max_dist:\n max_dist = dist\n center.append(d[max_dist])\n x += 1\n \n # x = sorted(d, reverse=True)\n # for i in range(k - 1):\n # center.append(d[x[i]])\n\n return center\n#Clustering of points according to centers\ndef clustering(data, centers, first_cluster=False):\n for point in data:\n nearest_center = 0\n nearest_center_dist = None\n for i in range(0, len(centers)):\n euclidean_dist = 0\n for d in range(0, 2):\n dist = abs(point[d] - centers[i][d])\n euclidean_dist += dist**2\n euclidean_dist = np.sqrt(euclidean_dist)\n if nearest_center_dist == None:\n nearest_center_dist = euclidean_dist\n nearest_center = i\n elif nearest_center_dist > euclidean_dist:\n nearest_center_dist = euclidean_dist\n nearest_center = i\n if first_cluster:\n point.append(nearest_center)\n else:\n point[-1] = nearest_center\n return data\n\n#Calculation of centroid\ndef centroid(data, centers):\n centers_updated = []\n for i in range(len(centers)):\n new_center = []\n n_of_points = 0\n sum_pts = []\n for point in data:\n if point[-1] == i:\n n_of_points += 1\n for dim in range(0, 2):\n if dim < len(sum_pts):\n sum_pts[dim] += point[dim]\n else:\n sum_pts.append(point[dim])\n if len(sum_pts) != 0:\n for dim in range(0, 2):\n new_center.append(sum_pts[dim]/n_of_points)\n centers_updated.append(new_center)\n else:\n centers_updated.append(centers[i])\n return centers_updated\n\n# Trains and returns final list of centriods\ndef Kmeans(data, k=2, iter=3000):\n #centers = centers_str1(data, k)\n centers = centers_str2(data ,k)\n\n clustered_data = clustering(data, centers, first_cluster=True)\n\n temp = []\n for i in range(iter):\n centers = centroid(clustered_data, centers)\n if centers == temp:\n break\n clustered_data = clustering(data, centers, first_cluster=False)\n temp = copy.deepcopy(centers)\n\n return centers\n\n#Given a point, predicts the cluster that it belongs to\ndef predict_cluster(point, centers):\n nearest_center = None\n nearest_dist = None\n \n for i in range(len(centers)):\n euclidean_dist = 0\n for d in range(0, 2):\n dist = abs(point[d] - centers[i][d])\n euclidean_dist += dist**2\n euclidean_dist = np.sqrt(euclidean_dist)\n if nearest_dist == None:\n nearest_dist = euclidean_dist\n nearest_center = i\n elif nearest_dist > euclidean_dist:\n nearest_dist = euclidean_dist\n nearest_center = i\n #print('center:',i, 'dist:',euclidean_dist[i])\n\n return centers[nearest_center]\n\n\n#%%\n#######################################################################################\n\n#Extracting data from given .mat file\ndata = scipy.io.loadmat('AllSamples.mat')\npts = data['AllSamples']\n\n#Running for all values from k = 2 to k = 10\nobjective_function = []\nfor k in range(2, 11):\n #Calling Kmeans for clustering\n centers = Kmeans(pts.tolist(), k)\n\n #Displaying graphs of points with centroids marked in red\n plt.scatter(pts[ :, 0], pts[ :, 1])\n for i in range(k):\n plt.plot(centers[i][0], centers[i][1], \"ro\")\n plt.xlabel(\"x1\")\n plt.ylabel(\"x2\")\n plt.title(\"Clustering (k = \" + str(k) + \")\")\n plt.show()\n\n #Calculating Objective function for each k\n dist = []\n for i in range(k):\n dist.append(0)\n\n for point in pts:\n center = predict_cluster(point, centers)\n j = centers.index(center)\n dist[j] += ( ((point[0] - center[0])**2) + ((point[1] - center[1])**2) )**(0.5)\n\n sum = 0\n for i in range(k):\n sum += (dist[i]**2) / 100\n\n objective_function.append(sum)\n\n\n#Plotting Objective function for each k\nplt.plot(range(2, 11), objective_function, c=\"white\")\nplt.ylabel(\"Objective Function\")\nplt.xlabel(\"Number of clusters\")\nplt.title(\"Elbow Method(Strategy 2)\")\nplt.show()\n\n\n# %%\n"},{"alpha_fraction":0.5657268166542053,"alphanum_fraction":0.6122299432754517,"avg_line_length":23.383928298950195,"blob_id":"b3d56387f0f224f1f5a9d93545200d460e387c9d","content_id":"89d66bd69ef8aab41614a205c07ac99cd1d92c8f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2731,"license_type":"no_license","max_line_length":95,"num_lines":112,"path":"/Part 1/Project 1.py","repo_name":"karanajith2096/SML-Project","src_encoding":"UTF-8","text":"#This is the python code for Naive Bayes classiifcation\n#on MNIST handwritten digits dataset. \n\nimport scipy.io\nimport numpy as np\nimport numpy.ma as ma\nimport math\nfrom scipy.stats import multivariate_normal\n#Fucntion to calculate Standard Deviation\ndef stDev(array):\n mean = np.mean(array)\n var = sum([pow(x - mean, 2) for x in array])/ float(len(array)-1)\n return math.sqrt(var)\n\n#Function to calculate log likelyhood for P(X|Y)\ndef logLikely(array, mean, std):\n p = 1\n for i in range(len(array)):\n p *= pdf(array[i], mean[i], std[i])\n return p\n\n#Function to calculate Probability Density Function\ndef pdf(val, mean, std):\n d = val - mean\n if std != 0:\n return math.exp(-1 * (pow(d, 2)/(2 * (pow(std, 2))))) / ((pow(2 * math.pi, 0.5)) * std)\n else:\n return 1\n#########################################################################################\n#Loading data \ndata = scipy.io.loadmat('mnist_data.mat')\n\n#Getting count of the images of each type from training set labels\nnum_seven = data['trY'][0].tolist().count(0.0)\nnum_eight = data['trY'][0].tolist().count(1.0)\n\n#Extracting training data images for 7 and 8\ntrain_7 = data['trX'][: num_seven]\ntrain_8 = data['trX'][num_seven :]\n\n#Extracting testing images\ntest = data['tsX']\n\n#Extracting testing labels\nlabels = data['tsY']\n\n#Transpose of data\ntrain_7 = train_7.T\ntrain_8 = train_8.T\n\n#Calculating mean and variance for training data\nmean_7 = []\nfor i in range(784):\n mean_7.append(np.mean(train_7[i]))\n\nvar_7 = []\nfor i in range(784):\n var_7.append(np.var(train_7[i]))\n\nstd_7 = []\nfor i in range(784):\n std_7.append(stDev(train_7[i]))\n\nmean_8 = []\nfor i in range(784):\n mean_8.append(np.mean(train_8[i]))\n\nvar_8 = []\nfor i in range(784):\n var_8.append(np.var(train_8[i]))\n\nstd_8 = []\nfor i in range(784):\n std_8.append(stDev(train_8[i]))\n\n\n#Calculating priors for each type \nprior_7 = num_seven / (num_seven + num_eight)\nprior_8 = 1.0 - prior_7\n\n#Calculating posterior probablity for testing data\nout = []\nfor i in range(2002):\n post_7 = logLikely(test[i], mean_7, std_7) + np.log(prior_7)\n post_8 = logLikely(test[i], mean_8, std_8) + np.log(prior_8)\n if post_7 > post_8:\n out.append(0.0)\n else:\n out.append(1.0)\n\n#Cross checking predicted labels with actual labels\ncount_7 = 0\ncount_8 = 0\ni = 0\nwhile i < 1028:\n if out[i] == labels[0, i]:\n count_7 += 1\n i += 1\n\nwhile i < 2002:\n if out[i] == labels[0, i]:\n count_8 += 1\n i += 1\n\n#Calculating accuracy\nacc_7 = count_7 / 1028\nacc_8 = count_8 / 974\nacc = (count_7 + count_8) / 2002\n\nprint(\"Accuracy for '7': \" + str(acc_7))\nprint(\"Accuracy for '8': \" + str(acc_8))\nprint(\"Overall accruacy: \" + str(acc))\n"},{"alpha_fraction":0.5539906024932861,"alphanum_fraction":0.567180871963501,"avg_line_length":29.02013397216797,"blob_id":"c247de56901b54f7833a7e19d6b61a1887b9c8a8","content_id":"ec812d1d17539151c0db37975f9f06a6e88472c9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4473,"license_type":"no_license","max_line_length":101,"num_lines":149,"path":"/Part 2/K-means (Part 1).py","repo_name":"karanajith2096/SML-Project","src_encoding":"UTF-8","text":"#%%\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\nimport scipy.io\nimport pandas as pd\nimport copy\n\n#Picks k random points from given dataset\ndef centers_str1(points, k):\n l = len(points)\n centers = []\n for i in range(k):\n rand = random.randint(0,l - 1)\n centers.append(points[rand])\n return centers\n\n#Clustering of points according to centers\ndef clustering(data, centers, first_cluster=False):\n for point in data:\n nearest_center = 0\n nearest_center_dist = None\n for i in range(0, len(centers)):\n euclidean_dist = 0\n for d in range(0, 2):\n dist = abs(point[d] - centers[i][d])\n euclidean_dist += dist**2\n euclidean_dist = np.sqrt(euclidean_dist)\n if nearest_center_dist == None:\n nearest_center_dist = euclidean_dist\n nearest_center = i\n elif nearest_center_dist > euclidean_dist:\n nearest_center_dist = euclidean_dist\n nearest_center = i\n if first_cluster:\n point.append(nearest_center)\n else:\n point[-1] = nearest_center\n return data\n\n#Calculation of centroid\ndef centroid(data, centers):\n centers_updated = []\n for i in range(len(centers)):\n new_center = []\n n_of_points = 0\n sum_pts = []\n for point in data:\n if point[-1] == i:\n n_of_points += 1\n for dim in range(0, 2):\n if dim < len(sum_pts):\n sum_pts[dim] += point[dim]\n else:\n sum_pts.append(point[dim])\n if len(sum_pts) != 0:\n for dim in range(0, 2):\n new_center.append(sum_pts[dim]/n_of_points)\n centers_updated.append(new_center)\n else:\n centers_updated.append(centers[i])\n return centers_updated\n\n# Trains and returns final list of centriods\ndef Kmeans(data, k=2, iter=3000):\n centers = centers_str1(data, k)\n #centers = centers_str2(data ,k)\n\n clustered_data = clustering(data, centers, first_cluster=True)\n\n temp = []\n for i in range(iter):\n centers = centroid(clustered_data, centers)\n if centers == temp:\n break\n clustered_data = clustering(data, centers, first_cluster=False)\n temp = copy.deepcopy(centers)\n\n return centers\n\n#Given a point, predicts the cluster that it belongs to\ndef predict_cluster(point, centers):\n nearest_center = None\n nearest_dist = None\n \n for i in range(len(centers)):\n euclidean_dist = 0\n for d in range(0, 2):\n dist = abs(point[d] - centers[i][d])\n euclidean_dist += dist**2\n euclidean_dist = np.sqrt(euclidean_dist)\n if nearest_dist == None:\n nearest_dist = euclidean_dist\n nearest_center = i\n elif nearest_dist > euclidean_dist:\n nearest_dist = euclidean_dist\n nearest_center = i\n #print('center:',i, 'dist:',euclidean_dist[i])\n\n return centers[nearest_center]\n\n\n#%%\n#######################################################################################\n\n#Extracting data from given .mat file\ndata = scipy.io.loadmat('AllSamples.mat')\npts = data['AllSamples']\n\n#Running for all cases from k = 2 to k = 10\nobjective_function = []\nfor k in range(2, 11):\n #Calling Kmeans function for clustering\n centers = Kmeans(pts.tolist(), k)\n\n #Diplaying graph of points with centroids marked in red\n plt.scatter(pts[ :, 0], pts[ :, 1])\n for i in range(k):\n plt.plot(centers[i][0], centers[i][1], \"ro\")\n plt.xlabel(\"x1\")\n plt.ylabel(\"x2\")\n plt.title(\"Clustering (k = \" + str(k) + \")\")\n plt.show()\n\n #Calculating Objective function for each k\n dist_from_centroid = []\n for i in range(k):\n dist_from_centroid.append(0)\n\n for point in pts:\n center = predict_cluster(point, centers)\n j = centers.index(center)\n dist_from_centroid[j] += ( ((point[0] - center[0])**2) + ((point[1] - center[1])**2) )**(0.5)\n\n sum = 0\n for i in range(k):\n sum += dist_from_centroid[i]**2 / 100\n\n objective_function.append(sum)\n\n#Plotting graph of Objective function for each k\nplt.plot(range(2, 11), objective_function, c=\"white\")\nplt.ylabel(\"Objective Function\")\nplt.xlabel(\"Number of clusters\")\nplt.title(\"Elbow Method(Strategy 1)\")\nplt.show()\n\n\n# %%\n"},{"alpha_fraction":0.7068403959274292,"alphanum_fraction":0.7491856813430786,"avg_line_length":42.57143020629883,"blob_id":"14d057bfb35af2f5d4869c1b72dad73d442b74f8","content_id":"35ebdc0a79cbdc61b81e2c7cd00e612012aa8a7e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":307,"license_type":"no_license","max_line_length":182,"num_lines":7,"path":"/README.md","repo_name":"karanajith2096/SML-Project","src_encoding":"UTF-8","text":"# SML-Project (Fall 2019)\n\n## Part 1: Naive Bayes and Logistic Regression\n \n**Submission date:** 22nd September, 2019\n \n**Notes:** Implemented Naive Bayes Classification and Logistic Regression on given dataset. The dataset was the MNIST digits dataset with training and testing images for '7' and '8'. \n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5905118584632874,\n \"alphanum_fraction\": 0.6223470568656921,\n \"avg_line_length\": 22.130434036254883,\n \"blob_id\": \"790082dce1bf3b4784bea33e83d0e23c55598b76\",\n \"content_id\": \"8dde391d07d6f969c96199bb5fac839aa3afe909\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1602,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 69,\n \"path\": \"/Part 1/Logistic Regression.py\",\n \"repo_name\": \"karanajith2096/SML-Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#This is the Python code for Logistic Regression \\n#on MNIST handwritten digits dataset.\\n\\n\\nimport scipy.io\\nimport numpy as np\\nimport numpy.ma as ma\\nfrom sklearn.naive_bayes import GaussianNB\\n\\n#Sigmoid function\\ndef sigmoid(score):\\n return 1 / (1 + np.exp(-score))\\n\\n#Logistic regression function to do the training of model\\ndef LogReg(features, t, num_steps, rate):\\n w = np.zeros(features.shape[1])\\n for step in range(num_steps):\\n score = np.dot(features, w)\\n p = sigmoid(score)\\n\\n #Using gradient to update weights\\n err_out = t - p\\n grad = np.dot(features.T, err_out)\\n w += rate * grad\\n \\n return w\\n\\n\\n#############################################################\\n#Loading data\\ndata = scipy.io.loadmat('mnist_data.mat')\\n\\n#Extracting training data\\ntrain_data = data['trX']\\ntrain_labels = data['trY'][0]\\n\\n#Extracting testing data\\ntest_data = data['tsX']\\ntest_labels = data['tsY'][0]\\n\\n#Calculating Weight\\nweight = LogReg(train_data, train_labels, 7000, 2e-3)\\n\\n#Calcuating final probability and the predicted labels\\nfinal = np.dot(test_data, weight)\\np = np.round(sigmoid(final))\\n\\n#Cross checking predicted labels with actual labels\\ncount_7 = 0\\ncount_8 = 0\\ni = 0\\nwhile i < 1028:\\n if p[i] == test_labels[i]:\\n count_7 += 1\\n i += 1\\n\\nwhile i < 2002:\\n if p[i] == test_labels[i]:\\n count_8 += 1\\n i += 1\\n\\n#Calculating accuracy\\nacc_7 = count_7 / 1028\\nacc_8 = count_8 / 974\\nacc = (count_7 + count_8) / 2002\\n\\nprint(\\\"Accuracy for '7': \\\" + str(acc_7))\\nprint(\\\"Accuracy for '8': \\\" + str(acc_8))\\nprint(\\\"Overall Accuracy: \\\" + str(acc))\\n\\n\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5223791599273682,\n \"alphanum_fraction\": 0.5369007587432861,\n \"avg_line_length\": 28.226743698120117,\n \"blob_id\": \"0b3f5a4d93ead9de36e1df1d3fc37d7a40b1aa29\",\n \"content_id\": \"f0d69fd703f6bd34c99fce6b27a4a91ab153f836\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5027,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 172,\n \"path\": \"/Part 2/K-means (Part 2).py\",\n \"repo_name\": \"karanajith2096/SML-Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#%%\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nimport random\\nimport scipy.io\\nimport pandas as pd\\nimport copy\\n\\n#Picks initial centers according to the second strategy in question\\ndef centers_str2(points, k):\\n rand = random.randint(0, len(points) - 1)\\n c = points[rand]\\n center = []\\n center.append(c)\\n max_dist = 0\\n x = 1\\n while x < k:\\n d = dict()\\n max_dist = 0\\n for p in points:\\n if p in center:\\n continue\\n else:\\n dist = 0\\n for i in center:\\n dist += ( ((p[0] - i[0])**2) + ((p[1] - i[1])**2) )**(1 / 2)\\n \\n dist = dist / len(center)\\n d[dist] = p\\n if dist > max_dist:\\n max_dist = dist\\n center.append(d[max_dist])\\n x += 1\\n \\n # x = sorted(d, reverse=True)\\n # for i in range(k - 1):\\n # center.append(d[x[i]])\\n\\n return center\\n#Clustering of points according to centers\\ndef clustering(data, centers, first_cluster=False):\\n for point in data:\\n nearest_center = 0\\n nearest_center_dist = None\\n for i in range(0, len(centers)):\\n euclidean_dist = 0\\n for d in range(0, 2):\\n dist = abs(point[d] - centers[i][d])\\n euclidean_dist += dist**2\\n euclidean_dist = np.sqrt(euclidean_dist)\\n if nearest_center_dist == None:\\n nearest_center_dist = euclidean_dist\\n nearest_center = i\\n elif nearest_center_dist > euclidean_dist:\\n nearest_center_dist = euclidean_dist\\n nearest_center = i\\n if first_cluster:\\n point.append(nearest_center)\\n else:\\n point[-1] = nearest_center\\n return data\\n\\n#Calculation of centroid\\ndef centroid(data, centers):\\n centers_updated = []\\n for i in range(len(centers)):\\n new_center = []\\n n_of_points = 0\\n sum_pts = []\\n for point in data:\\n if point[-1] == i:\\n n_of_points += 1\\n for dim in range(0, 2):\\n if dim < len(sum_pts):\\n sum_pts[dim] += point[dim]\\n else:\\n sum_pts.append(point[dim])\\n if len(sum_pts) != 0:\\n for dim in range(0, 2):\\n new_center.append(sum_pts[dim]/n_of_points)\\n centers_updated.append(new_center)\\n else:\\n centers_updated.append(centers[i])\\n return centers_updated\\n\\n# Trains and returns final list of centriods\\ndef Kmeans(data, k=2, iter=3000):\\n #centers = centers_str1(data, k)\\n centers = centers_str2(data ,k)\\n\\n clustered_data = clustering(data, centers, first_cluster=True)\\n\\n temp = []\\n for i in range(iter):\\n centers = centroid(clustered_data, centers)\\n if centers == temp:\\n break\\n clustered_data = clustering(data, centers, first_cluster=False)\\n temp = copy.deepcopy(centers)\\n\\n return centers\\n\\n#Given a point, predicts the cluster that it belongs to\\ndef predict_cluster(point, centers):\\n nearest_center = None\\n nearest_dist = None\\n \\n for i in range(len(centers)):\\n euclidean_dist = 0\\n for d in range(0, 2):\\n dist = abs(point[d] - centers[i][d])\\n euclidean_dist += dist**2\\n euclidean_dist = np.sqrt(euclidean_dist)\\n if nearest_dist == None:\\n nearest_dist = euclidean_dist\\n nearest_center = i\\n elif nearest_dist > euclidean_dist:\\n nearest_dist = euclidean_dist\\n nearest_center = i\\n #print('center:',i, 'dist:',euclidean_dist[i])\\n\\n return centers[nearest_center]\\n\\n\\n#%%\\n#######################################################################################\\n\\n#Extracting data from given .mat file\\ndata = scipy.io.loadmat('AllSamples.mat')\\npts = data['AllSamples']\\n\\n#Running for all values from k = 2 to k = 10\\nobjective_function = []\\nfor k in range(2, 11):\\n #Calling Kmeans for clustering\\n centers = Kmeans(pts.tolist(), k)\\n\\n #Displaying graphs of points with centroids marked in red\\n plt.scatter(pts[ :, 0], pts[ :, 1])\\n for i in range(k):\\n plt.plot(centers[i][0], centers[i][1], \\\"ro\\\")\\n plt.xlabel(\\\"x1\\\")\\n plt.ylabel(\\\"x2\\\")\\n plt.title(\\\"Clustering (k = \\\" + str(k) + \\\")\\\")\\n plt.show()\\n\\n #Calculating Objective function for each k\\n dist = []\\n for i in range(k):\\n dist.append(0)\\n\\n for point in pts:\\n center = predict_cluster(point, centers)\\n j = centers.index(center)\\n dist[j] += ( ((point[0] - center[0])**2) + ((point[1] - center[1])**2) )**(0.5)\\n\\n sum = 0\\n for i in range(k):\\n sum += (dist[i]**2) / 100\\n\\n objective_function.append(sum)\\n\\n\\n#Plotting Objective function for each k\\nplt.plot(range(2, 11), objective_function, c=\\\"white\\\")\\nplt.ylabel(\\\"Objective Function\\\")\\nplt.xlabel(\\\"Number of clusters\\\")\\nplt.title(\\\"Elbow Method(Strategy 2)\\\")\\nplt.show()\\n\\n\\n# %%\\n\"\n },\n {\n \"alpha_fraction\": 0.5657268166542053,\n \"alphanum_fraction\": 0.6122299432754517,\n \"avg_line_length\": 23.383928298950195,\n \"blob_id\": \"b3d56387f0f224f1f5a9d93545200d460e387c9d\",\n \"content_id\": \"89d66bd69ef8aab41614a205c07ac99cd1d92c8f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2731,\n \"license_type\": \"no_license\",\n \"max_line_length\": 95,\n \"num_lines\": 112,\n \"path\": \"/Part 1/Project 1.py\",\n \"repo_name\": \"karanajith2096/SML-Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#This is the python code for Naive Bayes classiifcation\\n#on MNIST handwritten digits dataset. \\n\\nimport scipy.io\\nimport numpy as np\\nimport numpy.ma as ma\\nimport math\\nfrom scipy.stats import multivariate_normal\\n#Fucntion to calculate Standard Deviation\\ndef stDev(array):\\n mean = np.mean(array)\\n var = sum([pow(x - mean, 2) for x in array])/ float(len(array)-1)\\n return math.sqrt(var)\\n\\n#Function to calculate log likelyhood for P(X|Y)\\ndef logLikely(array, mean, std):\\n p = 1\\n for i in range(len(array)):\\n p *= pdf(array[i], mean[i], std[i])\\n return p\\n\\n#Function to calculate Probability Density Function\\ndef pdf(val, mean, std):\\n d = val - mean\\n if std != 0:\\n return math.exp(-1 * (pow(d, 2)/(2 * (pow(std, 2))))) / ((pow(2 * math.pi, 0.5)) * std)\\n else:\\n return 1\\n#########################################################################################\\n#Loading data \\ndata = scipy.io.loadmat('mnist_data.mat')\\n\\n#Getting count of the images of each type from training set labels\\nnum_seven = data['trY'][0].tolist().count(0.0)\\nnum_eight = data['trY'][0].tolist().count(1.0)\\n\\n#Extracting training data images for 7 and 8\\ntrain_7 = data['trX'][: num_seven]\\ntrain_8 = data['trX'][num_seven :]\\n\\n#Extracting testing images\\ntest = data['tsX']\\n\\n#Extracting testing labels\\nlabels = data['tsY']\\n\\n#Transpose of data\\ntrain_7 = train_7.T\\ntrain_8 = train_8.T\\n\\n#Calculating mean and variance for training data\\nmean_7 = []\\nfor i in range(784):\\n mean_7.append(np.mean(train_7[i]))\\n\\nvar_7 = []\\nfor i in range(784):\\n var_7.append(np.var(train_7[i]))\\n\\nstd_7 = []\\nfor i in range(784):\\n std_7.append(stDev(train_7[i]))\\n\\nmean_8 = []\\nfor i in range(784):\\n mean_8.append(np.mean(train_8[i]))\\n\\nvar_8 = []\\nfor i in range(784):\\n var_8.append(np.var(train_8[i]))\\n\\nstd_8 = []\\nfor i in range(784):\\n std_8.append(stDev(train_8[i]))\\n\\n\\n#Calculating priors for each type \\nprior_7 = num_seven / (num_seven + num_eight)\\nprior_8 = 1.0 - prior_7\\n\\n#Calculating posterior probablity for testing data\\nout = []\\nfor i in range(2002):\\n post_7 = logLikely(test[i], mean_7, std_7) + np.log(prior_7)\\n post_8 = logLikely(test[i], mean_8, std_8) + np.log(prior_8)\\n if post_7 > post_8:\\n out.append(0.0)\\n else:\\n out.append(1.0)\\n\\n#Cross checking predicted labels with actual labels\\ncount_7 = 0\\ncount_8 = 0\\ni = 0\\nwhile i < 1028:\\n if out[i] == labels[0, i]:\\n count_7 += 1\\n i += 1\\n\\nwhile i < 2002:\\n if out[i] == labels[0, i]:\\n count_8 += 1\\n i += 1\\n\\n#Calculating accuracy\\nacc_7 = count_7 / 1028\\nacc_8 = count_8 / 974\\nacc = (count_7 + count_8) / 2002\\n\\nprint(\\\"Accuracy for '7': \\\" + str(acc_7))\\nprint(\\\"Accuracy for '8': \\\" + str(acc_8))\\nprint(\\\"Overall accruacy: \\\" + str(acc))\\n\"\n },\n {\n \"alpha_fraction\": 0.5539906024932861,\n \"alphanum_fraction\": 0.567180871963501,\n \"avg_line_length\": 29.02013397216797,\n \"blob_id\": \"c247de56901b54f7833a7e19d6b61a1887b9c8a8\",\n \"content_id\": \"ec812d1d17539151c0db37975f9f06a6e88472c9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4473,\n \"license_type\": \"no_license\",\n \"max_line_length\": 101,\n \"num_lines\": 149,\n \"path\": \"/Part 2/K-means (Part 1).py\",\n \"repo_name\": \"karanajith2096/SML-Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#%%\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nimport random\\nimport scipy.io\\nimport pandas as pd\\nimport copy\\n\\n#Picks k random points from given dataset\\ndef centers_str1(points, k):\\n l = len(points)\\n centers = []\\n for i in range(k):\\n rand = random.randint(0,l - 1)\\n centers.append(points[rand])\\n return centers\\n\\n#Clustering of points according to centers\\ndef clustering(data, centers, first_cluster=False):\\n for point in data:\\n nearest_center = 0\\n nearest_center_dist = None\\n for i in range(0, len(centers)):\\n euclidean_dist = 0\\n for d in range(0, 2):\\n dist = abs(point[d] - centers[i][d])\\n euclidean_dist += dist**2\\n euclidean_dist = np.sqrt(euclidean_dist)\\n if nearest_center_dist == None:\\n nearest_center_dist = euclidean_dist\\n nearest_center = i\\n elif nearest_center_dist > euclidean_dist:\\n nearest_center_dist = euclidean_dist\\n nearest_center = i\\n if first_cluster:\\n point.append(nearest_center)\\n else:\\n point[-1] = nearest_center\\n return data\\n\\n#Calculation of centroid\\ndef centroid(data, centers):\\n centers_updated = []\\n for i in range(len(centers)):\\n new_center = []\\n n_of_points = 0\\n sum_pts = []\\n for point in data:\\n if point[-1] == i:\\n n_of_points += 1\\n for dim in range(0, 2):\\n if dim < len(sum_pts):\\n sum_pts[dim] += point[dim]\\n else:\\n sum_pts.append(point[dim])\\n if len(sum_pts) != 0:\\n for dim in range(0, 2):\\n new_center.append(sum_pts[dim]/n_of_points)\\n centers_updated.append(new_center)\\n else:\\n centers_updated.append(centers[i])\\n return centers_updated\\n\\n# Trains and returns final list of centriods\\ndef Kmeans(data, k=2, iter=3000):\\n centers = centers_str1(data, k)\\n #centers = centers_str2(data ,k)\\n\\n clustered_data = clustering(data, centers, first_cluster=True)\\n\\n temp = []\\n for i in range(iter):\\n centers = centroid(clustered_data, centers)\\n if centers == temp:\\n break\\n clustered_data = clustering(data, centers, first_cluster=False)\\n temp = copy.deepcopy(centers)\\n\\n return centers\\n\\n#Given a point, predicts the cluster that it belongs to\\ndef predict_cluster(point, centers):\\n nearest_center = None\\n nearest_dist = None\\n \\n for i in range(len(centers)):\\n euclidean_dist = 0\\n for d in range(0, 2):\\n dist = abs(point[d] - centers[i][d])\\n euclidean_dist += dist**2\\n euclidean_dist = np.sqrt(euclidean_dist)\\n if nearest_dist == None:\\n nearest_dist = euclidean_dist\\n nearest_center = i\\n elif nearest_dist > euclidean_dist:\\n nearest_dist = euclidean_dist\\n nearest_center = i\\n #print('center:',i, 'dist:',euclidean_dist[i])\\n\\n return centers[nearest_center]\\n\\n\\n#%%\\n#######################################################################################\\n\\n#Extracting data from given .mat file\\ndata = scipy.io.loadmat('AllSamples.mat')\\npts = data['AllSamples']\\n\\n#Running for all cases from k = 2 to k = 10\\nobjective_function = []\\nfor k in range(2, 11):\\n #Calling Kmeans function for clustering\\n centers = Kmeans(pts.tolist(), k)\\n\\n #Diplaying graph of points with centroids marked in red\\n plt.scatter(pts[ :, 0], pts[ :, 1])\\n for i in range(k):\\n plt.plot(centers[i][0], centers[i][1], \\\"ro\\\")\\n plt.xlabel(\\\"x1\\\")\\n plt.ylabel(\\\"x2\\\")\\n plt.title(\\\"Clustering (k = \\\" + str(k) + \\\")\\\")\\n plt.show()\\n\\n #Calculating Objective function for each k\\n dist_from_centroid = []\\n for i in range(k):\\n dist_from_centroid.append(0)\\n\\n for point in pts:\\n center = predict_cluster(point, centers)\\n j = centers.index(center)\\n dist_from_centroid[j] += ( ((point[0] - center[0])**2) + ((point[1] - center[1])**2) )**(0.5)\\n\\n sum = 0\\n for i in range(k):\\n sum += dist_from_centroid[i]**2 / 100\\n\\n objective_function.append(sum)\\n\\n#Plotting graph of Objective function for each k\\nplt.plot(range(2, 11), objective_function, c=\\\"white\\\")\\nplt.ylabel(\\\"Objective Function\\\")\\nplt.xlabel(\\\"Number of clusters\\\")\\nplt.title(\\\"Elbow Method(Strategy 1)\\\")\\nplt.show()\\n\\n\\n# %%\\n\"\n },\n {\n \"alpha_fraction\": 0.7068403959274292,\n \"alphanum_fraction\": 0.7491856813430786,\n \"avg_line_length\": 42.57143020629883,\n \"blob_id\": \"14d057bfb35af2f5d4869c1b72dad73d442b74f8\",\n \"content_id\": \"35ebdc0a79cbdc61b81e2c7cd00e612012aa8a7e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 307,\n \"license_type\": \"no_license\",\n \"max_line_length\": 182,\n \"num_lines\": 7,\n \"path\": \"/README.md\",\n \"repo_name\": \"karanajith2096/SML-Project\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# SML-Project (Fall 2019)\\n\\n## Part 1: Naive Bayes and Logistic Regression\\n \\n**Submission date:** 22nd September, 2019\\n \\n**Notes:** Implemented Naive Bayes Classification and Logistic Regression on given dataset. The dataset was the MNIST digits dataset with training and testing images for '7' and '8'. \\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":5,"string":"5"}}},{"rowIdx":2979137,"cells":{"repo_name":{"kind":"string","value":"geasyheart/voice_change"},"repo_url":{"kind":"string","value":"https://github.com/geasyheart/voice_change"},"snapshot_id":{"kind":"string","value":"d0d663a4f9e87bc2e6bf44723d1f0b922db4d1dd"},"revision_id":{"kind":"string","value":"72cc2d7da36d5472da59e5ae38b30b23f967faff"},"directory_id":{"kind":"string","value":"425ebca03e41716e61b795ec00b8012316699a9d"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-02-10T03:56:39.246843","string":"2023-02-10T03:56:39.246843"},"revision_date":{"kind":"timestamp","value":"2019-10-24T13:00:00","string":"2019-10-24T13:00:00"},"committer_date":{"kind":"timestamp","value":"2019-10-24T13:00:00","string":"2019-10-24T13:00:00"},"github_id":{"kind":"number","value":192755360,"string":"192,755,360"},"star_events_count":{"kind":"number","value":7,"string":"7"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"timestamp","value":"2019-06-19T15:07:07","string":"2019-06-19T15:07:07"},"gha_updated_at":{"kind":"timestamp","value":"2022-06-21T00:21:08","string":"2022-06-21T00:21:08"},"gha_pushed_at":{"kind":"timestamp","value":"2023-02-02T06:39:54","string":"2023-02-02T06:39:54"},"gha_language":{"kind":"string","value":"Dart"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6988847851753235,"alphanum_fraction":0.7249070405960083,"avg_line_length":24.571428298950195,"blob_id":"2f81bf11fbd7d409eb46c24074c8dc67ad75c635","content_id":"f3ec1ba121f91845f41853f1a4ae163b56f1e166","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":816,"license_type":"no_license","max_line_length":125,"num_lines":21,"path":"/README.md","repo_name":"geasyheart/voice_change","src_encoding":"UTF-8","text":"\n## Getting Started\n\n1. cd server/ && virtualenv -p python3 venv && source venv/bin/activate && pip install -r requirements.txt && python main.py\n2. modify `g/env.dart` `SERVER_URL` address，notice isn`t 127.0.0.1\n3. cd projectPath && flutter packages get\n4. cd projectPath && flutter run\n\n### Preview\n\n\n\n\n\n\n\n\n### Thanks\n\n在整个页面布局上，也参考了一些网上资源，很感谢你们的奉献，帮助初学者(或后端～)可以快速写界面，此处不再一一列举。\n最后这个项目也是第一次写，很感谢flutter的快速开发以及官方更新速度～，还有非常感谢官方文档，顺带学习到了很多移动开发知识，\n如写的不好的地方，也请多担待，当然非常乐意接受指教～\n"},{"alpha_fraction":0.5384349226951599,"alphanum_fraction":0.5552285313606262,"avg_line_length":27.736318588256836,"blob_id":"e536ec2fd2f2f0b7b14a570687387ce032e64086","content_id":"6033f9c13585d8b7c9652eba942495d799eb474b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5976,"license_type":"no_license","max_line_length":83,"num_lines":201,"path":"/server/main.py","repo_name":"geasyheart/voice_change","src_encoding":"UTF-8","text":"import flask\nfrom werkzeug.utils import secure_filename\nimport os\nfrom flask import request\nimport random\nfrom flask import abort\nfrom flask import request, jsonify, Flask\nfrom itsdangerous import TimedJSONWebSignatureSerializer\nfrom flask import send_file\nfrom urllib.parse import urljoin, urlsplit\nfrom flask_sslify import SSLify\nfrom werkzeug.contrib.fixers import ProxyFix\nTOKEN = \"asdfadfadfas\"\n\na = TimedJSONWebSignatureSerializer(TOKEN, 60)\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = \"./videos\"\n\n# sslify = SSLify(app)\napp.wsgi_app = ProxyFix(app.wsgi_app)\n\nGLABAL_TOKEN = {}\nGLABAL_FILE = {}\nclass APIException(Exception):\n errCode = 0\n errMsg = \"\"\n def __init__(self, errMsg=\"\"):\n if errMsg:\n self.errMsg = errMsg\n\n def to_json(self):\n return {\n \"errCode\": self.errCode,\n \"errMsg\": self.errMsg\n }\n\ndef fake_login_required(func):\n def wrapper(*args, **kwargs):\n auth = request.headers.get(\"Authorization\", \"\")\n if not auth:\n return func(*args, **kwargs)\n token = auth.rsplit(\" \")[-1]\n try:\n a.loads(token)\n except:\n raise APIException(\"认证失败:{}\".format(token)) \n else:\n return func(*args, **kwargs)\n return wrapper\n\ndef capture(func):\n def wrapper(*args, **kwargs):\n try:\n return func(*args, **kwargs)\n except APIException as e:\n return jsonify(e.to_json())\n return wrapper\n\n@capture\n@fake_login_required\n@app.route(\"/api/v1/video/upload\", methods=['POST'])\ndef upload():\n file = request.files['file']\n device_width = (request.json or request.form).get('width')\n device_height = (request.json or request.form).get('height')\n filename = secure_filename(file.filename)\n print(f'设备的高度:{device_height},宽度:{device_width}，视频名称:{filename}')\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n u = str(random.random() * 1000000000)\n GLABAL_FILE[u] = filename\n return jsonify({\"errCode\": 0,\"errMsg\": \"\", \"data\": u})\n\n@app.route(\"/api/v1/verify_code\", methods=[\"POST\"])\ndef aa():\n phone = request.json.get(\"phone\")\n print(\"获取手机号\", phone)\n return jsonify({\"errCode\": 0, \"errMsg\":\"\", \"data\":\"777777\"})\n\n@app.route(\"/api/v1/user/login\", methods=[\"POST\"])\ndef aab():\n phone = request.json.get(\"phone\")\n verifyCode = request.json.get(\"verifyCode\")\n print(\"获取手机号\", phone)\n print(\"获取验证码\", verifyCode)\n if phone == \"11111111111\":\n \n token = a.dumps({\"uid\": 1, \"phone\": phone}).decode()\n GLABAL_TOKEN[token] = phone\n return jsonify({\"errCode\": 0, \"errMsg\":\"登录成功\",\"token\": token})\n return jsonify({\"errCode\": 123, \"errMsg\":\"验证码错误\"})\n\n\n@capture\n@fake_login_required\n@app.route(\"/api/v1/user/info\", methods=[\"GET\"])\ndef info():\n auth = request.headers.get(\"Authorization\", \"\")\n token = auth.rsplit(\" \")[-1]\n return jsonify({\"errCode\": 0, \"data\":{\"phone\": GLABAL_TOKEN.get(token)}})\n\n@capture\n@fake_login_required\n@app.route(\"/api/v1/user/logout\", methods=[\"POST\"])\ndef logout():\n auth = request.headers.get(\"Authorization\", \"\")\n token = auth.rsplit(\" \")[-1]\n try:\n GLABAL_TOKEN.pop(token)\n except KeyError:\n pass\n return jsonify({'errrCode': 0, 'errMsg': \"success.\"})\n@capture\n@fake_login_required\n@app.route(\"/api/v1/user/peoples\", methods=[\"GET\"])\ndef peoples():\n print(request.headers)\n return jsonify({\n \"errCode\": 0,\n \"errMsg\": \"\",\n \"data\": {\n \"lineCount\": 3,\n \"choicePeoples\": [\n {\n \"icon\": \"assets/images/avatars/1.png\",\n \"name\": \"人物1\",\n \"lock\": True\n },\n {\n \"icon\": \"assets/images/avatars/2.png\",\n \"name\": \"人物2\",\n \"lock\": False\n },\n {\n \"icon\": \"assets/images/avatars/3.png\",\n \"name\": \"人物3\",\n \"lock\": True\n },\n {\n \"icon\": \"assets/images/avatars/4.png\",\n \"name\": \"人物4\",\n \"lock\": True\n },\n {\n \"icon\": \"assets/images/avatars/5.png\",\n \"name\": \"人物5\",\n \"lock\": True\n },\n {\n \"icon\": \"assets/images/avatars/6.png\",\n \"name\": \"人物1\",\n \"lock\": False\n },\n {\n \"icon\": \"assets/images/avatars/7.png\",\n \"name\": \"人物7\",\n \"lock\": True\n },\n ]\n }\n })\n\n\n@app.route('/api/v1/video/url', methods=['POST'])\ndef retrieve_video_url():\n # 不断轮训，知道处理完成，获取url\n mark = (request.form or request.json).get(\"mark\")\n \n import time\n time.sleep(1)\n value = random.choice([1,2])\n print(f\"{mark} 正在处理中。。。选择的值为:{value}\")\n if value != 1:\n return jsonify({\"errCode\": 123, \"errMsg\": \"未处理完成\"})\n\n s = urlsplit(request.url)\n url = f\"{s.scheme}://{s.netloc}/api/v1/video/{GLABAL_FILE[mark]}\"\n # \"https://www.sample-videos.com/video123/mp4/720/big_buck_bunny_720p_20mb.mp4\"\n return jsonify({\n \"errCode\": 0,\n \"errMsg\": \"处理完成\", \n \"data\": \"https://netease.im/res/video/face/video03.mp4\"\n})\n \n\n\n@app.route(\"/api/v1/video/<string:address>\", methods=[\"GET\"])\ndef retrieve_video(address):\n print(address)\n return send_file(f\"./videos/{address}\")\n\n\n@app.route(\"/api/v1/video/text\", methods=[\"POST\"])\ndef post_text():\n text = (request.form or request.json).get(\"text\")\n mark = (request.form or request.json).get(\"mark\")\n print(f\"文件{GLABAL_FILE.get(mark)}上传的对应文字为:{text}\")\n return jsonify({\"errCode\": 0, \"errMsg\": \"\"})\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\")\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6988847851753235,\n \"alphanum_fraction\": 0.7249070405960083,\n \"avg_line_length\": 24.571428298950195,\n \"blob_id\": \"2f81bf11fbd7d409eb46c24074c8dc67ad75c635\",\n \"content_id\": \"f3ec1ba121f91845f41853f1a4ae163b56f1e166\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 816,\n \"license_type\": \"no_license\",\n \"max_line_length\": 125,\n \"num_lines\": 21,\n \"path\": \"/README.md\",\n \"repo_name\": \"geasyheart/voice_change\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n## Getting Started\\n\\n1. cd server/ && virtualenv -p python3 venv && source venv/bin/activate && pip install -r requirements.txt && python main.py\\n2. modify `g/env.dart` `SERVER_URL` address，notice isn`t 127.0.0.1\\n3. cd projectPath && flutter packages get\\n4. cd projectPath && flutter run\\n\\n### Preview\\n\\n\\n\\n\\n\\n\\n\\n\\n### Thanks\\n\\n在整个页面布局上，也参考了一些网上资源，很感谢你们的奉献，帮助初学者(或后端～)可以快速写界面，此处不再一一列举。\\n最后这个项目也是第一次写，很感谢flutter的快速开发以及官方更新速度～，还有非常感谢官方文档，顺带学习到了很多移动开发知识，\\n如写的不好的地方，也请多担待，当然非常乐意接受指教～\\n\"\n },\n {\n \"alpha_fraction\": 0.5384349226951599,\n \"alphanum_fraction\": 0.5552285313606262,\n \"avg_line_length\": 27.736318588256836,\n \"blob_id\": \"e536ec2fd2f2f0b7b14a570687387ce032e64086\",\n \"content_id\": \"6033f9c13585d8b7c9652eba942495d799eb474b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5976,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 201,\n \"path\": \"/server/main.py\",\n \"repo_name\": \"geasyheart/voice_change\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import flask\\nfrom werkzeug.utils import secure_filename\\nimport os\\nfrom flask import request\\nimport random\\nfrom flask import abort\\nfrom flask import request, jsonify, Flask\\nfrom itsdangerous import TimedJSONWebSignatureSerializer\\nfrom flask import send_file\\nfrom urllib.parse import urljoin, urlsplit\\nfrom flask_sslify import SSLify\\nfrom werkzeug.contrib.fixers import ProxyFix\\nTOKEN = \\\"asdfadfadfas\\\"\\n\\na = TimedJSONWebSignatureSerializer(TOKEN, 60)\\n\\napp = Flask(__name__)\\napp.config['UPLOAD_FOLDER'] = \\\"./videos\\\"\\n\\n# sslify = SSLify(app)\\napp.wsgi_app = ProxyFix(app.wsgi_app)\\n\\nGLABAL_TOKEN = {}\\nGLABAL_FILE = {}\\nclass APIException(Exception):\\n errCode = 0\\n errMsg = \\\"\\\"\\n def __init__(self, errMsg=\\\"\\\"):\\n if errMsg:\\n self.errMsg = errMsg\\n\\n def to_json(self):\\n return {\\n \\\"errCode\\\": self.errCode,\\n \\\"errMsg\\\": self.errMsg\\n }\\n\\ndef fake_login_required(func):\\n def wrapper(*args, **kwargs):\\n auth = request.headers.get(\\\"Authorization\\\", \\\"\\\")\\n if not auth:\\n return func(*args, **kwargs)\\n token = auth.rsplit(\\\" \\\")[-1]\\n try:\\n a.loads(token)\\n except:\\n raise APIException(\\\"认证失败:{}\\\".format(token)) \\n else:\\n return func(*args, **kwargs)\\n return wrapper\\n\\ndef capture(func):\\n def wrapper(*args, **kwargs):\\n try:\\n return func(*args, **kwargs)\\n except APIException as e:\\n return jsonify(e.to_json())\\n return wrapper\\n\\n@capture\\n@fake_login_required\\n@app.route(\\\"/api/v1/video/upload\\\", methods=['POST'])\\ndef upload():\\n file = request.files['file']\\n device_width = (request.json or request.form).get('width')\\n device_height = (request.json or request.form).get('height')\\n filename = secure_filename(file.filename)\\n print(f'设备的高度:{device_height},宽度:{device_width}，视频名称:{filename}')\\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\\n u = str(random.random() * 1000000000)\\n GLABAL_FILE[u] = filename\\n return jsonify({\\\"errCode\\\": 0,\\\"errMsg\\\": \\\"\\\", \\\"data\\\": u})\\n\\n@app.route(\\\"/api/v1/verify_code\\\", methods=[\\\"POST\\\"])\\ndef aa():\\n phone = request.json.get(\\\"phone\\\")\\n print(\\\"获取手机号\\\", phone)\\n return jsonify({\\\"errCode\\\": 0, \\\"errMsg\\\":\\\"\\\", \\\"data\\\":\\\"777777\\\"})\\n\\n@app.route(\\\"/api/v1/user/login\\\", methods=[\\\"POST\\\"])\\ndef aab():\\n phone = request.json.get(\\\"phone\\\")\\n verifyCode = request.json.get(\\\"verifyCode\\\")\\n print(\\\"获取手机号\\\", phone)\\n print(\\\"获取验证码\\\", verifyCode)\\n if phone == \\\"11111111111\\\":\\n \\n token = a.dumps({\\\"uid\\\": 1, \\\"phone\\\": phone}).decode()\\n GLABAL_TOKEN[token] = phone\\n return jsonify({\\\"errCode\\\": 0, \\\"errMsg\\\":\\\"登录成功\\\",\\\"token\\\": token})\\n return jsonify({\\\"errCode\\\": 123, \\\"errMsg\\\":\\\"验证码错误\\\"})\\n\\n\\n@capture\\n@fake_login_required\\n@app.route(\\\"/api/v1/user/info\\\", methods=[\\\"GET\\\"])\\ndef info():\\n auth = request.headers.get(\\\"Authorization\\\", \\\"\\\")\\n token = auth.rsplit(\\\" \\\")[-1]\\n return jsonify({\\\"errCode\\\": 0, \\\"data\\\":{\\\"phone\\\": GLABAL_TOKEN.get(token)}})\\n\\n@capture\\n@fake_login_required\\n@app.route(\\\"/api/v1/user/logout\\\", methods=[\\\"POST\\\"])\\ndef logout():\\n auth = request.headers.get(\\\"Authorization\\\", \\\"\\\")\\n token = auth.rsplit(\\\" \\\")[-1]\\n try:\\n GLABAL_TOKEN.pop(token)\\n except KeyError:\\n pass\\n return jsonify({'errrCode': 0, 'errMsg': \\\"success.\\\"})\\n@capture\\n@fake_login_required\\n@app.route(\\\"/api/v1/user/peoples\\\", methods=[\\\"GET\\\"])\\ndef peoples():\\n print(request.headers)\\n return jsonify({\\n \\\"errCode\\\": 0,\\n \\\"errMsg\\\": \\\"\\\",\\n \\\"data\\\": {\\n \\\"lineCount\\\": 3,\\n \\\"choicePeoples\\\": [\\n {\\n \\\"icon\\\": \\\"assets/images/avatars/1.png\\\",\\n \\\"name\\\": \\\"人物1\\\",\\n \\\"lock\\\": True\\n },\\n {\\n \\\"icon\\\": \\\"assets/images/avatars/2.png\\\",\\n \\\"name\\\": \\\"人物2\\\",\\n \\\"lock\\\": False\\n },\\n {\\n \\\"icon\\\": \\\"assets/images/avatars/3.png\\\",\\n \\\"name\\\": \\\"人物3\\\",\\n \\\"lock\\\": True\\n },\\n {\\n \\\"icon\\\": \\\"assets/images/avatars/4.png\\\",\\n \\\"name\\\": \\\"人物4\\\",\\n \\\"lock\\\": True\\n },\\n {\\n \\\"icon\\\": \\\"assets/images/avatars/5.png\\\",\\n \\\"name\\\": \\\"人物5\\\",\\n \\\"lock\\\": True\\n },\\n {\\n \\\"icon\\\": \\\"assets/images/avatars/6.png\\\",\\n \\\"name\\\": \\\"人物1\\\",\\n \\\"lock\\\": False\\n },\\n {\\n \\\"icon\\\": \\\"assets/images/avatars/7.png\\\",\\n \\\"name\\\": \\\"人物7\\\",\\n \\\"lock\\\": True\\n },\\n ]\\n }\\n })\\n\\n\\n@app.route('/api/v1/video/url', methods=['POST'])\\ndef retrieve_video_url():\\n # 不断轮训，知道处理完成，获取url\\n mark = (request.form or request.json).get(\\\"mark\\\")\\n \\n import time\\n time.sleep(1)\\n value = random.choice([1,2])\\n print(f\\\"{mark} 正在处理中。。。选择的值为:{value}\\\")\\n if value != 1:\\n return jsonify({\\\"errCode\\\": 123, \\\"errMsg\\\": \\\"未处理完成\\\"})\\n\\n s = urlsplit(request.url)\\n url = f\\\"{s.scheme}://{s.netloc}/api/v1/video/{GLABAL_FILE[mark]}\\\"\\n # \\\"https://www.sample-videos.com/video123/mp4/720/big_buck_bunny_720p_20mb.mp4\\\"\\n return jsonify({\\n \\\"errCode\\\": 0,\\n \\\"errMsg\\\": \\\"处理完成\\\", \\n \\\"data\\\": \\\"https://netease.im/res/video/face/video03.mp4\\\"\\n})\\n \\n\\n\\n@app.route(\\\"/api/v1/video/<string:address>\\\", methods=[\\\"GET\\\"])\\ndef retrieve_video(address):\\n print(address)\\n return send_file(f\\\"./videos/{address}\\\")\\n\\n\\n@app.route(\\\"/api/v1/video/text\\\", methods=[\\\"POST\\\"])\\ndef post_text():\\n text = (request.form or request.json).get(\\\"text\\\")\\n mark = (request.form or request.json).get(\\\"mark\\\")\\n print(f\\\"文件{GLABAL_FILE.get(mark)}上传的对应文字为:{text}\\\")\\n return jsonify({\\\"errCode\\\": 0, \\\"errMsg\\\": \\\"\\\"})\\n\\nif __name__ == \\\"__main__\\\":\\n app.run(host=\\\"0.0.0.0\\\")\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979138,"cells":{"repo_name":{"kind":"string","value":"Rudra705/counting-teller"},"repo_url":{"kind":"string","value":"https://github.com/Rudra705/counting-teller"},"snapshot_id":{"kind":"string","value":"e1af869f368f83b3a259bbc89e78a9415d63ef19"},"revision_id":{"kind":"string","value":"239018a1f7eb1fb6dc374367834876f052ae918b"},"directory_id":{"kind":"string","value":"66bcb8d948aa4c3cdd4ee57c969f2a8a598a66ee"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-08-30T06:28:02.891989","string":"2023-08-30T06:28:02.891989"},"revision_date":{"kind":"timestamp","value":"2021-11-05T06:29:28","string":"2021-11-05T06:29:28"},"committer_date":{"kind":"timestamp","value":"2021-11-05T06:29:28","string":"2021-11-05T06:29:28"},"github_id":{"kind":"number","value":424848840,"string":"424,848,840"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.609375,"alphanum_fraction":0.6354166865348816,"avg_line_length":16.454545974731445,"blob_id":"7e63fe327cb646e2e9d7e5d4d47da4b92b9fde72","content_id":"0084c7d4bacc8202c17390162fda73ebccbea273","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":192,"license_type":"no_license","max_line_length":66,"num_lines":11,"path":"/counting-teller.py","repo_name":"Rudra705/counting-teller","src_encoding":"UTF-8","text":"i = 1\nl = int(input(\"Enter the number till you want the counting...\\n\"))\nprint(\"Starting!\")\nimport time\nfor i in range(l):\n\ti = i + 1\n\tprint(i)\n\ttime.sleep(0.5) \n\nprint(\"Done!\")\ntime.sleep(2)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.609375,\n \"alphanum_fraction\": 0.6354166865348816,\n \"avg_line_length\": 16.454545974731445,\n \"blob_id\": \"7e63fe327cb646e2e9d7e5d4d47da4b92b9fde72\",\n \"content_id\": \"0084c7d4bacc8202c17390162fda73ebccbea273\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 192,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 11,\n \"path\": \"/counting-teller.py\",\n \"repo_name\": \"Rudra705/counting-teller\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"i = 1\\nl = int(input(\\\"Enter the number till you want the counting...\\\\n\\\"))\\nprint(\\\"Starting!\\\")\\nimport time\\nfor i in range(l):\\n\\ti = i + 1\\n\\tprint(i)\\n\\ttime.sleep(0.5) \\n\\nprint(\\\"Done!\\\")\\ntime.sleep(2)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979139,"cells":{"repo_name":{"kind":"string","value":"DarthDanAmesh/Malaika"},"repo_url":{"kind":"string","value":"https://github.com/DarthDanAmesh/Malaika"},"snapshot_id":{"kind":"string","value":"b313944b339d7c915e54ac1dd0c0eedcc273401b"},"revision_id":{"kind":"string","value":"396964061dd6696350292aee8f600681f2ef0608"},"directory_id":{"kind":"string","value":"4e6d15fcab2c48d6d27a337128c249519bd03a47"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-04-15T11:50:13.148706","string":"2021-04-15T11:50:13.148706"},"revision_date":{"kind":"timestamp","value":"2017-05-19T13:19:30","string":"2017-05-19T13:19:30"},"committer_date":{"kind":"timestamp","value":"2017-05-19T13:19:30","string":"2017-05-19T13:19:30"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6456400752067566,"alphanum_fraction":0.6530612111091614,"avg_line_length":28.94444465637207,"blob_id":"a73ddae373ff38fa37ce7f556e7d51754a55f2e1","content_id":"c8d5994ac96e248943940c439d8ea098ed97afbd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":539,"license_type":"no_license","max_line_length":85,"num_lines":18,"path":"/staff/models.py","repo_name":"DarthDanAmesh/Malaika","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models\nfrom malaika.models import PersonDetails\n\n# Create your models here.\nclass Doctor(PersonDetails):\n SPECIALIZATION_CHOICES = {\n ('General','General'),\n ('Dentist','Dentist'),\n ('Physiotherapy','Physiotherapy'),\n ('Pharmacy','Pharmacy'),\n }\n specialization = models.CharField(max_length=200, choices=SPECIALIZATION_CHOICES)\n\n def __str__(self):\n return '{}-{}'.format(self.first_name,self.last_name)\n"},{"alpha_fraction":0.5656402111053467,"alphanum_fraction":0.6175040602684021,"avg_line_length":24.70833396911621,"blob_id":"f248f5e851e8781fcdaeead4d8d1ee5e263a754b","content_id":"190db286f59d92a920e9019038c4421aa19fb258","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":617,"license_type":"no_license","max_line_length":104,"num_lines":24,"path":"/operations/migrations/0003_auto_20170412_0929.py","repo_name":"DarthDanAmesh/Malaika","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n# Generated by Django 1.11 on 2017-04-12 09:29\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('operations', '0002_auto_20170412_0926'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='treatment',\n name='diagnosis',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='malaika.Diagnose'),\n ),\n migrations.DeleteModel(\n name='Diagnose',\n ),\n ]\n"},{"alpha_fraction":0.5335720777511597,"alphanum_fraction":0.5550581812858582,"avg_line_length":32.84848403930664,"blob_id":"d3d30f764c5eb8e5d042f519d582590316ec5ccd","content_id":"7845ccae22e8df54f847083e276482659557680d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1117,"license_type":"no_license","max_line_length":144,"num_lines":33,"path":"/malaika/migrations/0001_initial.py","repo_name":"DarthDanAmesh/Malaika","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n# Generated by Django 1.11 on 2017-04-12 09:24\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Diagnose',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=200)),\n ('code', models.IntegerField(default=0, unique=True)),\n ('description', models.TextField()),\n ],\n ),\n migrations.CreateModel(\n name='Room',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('room_number', models.IntegerField(default=0, unique=True)),\n ('room_type', models.CharField(choices=[('Maternity', 'Maternity'), ('General', 'General'), ('Ward', 'Ward')], max_length=200)),\n ],\n ),\n ]\n"},{"alpha_fraction":0.5420382022857666,"alphanum_fraction":0.5640127658843994,"avg_line_length":42.01369857788086,"blob_id":"724b1c3d54c64e83f0cfbce070be58a039efab3e","content_id":"7956cd90a880ae7c1e2af69b3968d064fc57b50e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3140,"license_type":"no_license","max_line_length":117,"num_lines":73,"path":"/reception/migrations/0001_initial.py","repo_name":"DarthDanAmesh/Malaika","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n# Generated by Django 1.11 on 2017-04-12 09:24\nfrom __future__ import unicode_literals\n\nimport datetime\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ('malaika', '0001_initial'),\n ('staff', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='In_patient',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('date_of_adm', models.DateField(default=datetime.date(2017, 4, 12))),\n ('date_of_discarge', models.DateField()),\n ('diagnosis', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='malaika.Diagnose')),\n ('doctor', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='staff.Doctor')),\n ],\n ),\n migrations.CreateModel(\n name='Out_patient',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('date', models.DateField(default=datetime.date(2017, 4, 12))),\n ('diagnosis', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='malaika.Diagnose')),\n ('doctor', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='staff.Doctor')),\n ],\n ),\n migrations.CreateModel(\n name='Patient',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('first_name', models.CharField(max_length=200)),\n ('last_name', models.CharField(max_length=200)),\n ('gender', models.CharField(choices=[('Male', 'Male'), ('Female', 'Female')], max_length=10)),\n ('dob', models.DateField(default=datetime.date(2017, 4, 12))),\n ('age', models.IntegerField(default=0)),\n ('country', models.CharField(max_length=200)),\n ('city', models.CharField(max_length=200)),\n ('address', models.CharField(max_length=200)),\n ('contact_no', models.CharField(max_length=200)),\n ('next_of_kin', models.CharField(max_length=200)),\n ],\n options={\n 'abstract': False,\n },\n ),\n migrations.AddField(\n model_name='out_patient',\n name='patient',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='reception.Patient'),\n ),\n migrations.AddField(\n model_name='in_patient',\n name='patient',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='reception.Patient'),\n ),\n migrations.AddField(\n model_name='in_patient',\n name='room',\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='malaika.Room'),\n ),\n ]\n"},{"alpha_fraction":0.6247122287750244,"alphanum_fraction":0.6454336047172546,"avg_line_length":27.955554962158203,"blob_id":"3bb666806b493ab283161603242c4302fe32de97","content_id":"9c8fa9134e4f617ee0e318565b63e1d405ae7c5c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1303,"license_type":"no_license","max_line_length":68,"num_lines":45,"path":"/malaika/models.py","repo_name":"DarthDanAmesh/Malaika","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models\n\nfrom django.utils import timezone\n\nclass PersonDetails(models.Model):\n GENDER_CHOICES = {\n ('Male', 'Male'),\n ('Female', 'Female'),\n }\n first_name = models.CharField(max_length=200)\n last_name = models.CharField(max_length=200)\n gender = models.CharField(max_length=10, choices=GENDER_CHOICES)\n dob = models.DateField(default=timezone.now().date())\n age = models.IntegerField(default=0)\n country = models.CharField(max_length=200)\n city = models.CharField(max_length=200)\n address = models.CharField(max_length=200)\n\n class Meta:\n abstract = True\n\n\nclass Room(models.Model):\n ROOM_TYPES = {\n ('General','General'),\n ('Maternity','Maternity'),\n ('Ward','Ward'),\n }\n room_number = models.IntegerField(default=0, unique=True)\n room_type = models.CharField(max_length=200, choices=ROOM_TYPES)\n\n def __str__(self):\n return '{}-{}'.format(self.room_number, self.room_type)\n\n\nclass Diagnose(models.Model):\n name = models.CharField(max_length=200)\n code = models.IntegerField(default=0, unique=True)\n description = models.TextField()\n\n def __str__(self):\n return '{}-{}'.format(self.code, self.name)\n"},{"alpha_fraction":0.5221638083457947,"alphanum_fraction":0.5552216172218323,"avg_line_length":37.02857208251953,"blob_id":"8b17b45ceeaaaef2c31aed652bdfa8da1d42f922","content_id":"499f385afcb0521e104b936a34163b8e2165fdde","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1331,"license_type":"no_license","max_line_length":189,"num_lines":35,"path":"/staff/migrations/0001_initial.py","repo_name":"DarthDanAmesh/Malaika","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n# Generated by Django 1.11 on 2017-04-12 09:24\nfrom __future__ import unicode_literals\n\nimport datetime\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Doctor',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('first_name', models.CharField(max_length=200)),\n ('last_name', models.CharField(max_length=200)),\n ('gender', models.CharField(choices=[('Male', 'Male'), ('Female', 'Female')], max_length=10)),\n ('dob', models.DateField(default=datetime.date(2017, 4, 12))),\n ('age', models.IntegerField(default=0)),\n ('country', models.CharField(max_length=200)),\n ('city', models.CharField(max_length=200)),\n ('address', models.CharField(max_length=200)),\n ('specialization', models.CharField(choices=[('General', 'General'), ('Dentist', 'Dentist'), ('Pharmacy', 'Pharmacy'), ('Physiotherapy', 'Physiotherapy')], max_length=200)),\n ],\n options={\n 'abstract': False,\n },\n ),\n ]\n"},{"alpha_fraction":0.6990595459938049,"alphanum_fraction":0.7045454382896423,"avg_line_length":30.899999618530273,"blob_id":"b4a84a0d05fb3201a72260d3874ac4c4bc59897f","content_id":"fc2360c1ed364f9c26aca8860562c0265db9d346","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1276,"license_type":"no_license","max_line_length":66,"num_lines":40,"path":"/reception/models.py","repo_name":"DarthDanAmesh/Malaika","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\n# project imports\nfrom django.db import models\nfrom malaika.models import PersonDetails, Room\nfrom staff.models import Doctor\nfrom malaika.models import Diagnose\n\nfrom django.utils import timezone\n\n\nclass Patient(PersonDetails):\n contact_no = models.CharField(max_length=200)\n next_of_kin = models.CharField(max_length=200)\n\n def __str__(self):\n return '{} {}'.format(self.first_name,self.last_name)\n\n\nclass In_patient(models.Model):\n patient = models.ForeignKey(Patient, on_delete=models.CASCADE)\n date_of_adm = models.DateField(default=timezone.now().date())\n date_of_discarge = models.DateField()\n diagnosis = models.ForeignKey(Diagnose, on_delete=None)\n doctor = models.ForeignKey(Doctor, on_delete=None)\n room = models.ForeignKey(Room, on_delete=None)\n\n def __str__(self):\n return '{}-{}'.format(self.patient, self.room)\n\n\nclass Out_patient(models.Model):\n patient = models.ForeignKey(Patient, on_delete=models.CASCADE)\n date = models.DateField(default=timezone.now().date())\n diagnosis = models.ForeignKey(Diagnose, on_delete=None)\n doctor = models.ForeignKey(Doctor, on_delete=None)\n\n def __str__(self):\n return '{}'.format(self.patient)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6456400752067566,\n \"alphanum_fraction\": 0.6530612111091614,\n \"avg_line_length\": 28.94444465637207,\n \"blob_id\": \"a73ddae373ff38fa37ce7f556e7d51754a55f2e1\",\n \"content_id\": \"c8d5994ac96e248943940c439d8ea098ed97afbd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 539,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 18,\n \"path\": \"/staff/models.py\",\n \"repo_name\": \"DarthDanAmesh/Malaika\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom __future__ import unicode_literals\\n\\nfrom django.db import models\\nfrom malaika.models import PersonDetails\\n\\n# Create your models here.\\nclass Doctor(PersonDetails):\\n SPECIALIZATION_CHOICES = {\\n ('General','General'),\\n ('Dentist','Dentist'),\\n ('Physiotherapy','Physiotherapy'),\\n ('Pharmacy','Pharmacy'),\\n }\\n specialization = models.CharField(max_length=200, choices=SPECIALIZATION_CHOICES)\\n\\n def __str__(self):\\n return '{}-{}'.format(self.first_name,self.last_name)\\n\"\n },\n {\n \"alpha_fraction\": 0.5656402111053467,\n \"alphanum_fraction\": 0.6175040602684021,\n \"avg_line_length\": 24.70833396911621,\n \"blob_id\": \"f248f5e851e8781fcdaeead4d8d1ee5e263a754b\",\n \"content_id\": \"190db286f59d92a920e9019038c4421aa19fb258\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 617,\n \"license_type\": \"no_license\",\n \"max_line_length\": 104,\n \"num_lines\": 24,\n \"path\": \"/operations/migrations/0003_auto_20170412_0929.py\",\n \"repo_name\": \"DarthDanAmesh/Malaika\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n# Generated by Django 1.11 on 2017-04-12 09:29\\nfrom __future__ import unicode_literals\\n\\nfrom django.db import migrations, models\\nimport django.db.models.deletion\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('operations', '0002_auto_20170412_0926'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='treatment',\\n name='diagnosis',\\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='malaika.Diagnose'),\\n ),\\n migrations.DeleteModel(\\n name='Diagnose',\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5335720777511597,\n \"alphanum_fraction\": 0.5550581812858582,\n \"avg_line_length\": 32.84848403930664,\n \"blob_id\": \"d3d30f764c5eb8e5d042f519d582590316ec5ccd\",\n \"content_id\": \"7845ccae22e8df54f847083e276482659557680d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1117,\n \"license_type\": \"no_license\",\n \"max_line_length\": 144,\n \"num_lines\": 33,\n \"path\": \"/malaika/migrations/0001_initial.py\",\n \"repo_name\": \"DarthDanAmesh/Malaika\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n# Generated by Django 1.11 on 2017-04-12 09:24\\nfrom __future__ import unicode_literals\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n initial = True\\n\\n dependencies = [\\n ]\\n\\n operations = [\\n migrations.CreateModel(\\n name='Diagnose',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('name', models.CharField(max_length=200)),\\n ('code', models.IntegerField(default=0, unique=True)),\\n ('description', models.TextField()),\\n ],\\n ),\\n migrations.CreateModel(\\n name='Room',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('room_number', models.IntegerField(default=0, unique=True)),\\n ('room_type', models.CharField(choices=[('Maternity', 'Maternity'), ('General', 'General'), ('Ward', 'Ward')], max_length=200)),\\n ],\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5420382022857666,\n \"alphanum_fraction\": 0.5640127658843994,\n \"avg_line_length\": 42.01369857788086,\n \"blob_id\": \"724b1c3d54c64e83f0cfbce070be58a039efab3e\",\n \"content_id\": \"7956cd90a880ae7c1e2af69b3968d064fc57b50e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3140,\n \"license_type\": \"no_license\",\n \"max_line_length\": 117,\n \"num_lines\": 73,\n \"path\": \"/reception/migrations/0001_initial.py\",\n \"repo_name\": \"DarthDanAmesh/Malaika\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n# Generated by Django 1.11 on 2017-04-12 09:24\\nfrom __future__ import unicode_literals\\n\\nimport datetime\\nfrom django.db import migrations, models\\nimport django.db.models.deletion\\n\\n\\nclass Migration(migrations.Migration):\\n\\n initial = True\\n\\n dependencies = [\\n ('malaika', '0001_initial'),\\n ('staff', '0001_initial'),\\n ]\\n\\n operations = [\\n migrations.CreateModel(\\n name='In_patient',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('date_of_adm', models.DateField(default=datetime.date(2017, 4, 12))),\\n ('date_of_discarge', models.DateField()),\\n ('diagnosis', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='malaika.Diagnose')),\\n ('doctor', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='staff.Doctor')),\\n ],\\n ),\\n migrations.CreateModel(\\n name='Out_patient',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('date', models.DateField(default=datetime.date(2017, 4, 12))),\\n ('diagnosis', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='malaika.Diagnose')),\\n ('doctor', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='staff.Doctor')),\\n ],\\n ),\\n migrations.CreateModel(\\n name='Patient',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('first_name', models.CharField(max_length=200)),\\n ('last_name', models.CharField(max_length=200)),\\n ('gender', models.CharField(choices=[('Male', 'Male'), ('Female', 'Female')], max_length=10)),\\n ('dob', models.DateField(default=datetime.date(2017, 4, 12))),\\n ('age', models.IntegerField(default=0)),\\n ('country', models.CharField(max_length=200)),\\n ('city', models.CharField(max_length=200)),\\n ('address', models.CharField(max_length=200)),\\n ('contact_no', models.CharField(max_length=200)),\\n ('next_of_kin', models.CharField(max_length=200)),\\n ],\\n options={\\n 'abstract': False,\\n },\\n ),\\n migrations.AddField(\\n model_name='out_patient',\\n name='patient',\\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='reception.Patient'),\\n ),\\n migrations.AddField(\\n model_name='in_patient',\\n name='patient',\\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='reception.Patient'),\\n ),\\n migrations.AddField(\\n model_name='in_patient',\\n name='room',\\n field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='malaika.Room'),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6247122287750244,\n \"alphanum_fraction\": 0.6454336047172546,\n \"avg_line_length\": 27.955554962158203,\n \"blob_id\": \"3bb666806b493ab283161603242c4302fe32de97\",\n \"content_id\": \"9c8fa9134e4f617ee0e318565b63e1d405ae7c5c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1303,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 45,\n \"path\": \"/malaika/models.py\",\n \"repo_name\": \"DarthDanAmesh/Malaika\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom __future__ import unicode_literals\\n\\nfrom django.db import models\\n\\nfrom django.utils import timezone\\n\\nclass PersonDetails(models.Model):\\n GENDER_CHOICES = {\\n ('Male', 'Male'),\\n ('Female', 'Female'),\\n }\\n first_name = models.CharField(max_length=200)\\n last_name = models.CharField(max_length=200)\\n gender = models.CharField(max_length=10, choices=GENDER_CHOICES)\\n dob = models.DateField(default=timezone.now().date())\\n age = models.IntegerField(default=0)\\n country = models.CharField(max_length=200)\\n city = models.CharField(max_length=200)\\n address = models.CharField(max_length=200)\\n\\n class Meta:\\n abstract = True\\n\\n\\nclass Room(models.Model):\\n ROOM_TYPES = {\\n ('General','General'),\\n ('Maternity','Maternity'),\\n ('Ward','Ward'),\\n }\\n room_number = models.IntegerField(default=0, unique=True)\\n room_type = models.CharField(max_length=200, choices=ROOM_TYPES)\\n\\n def __str__(self):\\n return '{}-{}'.format(self.room_number, self.room_type)\\n\\n\\nclass Diagnose(models.Model):\\n name = models.CharField(max_length=200)\\n code = models.IntegerField(default=0, unique=True)\\n description = models.TextField()\\n\\n def __str__(self):\\n return '{}-{}'.format(self.code, self.name)\\n\"\n },\n {\n \"alpha_fraction\": 0.5221638083457947,\n \"alphanum_fraction\": 0.5552216172218323,\n \"avg_line_length\": 37.02857208251953,\n \"blob_id\": \"8b17b45ceeaaaef2c31aed652bdfa8da1d42f922\",\n \"content_id\": \"499f385afcb0521e104b936a34163b8e2165fdde\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1331,\n \"license_type\": \"no_license\",\n \"max_line_length\": 189,\n \"num_lines\": 35,\n \"path\": \"/staff/migrations/0001_initial.py\",\n \"repo_name\": \"DarthDanAmesh/Malaika\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n# Generated by Django 1.11 on 2017-04-12 09:24\\nfrom __future__ import unicode_literals\\n\\nimport datetime\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n initial = True\\n\\n dependencies = [\\n ]\\n\\n operations = [\\n migrations.CreateModel(\\n name='Doctor',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('first_name', models.CharField(max_length=200)),\\n ('last_name', models.CharField(max_length=200)),\\n ('gender', models.CharField(choices=[('Male', 'Male'), ('Female', 'Female')], max_length=10)),\\n ('dob', models.DateField(default=datetime.date(2017, 4, 12))),\\n ('age', models.IntegerField(default=0)),\\n ('country', models.CharField(max_length=200)),\\n ('city', models.CharField(max_length=200)),\\n ('address', models.CharField(max_length=200)),\\n ('specialization', models.CharField(choices=[('General', 'General'), ('Dentist', 'Dentist'), ('Pharmacy', 'Pharmacy'), ('Physiotherapy', 'Physiotherapy')], max_length=200)),\\n ],\\n options={\\n 'abstract': False,\\n },\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6990595459938049,\n \"alphanum_fraction\": 0.7045454382896423,\n \"avg_line_length\": 30.899999618530273,\n \"blob_id\": \"b4a84a0d05fb3201a72260d3874ac4c4bc59897f\",\n \"content_id\": \"fc2360c1ed364f9c26aca8860562c0265db9d346\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1276,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 40,\n \"path\": \"/reception/models.py\",\n \"repo_name\": \"DarthDanAmesh/Malaika\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom __future__ import unicode_literals\\n\\n# project imports\\nfrom django.db import models\\nfrom malaika.models import PersonDetails, Room\\nfrom staff.models import Doctor\\nfrom malaika.models import Diagnose\\n\\nfrom django.utils import timezone\\n\\n\\nclass Patient(PersonDetails):\\n contact_no = models.CharField(max_length=200)\\n next_of_kin = models.CharField(max_length=200)\\n\\n def __str__(self):\\n return '{} {}'.format(self.first_name,self.last_name)\\n\\n\\nclass In_patient(models.Model):\\n patient = models.ForeignKey(Patient, on_delete=models.CASCADE)\\n date_of_adm = models.DateField(default=timezone.now().date())\\n date_of_discarge = models.DateField()\\n diagnosis = models.ForeignKey(Diagnose, on_delete=None)\\n doctor = models.ForeignKey(Doctor, on_delete=None)\\n room = models.ForeignKey(Room, on_delete=None)\\n\\n def __str__(self):\\n return '{}-{}'.format(self.patient, self.room)\\n\\n\\nclass Out_patient(models.Model):\\n patient = models.ForeignKey(Patient, on_delete=models.CASCADE)\\n date = models.DateField(default=timezone.now().date())\\n diagnosis = models.ForeignKey(Diagnose, on_delete=None)\\n doctor = models.ForeignKey(Doctor, on_delete=None)\\n\\n def __str__(self):\\n return '{}'.format(self.patient)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":7,"string":"7"}}},{"rowIdx":2979140,"cells":{"repo_name":{"kind":"string","value":"BayCEER/python-baySerial"},"repo_url":{"kind":"string","value":"https://github.com/BayCEER/python-baySerial"},"snapshot_id":{"kind":"string","value":"a18685a4a900a1b708f5d40a02f15f834e514dd7"},"revision_id":{"kind":"string","value":"6c193a475c764f92ba15de90dc2bf96983767ed1"},"directory_id":{"kind":"string","value":"04275cdc67729399643eeffd5f33269ee44807f3"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-06-27T03:26:23.335648","string":"2020-06-27T03:26:23.335648"},"revision_date":{"kind":"timestamp","value":"2019-08-01T06:04:16","string":"2019-08-01T06:04:16"},"committer_date":{"kind":"timestamp","value":"2019-08-01T06:04:16","string":"2019-08-01T06:04:16"},"github_id":{"kind":"number","value":199831537,"string":"199,831,537"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.699999988079071,"alphanum_fraction":0.699999988079071,"avg_line_length":12.666666984558105,"blob_id":"038a42e368aed5ecc9ab5a53f5eb74132e8db67e","content_id":"530334dcdea219041d33ec6bee5a7ad533583c21","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":40,"license_type":"no_license","max_line_length":23,"num_lines":3,"path":"/baySerial/__init__.py","repo_name":"BayCEER/python-baySerial","src_encoding":"UTF-8","text":"\"\"\"baySerial\"\"\"\n\nfrom baySerial import *"},{"alpha_fraction":0.4893918037414551,"alphanum_fraction":0.5091937780380249,"avg_line_length":28.650793075561523,"blob_id":"eb72c2ec4cd42efa44ad97356848f92aeb9e8f16","content_id":"3fccb83b972678a5d01a1f97cbc5cb89d97350f9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5656,"license_type":"no_license","max_line_length":100,"num_lines":189,"path":"/baySerial/baySerial.py","repo_name":"BayCEER/python-baySerial","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# Python port of BaySerial PHP library\n# Author: Stefan Holzheu <stefan.holzheu@bayceer.uni-bayreuth.de>\n#\n# \n# \nfrom time import time, sleep\nimport serial\nfrom struct import pack, unpack\nfrom serial.serialutil import XON, XOFF, to_bytes\n\nXBEE_ESCAPE = to_bytes([0x7d])\nXBEE_DELIM = to_bytes([0x7e])\nAPI_DATA = to_bytes([0x1])\nAPI_ACK = to_bytes([0x2])\nTX_OK = to_bytes([0x1])\nTX_CHECKSUM_FAILED = to_bytes([0x2])\nTX_BREAK = to_bytes([0x3])\n\nclass BaySerial:\n def __init__(self):\n self.ser=None\n self.stack=[]\n self.count=0;\n \n def begin(self,dev,baud=38400):\n self.ser=serial.Serial(dev,baud,timeout=0)\n \n def close(self):\n self.ser.close()\n self.ser=None\n \n def send(self,frame,api=API_DATA,timeout=120):\n frame=XBEE_DELIM+self._escape(pack(\"<B\",len(frame))+api+frame+self._calcChecksum(api+frame))\n self.ser.write(frame)\n if(api != API_DATA):\n return 0;\n res = self.getFrame(timeout)\n if(res == False):\n return 2\n if(res['api']!=API_ACK):\n self.sendTXBreak()\n self.ser.write(frame)\n res = self.getFrame(timeout)\n if(res == False):\n return 2 \n \n if(res['api']==API_ACK):\n if(res['frame']==TX_OK):\n return 0\n if(res['frame']==TX_CHECKSUM_FAILED):\n return 1\n if(res['frame']==TX_BREAK):\n return 3\n return 2\n \n def read(self,count=1):\n data = self.ser.read(count)\n if( not len(data)):\n return self.count\n if( not len(self.stack)):\n delim_pos=data.find(XBEE_DELIM)\n if(delim_pos==-1):\n return 0\n data=data[delim_pos+1:len(data)]\n \n chunks=data.split(XBEE_DELIM)\n offset=len(self.stack)-1\n if(offset==-1):\n offset=0\n i=0\n while(i<len(chunks)):\n index=i+offset\n if(len(self.stack)==index):\n self.stack.append({'ts':time(),'ok':False})\n if(not 'rawframe' in self.stack[index]):\n self.stack[index]['rawframe']=chunks[i]\n else:\n self.stack[index]['rawframe']+=chunks[i]\n self.stack[index]['frame']=self._unescape(self.stack[index]['rawframe'])\n res=self._parseFrame(self.stack[index]['frame'])\n if(res==0):\n self.stack[index]['ok']=True\n self.count+=1\n if(res==1):\n del self.stack[index]\n offset=offset-1\n if((i+1)<len(chunks) and 'ok' in self.stack[index] and not self.stack[index]['ok']):\n del self.stack[index]\n offset=offset-1\n i=i+1\n \n anz=len(self.stack)\n if(anz>0 and not self.stack[anz-1]['ok']):\n anz=anz-1\n return anz\n \n \n \n \n def getFrame(self,timeout=120):\n while(self.read()==0 and timeout>0):\n sleep(0.001)\n timeout=timeout-0.001\n if(timeout<0):\n return False\n if(self.count==0):\n return False\n \n frame=self.stack[0]\n del self.stack[0]\n self.count-=1\n frame['api']=frame['frame'][1:2]\n frame['length']=frame['frame'][0:1]\n frame['frame']=frame['frame'][2:len(frame['frame'])-1]\n return frame\n\n \n def _parseFrame(self,frame):\n if(len(frame)<3):\n return 2\n l=len(frame)\n length=frame[0:1]\n checksum=frame[l-1:l]\n data=frame[1:l-1]\n calculatedLength=pack('<B',len(data)-1)\n if(calculatedLength==length):\n calculatedChecksum=self._calcChecksum(data)\n if(checksum == calculatedChecksum):\n self._sendAck(TX_OK)\n return 0\n else:\n self._sendAck(TX_CHECKSUM_FAILED)\n return 1\n return 2\n \n \n \n def _calcChecksum(self,data):\n checksum=0\n i=1\n while i<=len(data):\n checksum+=unpack('<B',data[i-1:i])[0]\n i=i+1\n checksum=checksum & 0xFF\n checksum = 0xFF - checksum\n checksum=pack('<B',checksum)\n return checksum\n \n def _escape(self,data):\n res=b''\n i=1\n while i<=len(data):\n if(data[i-1:i] in [XBEE_ESCAPE,XBEE_DELIM,XOFF,XON]):\n res=res+XBEE_ESCAPE+pack(\"<B\",0x20^unpack('<B',data[i-1:i])[0])\n else:\n res=res+data[i-1:i]\n i=i+1\n return res\n \n def _unescape(self,data):\n res=b''\n i=1\n while i<=len(data):\n if(data[i-1:i]==XBEE_ESCAPE):\n i=i+1\n if(i<=len(data)):\n res=res+pack(\"<B\",0x20^unpack('<B',data[i-1:i])[0])\n else:\n res=res+data[i-1:i]\n i=i+1\n return res \n \n def _sendAck(self,data):\n self.send(data,API_ACK)\n \n def sendTXBreak(self):\n self._sendAck(TX_BREAK)\n \n \n \n \n \n \n \n \n "},{"alpha_fraction":0.6483516693115234,"alphanum_fraction":0.6571428775787354,"avg_line_length":22.947368621826172,"blob_id":"d3f9fa3fa48bcc4c5e303b233b576985c2e6b9ec","content_id":"8940d982a5c186a2ff6318637ce18beca723302f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":455,"license_type":"no_license","max_line_length":57,"num_lines":19,"path":"/setup.py","repo_name":"BayCEER/python-baySerial","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n\ntry:\n from setuptools import setup\nexcept ImportError as ierr:\n print 'Import error :' + str(ierr)\n from distutils.core import setup\n\nsetup(\n name='baySerial',\n version='0.0.1',\n packages=['baySerial'],\n description='Python port of the baySerial protocoll',\n author='Stefan Holzheu',\n author_email='stefan.holzheu@bayceer.uni-bayreuth.de',\n license='GPL2',\n keywords='serial bayeos',\n classifiers=['Programming Language :: Python'],\n)\n"},{"alpha_fraction":0.6680412292480469,"alphanum_fraction":0.6773195862770081,"avg_line_length":20.954545974731445,"blob_id":"ab1a72bc9dec54fdd7b20a7ae329e67dc37a9035","content_id":"06354672e107f3c32e8358fff647dd823a9460fb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":970,"license_type":"no_license","max_line_length":74,"num_lines":44,"path":"/baySerial/samplescripts/thread.py","repo_name":"BayCEER/python-baySerial","src_encoding":"UTF-8","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport baySerial\nfrom bayeosgatewayclient import BayEOSWriter, BayEOSSender\nimport tempfile\nfrom os import path\nfrom threading import Thread, Lock\nfrom time import sleep\n\nPATH = path.join(tempfile.gettempdir(),'bayeos-device') \nwriter = BayEOSWriter(PATH)\n\nNAME = 'BayEOSSerial-Thread-Example'\nURL = 'http://bayconf.bayceer.uni-bayreuth.de/gateway/frame/saveFlat'\n\nwriter = BayEOSWriter(PATH)\nwriter.save_msg('Writer was started.')\n\nsender = BayEOSSender(PATH, NAME, URL)\nsender.start()\n\nser = baySerial.BaySerial()\nser.begin('/dev/ttyUSB0',9600)\n\nlock=Lock();\n\ndef handleSerial():\n while True:\n frame=ser.getFrame()\n if not frame==False:\n # get lock for writer\n lock.acquire()\n writer.save_frame(frame['frame'], frame['ts'], \"Serial1\",True)\n lock.release()\n\n\nt = Thread(target=handleSerial, args=())\nt.start()\n\n\nwhile(True):\n print \"Main loop\"\n sleep(10) \n\n\n\n"},{"alpha_fraction":0.6430867910385132,"alphanum_fraction":0.6623794436454773,"avg_line_length":19.53333282470703,"blob_id":"3969916af3aa1f296b556700df032c287e1f76b5","content_id":"477214b6b8ef15a8e276458e2de38d0c9b7b7090","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":311,"license_type":"no_license","max_line_length":54,"num_lines":15,"path":"/baySerial/samplescripts/sample.py","repo_name":"BayCEER/python-baySerial","src_encoding":"UTF-8","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport baySerial\nfrom bayeosgatewayclient import BayEOSFrame\n\nser = baySerial.BaySerial()\n\nser.begin('/dev/ttyUSB0',9600)\n\nwhile(True):\n frame=ser.getFrame()\n if(not frame==False):\n # print(frame)\n print(BayEOSFrame.parse_frame(frame['frame']))\n\n\n\n"},{"alpha_fraction":0.7231833934783936,"alphanum_fraction":0.7231833934783936,"avg_line_length":36.69565200805664,"blob_id":"d39501aacda4cc75467c34a8ebd0d58f41be5caa","content_id":"d14da6317437eef43a241edc951b0a865ff22116","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":867,"license_type":"no_license","max_line_length":132,"num_lines":23,"path":"/README.md","repo_name":"BayCEER/python-baySerial","src_encoding":"UTF-8","text":"# python-baySerial\nBaySerial implementation for Python\n\n## Installation\nYou can either use the setup.py script or a Linux binary to install the package.\n\n### Setup.py\nDo the following steps to install the package via the setup.py script:\n- git clone request ```git clone git://github.com/BayCEER/python-baySerial.git```\n- find the right directory ```cd python-baySerial```\n- run ```python setup.py install``` as root\n\n### Linux Binary (for Debian)\n- add the following repositories to /etc/apt/sources.list ```deb http://www.bayceer.uni-bayreuth.de/repos/apt/debian squeeze main```\n- install key ```wget -O - http://www.bayceer.uni-bayreuth.de/repos/apt/conf/bayceer_repo.gpg.key | apt-key add -```\n- ```apt-get update```\n- ```apt-get install python-baySerial```\n\nAlternatively:\n- run ```dpkg -i python-baySerial_*_all.deb``` as root\n\n## Usage\nSee examples for usage.\n"}],"string":"[\n {\n \"alpha_fraction\": 0.699999988079071,\n \"alphanum_fraction\": 0.699999988079071,\n \"avg_line_length\": 12.666666984558105,\n \"blob_id\": \"038a42e368aed5ecc9ab5a53f5eb74132e8db67e\",\n \"content_id\": \"530334dcdea219041d33ec6bee5a7ad533583c21\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 40,\n \"license_type\": \"no_license\",\n \"max_line_length\": 23,\n \"num_lines\": 3,\n \"path\": \"/baySerial/__init__.py\",\n \"repo_name\": \"BayCEER/python-baySerial\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"baySerial\\\"\\\"\\\"\\n\\nfrom baySerial import *\"\n },\n {\n \"alpha_fraction\": 0.4893918037414551,\n \"alphanum_fraction\": 0.5091937780380249,\n \"avg_line_length\": 28.650793075561523,\n \"blob_id\": \"eb72c2ec4cd42efa44ad97356848f92aeb9e8f16\",\n \"content_id\": \"3fccb83b972678a5d01a1f97cbc5cb89d97350f9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5656,\n \"license_type\": \"no_license\",\n \"max_line_length\": 100,\n \"num_lines\": 189,\n \"path\": \"/baySerial/baySerial.py\",\n \"repo_name\": \"BayCEER/python-baySerial\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n#\\n# This program is free software: you can redistribute it and/or modify\\n# it under the terms of the GNU General Public License as published by\\n# the Free Software Foundation, either version 3 of the License, or\\n# (at your option) any later version.\\n#\\n# This program is distributed in the hope that it will be useful,\\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\\n# GNU General Public License for more details.\\n#\\n# Python port of BaySerial PHP library\\n# Author: Stefan Holzheu <stefan.holzheu@bayceer.uni-bayreuth.de>\\n#\\n# \\n# \\nfrom time import time, sleep\\nimport serial\\nfrom struct import pack, unpack\\nfrom serial.serialutil import XON, XOFF, to_bytes\\n\\nXBEE_ESCAPE = to_bytes([0x7d])\\nXBEE_DELIM = to_bytes([0x7e])\\nAPI_DATA = to_bytes([0x1])\\nAPI_ACK = to_bytes([0x2])\\nTX_OK = to_bytes([0x1])\\nTX_CHECKSUM_FAILED = to_bytes([0x2])\\nTX_BREAK = to_bytes([0x3])\\n\\nclass BaySerial:\\n def __init__(self):\\n self.ser=None\\n self.stack=[]\\n self.count=0;\\n \\n def begin(self,dev,baud=38400):\\n self.ser=serial.Serial(dev,baud,timeout=0)\\n \\n def close(self):\\n self.ser.close()\\n self.ser=None\\n \\n def send(self,frame,api=API_DATA,timeout=120):\\n frame=XBEE_DELIM+self._escape(pack(\\\"<B\\\",len(frame))+api+frame+self._calcChecksum(api+frame))\\n self.ser.write(frame)\\n if(api != API_DATA):\\n return 0;\\n res = self.getFrame(timeout)\\n if(res == False):\\n return 2\\n if(res['api']!=API_ACK):\\n self.sendTXBreak()\\n self.ser.write(frame)\\n res = self.getFrame(timeout)\\n if(res == False):\\n return 2 \\n \\n if(res['api']==API_ACK):\\n if(res['frame']==TX_OK):\\n return 0\\n if(res['frame']==TX_CHECKSUM_FAILED):\\n return 1\\n if(res['frame']==TX_BREAK):\\n return 3\\n return 2\\n \\n def read(self,count=1):\\n data = self.ser.read(count)\\n if( not len(data)):\\n return self.count\\n if( not len(self.stack)):\\n delim_pos=data.find(XBEE_DELIM)\\n if(delim_pos==-1):\\n return 0\\n data=data[delim_pos+1:len(data)]\\n \\n chunks=data.split(XBEE_DELIM)\\n offset=len(self.stack)-1\\n if(offset==-1):\\n offset=0\\n i=0\\n while(i<len(chunks)):\\n index=i+offset\\n if(len(self.stack)==index):\\n self.stack.append({'ts':time(),'ok':False})\\n if(not 'rawframe' in self.stack[index]):\\n self.stack[index]['rawframe']=chunks[i]\\n else:\\n self.stack[index]['rawframe']+=chunks[i]\\n self.stack[index]['frame']=self._unescape(self.stack[index]['rawframe'])\\n res=self._parseFrame(self.stack[index]['frame'])\\n if(res==0):\\n self.stack[index]['ok']=True\\n self.count+=1\\n if(res==1):\\n del self.stack[index]\\n offset=offset-1\\n if((i+1)<len(chunks) and 'ok' in self.stack[index] and not self.stack[index]['ok']):\\n del self.stack[index]\\n offset=offset-1\\n i=i+1\\n \\n anz=len(self.stack)\\n if(anz>0 and not self.stack[anz-1]['ok']):\\n anz=anz-1\\n return anz\\n \\n \\n \\n \\n def getFrame(self,timeout=120):\\n while(self.read()==0 and timeout>0):\\n sleep(0.001)\\n timeout=timeout-0.001\\n if(timeout<0):\\n return False\\n if(self.count==0):\\n return False\\n \\n frame=self.stack[0]\\n del self.stack[0]\\n self.count-=1\\n frame['api']=frame['frame'][1:2]\\n frame['length']=frame['frame'][0:1]\\n frame['frame']=frame['frame'][2:len(frame['frame'])-1]\\n return frame\\n\\n \\n def _parseFrame(self,frame):\\n if(len(frame)<3):\\n return 2\\n l=len(frame)\\n length=frame[0:1]\\n checksum=frame[l-1:l]\\n data=frame[1:l-1]\\n calculatedLength=pack('<B',len(data)-1)\\n if(calculatedLength==length):\\n calculatedChecksum=self._calcChecksum(data)\\n if(checksum == calculatedChecksum):\\n self._sendAck(TX_OK)\\n return 0\\n else:\\n self._sendAck(TX_CHECKSUM_FAILED)\\n return 1\\n return 2\\n \\n \\n \\n def _calcChecksum(self,data):\\n checksum=0\\n i=1\\n while i<=len(data):\\n checksum+=unpack('<B',data[i-1:i])[0]\\n i=i+1\\n checksum=checksum & 0xFF\\n checksum = 0xFF - checksum\\n checksum=pack('<B',checksum)\\n return checksum\\n \\n def _escape(self,data):\\n res=b''\\n i=1\\n while i<=len(data):\\n if(data[i-1:i] in [XBEE_ESCAPE,XBEE_DELIM,XOFF,XON]):\\n res=res+XBEE_ESCAPE+pack(\\\"<B\\\",0x20^unpack('<B',data[i-1:i])[0])\\n else:\\n res=res+data[i-1:i]\\n i=i+1\\n return res\\n \\n def _unescape(self,data):\\n res=b''\\n i=1\\n while i<=len(data):\\n if(data[i-1:i]==XBEE_ESCAPE):\\n i=i+1\\n if(i<=len(data)):\\n res=res+pack(\\\"<B\\\",0x20^unpack('<B',data[i-1:i])[0])\\n else:\\n res=res+data[i-1:i]\\n i=i+1\\n return res \\n \\n def _sendAck(self,data):\\n self.send(data,API_ACK)\\n \\n def sendTXBreak(self):\\n self._sendAck(TX_BREAK)\\n \\n \\n \\n \\n \\n \\n \\n \\n \"\n },\n {\n \"alpha_fraction\": 0.6483516693115234,\n \"alphanum_fraction\": 0.6571428775787354,\n \"avg_line_length\": 22.947368621826172,\n \"blob_id\": \"d3f9fa3fa48bcc4c5e303b233b576985c2e6b9ec\",\n \"content_id\": \"8940d982a5c186a2ff6318637ce18beca723302f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 455,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 19,\n \"path\": \"/setup.py\",\n \"repo_name\": \"BayCEER/python-baySerial\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n\\ntry:\\n from setuptools import setup\\nexcept ImportError as ierr:\\n print 'Import error :' + str(ierr)\\n from distutils.core import setup\\n\\nsetup(\\n name='baySerial',\\n version='0.0.1',\\n packages=['baySerial'],\\n description='Python port of the baySerial protocoll',\\n author='Stefan Holzheu',\\n author_email='stefan.holzheu@bayceer.uni-bayreuth.de',\\n license='GPL2',\\n keywords='serial bayeos',\\n classifiers=['Programming Language :: Python'],\\n)\\n\"\n },\n {\n \"alpha_fraction\": 0.6680412292480469,\n \"alphanum_fraction\": 0.6773195862770081,\n \"avg_line_length\": 20.954545974731445,\n \"blob_id\": \"ab1a72bc9dec54fdd7b20a7ae329e67dc37a9035\",\n \"content_id\": \"06354672e107f3c32e8358fff647dd823a9460fb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 970,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 44,\n \"path\": \"/baySerial/samplescripts/thread.py\",\n \"repo_name\": \"BayCEER/python-baySerial\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n# -*- coding: utf-8 -*-\\n\\nimport baySerial\\nfrom bayeosgatewayclient import BayEOSWriter, BayEOSSender\\nimport tempfile\\nfrom os import path\\nfrom threading import Thread, Lock\\nfrom time import sleep\\n\\nPATH = path.join(tempfile.gettempdir(),'bayeos-device') \\nwriter = BayEOSWriter(PATH)\\n\\nNAME = 'BayEOSSerial-Thread-Example'\\nURL = 'http://bayconf.bayceer.uni-bayreuth.de/gateway/frame/saveFlat'\\n\\nwriter = BayEOSWriter(PATH)\\nwriter.save_msg('Writer was started.')\\n\\nsender = BayEOSSender(PATH, NAME, URL)\\nsender.start()\\n\\nser = baySerial.BaySerial()\\nser.begin('/dev/ttyUSB0',9600)\\n\\nlock=Lock();\\n\\ndef handleSerial():\\n while True:\\n frame=ser.getFrame()\\n if not frame==False:\\n # get lock for writer\\n lock.acquire()\\n writer.save_frame(frame['frame'], frame['ts'], \\\"Serial1\\\",True)\\n lock.release()\\n\\n\\nt = Thread(target=handleSerial, args=())\\nt.start()\\n\\n\\nwhile(True):\\n print \\\"Main loop\\\"\\n sleep(10) \\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6430867910385132,\n \"alphanum_fraction\": 0.6623794436454773,\n \"avg_line_length\": 19.53333282470703,\n \"blob_id\": \"3969916af3aa1f296b556700df032c287e1f76b5\",\n \"content_id\": \"477214b6b8ef15a8e276458e2de38d0c9b7b7090\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 311,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 15,\n \"path\": \"/baySerial/samplescripts/sample.py\",\n \"repo_name\": \"BayCEER/python-baySerial\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n# -*- coding: utf-8 -*-\\n\\nimport baySerial\\nfrom bayeosgatewayclient import BayEOSFrame\\n\\nser = baySerial.BaySerial()\\n\\nser.begin('/dev/ttyUSB0',9600)\\n\\nwhile(True):\\n frame=ser.getFrame()\\n if(not frame==False):\\n # print(frame)\\n print(BayEOSFrame.parse_frame(frame['frame']))\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7231833934783936,\n \"alphanum_fraction\": 0.7231833934783936,\n \"avg_line_length\": 36.69565200805664,\n \"blob_id\": \"d39501aacda4cc75467c34a8ebd0d58f41be5caa\",\n \"content_id\": \"d14da6317437eef43a241edc951b0a865ff22116\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 867,\n \"license_type\": \"no_license\",\n \"max_line_length\": 132,\n \"num_lines\": 23,\n \"path\": \"/README.md\",\n \"repo_name\": \"BayCEER/python-baySerial\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# python-baySerial\\nBaySerial implementation for Python\\n\\n## Installation\\nYou can either use the setup.py script or a Linux binary to install the package.\\n\\n### Setup.py\\nDo the following steps to install the package via the setup.py script:\\n- git clone request ```git clone git://github.com/BayCEER/python-baySerial.git```\\n- find the right directory ```cd python-baySerial```\\n- run ```python setup.py install``` as root\\n\\n### Linux Binary (for Debian)\\n- add the following repositories to /etc/apt/sources.list ```deb http://www.bayceer.uni-bayreuth.de/repos/apt/debian squeeze main```\\n- install key ```wget -O - http://www.bayceer.uni-bayreuth.de/repos/apt/conf/bayceer_repo.gpg.key | apt-key add -```\\n- ```apt-get update```\\n- ```apt-get install python-baySerial```\\n\\nAlternatively:\\n- run ```dpkg -i python-baySerial_*_all.deb``` as root\\n\\n## Usage\\nSee examples for usage.\\n\"\n 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like","value":[{"alpha_fraction":0.6368222236633301,"alphanum_fraction":0.6481714844703674,"avg_line_length":25.433332443237305,"blob_id":"e0f3484fba242d67675355c36a7b3b5e7086df87","content_id":"d4b5fca6f7b97ec73e20b081a212e38e6ac9cc2d","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":793,"license_type":"permissive","max_line_length":88,"num_lines":30,"path":"/hooks/upgrade-charm","repo_name":"openstack/charm-heat","src_encoding":"UTF-8","text":"#!/bin/bash -e\n# Wrapper to deal with newer Ubuntu versions that don't have py2 installed\n# by default.\n\ndeclare -a DEPS=('apt' 'netaddr' 'netifaces' 'pip' 'yaml' 'dnspython' 'openstackclient')\n\n# drop this when trusty support is ended. Only need python3 at that point\nrelease=$(lsb_release -c -s)\nif [ \"$release\" == \"trusty\" ]; then\n PYTHON=\"python\"\nelse\n PYTHON=\"python3\"\n # if python-openstackclient is installed then remove it\n if dpkg -s python-openstackclient 2>&1 > /dev/null; then\n apt purge -y python-openstackclient\n fi\nfi\n\ncheck_and_install() {\n pkg=\"${1}-${2}\"\n if ! dpkg -s ${pkg} 2>&1 > /dev/null; then\n apt-get -y install ${pkg}\n fi\n}\n\nfor dep in ${DEPS[@]}; do\n check_and_install ${PYTHON} ${dep}\ndone\n\nexec ./hooks/upgrade-charm.real\n"},{"alpha_fraction":0.7232415676116943,"alphanum_fraction":0.7247706651687622,"avg_line_length":28.727272033691406,"blob_id":"201a3ba660cf4122186d6da0a2deef4f6f788e7b","content_id":"8b4a0a384f874abd2d5c93ad5319b2a2870708f7","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":654,"license_type":"permissive","max_line_length":106,"num_lines":22,"path":"/actions/domain-setup","repo_name":"openstack/charm-heat","src_encoding":"UTF-8","text":"#!/bin/bash\n\nset -e\n\n. /root/admin-openrc-v3\n\nopenstack domain show heat || {\n openstack domain create --description \"Stack projects and users\" heat\n}\n\nopenstack user show --domain heat heat_domain_admin || {\n openstack user create --domain heat --password `leader-get heat-domain-admin-passwd` heat_domain_admin\n}\n\nopenstack role add --domain heat --user-domain heat --user heat_domain_admin admin || {\n # NOTE(jamespage): Fallback to trying older style --user-domain -less configuration\n openstack role add --domain heat --user heat_domain_admin admin\n}\n\nopenstack role show heat_stack_user || {\n openstack role create heat_stack_user\n}\n"},{"alpha_fraction":0.6153894066810608,"alphanum_fraction":0.625981330871582,"avg_line_length":39.73604202270508,"blob_id":"6c796b7ee89ef71df854b321769e64ea96c74c82","content_id":"5858a39a33d8eab21b9295e27b467ffd3330feae","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":16050,"license_type":"permissive","max_line_length":79,"num_lines":394,"path":"/unit_tests/test_heat_relations.py","repo_name":"openstack/charm-heat","src_encoding":"UTF-8","text":"# Copyright 2016 Canonical Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os.path\nimport sys\n\nfrom unittest.mock import call, patch, MagicMock\nfrom test_utils import CharmTestCase, patch_open\n\n# python-apt is not installed as part of test-requirements but is imported by\n# some charmhelpers modules so create a fake import.\nmock_apt = MagicMock()\nsys.modules['apt'] = mock_apt\nmock_apt.apt_pkg = MagicMock()\n\n\nimport heat_utils as utils\n\n_reg = utils.register_configs\n_map = utils.restart_map\n\nutils.register_configs = MagicMock()\nutils.restart_map = MagicMock()\n\nwith patch('charmhelpers.contrib.hardening.harden.harden') as mock_dec:\n mock_dec.side_effect = (lambda *dargs, **dkwargs: lambda f:\n lambda *args, **kwargs: f(*args, **kwargs))\n import heat_relations as relations\n\nutils.register_configs = _reg\nutils.restart_map = _map\n\nTO_PATCH = [\n # charmhelpers.core.hookenv\n 'Hooks',\n 'config',\n 'is_leader',\n 'leader_set',\n 'log',\n 'open_port',\n 'relation_set',\n 'related_units',\n # charmhelpers.core.host\n 'apt_install',\n 'apt_update',\n 'restart_on_change',\n 'service_restart',\n # charmhelpers.contrib.openstack.utils\n 'charm_dir',\n 'configure_installation_source',\n 'determine_packages',\n 'openstack_upgrade_available',\n 'os_release',\n 'sync_db_with_multi_ipv6_addresses',\n # charmhelpers.contrib.openstack.ha.utils\n 'generate_ha_relation_data',\n # charmhelpers.contrib.hahelpers.cluster_utils\n # heat_utils\n 'restart_map',\n 'register_configs',\n 'do_openstack_upgrade',\n 'remove_old_packages',\n 'services',\n # other\n 'execd_preinstall',\n 'log',\n 'migrate_database',\n 'is_elected_leader',\n 'relation_ids',\n 'relation_get',\n 'local_unit',\n 'get_relation_ip',\n 'is_db_maintenance_mode',\n]\n\n\nclass HeatRelationTests(CharmTestCase):\n\n def setUp(self):\n super(HeatRelationTests, self).setUp(relations, TO_PATCH)\n self.config.side_effect = self.test_config.get\n self.charm_dir.return_value = '/var/lib/juju/charms/heat/charm'\n\n @patch.object(relations.policyd, 'maybe_do_policyd_overrides')\n def test_install_hook(self, mock_maybe_do_policyd_overrides):\n repo = 'cloud:precise-havana'\n self.determine_packages.return_value = [\n 'python-keystoneclient', 'uuid', 'heat-api',\n 'heat-api-cfn', 'heat-engine']\n self.test_config.set('openstack-origin', repo)\n relations.install()\n self.configure_installation_source.assert_called_with(repo)\n self.assertTrue(self.apt_update.called)\n self.apt_install.assert_called_with(['python-keystoneclient',\n 'uuid', 'heat-api',\n 'heat-api-cfn',\n 'heat-engine'], fatal=True)\n self.assertTrue(self.execd_preinstall.called)\n\n @patch.object(relations, 'configure_https')\n @patch.object(relations.policyd,\n 'maybe_do_policyd_overrides_on_config_changed')\n @patch.object(relations, 'update_nrpe_config')\n def test_config_changed_no_upgrade(\n self,\n mock_update_nrpe_config,\n maybe_do_policyd_overrides_on_config_changed,\n mock_configure_https,\n ):\n self.openstack_upgrade_available.return_value = False\n relations.config_changed()\n\n @patch.object(relations, 'configure_https')\n @patch.object(relations.policyd,\n 'maybe_do_policyd_overrides_on_config_changed')\n @patch.object(relations, 'update_nrpe_config')\n def test_config_changed_with_upgrade(\n self,\n mock_update_nrpe_config,\n maybe_do_policyd_overrides_on_config_changed,\n mock_configure_https\n ):\n self.openstack_upgrade_available.return_value = True\n relations.config_changed()\n self.assertTrue(self.do_openstack_upgrade.called)\n\n @patch.object(relations, 'configure_https')\n @patch.object(relations.policyd,\n 'maybe_do_policyd_overrides_on_config_changed')\n @patch.object(relations, 'update_nrpe_config')\n def test_config_changed_with_openstack_upgrade_action(\n self,\n mock_update_nrpe_config,\n maybe_do_policyd_overrides_on_config_changed,\n mock_configure_https\n ):\n self.openstack_upgrade_available.return_value = True\n self.test_config.set('action-managed-upgrade', True)\n\n relations.config_changed()\n\n self.assertFalse(self.do_openstack_upgrade.called)\n\n @patch('os.path.isfile')\n @patch('os.remove')\n @patch.object(relations, 'leader_elected')\n @patch.object(relations.policyd, 'maybe_do_policyd_overrides')\n @patch.object(relations, 'update_nrpe_config')\n def test_upgrade_charm(self,\n mock_update_nrpe_config,\n mock_maybe_do_policyd_overrides,\n leader_elected, os_remove, os_path_isfile):\n os_path_isfile.return_value = False\n self.is_leader.return_value = False\n self.remove_old_packages.return_value = True\n self.services.return_value = ['heat-api']\n\n relations.upgrade_charm()\n leader_elected.assert_called_once_with()\n os_path_isfile.assert_not_called()\n # now say we are the leader\n self.is_leader.return_value = True\n os_path_isfile.return_value = False\n\n relations.upgrade_charm()\n self.leader_set.assert_not_called()\n os_path_isfile.return_value = True\n with patch_open() as (mock_open, mock_file):\n mock_file.read.return_value = \"abc\"\n relations.upgrade_charm()\n filename = os.path.join(relations.HEAT_PATH, 'encryption-key')\n mock_open.assert_called_once_with(filename, 'r')\n self.leader_set.assert_called_once_with(\n {'heat-auth-encryption-key': 'abc'})\n os_remove.assert_called_once_with(filename)\n self.remove_old_packages.assert_called_with()\n self.service_restart.assert_called_with('heat-api')\n\n def test_db_joined(self):\n self.get_relation_ip.return_value = '192.168.20.1'\n relations.db_joined()\n self.relation_set.assert_called_with(heat_database='heat',\n heat_username='heat',\n heat_hostname='192.168.20.1')\n self.assertTrue(self.get_relation_ip.called)\n\n def _shared_db_test(self, configs):\n self.is_db_maintenance_mode.return_value = False\n self.relation_get.return_value = 'heat/0 heat/1'\n self.local_unit.return_value = 'heat/0'\n configs.complete_contexts = MagicMock()\n configs.complete_contexts.return_value = ['shared-db']\n configs.write = MagicMock()\n relations.db_changed()\n self.assertTrue(self.migrate_database.called)\n\n @patch.object(relations, 'CONFIGS')\n def test_db_changed(self, configs):\n self._shared_db_test(configs)\n self.assertEqual([call('/etc/heat/heat.conf')],\n configs.write.call_args_list)\n\n @patch.object(relations, 'CONFIGS')\n def test_db_changed_missing_relation_data(self, configs):\n self.is_db_maintenance_mode.return_value = False\n configs.complete_contexts = MagicMock()\n configs.complete_contexts.return_value = []\n relations.db_changed()\n self.log.assert_called_with(\n 'shared-db relation incomplete. Peer not ready?'\n )\n\n def test_amqp_joined(self):\n relations.amqp_joined()\n self.relation_set.assert_called_with(\n username='heat',\n vhost='openstack',\n relation_id=None,\n ttlname='heat_expiry',\n ttlreg='heat-engine-listener|engine_worker',\n ttl=3600000)\n\n def test_amqp_joined_passes_relation_id(self):\n \"Ensures relation_id correct passed to relation_set\"\n relations.amqp_joined(relation_id='heat:1')\n self.relation_set.assert_called_with(\n username='heat', vhost='openstack', relation_id='heat:1',\n ttlname='heat_expiry',\n ttlreg='heat-engine-listener|engine_worker', ttl=3600000)\n\n @patch.object(relations, 'CONFIGS')\n def test_amqp_changed_relation_data(self, configs):\n configs.complete_contexts = MagicMock()\n configs.complete_contexts.return_value = ['amqp']\n configs.write = MagicMock()\n relations.amqp_changed()\n self.assertEqual([call('/etc/heat/heat.conf')],\n configs.write.call_args_list)\n\n @patch.object(relations, 'CONFIGS')\n def test_amqp_changed_missing_relation_data(self, configs):\n configs.complete_contexts = MagicMock()\n configs.complete_contexts.return_value = []\n relations.amqp_changed()\n self.assertTrue(self.log.called)\n\n @patch.object(relations, 'CONFIGS')\n def test_relation_broken(self, configs):\n relations.relation_broken()\n self.assertTrue(configs.write_all.called)\n\n @patch.object(relations, 'canonical_url')\n def test_identity_service_joined(self, _canonical_url):\n \"It properly requests unclustered endpoint via identity-service\"\n _canonical_url.return_value = 'http://heatnode1'\n relations.identity_joined()\n expected = {\n 'heat_service': 'heat',\n 'heat_region': 'RegionOne',\n 'heat_public_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\n 'heat_admin_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\n 'heat_internal_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\n 'heat-cfn_service': 'heat-cfn',\n 'heat-cfn_region': 'RegionOne',\n 'heat-cfn_public_url': 'http://heatnode1:8000/v1',\n 'heat-cfn_admin_url': 'http://heatnode1:8000/v1',\n 'heat-cfn_internal_url': 'http://heatnode1:8000/v1',\n 'relation_id': None,\n }\n self.relation_set.assert_called_with(**expected)\n\n @patch.object(relations, 'canonical_url')\n def test_identity_service_joined_with_relation_id(self, _canonical_url):\n _canonical_url.return_value = 'http://heatnode1'\n relations.identity_joined(rid='identity-service:0')\n ex = {\n 'heat_service': 'heat',\n 'heat_region': 'RegionOne',\n 'heat_public_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\n 'heat_admin_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\n 'heat_internal_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\n 'heat-cfn_service': 'heat-cfn',\n 'heat-cfn_region': 'RegionOne',\n 'heat-cfn_public_url': 'http://heatnode1:8000/v1',\n 'heat-cfn_admin_url': 'http://heatnode1:8000/v1',\n 'heat-cfn_internal_url': 'http://heatnode1:8000/v1',\n 'relation_id': 'identity-service:0',\n }\n self.relation_set.assert_called_with(**ex)\n\n @patch('charmhelpers.contrib.openstack.ip.unit_get',\n lambda *args: 'heatnode1')\n @patch('charmhelpers.contrib.openstack.ip.is_clustered',\n lambda *args: False)\n @patch('charmhelpers.contrib.openstack.ip.service_name')\n @patch('charmhelpers.contrib.openstack.ip.config')\n def test_identity_service_public_address_override(self, ip_config,\n _service_name):\n ip_config.side_effect = self.test_config.get\n _service_name.return_value = 'heat'\n self.test_config.set('os-public-hostname', 'heat.example.org')\n relations.identity_joined(rid='identity-service:0')\n exp = {\n 'heat_service': 'heat',\n 'heat_region': 'RegionOne',\n 'heat_public_url': 'http://heat.example.org:8004/v1/$(tenant_id)s',\n 'heat_admin_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\n 'heat_internal_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\n 'heat-cfn_service': 'heat-cfn',\n 'heat-cfn_region': 'RegionOne',\n 'heat-cfn_public_url': 'http://heat.example.org:8000/v1',\n 'heat-cfn_admin_url': 'http://heatnode1:8000/v1',\n 'heat-cfn_internal_url': 'http://heatnode1:8000/v1',\n 'relation_id': 'identity-service:0',\n }\n self.relation_set.assert_called_with(**exp)\n\n @patch.object(relations, 'configure_https')\n @patch.object(relations, 'CONFIGS')\n def test_identity_changed(self, configs, mock_configure_https):\n configs.complete_contexts.return_value = ['identity-service']\n relations.identity_changed()\n self.assertTrue(configs.write_all.called)\n\n @patch.object(relations, 'CONFIGS')\n def test_identity_changed_incomplete(self, configs):\n configs.complete_contexts.return_value = []\n relations.identity_changed()\n self.assertTrue(self.log.called)\n self.assertFalse(configs.write.called)\n\n def test_db_joined_with_ipv6(self):\n 'It properly requests access to a shared-db service'\n self.sync_db_with_multi_ipv6_addresses.return_value = MagicMock()\n self.test_config.set('prefer-ipv6', True)\n relations.db_joined()\n self.sync_db_with_multi_ipv6_addresses.assert_called_with(\n 'heat', 'heat', relation_prefix='heat')\n\n @patch.object(relations, 'CONFIGS')\n def test_non_leader_db_changed(self, configs):\n self.is_elected_leader.return_value = False\n configs.complete_contexts.return_value = []\n self.relation_get.return_value = 'heat/0 heat/1'\n self.local_unit.return_value = 'heat/0'\n configs.complete_contexts = MagicMock()\n configs.complete_contexts.return_value = ['shared-db']\n configs.write = MagicMock()\n relations.db_changed()\n self.assertFalse(self.migrate_database.called)\n\n def test_ha_relation_joined(self):\n self.generate_ha_relation_data.return_value = {'rel_data': 'data'}\n relations.ha_joined(relation_id='rid:23')\n self.relation_set.assert_called_once_with(\n relation_id='rid:23', rel_data='data')\n\n @patch.object(relations, \"apt_install\")\n @patch.object(relations.nrpe, \"get_nagios_hostname\")\n @patch.object(relations.nrpe, \"get_nagios_unit_name\")\n @patch.object(relations.nrpe, \"NRPE\")\n @patch.object(relations.nrpe, \"copy_nrpe_checks\")\n @patch.object(relations.nrpe, \"add_init_service_checks\")\n @patch.object(relations.nrpe, \"add_haproxy_checks\")\n def test_update_nrpe_config(self, mock_add_haproxy, mock_add_init_svc,\n mock_copy_nrpe_checks, mock_nrpe,\n mock_nagios_unitname, mock_nagios_hostname,\n mock_apt_install):\n relations.update_nrpe_config()\n mock_apt_install.assert_called_once_with(\"python-dbus\")\n mock_nagios_hostname.assert_called_once_with()\n mock_nagios_unitname.assert_called_once_with()\n mock_nrpe.assert_called_once_with(\n hostname=mock_nagios_hostname.return_value)\n mock_copy_nrpe_checks.assert_called_once_with()\n mock_add_init_svc.assert_called_once_with(\n mock_nrpe.return_value,\n relations.services(),\n mock_nagios_unitname.return_value,\n )\n mock_add_haproxy.assert_called_once_with(\n mock_nrpe.return_value, mock_nagios_unitname.return_value)\n mock_nrpe.return_value.write.assert_called_once_with()\n"},{"alpha_fraction":0.6557239294052124,"alphanum_fraction":0.6601009964942932,"avg_line_length":38.078948974609375,"blob_id":"f9bebe20eb7d04c80e09ddaa5fbdd26c8ef409e4","content_id":"1445d85ab7a13bef40ce9430e7bee319d2a1b067","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5940,"license_type":"permissive","max_line_length":78,"num_lines":152,"path":"/unit_tests/test_heat_utils.py","repo_name":"openstack/charm-heat","src_encoding":"UTF-8","text":"# Copyright 2016 Canonical Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom copy import deepcopy\nfrom collections import OrderedDict\nfrom unittest.mock import patch, MagicMock, call\nfrom test_utils import CharmTestCase\n\nfrom charmhelpers.core import hookenv\n\n_conf = hookenv.config\nhookenv.config = MagicMock()\n\nimport heat_utils as utils\n\nhookenv.config = _conf\n\nTO_PATCH = [\n 'config',\n 'log',\n 'os_release',\n 'get_os_codename_install_source',\n 'configure_installation_source',\n 'apt_install',\n 'apt_update',\n 'apt_upgrade',\n 'check_call',\n 'service_start',\n 'service_stop',\n 'token_cache_pkgs',\n 'enable_memcache',\n 'os'\n]\n\n\n# Restart map should be constructed such that API services restart\n# before frontends (haproxy/apaceh) to avoid port conflicts.\nRESTART_MAP = OrderedDict([\n ('/etc/heat/heat.conf', ['heat-api', 'heat-api-cfn', 'heat-engine']),\n ('/etc/heat/api-paste.ini', ['heat-api', 'heat-api-cfn']),\n ('/etc/haproxy/haproxy.cfg', ['haproxy']),\n ('/etc/apache2/sites-available/openstack_https_frontend', ['apache2']),\n ('/etc/apache2/sites-available/openstack_https_frontend.conf',\n ['apache2']),\n ('/etc/memcached.conf', ['memcached']),\n ('/etc/apache2/ports.conf', ['apache2']),\n])\n\n\nclass HeatUtilsTests(CharmTestCase):\n\n def setUp(self):\n super(HeatUtilsTests, self).setUp(utils, TO_PATCH)\n self.config.side_effect = self.test_config.get\n\n @patch('charmhelpers.contrib.openstack.context.SubordinateConfigContext')\n def test_determine_packages(self, subcontext):\n self.os_release.return_value = 'havana'\n pkgs = utils.determine_packages()\n ex = list(set(utils.BASE_PACKAGES + utils.BASE_SERVICES))\n self.assertEqual(ex, pkgs)\n\n @patch('charmhelpers.contrib.openstack.context.SubordinateConfigContext')\n def test_determine_packages_queens(self, subcontext):\n self.os_release.return_value = 'queens'\n self.token_cache_pkgs.return_value = ['python-memcache', 'memcached']\n pkgs = utils.determine_packages()\n ex = list(set(utils.BASE_PACKAGES + ['python-memcache', 'memcached'] +\n utils.BASE_SERVICES))\n self.assertEqual(sorted(ex), sorted(pkgs))\n\n @patch('charmhelpers.contrib.openstack.context.SubordinateConfigContext')\n def test_determine_packages_rocky(self, subcontext):\n self.os_release.return_value = 'rocky'\n self.token_cache_pkgs.return_value = ['python-memcache', 'memcached']\n pkgs = utils.determine_packages()\n ex = list(set(\n [p for p in utils.BASE_PACKAGES if not p.startswith('python-')] +\n ['memcached'] + utils.BASE_SERVICES + utils.PY3_PACKAGES))\n self.assertEqual(sorted(ex), sorted(pkgs))\n\n def test_determine_purge_packages(self):\n 'Ensure no packages are identified for purge prior to rocky'\n self.os_release.return_value = 'queens'\n self.assertEqual(utils.determine_purge_packages(), [])\n\n def test_determine_purge_packages_rocky(self):\n 'Ensure python packages are identified for purge at rocky'\n self.os_release.return_value = 'rocky'\n self.assertEqual(utils.determine_purge_packages(),\n [p for p in utils.BASE_PACKAGES\n if p.startswith('python-')] +\n ['python-heat', 'python-memcache'])\n\n def test_restart_map(self):\n # Icehouse\n self.os_release.return_value = \"icehouse\"\n self.enable_memcache.return_value = False\n self.os.path.exists.return_value = False\n _restart_map = deepcopy(RESTART_MAP)\n _restart_map.pop(\n \"/etc/apache2/sites-available/openstack_https_frontend.conf\")\n _restart_map.pop(\"/etc/memcached.conf\")\n self.assertEqual(_restart_map, utils.restart_map())\n\n # Mitaka\n self.os_release.return_value = \"mitaka\"\n self.enable_memcache.return_value = True\n self.os.path.exists.return_value = True\n _restart_map = deepcopy(RESTART_MAP)\n _restart_map.pop(\n \"/etc/apache2/sites-available/openstack_https_frontend\")\n self.assertEqual(_restart_map, utils.restart_map())\n\n def test_openstack_upgrade(self):\n self.config.side_effect = None\n self.config.return_value = 'cloud:precise-havana'\n self.get_os_codename_install_source.return_value = 'havana'\n self.os_release.return_value = 'icehouse'\n configs = MagicMock()\n utils.do_openstack_upgrade(configs)\n self.assertTrue(self.apt_update.called)\n self.assertTrue(self.apt_upgrade.called)\n self.assertTrue(self.apt_install.called)\n configs.set_release.assert_called_with(openstack_release='havana')\n self.assertTrue(configs.write_all.called)\n\n def test_api_ports(self):\n cfn = utils.api_port('heat-api-cfn')\n self.assertEqual(cfn, 8000)\n cfn = utils.api_port('heat-api')\n self.assertEqual(cfn, 8004)\n\n def test_migrate_database(self):\n utils.migrate_database()\n self.assertTrue(self.log.called)\n self.check_call.assert_called_with(['heat-manage', 'db_sync'])\n expected = [call('heat-api'), call('heat-api-cfn'),\n call('heat-engine'), call('apache2')]\n self.service_stop.assert_has_calls(expected, any_order=True)\n self.service_start.assert_has_calls(expected, any_order=True)\n"},{"alpha_fraction":0.7702735066413879,"alphanum_fraction":0.771938145160675,"avg_line_length":34.63559341430664,"blob_id":"0cb8cb9f0fe789af388af2c7e27e8b5e4b4bb699","content_id":"51183240cfa0cb8bf6525ce61d4603e4fa02d8ba","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":4205,"license_type":"permissive","max_line_length":121,"num_lines":118,"path":"/README.md","repo_name":"openstack/charm-heat","src_encoding":"UTF-8","text":"# Overview\n\nHeat is the main project in the OpenStack Orchestration program. It implements\nan orchestration engine to launch multiple composite cloud applications based\non templates in the form of text files that can be treated like code.\n\nThis charm deploys the Heat infrastructure.\n\n# Usage\n\nHeat requires the existence of the other core OpenStack services deployed via\nJuju charms, specifically: mysql, rabbitmq-server, keystone and\nnova-cloud-controller. The following assumes these services have already\nbeen deployed.\n\nAfter deployment of the cloud, the domain-setup action must be run to configure\nrequired domains, roles and users in the cloud for Heat stacks.\n\nFor juju 2.x deployments use:\n\n juju run-action heat/0 domain-setup\n\nIf using juju 1.x run:\n\n juju action do heat/0 domain-setup\n\nThis is only required for >= OpenStack Kilo.\n\n## High availability\n\nWhen more than one unit is deployed with the [hacluster][hacluster-charm]\napplication the charm will bring up an HA active/active cluster.\n\nThere are two mutually exclusive high availability options: using virtual IP(s)\nor DNS. In both cases the hacluster subordinate charm is used to provide the\nCorosync and Pacemaker backend HA functionality.\n\nSee [OpenStack high availability][cdg-ha-apps] in the [OpenStack Charms\nDeployment Guide][cdg] for details.\n\n## Spaces\n\nThis charm supports the use of Juju Network Spaces, allowing the charm to be\nbound to network space configurations managed directly by Juju. This is only\nsupported with Juju 2.0 and above.\n\nAPI endpoints can be bound to distinct network spaces supporting the network\nseparation of public, internal and admin endpoints.\n\nAccess to the underlying MySQL instance can also be bound to a specific space\nusing the shared-db relation.\n\nTo use this feature, use the --bind option when deploying the charm:\n\n juju deploy heat --bind \\\n \"public=public-space \\\n internal=internal-space \\\n admin=admin-space \\\n shared-db=internal-space\"\n\nAlternatively, these can also be provided as part of a juju native bundle\nconfiguration:\n\n```yaml\n heat:\n charm: cs:xenial/heat\n num_units: 1\n bindings:\n public: public-space\n admin: admin-space\n internal: internal-space\n shared-db: internal-space\n```\n\nNOTE: Spaces must be configured in the underlying provider prior to attempting\nto use them.\n\nNOTE: Existing deployments using os-*-network configuration options will\ncontinue to function; these options are preferred over any network space\nbinding provided if set.\n\n## Policy Overrides\n\nPolicy overrides is an **advanced** feature that allows an operator to override\nthe default policy of an OpenStack service. The policies that the service\nsupports, the defaults it implements in its code, and the defaults that a charm\nmay include should all be clearly understood before proceeding.\n\n> **Caution**: It is possible to break the system (for tenants and other\n services) if policies are incorrectly applied to the service.\n\nPolicy statements are placed in a YAML file. This file (or files) is then (ZIP)\ncompressed into a single file and used as an application resource. The override\nis then enabled via a Boolean charm option.\n\nHere are the essential commands (filenames are arbitrary):\n\n zip overrides.zip override-file.yaml\n juju attach-resource heat policyd-override=overrides.zip\n juju config heat use-policyd-override=true\n\nSee appendix [Policy Overrides][cdg-appendix-n] in the [OpenStack Charms\nDeployment Guide][cdg] for a thorough treatment of this feature.\n\n# Bugs\n\nPlease report bugs on [Launchpad][lp-bugs-charm-heat].\n\nFor general charm questions refer to the OpenStack [Charm Guide][cg].\n\n<!-- LINKS -->\n\n[cg]: https://docs.openstack.org/charm-guide\n[cdg]: https://docs.openstack.org/project-deploy-guide/charm-deployment-guide\n[cdg-appendix-n]: https://docs.openstack.org/project-deploy-guide/charm-deployment-guide/latest/app-policy-overrides.html\n[lp-bugs-charm-heat]: https://bugs.launchpad.net/charm-heat/+filebug\n[hacluster-charm]: https://jaas.ai/hacluster\n[cdg-ha-apps]: https://docs.openstack.org/project-deploy-guide/charm-deployment-guide/latest/app-ha.html#ha-applications\n"},{"alpha_fraction":0.6437255144119263,"alphanum_fraction":0.6470350027084351,"avg_line_length":29.910228729248047,"blob_id":"444022f07ae76eeb5af683bc06bfcd709018fc29","content_id":"8988218fa9de88131e7fe33b7b230e6d619c3dbf","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":14806,"license_type":"permissive","max_line_length":79,"num_lines":479,"path":"/hooks/heat_relations.py","repo_name":"openstack/charm-heat","src_encoding":"UTF-8","text":"#!/usr/bin/env python3\n#\n# Copyright 2016 Canonical Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nimport shutil\nimport subprocess\nimport sys\n\nfrom charmhelpers.core.hookenv import (\n Hooks,\n UnregisteredHookError,\n config,\n charm_dir,\n log,\n relation_ids,\n relation_get,\n relation_set,\n related_units,\n local_unit,\n open_port,\n status_set,\n leader_get,\n leader_set,\n is_leader,\n WARNING,\n)\n\nfrom charmhelpers.core.host import (\n restart_on_change,\n service_reload,\n pwgen,\n service_restart,\n)\n\nfrom charmhelpers.fetch import (\n apt_install,\n apt_update\n)\n\nfrom charmhelpers.contrib.hahelpers.cluster import (\n is_elected_leader,\n)\n\nfrom charmhelpers.contrib.network.ip import (\n get_relation_ip,\n)\n\nfrom charmhelpers.contrib.openstack.utils import (\n configure_installation_source,\n openstack_upgrade_available,\n os_release,\n series_upgrade_complete,\n series_upgrade_prepare,\n sync_db_with_multi_ipv6_addresses,\n is_db_maintenance_mode,\n)\n\nfrom charmhelpers.contrib.openstack.ha.utils import (\n generate_ha_relation_data,\n)\n\nfrom charmhelpers.contrib.openstack.ip import (\n canonical_url,\n ADMIN,\n INTERNAL,\n PUBLIC,\n)\n\nfrom heat_utils import (\n do_openstack_upgrade,\n restart_map,\n determine_packages,\n migrate_database,\n register_configs,\n CLUSTER_RES,\n HEAT_CONF,\n setup_ipv6,\n pause_unit_helper,\n resume_unit_helper,\n assess_status,\n remove_old_packages,\n services,\n)\n\nfrom heat_context import (\n API_PORTS,\n HEAT_PATH,\n)\n\nfrom charmhelpers.contrib.charmsupport import nrpe\nfrom charmhelpers.contrib.hardening.harden import harden\nfrom charmhelpers.contrib.openstack.context import ADDRESS_TYPES\nfrom charmhelpers.payload.execd import execd_preinstall\n\nfrom charmhelpers.contrib.openstack.cert_utils import (\n get_certificate_request,\n process_certificates,\n)\n\nimport charmhelpers.contrib.openstack.policyd as policyd\n\nhooks = Hooks()\nCONFIGS = register_configs()\n\n\n@hooks.hook('install.real')\n@harden()\ndef install():\n status_set('maintenance', 'Executing pre-install')\n execd_preinstall()\n configure_installation_source(config('openstack-origin'))\n status_set('maintenance', 'Installing apt packages')\n apt_update()\n # (ajkavanagh) LP: #1989538\n # Tactical fix to force openstack-release to match the configured\n # installation source; note that it comes after apt_update().\n apt_install(['openstack-release'], fatal=False, quiet=True)\n apt_install(determine_packages(), fatal=True)\n\n _files = os.path.join(charm_dir(), 'files')\n if os.path.isdir(_files):\n for f in os.listdir(_files):\n f = os.path.join(_files, f)\n log('Installing {} to /usr/bin'.format(f))\n shutil.copy2(f, '/usr/bin')\n\n for port in API_PORTS.values():\n open_port(port)\n # call the policy overrides handler which will install any policy overrides\n policyd.maybe_do_policyd_overrides(\n os_release('heat-common'),\n 'heat',\n restart_handler=restart_heat_api,\n )\n\n\ndef restart_heat_api():\n service_restart('heat-api')\n\n\n@hooks.hook('config-changed')\n@restart_on_change(restart_map())\n@harden()\ndef config_changed():\n if not config('action-managed-upgrade'):\n if openstack_upgrade_available('heat-common'):\n status_set('maintenance', 'Running openstack upgrade')\n do_openstack_upgrade(CONFIGS)\n\n if config('prefer-ipv6'):\n status_set('maintenance', 'configuring ipv6')\n setup_ipv6()\n sync_db_with_multi_ipv6_addresses(config('database'),\n config('database-user'),\n relation_prefix='heat')\n\n CONFIGS.write_all()\n configure_https()\n update_nrpe_config()\n\n for rid in relation_ids('cluster'):\n cluster_joined(relation_id=rid)\n for r_id in relation_ids('ha'):\n ha_joined(relation_id=r_id)\n for rid in relation_ids('amqp'):\n amqp_joined(relation_id=rid)\n\n # call the policy overrides handler which will install any policy overrides\n policyd.maybe_do_policyd_overrides_on_config_changed(\n os_release('heat-common'),\n 'heat',\n restart_handler=restart_heat_api,\n )\n\n\n@hooks.hook('upgrade-charm.real')\n@harden()\ndef upgrade_charm():\n apt_install(determine_packages(), fatal=True)\n if remove_old_packages():\n log(\"Package purge detected, restarting services\", \"INFO\")\n for s in services():\n service_restart(s)\n if is_leader():\n # if we are upgrading, then the old version might have used the\n # HEAT_PATH/encryption-key. So we grab the key from that, and put it in\n # leader settings to ensure that the key remains the same during an\n # upgrade.\n encryption_path = os.path.join(HEAT_PATH, 'encryption-key')\n if os.path.isfile(encryption_path):\n with open(encryption_path, 'r') as f:\n encryption_key = f.read()\n try:\n leader_set({'heat-auth-encryption-key': encryption_key})\n except subprocess.CalledProcessError as e:\n log(\"upgrade: leader_set: heat-auth-encryption-key failed,\"\n \" didn't delete the existing file: {}.\\n\"\n \"Error was: {}\".format(encryption_path, str(e)),\n level=WARNING)\n else:\n # now we just delete the file\n os.remove(encryption_path)\n leader_elected()\n update_nrpe_config()\n\n # call the policy overrides handler which will install any policy overrides\n policyd.maybe_do_policyd_overrides(\n os_release('heat-common'),\n 'heat',\n restart_handler=restart_heat_api,\n )\n\n\n@hooks.hook('amqp-relation-joined')\ndef amqp_joined(relation_id=None):\n relation_set(relation_id=relation_id,\n username=config('rabbit-user'), vhost=config('rabbit-vhost'),\n ttlname='heat_expiry',\n ttlreg='heat-engine-listener|engine_worker',\n ttl=config('ttl'))\n\n\n@hooks.hook('amqp-relation-changed')\n@restart_on_change(restart_map())\ndef amqp_changed():\n if 'amqp' not in CONFIGS.complete_contexts():\n log('amqp relation incomplete. Peer not ready?')\n return\n CONFIGS.write(HEAT_CONF)\n\n\n@hooks.hook('shared-db-relation-joined')\ndef db_joined():\n if config('prefer-ipv6'):\n sync_db_with_multi_ipv6_addresses(config('database'),\n config('database-user'),\n relation_prefix='heat')\n else:\n # Avoid churn check for access-network early\n access_network = None\n for unit in related_units():\n access_network = relation_get(unit=unit,\n attribute='access-network')\n if access_network:\n break\n host = get_relation_ip('shared-db', cidr_network=access_network)\n\n relation_set(heat_database=config('database'),\n heat_username=config('database-user'),\n heat_hostname=host)\n\n\n@hooks.hook('shared-db-relation-changed')\n@restart_on_change(restart_map())\ndef db_changed():\n if is_db_maintenance_mode():\n log('Database maintenance mode, aborting hook.')\n return\n if 'shared-db' not in CONFIGS.complete_contexts():\n log('shared-db relation incomplete. Peer not ready?')\n return\n CONFIGS.write(HEAT_CONF)\n\n if is_elected_leader(CLUSTER_RES):\n allowed_units = relation_get('heat_allowed_units')\n if allowed_units and local_unit() in allowed_units.split():\n log('Cluster leader, performing db sync')\n migrate_database()\n else:\n log('allowed_units either not presented, or local unit '\n 'not in acl list: {}'.format(repr(allowed_units)))\n\n\ndef configure_https():\n \"\"\"Enables SSL API Apache config if appropriate.\"\"\"\n # need to write all to ensure changes to the entire request pipeline\n # propagate (c-api, haprxy, apache)\n CONFIGS.write_all()\n if 'https' in CONFIGS.complete_contexts():\n cmd = ['a2ensite', 'openstack_https_frontend']\n subprocess.check_call(cmd)\n else:\n cmd = ['a2dissite', 'openstack_https_frontend']\n subprocess.check_call(cmd)\n\n # TODO: improve this by checking if local CN certs are available\n # first then checking reload status (see LP #1433114).\n service_reload('apache2', restart_on_failure=True)\n\n for rid in relation_ids('identity-service'):\n identity_joined(rid=rid)\n\n\n@hooks.hook('identity-service-relation-joined')\ndef identity_joined(rid=None):\n public_url_base = canonical_url(CONFIGS, PUBLIC)\n internal_url_base = canonical_url(CONFIGS, INTERNAL)\n admin_url_base = canonical_url(CONFIGS, ADMIN)\n\n api_url_template = '{}:8004/v1/$(tenant_id)s'\n public_api_endpoint = (api_url_template.format(public_url_base))\n internal_api_endpoint = (api_url_template.format(internal_url_base))\n admin_api_endpoint = (api_url_template.format(admin_url_base))\n\n cfn_url_template = '{}:8000/v1'\n public_cfn_endpoint = (cfn_url_template.format(public_url_base))\n internal_cfn_endpoint = (cfn_url_template.format(internal_url_base))\n admin_cfn_endpoint = (cfn_url_template.format(admin_url_base))\n\n relation_data = {\n 'heat_service': 'heat',\n 'heat_region': config('region'),\n 'heat_public_url': public_api_endpoint,\n 'heat_admin_url': admin_api_endpoint,\n 'heat_internal_url': internal_api_endpoint,\n 'heat-cfn_service': 'heat-cfn',\n 'heat-cfn_region': config('region'),\n 'heat-cfn_public_url': public_cfn_endpoint,\n 'heat-cfn_admin_url': admin_cfn_endpoint,\n 'heat-cfn_internal_url': internal_cfn_endpoint,\n }\n\n relation_set(relation_id=rid, **relation_data)\n\n\n@hooks.hook('identity-service-relation-changed')\n@restart_on_change(restart_map())\ndef identity_changed():\n if 'identity-service' not in CONFIGS.complete_contexts():\n log('identity-service relation incomplete. Peer not ready?')\n return\n\n CONFIGS.write_all()\n configure_https()\n\n\n@hooks.hook('amqp-relation-broken',\n 'identity-service-relation-broken',\n 'shared-db-relation-broken')\ndef relation_broken():\n CONFIGS.write_all()\n\n\n@hooks.hook('leader-elected')\ndef leader_elected():\n if is_leader():\n if not leader_get('heat-domain-admin-passwd'):\n try:\n leader_set({'heat-domain-admin-passwd': pwgen(32)})\n except subprocess.CalledProcessError as e:\n log('leader_set: heat-domain-admin-password failed: {}'\n .format(str(e)), level=WARNING)\n if not leader_get('heat-auth-encryption-key'):\n try:\n leader_set({'heat-auth-encryption-key': pwgen(32)})\n except subprocess.CalledProcessError as e:\n log('leader_set: heat-domain-admin-password failed: {}'\n .format(str(e)), level=WARNING)\n\n\n@hooks.hook('cluster-relation-joined')\ndef cluster_joined(relation_id=None):\n settings = {}\n\n for addr_type in ADDRESS_TYPES:\n address = get_relation_ip(\n addr_type,\n cidr_network=config('os-{}-network'.format(addr_type)))\n if address:\n settings['{}-address'.format(addr_type)] = address\n\n settings['private-address'] = get_relation_ip('cluster')\n\n relation_set(relation_id=relation_id, relation_settings=settings)\n\n\n@hooks.hook('cluster-relation-changed',\n 'cluster-relation-departed')\n@restart_on_change(restart_map(), stopstart=True)\ndef cluster_changed():\n CONFIGS.write_all()\n\n\n@hooks.hook('ha-relation-joined')\ndef ha_joined(relation_id=None):\n settings = generate_ha_relation_data('heat')\n relation_set(relation_id=relation_id, **settings)\n\n\n@hooks.hook('ha-relation-changed')\ndef ha_changed():\n clustered = relation_get('clustered')\n if not clustered or clustered in [None, 'None', '']:\n log('ha_changed: hacluster subordinate not fully clustered.')\n else:\n log('Cluster configured, notifying other services and updating '\n 'keystone endpoint configuration')\n for rid in relation_ids('identity-service'):\n identity_joined(rid=rid)\n\n\n@hooks.hook('heat-plugin-subordinate-relation-joined',\n 'heat-plugin-subordinate-relation-changed')\n@restart_on_change(restart_map(), stopstart=True)\ndef heat_plugin_subordinate_relation_joined(relid=None):\n CONFIGS.write_all()\n\n\n@hooks.hook('update-status')\n@harden()\ndef update_status():\n log('Updating status.')\n\n\n@hooks.hook('certificates-relation-joined')\ndef certs_joined(relation_id=None):\n relation_set(\n relation_id=relation_id,\n relation_settings=get_certificate_request())\n\n\n@hooks.hook('certificates-relation-changed')\n@restart_on_change(restart_map(), stopstart=True)\ndef certs_changed(relation_id=None, unit=None):\n process_certificates('heat', relation_id, unit)\n configure_https()\n\n\n@hooks.hook('pre-series-upgrade')\ndef pre_series_upgrade():\n log(\"Running prepare series upgrade hook\", \"INFO\")\n series_upgrade_prepare(\n pause_unit_helper, CONFIGS)\n\n\n@hooks.hook('post-series-upgrade')\ndef post_series_upgrade():\n log(\"Running complete series upgrade hook\", \"INFO\")\n series_upgrade_complete(\n resume_unit_helper, CONFIGS)\n\n\n@hooks.hook('nrpe-external-master-relation-joined',\n 'nrpe-external-master-relation-changed')\ndef update_nrpe_config():\n # python-dbus is used by check_upstart_job\n apt_install('python-dbus')\n hostname = nrpe.get_nagios_hostname()\n current_unit = nrpe.get_nagios_unit_name()\n nrpe_setup = nrpe.NRPE(hostname=hostname)\n nrpe.copy_nrpe_checks()\n nrpe.add_init_service_checks(nrpe_setup, services(), current_unit)\n nrpe.add_haproxy_checks(nrpe_setup, current_unit)\n nrpe_setup.write()\n\n\ndef main():\n try:\n hooks.execute(sys.argv)\n except UnregisteredHookError as e:\n log('Unknown hook {} - skipping.'.format(e))\n assess_status(CONFIGS)\n\n\nif __name__ == '__main__':\n main()\n"},{"alpha_fraction":0.581091046333313,"alphanum_fraction":0.5871728658676147,"avg_line_length":31.491018295288086,"blob_id":"38130d5dcbdc9079d9e404c53f9046aa62e7e22d","content_id":"70bbb793dcdc4c62c6888f3cc12c61d81091d9af","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5426,"license_type":"permissive","max_line_length":79,"num_lines":167,"path":"/hooks/heat_context.py","repo_name":"openstack/charm-heat","src_encoding":"UTF-8","text":"# Copyright 2016 Canonical Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom charmhelpers.contrib.openstack import context\nfrom charmhelpers.core.hookenv import (\n config,\n relation_ids,\n related_units,\n relation_get,\n leader_get,\n)\nfrom charmhelpers.contrib.hahelpers.cluster import (\n determine_apache_port,\n determine_api_port,\n https,\n)\n\nHEAT_PATH = '/var/lib/heat/'\nAPI_PORTS = {\n 'heat-api-cfn': 8000,\n 'heat-api': 8004\n}\n\n\ndef generate_ec2_tokens(protocol, host, port):\n ec2_tokens = '%s://%s:%s/v2.0/ec2tokens' % (protocol, host, port)\n return ec2_tokens\n\n\nclass HeatIdentityServiceContext(context.IdentityServiceContext):\n def __call__(self):\n ctxt = super(HeatIdentityServiceContext, self).__call__()\n if not ctxt:\n return\n\n # the ec2 api needs to know the location of the keystone ec2\n # tokens endpoint, set in nova.conf\n ec2_tokens = generate_ec2_tokens(ctxt['service_protocol'] or 'http',\n ctxt['service_host'],\n ctxt['service_port'])\n ctxt['keystone_ec2_url'] = ec2_tokens\n ctxt['region'] = config('region')\n return ctxt\n\n\ndef get_encryption_key():\n encryption_key = config(\"encryption-key\")\n if not encryption_key:\n encryption_key = leader_get('heat-auth-encryption-key')\n return encryption_key\n\n\nclass HeatSecurityContext(context.OSContextGenerator):\n\n def __call__(self):\n ctxt = {}\n # check if we have stored encryption key\n ctxt['encryption_key'] = get_encryption_key()\n ctxt['heat_domain_admin_passwd'] = (\n leader_get('heat-domain-admin-passwd'))\n return ctxt\n\n\nclass HeatHAProxyContext(context.OSContextGenerator):\n interfaces = ['heat-haproxy']\n\n def __call__(self):\n \"\"\"Extends the main charmhelpers HAProxyContext with a port mapping\n specific to this charm.\n Also used to extend cinder.conf context with correct api_listening_port\n \"\"\"\n haproxy_port = API_PORTS['heat-api']\n api_port = determine_api_port(haproxy_port, singlenode_mode=True)\n apache_port = determine_apache_port(haproxy_port, singlenode_mode=True)\n\n haproxy_cfn_port = API_PORTS['heat-api-cfn']\n api_cfn_port = determine_api_port(haproxy_cfn_port,\n singlenode_mode=True)\n apache_cfn_port = determine_apache_port(haproxy_cfn_port,\n singlenode_mode=True)\n\n healthcheck = [{\n 'option': 'httpchk GET /healthcheck',\n 'http-check': 'expect status 200',\n }]\n\n backend_options = {\n 'heat_api': healthcheck,\n 'heat_cfn_api': healthcheck\n }\n\n ctxt = {\n 'service_ports': {'heat_api': [haproxy_port, apache_port],\n 'heat_cfn_api': [haproxy_cfn_port,\n apache_cfn_port]},\n 'api_listen_port': api_port,\n 'api_cfn_listen_port': api_cfn_port,\n 'backend_options': backend_options,\n 'https': https(),\n }\n return ctxt\n\n\nclass HeatApacheSSLContext(context.ApacheSSLContext):\n\n external_ports = API_PORTS.values()\n service_namespace = 'heat'\n\n\nclass HeatPluginContext(context.SubordinateConfigContext):\n interfaces = 'heat-plugin-subordinate'\n\n def __init__(self):\n super(HeatPluginContext, self).__init__(\n interface='heat-plugin-subordinate',\n service='heat',\n config_file='/etc/heat/heat.conf')\n\n def __call__(self):\n ctxt = super(HeatPluginContext, self).__call__()\n defaults = {\n 'plugin-dirs': {\n 'templ_key': 'plugin_dirs',\n 'value': '/usr/lib64/heat,/usr/lib/heat',\n },\n }\n for rid in relation_ids('heat-plugin-subordinate'):\n for unit in related_units(rid):\n rdata = relation_get(rid=rid, unit=unit)\n for key in defaults.keys():\n remote_value = rdata.get(key)\n ctxt_key = defaults[key]['templ_key']\n if remote_value:\n ctxt[ctxt_key] = remote_value\n else:\n ctxt[ctxt_key] = defaults[key]['value']\n return ctxt\n return ctxt\n\n\nclass InstanceUserContext(context.OSContextGenerator):\n\n def __call__(self):\n ctxt = {}\n\n instance_user = ''\n if config('instance-user'):\n instance_user = config('instance-user')\n ctxt['instance_user'] = instance_user\n return ctxt\n\n\nclass QuotaConfigurationContext(context.OSContextGenerator):\n def __call__(self):\n ctxt = {\"max_stacks_per_tenant\": config('max-stacks-per-tenant')}\n return ctxt\n"},{"alpha_fraction":0.6540936231613159,"alphanum_fraction":0.6577218174934387,"avg_line_length":31.60714340209961,"blob_id":"b956571172119fc53d1e7d189ddc22fc87f5a82f","content_id":"f0d4edb23b7cddb292fbd56b91204bd020f10d08","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":14608,"license_type":"permissive","max_line_length":79,"num_lines":448,"path":"/hooks/heat_utils.py","repo_name":"openstack/charm-heat","src_encoding":"UTF-8","text":"# Copyright 2016 Canonical Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\n\nfrom copy import deepcopy\nfrom collections import OrderedDict\nfrom subprocess import check_call\n\nfrom charmhelpers.contrib.openstack import context, templating\n\nfrom charmhelpers.contrib.openstack.utils import (\n configure_installation_source,\n get_os_codename_install_source,\n os_release,\n token_cache_pkgs,\n enable_memcache,\n CompareOpenStackReleases,\n os_application_version_set,\n make_assess_status_func,\n pause_unit,\n resume_unit,\n)\n\nfrom charmhelpers.contrib.hahelpers.cluster import (\n get_hacluster_config,\n get_managed_services_and_ports,\n)\n\n\nfrom charmhelpers.fetch import (\n add_source,\n apt_install,\n apt_update,\n apt_upgrade,\n apt_purge,\n apt_autoremove,\n filter_missing_packages,\n)\n\nfrom charmhelpers.core.hookenv import (\n log,\n config,\n relation_ids,\n)\n\nfrom charmhelpers.core.host import (\n lsb_release,\n service_start,\n service_stop,\n CompareHostReleases,\n)\n\nfrom heat_context import (\n API_PORTS,\n HeatIdentityServiceContext,\n HeatSecurityContext,\n InstanceUserContext,\n HeatApacheSSLContext,\n HeatHAProxyContext,\n HeatPluginContext,\n QuotaConfigurationContext,\n)\n\nTEMPLATES = 'templates/'\n\n# The interface is said to be satisfied if anyone of the interfaces in\n# the list has a complete context.\nREQUIRED_INTERFACES = {\n 'database': ['shared-db'],\n 'messaging': ['amqp'],\n 'identity': ['identity-service'],\n}\n\nBASE_PACKAGES = [\n 'python-keystoneclient',\n 'python-swiftclient', # work-around missing epoch in juno heat package\n 'uuid',\n 'apache2',\n 'haproxy',\n]\n\nPY3_PACKAGES = [\n 'python3-heat',\n 'python3-keystoneclient',\n 'python3-memcache',\n 'python3-swiftclient',\n 'python3-six',\n\n # NOTE(lourot): these dependencies are declared as \"recommended\" on the\n # heat-engine package but starting from focal-victoria they are actually\n # hard dependencies. Until this is fixed we need this workaround. See\n # lp:1893935\n 'python3-vitrageclient',\n 'python3-zunclient',\n]\n\nVERSION_PACKAGE = 'heat-common'\n\nBASE_SERVICES = [\n 'heat-api',\n 'heat-api-cfn',\n 'heat-engine'\n]\n\n# Cluster resource used to determine leadership when hacluster'd\nCLUSTER_RES = 'grp_heat_vips'\nSVC = 'heat'\nHEAT_DIR = '/etc/heat'\nHEAT_CONF = '/etc/heat/heat.conf'\nHEAT_API_PASTE = '/etc/heat/api-paste.ini'\nHAPROXY_CONF = '/etc/haproxy/haproxy.cfg'\nAPACHE_PORTS_CONF = '/etc/apache2/ports.conf'\nHTTPS_APACHE_CONF = '/etc/apache2/sites-available/openstack_https_frontend'\nHTTPS_APACHE_24_CONF = os.path.join('/etc/apache2/sites-available',\n 'openstack_https_frontend.conf')\nADMIN_OPENRC = '/root/admin-openrc-v3'\nMEMCACHED_CONF = '/etc/memcached.conf'\n\nCONFIG_FILES = OrderedDict([\n (HEAT_CONF, {\n 'services': BASE_SERVICES,\n 'contexts': [context.AMQPContext(ssl_dir=HEAT_DIR),\n context.SharedDBContext(relation_prefix='heat',\n ssl_dir=HEAT_DIR),\n context.OSConfigFlagContext(),\n context.InternalEndpointContext(),\n HeatIdentityServiceContext(service=SVC, service_user=SVC),\n HeatHAProxyContext(),\n HeatSecurityContext(),\n InstanceUserContext(),\n HeatPluginContext(),\n QuotaConfigurationContext(),\n context.SyslogContext(),\n context.LogLevelContext(),\n context.WorkerConfigContext(),\n context.BindHostContext(),\n context.MemcacheContext(),\n context.OSConfigFlagContext()],\n }),\n (HEAT_API_PASTE, {\n 'services': [s for s in BASE_SERVICES if 'api' in s],\n 'contexts': [HeatIdentityServiceContext()],\n }),\n (HAPROXY_CONF, {\n 'contexts': [context.HAProxyContext(singlenode_mode=True),\n HeatHAProxyContext()],\n 'services': ['haproxy'],\n }),\n (HTTPS_APACHE_CONF, {\n 'contexts': [HeatApacheSSLContext()],\n 'services': ['apache2'],\n }),\n (HTTPS_APACHE_24_CONF, {\n 'contexts': [HeatApacheSSLContext()],\n 'services': ['apache2'],\n }),\n (ADMIN_OPENRC, {\n 'contexts': [HeatIdentityServiceContext(service=SVC,\n service_user=SVC)],\n 'services': []\n }),\n (MEMCACHED_CONF, {\n 'contexts': [context.MemcacheContext()],\n 'services': ['memcached'],\n }),\n (APACHE_PORTS_CONF, {\n 'contexts': [],\n 'services': ['apache2'],\n }),\n])\n\n\ndef resource_map(release=None):\n \"\"\"\n Dynamically generate a map of resources that will be managed for a single\n hook execution.\n \"\"\"\n _release = release or os_release('heat-common', base='icehouse')\n _resource_map = deepcopy(CONFIG_FILES)\n\n if os.path.exists('/etc/apache2/conf-available'):\n _resource_map.pop(HTTPS_APACHE_CONF)\n else:\n _resource_map.pop(HTTPS_APACHE_24_CONF)\n\n if not enable_memcache(release=_release):\n _resource_map.pop(MEMCACHED_CONF)\n\n return _resource_map\n\n\ndef register_configs(release=None):\n \"\"\"Register config files with their respective contexts.\n Regstration of some configs may not be required depending on\n existing of certain relations.\n \"\"\"\n release = release or os_release('heat-common', base='icehouse')\n configs = templating.OSConfigRenderer(templates_dir=TEMPLATES,\n openstack_release=release)\n for cfg, rscs in resource_map(release).items():\n configs.register(cfg, rscs['contexts'])\n return configs\n\n\ndef api_port(service):\n return API_PORTS[service]\n\n\ndef determine_packages():\n release = CompareOpenStackReleases(os_release('heat-common'))\n\n # currently all packages match service names\n packages = BASE_PACKAGES + BASE_SERVICES\n packages.extend(token_cache_pkgs(source=config('openstack-origin')))\n\n if release >= 'rocky':\n packages = [p for p in packages if not p.startswith('python-')]\n packages.extend(PY3_PACKAGES)\n\n return list(set(packages))\n\n\ndef determine_purge_packages():\n '''\n Determine list of packages that where previously installed which are no\n longer needed.\n\n :returns: list of package names\n '''\n release = CompareOpenStackReleases(os_release('heat-common'))\n if release >= 'rocky':\n pkgs = [p for p in BASE_PACKAGES if p.startswith('python-')]\n pkgs.extend(['python-heat', 'python-memcache'])\n return pkgs\n return []\n\n\ndef remove_old_packages():\n '''Purge any packages that need ot be removed.\n\n :returns: bool Whether packages were removed.\n '''\n installed_packages = filter_missing_packages(determine_purge_packages())\n if installed_packages:\n apt_purge(installed_packages, fatal=True)\n apt_autoremove(purge=True, fatal=True)\n return bool(installed_packages)\n\n\ndef do_openstack_upgrade(configs):\n \"\"\"Perform an uprade of heat.\n\n Takes care of upgrading packages,\n rewriting configs and potentially any other post-upgrade\n actions.\n\n :param configs: The charms main OSConfigRenderer object.\n\n \"\"\"\n new_src = config('openstack-origin')\n new_os_rel = get_os_codename_install_source(new_src)\n\n log('Performing OpenStack upgrade to %s.' % (new_os_rel))\n\n configure_installation_source(new_src)\n dpkg_opts = [\n '--option', 'Dpkg::Options::=--force-confnew',\n '--option', 'Dpkg::Options::=--force-confdef',\n ]\n apt_update()\n apt_upgrade(options=dpkg_opts, fatal=True, dist=True)\n apt_install(packages=determine_packages(), options=dpkg_opts, fatal=True)\n\n remove_old_packages()\n\n # set CONFIGS to load templates from new release and regenerate config\n configs.set_release(openstack_release=new_os_rel)\n configs.write_all()\n\n migrate_database()\n\n\ndef restart_map():\n '''Determine the correct resource map to be passed to\n charmhelpers.core.restart_on_change() based on the services configured.\n\n :returns: dict: A dictionary mapping config file to lists of services\n that should be restarted when file changes.\n '''\n return OrderedDict([(cfg, v['services'])\n for cfg, v in resource_map().items()\n if v['services']])\n\n\ndef services():\n \"\"\"Returns a list of services associate with this charm\"\"\"\n _services = []\n for v in restart_map().values():\n _services = _services + v\n return list(set(_services))\n\n\ndef migrate_database():\n \"\"\"Runs heat-manage to initialize a new database or migrate existing\"\"\"\n log('Migrating the heat database.')\n [service_stop(s) for s in services()]\n check_call(['heat-manage', 'db_sync'])\n [service_start(s) for s in services()]\n\n\ndef setup_ipv6():\n ubuntu_rel = lsb_release()['DISTRIB_CODENAME'].lower()\n if CompareHostReleases(ubuntu_rel) < \"trusty\":\n raise Exception(\"IPv6 is not supported in the charms for Ubuntu \"\n \"versions less than Trusty 14.04\")\n\n # Need haproxy >= 1.5.3 for ipv6 so for Trusty if we are <= Kilo we need to\n # use trusty-backports otherwise we can use the UCA.\n if (ubuntu_rel == 'trusty' and\n CompareOpenStackReleases(os_release('heat-common')) < 'liberty'):\n add_source('deb http://archive.ubuntu.com/ubuntu trusty-backports '\n 'main')\n apt_update()\n apt_install('haproxy/trusty-backports', fatal=True)\n\n\ndef check_optional_relations(configs):\n \"\"\"Check that if we have a relation_id for high availability that we can\n get the hacluster config. If we can't then we are blocked.\n\n This function is called from assess_status/set_os_workload_status as the\n charm_func and needs to return either None, None if there is no problem or\n the status, message if there is a problem.\n\n :param configs: an OSConfigRender() instance.\n :return 2-tuple: (string, string) = (status, message)\n \"\"\"\n if relation_ids('ha'):\n try:\n get_hacluster_config()\n except Exception:\n return ('blocked',\n 'hacluster missing configuration: '\n 'vip, vip_iface, vip_cidr')\n # return 'unknown' as the lowest priority to not clobber an existing\n # status.\n return \"unknown\", \"\"\n\n\ndef get_optional_interfaces():\n \"\"\"Return the optional interfaces that should be checked if the relavent\n relations have appeared.\n\n :returns: {general_interface: [specific_int1, specific_int2, ...], ...}\n \"\"\"\n optional_interfaces = {}\n if relation_ids('ha'):\n optional_interfaces['ha'] = ['cluster']\n\n return optional_interfaces\n\n\ndef assess_status(configs):\n \"\"\"Assess status of current unit\n Decides what the state of the unit should be based on the current\n configuration.\n SIDE EFFECT: calls set_os_workload_status(...) which sets the workload\n status of the unit.\n Also calls status_set(...) directly if paused state isn't complete.\n @param configs: a templating.OSConfigRenderer() object\n @returns None - this function is executed for its side-effect\n \"\"\"\n assess_status_func(configs)()\n os_application_version_set(VERSION_PACKAGE)\n\n\ndef assess_status_func(configs):\n \"\"\"Helper function to create the function that will assess_status() for\n the unit.\n Uses charmhelpers.contrib.openstack.utils.make_assess_status_func() to\n create the appropriate status function and then returns it.\n Used directly by assess_status() and also for pausing and resuming\n the unit.\n\n NOTE: REQUIRED_INTERFACES is augmented with the optional interfaces\n depending on the current config before being passed to the\n make_assess_status_func() function.\n\n NOTE(ajkavanagh) ports are not checked due to race hazards with services\n that don't behave sychronously w.r.t their service scripts. e.g.\n apache2.\n @param configs: a templating.OSConfigRenderer() object\n @return f() -> None : a function that assesses the unit's workload status\n \"\"\"\n required_interfaces = REQUIRED_INTERFACES.copy()\n required_interfaces.update(get_optional_interfaces())\n _services, _ = get_managed_services_and_ports(services(), [])\n return make_assess_status_func(\n configs, required_interfaces,\n charm_func=check_optional_relations,\n services=_services, ports=None)\n\n\ndef pause_unit_helper(configs):\n \"\"\"Helper function to pause a unit, and then call assess_status(...) in\n effect, so that the status is correctly updated.\n Uses charmhelpers.contrib.openstack.utils.pause_unit() to do the work.\n @param configs: a templating.OSConfigRenderer() object\n @returns None - this function is executed for its side-effect\n \"\"\"\n _pause_resume_helper(pause_unit, configs)\n\n\ndef resume_unit_helper(configs):\n \"\"\"Helper function to resume a unit, and then call assess_status(...) in\n effect, so that the status is correctly updated.\n Uses charmhelpers.contrib.openstack.utils.resume_unit() to do the work.\n @param configs: a templating.OSConfigRenderer() object\n @returns None - this function is executed for its side-effect\n \"\"\"\n _pause_resume_helper(resume_unit, configs)\n\n\ndef _pause_resume_helper(f, configs):\n \"\"\"Helper function that uses the make_assess_status_func(...) from\n charmhelpers.contrib.openstack.utils to create an assess_status(...)\n function that can be used with the pause/resume of the unit\n @param f: the function to be used with the assess_status(...) function\n @returns None - this function is executed for its side-effect\n \"\"\"\n # TODO(ajkavanagh) - ports= has been left off because of the race hazard\n # that exists due to service_start()\n _services, _ = get_managed_services_and_ports(services(), [])\n f(assess_status_func(configs),\n services=_services,\n ports=None)\n"},{"alpha_fraction":0.5509568452835083,"alphanum_fraction":0.5613410472869873,"avg_line_length":34.478946685791016,"blob_id":"1c67a5b9309831a10c673fedd068b887899b908b","content_id":"b51dd673189f48f571a6094fc754710301a4d99a","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6741,"license_type":"permissive","max_line_length":78,"num_lines":190,"path":"/unit_tests/test_heat_context.py","repo_name":"openstack/charm-heat","src_encoding":"UTF-8","text":"# Copyright 2016 Canonical Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport charmhelpers\nimport heat_context\nimport json\nfrom unittest.mock import patch\nfrom test_utils import CharmTestCase\n\nTO_PATCH = [\n 'get_encryption_key',\n 'generate_ec2_tokens',\n 'config',\n 'leader_get',\n 'relation_get',\n 'relation_ids',\n 'related_units',\n]\n\n\nclass TestHeatContext(CharmTestCase):\n\n def setUp(self):\n super(TestHeatContext, self).setUp(heat_context, TO_PATCH)\n\n def test_encryption_configuration(self):\n self.get_encryption_key.return_value = 'key'\n self.leader_get.return_value = 'password'\n self.assertEqual(\n heat_context.HeatSecurityContext()(),\n {'encryption_key': 'key',\n 'heat_domain_admin_passwd': 'password'})\n self.leader_get.assert_called_with('heat-domain-admin-passwd')\n\n def test_instance_user_empty_configuration(self):\n self.config.return_value = None\n self.assertEqual(\n heat_context.InstanceUserContext()(),\n {'instance_user': ''})\n\n @patch('charmhelpers.contrib.openstack.'\n 'context.IdentityServiceContext.__call__')\n def test_identity_configuration(self, __call__):\n __call__.return_value = {\n 'service_port': 'port',\n 'service_host': 'host',\n 'auth_host': 'host',\n 'auth_port': 'port',\n 'admin_tenant_name': 'tenant',\n 'admin_user': 'user',\n 'admin_password': 'pass',\n 'service_protocol': 'http',\n 'auth_protocol': 'http'}\n self.generate_ec2_tokens.return_value = \\\n 'http://host:port/v2.0/ec2tokens'\n\n final_result = __call__.return_value\n final_result['keystone_ec2_url'] = \\\n self.generate_ec2_tokens.return_value\n\n self.assertEqual(\n heat_context.HeatIdentityServiceContext()(), final_result)\n\n def test_quota_configuration_context(self):\n expected = {'max_stacks_per_tenant': '999'}\n self.config.side_effect = self.test_config.get\n self.test_config.set('max-stacks-per-tenant', '999')\n self.assertEqual(heat_context.QuotaConfigurationContext()(), expected)\n\n @patch('charmhelpers.contrib.hahelpers.cluster.https')\n @patch('heat_context.https')\n def test_haproxy_context(self, mock_https, mock_ch_https):\n for https_mode in [False, True]:\n api_cfn_listen_port = 7990\n api_listen_port = 7994\n if https_mode:\n api_cfn_listen_port = 7980\n api_listen_port = 7984\n mock_https.return_value = https_mode\n mock_ch_https.return_value = https_mode\n haproxy_context = heat_context.HeatHAProxyContext()\n healthcheck = [{\n 'option': 'httpchk GET /healthcheck',\n 'http-check': 'expect status 200',\n }]\n backend_options = {\n 'heat_api': healthcheck,\n 'heat_cfn_api': healthcheck\n }\n expected = {\n 'service_ports': {\n 'heat_api': [8004, 7994],\n 'heat_cfn_api': [8000, 7990]\n },\n 'api_listen_port': api_listen_port,\n 'api_cfn_listen_port': api_cfn_listen_port,\n 'backend_options': backend_options,\n 'https': https_mode,\n }\n self.assertEqual(expected, haproxy_context())\n\n\nclass HeatPluginContextTest(CharmTestCase):\n\n def setUp(self):\n super(HeatPluginContextTest, self).setUp(heat_context, TO_PATCH)\n self.relation_get.side_effect = self.test_relation.get\n\n def tearDown(self):\n super(HeatPluginContextTest, self).tearDown()\n\n def test_init(self):\n heatp_ctxt = heat_context.HeatPluginContext()\n self.assertEqual(\n heatp_ctxt.interfaces,\n ['heat-plugin-subordinate']\n )\n self.assertEqual(heatp_ctxt.services, ['heat'])\n self.assertEqual(\n heatp_ctxt.config_file,\n '/etc/heat/heat.conf'\n )\n\n @patch.object(charmhelpers.contrib.openstack.context, 'log')\n @patch.object(charmhelpers.contrib.openstack.context, 'relation_get')\n @patch.object(charmhelpers.contrib.openstack.context, 'related_units')\n @patch.object(charmhelpers.contrib.openstack.context, 'relation_ids')\n def ctxt_check(self, rel_settings, expect, _rids, _runits, _rget, _log):\n self.test_relation.set(rel_settings)\n _runits.return_value = ['unit1']\n _rids.return_value = ['rid2']\n _rget.side_effect = self.test_relation.get\n self.relation_ids.return_value = ['rid2']\n self.related_units.return_value = ['unit1']\n heatp_ctxt = heat_context.HeatPluginContext()()\n self.assertEqual(heatp_ctxt, expect)\n\n def test_defaults(self):\n self.ctxt_check(\n {},\n {\n 'plugin_dirs': '/usr/lib64/heat,/usr/lib/heat',\n }\n )\n\n def test_overrides(self):\n self.ctxt_check(\n {'plugin-dirs': '/usr/lib64/heat,/usr/lib/heat,/usr/local/lib'},\n {\n 'plugin_dirs': '/usr/lib64/heat,/usr/lib/heat,/usr/local/lib',\n }\n )\n\n def test_subordinateconfig(self):\n principle_config = {\n \"heat\": {\n \"/etc/heat/heat.conf\": {\n \"sections\": {\n 'DEFAULT': [\n ('heatboost', True)\n ],\n 'heatplugin': [\n ('superkey', 'supervalue')\n ],\n }\n }\n }\n }\n self.ctxt_check(\n {\n 'plugin-dirs': '/usr/lib64/heat,/usr/lib/heat,/usr/local/lib',\n 'subordinate_configuration': json.dumps(principle_config)\n },\n {\n 'plugin_dirs': '/usr/lib64/heat,/usr/lib/heat,/usr/local/lib',\n 'sections': {u'DEFAULT': [[u'heatboost', True]],\n u'heatplugin': [[u'superkey', u'supervalue']]},\n }\n )\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6368222236633301,\n \"alphanum_fraction\": 0.6481714844703674,\n \"avg_line_length\": 25.433332443237305,\n \"blob_id\": \"e0f3484fba242d67675355c36a7b3b5e7086df87\",\n \"content_id\": \"d4b5fca6f7b97ec73e20b081a212e38e6ac9cc2d\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 793,\n \"license_type\": \"permissive\",\n \"max_line_length\": 88,\n \"num_lines\": 30,\n \"path\": \"/hooks/upgrade-charm\",\n \"repo_name\": \"openstack/charm-heat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash -e\\n# Wrapper to deal with newer Ubuntu versions that don't have py2 installed\\n# by default.\\n\\ndeclare -a DEPS=('apt' 'netaddr' 'netifaces' 'pip' 'yaml' 'dnspython' 'openstackclient')\\n\\n# drop this when trusty support is ended. Only need python3 at that point\\nrelease=$(lsb_release -c -s)\\nif [ \\\"$release\\\" == \\\"trusty\\\" ]; then\\n PYTHON=\\\"python\\\"\\nelse\\n PYTHON=\\\"python3\\\"\\n # if python-openstackclient is installed then remove it\\n if dpkg -s python-openstackclient 2>&1 > /dev/null; then\\n apt purge -y python-openstackclient\\n fi\\nfi\\n\\ncheck_and_install() {\\n pkg=\\\"${1}-${2}\\\"\\n if ! dpkg -s ${pkg} 2>&1 > /dev/null; then\\n apt-get -y install ${pkg}\\n fi\\n}\\n\\nfor dep in ${DEPS[@]}; do\\n check_and_install ${PYTHON} ${dep}\\ndone\\n\\nexec ./hooks/upgrade-charm.real\\n\"\n },\n {\n \"alpha_fraction\": 0.7232415676116943,\n \"alphanum_fraction\": 0.7247706651687622,\n \"avg_line_length\": 28.727272033691406,\n \"blob_id\": \"201a3ba660cf4122186d6da0a2deef4f6f788e7b\",\n \"content_id\": \"8b4a0a384f874abd2d5c93ad5319b2a2870708f7\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 654,\n \"license_type\": \"permissive\",\n \"max_line_length\": 106,\n \"num_lines\": 22,\n \"path\": \"/actions/domain-setup\",\n \"repo_name\": \"openstack/charm-heat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n\\nset -e\\n\\n. /root/admin-openrc-v3\\n\\nopenstack domain show heat || {\\n openstack domain create --description \\\"Stack projects and users\\\" heat\\n}\\n\\nopenstack user show --domain heat heat_domain_admin || {\\n openstack user create --domain heat --password `leader-get heat-domain-admin-passwd` heat_domain_admin\\n}\\n\\nopenstack role add --domain heat --user-domain heat --user heat_domain_admin admin || {\\n # NOTE(jamespage): Fallback to trying older style --user-domain -less configuration\\n openstack role add --domain heat --user heat_domain_admin admin\\n}\\n\\nopenstack role show heat_stack_user || {\\n openstack role create heat_stack_user\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6153894066810608,\n \"alphanum_fraction\": 0.625981330871582,\n \"avg_line_length\": 39.73604202270508,\n \"blob_id\": \"6c796b7ee89ef71df854b321769e64ea96c74c82\",\n \"content_id\": \"5858a39a33d8eab21b9295e27b467ffd3330feae\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 16050,\n \"license_type\": \"permissive\",\n \"max_line_length\": 79,\n \"num_lines\": 394,\n \"path\": \"/unit_tests/test_heat_relations.py\",\n \"repo_name\": \"openstack/charm-heat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Copyright 2016 Canonical Ltd\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\n\\nimport os.path\\nimport sys\\n\\nfrom unittest.mock import call, patch, MagicMock\\nfrom test_utils import CharmTestCase, patch_open\\n\\n# python-apt is not installed as part of test-requirements but is imported by\\n# some charmhelpers modules so create a fake import.\\nmock_apt = MagicMock()\\nsys.modules['apt'] = mock_apt\\nmock_apt.apt_pkg = MagicMock()\\n\\n\\nimport heat_utils as utils\\n\\n_reg = utils.register_configs\\n_map = utils.restart_map\\n\\nutils.register_configs = MagicMock()\\nutils.restart_map = MagicMock()\\n\\nwith patch('charmhelpers.contrib.hardening.harden.harden') as mock_dec:\\n mock_dec.side_effect = (lambda *dargs, **dkwargs: lambda f:\\n lambda *args, **kwargs: f(*args, **kwargs))\\n import heat_relations as relations\\n\\nutils.register_configs = _reg\\nutils.restart_map = _map\\n\\nTO_PATCH = [\\n # charmhelpers.core.hookenv\\n 'Hooks',\\n 'config',\\n 'is_leader',\\n 'leader_set',\\n 'log',\\n 'open_port',\\n 'relation_set',\\n 'related_units',\\n # charmhelpers.core.host\\n 'apt_install',\\n 'apt_update',\\n 'restart_on_change',\\n 'service_restart',\\n # charmhelpers.contrib.openstack.utils\\n 'charm_dir',\\n 'configure_installation_source',\\n 'determine_packages',\\n 'openstack_upgrade_available',\\n 'os_release',\\n 'sync_db_with_multi_ipv6_addresses',\\n # charmhelpers.contrib.openstack.ha.utils\\n 'generate_ha_relation_data',\\n # charmhelpers.contrib.hahelpers.cluster_utils\\n # heat_utils\\n 'restart_map',\\n 'register_configs',\\n 'do_openstack_upgrade',\\n 'remove_old_packages',\\n 'services',\\n # other\\n 'execd_preinstall',\\n 'log',\\n 'migrate_database',\\n 'is_elected_leader',\\n 'relation_ids',\\n 'relation_get',\\n 'local_unit',\\n 'get_relation_ip',\\n 'is_db_maintenance_mode',\\n]\\n\\n\\nclass HeatRelationTests(CharmTestCase):\\n\\n def setUp(self):\\n super(HeatRelationTests, self).setUp(relations, TO_PATCH)\\n self.config.side_effect = self.test_config.get\\n self.charm_dir.return_value = '/var/lib/juju/charms/heat/charm'\\n\\n @patch.object(relations.policyd, 'maybe_do_policyd_overrides')\\n def test_install_hook(self, mock_maybe_do_policyd_overrides):\\n repo = 'cloud:precise-havana'\\n self.determine_packages.return_value = [\\n 'python-keystoneclient', 'uuid', 'heat-api',\\n 'heat-api-cfn', 'heat-engine']\\n self.test_config.set('openstack-origin', repo)\\n relations.install()\\n self.configure_installation_source.assert_called_with(repo)\\n self.assertTrue(self.apt_update.called)\\n self.apt_install.assert_called_with(['python-keystoneclient',\\n 'uuid', 'heat-api',\\n 'heat-api-cfn',\\n 'heat-engine'], fatal=True)\\n self.assertTrue(self.execd_preinstall.called)\\n\\n @patch.object(relations, 'configure_https')\\n @patch.object(relations.policyd,\\n 'maybe_do_policyd_overrides_on_config_changed')\\n @patch.object(relations, 'update_nrpe_config')\\n def test_config_changed_no_upgrade(\\n self,\\n mock_update_nrpe_config,\\n maybe_do_policyd_overrides_on_config_changed,\\n mock_configure_https,\\n ):\\n self.openstack_upgrade_available.return_value = False\\n relations.config_changed()\\n\\n @patch.object(relations, 'configure_https')\\n @patch.object(relations.policyd,\\n 'maybe_do_policyd_overrides_on_config_changed')\\n @patch.object(relations, 'update_nrpe_config')\\n def test_config_changed_with_upgrade(\\n self,\\n mock_update_nrpe_config,\\n maybe_do_policyd_overrides_on_config_changed,\\n mock_configure_https\\n ):\\n self.openstack_upgrade_available.return_value = True\\n relations.config_changed()\\n self.assertTrue(self.do_openstack_upgrade.called)\\n\\n @patch.object(relations, 'configure_https')\\n @patch.object(relations.policyd,\\n 'maybe_do_policyd_overrides_on_config_changed')\\n @patch.object(relations, 'update_nrpe_config')\\n def test_config_changed_with_openstack_upgrade_action(\\n self,\\n mock_update_nrpe_config,\\n maybe_do_policyd_overrides_on_config_changed,\\n mock_configure_https\\n ):\\n self.openstack_upgrade_available.return_value = True\\n self.test_config.set('action-managed-upgrade', True)\\n\\n relations.config_changed()\\n\\n self.assertFalse(self.do_openstack_upgrade.called)\\n\\n @patch('os.path.isfile')\\n @patch('os.remove')\\n @patch.object(relations, 'leader_elected')\\n @patch.object(relations.policyd, 'maybe_do_policyd_overrides')\\n @patch.object(relations, 'update_nrpe_config')\\n def test_upgrade_charm(self,\\n mock_update_nrpe_config,\\n mock_maybe_do_policyd_overrides,\\n leader_elected, os_remove, os_path_isfile):\\n os_path_isfile.return_value = False\\n self.is_leader.return_value = False\\n self.remove_old_packages.return_value = True\\n self.services.return_value = ['heat-api']\\n\\n relations.upgrade_charm()\\n leader_elected.assert_called_once_with()\\n os_path_isfile.assert_not_called()\\n # now say we are the leader\\n self.is_leader.return_value = True\\n os_path_isfile.return_value = False\\n\\n relations.upgrade_charm()\\n self.leader_set.assert_not_called()\\n os_path_isfile.return_value = True\\n with patch_open() as (mock_open, mock_file):\\n mock_file.read.return_value = \\\"abc\\\"\\n relations.upgrade_charm()\\n filename = os.path.join(relations.HEAT_PATH, 'encryption-key')\\n mock_open.assert_called_once_with(filename, 'r')\\n self.leader_set.assert_called_once_with(\\n {'heat-auth-encryption-key': 'abc'})\\n os_remove.assert_called_once_with(filename)\\n self.remove_old_packages.assert_called_with()\\n self.service_restart.assert_called_with('heat-api')\\n\\n def test_db_joined(self):\\n self.get_relation_ip.return_value = '192.168.20.1'\\n relations.db_joined()\\n self.relation_set.assert_called_with(heat_database='heat',\\n heat_username='heat',\\n heat_hostname='192.168.20.1')\\n self.assertTrue(self.get_relation_ip.called)\\n\\n def _shared_db_test(self, configs):\\n self.is_db_maintenance_mode.return_value = False\\n self.relation_get.return_value = 'heat/0 heat/1'\\n self.local_unit.return_value = 'heat/0'\\n configs.complete_contexts = MagicMock()\\n configs.complete_contexts.return_value = ['shared-db']\\n configs.write = MagicMock()\\n relations.db_changed()\\n self.assertTrue(self.migrate_database.called)\\n\\n @patch.object(relations, 'CONFIGS')\\n def test_db_changed(self, configs):\\n self._shared_db_test(configs)\\n self.assertEqual([call('/etc/heat/heat.conf')],\\n configs.write.call_args_list)\\n\\n @patch.object(relations, 'CONFIGS')\\n def test_db_changed_missing_relation_data(self, configs):\\n self.is_db_maintenance_mode.return_value = False\\n configs.complete_contexts = MagicMock()\\n configs.complete_contexts.return_value = []\\n relations.db_changed()\\n self.log.assert_called_with(\\n 'shared-db relation incomplete. Peer not ready?'\\n )\\n\\n def test_amqp_joined(self):\\n relations.amqp_joined()\\n self.relation_set.assert_called_with(\\n username='heat',\\n vhost='openstack',\\n relation_id=None,\\n ttlname='heat_expiry',\\n ttlreg='heat-engine-listener|engine_worker',\\n ttl=3600000)\\n\\n def test_amqp_joined_passes_relation_id(self):\\n \\\"Ensures relation_id correct passed to relation_set\\\"\\n relations.amqp_joined(relation_id='heat:1')\\n self.relation_set.assert_called_with(\\n username='heat', vhost='openstack', relation_id='heat:1',\\n ttlname='heat_expiry',\\n ttlreg='heat-engine-listener|engine_worker', ttl=3600000)\\n\\n @patch.object(relations, 'CONFIGS')\\n def test_amqp_changed_relation_data(self, configs):\\n configs.complete_contexts = MagicMock()\\n configs.complete_contexts.return_value = ['amqp']\\n configs.write = MagicMock()\\n relations.amqp_changed()\\n self.assertEqual([call('/etc/heat/heat.conf')],\\n configs.write.call_args_list)\\n\\n @patch.object(relations, 'CONFIGS')\\n def test_amqp_changed_missing_relation_data(self, configs):\\n configs.complete_contexts = MagicMock()\\n configs.complete_contexts.return_value = []\\n relations.amqp_changed()\\n self.assertTrue(self.log.called)\\n\\n @patch.object(relations, 'CONFIGS')\\n def test_relation_broken(self, configs):\\n relations.relation_broken()\\n self.assertTrue(configs.write_all.called)\\n\\n @patch.object(relations, 'canonical_url')\\n def test_identity_service_joined(self, _canonical_url):\\n \\\"It properly requests unclustered endpoint via identity-service\\\"\\n _canonical_url.return_value = 'http://heatnode1'\\n relations.identity_joined()\\n expected = {\\n 'heat_service': 'heat',\\n 'heat_region': 'RegionOne',\\n 'heat_public_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\\n 'heat_admin_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\\n 'heat_internal_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\\n 'heat-cfn_service': 'heat-cfn',\\n 'heat-cfn_region': 'RegionOne',\\n 'heat-cfn_public_url': 'http://heatnode1:8000/v1',\\n 'heat-cfn_admin_url': 'http://heatnode1:8000/v1',\\n 'heat-cfn_internal_url': 'http://heatnode1:8000/v1',\\n 'relation_id': None,\\n }\\n self.relation_set.assert_called_with(**expected)\\n\\n @patch.object(relations, 'canonical_url')\\n def test_identity_service_joined_with_relation_id(self, _canonical_url):\\n _canonical_url.return_value = 'http://heatnode1'\\n relations.identity_joined(rid='identity-service:0')\\n ex = {\\n 'heat_service': 'heat',\\n 'heat_region': 'RegionOne',\\n 'heat_public_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\\n 'heat_admin_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\\n 'heat_internal_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\\n 'heat-cfn_service': 'heat-cfn',\\n 'heat-cfn_region': 'RegionOne',\\n 'heat-cfn_public_url': 'http://heatnode1:8000/v1',\\n 'heat-cfn_admin_url': 'http://heatnode1:8000/v1',\\n 'heat-cfn_internal_url': 'http://heatnode1:8000/v1',\\n 'relation_id': 'identity-service:0',\\n }\\n self.relation_set.assert_called_with(**ex)\\n\\n @patch('charmhelpers.contrib.openstack.ip.unit_get',\\n lambda *args: 'heatnode1')\\n @patch('charmhelpers.contrib.openstack.ip.is_clustered',\\n lambda *args: False)\\n @patch('charmhelpers.contrib.openstack.ip.service_name')\\n @patch('charmhelpers.contrib.openstack.ip.config')\\n def test_identity_service_public_address_override(self, ip_config,\\n _service_name):\\n ip_config.side_effect = self.test_config.get\\n _service_name.return_value = 'heat'\\n self.test_config.set('os-public-hostname', 'heat.example.org')\\n relations.identity_joined(rid='identity-service:0')\\n exp = {\\n 'heat_service': 'heat',\\n 'heat_region': 'RegionOne',\\n 'heat_public_url': 'http://heat.example.org:8004/v1/$(tenant_id)s',\\n 'heat_admin_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\\n 'heat_internal_url': 'http://heatnode1:8004/v1/$(tenant_id)s',\\n 'heat-cfn_service': 'heat-cfn',\\n 'heat-cfn_region': 'RegionOne',\\n 'heat-cfn_public_url': 'http://heat.example.org:8000/v1',\\n 'heat-cfn_admin_url': 'http://heatnode1:8000/v1',\\n 'heat-cfn_internal_url': 'http://heatnode1:8000/v1',\\n 'relation_id': 'identity-service:0',\\n }\\n self.relation_set.assert_called_with(**exp)\\n\\n @patch.object(relations, 'configure_https')\\n @patch.object(relations, 'CONFIGS')\\n def test_identity_changed(self, configs, mock_configure_https):\\n configs.complete_contexts.return_value = ['identity-service']\\n relations.identity_changed()\\n self.assertTrue(configs.write_all.called)\\n\\n @patch.object(relations, 'CONFIGS')\\n def test_identity_changed_incomplete(self, configs):\\n configs.complete_contexts.return_value = []\\n relations.identity_changed()\\n self.assertTrue(self.log.called)\\n self.assertFalse(configs.write.called)\\n\\n def test_db_joined_with_ipv6(self):\\n 'It properly requests access to a shared-db service'\\n self.sync_db_with_multi_ipv6_addresses.return_value = MagicMock()\\n self.test_config.set('prefer-ipv6', True)\\n relations.db_joined()\\n self.sync_db_with_multi_ipv6_addresses.assert_called_with(\\n 'heat', 'heat', relation_prefix='heat')\\n\\n @patch.object(relations, 'CONFIGS')\\n def test_non_leader_db_changed(self, configs):\\n self.is_elected_leader.return_value = False\\n configs.complete_contexts.return_value = []\\n self.relation_get.return_value = 'heat/0 heat/1'\\n self.local_unit.return_value = 'heat/0'\\n configs.complete_contexts = MagicMock()\\n configs.complete_contexts.return_value = ['shared-db']\\n configs.write = MagicMock()\\n relations.db_changed()\\n self.assertFalse(self.migrate_database.called)\\n\\n def test_ha_relation_joined(self):\\n self.generate_ha_relation_data.return_value = {'rel_data': 'data'}\\n relations.ha_joined(relation_id='rid:23')\\n self.relation_set.assert_called_once_with(\\n relation_id='rid:23', rel_data='data')\\n\\n @patch.object(relations, \\\"apt_install\\\")\\n @patch.object(relations.nrpe, \\\"get_nagios_hostname\\\")\\n @patch.object(relations.nrpe, \\\"get_nagios_unit_name\\\")\\n @patch.object(relations.nrpe, \\\"NRPE\\\")\\n @patch.object(relations.nrpe, \\\"copy_nrpe_checks\\\")\\n @patch.object(relations.nrpe, \\\"add_init_service_checks\\\")\\n @patch.object(relations.nrpe, \\\"add_haproxy_checks\\\")\\n def test_update_nrpe_config(self, mock_add_haproxy, mock_add_init_svc,\\n mock_copy_nrpe_checks, mock_nrpe,\\n mock_nagios_unitname, mock_nagios_hostname,\\n mock_apt_install):\\n relations.update_nrpe_config()\\n mock_apt_install.assert_called_once_with(\\\"python-dbus\\\")\\n mock_nagios_hostname.assert_called_once_with()\\n mock_nagios_unitname.assert_called_once_with()\\n mock_nrpe.assert_called_once_with(\\n hostname=mock_nagios_hostname.return_value)\\n mock_copy_nrpe_checks.assert_called_once_with()\\n mock_add_init_svc.assert_called_once_with(\\n mock_nrpe.return_value,\\n relations.services(),\\n mock_nagios_unitname.return_value,\\n )\\n mock_add_haproxy.assert_called_once_with(\\n mock_nrpe.return_value, mock_nagios_unitname.return_value)\\n mock_nrpe.return_value.write.assert_called_once_with()\\n\"\n },\n {\n \"alpha_fraction\": 0.6557239294052124,\n \"alphanum_fraction\": 0.6601009964942932,\n \"avg_line_length\": 38.078948974609375,\n \"blob_id\": \"f9bebe20eb7d04c80e09ddaa5fbdd26c8ef409e4\",\n \"content_id\": \"1445d85ab7a13bef40ce9430e7bee319d2a1b067\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5940,\n \"license_type\": \"permissive\",\n \"max_line_length\": 78,\n \"num_lines\": 152,\n \"path\": \"/unit_tests/test_heat_utils.py\",\n \"repo_name\": \"openstack/charm-heat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Copyright 2016 Canonical Ltd\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\n\\nfrom copy import deepcopy\\nfrom collections import OrderedDict\\nfrom unittest.mock import patch, MagicMock, call\\nfrom test_utils import CharmTestCase\\n\\nfrom charmhelpers.core import hookenv\\n\\n_conf = hookenv.config\\nhookenv.config = MagicMock()\\n\\nimport heat_utils as utils\\n\\nhookenv.config = _conf\\n\\nTO_PATCH = [\\n 'config',\\n 'log',\\n 'os_release',\\n 'get_os_codename_install_source',\\n 'configure_installation_source',\\n 'apt_install',\\n 'apt_update',\\n 'apt_upgrade',\\n 'check_call',\\n 'service_start',\\n 'service_stop',\\n 'token_cache_pkgs',\\n 'enable_memcache',\\n 'os'\\n]\\n\\n\\n# Restart map should be constructed such that API services restart\\n# before frontends (haproxy/apaceh) to avoid port conflicts.\\nRESTART_MAP = OrderedDict([\\n ('/etc/heat/heat.conf', ['heat-api', 'heat-api-cfn', 'heat-engine']),\\n ('/etc/heat/api-paste.ini', ['heat-api', 'heat-api-cfn']),\\n ('/etc/haproxy/haproxy.cfg', ['haproxy']),\\n ('/etc/apache2/sites-available/openstack_https_frontend', ['apache2']),\\n ('/etc/apache2/sites-available/openstack_https_frontend.conf',\\n ['apache2']),\\n ('/etc/memcached.conf', ['memcached']),\\n ('/etc/apache2/ports.conf', ['apache2']),\\n])\\n\\n\\nclass HeatUtilsTests(CharmTestCase):\\n\\n def setUp(self):\\n super(HeatUtilsTests, self).setUp(utils, TO_PATCH)\\n self.config.side_effect = self.test_config.get\\n\\n @patch('charmhelpers.contrib.openstack.context.SubordinateConfigContext')\\n def test_determine_packages(self, subcontext):\\n self.os_release.return_value = 'havana'\\n pkgs = utils.determine_packages()\\n ex = list(set(utils.BASE_PACKAGES + utils.BASE_SERVICES))\\n self.assertEqual(ex, pkgs)\\n\\n @patch('charmhelpers.contrib.openstack.context.SubordinateConfigContext')\\n def test_determine_packages_queens(self, subcontext):\\n self.os_release.return_value = 'queens'\\n self.token_cache_pkgs.return_value = ['python-memcache', 'memcached']\\n pkgs = utils.determine_packages()\\n ex = list(set(utils.BASE_PACKAGES + ['python-memcache', 'memcached'] +\\n utils.BASE_SERVICES))\\n self.assertEqual(sorted(ex), sorted(pkgs))\\n\\n @patch('charmhelpers.contrib.openstack.context.SubordinateConfigContext')\\n def test_determine_packages_rocky(self, subcontext):\\n self.os_release.return_value = 'rocky'\\n self.token_cache_pkgs.return_value = ['python-memcache', 'memcached']\\n pkgs = utils.determine_packages()\\n ex = list(set(\\n [p for p in utils.BASE_PACKAGES if not p.startswith('python-')] +\\n ['memcached'] + utils.BASE_SERVICES + utils.PY3_PACKAGES))\\n self.assertEqual(sorted(ex), sorted(pkgs))\\n\\n def test_determine_purge_packages(self):\\n 'Ensure no packages are identified for purge prior to rocky'\\n self.os_release.return_value = 'queens'\\n self.assertEqual(utils.determine_purge_packages(), [])\\n\\n def test_determine_purge_packages_rocky(self):\\n 'Ensure python packages are identified for purge at rocky'\\n self.os_release.return_value = 'rocky'\\n self.assertEqual(utils.determine_purge_packages(),\\n [p for p in utils.BASE_PACKAGES\\n if p.startswith('python-')] +\\n ['python-heat', 'python-memcache'])\\n\\n def test_restart_map(self):\\n # Icehouse\\n self.os_release.return_value = \\\"icehouse\\\"\\n self.enable_memcache.return_value = False\\n self.os.path.exists.return_value = False\\n _restart_map = deepcopy(RESTART_MAP)\\n _restart_map.pop(\\n \\\"/etc/apache2/sites-available/openstack_https_frontend.conf\\\")\\n _restart_map.pop(\\\"/etc/memcached.conf\\\")\\n self.assertEqual(_restart_map, utils.restart_map())\\n\\n # Mitaka\\n self.os_release.return_value = \\\"mitaka\\\"\\n self.enable_memcache.return_value = True\\n self.os.path.exists.return_value = True\\n _restart_map = deepcopy(RESTART_MAP)\\n _restart_map.pop(\\n \\\"/etc/apache2/sites-available/openstack_https_frontend\\\")\\n self.assertEqual(_restart_map, utils.restart_map())\\n\\n def test_openstack_upgrade(self):\\n self.config.side_effect = None\\n self.config.return_value = 'cloud:precise-havana'\\n self.get_os_codename_install_source.return_value = 'havana'\\n self.os_release.return_value = 'icehouse'\\n configs = MagicMock()\\n utils.do_openstack_upgrade(configs)\\n self.assertTrue(self.apt_update.called)\\n self.assertTrue(self.apt_upgrade.called)\\n self.assertTrue(self.apt_install.called)\\n configs.set_release.assert_called_with(openstack_release='havana')\\n self.assertTrue(configs.write_all.called)\\n\\n def test_api_ports(self):\\n cfn = utils.api_port('heat-api-cfn')\\n self.assertEqual(cfn, 8000)\\n cfn = utils.api_port('heat-api')\\n self.assertEqual(cfn, 8004)\\n\\n def test_migrate_database(self):\\n utils.migrate_database()\\n self.assertTrue(self.log.called)\\n self.check_call.assert_called_with(['heat-manage', 'db_sync'])\\n expected = [call('heat-api'), call('heat-api-cfn'),\\n call('heat-engine'), call('apache2')]\\n self.service_stop.assert_has_calls(expected, any_order=True)\\n self.service_start.assert_has_calls(expected, any_order=True)\\n\"\n },\n {\n \"alpha_fraction\": 0.7702735066413879,\n \"alphanum_fraction\": 0.771938145160675,\n \"avg_line_length\": 34.63559341430664,\n \"blob_id\": \"0cb8cb9f0fe789af388af2c7e27e8b5e4b4bb699\",\n \"content_id\": \"51183240cfa0cb8bf6525ce61d4603e4fa02d8ba\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 4205,\n \"license_type\": \"permissive\",\n \"max_line_length\": 121,\n \"num_lines\": 118,\n \"path\": \"/README.md\",\n \"repo_name\": \"openstack/charm-heat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Overview\\n\\nHeat is the main project in the OpenStack Orchestration program. It implements\\nan orchestration engine to launch multiple composite cloud applications based\\non templates in the form of text files that can be treated like code.\\n\\nThis charm deploys the Heat infrastructure.\\n\\n# Usage\\n\\nHeat requires the existence of the other core OpenStack services deployed via\\nJuju charms, specifically: mysql, rabbitmq-server, keystone and\\nnova-cloud-controller. The following assumes these services have already\\nbeen deployed.\\n\\nAfter deployment of the cloud, the domain-setup action must be run to configure\\nrequired domains, roles and users in the cloud for Heat stacks.\\n\\nFor juju 2.x deployments use:\\n\\n juju run-action heat/0 domain-setup\\n\\nIf using juju 1.x run:\\n\\n juju action do heat/0 domain-setup\\n\\nThis is only required for >= OpenStack Kilo.\\n\\n## High availability\\n\\nWhen more than one unit is deployed with the [hacluster][hacluster-charm]\\napplication the charm will bring up an HA active/active cluster.\\n\\nThere are two mutually exclusive high availability options: using virtual IP(s)\\nor DNS. In both cases the hacluster subordinate charm is used to provide the\\nCorosync and Pacemaker backend HA functionality.\\n\\nSee [OpenStack high availability][cdg-ha-apps] in the [OpenStack Charms\\nDeployment Guide][cdg] for details.\\n\\n## Spaces\\n\\nThis charm supports the use of Juju Network Spaces, allowing the charm to be\\nbound to network space configurations managed directly by Juju. This is only\\nsupported with Juju 2.0 and above.\\n\\nAPI endpoints can be bound to distinct network spaces supporting the network\\nseparation of public, internal and admin endpoints.\\n\\nAccess to the underlying MySQL instance can also be bound to a specific space\\nusing the shared-db relation.\\n\\nTo use this feature, use the --bind option when deploying the charm:\\n\\n juju deploy heat --bind \\\\\\n \\\"public=public-space \\\\\\n internal=internal-space \\\\\\n admin=admin-space \\\\\\n shared-db=internal-space\\\"\\n\\nAlternatively, these can also be provided as part of a juju native bundle\\nconfiguration:\\n\\n```yaml\\n heat:\\n charm: cs:xenial/heat\\n num_units: 1\\n bindings:\\n public: public-space\\n admin: admin-space\\n internal: internal-space\\n shared-db: internal-space\\n```\\n\\nNOTE: Spaces must be configured in the underlying provider prior to attempting\\nto use them.\\n\\nNOTE: Existing deployments using os-*-network configuration options will\\ncontinue to function; these options are preferred over any network space\\nbinding provided if set.\\n\\n## Policy Overrides\\n\\nPolicy overrides is an **advanced** feature that allows an operator to override\\nthe default policy of an OpenStack service. The policies that the service\\nsupports, the defaults it implements in its code, and the defaults that a charm\\nmay include should all be clearly understood before proceeding.\\n\\n> **Caution**: It is possible to break the system (for tenants and other\\n services) if policies are incorrectly applied to the service.\\n\\nPolicy statements are placed in a YAML file. This file (or files) is then (ZIP)\\ncompressed into a single file and used as an application resource. The override\\nis then enabled via a Boolean charm option.\\n\\nHere are the essential commands (filenames are arbitrary):\\n\\n zip overrides.zip override-file.yaml\\n juju attach-resource heat policyd-override=overrides.zip\\n juju config heat use-policyd-override=true\\n\\nSee appendix [Policy Overrides][cdg-appendix-n] in the [OpenStack Charms\\nDeployment Guide][cdg] for a thorough treatment of this feature.\\n\\n# Bugs\\n\\nPlease report bugs on [Launchpad][lp-bugs-charm-heat].\\n\\nFor general charm questions refer to the OpenStack [Charm Guide][cg].\\n\\n<!-- LINKS -->\\n\\n[cg]: https://docs.openstack.org/charm-guide\\n[cdg]: https://docs.openstack.org/project-deploy-guide/charm-deployment-guide\\n[cdg-appendix-n]: https://docs.openstack.org/project-deploy-guide/charm-deployment-guide/latest/app-policy-overrides.html\\n[lp-bugs-charm-heat]: https://bugs.launchpad.net/charm-heat/+filebug\\n[hacluster-charm]: https://jaas.ai/hacluster\\n[cdg-ha-apps]: https://docs.openstack.org/project-deploy-guide/charm-deployment-guide/latest/app-ha.html#ha-applications\\n\"\n },\n {\n \"alpha_fraction\": 0.6437255144119263,\n \"alphanum_fraction\": 0.6470350027084351,\n \"avg_line_length\": 29.910228729248047,\n \"blob_id\": \"444022f07ae76eeb5af683bc06bfcd709018fc29\",\n \"content_id\": \"8988218fa9de88131e7fe33b7b230e6d619c3dbf\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 14806,\n \"license_type\": \"permissive\",\n \"max_line_length\": 79,\n \"num_lines\": 479,\n \"path\": \"/hooks/heat_relations.py\",\n \"repo_name\": \"openstack/charm-heat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python3\\n#\\n# Copyright 2016 Canonical Ltd\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\n\\nimport os\\nimport shutil\\nimport subprocess\\nimport sys\\n\\nfrom charmhelpers.core.hookenv import (\\n Hooks,\\n UnregisteredHookError,\\n config,\\n charm_dir,\\n log,\\n relation_ids,\\n relation_get,\\n relation_set,\\n related_units,\\n local_unit,\\n open_port,\\n status_set,\\n leader_get,\\n leader_set,\\n is_leader,\\n WARNING,\\n)\\n\\nfrom charmhelpers.core.host import (\\n restart_on_change,\\n service_reload,\\n pwgen,\\n service_restart,\\n)\\n\\nfrom charmhelpers.fetch import (\\n apt_install,\\n apt_update\\n)\\n\\nfrom charmhelpers.contrib.hahelpers.cluster import (\\n is_elected_leader,\\n)\\n\\nfrom charmhelpers.contrib.network.ip import (\\n get_relation_ip,\\n)\\n\\nfrom charmhelpers.contrib.openstack.utils import (\\n configure_installation_source,\\n openstack_upgrade_available,\\n os_release,\\n series_upgrade_complete,\\n series_upgrade_prepare,\\n sync_db_with_multi_ipv6_addresses,\\n is_db_maintenance_mode,\\n)\\n\\nfrom charmhelpers.contrib.openstack.ha.utils import (\\n generate_ha_relation_data,\\n)\\n\\nfrom charmhelpers.contrib.openstack.ip import (\\n canonical_url,\\n ADMIN,\\n INTERNAL,\\n PUBLIC,\\n)\\n\\nfrom heat_utils import (\\n do_openstack_upgrade,\\n restart_map,\\n determine_packages,\\n migrate_database,\\n register_configs,\\n CLUSTER_RES,\\n HEAT_CONF,\\n setup_ipv6,\\n pause_unit_helper,\\n resume_unit_helper,\\n assess_status,\\n remove_old_packages,\\n services,\\n)\\n\\nfrom heat_context import (\\n API_PORTS,\\n HEAT_PATH,\\n)\\n\\nfrom charmhelpers.contrib.charmsupport import nrpe\\nfrom charmhelpers.contrib.hardening.harden import harden\\nfrom charmhelpers.contrib.openstack.context import ADDRESS_TYPES\\nfrom charmhelpers.payload.execd import execd_preinstall\\n\\nfrom charmhelpers.contrib.openstack.cert_utils import (\\n get_certificate_request,\\n process_certificates,\\n)\\n\\nimport charmhelpers.contrib.openstack.policyd as policyd\\n\\nhooks = Hooks()\\nCONFIGS = register_configs()\\n\\n\\n@hooks.hook('install.real')\\n@harden()\\ndef install():\\n status_set('maintenance', 'Executing pre-install')\\n execd_preinstall()\\n configure_installation_source(config('openstack-origin'))\\n status_set('maintenance', 'Installing apt packages')\\n apt_update()\\n # (ajkavanagh) LP: #1989538\\n # Tactical fix to force openstack-release to match the configured\\n # installation source; note that it comes after apt_update().\\n apt_install(['openstack-release'], fatal=False, quiet=True)\\n apt_install(determine_packages(), fatal=True)\\n\\n _files = os.path.join(charm_dir(), 'files')\\n if os.path.isdir(_files):\\n for f in os.listdir(_files):\\n f = os.path.join(_files, f)\\n log('Installing {} to /usr/bin'.format(f))\\n shutil.copy2(f, '/usr/bin')\\n\\n for port in API_PORTS.values():\\n open_port(port)\\n # call the policy overrides handler which will install any policy overrides\\n policyd.maybe_do_policyd_overrides(\\n os_release('heat-common'),\\n 'heat',\\n restart_handler=restart_heat_api,\\n )\\n\\n\\ndef restart_heat_api():\\n service_restart('heat-api')\\n\\n\\n@hooks.hook('config-changed')\\n@restart_on_change(restart_map())\\n@harden()\\ndef config_changed():\\n if not config('action-managed-upgrade'):\\n if openstack_upgrade_available('heat-common'):\\n status_set('maintenance', 'Running openstack upgrade')\\n do_openstack_upgrade(CONFIGS)\\n\\n if config('prefer-ipv6'):\\n status_set('maintenance', 'configuring ipv6')\\n setup_ipv6()\\n sync_db_with_multi_ipv6_addresses(config('database'),\\n config('database-user'),\\n relation_prefix='heat')\\n\\n CONFIGS.write_all()\\n configure_https()\\n update_nrpe_config()\\n\\n for rid in relation_ids('cluster'):\\n cluster_joined(relation_id=rid)\\n for r_id in relation_ids('ha'):\\n ha_joined(relation_id=r_id)\\n for rid in relation_ids('amqp'):\\n amqp_joined(relation_id=rid)\\n\\n # call the policy overrides handler which will install any policy overrides\\n policyd.maybe_do_policyd_overrides_on_config_changed(\\n os_release('heat-common'),\\n 'heat',\\n restart_handler=restart_heat_api,\\n )\\n\\n\\n@hooks.hook('upgrade-charm.real')\\n@harden()\\ndef upgrade_charm():\\n apt_install(determine_packages(), fatal=True)\\n if remove_old_packages():\\n log(\\\"Package purge detected, restarting services\\\", \\\"INFO\\\")\\n for s in services():\\n service_restart(s)\\n if is_leader():\\n # if we are upgrading, then the old version might have used the\\n # HEAT_PATH/encryption-key. So we grab the key from that, and put it in\\n # leader settings to ensure that the key remains the same during an\\n # upgrade.\\n encryption_path = os.path.join(HEAT_PATH, 'encryption-key')\\n if os.path.isfile(encryption_path):\\n with open(encryption_path, 'r') as f:\\n encryption_key = f.read()\\n try:\\n leader_set({'heat-auth-encryption-key': encryption_key})\\n except subprocess.CalledProcessError as e:\\n log(\\\"upgrade: leader_set: heat-auth-encryption-key failed,\\\"\\n \\\" didn't delete the existing file: {}.\\\\n\\\"\\n \\\"Error was: {}\\\".format(encryption_path, str(e)),\\n level=WARNING)\\n else:\\n # now we just delete the file\\n os.remove(encryption_path)\\n leader_elected()\\n update_nrpe_config()\\n\\n # call the policy overrides handler which will install any policy overrides\\n policyd.maybe_do_policyd_overrides(\\n os_release('heat-common'),\\n 'heat',\\n restart_handler=restart_heat_api,\\n )\\n\\n\\n@hooks.hook('amqp-relation-joined')\\ndef amqp_joined(relation_id=None):\\n relation_set(relation_id=relation_id,\\n username=config('rabbit-user'), vhost=config('rabbit-vhost'),\\n ttlname='heat_expiry',\\n ttlreg='heat-engine-listener|engine_worker',\\n ttl=config('ttl'))\\n\\n\\n@hooks.hook('amqp-relation-changed')\\n@restart_on_change(restart_map())\\ndef amqp_changed():\\n if 'amqp' not in CONFIGS.complete_contexts():\\n log('amqp relation incomplete. Peer not ready?')\\n return\\n CONFIGS.write(HEAT_CONF)\\n\\n\\n@hooks.hook('shared-db-relation-joined')\\ndef db_joined():\\n if config('prefer-ipv6'):\\n sync_db_with_multi_ipv6_addresses(config('database'),\\n config('database-user'),\\n relation_prefix='heat')\\n else:\\n # Avoid churn check for access-network early\\n access_network = None\\n for unit in related_units():\\n access_network = relation_get(unit=unit,\\n attribute='access-network')\\n if access_network:\\n break\\n host = get_relation_ip('shared-db', cidr_network=access_network)\\n\\n relation_set(heat_database=config('database'),\\n heat_username=config('database-user'),\\n heat_hostname=host)\\n\\n\\n@hooks.hook('shared-db-relation-changed')\\n@restart_on_change(restart_map())\\ndef db_changed():\\n if is_db_maintenance_mode():\\n log('Database maintenance mode, aborting hook.')\\n return\\n if 'shared-db' not in CONFIGS.complete_contexts():\\n log('shared-db relation incomplete. Peer not ready?')\\n return\\n CONFIGS.write(HEAT_CONF)\\n\\n if is_elected_leader(CLUSTER_RES):\\n allowed_units = relation_get('heat_allowed_units')\\n if allowed_units and local_unit() in allowed_units.split():\\n log('Cluster leader, performing db sync')\\n migrate_database()\\n else:\\n log('allowed_units either not presented, or local unit '\\n 'not in acl list: {}'.format(repr(allowed_units)))\\n\\n\\ndef configure_https():\\n \\\"\\\"\\\"Enables SSL API Apache config if appropriate.\\\"\\\"\\\"\\n # need to write all to ensure changes to the entire request pipeline\\n # propagate (c-api, haprxy, apache)\\n CONFIGS.write_all()\\n if 'https' in CONFIGS.complete_contexts():\\n cmd = ['a2ensite', 'openstack_https_frontend']\\n subprocess.check_call(cmd)\\n else:\\n cmd = ['a2dissite', 'openstack_https_frontend']\\n subprocess.check_call(cmd)\\n\\n # TODO: improve this by checking if local CN certs are available\\n # first then checking reload status (see LP #1433114).\\n service_reload('apache2', restart_on_failure=True)\\n\\n for rid in relation_ids('identity-service'):\\n identity_joined(rid=rid)\\n\\n\\n@hooks.hook('identity-service-relation-joined')\\ndef identity_joined(rid=None):\\n public_url_base = canonical_url(CONFIGS, PUBLIC)\\n internal_url_base = canonical_url(CONFIGS, INTERNAL)\\n admin_url_base = canonical_url(CONFIGS, ADMIN)\\n\\n api_url_template = '{}:8004/v1/$(tenant_id)s'\\n public_api_endpoint = (api_url_template.format(public_url_base))\\n internal_api_endpoint = (api_url_template.format(internal_url_base))\\n admin_api_endpoint = (api_url_template.format(admin_url_base))\\n\\n cfn_url_template = '{}:8000/v1'\\n public_cfn_endpoint = (cfn_url_template.format(public_url_base))\\n internal_cfn_endpoint = (cfn_url_template.format(internal_url_base))\\n admin_cfn_endpoint = (cfn_url_template.format(admin_url_base))\\n\\n relation_data = {\\n 'heat_service': 'heat',\\n 'heat_region': config('region'),\\n 'heat_public_url': public_api_endpoint,\\n 'heat_admin_url': admin_api_endpoint,\\n 'heat_internal_url': internal_api_endpoint,\\n 'heat-cfn_service': 'heat-cfn',\\n 'heat-cfn_region': config('region'),\\n 'heat-cfn_public_url': public_cfn_endpoint,\\n 'heat-cfn_admin_url': admin_cfn_endpoint,\\n 'heat-cfn_internal_url': internal_cfn_endpoint,\\n }\\n\\n relation_set(relation_id=rid, **relation_data)\\n\\n\\n@hooks.hook('identity-service-relation-changed')\\n@restart_on_change(restart_map())\\ndef identity_changed():\\n if 'identity-service' not in CONFIGS.complete_contexts():\\n log('identity-service relation incomplete. Peer not ready?')\\n return\\n\\n CONFIGS.write_all()\\n configure_https()\\n\\n\\n@hooks.hook('amqp-relation-broken',\\n 'identity-service-relation-broken',\\n 'shared-db-relation-broken')\\ndef relation_broken():\\n CONFIGS.write_all()\\n\\n\\n@hooks.hook('leader-elected')\\ndef leader_elected():\\n if is_leader():\\n if not leader_get('heat-domain-admin-passwd'):\\n try:\\n leader_set({'heat-domain-admin-passwd': pwgen(32)})\\n except subprocess.CalledProcessError as e:\\n log('leader_set: heat-domain-admin-password failed: {}'\\n .format(str(e)), level=WARNING)\\n if not leader_get('heat-auth-encryption-key'):\\n try:\\n leader_set({'heat-auth-encryption-key': pwgen(32)})\\n except subprocess.CalledProcessError as e:\\n log('leader_set: heat-domain-admin-password failed: {}'\\n .format(str(e)), level=WARNING)\\n\\n\\n@hooks.hook('cluster-relation-joined')\\ndef cluster_joined(relation_id=None):\\n settings = {}\\n\\n for addr_type in ADDRESS_TYPES:\\n address = get_relation_ip(\\n addr_type,\\n cidr_network=config('os-{}-network'.format(addr_type)))\\n if address:\\n settings['{}-address'.format(addr_type)] = address\\n\\n settings['private-address'] = get_relation_ip('cluster')\\n\\n relation_set(relation_id=relation_id, relation_settings=settings)\\n\\n\\n@hooks.hook('cluster-relation-changed',\\n 'cluster-relation-departed')\\n@restart_on_change(restart_map(), stopstart=True)\\ndef cluster_changed():\\n CONFIGS.write_all()\\n\\n\\n@hooks.hook('ha-relation-joined')\\ndef ha_joined(relation_id=None):\\n settings = generate_ha_relation_data('heat')\\n relation_set(relation_id=relation_id, **settings)\\n\\n\\n@hooks.hook('ha-relation-changed')\\ndef ha_changed():\\n clustered = relation_get('clustered')\\n if not clustered or clustered in [None, 'None', '']:\\n log('ha_changed: hacluster subordinate not fully clustered.')\\n else:\\n log('Cluster configured, notifying other services and updating '\\n 'keystone endpoint configuration')\\n for rid in relation_ids('identity-service'):\\n identity_joined(rid=rid)\\n\\n\\n@hooks.hook('heat-plugin-subordinate-relation-joined',\\n 'heat-plugin-subordinate-relation-changed')\\n@restart_on_change(restart_map(), stopstart=True)\\ndef heat_plugin_subordinate_relation_joined(relid=None):\\n CONFIGS.write_all()\\n\\n\\n@hooks.hook('update-status')\\n@harden()\\ndef update_status():\\n log('Updating status.')\\n\\n\\n@hooks.hook('certificates-relation-joined')\\ndef certs_joined(relation_id=None):\\n relation_set(\\n relation_id=relation_id,\\n relation_settings=get_certificate_request())\\n\\n\\n@hooks.hook('certificates-relation-changed')\\n@restart_on_change(restart_map(), stopstart=True)\\ndef certs_changed(relation_id=None, unit=None):\\n process_certificates('heat', relation_id, unit)\\n configure_https()\\n\\n\\n@hooks.hook('pre-series-upgrade')\\ndef pre_series_upgrade():\\n log(\\\"Running prepare series upgrade hook\\\", \\\"INFO\\\")\\n series_upgrade_prepare(\\n pause_unit_helper, CONFIGS)\\n\\n\\n@hooks.hook('post-series-upgrade')\\ndef post_series_upgrade():\\n log(\\\"Running complete series upgrade hook\\\", \\\"INFO\\\")\\n series_upgrade_complete(\\n resume_unit_helper, CONFIGS)\\n\\n\\n@hooks.hook('nrpe-external-master-relation-joined',\\n 'nrpe-external-master-relation-changed')\\ndef update_nrpe_config():\\n # python-dbus is used by check_upstart_job\\n apt_install('python-dbus')\\n hostname = nrpe.get_nagios_hostname()\\n current_unit = nrpe.get_nagios_unit_name()\\n nrpe_setup = nrpe.NRPE(hostname=hostname)\\n nrpe.copy_nrpe_checks()\\n nrpe.add_init_service_checks(nrpe_setup, services(), current_unit)\\n nrpe.add_haproxy_checks(nrpe_setup, current_unit)\\n nrpe_setup.write()\\n\\n\\ndef main():\\n try:\\n hooks.execute(sys.argv)\\n except UnregisteredHookError as e:\\n log('Unknown hook {} - skipping.'.format(e))\\n assess_status(CONFIGS)\\n\\n\\nif __name__ == '__main__':\\n main()\\n\"\n },\n {\n \"alpha_fraction\": 0.581091046333313,\n \"alphanum_fraction\": 0.5871728658676147,\n \"avg_line_length\": 31.491018295288086,\n \"blob_id\": \"38130d5dcbdc9079d9e404c53f9046aa62e7e22d\",\n \"content_id\": \"70bbb793dcdc4c62c6888f3cc12c61d81091d9af\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5426,\n \"license_type\": \"permissive\",\n \"max_line_length\": 79,\n \"num_lines\": 167,\n \"path\": \"/hooks/heat_context.py\",\n \"repo_name\": \"openstack/charm-heat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Copyright 2016 Canonical Ltd\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\n\\nfrom charmhelpers.contrib.openstack import context\\nfrom charmhelpers.core.hookenv import (\\n config,\\n relation_ids,\\n related_units,\\n relation_get,\\n leader_get,\\n)\\nfrom charmhelpers.contrib.hahelpers.cluster import (\\n determine_apache_port,\\n determine_api_port,\\n https,\\n)\\n\\nHEAT_PATH = '/var/lib/heat/'\\nAPI_PORTS = {\\n 'heat-api-cfn': 8000,\\n 'heat-api': 8004\\n}\\n\\n\\ndef generate_ec2_tokens(protocol, host, port):\\n ec2_tokens = '%s://%s:%s/v2.0/ec2tokens' % (protocol, host, port)\\n return ec2_tokens\\n\\n\\nclass HeatIdentityServiceContext(context.IdentityServiceContext):\\n def __call__(self):\\n ctxt = super(HeatIdentityServiceContext, self).__call__()\\n if not ctxt:\\n return\\n\\n # the ec2 api needs to know the location of the keystone ec2\\n # tokens endpoint, set in nova.conf\\n ec2_tokens = generate_ec2_tokens(ctxt['service_protocol'] or 'http',\\n ctxt['service_host'],\\n ctxt['service_port'])\\n ctxt['keystone_ec2_url'] = ec2_tokens\\n ctxt['region'] = config('region')\\n return ctxt\\n\\n\\ndef get_encryption_key():\\n encryption_key = config(\\\"encryption-key\\\")\\n if not encryption_key:\\n encryption_key = leader_get('heat-auth-encryption-key')\\n return encryption_key\\n\\n\\nclass HeatSecurityContext(context.OSContextGenerator):\\n\\n def __call__(self):\\n ctxt = {}\\n # check if we have stored encryption key\\n ctxt['encryption_key'] = get_encryption_key()\\n ctxt['heat_domain_admin_passwd'] = (\\n leader_get('heat-domain-admin-passwd'))\\n return ctxt\\n\\n\\nclass HeatHAProxyContext(context.OSContextGenerator):\\n interfaces = ['heat-haproxy']\\n\\n def __call__(self):\\n \\\"\\\"\\\"Extends the main charmhelpers HAProxyContext with a port mapping\\n specific to this charm.\\n Also used to extend cinder.conf context with correct api_listening_port\\n \\\"\\\"\\\"\\n haproxy_port = API_PORTS['heat-api']\\n api_port = determine_api_port(haproxy_port, singlenode_mode=True)\\n apache_port = determine_apache_port(haproxy_port, singlenode_mode=True)\\n\\n haproxy_cfn_port = API_PORTS['heat-api-cfn']\\n api_cfn_port = determine_api_port(haproxy_cfn_port,\\n singlenode_mode=True)\\n apache_cfn_port = determine_apache_port(haproxy_cfn_port,\\n singlenode_mode=True)\\n\\n healthcheck = [{\\n 'option': 'httpchk GET /healthcheck',\\n 'http-check': 'expect status 200',\\n }]\\n\\n backend_options = {\\n 'heat_api': healthcheck,\\n 'heat_cfn_api': healthcheck\\n }\\n\\n ctxt = {\\n 'service_ports': {'heat_api': [haproxy_port, apache_port],\\n 'heat_cfn_api': [haproxy_cfn_port,\\n apache_cfn_port]},\\n 'api_listen_port': api_port,\\n 'api_cfn_listen_port': api_cfn_port,\\n 'backend_options': backend_options,\\n 'https': https(),\\n }\\n return ctxt\\n\\n\\nclass HeatApacheSSLContext(context.ApacheSSLContext):\\n\\n external_ports = API_PORTS.values()\\n service_namespace = 'heat'\\n\\n\\nclass HeatPluginContext(context.SubordinateConfigContext):\\n interfaces = 'heat-plugin-subordinate'\\n\\n def __init__(self):\\n super(HeatPluginContext, self).__init__(\\n interface='heat-plugin-subordinate',\\n service='heat',\\n config_file='/etc/heat/heat.conf')\\n\\n def __call__(self):\\n ctxt = super(HeatPluginContext, self).__call__()\\n defaults = {\\n 'plugin-dirs': {\\n 'templ_key': 'plugin_dirs',\\n 'value': '/usr/lib64/heat,/usr/lib/heat',\\n },\\n }\\n for rid in relation_ids('heat-plugin-subordinate'):\\n for unit in related_units(rid):\\n rdata = relation_get(rid=rid, unit=unit)\\n for key in defaults.keys():\\n remote_value = rdata.get(key)\\n ctxt_key = defaults[key]['templ_key']\\n if remote_value:\\n ctxt[ctxt_key] = remote_value\\n else:\\n ctxt[ctxt_key] = defaults[key]['value']\\n return ctxt\\n return ctxt\\n\\n\\nclass InstanceUserContext(context.OSContextGenerator):\\n\\n def __call__(self):\\n ctxt = {}\\n\\n instance_user = ''\\n if config('instance-user'):\\n instance_user = config('instance-user')\\n ctxt['instance_user'] = instance_user\\n return ctxt\\n\\n\\nclass QuotaConfigurationContext(context.OSContextGenerator):\\n def __call__(self):\\n ctxt = {\\\"max_stacks_per_tenant\\\": config('max-stacks-per-tenant')}\\n return ctxt\\n\"\n },\n {\n \"alpha_fraction\": 0.6540936231613159,\n \"alphanum_fraction\": 0.6577218174934387,\n \"avg_line_length\": 31.60714340209961,\n \"blob_id\": \"b956571172119fc53d1e7d189ddc22fc87f5a82f\",\n \"content_id\": \"f0d4edb23b7cddb292fbd56b91204bd020f10d08\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 14608,\n \"license_type\": \"permissive\",\n \"max_line_length\": 79,\n \"num_lines\": 448,\n \"path\": \"/hooks/heat_utils.py\",\n \"repo_name\": \"openstack/charm-heat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Copyright 2016 Canonical Ltd\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\n\\nimport os\\n\\nfrom copy import deepcopy\\nfrom collections import OrderedDict\\nfrom subprocess import check_call\\n\\nfrom charmhelpers.contrib.openstack import context, templating\\n\\nfrom charmhelpers.contrib.openstack.utils import (\\n configure_installation_source,\\n get_os_codename_install_source,\\n os_release,\\n token_cache_pkgs,\\n enable_memcache,\\n CompareOpenStackReleases,\\n os_application_version_set,\\n make_assess_status_func,\\n pause_unit,\\n resume_unit,\\n)\\n\\nfrom charmhelpers.contrib.hahelpers.cluster import (\\n get_hacluster_config,\\n get_managed_services_and_ports,\\n)\\n\\n\\nfrom charmhelpers.fetch import (\\n add_source,\\n apt_install,\\n apt_update,\\n apt_upgrade,\\n apt_purge,\\n apt_autoremove,\\n filter_missing_packages,\\n)\\n\\nfrom charmhelpers.core.hookenv import (\\n log,\\n config,\\n relation_ids,\\n)\\n\\nfrom charmhelpers.core.host import (\\n lsb_release,\\n service_start,\\n service_stop,\\n CompareHostReleases,\\n)\\n\\nfrom heat_context import (\\n API_PORTS,\\n HeatIdentityServiceContext,\\n HeatSecurityContext,\\n InstanceUserContext,\\n HeatApacheSSLContext,\\n HeatHAProxyContext,\\n HeatPluginContext,\\n QuotaConfigurationContext,\\n)\\n\\nTEMPLATES = 'templates/'\\n\\n# The interface is said to be satisfied if anyone of the interfaces in\\n# the list has a complete context.\\nREQUIRED_INTERFACES = {\\n 'database': ['shared-db'],\\n 'messaging': ['amqp'],\\n 'identity': ['identity-service'],\\n}\\n\\nBASE_PACKAGES = [\\n 'python-keystoneclient',\\n 'python-swiftclient', # work-around missing epoch in juno heat package\\n 'uuid',\\n 'apache2',\\n 'haproxy',\\n]\\n\\nPY3_PACKAGES = [\\n 'python3-heat',\\n 'python3-keystoneclient',\\n 'python3-memcache',\\n 'python3-swiftclient',\\n 'python3-six',\\n\\n # NOTE(lourot): these dependencies are declared as \\\"recommended\\\" on the\\n # heat-engine package but starting from focal-victoria they are actually\\n # hard dependencies. Until this is fixed we need this workaround. See\\n # lp:1893935\\n 'python3-vitrageclient',\\n 'python3-zunclient',\\n]\\n\\nVERSION_PACKAGE = 'heat-common'\\n\\nBASE_SERVICES = [\\n 'heat-api',\\n 'heat-api-cfn',\\n 'heat-engine'\\n]\\n\\n# Cluster resource used to determine leadership when hacluster'd\\nCLUSTER_RES = 'grp_heat_vips'\\nSVC = 'heat'\\nHEAT_DIR = '/etc/heat'\\nHEAT_CONF = '/etc/heat/heat.conf'\\nHEAT_API_PASTE = '/etc/heat/api-paste.ini'\\nHAPROXY_CONF = '/etc/haproxy/haproxy.cfg'\\nAPACHE_PORTS_CONF = '/etc/apache2/ports.conf'\\nHTTPS_APACHE_CONF = '/etc/apache2/sites-available/openstack_https_frontend'\\nHTTPS_APACHE_24_CONF = os.path.join('/etc/apache2/sites-available',\\n 'openstack_https_frontend.conf')\\nADMIN_OPENRC = '/root/admin-openrc-v3'\\nMEMCACHED_CONF = '/etc/memcached.conf'\\n\\nCONFIG_FILES = OrderedDict([\\n (HEAT_CONF, {\\n 'services': BASE_SERVICES,\\n 'contexts': [context.AMQPContext(ssl_dir=HEAT_DIR),\\n context.SharedDBContext(relation_prefix='heat',\\n ssl_dir=HEAT_DIR),\\n context.OSConfigFlagContext(),\\n context.InternalEndpointContext(),\\n HeatIdentityServiceContext(service=SVC, service_user=SVC),\\n HeatHAProxyContext(),\\n HeatSecurityContext(),\\n InstanceUserContext(),\\n HeatPluginContext(),\\n QuotaConfigurationContext(),\\n context.SyslogContext(),\\n context.LogLevelContext(),\\n context.WorkerConfigContext(),\\n context.BindHostContext(),\\n context.MemcacheContext(),\\n context.OSConfigFlagContext()],\\n }),\\n (HEAT_API_PASTE, {\\n 'services': [s for s in BASE_SERVICES if 'api' in s],\\n 'contexts': [HeatIdentityServiceContext()],\\n }),\\n (HAPROXY_CONF, {\\n 'contexts': [context.HAProxyContext(singlenode_mode=True),\\n HeatHAProxyContext()],\\n 'services': ['haproxy'],\\n }),\\n (HTTPS_APACHE_CONF, {\\n 'contexts': [HeatApacheSSLContext()],\\n 'services': ['apache2'],\\n }),\\n (HTTPS_APACHE_24_CONF, {\\n 'contexts': [HeatApacheSSLContext()],\\n 'services': ['apache2'],\\n }),\\n (ADMIN_OPENRC, {\\n 'contexts': [HeatIdentityServiceContext(service=SVC,\\n service_user=SVC)],\\n 'services': []\\n }),\\n (MEMCACHED_CONF, {\\n 'contexts': [context.MemcacheContext()],\\n 'services': ['memcached'],\\n }),\\n (APACHE_PORTS_CONF, {\\n 'contexts': [],\\n 'services': ['apache2'],\\n }),\\n])\\n\\n\\ndef resource_map(release=None):\\n \\\"\\\"\\\"\\n Dynamically generate a map of resources that will be managed for a single\\n hook execution.\\n \\\"\\\"\\\"\\n _release = release or os_release('heat-common', base='icehouse')\\n _resource_map = deepcopy(CONFIG_FILES)\\n\\n if os.path.exists('/etc/apache2/conf-available'):\\n _resource_map.pop(HTTPS_APACHE_CONF)\\n else:\\n _resource_map.pop(HTTPS_APACHE_24_CONF)\\n\\n if not enable_memcache(release=_release):\\n _resource_map.pop(MEMCACHED_CONF)\\n\\n return _resource_map\\n\\n\\ndef register_configs(release=None):\\n \\\"\\\"\\\"Register config files with their respective contexts.\\n Regstration of some configs may not be required depending on\\n existing of certain relations.\\n \\\"\\\"\\\"\\n release = release or os_release('heat-common', base='icehouse')\\n configs = templating.OSConfigRenderer(templates_dir=TEMPLATES,\\n openstack_release=release)\\n for cfg, rscs in resource_map(release).items():\\n configs.register(cfg, rscs['contexts'])\\n return configs\\n\\n\\ndef api_port(service):\\n return API_PORTS[service]\\n\\n\\ndef determine_packages():\\n release = CompareOpenStackReleases(os_release('heat-common'))\\n\\n # currently all packages match service names\\n packages = BASE_PACKAGES + BASE_SERVICES\\n packages.extend(token_cache_pkgs(source=config('openstack-origin')))\\n\\n if release >= 'rocky':\\n packages = [p for p in packages if not p.startswith('python-')]\\n packages.extend(PY3_PACKAGES)\\n\\n return list(set(packages))\\n\\n\\ndef determine_purge_packages():\\n '''\\n Determine list of packages that where previously installed which are no\\n longer needed.\\n\\n :returns: list of package names\\n '''\\n release = CompareOpenStackReleases(os_release('heat-common'))\\n if release >= 'rocky':\\n pkgs = [p for p in BASE_PACKAGES if p.startswith('python-')]\\n pkgs.extend(['python-heat', 'python-memcache'])\\n return pkgs\\n return []\\n\\n\\ndef remove_old_packages():\\n '''Purge any packages that need ot be removed.\\n\\n :returns: bool Whether packages were removed.\\n '''\\n installed_packages = filter_missing_packages(determine_purge_packages())\\n if installed_packages:\\n apt_purge(installed_packages, fatal=True)\\n apt_autoremove(purge=True, fatal=True)\\n return bool(installed_packages)\\n\\n\\ndef do_openstack_upgrade(configs):\\n \\\"\\\"\\\"Perform an uprade of heat.\\n\\n Takes care of upgrading packages,\\n rewriting configs and potentially any other post-upgrade\\n actions.\\n\\n :param configs: The charms main OSConfigRenderer object.\\n\\n \\\"\\\"\\\"\\n new_src = config('openstack-origin')\\n new_os_rel = get_os_codename_install_source(new_src)\\n\\n log('Performing OpenStack upgrade to %s.' % (new_os_rel))\\n\\n configure_installation_source(new_src)\\n dpkg_opts = [\\n '--option', 'Dpkg::Options::=--force-confnew',\\n '--option', 'Dpkg::Options::=--force-confdef',\\n ]\\n apt_update()\\n apt_upgrade(options=dpkg_opts, fatal=True, dist=True)\\n apt_install(packages=determine_packages(), options=dpkg_opts, fatal=True)\\n\\n remove_old_packages()\\n\\n # set CONFIGS to load templates from new release and regenerate config\\n configs.set_release(openstack_release=new_os_rel)\\n configs.write_all()\\n\\n migrate_database()\\n\\n\\ndef restart_map():\\n '''Determine the correct resource map to be passed to\\n charmhelpers.core.restart_on_change() based on the services configured.\\n\\n :returns: dict: A dictionary mapping config file to lists of services\\n that should be restarted when file changes.\\n '''\\n return OrderedDict([(cfg, v['services'])\\n for cfg, v in resource_map().items()\\n if v['services']])\\n\\n\\ndef services():\\n \\\"\\\"\\\"Returns a list of services associate with this charm\\\"\\\"\\\"\\n _services = []\\n for v in restart_map().values():\\n _services = _services + v\\n return list(set(_services))\\n\\n\\ndef migrate_database():\\n \\\"\\\"\\\"Runs heat-manage to initialize a new database or migrate existing\\\"\\\"\\\"\\n log('Migrating the heat database.')\\n [service_stop(s) for s in services()]\\n check_call(['heat-manage', 'db_sync'])\\n [service_start(s) for s in services()]\\n\\n\\ndef setup_ipv6():\\n ubuntu_rel = lsb_release()['DISTRIB_CODENAME'].lower()\\n if CompareHostReleases(ubuntu_rel) < \\\"trusty\\\":\\n raise Exception(\\\"IPv6 is not supported in the charms for Ubuntu \\\"\\n \\\"versions less than Trusty 14.04\\\")\\n\\n # Need haproxy >= 1.5.3 for ipv6 so for Trusty if we are <= Kilo we need to\\n # use trusty-backports otherwise we can use the UCA.\\n if (ubuntu_rel == 'trusty' and\\n CompareOpenStackReleases(os_release('heat-common')) < 'liberty'):\\n add_source('deb http://archive.ubuntu.com/ubuntu trusty-backports '\\n 'main')\\n apt_update()\\n apt_install('haproxy/trusty-backports', fatal=True)\\n\\n\\ndef check_optional_relations(configs):\\n \\\"\\\"\\\"Check that if we have a relation_id for high availability that we can\\n get the hacluster config. If we can't then we are blocked.\\n\\n This function is called from assess_status/set_os_workload_status as the\\n charm_func and needs to return either None, None if there is no problem or\\n the status, message if there is a problem.\\n\\n :param configs: an OSConfigRender() instance.\\n :return 2-tuple: (string, string) = (status, message)\\n \\\"\\\"\\\"\\n if relation_ids('ha'):\\n try:\\n get_hacluster_config()\\n except Exception:\\n return ('blocked',\\n 'hacluster missing configuration: '\\n 'vip, vip_iface, vip_cidr')\\n # return 'unknown' as the lowest priority to not clobber an existing\\n # status.\\n return \\\"unknown\\\", \\\"\\\"\\n\\n\\ndef get_optional_interfaces():\\n \\\"\\\"\\\"Return the optional interfaces that should be checked if the relavent\\n relations have appeared.\\n\\n :returns: {general_interface: [specific_int1, specific_int2, ...], ...}\\n \\\"\\\"\\\"\\n optional_interfaces = {}\\n if relation_ids('ha'):\\n optional_interfaces['ha'] = ['cluster']\\n\\n return optional_interfaces\\n\\n\\ndef assess_status(configs):\\n \\\"\\\"\\\"Assess status of current unit\\n Decides what the state of the unit should be based on the current\\n configuration.\\n SIDE EFFECT: calls set_os_workload_status(...) which sets the workload\\n status of the unit.\\n Also calls status_set(...) directly if paused state isn't complete.\\n @param configs: a templating.OSConfigRenderer() object\\n @returns None - this function is executed for its side-effect\\n \\\"\\\"\\\"\\n assess_status_func(configs)()\\n os_application_version_set(VERSION_PACKAGE)\\n\\n\\ndef assess_status_func(configs):\\n \\\"\\\"\\\"Helper function to create the function that will assess_status() for\\n the unit.\\n Uses charmhelpers.contrib.openstack.utils.make_assess_status_func() to\\n create the appropriate status function and then returns it.\\n Used directly by assess_status() and also for pausing and resuming\\n the unit.\\n\\n NOTE: REQUIRED_INTERFACES is augmented with the optional interfaces\\n depending on the current config before being passed to the\\n make_assess_status_func() function.\\n\\n NOTE(ajkavanagh) ports are not checked due to race hazards with services\\n that don't behave sychronously w.r.t their service scripts. e.g.\\n apache2.\\n @param configs: a templating.OSConfigRenderer() object\\n @return f() -> None : a function that assesses the unit's workload status\\n \\\"\\\"\\\"\\n required_interfaces = REQUIRED_INTERFACES.copy()\\n required_interfaces.update(get_optional_interfaces())\\n _services, _ = get_managed_services_and_ports(services(), [])\\n return make_assess_status_func(\\n configs, required_interfaces,\\n charm_func=check_optional_relations,\\n services=_services, ports=None)\\n\\n\\ndef pause_unit_helper(configs):\\n \\\"\\\"\\\"Helper function to pause a unit, and then call assess_status(...) in\\n effect, so that the status is correctly updated.\\n Uses charmhelpers.contrib.openstack.utils.pause_unit() to do the work.\\n @param configs: a templating.OSConfigRenderer() object\\n @returns None - this function is executed for its side-effect\\n \\\"\\\"\\\"\\n _pause_resume_helper(pause_unit, configs)\\n\\n\\ndef resume_unit_helper(configs):\\n \\\"\\\"\\\"Helper function to resume a unit, and then call assess_status(...) in\\n effect, so that the status is correctly updated.\\n Uses charmhelpers.contrib.openstack.utils.resume_unit() to do the work.\\n @param configs: a templating.OSConfigRenderer() object\\n @returns None - this function is executed for its side-effect\\n \\\"\\\"\\\"\\n _pause_resume_helper(resume_unit, configs)\\n\\n\\ndef _pause_resume_helper(f, configs):\\n \\\"\\\"\\\"Helper function that uses the make_assess_status_func(...) from\\n charmhelpers.contrib.openstack.utils to create an assess_status(...)\\n function that can be used with the pause/resume of the unit\\n @param f: the function to be used with the assess_status(...) function\\n @returns None - this function is executed for its side-effect\\n \\\"\\\"\\\"\\n # TODO(ajkavanagh) - ports= has been left off because of the race hazard\\n # that exists due to service_start()\\n _services, _ = get_managed_services_and_ports(services(), [])\\n f(assess_status_func(configs),\\n services=_services,\\n ports=None)\\n\"\n },\n {\n \"alpha_fraction\": 0.5509568452835083,\n \"alphanum_fraction\": 0.5613410472869873,\n \"avg_line_length\": 34.478946685791016,\n \"blob_id\": \"1c67a5b9309831a10c673fedd068b887899b908b\",\n \"content_id\": \"b51dd673189f48f571a6094fc754710301a4d99a\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6741,\n \"license_type\": \"permissive\",\n \"max_line_length\": 78,\n \"num_lines\": 190,\n \"path\": \"/unit_tests/test_heat_context.py\",\n \"repo_name\": \"openstack/charm-heat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Copyright 2016 Canonical Ltd\\n#\\n# Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n# you may not use this file except in compliance with the License.\\n# You may obtain a copy of the License at\\n#\\n# http://www.apache.org/licenses/LICENSE-2.0\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\n\\nimport charmhelpers\\nimport heat_context\\nimport json\\nfrom unittest.mock import patch\\nfrom test_utils import CharmTestCase\\n\\nTO_PATCH = [\\n 'get_encryption_key',\\n 'generate_ec2_tokens',\\n 'config',\\n 'leader_get',\\n 'relation_get',\\n 'relation_ids',\\n 'related_units',\\n]\\n\\n\\nclass TestHeatContext(CharmTestCase):\\n\\n def setUp(self):\\n super(TestHeatContext, self).setUp(heat_context, TO_PATCH)\\n\\n def test_encryption_configuration(self):\\n self.get_encryption_key.return_value = 'key'\\n self.leader_get.return_value = 'password'\\n self.assertEqual(\\n heat_context.HeatSecurityContext()(),\\n {'encryption_key': 'key',\\n 'heat_domain_admin_passwd': 'password'})\\n self.leader_get.assert_called_with('heat-domain-admin-passwd')\\n\\n def test_instance_user_empty_configuration(self):\\n self.config.return_value = None\\n self.assertEqual(\\n heat_context.InstanceUserContext()(),\\n {'instance_user': ''})\\n\\n @patch('charmhelpers.contrib.openstack.'\\n 'context.IdentityServiceContext.__call__')\\n def test_identity_configuration(self, __call__):\\n __call__.return_value = {\\n 'service_port': 'port',\\n 'service_host': 'host',\\n 'auth_host': 'host',\\n 'auth_port': 'port',\\n 'admin_tenant_name': 'tenant',\\n 'admin_user': 'user',\\n 'admin_password': 'pass',\\n 'service_protocol': 'http',\\n 'auth_protocol': 'http'}\\n self.generate_ec2_tokens.return_value = \\\\\\n 'http://host:port/v2.0/ec2tokens'\\n\\n final_result = __call__.return_value\\n final_result['keystone_ec2_url'] = \\\\\\n self.generate_ec2_tokens.return_value\\n\\n self.assertEqual(\\n heat_context.HeatIdentityServiceContext()(), final_result)\\n\\n def test_quota_configuration_context(self):\\n expected = {'max_stacks_per_tenant': '999'}\\n self.config.side_effect = self.test_config.get\\n self.test_config.set('max-stacks-per-tenant', '999')\\n self.assertEqual(heat_context.QuotaConfigurationContext()(), expected)\\n\\n @patch('charmhelpers.contrib.hahelpers.cluster.https')\\n @patch('heat_context.https')\\n def test_haproxy_context(self, mock_https, mock_ch_https):\\n for https_mode in [False, True]:\\n api_cfn_listen_port = 7990\\n api_listen_port = 7994\\n if https_mode:\\n api_cfn_listen_port = 7980\\n api_listen_port = 7984\\n mock_https.return_value = https_mode\\n mock_ch_https.return_value = https_mode\\n haproxy_context = heat_context.HeatHAProxyContext()\\n healthcheck = [{\\n 'option': 'httpchk GET /healthcheck',\\n 'http-check': 'expect status 200',\\n }]\\n backend_options = {\\n 'heat_api': healthcheck,\\n 'heat_cfn_api': healthcheck\\n }\\n expected = {\\n 'service_ports': {\\n 'heat_api': [8004, 7994],\\n 'heat_cfn_api': [8000, 7990]\\n },\\n 'api_listen_port': api_listen_port,\\n 'api_cfn_listen_port': api_cfn_listen_port,\\n 'backend_options': backend_options,\\n 'https': https_mode,\\n }\\n self.assertEqual(expected, haproxy_context())\\n\\n\\nclass HeatPluginContextTest(CharmTestCase):\\n\\n def setUp(self):\\n super(HeatPluginContextTest, self).setUp(heat_context, TO_PATCH)\\n self.relation_get.side_effect = self.test_relation.get\\n\\n def tearDown(self):\\n super(HeatPluginContextTest, self).tearDown()\\n\\n def test_init(self):\\n heatp_ctxt = heat_context.HeatPluginContext()\\n self.assertEqual(\\n heatp_ctxt.interfaces,\\n ['heat-plugin-subordinate']\\n )\\n self.assertEqual(heatp_ctxt.services, ['heat'])\\n self.assertEqual(\\n heatp_ctxt.config_file,\\n '/etc/heat/heat.conf'\\n )\\n\\n @patch.object(charmhelpers.contrib.openstack.context, 'log')\\n @patch.object(charmhelpers.contrib.openstack.context, 'relation_get')\\n @patch.object(charmhelpers.contrib.openstack.context, 'related_units')\\n @patch.object(charmhelpers.contrib.openstack.context, 'relation_ids')\\n def ctxt_check(self, rel_settings, expect, _rids, _runits, _rget, _log):\\n self.test_relation.set(rel_settings)\\n _runits.return_value = ['unit1']\\n _rids.return_value = ['rid2']\\n _rget.side_effect = self.test_relation.get\\n self.relation_ids.return_value = ['rid2']\\n self.related_units.return_value = ['unit1']\\n heatp_ctxt = heat_context.HeatPluginContext()()\\n self.assertEqual(heatp_ctxt, expect)\\n\\n def test_defaults(self):\\n self.ctxt_check(\\n {},\\n {\\n 'plugin_dirs': '/usr/lib64/heat,/usr/lib/heat',\\n }\\n )\\n\\n def test_overrides(self):\\n self.ctxt_check(\\n {'plugin-dirs': '/usr/lib64/heat,/usr/lib/heat,/usr/local/lib'},\\n {\\n 'plugin_dirs': '/usr/lib64/heat,/usr/lib/heat,/usr/local/lib',\\n }\\n )\\n\\n def test_subordinateconfig(self):\\n principle_config = {\\n \\\"heat\\\": {\\n \\\"/etc/heat/heat.conf\\\": {\\n \\\"sections\\\": {\\n 'DEFAULT': [\\n ('heatboost', True)\\n ],\\n 'heatplugin': [\\n ('superkey', 'supervalue')\\n ],\\n }\\n }\\n }\\n }\\n self.ctxt_check(\\n {\\n 'plugin-dirs': '/usr/lib64/heat,/usr/lib/heat,/usr/local/lib',\\n 'subordinate_configuration': json.dumps(principle_config)\\n },\\n {\\n 'plugin_dirs': '/usr/lib64/heat,/usr/lib/heat,/usr/local/lib',\\n 'sections': {u'DEFAULT': [[u'heatboost', True]],\\n u'heatplugin': [[u'superkey', u'supervalue']]},\\n }\\n )\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":9,"string":"9"}}},{"rowIdx":2979142,"cells":{"repo_name":{"kind":"string","value":"zhanglian-boob/zhanglian_Home1"},"repo_url":{"kind":"string","value":"https://github.com/zhanglian-boob/zhanglian_Home1"},"snapshot_id":{"kind":"string","value":"8555196c907006201fd7887a010aed1dccc35fb9"},"revision_id":{"kind":"string","value":"da31448f987aba57076f29444fa103d803199e26"},"directory_id":{"kind":"string","value":"8d9791ead2142aaaf6cd5eab2708395e9827c41d"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-09-24T22:09:41.960731","string":"2020-09-24T22:09:41.960731"},"revision_date":{"kind":"timestamp","value":"2019-12-05T15:07:27","string":"2019-12-05T15:07:27"},"committer_date":{"kind":"timestamp","value":"2019-12-05T15:07:27","string":"2019-12-05T15:07:27"},"github_id":{"kind":"number","value":225854760,"string":"225,854,760"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.629146933555603,"alphanum_fraction":0.6350710988044739,"avg_line_length":32.720001220703125,"blob_id":"a12247b36b1a6916c01ad633193c34c0f50b9699","content_id":"7e5d34705238421bb2f7980beb2ab63d1d9c46aa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":960,"license_type":"no_license","max_line_length":65,"num_lines":25,"path":"/unitetest2/以对象形式进行读取/test_case.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import unittest\nfrom logging_encap import MyLogger\nfrom regist import register\nfrom read_excel02 import ReadExcel\n\n\nclass RegistTest(unittest.TestCase):\n excel = ReadExcel('cases.xlsx', 'test')\n #重写__init__方法，多加一个参数进去\n def __init__(self, methodName, data_case):\n self.data_case = data_case\n super().__init__(methodName)\n\n def regist_test_pass(self):\n row = self.data_case.case_id+1\n data = eval(self.data_case.data)\n expected = eval(self.data_case.expected)\n result = register(*data) #调用程序，获取实际结果\n try:\n self.assertEqual(expected, result)\n except AssertionError as e:\n self.excel.write_data(row=row, column=4, value='未通过')\n raise e #将异常再抛出去，没有异常就会全部通过，所以要再把异常抛出去\n else:\n self.excel.write_data(row=row, column=4, value='通过')\n\n"},{"alpha_fraction":0.7410628199577332,"alphanum_fraction":0.7449275255203247,"avg_line_length":22.5,"blob_id":"c54c9c97a24bbee1b9929ba0bfa92d8547ae303e","content_id":"a32c865a472c3b2c27aa5cf214743e903c91f6ae","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1513,"license_type":"no_license","max_line_length":101,"num_lines":44,"path":"/unitetest2/log_test.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"# import logging\n# #默认的日志收集器\n#\n# #创建一个默认收集器，如果不传参数，则返回默认的日志收集器（root）\n# my = logging.getLogger()\n# my.setLevel('DEBUG')\n#\n# a = 100\n# print(\"调试a\")\n#\n# logging.debug('这是一个debug的日志')\n# logging.info('这是一个info的日志')\n# logging.warning('这是一个warning的日志')\n# logging.error('这是一个error的日志')\n# logging.critical('这是一个critical的日志')\n\n#自定义的日志收集器\nimport logging\n#参数传入自定义的收集器名称\nmy_log = logging.getLogger('my_log')\nmy_log.setLevel('INFO') #设置输入到收集器里面的等级\n\n#日志收集器的格式设置\nformater = logging.Formatter('%(asctime)s--%(filename)s--line:%(lineno)d--%(levelname)s:%(message)s')\n\n#日志的输出,创建一个输出到控制台的输出渠道\nsh = logging.StreamHandler()\nsh.setLevel('ERROR') #设置输出的等级，最后以输出的等级为准，设置等级时需要大写\n#将输出渠道添加到日志收集器中\nmy_log.addHandler(sh)\nsh.setFormatter(formater) #将日志输出格式传过去\n\n\n#创建一个输出到文件的渠道\nfh = logging.FileHandler(filename='test.log', encoding='utf8')\nfh.setLevel('INFO')\nmy_log.addHandler(fh)\nfh.setFormatter(formater)\n\nmy_log.debug('这是一个debug的日志')\nmy_log.info('这是一个info的日志')\nmy_log.warning('这是一个warning的日志')\nmy_log.error('这是一个error的日志')\nmy_log.critical('这是一个critical的日志')\n\n"},{"alpha_fraction":0.5269636511802673,"alphanum_fraction":0.5316529870033264,"avg_line_length":26.047618865966797,"blob_id":"44cc63d5c0fa09e525fbd7d2acb8e040c93a115a","content_id":"3fb329eb35e0bca34a9eb67502ecaf669e9282d3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2134,"license_type":"no_license","max_line_length":71,"num_lines":63,"path":"/unitetest2/以字典形式进行读取/read_excel02.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import openpyxl\n\n\nclass CaseData(object):\n '''测试用例数据类，专门用来创建对象的，不用写'''\n pass\n\n\nclass ReadExcel(object):\n def __init__(self, filename, sheetname):\n self.filename = filename\n self.sheetname = sheetname\n\n def open(self):\n self.wb = openpyxl.load_workbook(self.filename)\n self.sh = self.wb[self.sheetname]\n\n def read_data(self):\n '''读取数据'''\n self.open()\n self.max_row = self.sh.max_row\n # 按行依次读取格子中的内容,读取出来后每一行为一个元组，存储在列表中\n rows = list(self.sh.rows)\n # 获取表头\n title = []\n for row in rows[0]:\n title.append(row.value)\n # 获取列表中的数据\n datas = []\n for r_obj in rows[1:]:\n data = [] #创建列表，将每一行遍历出来的数据放入列表中\n for r in r_obj:\n data.append(r.value)\n data_obj = dict(zip(title, data)) # 将表头和数据进行打包，组成列表嵌套字典的形式\n datas.append(data_obj)\n self.wb.close()\n return datas\n\n def read_data_obj(self):\n '''读取数据后，返回的是列表嵌套对象的方式'''\n self.open()\n self.max_row = self.sh.max_row\n # 按行依次读取格子中的内容,读取出来后每一行为一个元组，存储在列表中\n rows = list(self.sh.rows)\n title = [row.value for row in rows[0]] #列表推导式，获取表头\n # 获取列表中的数据\n datas = []\n for r_obj in rows[1:]:\n data = [r.value for r in r_obj] #列表推导式,只适用于for下面跟简单的一行代码\n case = CaseData() #创建一个用例数据对象\n for i in zip(title, data):\n setattr(case, i[0], i[1])\n datas.append(case)\n return datas\n\n def write_data(self):\n '''写入数据'''\n pass\n\nif __name__=='__main__':\n read = ReadExcel('cases.xlsx', 'test')\n data2 = read.read_data()\n print(data2)\n\n\n"},{"alpha_fraction":0.6031566858291626,"alphanum_fraction":0.6042841076850891,"avg_line_length":25.08823585510254,"blob_id":"1b6987ce4b6246ac45e91e653fd455c35d7caf96","content_id":"072e40c5a59d051b75419ca8da65c56e870b897f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1127,"license_type":"no_license","max_line_length":109,"num_lines":34,"path":"/unitetest2/以对象形式进行读取/logging_encap.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import logging\n\n\nclass MyLogger(object):\n\n @classmethod\n def createlogger(cls):\n # 参数传入自定义的收集器名称\n my_log = logging.getLogger('my_log')\n my_log.setLevel('INFO') # 设置输入到收集器里面的等级\n\n # 日志收集器的格式设置\n formater = logging.Formatter('%(asctime)s--%(filename)s--line:%(lineno)d--%(levelname)s:%(message)s')\n\n # 日志的输出,创建一个输出到控制台的输出渠道\n sh = logging.StreamHandler()\n sh.setLevel('INFO') # 设置输出的等级，最后以输出的等级为准，设置等级时需要大写\n # 将输出渠道添加到日志收集器中\n my_log.addHandler(sh)\n sh.setFormatter(formater) # 将日志输出格式传过去\n\n # 创建一个输出到文件的渠道\n fh = logging.FileHandler(filename='test.log', encoding='utf8')\n fh.setLevel('INFO')\n my_log.addHandler(fh)\n fh.setFormatter(formater)\n return my_log\n\n\nif __name__ == '__main__':\n log = MyLogger.createlogger()\n\n log.info('hello')\n log.error('world')\n"},{"alpha_fraction":0.5518035292625427,"alphanum_fraction":0.5579432249069214,"avg_line_length":26.680850982666016,"blob_id":"a15f0fa6546ce533d75cdaf34d1228849302c612","content_id":"40b6674b0aa4722340d222eb6cccfec32792f8bf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1553,"license_type":"no_license","max_line_length":70,"num_lines":47,"path":"/unitetest2/以对象形式进行读取/read_excel02.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import openpyxl\n\n\nclass CaseData(object):\n '''测试用例数据类，专门用来创建对象的，不用写'''\n pass\n\n\nclass ReadExcel(object):\n def __init__(self, filename, sheetname):\n self.filename = filename\n self.sheetname = sheetname\n\n def open(self):\n self.wb = openpyxl.load_workbook(self.filename)\n self.sh = self.wb[self.sheetname]\n\n def read_data_obj(self):\n '''读取数据后，返回的是列表嵌套对象的方式'''\n self.open()\n self.max_row = self.sh.max_row\n # 按行依次读取格子中的内容,读取出来后每一行为一个元组，存储在列表中\n rows = list(self.sh.rows)\n title = [row.value for row in rows[0]] #列表推导式，获取表头\n # 获取列表中的数据\n datas = []\n for r_obj in rows[1:]:\n data = [r.value for r in r_obj] #列表推导式,只适用于for下面跟简单的一行代码\n case = CaseData() #创建一个用例数据对象\n for i in zip(title, data):\n setattr(case, i[0], i[1])\n datas.append(case)\n self.wb.close()\n return datas\n\n def write_data(self, row, column, value):\n '''写入数据'''\n self.open()\n self.sh.cell(row=row, column=column, value=value)\n self.wb.save(self.filename)\n self.wb.close()\n\nif __name__=='__main__':\n read = ReadExcel('cases.xlsx', 'test')\n # data2 = read.read_data_obj()\n # print(data2)\n read.write_data(5, 5, '通过')\n\n\n"},{"alpha_fraction":0.5627943277359009,"alphanum_fraction":0.5659340620040894,"avg_line_length":34.36111068725586,"blob_id":"523ff2fa80b7cf7e77027d7c64ff7f0c69766736","content_id":"1f6e5e0807b08f0ca1d5ac4875212f542b58552b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1320,"license_type":"no_license","max_line_length":85,"num_lines":36,"path":"/unitetest2/ddt_file/test_case.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import unittest\nfrom regist import register\nfrom read_excel02 import ReadExcel\nfrom all.unitetest2.ddt_file.ddt import ddt, data\nfrom logging_encap import my_log\nfrom my_config.my_yaml_config import my_yaml\nfrom logging_encap import my_log\n\n\n\n@ddt\nclass RegistTest(unittest.TestCase):\n excel = ReadExcel(my_yaml.read_yaml('excel', 'excel_name'), 'test')\n cases = excel.read_data_obj()\n\n @data(*cases)\n def test_regist(self, case):\n #准备用例数据\n row = case.case_id+1\n expected = eval(case.expected)\n data = eval(case.data)\n result = register(*data)\n try:\n self.assertEqual(expected, result)\n except AssertionError as e:\n log.info(F'用例:{case.title}, 未执行通过')\n self.excel.write_data(row=row,\n column=my_yaml.read_yaml('excel', 'result_column'),\n value=my_yaml.read_yaml('msg', 'success_msg'))\n my_log.error(F'断言异常{e}')\n raise e\n else:\n log.info(F'用例:{case.title}, 执行通过')\n self.excel.write_data(row=row,\n column=my_yaml.read_yaml('excel', 'result_column'),\n value=my_yaml.read_yaml('msg', 'failure_msg'))\n\n"},{"alpha_fraction":0.6505235433578491,"alphanum_fraction":0.6570680737495422,"avg_line_length":24.46666717529297,"blob_id":"8a23d7e70e9f74e09489578dd7e227e975e70261","content_id":"f6f36f2e0a951141c8d108de271c2426c8176f58","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1052,"license_type":"no_license","max_line_length":56,"num_lines":30,"path":"/unitetest2/以对象形式进行读取/test_test.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"# 对下面功能函数 设计用例进行单元测试，\n# 要求 :1、 设计至少五条用例\n# 2、至少用两种方式往测试集合（套件）中添加测试用例\n# 3、执行测试集合（套件）中的测试用例，生成测试报告\n\nimport unittest\nfrom test_case import RegistTest\nfrom regist import register\nfrom HTMLTestRunnerNew import HTMLTestRunner\nfrom read_excel02 import ReadExcel\n\n#创建测试套件\nsuite = unittest.TestSuite()\nread = ReadExcel('cases.xlsx', 'test')\ndatas = read.read_data_obj()\nprint(datas)\n\n#将测试用例加入到测试套件中\nfor data in datas:\n case = RegistTest('regist_test_pass', data)\n suite.addTest(case)\n\n#创建一个测试运行的程序对象\nwith open('report.html', 'wb') as fb:\n runner = HTMLTestRunner(stream=fb,\n title='关于注册的测试报告',\n description='这是一个关于注册的测试报告',\n tester='橘子')\n#运行程序，执行测试用例\n runner.run(suite)\n"},{"alpha_fraction":0.6043233275413513,"alphanum_fraction":0.6137217879295349,"avg_line_length":21.95652198791504,"blob_id":"47207178b0e9fd21d4e8a15c7806a646a093fe5c","content_id":"a1f376d5c07dff42a9e342aa1796c4a9b5667e2b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1260,"license_type":"no_license","max_line_length":58,"num_lines":46,"path":"/unitetest2/testcase.conf.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"#读取配置文件\n#引入自带的方法configparser\nfrom configparser import ConfigParser\n#创建配置的对象\nconfig = ConfigParser()\n#读取数据，指定读取文件的路径\nconfig.read('testcase.ini', encoding='utf8')\n#getint(区域名，选项名)\nresult1 = config.get('users', 'name')\nprint(result1)\n\n\n#配置文件中进行写入\n#引入配置文件的方法\n# from configparser import ConfigParser\n# #创建对象\n# config = ConfigParser()\n# #写入数据,将配置文件看作一个空字典来进行写入\n# datas = {\n# \"excel\": {\"case_path\": \"casepath.txt\"},\n# \"users\": {\"name\": \"xiaoming\", \"age\": 19}\n# }\n# for i in datas:\n# config[i] = datas[i]\n# with open('testcase.ini', 'w', encoding='utf8') as file:\n# config.write(file)\n\n#使用yaml进行读取数据\n# import yaml\n# with open('testcase.yaml', 'r', encoding='utf8') as fb:\n# data = yaml.load(fb, Loader=yaml.FullLoader)\n# print(data)\n#\n# #使用yaml写入数据\n# import yaml\n# datas = {\n# \"excel\": {\"case_path\": \"casepath.txt\"\n# },\n# \"users\": {\"name\": \"xiaoming\",\n# \"age\": 19\n# }\n# }\n# with open('testcase.yaml', 'w', encoding='utf8') as wb:\n# yaml.dump(datas, wb, allow_unicode=True)\n\nimport yaml\n\n\n\n\n\n\n\n\n"},{"alpha_fraction":0.640625,"alphanum_fraction":0.640625,"avg_line_length":28.866666793823242,"blob_id":"d9e3fe7df4f719aabacfd7323967902759ae5196","content_id":"cd89fc8d651e52c9a3f05700488958ce55c93eb7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":496,"license_type":"no_license","max_line_length":51,"num_lines":15,"path":"/unitetest2/以字典形式进行读取/test_case.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import unittest\nfrom regist import register\n\n\nclass RegistTest(unittest.TestCase):\n #重写__init__方法，多加一个参数进去\n def __init__(self, methodName, data_case):\n self.data_case = data_case\n super().__init__(methodName)\n\n def regist_test_pass(self):\n data = eval(self.data_case['data'])\n expected = eval(self.data_case['expected'])\n result = register(*data) #调用程序，获取实际结果\n self.assertEqual(expected, result)\n"},{"alpha_fraction":0.5189328789710999,"alphanum_fraction":0.5215146541595459,"avg_line_length":24.217391967773438,"blob_id":"dad6b0f8109bcec23ddcdd18a6c456e882c9346e","content_id":"6fa8c737ea6a5398d1ef58715bd6c834227bc297","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1272,"license_type":"no_license","max_line_length":79,"num_lines":46,"path":"/unitetest2/ddt_file/handle_requests.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import requests\n\n\nclass MyRequests(object):\n def __init__(self):\n self.my_session = requests.Session()\n\n def add_headers(self, headers):\n '''添加公共请求头'''\n self.my_session.headers.update(headers)\n\n def send(self, url, method='post', data=None, is_json=True, **kwargs):\n if isinstance(data,str):\n try:\n data = json.loads(data)\n except Exception as e:\n print('使用日志器来记录日志')\n data = eval(data)\n\n\n method = method.lower()\n if method == 'get':\n res = self.my_session.request(method, url, json=data, **kwargs)\n elif method in ('post', 'put', 'delete', 'patch'):\n if is_json:\n res = self.my_session.request(method, url, json=data, **kwargs)\n else:\n res = self.my_session.request(method, url, data=data,**kwargs)\n else:\n res = None\n print(F'不支持【{method}】请求方法')\n return res\n\n def close(self):\n self.my_session.close()\n\n\nif __name__ == '__main__':\n #1、构建请求的url\n login_url =\n recharge_url =\n\n #2、创建请求参数（包括请求头和请求体）\n\n\n #3、执行登陆\n\n\n"},{"alpha_fraction":0.634178876876831,"alphanum_fraction":0.634178876876831,"avg_line_length":28.920000076293945,"blob_id":"3479f3cfdcb3435eb7e98e408ea9fafc37f18ba2","content_id":"09a7cf16a3ded53db0896ab41c46a1cdd04b2acc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":821,"license_type":"no_license","max_line_length":81,"num_lines":25,"path":"/unitetest2/ddt_file/run_test.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import unittest\nimport test_case\n\n\nfrom HTMLTestRunnerNew import HTMLTestRunner\nfrom logging_encap import my_log\nfrom my_config.my_yaml_config import my_yaml\n#创建测试套件\nsuite = unittest.TestSuite()\nmy_log.info('测试套件创建成功！')\n\n#加载用例到套件\nloader = unittest.TestLoader()\nsuite.addTest(loader.loadTestsFromModule(test_case))\n\n\n#创建测试运行程序\nwith open(my_yaml.read_yaml('report', 'report_name'), 'wb') as fb:\n runner = HTMLTestRunner(stream=fb,\n title= my_yaml.read_yaml('report', 'report_title'),\n description=\n my_yaml.read_yaml('report', 'report_description'),\n tester= my_yaml.read_yaml('report', 'report_tester'))\n #执行测试用例\n runner.run(suite)\n\n"},{"alpha_fraction":0.6111111044883728,"alphanum_fraction":0.6388888955116272,"avg_line_length":10.833333015441895,"blob_id":"0bbfebf4fa9f43d04aa1491a9f79b9f804d6fe8a","content_id":"d2fee6b189c4ba1de9732974e988e3d1e7e296d0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"INI","length_bytes":72,"license_type":"no_license","max_line_length":26,"num_lines":6,"path":"/unitetest2/testcase.ini","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"[excel]\ncase_path = 'casepath.txt'\n\n[users]\nname = 'xiaoming'\nage = 19\n\n"},{"alpha_fraction":0.5726290345191956,"alphanum_fraction":0.5786314606666565,"avg_line_length":29.66666603088379,"blob_id":"657268516bd8c9d05b7d968f0978a165b36184c8","content_id":"d3403486f04b8a8e578cae9923af7ee3a5018c50","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":833,"license_type":"no_license","max_line_length":61,"num_lines":27,"path":"/unitetest2/ddt_file/my_config/my_yaml_config.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import yaml\n\n\nclass ReadWriteYaml(object):\n def __init__(self, filename):\n with open(filename, 'r', encoding='utf8') as my_file:\n self.datas = yaml.full_load(my_file)\n\n def read_yaml(self, section, option):\n return self.datas[section][option]\n\n def write_yaml(self, filename, data):\n with open(filename, 'w', encoding='utf8') as wf:\n yaml.dump(data, wf, allow_unicode=True)\n with open(filename, 'r', encoding='utf8') as wf:\n self.datas = yaml.full_load(wf)\n\n\nmy_yaml = ReadWriteYaml('my_config\\\\testcase.yaml')\n\nif __name__ == '__main__':\n datas = {\n \"excel\": {\"case_path\": \"casepath.txt\"},\n \"users\": {\"name\": \"xiaoming\", \"age\": 20}}\n my_yaml.write_yaml('testcase.yaml', datas)\n b = my_yaml.read_yaml('users', 'name')\n print(b)\n\n\n\n\n\n"},{"alpha_fraction":0.6679389476776123,"alphanum_fraction":0.6717557311058044,"avg_line_length":16.89655113220215,"blob_id":"2940f5512d5d8eb3062debd84f516b8ceb6df0ba","content_id":"b5f4e802560ded8648faf7b2cb0fc85a25e20320","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":746,"license_type":"no_license","max_line_length":71,"num_lines":29,"path":"/unitetest2/以对象形式进行读取/读取excel数据.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import openpyxl\n\n#打开excel文件和对应的表单\nwb = openpyxl.load_workbook('cases.xlsx')\nsh = wb['test']\n\n#获取表单中的数据并整合为列表嵌套字典的形式\nmax_row = sh.max_row\n\n#按行依次读取格子中的内容,读取出来后每一行为一个元组，存储在列表中\nrows = list(sh.rows)\n#print(rows)\n\n#获取表头(PS:表头在第一行)\n\ntitle = []\nfor row in rows[0]:\n title.append(row.value)\n#print(title)\n\n#获取列表中的数据\ndatas = []\nfor r_obj in rows[1:]:\n data = []\n for r in r_obj:\n data.append(r.value)\n data_obj = dict(zip(title, data)) #将表头和数据进行打包，组成列表嵌套字典的形式，以方便获取数据\\\n datas.append(data_obj)\nprint(datas)\n\n\n\n\n\n"},{"alpha_fraction":0.6450777053833008,"alphanum_fraction":0.6459412574768066,"avg_line_length":29.473684310913086,"blob_id":"8824d5c7d61f75737f5204c4d576384a76f77fec","content_id":"084521b379452acb39bdbcfa5caa9c7f98820536","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1516,"license_type":"no_license","max_line_length":108,"num_lines":38,"path":"/unitetest2/ddt_file/logging_encap.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import logging\nfrom my_config.my_yaml_config import my_yaml\n\n\nclass MyLogger(object):\n\n @classmethod\n def createlogger(cls):\n # 参数传入自定义的收集器名称\n my_log = logging.getLogger('my_log')\n my_log.setLevel(my_yaml.read_yaml('log', 'log_setlevel_input'))\n # 设置输入到收集器里面的等级\n\n # 日志收集器的格式设置\n formater = logging.Formatter(my_yaml.read_yaml('log', 'log_formater'))\n\n # 日志的输出,创建一个输出到控制台的输出渠道\n sh = logging.StreamHandler()\n sh.setLevel(my_yaml.read_yaml('log', 'log_setlevel_output_contril')) # 设置输出的等级，最后以输出的等级为准，设置等级时需要大写\n # 将输出渠道添加到日志收集器中\n my_log.addHandler(sh)\n sh.setFormatter(formater) # 将日志输出格式传过去\n\n # 创建一个输出到文件的渠道\n fh = logging.FileHandler(my_yaml.read_yaml('log', 'log_filename'), encoding='utf8')\n fh.setLevel(my_yaml.read_yaml('log', 'log_setlevel_output_file'))\n my_log.addHandler(fh)\n fh.setFormatter(formater)\n return my_log\n\n\nmy_log = MyLogger.createlogger()\n#封装的时候建好对象，这样使用时可直接导入该日志收集器，可以解决在不同地方重复创建导致收集器多个，日志信息展示多条的问题\nif __name__ == '__main__':\n log = MyLogger.createlogger()\n\n log.info('hello')\n log.error('world')\n"},{"alpha_fraction":0.6767676472663879,"alphanum_fraction":0.705050528049469,"avg_line_length":13.529411315917969,"blob_id":"32e16e9163081080f2c2999e88a155b40bdbbbe1","content_id":"eb4b4917881c688609203342423464914230565c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":663,"license_type":"no_license","max_line_length":48,"num_lines":34,"path":"/unitetest2/excel文件操作练习.py","repo_name":"zhanglian-boob/zhanglian_Home1","src_encoding":"UTF-8","text":"import openpyxl\n\n#打开excel文件，\nwb = openpyxl.load_workbook('cases.xlsx')\n\n#选择工作簿中的某个表单\n\nsh = wb['test']\nprint(sh)\n\n#读取表格的内容\n\na = sh.cell(row=3, column=1).value #读取第三行第一列的数据\n\n#往表格中填写数据\nsh.cell(row=3, column=2).value = 9999\nsh.cell(row=3, column=3, value=8888)\n\n#写完数据一定要保存，保存后才会生效\nwb.save('cases.xlsx')\n\n#获取表单中最大的行\nmax_row = sh.max_row\nmax_column = sh.max_column\nprint(max_row)\n\n#获取最大的列\nmax_column = sh.max_column\nprint(max_column)\n\n#按行获取表格中的内容\nrows = sh.rows\nfor r in rows:\n print(list(rows))\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.629146933555603,\n \"alphanum_fraction\": 0.6350710988044739,\n \"avg_line_length\": 32.720001220703125,\n \"blob_id\": \"a12247b36b1a6916c01ad633193c34c0f50b9699\",\n \"content_id\": \"7e5d34705238421bb2f7980beb2ab63d1d9c46aa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 960,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 25,\n \"path\": \"/unitetest2/以对象形式进行读取/test_case.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import unittest\\nfrom logging_encap import MyLogger\\nfrom regist import register\\nfrom read_excel02 import ReadExcel\\n\\n\\nclass RegistTest(unittest.TestCase):\\n excel = ReadExcel('cases.xlsx', 'test')\\n #重写__init__方法，多加一个参数进去\\n def __init__(self, methodName, data_case):\\n self.data_case = data_case\\n super().__init__(methodName)\\n\\n def regist_test_pass(self):\\n row = self.data_case.case_id+1\\n data = eval(self.data_case.data)\\n expected = eval(self.data_case.expected)\\n result = register(*data) #调用程序，获取实际结果\\n try:\\n self.assertEqual(expected, result)\\n except AssertionError as e:\\n self.excel.write_data(row=row, column=4, value='未通过')\\n raise e #将异常再抛出去，没有异常就会全部通过，所以要再把异常抛出去\\n else:\\n self.excel.write_data(row=row, column=4, value='通过')\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7410628199577332,\n \"alphanum_fraction\": 0.7449275255203247,\n \"avg_line_length\": 22.5,\n \"blob_id\": \"c54c9c97a24bbee1b9929ba0bfa92d8547ae303e\",\n \"content_id\": \"a32c865a472c3b2c27aa5cf214743e903c91f6ae\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1513,\n \"license_type\": \"no_license\",\n \"max_line_length\": 101,\n \"num_lines\": 44,\n \"path\": \"/unitetest2/log_test.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import logging\\n# #默认的日志收集器\\n#\\n# #创建一个默认收集器，如果不传参数，则返回默认的日志收集器（root）\\n# my = logging.getLogger()\\n# my.setLevel('DEBUG')\\n#\\n# a = 100\\n# print(\\\"调试a\\\")\\n#\\n# logging.debug('这是一个debug的日志')\\n# logging.info('这是一个info的日志')\\n# logging.warning('这是一个warning的日志')\\n# logging.error('这是一个error的日志')\\n# logging.critical('这是一个critical的日志')\\n\\n#自定义的日志收集器\\nimport logging\\n#参数传入自定义的收集器名称\\nmy_log = logging.getLogger('my_log')\\nmy_log.setLevel('INFO') #设置输入到收集器里面的等级\\n\\n#日志收集器的格式设置\\nformater = logging.Formatter('%(asctime)s--%(filename)s--line:%(lineno)d--%(levelname)s:%(message)s')\\n\\n#日志的输出,创建一个输出到控制台的输出渠道\\nsh = logging.StreamHandler()\\nsh.setLevel('ERROR') #设置输出的等级，最后以输出的等级为准，设置等级时需要大写\\n#将输出渠道添加到日志收集器中\\nmy_log.addHandler(sh)\\nsh.setFormatter(formater) #将日志输出格式传过去\\n\\n\\n#创建一个输出到文件的渠道\\nfh = logging.FileHandler(filename='test.log', encoding='utf8')\\nfh.setLevel('INFO')\\nmy_log.addHandler(fh)\\nfh.setFormatter(formater)\\n\\nmy_log.debug('这是一个debug的日志')\\nmy_log.info('这是一个info的日志')\\nmy_log.warning('这是一个warning的日志')\\nmy_log.error('这是一个error的日志')\\nmy_log.critical('这是一个critical的日志')\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5269636511802673,\n \"alphanum_fraction\": 0.5316529870033264,\n \"avg_line_length\": 26.047618865966797,\n \"blob_id\": \"44cc63d5c0fa09e525fbd7d2acb8e040c93a115a\",\n \"content_id\": \"3fb329eb35e0bca34a9eb67502ecaf669e9282d3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2134,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 63,\n \"path\": \"/unitetest2/以字典形式进行读取/read_excel02.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import openpyxl\\n\\n\\nclass CaseData(object):\\n '''测试用例数据类，专门用来创建对象的，不用写'''\\n pass\\n\\n\\nclass ReadExcel(object):\\n def __init__(self, filename, sheetname):\\n self.filename = filename\\n self.sheetname = sheetname\\n\\n def open(self):\\n self.wb = openpyxl.load_workbook(self.filename)\\n self.sh = self.wb[self.sheetname]\\n\\n def read_data(self):\\n '''读取数据'''\\n self.open()\\n self.max_row = self.sh.max_row\\n # 按行依次读取格子中的内容,读取出来后每一行为一个元组，存储在列表中\\n rows = list(self.sh.rows)\\n # 获取表头\\n title = []\\n for row in rows[0]:\\n title.append(row.value)\\n # 获取列表中的数据\\n datas = []\\n for r_obj in rows[1:]:\\n data = [] #创建列表，将每一行遍历出来的数据放入列表中\\n for r in r_obj:\\n data.append(r.value)\\n data_obj = dict(zip(title, data)) # 将表头和数据进行打包，组成列表嵌套字典的形式\\n datas.append(data_obj)\\n self.wb.close()\\n return datas\\n\\n def read_data_obj(self):\\n '''读取数据后，返回的是列表嵌套对象的方式'''\\n self.open()\\n self.max_row = self.sh.max_row\\n # 按行依次读取格子中的内容,读取出来后每一行为一个元组，存储在列表中\\n rows = list(self.sh.rows)\\n title = [row.value for row in rows[0]] #列表推导式，获取表头\\n # 获取列表中的数据\\n datas = []\\n for r_obj in rows[1:]:\\n data = [r.value for r in r_obj] #列表推导式,只适用于for下面跟简单的一行代码\\n case = CaseData() #创建一个用例数据对象\\n for i in zip(title, data):\\n setattr(case, i[0], i[1])\\n datas.append(case)\\n return datas\\n\\n def write_data(self):\\n '''写入数据'''\\n pass\\n\\nif __name__=='__main__':\\n read = ReadExcel('cases.xlsx', 'test')\\n data2 = read.read_data()\\n print(data2)\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6031566858291626,\n \"alphanum_fraction\": 0.6042841076850891,\n \"avg_line_length\": 25.08823585510254,\n \"blob_id\": \"1b6987ce4b6246ac45e91e653fd455c35d7caf96\",\n \"content_id\": \"072e40c5a59d051b75419ca8da65c56e870b897f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1127,\n \"license_type\": \"no_license\",\n \"max_line_length\": 109,\n \"num_lines\": 34,\n \"path\": \"/unitetest2/以对象形式进行读取/logging_encap.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import logging\\n\\n\\nclass MyLogger(object):\\n\\n @classmethod\\n def createlogger(cls):\\n # 参数传入自定义的收集器名称\\n my_log = logging.getLogger('my_log')\\n my_log.setLevel('INFO') # 设置输入到收集器里面的等级\\n\\n # 日志收集器的格式设置\\n formater = logging.Formatter('%(asctime)s--%(filename)s--line:%(lineno)d--%(levelname)s:%(message)s')\\n\\n # 日志的输出,创建一个输出到控制台的输出渠道\\n sh = logging.StreamHandler()\\n sh.setLevel('INFO') # 设置输出的等级，最后以输出的等级为准，设置等级时需要大写\\n # 将输出渠道添加到日志收集器中\\n my_log.addHandler(sh)\\n sh.setFormatter(formater) # 将日志输出格式传过去\\n\\n # 创建一个输出到文件的渠道\\n fh = logging.FileHandler(filename='test.log', encoding='utf8')\\n fh.setLevel('INFO')\\n my_log.addHandler(fh)\\n fh.setFormatter(formater)\\n return my_log\\n\\n\\nif __name__ == '__main__':\\n log = MyLogger.createlogger()\\n\\n log.info('hello')\\n log.error('world')\\n\"\n },\n {\n \"alpha_fraction\": 0.5518035292625427,\n \"alphanum_fraction\": 0.5579432249069214,\n \"avg_line_length\": 26.680850982666016,\n \"blob_id\": \"a15f0fa6546ce533d75cdaf34d1228849302c612\",\n \"content_id\": \"40b6674b0aa4722340d222eb6cccfec32792f8bf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1553,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 47,\n \"path\": \"/unitetest2/以对象形式进行读取/read_excel02.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import openpyxl\\n\\n\\nclass CaseData(object):\\n '''测试用例数据类，专门用来创建对象的，不用写'''\\n pass\\n\\n\\nclass ReadExcel(object):\\n def __init__(self, filename, sheetname):\\n self.filename = filename\\n self.sheetname = sheetname\\n\\n def open(self):\\n self.wb = openpyxl.load_workbook(self.filename)\\n self.sh = self.wb[self.sheetname]\\n\\n def read_data_obj(self):\\n '''读取数据后，返回的是列表嵌套对象的方式'''\\n self.open()\\n self.max_row = self.sh.max_row\\n # 按行依次读取格子中的内容,读取出来后每一行为一个元组，存储在列表中\\n rows = list(self.sh.rows)\\n title = [row.value for row in rows[0]] #列表推导式，获取表头\\n # 获取列表中的数据\\n datas = []\\n for r_obj in rows[1:]:\\n data = [r.value for r in r_obj] #列表推导式,只适用于for下面跟简单的一行代码\\n case = CaseData() #创建一个用例数据对象\\n for i in zip(title, data):\\n setattr(case, i[0], i[1])\\n datas.append(case)\\n self.wb.close()\\n return datas\\n\\n def write_data(self, row, column, value):\\n '''写入数据'''\\n self.open()\\n self.sh.cell(row=row, column=column, value=value)\\n self.wb.save(self.filename)\\n self.wb.close()\\n\\nif __name__=='__main__':\\n read = ReadExcel('cases.xlsx', 'test')\\n # data2 = read.read_data_obj()\\n # print(data2)\\n read.write_data(5, 5, '通过')\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5627943277359009,\n \"alphanum_fraction\": 0.5659340620040894,\n \"avg_line_length\": 34.36111068725586,\n \"blob_id\": \"523ff2fa80b7cf7e77027d7c64ff7f0c69766736\",\n \"content_id\": \"1f6e5e0807b08f0ca1d5ac4875212f542b58552b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1320,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 36,\n \"path\": \"/unitetest2/ddt_file/test_case.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import unittest\\nfrom regist import register\\nfrom read_excel02 import ReadExcel\\nfrom all.unitetest2.ddt_file.ddt import ddt, data\\nfrom logging_encap import my_log\\nfrom my_config.my_yaml_config import my_yaml\\nfrom logging_encap import my_log\\n\\n\\n\\n@ddt\\nclass RegistTest(unittest.TestCase):\\n excel = ReadExcel(my_yaml.read_yaml('excel', 'excel_name'), 'test')\\n cases = excel.read_data_obj()\\n\\n @data(*cases)\\n def test_regist(self, case):\\n #准备用例数据\\n row = case.case_id+1\\n expected = eval(case.expected)\\n data = eval(case.data)\\n result = register(*data)\\n try:\\n self.assertEqual(expected, result)\\n except AssertionError as e:\\n log.info(F'用例:{case.title}, 未执行通过')\\n self.excel.write_data(row=row,\\n column=my_yaml.read_yaml('excel', 'result_column'),\\n value=my_yaml.read_yaml('msg', 'success_msg'))\\n my_log.error(F'断言异常{e}')\\n raise e\\n else:\\n log.info(F'用例:{case.title}, 执行通过')\\n self.excel.write_data(row=row,\\n column=my_yaml.read_yaml('excel', 'result_column'),\\n value=my_yaml.read_yaml('msg', 'failure_msg'))\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6505235433578491,\n \"alphanum_fraction\": 0.6570680737495422,\n \"avg_line_length\": 24.46666717529297,\n \"blob_id\": \"8a23d7e70e9f74e09489578dd7e227e975e70261\",\n \"content_id\": \"f6f36f2e0a951141c8d108de271c2426c8176f58\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1052,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 30,\n \"path\": \"/unitetest2/以对象形式进行读取/test_test.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 对下面功能函数 设计用例进行单元测试，\\n# 要求 :1、 设计至少五条用例\\n# 2、至少用两种方式往测试集合（套件）中添加测试用例\\n# 3、执行测试集合（套件）中的测试用例，生成测试报告\\n\\nimport unittest\\nfrom test_case import RegistTest\\nfrom regist import register\\nfrom HTMLTestRunnerNew import HTMLTestRunner\\nfrom read_excel02 import ReadExcel\\n\\n#创建测试套件\\nsuite = unittest.TestSuite()\\nread = ReadExcel('cases.xlsx', 'test')\\ndatas = read.read_data_obj()\\nprint(datas)\\n\\n#将测试用例加入到测试套件中\\nfor data in datas:\\n case = RegistTest('regist_test_pass', data)\\n suite.addTest(case)\\n\\n#创建一个测试运行的程序对象\\nwith open('report.html', 'wb') as fb:\\n runner = HTMLTestRunner(stream=fb,\\n title='关于注册的测试报告',\\n description='这是一个关于注册的测试报告',\\n tester='橘子')\\n#运行程序，执行测试用例\\n runner.run(suite)\\n\"\n },\n {\n \"alpha_fraction\": 0.6043233275413513,\n \"alphanum_fraction\": 0.6137217879295349,\n \"avg_line_length\": 21.95652198791504,\n \"blob_id\": \"47207178b0e9fd21d4e8a15c7806a646a093fe5c\",\n \"content_id\": \"a1f376d5c07dff42a9e342aa1796c4a9b5667e2b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1260,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 46,\n \"path\": \"/unitetest2/testcase.conf.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#读取配置文件\\n#引入自带的方法configparser\\nfrom configparser import ConfigParser\\n#创建配置的对象\\nconfig = ConfigParser()\\n#读取数据，指定读取文件的路径\\nconfig.read('testcase.ini', encoding='utf8')\\n#getint(区域名，选项名)\\nresult1 = config.get('users', 'name')\\nprint(result1)\\n\\n\\n#配置文件中进行写入\\n#引入配置文件的方法\\n# from configparser import ConfigParser\\n# #创建对象\\n# config = ConfigParser()\\n# #写入数据,将配置文件看作一个空字典来进行写入\\n# datas = {\\n# \\\"excel\\\": {\\\"case_path\\\": \\\"casepath.txt\\\"},\\n# \\\"users\\\": {\\\"name\\\": \\\"xiaoming\\\", \\\"age\\\": 19}\\n# }\\n# for i in datas:\\n# config[i] = datas[i]\\n# with open('testcase.ini', 'w', encoding='utf8') as file:\\n# config.write(file)\\n\\n#使用yaml进行读取数据\\n# import yaml\\n# with open('testcase.yaml', 'r', encoding='utf8') as fb:\\n# data = yaml.load(fb, Loader=yaml.FullLoader)\\n# print(data)\\n#\\n# #使用yaml写入数据\\n# import yaml\\n# datas = {\\n# \\\"excel\\\": {\\\"case_path\\\": \\\"casepath.txt\\\"\\n# },\\n# \\\"users\\\": {\\\"name\\\": \\\"xiaoming\\\",\\n# \\\"age\\\": 19\\n# }\\n# }\\n# with open('testcase.yaml', 'w', encoding='utf8') as wb:\\n# yaml.dump(datas, wb, allow_unicode=True)\\n\\nimport yaml\\n\\n\\n\\n\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.640625,\n \"alphanum_fraction\": 0.640625,\n \"avg_line_length\": 28.866666793823242,\n \"blob_id\": \"d9e3fe7df4f719aabacfd7323967902759ae5196\",\n \"content_id\": \"cd89fc8d651e52c9a3f05700488958ce55c93eb7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 496,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 15,\n \"path\": \"/unitetest2/以字典形式进行读取/test_case.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import unittest\\nfrom regist import register\\n\\n\\nclass RegistTest(unittest.TestCase):\\n #重写__init__方法，多加一个参数进去\\n def __init__(self, methodName, data_case):\\n self.data_case = data_case\\n super().__init__(methodName)\\n\\n def regist_test_pass(self):\\n data = eval(self.data_case['data'])\\n expected = eval(self.data_case['expected'])\\n result = register(*data) #调用程序，获取实际结果\\n self.assertEqual(expected, result)\\n\"\n },\n {\n \"alpha_fraction\": 0.5189328789710999,\n \"alphanum_fraction\": 0.5215146541595459,\n \"avg_line_length\": 24.217391967773438,\n \"blob_id\": \"dad6b0f8109bcec23ddcdd18a6c456e882c9346e\",\n \"content_id\": \"6fa8c737ea6a5398d1ef58715bd6c834227bc297\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1272,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 46,\n \"path\": \"/unitetest2/ddt_file/handle_requests.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import requests\\n\\n\\nclass MyRequests(object):\\n def __init__(self):\\n self.my_session = requests.Session()\\n\\n def add_headers(self, headers):\\n '''添加公共请求头'''\\n self.my_session.headers.update(headers)\\n\\n def send(self, url, method='post', data=None, is_json=True, **kwargs):\\n if isinstance(data,str):\\n try:\\n data = json.loads(data)\\n except Exception as e:\\n print('使用日志器来记录日志')\\n data = eval(data)\\n\\n\\n method = method.lower()\\n if method == 'get':\\n res = self.my_session.request(method, url, json=data, **kwargs)\\n elif method in ('post', 'put', 'delete', 'patch'):\\n if is_json:\\n res = self.my_session.request(method, url, json=data, **kwargs)\\n else:\\n res = self.my_session.request(method, url, data=data,**kwargs)\\n else:\\n res = None\\n print(F'不支持【{method}】请求方法')\\n return res\\n\\n def close(self):\\n self.my_session.close()\\n\\n\\nif __name__ == '__main__':\\n #1、构建请求的url\\n login_url =\\n recharge_url =\\n\\n #2、创建请求参数（包括请求头和请求体）\\n\\n\\n #3、执行登陆\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.634178876876831,\n \"alphanum_fraction\": 0.634178876876831,\n \"avg_line_length\": 28.920000076293945,\n \"blob_id\": \"3479f3cfdcb3435eb7e98e408ea9fafc37f18ba2\",\n \"content_id\": \"09a7cf16a3ded53db0896ab41c46a1cdd04b2acc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 821,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 25,\n \"path\": \"/unitetest2/ddt_file/run_test.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import unittest\\nimport test_case\\n\\n\\nfrom HTMLTestRunnerNew import HTMLTestRunner\\nfrom logging_encap import my_log\\nfrom my_config.my_yaml_config import my_yaml\\n#创建测试套件\\nsuite = unittest.TestSuite()\\nmy_log.info('测试套件创建成功！')\\n\\n#加载用例到套件\\nloader = unittest.TestLoader()\\nsuite.addTest(loader.loadTestsFromModule(test_case))\\n\\n\\n#创建测试运行程序\\nwith open(my_yaml.read_yaml('report', 'report_name'), 'wb') as fb:\\n runner = HTMLTestRunner(stream=fb,\\n title= my_yaml.read_yaml('report', 'report_title'),\\n description=\\n my_yaml.read_yaml('report', 'report_description'),\\n tester= my_yaml.read_yaml('report', 'report_tester'))\\n #执行测试用例\\n runner.run(suite)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6111111044883728,\n \"alphanum_fraction\": 0.6388888955116272,\n \"avg_line_length\": 10.833333015441895,\n \"blob_id\": \"0bbfebf4fa9f43d04aa1491a9f79b9f804d6fe8a\",\n \"content_id\": \"d2fee6b189c4ba1de9732974e988e3d1e7e296d0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"INI\",\n \"length_bytes\": 72,\n \"license_type\": \"no_license\",\n \"max_line_length\": 26,\n \"num_lines\": 6,\n \"path\": \"/unitetest2/testcase.ini\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"[excel]\\ncase_path = 'casepath.txt'\\n\\n[users]\\nname = 'xiaoming'\\nage = 19\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5726290345191956,\n \"alphanum_fraction\": 0.5786314606666565,\n \"avg_line_length\": 29.66666603088379,\n \"blob_id\": \"657268516bd8c9d05b7d968f0978a165b36184c8\",\n \"content_id\": \"d3403486f04b8a8e578cae9923af7ee3a5018c50\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 833,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 27,\n \"path\": \"/unitetest2/ddt_file/my_config/my_yaml_config.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import yaml\\n\\n\\nclass ReadWriteYaml(object):\\n def __init__(self, filename):\\n with open(filename, 'r', encoding='utf8') as my_file:\\n self.datas = yaml.full_load(my_file)\\n\\n def read_yaml(self, section, option):\\n return self.datas[section][option]\\n\\n def write_yaml(self, filename, data):\\n with open(filename, 'w', encoding='utf8') as wf:\\n yaml.dump(data, wf, allow_unicode=True)\\n with open(filename, 'r', encoding='utf8') as wf:\\n self.datas = yaml.full_load(wf)\\n\\n\\nmy_yaml = ReadWriteYaml('my_config\\\\\\\\testcase.yaml')\\n\\nif __name__ == '__main__':\\n datas = {\\n \\\"excel\\\": {\\\"case_path\\\": \\\"casepath.txt\\\"},\\n \\\"users\\\": {\\\"name\\\": \\\"xiaoming\\\", \\\"age\\\": 20}}\\n my_yaml.write_yaml('testcase.yaml', datas)\\n b = my_yaml.read_yaml('users', 'name')\\n print(b)\\n\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6679389476776123,\n \"alphanum_fraction\": 0.6717557311058044,\n \"avg_line_length\": 16.89655113220215,\n \"blob_id\": \"2940f5512d5d8eb3062debd84f516b8ceb6df0ba\",\n \"content_id\": \"b5f4e802560ded8648faf7b2cb0fc85a25e20320\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 746,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 29,\n \"path\": \"/unitetest2/以对象形式进行读取/读取excel数据.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import openpyxl\\n\\n#打开excel文件和对应的表单\\nwb = openpyxl.load_workbook('cases.xlsx')\\nsh = wb['test']\\n\\n#获取表单中的数据并整合为列表嵌套字典的形式\\nmax_row = sh.max_row\\n\\n#按行依次读取格子中的内容,读取出来后每一行为一个元组，存储在列表中\\nrows = list(sh.rows)\\n#print(rows)\\n\\n#获取表头(PS:表头在第一行)\\n\\ntitle = []\\nfor row in rows[0]:\\n title.append(row.value)\\n#print(title)\\n\\n#获取列表中的数据\\ndatas = []\\nfor r_obj in rows[1:]:\\n data = []\\n for r in r_obj:\\n data.append(r.value)\\n data_obj = dict(zip(title, data)) #将表头和数据进行打包，组成列表嵌套字典的形式，以方便获取数据\\\\\\n datas.append(data_obj)\\nprint(datas)\\n\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6450777053833008,\n \"alphanum_fraction\": 0.6459412574768066,\n \"avg_line_length\": 29.473684310913086,\n \"blob_id\": \"8824d5c7d61f75737f5204c4d576384a76f77fec\",\n \"content_id\": \"084521b379452acb39bdbcfa5caa9c7f98820536\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1516,\n \"license_type\": \"no_license\",\n \"max_line_length\": 108,\n \"num_lines\": 38,\n \"path\": \"/unitetest2/ddt_file/logging_encap.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import logging\\nfrom my_config.my_yaml_config import my_yaml\\n\\n\\nclass MyLogger(object):\\n\\n @classmethod\\n def createlogger(cls):\\n # 参数传入自定义的收集器名称\\n my_log = logging.getLogger('my_log')\\n my_log.setLevel(my_yaml.read_yaml('log', 'log_setlevel_input'))\\n # 设置输入到收集器里面的等级\\n\\n # 日志收集器的格式设置\\n formater = logging.Formatter(my_yaml.read_yaml('log', 'log_formater'))\\n\\n # 日志的输出,创建一个输出到控制台的输出渠道\\n sh = logging.StreamHandler()\\n sh.setLevel(my_yaml.read_yaml('log', 'log_setlevel_output_contril')) # 设置输出的等级，最后以输出的等级为准，设置等级时需要大写\\n # 将输出渠道添加到日志收集器中\\n my_log.addHandler(sh)\\n sh.setFormatter(formater) # 将日志输出格式传过去\\n\\n # 创建一个输出到文件的渠道\\n fh = logging.FileHandler(my_yaml.read_yaml('log', 'log_filename'), encoding='utf8')\\n fh.setLevel(my_yaml.read_yaml('log', 'log_setlevel_output_file'))\\n my_log.addHandler(fh)\\n fh.setFormatter(formater)\\n return my_log\\n\\n\\nmy_log = MyLogger.createlogger()\\n#封装的时候建好对象，这样使用时可直接导入该日志收集器，可以解决在不同地方重复创建导致收集器多个，日志信息展示多条的问题\\nif __name__ == '__main__':\\n log = MyLogger.createlogger()\\n\\n log.info('hello')\\n log.error('world')\\n\"\n },\n {\n \"alpha_fraction\": 0.6767676472663879,\n \"alphanum_fraction\": 0.705050528049469,\n \"avg_line_length\": 13.529411315917969,\n \"blob_id\": \"32e16e9163081080f2c2999e88a155b40bdbbbe1\",\n \"content_id\": \"eb4b4917881c688609203342423464914230565c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 663,\n \"license_type\": \"no_license\",\n \"max_line_length\": 48,\n \"num_lines\": 34,\n \"path\": \"/unitetest2/excel文件操作练习.py\",\n \"repo_name\": \"zhanglian-boob/zhanglian_Home1\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import openpyxl\\n\\n#打开excel文件，\\nwb = openpyxl.load_workbook('cases.xlsx')\\n\\n#选择工作簿中的某个表单\\n\\nsh = wb['test']\\nprint(sh)\\n\\n#读取表格的内容\\n\\na = sh.cell(row=3, column=1).value #读取第三行第一列的数据\\n\\n#往表格中填写数据\\nsh.cell(row=3, column=2).value = 9999\\nsh.cell(row=3, column=3, value=8888)\\n\\n#写完数据一定要保存，保存后才会生效\\nwb.save('cases.xlsx')\\n\\n#获取表单中最大的行\\nmax_row = sh.max_row\\nmax_column = sh.max_column\\nprint(max_row)\\n\\n#获取最大的列\\nmax_column = sh.max_column\\nprint(max_column)\\n\\n#按行获取表格中的内容\\nrows = sh.rows\\nfor r in rows:\\n print(list(rows))\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":16,"string":"16"}}},{"rowIdx":2979143,"cells":{"repo_name":{"kind":"string","value":"reshavcodes/empty_directory_killer"},"repo_url":{"kind":"string","value":"https://github.com/reshavcodes/empty_directory_killer"},"snapshot_id":{"kind":"string","value":"0c0b237d0cce4ca32b3c43cbc188204158c7cd58"},"revision_id":{"kind":"string","value":"8d71d4aa537519572ad1c5cd2b43fe41e1fa4110"},"directory_id":{"kind":"string","value":"13cfa31834c6cc27ba1e295452ed1a69f95b4674"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-05-05T22:52:56.248179","string":"2023-05-05T22:52:56.248179"},"revision_date":{"kind":"timestamp","value":"2021-05-21T16:21:46","string":"2021-05-21T16:21:46"},"committer_date":{"kind":"timestamp","value":"2021-05-21T16:21:46","string":"2021-05-21T16:21:46"},"github_id":{"kind":"number","value":369590088,"string":"369,590,088"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.49433010816574097,"alphanum_fraction":0.4981100261211395,"avg_line_length":31.09722137451172,"blob_id":"b5f63253e7d04277c1c2cca7f19fc700c67965f6","content_id":"af575204d2cf5be1925d79fe4ecc0ed5dc7aa03f","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2381,"license_type":"permissive","max_line_length":73,"num_lines":72,"path":"/main.py","repo_name":"reshavcodes/empty_directory_killer","src_encoding":"UTF-8","text":"import os\r\nfrom time import sleep,time\r\nfrom rm_dir import remover\r\n\r\n\r\n#Function to scan and put empty directories in an dictionary\r\ndef dir_scanner(path):\r\n emptys=[]\r\n for paths,dirs,files in os.walk(path):\r\n if len(files)<=0 and len(dirs)<=0:\r\n emptys.append(paths)\r\n print(\"\\n***Empty directory scan complete***\\n\")\r\n return emptys\r\n \r\n\r\n\r\n#Deleting empty folders\r\ndef removeing(empty_list):\r\n \r\n for dirs_path in empty_list:\r\n k=dirs_path.split(\"\\\\\")\r\n remover(dirs_path)\r\n item=k.pop()\r\n print(f\"Removed \\\"{item}\\\" folder\")\r\n print(\"\\nDeletion Complete\")\r\n\r\n\r\n#Execution starts here\r\nif __name__==\"__main__\":\r\n print(\"\"\"\\n\r\n ^___________________________________________________________________^\r\n | Welcome to empty directory cleaner! |\r\n | Run me and I will clean all the blank folders from your device |\r\n | **Please don't mess up with the code before running or |\r\n | I'm not responsible if it malfunctioned** |\r\n | |\r\n | Checkout my github http://www.github.com/reshavcodes |\r\n | |\r\n |___________________________________________________________________|\r\n \"\"\")\r\n\r\n print(\"#____Enter the directory path to start scanning___#\\n\")\r\n path=input()\r\n scan=True\r\n\r\n#Checking if given path exists\r\n if os.path.exists(path):\r\n print(\"Scanning.......\")\r\n t1=time()\r\n\r\n#Getting the empty directories\r\n try:\r\n emptys=dir_scanner(path)\r\n except:\r\n print(\"Got error while Scanning please scan again\")\r\n scan=False\r\n\r\n#Checking if scanner got any emoty directories stops execution if found 0\r\n if len(emptys)>=1 and scan:\r\n print(f\"Found {len(emptys)} empty directories/folders\")\r\n sleep(2)\r\n print(\"***Starting to delete empty folders***\\n\")\r\n\r\n#Deleting those empty directories\r\n removeing(emptys)\r\n print(f\"Process complete in {round((time()-t1),2)}s time\")\r\n\r\n else:\r\n print(\"Found 0 empty folders\\nStopping program\")\r\n \r\n else:\r\n print(\"Wrong Path Entered, Please try entering correct path!!!\")"},{"alpha_fraction":0.8020833134651184,"alphanum_fraction":0.8020833134651184,"avg_line_length":56.599998474121094,"blob_id":"681542be7a97db74c2c103cc57cb2a0809bfcb75","content_id":"fc34cdd223cbc584f04e82ed894f431246aa940b","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":288,"license_type":"permissive","max_line_length":114,"num_lines":5,"path":"/README.md","repo_name":"reshavcodes/empty_directory_killer","src_encoding":"UTF-8","text":"# empty_directory_killer\n#This python script will delete all the empty directories from the drive/system/device from the desired given path\n\n#Just run the main.py file and it will automatically scan and delete all the empty directories in that path\n#Star the repo if you love the script.\n"},{"alpha_fraction":0.6140350699424744,"alphanum_fraction":0.6140350699424744,"avg_line_length":22.14285659790039,"blob_id":"f329b8dfdad14e9bff6608ce65ace209d6a3dac4","content_id":"bfa61f24110efe7c6ff86149a98900bd8546b1db","detected_licenses":["Apache-2.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":171,"license_type":"permissive","max_line_length":81,"num_lines":7,"path":"/rm_dir.py","repo_name":"reshavcodes/empty_directory_killer","src_encoding":"UTF-8","text":"import os\r\n\r\ndef remover(dirpath):\r\n try:\r\n os.rmdir(dirpath)\r\n except:\r\n print(\"Directory does not exist or I don't have permission to delete it\")\r\n\r\n"}],"string":"[\n {\n \"alpha_fraction\": 0.49433010816574097,\n \"alphanum_fraction\": 0.4981100261211395,\n \"avg_line_length\": 31.09722137451172,\n \"blob_id\": \"b5f63253e7d04277c1c2cca7f19fc700c67965f6\",\n \"content_id\": \"af575204d2cf5be1925d79fe4ecc0ed5dc7aa03f\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2381,\n \"license_type\": \"permissive\",\n \"max_line_length\": 73,\n \"num_lines\": 72,\n \"path\": \"/main.py\",\n \"repo_name\": \"reshavcodes/empty_directory_killer\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\r\\nfrom time import sleep,time\\r\\nfrom rm_dir import remover\\r\\n\\r\\n\\r\\n#Function to scan and put empty directories in an dictionary\\r\\ndef dir_scanner(path):\\r\\n emptys=[]\\r\\n for paths,dirs,files in os.walk(path):\\r\\n if len(files)<=0 and len(dirs)<=0:\\r\\n emptys.append(paths)\\r\\n print(\\\"\\\\n***Empty directory scan complete***\\\\n\\\")\\r\\n return emptys\\r\\n \\r\\n\\r\\n\\r\\n#Deleting empty folders\\r\\ndef removeing(empty_list):\\r\\n \\r\\n for dirs_path in empty_list:\\r\\n k=dirs_path.split(\\\"\\\\\\\\\\\")\\r\\n remover(dirs_path)\\r\\n item=k.pop()\\r\\n print(f\\\"Removed \\\\\\\"{item}\\\\\\\" folder\\\")\\r\\n print(\\\"\\\\nDeletion Complete\\\")\\r\\n\\r\\n\\r\\n#Execution starts here\\r\\nif __name__==\\\"__main__\\\":\\r\\n print(\\\"\\\"\\\"\\\\n\\r\\n ^___________________________________________________________________^\\r\\n | Welcome to empty directory cleaner! |\\r\\n | Run me and I will clean all the blank folders from your device |\\r\\n | **Please don't mess up with the code before running or |\\r\\n | I'm not responsible if it malfunctioned** |\\r\\n | |\\r\\n | Checkout my github http://www.github.com/reshavcodes |\\r\\n | |\\r\\n |___________________________________________________________________|\\r\\n \\\"\\\"\\\")\\r\\n\\r\\n print(\\\"#____Enter the directory path to start scanning___#\\\\n\\\")\\r\\n path=input()\\r\\n scan=True\\r\\n\\r\\n#Checking if given path exists\\r\\n if os.path.exists(path):\\r\\n print(\\\"Scanning.......\\\")\\r\\n t1=time()\\r\\n\\r\\n#Getting the empty directories\\r\\n try:\\r\\n emptys=dir_scanner(path)\\r\\n except:\\r\\n print(\\\"Got error while Scanning please scan again\\\")\\r\\n scan=False\\r\\n\\r\\n#Checking if scanner got any emoty directories stops execution if found 0\\r\\n if len(emptys)>=1 and scan:\\r\\n print(f\\\"Found {len(emptys)} empty directories/folders\\\")\\r\\n sleep(2)\\r\\n print(\\\"***Starting to delete empty folders***\\\\n\\\")\\r\\n\\r\\n#Deleting those empty directories\\r\\n removeing(emptys)\\r\\n print(f\\\"Process complete in {round((time()-t1),2)}s time\\\")\\r\\n\\r\\n else:\\r\\n print(\\\"Found 0 empty folders\\\\nStopping program\\\")\\r\\n \\r\\n else:\\r\\n print(\\\"Wrong Path Entered, Please try entering correct path!!!\\\")\"\n },\n {\n \"alpha_fraction\": 0.8020833134651184,\n \"alphanum_fraction\": 0.8020833134651184,\n \"avg_line_length\": 56.599998474121094,\n \"blob_id\": \"681542be7a97db74c2c103cc57cb2a0809bfcb75\",\n \"content_id\": \"fc34cdd223cbc584f04e82ed894f431246aa940b\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 288,\n \"license_type\": \"permissive\",\n \"max_line_length\": 114,\n \"num_lines\": 5,\n \"path\": \"/README.md\",\n \"repo_name\": \"reshavcodes/empty_directory_killer\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# empty_directory_killer\\n#This python script will delete all the empty directories from the drive/system/device from the desired given path\\n\\n#Just run the main.py file and it will automatically scan and delete all the empty directories in that path\\n#Star the repo if you love the script.\\n\"\n },\n {\n \"alpha_fraction\": 0.6140350699424744,\n \"alphanum_fraction\": 0.6140350699424744,\n \"avg_line_length\": 22.14285659790039,\n \"blob_id\": \"f329b8dfdad14e9bff6608ce65ace209d6a3dac4\",\n \"content_id\": \"bfa61f24110efe7c6ff86149a98900bd8546b1db\",\n \"detected_licenses\": [\n \"Apache-2.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 171,\n \"license_type\": \"permissive\",\n \"max_line_length\": 81,\n \"num_lines\": 7,\n \"path\": \"/rm_dir.py\",\n \"repo_name\": \"reshavcodes/empty_directory_killer\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\r\\n\\r\\ndef remover(dirpath):\\r\\n try:\\r\\n os.rmdir(dirpath)\\r\\n except:\\r\\n print(\\\"Directory does not exist or I don't have permission to delete it\\\")\\r\\n\\r\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979144,"cells":{"repo_name":{"kind":"string","value":"priyankark/LSI"},"repo_url":{"kind":"string","value":"https://github.com/priyankark/LSI"},"snapshot_id":{"kind":"string","value":"7aac559bd514463e37503521a0e8f060f386c7a0"},"revision_id":{"kind":"string","value":"c4eea605c55905d2c776ba5aa78e9e33dbc02ca5"},"directory_id":{"kind":"string","value":"dedb7d6451e37965e4f25e26c96ec6718aed250e"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-13T05:36:14.708119","string":"2021-01-13T05:36:14.708119"},"revision_date":{"kind":"timestamp","value":"2017-06-23T14:30:29","string":"2017-06-23T14:30:29"},"committer_date":{"kind":"timestamp","value":"2017-06-23T14:30:29","string":"2017-06-23T14:30:29"},"github_id":{"kind":"number","value":95101068,"string":"95,101,068"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7139001488685608,"alphanum_fraction":0.7408906817436218,"avg_line_length":36.9487190246582,"blob_id":"ba1bc6b1bcd43002142f775a27f214797c136d9d","content_id":"bf23b760cc624bcd4123c54f0e95da00d9f3c588","detected_licenses":["MIT","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1482,"license_type":"permissive","max_line_length":259,"num_lines":39,"path":"/GeoVis/examples/polygonmap_example.py","repo_name":"priyankark/LSI","src_encoding":"UTF-8","text":"\"\"\"\nExample Script for the Python Geographic Visualizer (GeoVis)\nhttps://github.com/karimbahgat/geovis\n\"\"\"\n\n#importing geovis from temporary location\nTEMP_GEOVIS_FOLDER = r\"C:\\Users\\BIGKIMO\\Documents\\GitHub\\geovis\"\nimport sys\nsys.path.append(TEMP_GEOVIS_FOLDER)\nimport geovis\n\n############\n#SETUP\n############\n#setup rendering options\ngeovis.SetRenderingOptions(renderer=\"aggdraw\", numpyspeed=True, reducevectors=False)\n#create map\ngeovis.SetMapBackground(geovis.Color(\"blue\", brightness=0.9))\ngeovis.SetMapZoom(x2x=[0,180],y2y=[0,90])\nnewmap = geovis.NewMap()\n\n############\n#LOAD AND SYMBOLIZE LAYERS\n############\ncountrylayer = geovis.Layer(filepath=r\"D:\\Test Data\\necountries\\necountries.shp\")\ncountrylayer.AddClassification(symboltype=\"fillcolor\", valuefield=\"pop_est\", symbolrange=[geovis.Color(\"white\"),geovis.Color(\"red\", intensity=0.9, brightness=0.8),geovis.Color(\"red\", intensity=0.9, brightness=0.5)], classifytype=\"natural breaks\", nrclasses=3)\ncountrylayer.AddClassification(symboltype=\"outlinewidth\", valuefield=\"pop_est\", symbolrange=[0.05,0.4], classifytype=\"natural breaks\", nrclasses=3)\nriverlayer = geovis.Layer(filepath=r\"D:\\Test Data\\lines\\ne_50m_rivers_lake_centerlines.shp\", fillcolor=geovis.Color(\"blue\",brightness=0.9))\n\n############\n#RENDER TO MAP\n############\n#add layers to map\nnewmap.AddToMap(countrylayer)\nnewmap.AddToMap(riverlayer)\n#add legend\nnewmap.AddLegend(countrylayer, upperleft=(0.03,0.15), bottomright=(0.6,0.4))\n#view map\nnewmap.ViewMap()\n\n\n"},{"alpha_fraction":0.7744807004928589,"alphanum_fraction":0.7823936939239502,"avg_line_length":29.636363983154297,"blob_id":"212c65af62465054fbb79044c811a14b02760ee8","content_id":"3b03ac8db6f80e41057913532c807f5e18f02c74","detected_licenses":["MIT","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1011,"license_type":"permissive","max_line_length":121,"num_lines":33,"path":"/GeoVis/examples/batch_add_to_map_example.py","repo_name":"priyankark/LSI","src_encoding":"UTF-8","text":"\"\"\"\nExample Script for the Python Geographic Visualizer (GeoVis)\nhttps://github.com/karimbahgat/geovis\n\"\"\"\n\n#importing geovis from temporary location\nTEMP_GEOVIS_FOLDER = r\"C:\\Users\\BIGKIMO\\Documents\\GitHub\\geovis\"\nimport sys\nsys.path.append(TEMP_GEOVIS_FOLDER)\nimport geovis\n\n#setting up for speed\ngeovis.SetRenderingOptions(reducevectors=True)\n\n#create map\nMAPCOLORSTYLE = \"pastelle\"\ngeovis.SetMapBackground(geovis.Color(\"blue\",style=MAPCOLORSTYLE))\nnewmap = geovis.NewMap()\n\n#add base shapefile layer\nbase_layer = geovis.Layer(\"D:\\Test Data\\Global Subadmins\\gadm2.shp\")\nnewmap.AddToMap(base_layer)\n\n#overlay with a folder of many shapefiles\nfor eachfolder, eachfile, eachext in geovis.ShapefileFolder(r\"D:\\Test Data\\DHS GPS\"):\n eachlayer = geovis.Layer(filepath=eachfolder+eachfile+eachext, fillcolor=geovis.Color(\"random\", style=MAPCOLORSTYLE))\n newmap.AddToMap(eachlayer)\n\n#add title\nnewmap.AddText(relx=0.5, rely=0.1, text=\"Batch Map Example\", textsize=0.06)\n\n#finally view the map\nnewmap.ViewMap()\n"},{"alpha_fraction":0.7129316329956055,"alphanum_fraction":0.7379823923110962,"avg_line_length":36.82051467895508,"blob_id":"53c540fea358f30132c7d71d08a4d2a547f29816","content_id":"01852ee7ebdd1c4b837a360dd31a16827aeb7e97","detected_licenses":["MIT","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1477,"license_type":"permissive","max_line_length":256,"num_lines":39,"path":"/GeoVis/examples/pointmap_example.py","repo_name":"priyankark/LSI","src_encoding":"UTF-8","text":"\"\"\"\nExample Script for the Python Geographic Visualizer (GeoVis)\nhttps://github.com/karimbahgat/geovis\n\"\"\"\n\n#importing geovis from temporary location\nTEMP_GEOVIS_FOLDER = r\"C:\\Users\\BIGKIMO\\Documents\\GitHub\\geovis\"\nimport sys\nsys.path.append(TEMP_GEOVIS_FOLDER)\nimport geovis\n\n############\n#SETUP\n############\n#set rendering options\ngeovis.SetRenderingOptions(renderer=\"aggdraw\", numpyspeed=True, reducevectors=False)\n#create map\ngeovis.SetMapBackground(geovis.Color(\"blue\", brightness=0.9))\ngeovis.SetMapZoom(x2x=[-120,40],y2y=[-60,20])\nnewmap = geovis.NewMap()\n\n############\n#LOAD AND SYMBOLIZE LAYERS\n############\ncountrylayer = geovis.Layer(filepath=r\"D:\\Test Data\\necountries\\necountries.shp\", fillcolor=geovis.Color(\"yellow\",brightness=0.8))\npointlayer = geovis.Layer(filepath=r\"D:\\Test Data\\GTD_Georef\\gtd_georef.shp\", symbolizer=\"square\")\npointlayer.AddClassification(symboltype=\"fillcolor\", valuefield=\"nwound\", symbolrange=[geovis.Color(\"white\"),geovis.Color(\"red\", intensity=0.9, brightness=0.9),geovis.Color(\"red\", intensity=0.9, brightness=0.5)], classifytype=\"natural breaks\", nrclasses=3)\npointlayer.AddClassification(symboltype=\"fillsize\", valuefield=\"nwound\", symbolrange=[0.3,2.8], classifytype=\"natural breaks\", nrclasses=3)\n\n############\n#RENDER TO MAP\n############\n#add layers to map\nnewmap.AddToMap(countrylayer)\nnewmap.AddToMap(pointlayer)\n#add legend\nnewmap.AddLegend(pointlayer, upperleft=(0.5,0.7), bottomright=(0.9,0.9))\n#view map\nnewmap.ViewMap()\n\n\n"},{"alpha_fraction":0.7591887712478638,"alphanum_fraction":0.7656383514404297,"avg_line_length":52.39004135131836,"blob_id":"9956b63386e45f642ec5a0930455cf0572dfe382","content_id":"95c482059a4c6e38e9dd6896e5d75b2f444ce490","detected_licenses":["MIT","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":12871,"license_type":"permissive","max_line_length":722,"num_lines":241,"path":"/GeoVis/README.md","repo_name":"priyankark/LSI","src_encoding":"UTF-8","text":"# Python Geographic Visualizer (GeoVis)\n\n\n\n**Version: 0.2.0**\n\n**Author: [Karim Bahgat](https://uit.academia.edu/KarimBahgat)**\n\n\n## Introduction\nPython Geographic Visualizer (GeoVis) is a standalone geographic visualization module for the Python programming language intended for easy everyday-use by novices and power-programmers alike. It has one-liners for quickly visualizing a shapefile, building and styling basic maps with multiple shapefile layers, and/or saving to imagefiles. Uses the built-in Tkinter or other third-party rendering modules to do its main work. The current version is functional, but should be considered a work in progress with potential bugs, so use with care. For now, only visualizes shapefiles that are in lat/long unprojected coordinate system. \n\n\n## Latest News\n\n- April 15, 2014: New v0.2.0 released, major update. \n - New features:\n - Symbolize shapefiles based on their attributes and add legend (categorical, equal classes, equal interval, natural breaks)\n - Choose between multiple symbolizer icons: circle, square, pyramid\n - Zoom the map to a region of interest\n - Manually draw basic geometries and write text on map\n - Functions that allow mid-script interactive user-inputs\n - Support for the Pillow version of PIL, and improved PIL quality by using antialiasing\n - Changed to MIT license to be more contributor friendly\n - Incompatible changes with previous version\n - Make and style a layer, then add to map, instead of adding to map directly from file\n - Color now has to be explicitly set and is no longer random by default\n - Support for PyCairo has been temporarily discontinued due to some difficulties, so is likely to have errors\n - Errors fixed\n - Fixed error with interactively saving image from the viewer window in PIL mode. \n- 23 Feb. 2014: A little too quick on the trigger finger for the first release. \n - Discovered and fixed a crucial import error. - Also expanded the README file and converted it to markdown, and added autogenerated Wiki documentation. \n- 21 Feb. 2014: First rough version of code released (v0.1.0).\n - Basic functionality\n - One liners for shapefile viewing and map saving\n - Multiple layers custom map creation\n - Customize each shapefile with colors and fillsize\n\n\n## Table of Contents\n- [About](#about)\n - [Dependencies](#dependencies)\n - [System Compatibility](#system-compatibility)\n - [License](#license)\n- [Getting Started](#getting-started)\n - [Installation](#installation)\n - [Importing](#importing)\n - [Instant Mapping](#instant-mapping)\n - [Customized Maps](#customized-maps)\n- [Advanced Usage](#advanced-usage)\n - [Choosing Your Renderer](#choosing-your-renderer)\n - [Zooming In](#zooming-in)\n - [Playing With Colors](#playing-with-colors)\n - [Batch Map Creation](#batch-map-creation)\n- [Help and Documentation](#help-and-documentation)\n- [Contributing](#contributing)\n- [Thanks To](#thanks-to)\n\n\n## About\n\n### Dependencies\nTechnically speaking, GeoVis has no external dependencies, but it is highly recommended that you install the [Aggdraw](http://effbot.org/zone/aggdraw-index.htm), [PIL](http://www.pythonware.com/products/pil/) or [PyCairo](http://cairographics.org/pycairo/) renderer libraries to do the rendering. GeoVis automatically detects which renderer module you have and uses the first it finds in the following order (aggdraw, PIL, pycairo). If you wish to manually choose a different renderer this has to be specified for each session. If none of these are available then GeoVis will default to using the built-in Tkinter Canvas as its renderer, but due to major limitations this is not recommended for viewing larger shapefiles. \n\n### System Compatibility\nShould work on Python version 2.x and Windows. Has not yet been tested on Python 3.x or other OS systems.\n\n### License\nContributors are wanted and needed, so this code is free to share, use, reuse,\nand modify according to the MIT license, see license.txt\n\n\n## Getting Started\n\n### Installation\n1. Download the GeoVis repository folder from GitHub.\n2. Either a or b:\n - a) Place the geovis folder in you Python site-packages folder for a \"permanent\" installation.\n - b) Or begin each session by typing `import sys` and `sys.path.append(\"folder path where the geovis folder is located\")`.\n\n### Importing\nAssuming you have already installed it as described in the Installation section, GeoVis is imported as:\n\n```python\nimport geovis\n```\n\nTo begin using geovis either check out the full list of commands in the [the USER_MANUAL](../master/USER_MANUAL.md), or keep reading below for a basic introduction. \n\n### Instant Mapping\nIf you are simply wanting to inspect some shapefile interactively, or for seeing how your processed shapefile turned out, then you do this with a simple one-liner:\n\n```python\ngeovis.ViewShapefile(\"C:/shapefile.shp\")\n```\n\nIf you quickly want to show someone else your shapefile over email or on a forum you can just as easily save you map to an image either by clicking the \"save image\" button in the interactive window viewer or with the following line:\n\n```python\ngeovis.SaveShapefileImage(\"C:/shapefile.shp\",\n savepath=\"C:/output_picture.png\")\n```\n\n### Customized Maps\nIt is also possible to build your map from scratch in order to create a more visually appealing map. \n\nFirst setup and create a newmap instance:\n\n```python\ngeovis.SetMapDimensions(width=4000, height=2000)\ngeovis.SetMapBackground(geovis.Color(\"blue\")\nnewmap = geovis.NewMap()\n```\n\nNext, each shapefile has to be loaded and symbolized into layer instances:\n\n```python\npolylayer = geovis.Layer(polypath, fillcolor=geovis.Color(\"random\"))\npointlayer = geovis.Layer(pointpath, fillcolor=geovis.Color(\"random\"))\n```\n\nIf you wish to, you can also visualize the underlying attributes of your layer by adding one or more classifications. For instance, out point layer can be made to vary from small to large size and green to red color based on its \"population\" attribute:\n\n```python\npointlayer.AddClassification(symboltype=\"fillsize\", valuefield=\"population\", symbolrange=[0.01,0.2], classifytype=\"natural breaks\", nrclasses=5)\npointlayer.AddClassification(symboltype=\"fillcolor\", valuefield=\"population\", symbolrange=[geovis.Color(\"green\"),geovis.Color(\"red\")], classifytype=\"natural breaks\", nrclasses=5)\n```\n\nIn which case you will probably want to add a legend so that you can see which symbols represent which values. Let's place it in the top left corner of the map:\n\n```python\npointlayer.AddLegend(layer=pointlayer, upperleft=[0.01,0.01], bottomright=[0.3,0.3])\n```\n\nFinally, render the layers to the newmap instance:\n\n```python\nnewmap.AddToMap(polylayer)\nnewmap.AddToMap(pointlayer)\n```\n\nFor a finishing touch, add a top-centered map title:\n\n```python\nnewmap.AddText(relx=0.5, rely=0.01, text=\"Example Map Title\", textsize=0.10, textanchor=\"n\")\n```\n\nAnd save the map:\n\n```python\nnewmap.SaveMap(\"C:/Users/BIGKIMO/Desktop/custombuiltmap.png\")\n```\n\n\n## Advanced Usage\n\n### Choosing Your Renderer\nIf you have more than one renderer and you want to choose which one to use, for instance PIL, you must do this at the beginning of each session (also, if you're going for maximum speed/less quality then enable reducing the number of vectors while you're at it, though this is not recommended for line-shapefiles):\n\n```python\ngeovis.SetRenderingOptions(renderer=\"PIL\", reducevectors=True)\n```\n\n### Zooming In\nBy default, the map you create will show the entire world. To zoom the map to a particular area or region of interest you simply set the zoomextents of the map, which must be done before you add your layers:\n\n```python\ngeovis.SetMapZoom(x2x=[-90,90], y2y=[-45,45])\n```\n\n### Playing With Colors\nThere are several ways to play with the colors in your map. The most basic stylizing tool you will want to know about is the Color creator (a wrapper around [Valentin Lab's Colour module](https://pypi.python.org/pypi/colour) with added convenience functionality). You can either create a random color:\n\n```python\nrandomcolor = geovis.Color(\"random\")\n```\n\nOr you can create a specific color the way you imagine it in your head by writing the color name and optionally tweaking the color intensity and brightness (float value between 0 and 1). Let's create a strong (high intensity) but dark (low brightness) red:\n\n```python\nstrongdarkred = geovis.Color(\"red\", intensity=0.8, brightness=0.2)\n```\n\nAlternatively, instead of creating a very specific color you can create a random color that still keeps within certain limits. For instance, specifying a low brightness value and low intensity value but not specifying a color name will produce a random matte-looking color. Better yet, you can set the style argument to \"matte\" (among many other style names, see the documentation for the full list) which automatically chooses the brightness and intensity for you:\n\n```python\nrandommattecolor = geovis.Color(style=\"matte\")\n```\n\nAssuming you now know how to set your own colors or color styles, these colors are useful since they can be used to specify the color of any number of symbol options passed as keyword arguments to GeoVis' various rendering functions (see the documentation for a full list of changable symbol options). For instance, let's save a shapefile image as before, but this time set the fillcolor of the shapefile polygons/lines/circles to our strong-dark-red that we defined previously. In addition we will increase the outline width to match the strong fillcolor (we leave the outline *color* to its defaul black since this fits with the map):\n\n```python\ngeovis.SaveShapefileImage(\"C:/shapefile.shp\",\n savepath=\"C:/output_picture.png\",\n fillcolor=strongdarkred,\n outlinewidth=5)\n```\n\n### Batch Map Creation\nSometimes it is necessary to quickly create a gallery of images of all your shapefiles in a given directory. GeoVis provides a general utility tool that can help you do this; it loops through, parses, and returns the foldername, filename, and extension of all shapefiles in a folder tree, which in turn can be used as input for the SaveShapefileImage function. So for instance we may write it as:\n\n```python\nfor eachfolder, eachshapefile, eachfiletype in geovis.ShapefileFolder(r\"D:\\Test Data\\GPS\"):\n shapefilepath = eachfolder+eachshapefile+eachfiletype\n savetopath = \"C:/Users/BIGKIMO/Desktop/mapgallery/\"+eachshapefile+\".png\"\n geovis.SaveShapefileImage(shapefilepath, savetopath)\n```\n\nThe filename, parent-folder, and file extension can be played around with to do many other batch operations as well, such as placing each map image next to (in the same folder as) each shapefile. \n\n\n## Help and Documentation\nThis brief introduction has only covered the most essential functionality of GeoVis. \nIf you need more information about or are experiencing problems with a particular function you can look up the full documentation of available functions, classes, and arguments by typing `help(geovis)`, or checking out [the USER_MANUAL](../master/USER_MANUAL.md).\n\nIf you still need help, you can either [submit an issue](https://github.com/karimbahgat/geovis/issues) here on GitHub, or contact me directly at: karim.bahgat.norway@gmail.com\n\n\n## Contributing\nI welcome any efforts at contributing to this project. Below are a list of current problems and limitations that I hope to change in future releases:\n\n- The shapefiles have to be in lat/long coordinates (i.e. unprojected) in order to be displayed correctly. That is, GeoVis does not yet handle projections or coordinate transformation.\n- Currently, zooming to a local scale can be made much more efficient by using spatial indexing such as QuadTree...\n- I have not yet figured out how to pass PyCairo rendered images to the Tkinter window for viewing, so for now it results in an error. \n- Currently has not been tested with, and probably won't work in Python 3.x due to syntax changes, so should look into fixing this. \n\n---\n\n---\n\n---\n\n### Thanks To\nGeoVis could not have been accomplished without the excellent work of other open-source modules which it uses behind-the-scenes: \n\n- Several contributors over at Stackoverflow for their help with various rendering related problems and optimizations.\n- Various bloggers whose code examples have in some cases been used directly in GeoVis (these parts of the code are marked with the original source)\n- For shapefile reading it uses a modified version of \n[Joel Lawhead's PyShp module](http://code.google.com/p/pyshp/). \n- For color-wizardry it builds on and expands [Valentin Lab's Colour module](https://pypi.python.org/pypi/colour). \n- And offcourse it relies on the rendering of whichever renderer you are using. \n\n"},{"alpha_fraction":0.646774172782898,"alphanum_fraction":0.64838707447052,"avg_line_length":35.47058868408203,"blob_id":"92496e1118df4bd7eacc7c14a84301760440c07c","content_id":"50a1584eb506f9c7d4b86cb9027e29d991c7065d","detected_licenses":["MIT","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":620,"license_type":"permissive","max_line_length":99,"num_lines":17,"path":"/GeoVis/developers/Auto document/create_docu_developers.py","repo_name":"priyankark/LSI","src_encoding":"UTF-8","text":"import sys\nsys.path.append(r\"C:\\Users\\BIGKIMO\\Dropbox\\Work\\Research\\Software\\Various Python Libraries\\GitDoc\")\nimport gitdoc\n\nFILENAME = \"geovis\"\nFOLDERPATH = r\"C:\\Users\\BIGKIMO\\Documents\\GitHub\\geovis\"\nOUTPATH = r\"C:\\Users\\BIGKIMO\\Documents\\GitHub\\geovis\\developers\"\nOUTNAME = \"dev_documentation\"\nEXCLUDETYPES = [\"module\"]\ngitdoc.Module2GitDown(FOLDERPATH,\n filename=FILENAME,\n outputfolder=OUTPATH,\n outputname=OUTNAME,\n excludetypes=EXCLUDETYPES,\n excludesecret=False,\n excludesupersecret=False\n )\n"},{"alpha_fraction":0.6734254956245422,"alphanum_fraction":0.6777265667915344,"avg_line_length":28.0625,"blob_id":"50384f73f6e669de1a5d8ff5a42455821f2b6dff","content_id":"d67cda3aaf339985d7c81155fea7a6e0c4fc2586","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3255,"license_type":"no_license","max_line_length":96,"num_lines":112,"path":"/SimpleGui.pyw","repo_name":"priyankark/LSI","src_encoding":"UTF-8","text":"import sys\nfrom PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\nfrom ExtractLine import *\nfrom qgis.core import *\nfrom qgis.gui import *\nimport GlobalValuesLib\nimport os\nimport threading\n\nqgis_prefix = os.getenv(\"QGISHOME\")\n\nclass MainExecutionThread(QThread):\n def __init__(self):\n QThread.__init__(self)\n\n def __del__(self):\n self.wait()\n\n def run(self):\n convertSHP(DXF_fileName)\n\n\n\n\n\nclass SimpleGui(QWidget):\n\n def __init__(self,parent=None):\n super(SimpleGui,self).__init__(parent)\n self.setGeometry(65,50,500,500)\n selectionBar=QHBoxLayout()\n self.mainExecutionThread=MainExecutionThread()\n\n self.btnDXF=QPushButton(\"Select .dxf file\",self)\n self.showSelection=QLineEdit()\n self.btnDXF.clicked.connect(lambda:self.getfiles(self.showSelection))\n self.btnExtractLines=QPushButton(\"Extract\")\n #self.btnExtractLines.clicked.connect(lambda:self.handleExtract(self.viewStatus))\n self.btnExtractLines.clicked.connect(lambda:self.handleExtract(self.mainExecutionThread))\n self.btnSHP=QPushButton(\"View SHP\")\n\n self.canvas = QgsMapCanvas()\n self.canvas.useImageToRender(False)\n self.btnSHP.clicked.connect(lambda:self.btnSHPFn(self.canvas))\n\n selectionBar.addWidget(self.showSelection)\n selectionBar.addWidget(self.btnDXF)\n selectionBar.addWidget(self.btnExtractLines)\n selectionBarWidget=QWidget()\n selectionBarWidget.setLayout(selectionBar)\n #self.viewStatus=QTextEdit()\n\n #layout.addWidget(self.btnDFX)\n mainLayout=QVBoxLayout()\n mainLayout.addWidget(selectionBarWidget)\n #mainLayout.addWidget(self.viewStatus)\n mainLayout.addWidget(self.canvas)\n mainLayout.addWidget(self.btnSHP)\n\n self.setLayout(mainLayout)\n\n #def handleExtract(self,viewStatus):\n def handleExtract(self,mainExecutionThread):\n #convertSHP(DXF_fileName,viewStatus)\n mainExecutionThread.start()\n\n\n def getfiles(self,showSelection):\n dlg=QFileDialog()\n dlg.setFileMode(QFileDialog.AnyFile)\n dlg.setFilter(\"DXF Files (*.dxf)\")\n filenames=QStringList()\n\n if dlg.exec_():\n filename=dlg.selectedFiles()\n showSelection.setText(str(filename[0]))\n global DXF_fileName\n DXF_fileName=str(filename[0])\n print DXF_fileName\n\n def btnSHPFn(self,canvas):\n\n file=open(GlobalValuesLib.SHP_location,'r')\n fileInfo = QFileInfo(GlobalValuesLib.SHP_location)\n layer = QgsVectorLayer(GlobalValuesLib.SHP_location,\"testlayer_shp\",\"ogr\")\n if not layer.isValid():\n print \"Layer failed to load\"\n QgsMapLayerRegistry.instance().addMapLayer(layer);\n canvas.setExtent(layer.extent())\n cl = QgsMapCanvasLayer(layer)\n layers = [cl]\n canvas.setLayerSet(layers)\n\n\n\n\n print GlobalValuesLib.SHP_location\n #geovis.ViewShapefile(GlobalValuesLib.SHP_location)\n\n\ndef main():\n app=QApplication(sys.argv)\n QgsApplication.setPrefixPath(\"/usr\", True)\n QgsApplication.initQgis()\n QApplication.processEvents()\n simpleGUI=SimpleGui()\n simpleGUI.show()\n sys.exit(app.exec_())\n\nif __name__==\"__main__\":\n main()\n"},{"alpha_fraction":0.8035264611244202,"alphanum_fraction":0.8035264611244202,"avg_line_length":38.70000076293945,"blob_id":"686a92eac5f43e77dbb6aad4285ad8be65685bef","content_id":"eba3146bb7d1b9fd928dd6c77d2563f8e537ca2e","detected_licenses":["MIT","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":397,"license_type":"permissive","max_line_length":117,"num_lines":10,"path":"/GeoVis/developers/Auto document/create_docu_users.py","repo_name":"priyankark/LSI","src_encoding":"UTF-8","text":"import sys\nsys.path.append(r\"C:\\Users\\BIGKIMO\\Documents\\GitHub\\GitDoc\")\nimport gitdoc\n\nFILENAME = \"geovis\"\nFOLDERPATH = r\"C:\\Users\\BIGKIMO\\Documents\\GitHub\\geovis\"\nOUTPATH = r\"C:\\Users\\BIGKIMO\\Documents\\GitHub\\geovis\"\nOUTNAME = \"USER_MANUAL\"\nEXCLUDETYPES = [\"module\",\"variable\"]\ngitdoc.DocumentModule(FOLDERPATH,filename=FILENAME,outputfolder=OUTPATH,outputname=OUTNAME,excludetypes=EXCLUDETYPES)\n"},{"alpha_fraction":0.8021978139877319,"alphanum_fraction":0.8021978139877319,"avg_line_length":27.4375,"blob_id":"f93f7f4d12cc706fe3ef1ff1936b494dedd50009","content_id":"b29291d80a332cd5a8c36da40c086b9193e53b6c","detected_licenses":["MIT","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":455,"license_type":"permissive","max_line_length":91,"num_lines":16,"path":"/GeoVis/examples/quickview_example.py","repo_name":"priyankark/LSI","src_encoding":"UTF-8","text":"\"\"\"\nExample Script for the Python Geographic Visualizer (GeoVis)\nhttps://github.com/karimbahgat/geovis\n\"\"\"\n\n#importing geovis from temporary location\nTEMP_GEOVIS_FOLDER = r\"C:\\Users\\BIGKIMO\\Documents\\GitHub\\geovis\"\nimport sys\nsys.path.append(TEMP_GEOVIS_FOLDER)\nimport geovis\n\n#setting up for speed\ngeovis.SetRenderingOptions(reducevectors=True)\n\n#view shapefile\ngeovis.ViewShapefile(r\"D:\\Test Data\\cshapes\\cshapes.shp\", maptitle=\"ViewShapefile Example\")\n"},{"alpha_fraction":0.6240951418876648,"alphanum_fraction":0.6415029168128967,"avg_line_length":44.606300354003906,"blob_id":"aaed22cb16f16504cb2a5911ae9ec17d7ffe9115","content_id":"fe31e3628b02a2949ead9fd16aa724f2b8e57bc2","detected_licenses":["MIT","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5802,"license_type":"permissive","max_line_length":206,"num_lines":127,"path":"/GeoVis/geovis/guihelper.py","repo_name":"priyankark/LSI","src_encoding":"UTF-8","text":"\n\"\"\"\nalmost good\nbut not sure how useful all of this is\ngets buggy/leans to one direction when using excessive padding isntead of just disappearing\nand seems that same can be done using relx/rely and nested widgets...?\nmaybe the charm is to not use the subpartition (only if you really cant do nesting)\nbut instead call PartitionSpace simply as an aid when doing relx/rely inside a nested widget\n\"\"\"\n\nclass Partition:\n def __init__(self, center, partitionwidth, partitionheight, direction):\n if direction == \"vertical\":\n center = (center[1],center[0])\n partitionwidth,partitionheight = partitionheight,partitionwidth\n self.center = center\n self.width = partitionwidth\n self.height = partitionheight\n midx,midy = center\n halfx = partitionwidth/2.0\n halfy = partitionheight/2.0\n self.nw = (midx-halfx, midy-halfy)\n self.n = (midx, midy-halfy)\n self.ne = (midx+halfx, midy-halfy)\n self.e = (midx+halfx, midy)\n self.se = (midx+halfx, midy+halfy)\n self.s = (midx, midy+halfy)\n self.sw = (midx-halfx, midy+halfy)\n self.w = (midx-halfx, midy)\n def __repr__(self):\n visualstring = \"partition:\\n\"\n visualstring += \"%s \\t %s \\t %s \\n\" % tuple([[str(nr)[:5] for nr in pair] for pair in (self.nw,self.n,self.ne)])\n visualstring += \"%s \\t %s \\t %s \\n\" % tuple([[str(nr)[:5] for nr in pair] for pair in (self.w,self.center,self.e)])\n visualstring += \"%s \\t %s \\t %s\" % tuple([[str(nr)[:5] for nr in pair] for pair in (self.sw,self.s,self.se)])\n return visualstring\n def SubPartition(self, partitions, padx, pady, direction=\"horizontal\"):\n \"only use if you really need to avoid nested widgets, eg if you need to structure your widgets over a large background image bc a background widget to nest them in would cover up parts of the image\"\n xtox = (self.w[0], self.e[0])\n ytoy = (self.n[1], self.s[1])\n return PartitionSpace(xtox, ytoy, partitions, padx, pady, direction)\n\ndef PartitionSpace(xtox, ytoy, partitions, padx, pady, direction=\"horizontal\"):\n startx, endx = xtox\n starty, endy = ytoy\n if direction == \"vertical\":\n startx,starty = starty,startx\n endx,endy = endy,endx\n padx,pady = pady,padx\n #prep\n allwidth = endx-startx\n allheight = endy-starty\n widthafterpad = allwidth-padx*(partitions+1)\n heightafterpad = allheight-pady*2\n partitionwidth = widthafterpad/float(partitions)\n partitionheight = heightafterpad\n #calc\n outpartitions = []\n tempx = startx+padx+partitionwidth/2.0\n tempy = starty+pady+partitionheight/2.0\n for _ in xrange(partitions):\n center = (tempx, tempy)\n outpartitions.append( Partition(center, partitionwidth, partitionheight, direction) )\n tempx += partitionwidth/2.0+padx+partitionwidth/2.0\n return outpartitions\n\nif __name__ == \"__main__\":\n import Tkinter as tk\n import random\n win = tk.Tk()\n testdims = (1100,500)\n testparts = 3\n\n #pixel positions test\n## testpadx,testpady = (90,20)\n## frame = tk.Frame(win, bg=\"red\", width=testdims[0], height=testdims[1])\n## frame.pack()\n## hm = tk.Label(frame, bg=\"yellow\")\n## hm.place(x=200,y=200)\n## for partition in PartitionSpace(200,testdims[0],0,testdims[1],testparts,testpadx,testpady):\n## but = tk.Label(frame, bg=\"blue\")\n## x,y = partition.center\n## print partition.w, x, partition.e\n## but.place(x=x, y=y, width=round(partition.width), height=round(partition.height), anchor=\"center\")\n\n #rel test\n testpadx,testpady = (0.01,0.01)\n frame = tk.Frame(win, bg=\"red\", width=testdims[0], height=testdims[1])\n frame.pack(fill=\"both\")\n hm = tk.Label(frame, bg=\"yellow\")\n hm.place(relx=0.3,rely=0.5, anchor=\"ne\")\n #horiz partitions\n partitions = PartitionSpace((0.3,1),(0.7,1),testparts,testpadx,testpady)\n subpart = partitions[0]\n for partition in partitions:\n but = tk.Label(frame, bg=\"blue\")\n x,y = partition.center\n but.place(relx=x, rely=y, relwidth=partition.width, relheight=partition.height, anchor=\"center\")\n #split each down the middle\n subpartitions = partition.SubPartition(2, 0.01, 0.02)\n #left side\n subpart = subpartitions[0]\n but = tk.Label(frame, bg=\"green\")\n x,y = subpart.center\n but.place(relx=x, rely=y, relwidth=subpart.width, relheight=subpart.height, anchor=\"center\")\n #then try downwards partition\n subsubpartitions = subpart.SubPartition(5, 0.006, 0.006, direction=\"vertical\")\n for subpart in subsubpartitions:\n but = tk.Label(frame, bg=\"red\")\n x,y = subpart.center\n but.place(relx=x, rely=y, relwidth=subpart.width, relheight=subpart.height, anchor=\"center\")\n #right side\n subpart = subpartitions[1]\n but = tk.Label(frame, bg=\"green\")\n x,y = subpart.center\n but.place(relx=x, rely=y, relwidth=subpart.width, relheight=subpart.height, anchor=\"center\")\n #then try downwards partition\n subsubpartitions = subpart.SubPartition(5, 0.006, 0.006, direction=\"vertical\")\n for subpart in subsubpartitions:\n but = tk.Label(frame, bg=\"white\", text=\"text\")\n x,y = subpart.center\n but.place(relx=x, rely=y, relwidth=subpart.width, relheight=subpart.height, anchor=\"center\")\n #finalize window\n coordsdisplay = tk.Label(win, text=\"mousepos\")\n coordsdisplay.place(relx=0.01, rely=0.01)\n def displaymousecoords(event):\n coordsdisplay[\"text\"] = \"%s,%s (%s,%s)\" %(event.x,event.y, event.x/float(win.winfo_width()),event.y/float(win.winfo_height()))\n win.bind(\"<Motion>\", displaymousecoords)\n win.mainloop()\n \n"},{"alpha_fraction":0.6251082420349121,"alphanum_fraction":0.635188639163971,"avg_line_length":39.21890640258789,"blob_id":"074b7e8f3dec48b3c817056b9507b333127a455e","content_id":"4a455ca138c14674ff5b6dbdb447212e4b6847a3","detected_licenses":["MIT","LicenseRef-scancode-unknown-license-reference"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":16170,"license_type":"permissive","max_line_length":218,"num_lines":402,"path":"/GeoVis/geovis/listy.py","repo_name":"priyankark/LSI","src_encoding":"UTF-8","text":"#IMPORTS\nimport sys, os, operator, itertools, random, math\n\n#GLOBALS\nNONEVALUE = None\n\n\n\"\"\"\nSEE ALSO\nhttp://code.activestate.com/recipes/189971-basic-linear-algebra-matrix/\nhttp://users.rcn.com/python/download/python.htm\n\"\"\"\n\n\n#FUNCTIONS\ndef Resize(rows, newlength, stretchmethod=\"not specified\", gapvalue=\"not specified\"):\n \"front end used by user, determines if special nested list resizing or single list\"\n if isinstance(rows[0], (list,tuple)):\n #input list is a sequence of list (but only the first item is checked)\n #needs to have same nr of list items in all sublists\n crosssection = itertools.izip(*rows)\n grad_crosssection = [ _Resize(spectrum,newlength,stretchmethod,gapvalue) for spectrum in crosssection ]\n gradient = [list(each) for each in itertools.izip(*grad_crosssection)]\n return gradient\n else:\n #just a single list of values\n return _Resize(rows, newlength, stretchmethod, gapvalue)\ndef Transpose(listoflists):\n \"must get a 2d grid, ie a list of lists, with all sublists having equal lengths\"\n transposed = [list(each) for each in itertools.izip(*listoflists)]\n return transposed\n\n#BUILTINS \ndef _Resize(rows, newlength, stretchmethod=\"not specified\", gapvalue=\"not specified\"):\n \"behind the scenes, does the actual work, only for a single flat list\"\n #return input as is if no difference in length\n if newlength == len(rows):\n return rows\n #set gap\n if gapvalue == \"not specified\":\n gapvalue = NONEVALUE\n #set auto stretchmode\n if stretchmethod == \"not specified\":\n if isinstance(rows[0], (int,float)):\n stretchmethod = \"interpolate\"\n else:\n stretchmethod = \"duplicate\"\n #reduce newlength \n newlength -= 1\n #assign first value\n outlist = [rows[0]]\n relspreadindexgen = (index/float(len(rows)-1) for index in xrange(1,len(rows))) #warning a little hacky by skipping first index cus is assigned auto\n relspreadindex = next(relspreadindexgen)\n spreadflag = False\n for each in xrange(1, newlength):\n #relative positions\n rel = each/float(newlength)\n relindex = (len(rows)-1) * rel\n basenr,decimals = str(relindex).split(\".\")\n relbwindex = float(\"0.\"+decimals)\n #determine equivalent value\n if stretchmethod==\"interpolate\":\n maybecurrelval = rows[int(relindex)]\n if maybecurrelval != gapvalue:\n #ALMOST THERE BUT NOT QUITE\n #DOES SOME WEIRD BACKTRACKING\n #...\n currelval = rows[int(relindex)]\n #make sure next value to interpolate to is valid\n testindex = int(relindex)+1\n while testindex < len(rows)-1 and rows[testindex] == gapvalue:\n #if not then interpolate until next valid item\n testindex += 1\n nextrelval = rows[testindex]\n #assign value\n relbwval = currelval + (nextrelval - currelval) * relbwindex #basenr pluss interindex percent interpolation of diff to next item\n elif stretchmethod==\"duplicate\":\n relbwval = rows[int(round(relindex))] #no interpolation possible, so just copy each time\n elif stretchmethod==\"spread\":\n if rel >= relspreadindex:\n spreadindex = int(len(rows)*relspreadindex)\n relbwval = rows[spreadindex] #spread values further apart so as to leave gaps in between\n relspreadindex = next(relspreadindexgen)\n else:\n relbwval = gapvalue\n #assign each value\n outlist.append(relbwval)\n #assign last value\n outlist.append(rows[-1])\n return outlist\ndef _InterpolateValue(value, otherinput, method):\n \"method can be linear, IDW, etc...\"\n pass\n\n#CLASSES\nclass _1dData:\n \"\"\"\nMost basic of all list types. Contains data values but is just a meaningless arbitrary list if not embedded in some other list type.\nIt can be embedded in a 2dsurfacegrid to represent a theme located along horizantal x lines.\nOr embedded in a 4dtimegrid to represent a theme changing over time, without any spatial properties.\nMaybe same as Listy below??\n\"\"\"\n pass\nclass _Cell:\n def __init__(self, xpos, ypos, value):\n self.x = xpos\n self.y = ypos\n self.value = value\nclass _2dSurfaceGrid:\n \"horizontal lines up and down along y axis\"\n #BUILTINS\n def __init__(self, twoDlist=None, emptydims=\"not specified\"):\n #add some error checking...\n #...\n if not twoDlist:\n if emptydims == \"not specified\":\n emptydims = (50,50)\n width,height = emptydims\n twoDlist = [[NONEVALUE for _ in xrange(width)] for _ in xrange(height)]\n self.grid = Listy(*twoDlist)\n self.height = len(self.grid.lists)\n self.width = len(self.grid.lists[0])\n self.knowncells = self._GetKnownCells()\n def __iter__(self):\n for ypos, horizline in enumerate(self.grid.lists):\n for xpos, xpoint in enumerate(horizline):\n yield _Cell(xpos, ypos, value=xpoint)\n def __str__(self):\n return str(self.grid)\n #FUNCTIONS\n def RandomPoints(self, value=\"random\", valuerange=\"not specified\", nrpoints=\"not specified\"):\n if nrpoints == \"not specified\":\n nrpoints = int(self.width*self.height*0.10) #10 percent of all cells\n if valuerange == \"not specified\":\n valuerange = (0,250)\n randomvalue = False\n if value == \"random\":\n randomvalue = True\n for _ in xrange(nrpoints):\n if randomvalue:\n value = random.randrange(*valuerange)\n xindex = random.randrange(self.width)\n yindex = random.randrange(self.height)\n self.grid.lists[yindex][xindex] = value\n self.knowncells = self._GetKnownCells()\n def Interpolate(self, method=\"IDW\", **options):\n \"ie fill any gaps in grid with interpolation\"\n if method == \"IDW\":\n self._IDW(options)\n def ChangeValues(self, expression):\n pass\n def SelectQuery(self, query):\n pass\n def Show(self):\n import numpy, PIL, PIL.Image, PIL.ImageTk, PIL.ImageDraw\n import Tkinter as tk\n import colour\n win = tk.Tk()\n nparr = numpy.array(self.grid.lists)\n npmin = numpy.min(nparr)\n npmax = numpy.max(nparr)\n minmaxdiff = npmax-npmin\n colorstops = [colour.Color(\"red\").rgb,colour.Color(\"yellow\").rgb,colour.Color(\"green\").rgb]\n colorstops = [list(each) for each in colorstops]\n colorgrad = Listy(*colorstops)\n colorgrad.Convert(\"250*value\")\n colorgrad.Resize(int(minmaxdiff))\n valuerange = range(int(npmin),int(npmax))\n colordict = dict(zip(valuerange,colorgrad.lists))\n print len(valuerange),len(colorgrad.lists),len(colordict)\n print \"minmax\",npmin,npmax\n for ypos,horizline in enumerate(self.grid.lists):\n for xpos,value in enumerate(horizline):\n relval = value/float(npmax)\n self.grid.lists[ypos][xpos] = colorgrad.lists[int((len(colorgrad.lists)-1)*relval)]\n nparr = numpy.array(self.grid.lists,\"uint8\")\n print \"np shape\",nparr.shape\n img = PIL.Image.fromarray(nparr)\n drawer = PIL.ImageDraw.ImageDraw(img)\n size = 3\n for knowncell in self.knowncells:\n x,y = (knowncell.x,knowncell.y)\n drawer.ellipse((x-size,y-size,x+size,y+size),fill=\"black\")\n img.save(\"C:/Users/BIGKIMO/Desktop/test.png\")\n tkimg = PIL.ImageTk.PhotoImage(img)\n lbl = tk.Label(win, image=tkimg)\n lbl.pack()\n win.mainloop()\n #INTERNAL USE ONLY\n def _GetKnownCells(self):\n knowncellslist = []\n for cell in self:\n if cell.value != NONEVALUE:\n knowncellslist.append(cell)\n return knowncellslist\n def _IDW(self, options):\n #retrieve input options\n neighbours = options.get(\"neighbours\",int(len(self.knowncells)*0.10)) #default neighbours is 10 percent of known points\n sensitivity = options.get(\"sensitivity\",3) #same as power, ie that high sensitivity means much more effect from far away points\n #some defs\n def _calcvalue(unknowncell, knowncells):\n weighted_values_sum = 0.0\n sum_of_weights = 0.0\n for knowncell in knowncells:\n weight = ((unknowncell.x-knowncell.x)**2 + (unknowncell.y-knowncell.y)**2)**(-sensitivity/2.0)\n sum_of_weights += weight\n weighted_values_sum += weight * knowncell.value\n return weighted_values_sum / sum_of_weights\n #calculate value\n for unknowncell in self:\n if unknowncell.value == NONEVALUE:\n #only calculate for unknown points\n self.grid.lists[unknowncell.y][unknowncell.x] = _calcvalue(unknowncell, self.knowncells)\n \nclass _3dSpaceGrid:\n \"\"\"z axis.\nWorks by rendering one surface at a time, starting with lowest, that way rendering higher up/closer to the eye points on top of lower/further away points which gives a 3d effect.\nJust need to find a way to transform each surface to the way it should look like from different angles.\nNote: Link to a function to create the transform coeffs for PIL's perspective transform: http://stackoverflow.com/questions/14177744/how-does-perspective-transformation-work-in-pil\nOR use ray tracing... http://scratchapixel.com/lessons/3d-basic-lessons/lesson-1-writing-a-simple-raytracer/source-code/\nALSO see basic 3d equations http://www.math.washington.edu/~king/coursedir/m445w04/notes/vector/equations.html\n\"\"\"\n pass\nclass _4dTimeGrid:\n \"\"\"time axis\nexample:\nfor 3dtime in 4dtimegrid:\n #loops all 3d spaces at different points in time\n for 2ddepth in 3dtime:\n #loops all 2d surfaces at different altitudes, from low to high\n for 1dheight in 2ddepth:\n #loops all leftright horizantal lines in a 2dgrid\n for datavalue in 1dheight:\n #loops all datavalues for whatever theme in a specific 1dline, in a 2dgrid, at a 3daltitude, at a 4d point in time\n\"\"\"\n pass\n\nclass Listy(list):\n \"A list-type class with extended functionality and methods. These extra features should only be bindings to functions alredy defined in the general listy module and can be used by anyone, not just the Listy class.\"\n def __init__(self, *sequences):\n self.lists = list(sequences)\n self.dtype = \"numbers\" #\"maybe do some automatic dtype detection\"\n def __str__(self):\n maxchar = 50\n printstr = \"[\\n\"\n for eachlist in self.lists:\n if len(str(eachlist)) > maxchar:\n printstr += str(eachlist)[:int(maxchar/2.0)]+\"...\"+str(eachlist)[-int(maxchar/2.0):]+\"\\n\"\n else:\n printstr += str(eachlist)+\"\\n\"\n printstr += \"]\\n\"\n return printstr\n #SHAPE AND ORIENTATION\n def Resize(self, newlength, listdim=None, stretchmethod=\"not specified\", gapvalue=\"not specified\"):\n if listdim:\n #resize only a certain dimension\n self.lists[listdim] = Resize(self.lists[listdim], newlength, stretchmethod, gapvalue)\n else:\n #resize all lists??? experimental...\n self.lists = Resize(self.lists, newlength, stretchmethod, gapvalue)\n def Transpose(self):\n self.lists = Transpose(self.lists)\n def Reshape(self, newshape):\n \"not sure yet how to do...\"\n pass\n def Split(self, groups_or_indexes):\n pass\n #TYPES\n def Convert(self, dataformat):\n \"it does actually work...but floating values will never be shortened bc no exact precision\"\n toplist = self.lists\n def execfunc(code, toplist, value, index):\n exec(code)\n def recurloop(toplist):\n for index, value in enumerate(toplist):\n if isinstance(value, list):\n listfound = True\n recurloop(value)\n else:\n conversioncode = \"toplist[index] = \"+dataformat\n execfunc(conversioncode, toplist, value, index)\n #begin\n recurloop(toplist)\n #ATTRIBUTES\n @property\n def minval(self):\n pass\n @property\n def maxval(self):\n pass\n @property\n def shape(self):\n return tuple([len(eachlist) for eachlist in self.lists])\n @property\n def structure(self):\n toplist = self.lists\n depth = 0\n spaces = \" \"\n structstring = str(len(toplist))+\"\\n\"\n def recurloop(toplist, structstring, depth, spaces):\n for item in toplist:\n if isinstance(item, list):\n listfound = True\n depth += 1\n structstring += spaces*depth + str(len(item)) + \"\\n\"\n toplist, structstring, depth, spaces = recurloop(item, structstring, depth, spaces)\n depth -= 1\n return toplist, structstring, depth, spaces\n #begin\n item, structstring, depth, spaces = recurloop(toplist, structstring, depth, spaces)\n return structstring\n\nif __name__ == \"__main__\":\n\n print \"\"\n print \"print and shape and resize test\"\n testlist = [random.randrange(50) for e in xrange(random.randrange(31))]\n testlisty = Listy(testlist, testlist)\n print testlisty\n print testlisty.shape\n testlisty.Resize(22, listdim=0)\n print testlisty.shape\n print testlisty\n\n print \"\"\n print \"hierarchical nested list structure test\"\n nestedlists = [[[\"anything\" for e in xrange(random.randrange(500))] for e in xrange(random.randrange(5))] for d in xrange(random.randrange(6))]\n nestedlisty = Listy(*nestedlists)\n print nestedlisty\n print nestedlisty.structure\n\n print \"\"\n print \"transpose test\"\n listoflists = [range(100) for _ in xrange(100)]\n gridlisty = Listy(*listoflists)\n print gridlisty\n gridlisty.Transpose()\n print gridlisty\n\n print \"\"\n print \"fill in blanks test\"\n listholes = [1,2,None,4,None,6]\n gridlisty = Listy(*listholes)\n print gridlisty\n gridlisty.Resize(12, stretchmethod=\"interpolate\")\n print gridlisty\n\n print \"\"\n print \"spread grid test\"\n listoflists = [range(6) for _ in xrange(6)]\n gridlisty = Listy(*listoflists)\n print gridlisty\n #expand sideways (instead of downwards which is default for the multilist resize func)\n gridlisty.Transpose()\n gridlisty.Resize(11, stretchmethod=\"spread\")\n gridlisty.Transpose()\n #resize again to also expand downwards\n gridlisty.Resize(11, stretchmethod=\"spread\")\n print gridlisty\n #finally try interpolating in between\n #THIS PART NOT WORKING PROPERLY, GOING ZIGZAG OVER GAPS\n## gridlisty.Resize(22, stretchmethod=\"interpolate\")\n## print gridlisty\n## gridlisty.Resize(11)\n## print gridlisty\n\n print \"\"\n print \"gridpoints interpolate test\"\n listoflists = [[random.randrange(200) for _ in xrange(10)] for _ in xrange(10)] #[range(10) for _ in xrange(10)]\n templisty = Listy(*listoflists)\n print templisty\n #spread to create holes\n templisty.Transpose()\n templisty.Resize(80, stretchmethod=\"spread\")\n templisty.Transpose()\n templisty.Resize(80, stretchmethod=\"spread\")\n #make into 2dgrid obj\n testgrid = _2dSurfaceGrid(templisty.lists)\n print \"spread done\"#,testgrid\n #interpolate\n testgrid.Interpolate(\"IDW\")\n print \"idw done\"#,testgrid.grid.lists\n #reduce decimals\n ##testgrid.grid.Convert(\"str(round(value,2))\")\n ##print testgrid\n #testgrid.Show()\n\n print \"\"\n print \"randompoints interpolate test\"\n #create empty 2dgrid obj\n testgrid = _2dSurfaceGrid(emptydims=(600,600))\n print \"grid made\"#,testgrid\n #put random points\n testgrid.RandomPoints(nrpoints=20)\n print \"points placed\"#,testgrid\n #interpolate\n import time\n t=time.clock()\n testgrid.Interpolate(\"IDW\", sensitivity=4)\n print time.clock()-t\n print \"idw done\"#,testgrid.grid.lists\n testgrid.Show()\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7139001488685608,\n \"alphanum_fraction\": 0.7408906817436218,\n \"avg_line_length\": 36.9487190246582,\n \"blob_id\": \"ba1bc6b1bcd43002142f775a27f214797c136d9d\",\n \"content_id\": \"bf23b760cc624bcd4123c54f0e95da00d9f3c588\",\n \"detected_licenses\": [\n \"MIT\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1482,\n \"license_type\": \"permissive\",\n \"max_line_length\": 259,\n \"num_lines\": 39,\n \"path\": \"/GeoVis/examples/polygonmap_example.py\",\n \"repo_name\": \"priyankark/LSI\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nExample Script for the Python Geographic Visualizer (GeoVis)\\nhttps://github.com/karimbahgat/geovis\\n\\\"\\\"\\\"\\n\\n#importing geovis from temporary location\\nTEMP_GEOVIS_FOLDER = r\\\"C:\\\\Users\\\\BIGKIMO\\\\Documents\\\\GitHub\\\\geovis\\\"\\nimport sys\\nsys.path.append(TEMP_GEOVIS_FOLDER)\\nimport geovis\\n\\n############\\n#SETUP\\n############\\n#setup rendering options\\ngeovis.SetRenderingOptions(renderer=\\\"aggdraw\\\", numpyspeed=True, reducevectors=False)\\n#create map\\ngeovis.SetMapBackground(geovis.Color(\\\"blue\\\", brightness=0.9))\\ngeovis.SetMapZoom(x2x=[0,180],y2y=[0,90])\\nnewmap = geovis.NewMap()\\n\\n############\\n#LOAD AND SYMBOLIZE LAYERS\\n############\\ncountrylayer = geovis.Layer(filepath=r\\\"D:\\\\Test Data\\\\necountries\\\\necountries.shp\\\")\\ncountrylayer.AddClassification(symboltype=\\\"fillcolor\\\", valuefield=\\\"pop_est\\\", symbolrange=[geovis.Color(\\\"white\\\"),geovis.Color(\\\"red\\\", intensity=0.9, brightness=0.8),geovis.Color(\\\"red\\\", intensity=0.9, brightness=0.5)], classifytype=\\\"natural breaks\\\", nrclasses=3)\\ncountrylayer.AddClassification(symboltype=\\\"outlinewidth\\\", valuefield=\\\"pop_est\\\", symbolrange=[0.05,0.4], classifytype=\\\"natural breaks\\\", nrclasses=3)\\nriverlayer = geovis.Layer(filepath=r\\\"D:\\\\Test Data\\\\lines\\\\ne_50m_rivers_lake_centerlines.shp\\\", fillcolor=geovis.Color(\\\"blue\\\",brightness=0.9))\\n\\n############\\n#RENDER TO MAP\\n############\\n#add layers to map\\nnewmap.AddToMap(countrylayer)\\nnewmap.AddToMap(riverlayer)\\n#add legend\\nnewmap.AddLegend(countrylayer, upperleft=(0.03,0.15), bottomright=(0.6,0.4))\\n#view map\\nnewmap.ViewMap()\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7744807004928589,\n \"alphanum_fraction\": 0.7823936939239502,\n \"avg_line_length\": 29.636363983154297,\n \"blob_id\": \"212c65af62465054fbb79044c811a14b02760ee8\",\n \"content_id\": \"3b03ac8db6f80e41057913532c807f5e18f02c74\",\n \"detected_licenses\": [\n \"MIT\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1011,\n \"license_type\": \"permissive\",\n \"max_line_length\": 121,\n \"num_lines\": 33,\n \"path\": \"/GeoVis/examples/batch_add_to_map_example.py\",\n \"repo_name\": \"priyankark/LSI\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nExample Script for the Python Geographic Visualizer (GeoVis)\\nhttps://github.com/karimbahgat/geovis\\n\\\"\\\"\\\"\\n\\n#importing geovis from temporary location\\nTEMP_GEOVIS_FOLDER = r\\\"C:\\\\Users\\\\BIGKIMO\\\\Documents\\\\GitHub\\\\geovis\\\"\\nimport sys\\nsys.path.append(TEMP_GEOVIS_FOLDER)\\nimport geovis\\n\\n#setting up for speed\\ngeovis.SetRenderingOptions(reducevectors=True)\\n\\n#create map\\nMAPCOLORSTYLE = \\\"pastelle\\\"\\ngeovis.SetMapBackground(geovis.Color(\\\"blue\\\",style=MAPCOLORSTYLE))\\nnewmap = geovis.NewMap()\\n\\n#add base shapefile layer\\nbase_layer = geovis.Layer(\\\"D:\\\\Test Data\\\\Global Subadmins\\\\gadm2.shp\\\")\\nnewmap.AddToMap(base_layer)\\n\\n#overlay with a folder of many shapefiles\\nfor eachfolder, eachfile, eachext in geovis.ShapefileFolder(r\\\"D:\\\\Test Data\\\\DHS GPS\\\"):\\n eachlayer = geovis.Layer(filepath=eachfolder+eachfile+eachext, fillcolor=geovis.Color(\\\"random\\\", style=MAPCOLORSTYLE))\\n newmap.AddToMap(eachlayer)\\n\\n#add title\\nnewmap.AddText(relx=0.5, rely=0.1, text=\\\"Batch Map Example\\\", textsize=0.06)\\n\\n#finally view the map\\nnewmap.ViewMap()\\n\"\n },\n {\n \"alpha_fraction\": 0.7129316329956055,\n \"alphanum_fraction\": 0.7379823923110962,\n \"avg_line_length\": 36.82051467895508,\n \"blob_id\": \"53c540fea358f30132c7d71d08a4d2a547f29816\",\n \"content_id\": \"01852ee7ebdd1c4b837a360dd31a16827aeb7e97\",\n \"detected_licenses\": [\n \"MIT\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1477,\n \"license_type\": \"permissive\",\n \"max_line_length\": 256,\n \"num_lines\": 39,\n \"path\": \"/GeoVis/examples/pointmap_example.py\",\n \"repo_name\": \"priyankark/LSI\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nExample Script for the Python Geographic Visualizer (GeoVis)\\nhttps://github.com/karimbahgat/geovis\\n\\\"\\\"\\\"\\n\\n#importing geovis from temporary location\\nTEMP_GEOVIS_FOLDER = r\\\"C:\\\\Users\\\\BIGKIMO\\\\Documents\\\\GitHub\\\\geovis\\\"\\nimport sys\\nsys.path.append(TEMP_GEOVIS_FOLDER)\\nimport geovis\\n\\n############\\n#SETUP\\n############\\n#set rendering options\\ngeovis.SetRenderingOptions(renderer=\\\"aggdraw\\\", numpyspeed=True, reducevectors=False)\\n#create map\\ngeovis.SetMapBackground(geovis.Color(\\\"blue\\\", brightness=0.9))\\ngeovis.SetMapZoom(x2x=[-120,40],y2y=[-60,20])\\nnewmap = geovis.NewMap()\\n\\n############\\n#LOAD AND SYMBOLIZE LAYERS\\n############\\ncountrylayer = geovis.Layer(filepath=r\\\"D:\\\\Test Data\\\\necountries\\\\necountries.shp\\\", fillcolor=geovis.Color(\\\"yellow\\\",brightness=0.8))\\npointlayer = geovis.Layer(filepath=r\\\"D:\\\\Test Data\\\\GTD_Georef\\\\gtd_georef.shp\\\", symbolizer=\\\"square\\\")\\npointlayer.AddClassification(symboltype=\\\"fillcolor\\\", valuefield=\\\"nwound\\\", symbolrange=[geovis.Color(\\\"white\\\"),geovis.Color(\\\"red\\\", intensity=0.9, brightness=0.9),geovis.Color(\\\"red\\\", intensity=0.9, brightness=0.5)], classifytype=\\\"natural breaks\\\", nrclasses=3)\\npointlayer.AddClassification(symboltype=\\\"fillsize\\\", valuefield=\\\"nwound\\\", symbolrange=[0.3,2.8], classifytype=\\\"natural breaks\\\", nrclasses=3)\\n\\n############\\n#RENDER TO MAP\\n############\\n#add layers to map\\nnewmap.AddToMap(countrylayer)\\nnewmap.AddToMap(pointlayer)\\n#add legend\\nnewmap.AddLegend(pointlayer, upperleft=(0.5,0.7), bottomright=(0.9,0.9))\\n#view map\\nnewmap.ViewMap()\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7591887712478638,\n \"alphanum_fraction\": 0.7656383514404297,\n \"avg_line_length\": 52.39004135131836,\n \"blob_id\": \"9956b63386e45f642ec5a0930455cf0572dfe382\",\n \"content_id\": \"95c482059a4c6e38e9dd6896e5d75b2f444ce490\",\n \"detected_licenses\": [\n \"MIT\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 12871,\n \"license_type\": \"permissive\",\n \"max_line_length\": 722,\n \"num_lines\": 241,\n \"path\": \"/GeoVis/README.md\",\n \"repo_name\": \"priyankark/LSI\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Python Geographic Visualizer (GeoVis)\\n\\n\\n\\n**Version: 0.2.0**\\n\\n**Author: [Karim Bahgat](https://uit.academia.edu/KarimBahgat)**\\n\\n\\n## Introduction\\nPython Geographic Visualizer (GeoVis) is a standalone geographic visualization module for the Python programming language intended for easy everyday-use by novices and power-programmers alike. It has one-liners for quickly visualizing a shapefile, building and styling basic maps with multiple shapefile layers, and/or saving to imagefiles. Uses the built-in Tkinter or other third-party rendering modules to do its main work. The current version is functional, but should be considered a work in progress with potential bugs, so use with care. For now, only visualizes shapefiles that are in lat/long unprojected coordinate system. \\n\\n\\n## Latest News\\n\\n- April 15, 2014: New v0.2.0 released, major update. \\n - New features:\\n - Symbolize shapefiles based on their attributes and add legend (categorical, equal classes, equal interval, natural breaks)\\n - Choose between multiple symbolizer icons: circle, square, pyramid\\n - Zoom the map to a region of interest\\n - Manually draw basic geometries and write text on map\\n - Functions that allow mid-script interactive user-inputs\\n - Support for the Pillow version of PIL, and improved PIL quality by using antialiasing\\n - Changed to MIT license to be more contributor friendly\\n - Incompatible changes with previous version\\n - Make and style a layer, then add to map, instead of adding to map directly from file\\n - Color now has to be explicitly set and is no longer random by default\\n - Support for PyCairo has been temporarily discontinued due to some difficulties, so is likely to have errors\\n - Errors fixed\\n - Fixed error with interactively saving image from the viewer window in PIL mode. \\n- 23 Feb. 2014: A little too quick on the trigger finger for the first release. \\n - Discovered and fixed a crucial import error. - Also expanded the README file and converted it to markdown, and added autogenerated Wiki documentation. \\n- 21 Feb. 2014: First rough version of code released (v0.1.0).\\n - Basic functionality\\n - One liners for shapefile viewing and map saving\\n - Multiple layers custom map creation\\n - Customize each shapefile with colors and fillsize\\n\\n\\n## Table of Contents\\n- [About](#about)\\n - [Dependencies](#dependencies)\\n - [System Compatibility](#system-compatibility)\\n - [License](#license)\\n- [Getting Started](#getting-started)\\n - [Installation](#installation)\\n - [Importing](#importing)\\n - [Instant Mapping](#instant-mapping)\\n - [Customized Maps](#customized-maps)\\n- [Advanced Usage](#advanced-usage)\\n - [Choosing Your Renderer](#choosing-your-renderer)\\n - [Zooming In](#zooming-in)\\n - [Playing With Colors](#playing-with-colors)\\n - [Batch Map Creation](#batch-map-creation)\\n- [Help and Documentation](#help-and-documentation)\\n- [Contributing](#contributing)\\n- [Thanks To](#thanks-to)\\n\\n\\n## About\\n\\n### Dependencies\\nTechnically speaking, GeoVis has no external dependencies, but it is highly recommended that you install the [Aggdraw](http://effbot.org/zone/aggdraw-index.htm), [PIL](http://www.pythonware.com/products/pil/) or [PyCairo](http://cairographics.org/pycairo/) renderer libraries to do the rendering. GeoVis automatically detects which renderer module you have and uses the first it finds in the following order (aggdraw, PIL, pycairo). If you wish to manually choose a different renderer this has to be specified for each session. If none of these are available then GeoVis will default to using the built-in Tkinter Canvas as its renderer, but due to major limitations this is not recommended for viewing larger shapefiles. \\n\\n### System Compatibility\\nShould work on Python version 2.x and Windows. Has not yet been tested on Python 3.x or other OS systems.\\n\\n### License\\nContributors are wanted and needed, so this code is free to share, use, reuse,\\nand modify according to the MIT license, see license.txt\\n\\n\\n## Getting Started\\n\\n### Installation\\n1. Download the GeoVis repository folder from GitHub.\\n2. Either a or b:\\n - a) Place the geovis folder in you Python site-packages folder for a \\\"permanent\\\" installation.\\n - b) Or begin each session by typing `import sys` and `sys.path.append(\\\"folder path where the geovis folder is located\\\")`.\\n\\n### Importing\\nAssuming you have already installed it as described in the Installation section, GeoVis is imported as:\\n\\n```python\\nimport geovis\\n```\\n\\nTo begin using geovis either check out the full list of commands in the [the USER_MANUAL](../master/USER_MANUAL.md), or keep reading below for a basic introduction. \\n\\n### Instant Mapping\\nIf you are simply wanting to inspect some shapefile interactively, or for seeing how your processed shapefile turned out, then you do this with a simple one-liner:\\n\\n```python\\ngeovis.ViewShapefile(\\\"C:/shapefile.shp\\\")\\n```\\n\\nIf you quickly want to show someone else your shapefile over email or on a forum you can just as easily save you map to an image either by clicking the \\\"save image\\\" button in the interactive window viewer or with the following line:\\n\\n```python\\ngeovis.SaveShapefileImage(\\\"C:/shapefile.shp\\\",\\n savepath=\\\"C:/output_picture.png\\\")\\n```\\n\\n### Customized Maps\\nIt is also possible to build your map from scratch in order to create a more visually appealing map. \\n\\nFirst setup and create a newmap instance:\\n\\n```python\\ngeovis.SetMapDimensions(width=4000, height=2000)\\ngeovis.SetMapBackground(geovis.Color(\\\"blue\\\")\\nnewmap = geovis.NewMap()\\n```\\n\\nNext, each shapefile has to be loaded and symbolized into layer instances:\\n\\n```python\\npolylayer = geovis.Layer(polypath, fillcolor=geovis.Color(\\\"random\\\"))\\npointlayer = geovis.Layer(pointpath, fillcolor=geovis.Color(\\\"random\\\"))\\n```\\n\\nIf you wish to, you can also visualize the underlying attributes of your layer by adding one or more classifications. For instance, out point layer can be made to vary from small to large size and green to red color based on its \\\"population\\\" attribute:\\n\\n```python\\npointlayer.AddClassification(symboltype=\\\"fillsize\\\", valuefield=\\\"population\\\", symbolrange=[0.01,0.2], classifytype=\\\"natural breaks\\\", nrclasses=5)\\npointlayer.AddClassification(symboltype=\\\"fillcolor\\\", valuefield=\\\"population\\\", symbolrange=[geovis.Color(\\\"green\\\"),geovis.Color(\\\"red\\\")], classifytype=\\\"natural breaks\\\", nrclasses=5)\\n```\\n\\nIn which case you will probably want to add a legend so that you can see which symbols represent which values. Let's place it in the top left corner of the map:\\n\\n```python\\npointlayer.AddLegend(layer=pointlayer, upperleft=[0.01,0.01], bottomright=[0.3,0.3])\\n```\\n\\nFinally, render the layers to the newmap instance:\\n\\n```python\\nnewmap.AddToMap(polylayer)\\nnewmap.AddToMap(pointlayer)\\n```\\n\\nFor a finishing touch, add a top-centered map title:\\n\\n```python\\nnewmap.AddText(relx=0.5, rely=0.01, text=\\\"Example Map Title\\\", textsize=0.10, textanchor=\\\"n\\\")\\n```\\n\\nAnd save the map:\\n\\n```python\\nnewmap.SaveMap(\\\"C:/Users/BIGKIMO/Desktop/custombuiltmap.png\\\")\\n```\\n\\n\\n## Advanced Usage\\n\\n### Choosing Your Renderer\\nIf you have more than one renderer and you want to choose which one to use, for instance PIL, you must do this at the beginning of each session (also, if you're going for maximum speed/less quality then enable reducing the number of vectors while you're at it, though this is not recommended for line-shapefiles):\\n\\n```python\\ngeovis.SetRenderingOptions(renderer=\\\"PIL\\\", reducevectors=True)\\n```\\n\\n### Zooming In\\nBy default, the map you create will show the entire world. To zoom the map to a particular area or region of interest you simply set the zoomextents of the map, which must be done before you add your layers:\\n\\n```python\\ngeovis.SetMapZoom(x2x=[-90,90], y2y=[-45,45])\\n```\\n\\n### Playing With Colors\\nThere are several ways to play with the colors in your map. The most basic stylizing tool you will want to know about is the Color creator (a wrapper around [Valentin Lab's Colour module](https://pypi.python.org/pypi/colour) with added convenience functionality). You can either create a random color:\\n\\n```python\\nrandomcolor = geovis.Color(\\\"random\\\")\\n```\\n\\nOr you can create a specific color the way you imagine it in your head by writing the color name and optionally tweaking the color intensity and brightness (float value between 0 and 1). Let's create a strong (high intensity) but dark (low brightness) red:\\n\\n```python\\nstrongdarkred = geovis.Color(\\\"red\\\", intensity=0.8, brightness=0.2)\\n```\\n\\nAlternatively, instead of creating a very specific color you can create a random color that still keeps within certain limits. For instance, specifying a low brightness value and low intensity value but not specifying a color name will produce a random matte-looking color. Better yet, you can set the style argument to \\\"matte\\\" (among many other style names, see the documentation for the full list) which automatically chooses the brightness and intensity for you:\\n\\n```python\\nrandommattecolor = geovis.Color(style=\\\"matte\\\")\\n```\\n\\nAssuming you now know how to set your own colors or color styles, these colors are useful since they can be used to specify the color of any number of symbol options passed as keyword arguments to GeoVis' various rendering functions (see the documentation for a full list of changable symbol options). For instance, let's save a shapefile image as before, but this time set the fillcolor of the shapefile polygons/lines/circles to our strong-dark-red that we defined previously. In addition we will increase the outline width to match the strong fillcolor (we leave the outline *color* to its defaul black since this fits with the map):\\n\\n```python\\ngeovis.SaveShapefileImage(\\\"C:/shapefile.shp\\\",\\n savepath=\\\"C:/output_picture.png\\\",\\n fillcolor=strongdarkred,\\n outlinewidth=5)\\n```\\n\\n### Batch Map Creation\\nSometimes it is necessary to quickly create a gallery of images of all your shapefiles in a given directory. GeoVis provides a general utility tool that can help you do this; it loops through, parses, and returns the foldername, filename, and extension of all shapefiles in a folder tree, which in turn can be used as input for the SaveShapefileImage function. So for instance we may write it as:\\n\\n```python\\nfor eachfolder, eachshapefile, eachfiletype in geovis.ShapefileFolder(r\\\"D:\\\\Test Data\\\\GPS\\\"):\\n shapefilepath = eachfolder+eachshapefile+eachfiletype\\n savetopath = \\\"C:/Users/BIGKIMO/Desktop/mapgallery/\\\"+eachshapefile+\\\".png\\\"\\n geovis.SaveShapefileImage(shapefilepath, savetopath)\\n```\\n\\nThe filename, parent-folder, and file extension can be played around with to do many other batch operations as well, such as placing each map image next to (in the same folder as) each shapefile. \\n\\n\\n## Help and Documentation\\nThis brief introduction has only covered the most essential functionality of GeoVis. \\nIf you need more information about or are experiencing problems with a particular function you can look up the full documentation of available functions, classes, and arguments by typing `help(geovis)`, or checking out [the USER_MANUAL](../master/USER_MANUAL.md).\\n\\nIf you still need help, you can either [submit an issue](https://github.com/karimbahgat/geovis/issues) here on GitHub, or contact me directly at: karim.bahgat.norway@gmail.com\\n\\n\\n## Contributing\\nI welcome any efforts at contributing to this project. Below are a list of current problems and limitations that I hope to change in future releases:\\n\\n- The shapefiles have to be in lat/long coordinates (i.e. unprojected) in order to be displayed correctly. That is, GeoVis does not yet handle projections or coordinate transformation.\\n- Currently, zooming to a local scale can be made much more efficient by using spatial indexing such as QuadTree...\\n- I have not yet figured out how to pass PyCairo rendered images to the Tkinter window for viewing, so for now it results in an error. \\n- Currently has not been tested with, and probably won't work in Python 3.x due to syntax changes, so should look into fixing this. \\n\\n---\\n\\n---\\n\\n---\\n\\n### Thanks To\\nGeoVis could not have been accomplished without the excellent work of other open-source modules which it uses behind-the-scenes: \\n\\n- Several contributors over at Stackoverflow for their help with various rendering related problems and optimizations.\\n- Various bloggers whose code examples have in some cases been used directly in GeoVis (these parts of the code are marked with the original source)\\n- For shapefile reading it uses a modified version of \\n[Joel Lawhead's PyShp module](http://code.google.com/p/pyshp/). \\n- For color-wizardry it builds on and expands [Valentin Lab's Colour module](https://pypi.python.org/pypi/colour). \\n- And offcourse it relies on the rendering of whichever renderer you are using. \\n\\n\"\n },\n {\n \"alpha_fraction\": 0.646774172782898,\n \"alphanum_fraction\": 0.64838707447052,\n \"avg_line_length\": 35.47058868408203,\n \"blob_id\": \"92496e1118df4bd7eacc7c14a84301760440c07c\",\n \"content_id\": \"50a1584eb506f9c7d4b86cb9027e29d991c7065d\",\n \"detected_licenses\": [\n \"MIT\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 620,\n \"license_type\": \"permissive\",\n \"max_line_length\": 99,\n \"num_lines\": 17,\n \"path\": \"/GeoVis/developers/Auto document/create_docu_developers.py\",\n \"repo_name\": \"priyankark/LSI\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.path.append(r\\\"C:\\\\Users\\\\BIGKIMO\\\\Dropbox\\\\Work\\\\Research\\\\Software\\\\Various Python Libraries\\\\GitDoc\\\")\\nimport gitdoc\\n\\nFILENAME = \\\"geovis\\\"\\nFOLDERPATH = r\\\"C:\\\\Users\\\\BIGKIMO\\\\Documents\\\\GitHub\\\\geovis\\\"\\nOUTPATH = r\\\"C:\\\\Users\\\\BIGKIMO\\\\Documents\\\\GitHub\\\\geovis\\\\developers\\\"\\nOUTNAME = \\\"dev_documentation\\\"\\nEXCLUDETYPES = [\\\"module\\\"]\\ngitdoc.Module2GitDown(FOLDERPATH,\\n filename=FILENAME,\\n outputfolder=OUTPATH,\\n outputname=OUTNAME,\\n excludetypes=EXCLUDETYPES,\\n excludesecret=False,\\n excludesupersecret=False\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.6734254956245422,\n \"alphanum_fraction\": 0.6777265667915344,\n \"avg_line_length\": 28.0625,\n \"blob_id\": \"50384f73f6e669de1a5d8ff5a42455821f2b6dff\",\n \"content_id\": \"d67cda3aaf339985d7c81155fea7a6e0c4fc2586\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3255,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 112,\n \"path\": \"/SimpleGui.pyw\",\n \"repo_name\": \"priyankark/LSI\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nfrom PyQt4.QtCore import *\\nfrom PyQt4.QtGui import *\\nfrom ExtractLine import *\\nfrom qgis.core import *\\nfrom qgis.gui import *\\nimport GlobalValuesLib\\nimport os\\nimport threading\\n\\nqgis_prefix = os.getenv(\\\"QGISHOME\\\")\\n\\nclass MainExecutionThread(QThread):\\n def __init__(self):\\n QThread.__init__(self)\\n\\n def __del__(self):\\n self.wait()\\n\\n def run(self):\\n convertSHP(DXF_fileName)\\n\\n\\n\\n\\n\\nclass SimpleGui(QWidget):\\n\\n def __init__(self,parent=None):\\n super(SimpleGui,self).__init__(parent)\\n self.setGeometry(65,50,500,500)\\n selectionBar=QHBoxLayout()\\n self.mainExecutionThread=MainExecutionThread()\\n\\n self.btnDXF=QPushButton(\\\"Select .dxf file\\\",self)\\n self.showSelection=QLineEdit()\\n self.btnDXF.clicked.connect(lambda:self.getfiles(self.showSelection))\\n self.btnExtractLines=QPushButton(\\\"Extract\\\")\\n #self.btnExtractLines.clicked.connect(lambda:self.handleExtract(self.viewStatus))\\n self.btnExtractLines.clicked.connect(lambda:self.handleExtract(self.mainExecutionThread))\\n self.btnSHP=QPushButton(\\\"View SHP\\\")\\n\\n self.canvas = QgsMapCanvas()\\n self.canvas.useImageToRender(False)\\n self.btnSHP.clicked.connect(lambda:self.btnSHPFn(self.canvas))\\n\\n selectionBar.addWidget(self.showSelection)\\n selectionBar.addWidget(self.btnDXF)\\n selectionBar.addWidget(self.btnExtractLines)\\n selectionBarWidget=QWidget()\\n selectionBarWidget.setLayout(selectionBar)\\n #self.viewStatus=QTextEdit()\\n\\n #layout.addWidget(self.btnDFX)\\n mainLayout=QVBoxLayout()\\n mainLayout.addWidget(selectionBarWidget)\\n #mainLayout.addWidget(self.viewStatus)\\n mainLayout.addWidget(self.canvas)\\n mainLayout.addWidget(self.btnSHP)\\n\\n self.setLayout(mainLayout)\\n\\n #def handleExtract(self,viewStatus):\\n def handleExtract(self,mainExecutionThread):\\n #convertSHP(DXF_fileName,viewStatus)\\n mainExecutionThread.start()\\n\\n\\n def getfiles(self,showSelection):\\n dlg=QFileDialog()\\n dlg.setFileMode(QFileDialog.AnyFile)\\n dlg.setFilter(\\\"DXF Files (*.dxf)\\\")\\n filenames=QStringList()\\n\\n if dlg.exec_():\\n filename=dlg.selectedFiles()\\n showSelection.setText(str(filename[0]))\\n global DXF_fileName\\n DXF_fileName=str(filename[0])\\n print DXF_fileName\\n\\n def btnSHPFn(self,canvas):\\n\\n file=open(GlobalValuesLib.SHP_location,'r')\\n fileInfo = QFileInfo(GlobalValuesLib.SHP_location)\\n layer = QgsVectorLayer(GlobalValuesLib.SHP_location,\\\"testlayer_shp\\\",\\\"ogr\\\")\\n if not layer.isValid():\\n print \\\"Layer failed to load\\\"\\n QgsMapLayerRegistry.instance().addMapLayer(layer);\\n canvas.setExtent(layer.extent())\\n cl = QgsMapCanvasLayer(layer)\\n layers = [cl]\\n canvas.setLayerSet(layers)\\n\\n\\n\\n\\n print GlobalValuesLib.SHP_location\\n #geovis.ViewShapefile(GlobalValuesLib.SHP_location)\\n\\n\\ndef main():\\n app=QApplication(sys.argv)\\n QgsApplication.setPrefixPath(\\\"/usr\\\", True)\\n QgsApplication.initQgis()\\n QApplication.processEvents()\\n simpleGUI=SimpleGui()\\n simpleGUI.show()\\n sys.exit(app.exec_())\\n\\nif __name__==\\\"__main__\\\":\\n main()\\n\"\n },\n {\n \"alpha_fraction\": 0.8035264611244202,\n \"alphanum_fraction\": 0.8035264611244202,\n \"avg_line_length\": 38.70000076293945,\n \"blob_id\": \"686a92eac5f43e77dbb6aad4285ad8be65685bef\",\n \"content_id\": \"eba3146bb7d1b9fd928dd6c77d2563f8e537ca2e\",\n \"detected_licenses\": [\n \"MIT\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 397,\n \"license_type\": \"permissive\",\n \"max_line_length\": 117,\n \"num_lines\": 10,\n \"path\": \"/GeoVis/developers/Auto document/create_docu_users.py\",\n \"repo_name\": \"priyankark/LSI\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.path.append(r\\\"C:\\\\Users\\\\BIGKIMO\\\\Documents\\\\GitHub\\\\GitDoc\\\")\\nimport gitdoc\\n\\nFILENAME = \\\"geovis\\\"\\nFOLDERPATH = r\\\"C:\\\\Users\\\\BIGKIMO\\\\Documents\\\\GitHub\\\\geovis\\\"\\nOUTPATH = r\\\"C:\\\\Users\\\\BIGKIMO\\\\Documents\\\\GitHub\\\\geovis\\\"\\nOUTNAME = \\\"USER_MANUAL\\\"\\nEXCLUDETYPES = [\\\"module\\\",\\\"variable\\\"]\\ngitdoc.DocumentModule(FOLDERPATH,filename=FILENAME,outputfolder=OUTPATH,outputname=OUTNAME,excludetypes=EXCLUDETYPES)\\n\"\n },\n {\n \"alpha_fraction\": 0.8021978139877319,\n \"alphanum_fraction\": 0.8021978139877319,\n \"avg_line_length\": 27.4375,\n \"blob_id\": \"f93f7f4d12cc706fe3ef1ff1936b494dedd50009\",\n \"content_id\": \"b29291d80a332cd5a8c36da40c086b9193e53b6c\",\n \"detected_licenses\": [\n \"MIT\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 455,\n \"license_type\": \"permissive\",\n \"max_line_length\": 91,\n \"num_lines\": 16,\n \"path\": \"/GeoVis/examples/quickview_example.py\",\n \"repo_name\": \"priyankark/LSI\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nExample Script for the Python Geographic Visualizer (GeoVis)\\nhttps://github.com/karimbahgat/geovis\\n\\\"\\\"\\\"\\n\\n#importing geovis from temporary location\\nTEMP_GEOVIS_FOLDER = r\\\"C:\\\\Users\\\\BIGKIMO\\\\Documents\\\\GitHub\\\\geovis\\\"\\nimport sys\\nsys.path.append(TEMP_GEOVIS_FOLDER)\\nimport geovis\\n\\n#setting up for speed\\ngeovis.SetRenderingOptions(reducevectors=True)\\n\\n#view shapefile\\ngeovis.ViewShapefile(r\\\"D:\\\\Test Data\\\\cshapes\\\\cshapes.shp\\\", maptitle=\\\"ViewShapefile Example\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.6240951418876648,\n \"alphanum_fraction\": 0.6415029168128967,\n \"avg_line_length\": 44.606300354003906,\n \"blob_id\": \"aaed22cb16f16504cb2a5911ae9ec17d7ffe9115\",\n \"content_id\": \"fe31e3628b02a2949ead9fd16aa724f2b8e57bc2\",\n \"detected_licenses\": [\n \"MIT\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5802,\n \"license_type\": \"permissive\",\n \"max_line_length\": 206,\n \"num_lines\": 127,\n \"path\": \"/GeoVis/geovis/guihelper.py\",\n \"repo_name\": \"priyankark/LSI\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\\"\\\"\\\"\\nalmost good\\nbut not sure how useful all of this is\\ngets buggy/leans to one direction when using excessive padding isntead of just disappearing\\nand seems that same can be done using relx/rely and nested widgets...?\\nmaybe the charm is to not use the subpartition (only if you really cant do nesting)\\nbut instead call PartitionSpace simply as an aid when doing relx/rely inside a nested widget\\n\\\"\\\"\\\"\\n\\nclass Partition:\\n def __init__(self, center, partitionwidth, partitionheight, direction):\\n if direction == \\\"vertical\\\":\\n center = (center[1],center[0])\\n partitionwidth,partitionheight = partitionheight,partitionwidth\\n self.center = center\\n self.width = partitionwidth\\n self.height = partitionheight\\n midx,midy = center\\n halfx = partitionwidth/2.0\\n halfy = partitionheight/2.0\\n self.nw = (midx-halfx, midy-halfy)\\n self.n = (midx, midy-halfy)\\n self.ne = (midx+halfx, midy-halfy)\\n self.e = (midx+halfx, midy)\\n self.se = (midx+halfx, midy+halfy)\\n self.s = (midx, midy+halfy)\\n self.sw = (midx-halfx, midy+halfy)\\n self.w = (midx-halfx, midy)\\n def __repr__(self):\\n visualstring = \\\"partition:\\\\n\\\"\\n visualstring += \\\"%s \\\\t %s \\\\t %s \\\\n\\\" % tuple([[str(nr)[:5] for nr in pair] for pair in (self.nw,self.n,self.ne)])\\n visualstring += \\\"%s \\\\t %s \\\\t %s \\\\n\\\" % tuple([[str(nr)[:5] for nr in pair] for pair in (self.w,self.center,self.e)])\\n visualstring += \\\"%s \\\\t %s \\\\t %s\\\" % tuple([[str(nr)[:5] for nr in pair] for pair in (self.sw,self.s,self.se)])\\n return visualstring\\n def SubPartition(self, partitions, padx, pady, direction=\\\"horizontal\\\"):\\n \\\"only use if you really need to avoid nested widgets, eg if you need to structure your widgets over a large background image bc a background widget to nest them in would cover up parts of the image\\\"\\n xtox = (self.w[0], self.e[0])\\n ytoy = (self.n[1], self.s[1])\\n return PartitionSpace(xtox, ytoy, partitions, padx, pady, direction)\\n\\ndef PartitionSpace(xtox, ytoy, partitions, padx, pady, direction=\\\"horizontal\\\"):\\n startx, endx = xtox\\n starty, endy = ytoy\\n if direction == \\\"vertical\\\":\\n startx,starty = starty,startx\\n endx,endy = endy,endx\\n padx,pady = pady,padx\\n #prep\\n allwidth = endx-startx\\n allheight = endy-starty\\n widthafterpad = allwidth-padx*(partitions+1)\\n heightafterpad = allheight-pady*2\\n partitionwidth = widthafterpad/float(partitions)\\n partitionheight = heightafterpad\\n #calc\\n outpartitions = []\\n tempx = startx+padx+partitionwidth/2.0\\n tempy = starty+pady+partitionheight/2.0\\n for _ in xrange(partitions):\\n center = (tempx, tempy)\\n outpartitions.append( Partition(center, partitionwidth, partitionheight, direction) )\\n tempx += partitionwidth/2.0+padx+partitionwidth/2.0\\n return outpartitions\\n\\nif __name__ == \\\"__main__\\\":\\n import Tkinter as tk\\n import random\\n win = tk.Tk()\\n testdims = (1100,500)\\n testparts = 3\\n\\n #pixel positions test\\n## testpadx,testpady = (90,20)\\n## frame = tk.Frame(win, bg=\\\"red\\\", width=testdims[0], height=testdims[1])\\n## frame.pack()\\n## hm = tk.Label(frame, bg=\\\"yellow\\\")\\n## hm.place(x=200,y=200)\\n## for partition in PartitionSpace(200,testdims[0],0,testdims[1],testparts,testpadx,testpady):\\n## but = tk.Label(frame, bg=\\\"blue\\\")\\n## x,y = partition.center\\n## print partition.w, x, partition.e\\n## but.place(x=x, y=y, width=round(partition.width), height=round(partition.height), anchor=\\\"center\\\")\\n\\n #rel test\\n testpadx,testpady = (0.01,0.01)\\n frame = tk.Frame(win, bg=\\\"red\\\", width=testdims[0], height=testdims[1])\\n frame.pack(fill=\\\"both\\\")\\n hm = tk.Label(frame, bg=\\\"yellow\\\")\\n hm.place(relx=0.3,rely=0.5, anchor=\\\"ne\\\")\\n #horiz partitions\\n partitions = PartitionSpace((0.3,1),(0.7,1),testparts,testpadx,testpady)\\n subpart = partitions[0]\\n for partition in partitions:\\n but = tk.Label(frame, bg=\\\"blue\\\")\\n x,y = partition.center\\n but.place(relx=x, rely=y, relwidth=partition.width, relheight=partition.height, anchor=\\\"center\\\")\\n #split each down the middle\\n subpartitions = partition.SubPartition(2, 0.01, 0.02)\\n #left side\\n subpart = subpartitions[0]\\n but = tk.Label(frame, bg=\\\"green\\\")\\n x,y = subpart.center\\n but.place(relx=x, rely=y, relwidth=subpart.width, relheight=subpart.height, anchor=\\\"center\\\")\\n #then try downwards partition\\n subsubpartitions = subpart.SubPartition(5, 0.006, 0.006, direction=\\\"vertical\\\")\\n for subpart in subsubpartitions:\\n but = tk.Label(frame, bg=\\\"red\\\")\\n x,y = subpart.center\\n but.place(relx=x, rely=y, relwidth=subpart.width, relheight=subpart.height, anchor=\\\"center\\\")\\n #right side\\n subpart = subpartitions[1]\\n but = tk.Label(frame, bg=\\\"green\\\")\\n x,y = subpart.center\\n but.place(relx=x, rely=y, relwidth=subpart.width, relheight=subpart.height, anchor=\\\"center\\\")\\n #then try downwards partition\\n subsubpartitions = subpart.SubPartition(5, 0.006, 0.006, direction=\\\"vertical\\\")\\n for subpart in subsubpartitions:\\n but = tk.Label(frame, bg=\\\"white\\\", text=\\\"text\\\")\\n x,y = subpart.center\\n but.place(relx=x, rely=y, relwidth=subpart.width, relheight=subpart.height, anchor=\\\"center\\\")\\n #finalize window\\n coordsdisplay = tk.Label(win, text=\\\"mousepos\\\")\\n coordsdisplay.place(relx=0.01, rely=0.01)\\n def displaymousecoords(event):\\n coordsdisplay[\\\"text\\\"] = \\\"%s,%s (%s,%s)\\\" %(event.x,event.y, event.x/float(win.winfo_width()),event.y/float(win.winfo_height()))\\n win.bind(\\\"<Motion>\\\", displaymousecoords)\\n win.mainloop()\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.6251082420349121,\n \"alphanum_fraction\": 0.635188639163971,\n \"avg_line_length\": 39.21890640258789,\n \"blob_id\": \"074b7e8f3dec48b3c817056b9507b333127a455e\",\n \"content_id\": \"4a455ca138c14674ff5b6dbdb447212e4b6847a3\",\n \"detected_licenses\": [\n \"MIT\",\n \"LicenseRef-scancode-unknown-license-reference\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 16170,\n \"license_type\": \"permissive\",\n \"max_line_length\": 218,\n \"num_lines\": 402,\n \"path\": \"/GeoVis/geovis/listy.py\",\n \"repo_name\": \"priyankark/LSI\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#IMPORTS\\nimport sys, os, operator, itertools, random, math\\n\\n#GLOBALS\\nNONEVALUE = None\\n\\n\\n\\\"\\\"\\\"\\nSEE ALSO\\nhttp://code.activestate.com/recipes/189971-basic-linear-algebra-matrix/\\nhttp://users.rcn.com/python/download/python.htm\\n\\\"\\\"\\\"\\n\\n\\n#FUNCTIONS\\ndef Resize(rows, newlength, stretchmethod=\\\"not specified\\\", gapvalue=\\\"not specified\\\"):\\n \\\"front end used by user, determines if special nested list resizing or single list\\\"\\n if isinstance(rows[0], (list,tuple)):\\n #input list is a sequence of list (but only the first item is checked)\\n #needs to have same nr of list items in all sublists\\n crosssection = itertools.izip(*rows)\\n grad_crosssection = [ _Resize(spectrum,newlength,stretchmethod,gapvalue) for spectrum in crosssection ]\\n gradient = [list(each) for each in itertools.izip(*grad_crosssection)]\\n return gradient\\n else:\\n #just a single list of values\\n return _Resize(rows, newlength, stretchmethod, gapvalue)\\ndef Transpose(listoflists):\\n \\\"must get a 2d grid, ie a list of lists, with all sublists having equal lengths\\\"\\n transposed = [list(each) for each in itertools.izip(*listoflists)]\\n return transposed\\n\\n#BUILTINS \\ndef _Resize(rows, newlength, stretchmethod=\\\"not specified\\\", gapvalue=\\\"not specified\\\"):\\n \\\"behind the scenes, does the actual work, only for a single flat list\\\"\\n #return input as is if no difference in length\\n if newlength == len(rows):\\n return rows\\n #set gap\\n if gapvalue == \\\"not specified\\\":\\n gapvalue = NONEVALUE\\n #set auto stretchmode\\n if stretchmethod == \\\"not specified\\\":\\n if isinstance(rows[0], (int,float)):\\n stretchmethod = \\\"interpolate\\\"\\n else:\\n stretchmethod = \\\"duplicate\\\"\\n #reduce newlength \\n newlength -= 1\\n #assign first value\\n outlist = [rows[0]]\\n relspreadindexgen = (index/float(len(rows)-1) for index in xrange(1,len(rows))) #warning a little hacky by skipping first index cus is assigned auto\\n relspreadindex = next(relspreadindexgen)\\n spreadflag = False\\n for each in xrange(1, newlength):\\n #relative positions\\n rel = each/float(newlength)\\n relindex = (len(rows)-1) * rel\\n basenr,decimals = str(relindex).split(\\\".\\\")\\n relbwindex = float(\\\"0.\\\"+decimals)\\n #determine equivalent value\\n if stretchmethod==\\\"interpolate\\\":\\n maybecurrelval = rows[int(relindex)]\\n if maybecurrelval != gapvalue:\\n #ALMOST THERE BUT NOT QUITE\\n #DOES SOME WEIRD BACKTRACKING\\n #...\\n currelval = rows[int(relindex)]\\n #make sure next value to interpolate to is valid\\n testindex = int(relindex)+1\\n while testindex < len(rows)-1 and rows[testindex] == gapvalue:\\n #if not then interpolate until next valid item\\n testindex += 1\\n nextrelval = rows[testindex]\\n #assign value\\n relbwval = currelval + (nextrelval - currelval) * relbwindex #basenr pluss interindex percent interpolation of diff to next item\\n elif stretchmethod==\\\"duplicate\\\":\\n relbwval = rows[int(round(relindex))] #no interpolation possible, so just copy each time\\n elif stretchmethod==\\\"spread\\\":\\n if rel >= relspreadindex:\\n spreadindex = int(len(rows)*relspreadindex)\\n relbwval = rows[spreadindex] #spread values further apart so as to leave gaps in between\\n relspreadindex = next(relspreadindexgen)\\n else:\\n relbwval = gapvalue\\n #assign each value\\n outlist.append(relbwval)\\n #assign last value\\n outlist.append(rows[-1])\\n return outlist\\ndef _InterpolateValue(value, otherinput, method):\\n \\\"method can be linear, IDW, etc...\\\"\\n pass\\n\\n#CLASSES\\nclass _1dData:\\n \\\"\\\"\\\"\\nMost basic of all list types. Contains data values but is just a meaningless arbitrary list if not embedded in some other list type.\\nIt can be embedded in a 2dsurfacegrid to represent a theme located along horizantal x lines.\\nOr embedded in a 4dtimegrid to represent a theme changing over time, without any spatial properties.\\nMaybe same as Listy below??\\n\\\"\\\"\\\"\\n pass\\nclass _Cell:\\n def __init__(self, xpos, ypos, value):\\n self.x = xpos\\n self.y = ypos\\n self.value = value\\nclass _2dSurfaceGrid:\\n \\\"horizontal lines up and down along y axis\\\"\\n #BUILTINS\\n def __init__(self, twoDlist=None, emptydims=\\\"not specified\\\"):\\n #add some error checking...\\n #...\\n if not twoDlist:\\n if emptydims == \\\"not specified\\\":\\n emptydims = (50,50)\\n width,height = emptydims\\n twoDlist = [[NONEVALUE for _ in xrange(width)] for _ in xrange(height)]\\n self.grid = Listy(*twoDlist)\\n self.height = len(self.grid.lists)\\n self.width = len(self.grid.lists[0])\\n self.knowncells = self._GetKnownCells()\\n def __iter__(self):\\n for ypos, horizline in enumerate(self.grid.lists):\\n for xpos, xpoint in enumerate(horizline):\\n yield _Cell(xpos, ypos, value=xpoint)\\n def __str__(self):\\n return str(self.grid)\\n #FUNCTIONS\\n def RandomPoints(self, value=\\\"random\\\", valuerange=\\\"not specified\\\", nrpoints=\\\"not specified\\\"):\\n if nrpoints == \\\"not specified\\\":\\n nrpoints = int(self.width*self.height*0.10) #10 percent of all cells\\n if valuerange == \\\"not specified\\\":\\n valuerange = (0,250)\\n randomvalue = False\\n if value == \\\"random\\\":\\n randomvalue = True\\n for _ in xrange(nrpoints):\\n if randomvalue:\\n value = random.randrange(*valuerange)\\n xindex = random.randrange(self.width)\\n yindex = random.randrange(self.height)\\n self.grid.lists[yindex][xindex] = value\\n self.knowncells = self._GetKnownCells()\\n def Interpolate(self, method=\\\"IDW\\\", **options):\\n \\\"ie fill any gaps in grid with interpolation\\\"\\n if method == \\\"IDW\\\":\\n self._IDW(options)\\n def ChangeValues(self, expression):\\n pass\\n def SelectQuery(self, query):\\n pass\\n def Show(self):\\n import numpy, PIL, PIL.Image, PIL.ImageTk, PIL.ImageDraw\\n import Tkinter as tk\\n import colour\\n win = tk.Tk()\\n nparr = numpy.array(self.grid.lists)\\n npmin = numpy.min(nparr)\\n npmax = numpy.max(nparr)\\n minmaxdiff = npmax-npmin\\n colorstops = [colour.Color(\\\"red\\\").rgb,colour.Color(\\\"yellow\\\").rgb,colour.Color(\\\"green\\\").rgb]\\n colorstops = [list(each) for each in colorstops]\\n colorgrad = Listy(*colorstops)\\n colorgrad.Convert(\\\"250*value\\\")\\n colorgrad.Resize(int(minmaxdiff))\\n valuerange = range(int(npmin),int(npmax))\\n colordict = dict(zip(valuerange,colorgrad.lists))\\n print len(valuerange),len(colorgrad.lists),len(colordict)\\n print \\\"minmax\\\",npmin,npmax\\n for ypos,horizline in enumerate(self.grid.lists):\\n for xpos,value in enumerate(horizline):\\n relval = value/float(npmax)\\n self.grid.lists[ypos][xpos] = colorgrad.lists[int((len(colorgrad.lists)-1)*relval)]\\n nparr = numpy.array(self.grid.lists,\\\"uint8\\\")\\n print \\\"np shape\\\",nparr.shape\\n img = PIL.Image.fromarray(nparr)\\n drawer = PIL.ImageDraw.ImageDraw(img)\\n size = 3\\n for knowncell in self.knowncells:\\n x,y = (knowncell.x,knowncell.y)\\n drawer.ellipse((x-size,y-size,x+size,y+size),fill=\\\"black\\\")\\n img.save(\\\"C:/Users/BIGKIMO/Desktop/test.png\\\")\\n tkimg = PIL.ImageTk.PhotoImage(img)\\n lbl = tk.Label(win, image=tkimg)\\n lbl.pack()\\n win.mainloop()\\n #INTERNAL USE ONLY\\n def _GetKnownCells(self):\\n knowncellslist = []\\n for cell in self:\\n if cell.value != NONEVALUE:\\n knowncellslist.append(cell)\\n return knowncellslist\\n def _IDW(self, options):\\n #retrieve input options\\n neighbours = options.get(\\\"neighbours\\\",int(len(self.knowncells)*0.10)) #default neighbours is 10 percent of known points\\n sensitivity = options.get(\\\"sensitivity\\\",3) #same as power, ie that high sensitivity means much more effect from far away points\\n #some defs\\n def _calcvalue(unknowncell, knowncells):\\n weighted_values_sum = 0.0\\n sum_of_weights = 0.0\\n for knowncell in knowncells:\\n weight = ((unknowncell.x-knowncell.x)**2 + (unknowncell.y-knowncell.y)**2)**(-sensitivity/2.0)\\n sum_of_weights += weight\\n weighted_values_sum += weight * knowncell.value\\n return weighted_values_sum / sum_of_weights\\n #calculate value\\n for unknowncell in self:\\n if unknowncell.value == NONEVALUE:\\n #only calculate for unknown points\\n self.grid.lists[unknowncell.y][unknowncell.x] = _calcvalue(unknowncell, self.knowncells)\\n \\nclass _3dSpaceGrid:\\n \\\"\\\"\\\"z axis.\\nWorks by rendering one surface at a time, starting with lowest, that way rendering higher up/closer to the eye points on top of lower/further away points which gives a 3d effect.\\nJust need to find a way to transform each surface to the way it should look like from different angles.\\nNote: Link to a function to create the transform coeffs for PIL's perspective transform: http://stackoverflow.com/questions/14177744/how-does-perspective-transformation-work-in-pil\\nOR use ray tracing... http://scratchapixel.com/lessons/3d-basic-lessons/lesson-1-writing-a-simple-raytracer/source-code/\\nALSO see basic 3d equations http://www.math.washington.edu/~king/coursedir/m445w04/notes/vector/equations.html\\n\\\"\\\"\\\"\\n pass\\nclass _4dTimeGrid:\\n \\\"\\\"\\\"time axis\\nexample:\\nfor 3dtime in 4dtimegrid:\\n #loops all 3d spaces at different points in time\\n for 2ddepth in 3dtime:\\n #loops all 2d surfaces at different altitudes, from low to high\\n for 1dheight in 2ddepth:\\n #loops all leftright horizantal lines in a 2dgrid\\n for datavalue in 1dheight:\\n #loops all datavalues for whatever theme in a specific 1dline, in a 2dgrid, at a 3daltitude, at a 4d point in time\\n\\\"\\\"\\\"\\n pass\\n\\nclass Listy(list):\\n \\\"A list-type class with extended functionality and methods. These extra features should only be bindings to functions alredy defined in the general listy module and can be used by anyone, not just the Listy class.\\\"\\n def __init__(self, *sequences):\\n self.lists = list(sequences)\\n self.dtype = \\\"numbers\\\" #\\\"maybe do some automatic dtype detection\\\"\\n def __str__(self):\\n maxchar = 50\\n printstr = \\\"[\\\\n\\\"\\n for eachlist in self.lists:\\n if len(str(eachlist)) > maxchar:\\n printstr += str(eachlist)[:int(maxchar/2.0)]+\\\"...\\\"+str(eachlist)[-int(maxchar/2.0):]+\\\"\\\\n\\\"\\n else:\\n printstr += str(eachlist)+\\\"\\\\n\\\"\\n printstr += \\\"]\\\\n\\\"\\n return printstr\\n #SHAPE AND ORIENTATION\\n def Resize(self, newlength, listdim=None, stretchmethod=\\\"not specified\\\", gapvalue=\\\"not specified\\\"):\\n if listdim:\\n #resize only a certain dimension\\n self.lists[listdim] = Resize(self.lists[listdim], newlength, stretchmethod, gapvalue)\\n else:\\n #resize all lists??? experimental...\\n self.lists = Resize(self.lists, newlength, stretchmethod, gapvalue)\\n def Transpose(self):\\n self.lists = Transpose(self.lists)\\n def Reshape(self, newshape):\\n \\\"not sure yet how to do...\\\"\\n pass\\n def Split(self, groups_or_indexes):\\n pass\\n #TYPES\\n def Convert(self, dataformat):\\n \\\"it does actually work...but floating values will never be shortened bc no exact precision\\\"\\n toplist = self.lists\\n def execfunc(code, toplist, value, index):\\n exec(code)\\n def recurloop(toplist):\\n for index, value in enumerate(toplist):\\n if isinstance(value, list):\\n listfound = True\\n recurloop(value)\\n else:\\n conversioncode = \\\"toplist[index] = \\\"+dataformat\\n execfunc(conversioncode, toplist, value, index)\\n #begin\\n recurloop(toplist)\\n #ATTRIBUTES\\n @property\\n def minval(self):\\n pass\\n @property\\n def maxval(self):\\n pass\\n @property\\n def shape(self):\\n return tuple([len(eachlist) for eachlist in self.lists])\\n @property\\n def structure(self):\\n toplist = self.lists\\n depth = 0\\n spaces = \\\" \\\"\\n structstring = str(len(toplist))+\\\"\\\\n\\\"\\n def recurloop(toplist, structstring, depth, spaces):\\n for item in toplist:\\n if isinstance(item, list):\\n listfound = True\\n depth += 1\\n structstring += spaces*depth + str(len(item)) + \\\"\\\\n\\\"\\n toplist, structstring, depth, spaces = recurloop(item, structstring, depth, spaces)\\n depth -= 1\\n return toplist, structstring, depth, spaces\\n #begin\\n item, structstring, depth, spaces = recurloop(toplist, structstring, depth, spaces)\\n return structstring\\n\\nif __name__ == \\\"__main__\\\":\\n\\n print \\\"\\\"\\n print \\\"print and shape and resize test\\\"\\n testlist = [random.randrange(50) for e in xrange(random.randrange(31))]\\n testlisty = Listy(testlist, testlist)\\n print testlisty\\n print testlisty.shape\\n testlisty.Resize(22, listdim=0)\\n print testlisty.shape\\n print testlisty\\n\\n print \\\"\\\"\\n print \\\"hierarchical nested list structure test\\\"\\n nestedlists = [[[\\\"anything\\\" for e in xrange(random.randrange(500))] for e in xrange(random.randrange(5))] for d in xrange(random.randrange(6))]\\n nestedlisty = Listy(*nestedlists)\\n print nestedlisty\\n print nestedlisty.structure\\n\\n print \\\"\\\"\\n print \\\"transpose test\\\"\\n listoflists = [range(100) for _ in xrange(100)]\\n gridlisty = Listy(*listoflists)\\n print gridlisty\\n gridlisty.Transpose()\\n print gridlisty\\n\\n print \\\"\\\"\\n print \\\"fill in blanks test\\\"\\n listholes = [1,2,None,4,None,6]\\n gridlisty = Listy(*listholes)\\n print gridlisty\\n gridlisty.Resize(12, stretchmethod=\\\"interpolate\\\")\\n print gridlisty\\n\\n print \\\"\\\"\\n print \\\"spread grid test\\\"\\n listoflists = [range(6) for _ in xrange(6)]\\n gridlisty = Listy(*listoflists)\\n print gridlisty\\n #expand sideways (instead of downwards which is default for the multilist resize func)\\n gridlisty.Transpose()\\n gridlisty.Resize(11, stretchmethod=\\\"spread\\\")\\n gridlisty.Transpose()\\n #resize again to also expand downwards\\n gridlisty.Resize(11, stretchmethod=\\\"spread\\\")\\n print gridlisty\\n #finally try interpolating in between\\n #THIS PART NOT WORKING PROPERLY, GOING ZIGZAG OVER GAPS\\n## gridlisty.Resize(22, stretchmethod=\\\"interpolate\\\")\\n## print gridlisty\\n## gridlisty.Resize(11)\\n## print gridlisty\\n\\n print \\\"\\\"\\n print \\\"gridpoints interpolate test\\\"\\n listoflists = [[random.randrange(200) for _ in xrange(10)] for _ in xrange(10)] #[range(10) for _ in xrange(10)]\\n templisty = Listy(*listoflists)\\n print templisty\\n #spread to create holes\\n templisty.Transpose()\\n templisty.Resize(80, stretchmethod=\\\"spread\\\")\\n templisty.Transpose()\\n templisty.Resize(80, stretchmethod=\\\"spread\\\")\\n #make into 2dgrid obj\\n testgrid = _2dSurfaceGrid(templisty.lists)\\n print \\\"spread done\\\"#,testgrid\\n #interpolate\\n testgrid.Interpolate(\\\"IDW\\\")\\n print \\\"idw done\\\"#,testgrid.grid.lists\\n #reduce decimals\\n ##testgrid.grid.Convert(\\\"str(round(value,2))\\\")\\n ##print testgrid\\n #testgrid.Show()\\n\\n print \\\"\\\"\\n print \\\"randompoints interpolate test\\\"\\n #create empty 2dgrid obj\\n testgrid = _2dSurfaceGrid(emptydims=(600,600))\\n print \\\"grid made\\\"#,testgrid\\n #put random points\\n testgrid.RandomPoints(nrpoints=20)\\n print \\\"points placed\\\"#,testgrid\\n #interpolate\\n import time\\n t=time.clock()\\n testgrid.Interpolate(\\\"IDW\\\", sensitivity=4)\\n print time.clock()-t\\n print \\\"idw done\\\"#,testgrid.grid.lists\\n testgrid.Show()\\n\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":10,"string":"10"}}},{"rowIdx":2979145,"cells":{"repo_name":{"kind":"string","value":"akankshanb/BeagleBone-Black-IOT"},"repo_url":{"kind":"string","value":"https://github.com/akankshanb/BeagleBone-Black-IOT"},"snapshot_id":{"kind":"string","value":"2e7fbf98c815be8ae64506d9ca55e6e64f09f737"},"revision_id":{"kind":"string","value":"89e16196713a173f3dc2e6b291c7926a6533fe1e"},"directory_id":{"kind":"string","value":"485eabf72cd25707307468539e455936fd4345bb"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-10-19T00:46:18.141765","string":"2021-10-19T00:46:18.141765"},"revision_date":{"kind":"timestamp","value":"2019-02-16T06:02:17","string":"2019-02-16T06:02:17"},"committer_date":{"kind":"timestamp","value":"2019-02-16T06:02:17","string":"2019-02-16T06:02:17"},"github_id":{"kind":"number","value":105971953,"string":"105,971,953"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5841584205627441,"alphanum_fraction":0.6633663177490234,"avg_line_length":17.799999237060547,"blob_id":"8b1489b79c6cea8cc90651d9a0e2ab593580bb0c","content_id":"0b7bde19e01bfe6cbdf536ad16c0bf8b3666fede","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":101,"license_type":"no_license","max_line_length":35,"num_lines":5,"path":"/fs.sh","repo_name":"akankshanb/BeagleBone-Black-IOT","src_encoding":"UTF-8","text":"#FS.SH(bash shell file)\r\n\r\n#!/bin/bash\r\nfswebcam -r 640x480 image.jpg -S 20\r\necho \"photo clicked\"\r\n\r\n"},{"alpha_fraction":0.7993842959403992,"alphanum_fraction":0.8004104495048523,"avg_line_length":388.79998779296875,"blob_id":"fbce8908b1fe025bd9ba341fe7a85d8361358246","content_id":"eeb6b51e9b1ffb50073809bfdad495747a1cf292","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1951,"license_type":"no_license","max_line_length":1340,"num_lines":5,"path":"/README.md","repo_name":"akankshanb/BeagleBone-Black-IOT","src_encoding":"UTF-8","text":"# BeagleBone-Black-IOT\n# IOT based Home automation system \n'''The project I made using BeagleBone Black involved a Webcam,PIR Motion sensor, a USB card reader, Speakers, USB hub,PCB design containing LEDs and resistors. The aim of the project was to build a Smart Security Doorbell which is able to perform the task of notifying the owner not home about an intruder or guest by using IOT. The basic layout of the doorbell consists of a Webcam to take a picture whenever\nthe PIR senses motion near the door.</br> The picture is sent to the owners Telegram App. This part completes informing the owner about a\nstranger near the house. Now to establish a communication system between the two subjects became the next step. This is because information could be shared between the owner and stranger, if he/ she happens to be an important person to the owner. How? This could be done by interfacing the audio feature between the Beaglebone microprocessor and the Telegram app. In Telegram , the audio feature is in the app itself, by long pressing the record button, the audio the owner wishes to sent to the person at the door is established. The output is connected to the speakers connected to the door. The wave file is converted to mp3 and connected to speakers. </br> Now the response of the person at the door needs to be sent to the owner .This is done via the webcam consisting of the microphone inbuilt. The person records the information and it get stored in the microprocessor. The wave file is then converted to mp3 and is sent to the Bot of the Telegram by a set of algorithms. This is how audio communication is established. This project not only helps in providing security near house but also is helpful in sharing important messages via internet between to people who may know each other. The coding part was done in Linux using embedded Python. The following libraries were imported – Adafruit_BBIO, Subprocess, Telepot, Time, Sys.'''\n"},{"alpha_fraction":0.6217948794364929,"alphanum_fraction":0.7051281929016113,"avg_line_length":22,"blob_id":"d9fe958feb34792e423102ed154f616ba774ef9f","content_id":"5b72e71004197a60a6e299386e4757ddfdca8ea8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":312,"license_type":"no_license","max_line_length":66,"num_lines":13,"path":"/send_photo.py","repo_name":"akankshanb/BeagleBone-Black-IOT","src_encoding":"UTF-8","text":"#SENDP.PY\r\n\r\nfrom twx.botapi import TelegramBot, ReplyKeyboardMarkup\r\n\r\nf = open('photo2.jpg','r+')\r\njpgdata = f.read()\r\n\r\nbot = TelegramBot('232181922:AAG5L_UGt9pEvfhL4LqZFTu3Hyjk8srYR-0')\r\nbot.update_bot_info().wait()\r\nuser_id = int(148942492)\r\nbot.send_photo(user_id,'/home/debian/photo2.jpg')\r\n\r\nf.close()\r\n"},{"alpha_fraction":0.47940340638160706,"alphanum_fraction":0.5220170617103577,"avg_line_length":25.076923370361328,"blob_id":"57d73a4d8949ff46525753f332e5cbecf0c67089","content_id":"300d6c047404527bdd7c76a9a9fe10198cdadf45","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1408,"license_type":"no_license","max_line_length":86,"num_lines":52,"path":"/telegram.py","repo_name":"akankshanb/BeagleBone-Black-IOT","src_encoding":"UTF-8","text":"TELEGRAM.PY\r\n\r\nimport telepot\r\nimport time\r\nbot = telepot.Bot('232181922:AAG5L_UGt9pEvfhL4LqZFTu3Hyjk8srYR-0')\r\nfrom pprint import pprint\r\ndef handle(msg):\r\n print msg.get('text')\r\n tmp = msg.get('photo')\r\n print tmp\r\n fid = tmp[0].get('file_id')\r\n fid.decode('utf-8','ignore')\r\n #print fid\r\n #print msg.get('file_id')\r\n bot.download_file(fid, '/root/xyz2.jpg')\r\nbot.message_loop(handle)\r\nprint ('Listening ...')\r\n\r\n# Keep the program running.\r\nwhile 1:\r\n time.sleep(10)\r\n\r\nAUDIO_BB2TEL.PY\r\n\r\nimport time\r\nimport Adafruit_BBIO.GPIO as GPIO\r\nimport telepot, sys, subprocess\r\n\r\nGPIO.setup('P9_15', GPIO.IN)\r\npush = \"P9_15\"\r\nwhile True:\r\n try:\r\n if GPIO.input(push):\r\n subprocess.call(\"./record.sh\", shell = True)\r\n b=telepot.Bot(\"232181922:AAG5L_UGt9pEvfhL4LqZFTu3Hyjk8srYR-0\")\r\n b.getMe()\r\n df = open('rec.mp3','rb')\r\n b.sendAudio(148942492, df)\r\n print(\"Audio sent\")\r\n else:\r\n print(\"no audio\")\r\n time.sleep(1)\r\n except KeyboardInterrupt:\r\n GPIO.cleanup()\r\n sys.exit(0)\r\n\r\nRECORD.SH\r\n\r\n#!/bin/bash\r\nrm rec.mp3\r\narecord -D plughw:1 --duration=10 -f cd -vv rec.mp3\r\necho \"recorded, thank u\"\r\n"},{"alpha_fraction":0.4183168411254883,"alphanum_fraction":0.4653465449810028,"avg_line_length":31.66666603088379,"blob_id":"8e7f96612445f7c718cce691de10aba824ecec5b","content_id":"1464934e51ad46fb2ed91d94d1f0239de04c991a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":808,"license_type":"no_license","max_line_length":86,"num_lines":24,"path":"/motion_detection.py","repo_name":"akankshanb/BeagleBone-Black-IOT","src_encoding":"UTF-8","text":"import Adafruit_BBIO.GPIO as GPIO\r\nimport time\r\nimport sys\r\nimport subprocess\r\nimport telepot\r\nGPIO.setup('P9_15',GPIO.IN)\r\nGPIO.setup('P9_16',GPIO.IN)\r\nwhile True:\r\n try:\r\n if GPIO.input('P9_15'):\r\n print(\"motion detected\")\r\n subprocess.call(\"./fs.sh\", shell=True)\r\n b=telepot.Bot('232181922:AAG5L_UGt9pEvfhL4LqZFTu3Hyjk8srYR-0')\r\n b.getMe()\r\n df = open('photo2.jpg','rb')\r\n b.sendPhoto(148942492,df)\r\n else:\r\n print(\"Motion not detected\")\r\n if GPIO.input('P9_16')\r\n\r\n time.sleep(1)\r\n except KeyboardInterrupt:\r\n GPIO.cleanup()\r\n sys.exit()\r\n"},{"alpha_fraction":0.6373626589775085,"alphanum_fraction":0.6593406796455383,"avg_line_length":11,"blob_id":"371bfa888dd543556d7e731863733902dcb595d5","content_id":"96e47f50cfe1396d0a65c062d138d516b9e862f5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":91,"license_type":"no_license","max_line_length":32,"num_lines":7,"path":"/initial.sh","repo_name":"akankshanb/BeagleBone-Black-IOT","src_encoding":"UTF-8","text":"INITIAL.SH \r\n\r\n#!/bin/bash\r\n\r\naplay -D plughw:1 owneraudio.mp3\r\n\r\necho \"initial msg sent\"\r\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5841584205627441,\n \"alphanum_fraction\": 0.6633663177490234,\n \"avg_line_length\": 17.799999237060547,\n \"blob_id\": \"8b1489b79c6cea8cc90651d9a0e2ab593580bb0c\",\n \"content_id\": \"0b7bde19e01bfe6cbdf536ad16c0bf8b3666fede\",\n \"detected_licenses\": [],\n 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\"akankshanb/BeagleBone-Black-IOT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# BeagleBone-Black-IOT\\n# IOT based Home automation system \\n'''The project I made using BeagleBone Black involved a Webcam,PIR Motion sensor, a USB card reader, Speakers, USB hub,PCB design containing LEDs and resistors. The aim of the project was to build a Smart Security Doorbell which is able to perform the task of notifying the owner not home about an intruder or guest by using IOT. The basic layout of the doorbell consists of a Webcam to take a picture whenever\\nthe PIR senses motion near the door.</br> The picture is sent to the owners Telegram App. This part completes informing the owner about a\\nstranger near the house. Now to establish a communication system between the two subjects became the next step. This is because information could be shared between the owner and stranger, if he/ she happens to be an important person to the owner. How? This could be done by interfacing the audio feature between the Beaglebone microprocessor and the Telegram app. In Telegram , the audio feature is in the app itself, by long pressing the record button, the audio the owner wishes to sent to the person at the door is established. The output is connected to the speakers connected to the door. The wave file is converted to mp3 and connected to speakers. </br> Now the response of the person at the door needs to be sent to the owner .This is done via the webcam consisting of the microphone inbuilt. The person records the information and it get stored in the microprocessor. The wave file is then converted to mp3 and is sent to the Bot of the Telegram by a set of algorithms. This is how audio communication is established. This project not only helps in providing security near house but also is helpful in sharing important messages via internet between to people who may know each other. The coding part was done in Linux using embedded Python. The following libraries were imported – Adafruit_BBIO, Subprocess, Telepot, Time, Sys.'''\\n\"\n },\n {\n \"alpha_fraction\": 0.6217948794364929,\n \"alphanum_fraction\": 0.7051281929016113,\n \"avg_line_length\": 22,\n \"blob_id\": \"d9fe958feb34792e423102ed154f616ba774ef9f\",\n \"content_id\": \"5b72e71004197a60a6e299386e4757ddfdca8ea8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 312,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 13,\n \"path\": \"/send_photo.py\",\n \"repo_name\": \"akankshanb/BeagleBone-Black-IOT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#SENDP.PY\\r\\n\\r\\nfrom twx.botapi import TelegramBot, ReplyKeyboardMarkup\\r\\n\\r\\nf = open('photo2.jpg','r+')\\r\\njpgdata = f.read()\\r\\n\\r\\nbot = TelegramBot('232181922:AAG5L_UGt9pEvfhL4LqZFTu3Hyjk8srYR-0')\\r\\nbot.update_bot_info().wait()\\r\\nuser_id = int(148942492)\\r\\nbot.send_photo(user_id,'/home/debian/photo2.jpg')\\r\\n\\r\\nf.close()\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.47940340638160706,\n \"alphanum_fraction\": 0.5220170617103577,\n \"avg_line_length\": 25.076923370361328,\n \"blob_id\": \"57d73a4d8949ff46525753f332e5cbecf0c67089\",\n \"content_id\": \"300d6c047404527bdd7c76a9a9fe10198cdadf45\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1408,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 52,\n \"path\": \"/telegram.py\",\n \"repo_name\": \"akankshanb/BeagleBone-Black-IOT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"TELEGRAM.PY\\r\\n\\r\\nimport telepot\\r\\nimport time\\r\\nbot = telepot.Bot('232181922:AAG5L_UGt9pEvfhL4LqZFTu3Hyjk8srYR-0')\\r\\nfrom pprint import pprint\\r\\ndef handle(msg):\\r\\n print msg.get('text')\\r\\n tmp = msg.get('photo')\\r\\n print tmp\\r\\n fid = tmp[0].get('file_id')\\r\\n fid.decode('utf-8','ignore')\\r\\n #print fid\\r\\n #print msg.get('file_id')\\r\\n bot.download_file(fid, '/root/xyz2.jpg')\\r\\nbot.message_loop(handle)\\r\\nprint ('Listening ...')\\r\\n\\r\\n# Keep the program running.\\r\\nwhile 1:\\r\\n time.sleep(10)\\r\\n\\r\\nAUDIO_BB2TEL.PY\\r\\n\\r\\nimport time\\r\\nimport Adafruit_BBIO.GPIO as GPIO\\r\\nimport telepot, sys, subprocess\\r\\n\\r\\nGPIO.setup('P9_15', GPIO.IN)\\r\\npush = \\\"P9_15\\\"\\r\\nwhile True:\\r\\n try:\\r\\n if GPIO.input(push):\\r\\n subprocess.call(\\\"./record.sh\\\", shell = True)\\r\\n b=telepot.Bot(\\\"232181922:AAG5L_UGt9pEvfhL4LqZFTu3Hyjk8srYR-0\\\")\\r\\n b.getMe()\\r\\n df = open('rec.mp3','rb')\\r\\n b.sendAudio(148942492, df)\\r\\n print(\\\"Audio sent\\\")\\r\\n else:\\r\\n print(\\\"no audio\\\")\\r\\n time.sleep(1)\\r\\n except KeyboardInterrupt:\\r\\n GPIO.cleanup()\\r\\n sys.exit(0)\\r\\n\\r\\nRECORD.SH\\r\\n\\r\\n#!/bin/bash\\r\\nrm rec.mp3\\r\\narecord -D plughw:1 --duration=10 -f cd -vv rec.mp3\\r\\necho \\\"recorded, thank u\\\"\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.4183168411254883,\n \"alphanum_fraction\": 0.4653465449810028,\n \"avg_line_length\": 31.66666603088379,\n \"blob_id\": \"8e7f96612445f7c718cce691de10aba824ecec5b\",\n \"content_id\": \"1464934e51ad46fb2ed91d94d1f0239de04c991a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 808,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 24,\n \"path\": \"/motion_detection.py\",\n \"repo_name\": \"akankshanb/BeagleBone-Black-IOT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import Adafruit_BBIO.GPIO as GPIO\\r\\nimport time\\r\\nimport sys\\r\\nimport subprocess\\r\\nimport telepot\\r\\nGPIO.setup('P9_15',GPIO.IN)\\r\\nGPIO.setup('P9_16',GPIO.IN)\\r\\nwhile True:\\r\\n try:\\r\\n if GPIO.input('P9_15'):\\r\\n print(\\\"motion detected\\\")\\r\\n subprocess.call(\\\"./fs.sh\\\", shell=True)\\r\\n b=telepot.Bot('232181922:AAG5L_UGt9pEvfhL4LqZFTu3Hyjk8srYR-0')\\r\\n b.getMe()\\r\\n df = open('photo2.jpg','rb')\\r\\n b.sendPhoto(148942492,df)\\r\\n else:\\r\\n print(\\\"Motion not detected\\\")\\r\\n if GPIO.input('P9_16')\\r\\n\\r\\n time.sleep(1)\\r\\n except KeyboardInterrupt:\\r\\n GPIO.cleanup()\\r\\n sys.exit()\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6373626589775085,\n \"alphanum_fraction\": 0.6593406796455383,\n \"avg_line_length\": 11,\n \"blob_id\": \"371bfa888dd543556d7e731863733902dcb595d5\",\n \"content_id\": \"96e47f50cfe1396d0a65c062d138d516b9e862f5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 91,\n \"license_type\": \"no_license\",\n \"max_line_length\": 32,\n \"num_lines\": 7,\n \"path\": \"/initial.sh\",\n \"repo_name\": \"akankshanb/BeagleBone-Black-IOT\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"INITIAL.SH \\r\\n\\r\\n#!/bin/bash\\r\\n\\r\\naplay -D plughw:1 owneraudio.mp3\\r\\n\\r\\necho \\\"initial msg sent\\\"\\r\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":2979146,"cells":{"repo_name":{"kind":"string","value":"neuralian/Spider"},"repo_url":{"kind":"string","value":"https://github.com/neuralian/Spider"},"snapshot_id":{"kind":"string","value":"f5a6f7df5a106c6b79f07d054ba3809025b619a0"},"revision_id":{"kind":"string","value":"456f061640ad210e8f35f9c2b46fed49cd835d73"},"directory_id":{"kind":"string","value":"16006a05ef6da4bc678d92772ce8ff2c63600f1d"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-12-02T11:35:45.769040","string":"2022-12-02T11:35:45.769040"},"revision_date":{"kind":"timestamp","value":"2020-08-17T08:21:26","string":"2020-08-17T08:21:26"},"committer_date":{"kind":"timestamp","value":"2020-08-17T08:21:26","string":"2020-08-17T08:21:26"},"github_id":{"kind":"number","value":269482371,"string":"269,482,371"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6552927494049072,"alphanum_fraction":0.6766188740730286,"avg_line_length":34.81944274902344,"blob_id":"943ce2e8d6232189df146542fa6a1547d50e3671","content_id":"d33ccdc739373c749a1640403d14bcab82f08f28","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2579,"license_type":"no_license","max_line_length":86,"num_lines":72,"path":"/spider_dev_legsegment_02.py","repo_name":"neuralian/Spider","src_encoding":"UTF-8","text":"import Part\nfrom FreeCAD import Base\n\n\n# tapered tube with elliptical cross section\n# major axis in x-direction (because Part.Ellipse works that way)\ndef makeSpiderLegSegment (baseDiamX = 1.0, baseDiamY = 0.8, \n tipDiamX = 0.8, tipDiamY = 0.6,\n segLength = 5.0, skinThick = 0.25,\n name = \"segment\"):\n \n # outer shell is a tapered ellipsoid loft \n ellipse1 = Part.Ellipse(Base.Vector(0,0,0), baseDiamX, baseDiamY)\n base1=Part.Wire(Part.Edge(ellipse1))\n\n ellipse2 = Part.Ellipse(Base.Vector(0,0,0), tipDiamX, tipDiamY)\n ellipse2.translate(Base.Vector(0,0,segLength))\n tip1 = Part.Wire(Part.Edge(ellipse2))\n\n shell = App.ActiveDocument.addObject(\"Part::Feature\", \"shell\")\n shell.Shape = Part.makeLoft([base1, tip1], True)\n\n # hollow core is a tapered ellipsoid loft\n ellipse3 = Part.Ellipse(Base.Vector(0,0,0), \n baseDiamX-skinThick, baseDiamY-skinThick)\n base2=Part.Wire(Part.Edge(ellipse3))\n\n ellipse4 = Part.Ellipse(Base.Vector(0,0,0), \n tipDiamX-skinThick, tipDiamY-skinThick)\n ellipse4.translate(Base.Vector(0,0,segLength))\n tip2 = Part.Wire(Part.Edge(ellipse4))\n\n core = App.ActiveDocument.addObject(\"Part::Feature\", \"core\")\n core.Shape = Part.makeLoft([base2, tip2], True)\n\n # Make tube by cutting core from shell\n legSegment = App.ActiveDocument.addObject(\"Part::Cut\", name)\n App.ActiveDocument.ActiveObject.Base = shell \n App.ActiveDocument.ActiveObject.Tool = core \n \n \n # core.Document.removeObject(\"core\")\n # shell.Document.removeObject(\"shell\")\n \n return legSegment\n \ndef toCompound(theObjectName):\n \n theObject = App.activeDocument().getObject(theObjectName)\n newObject = App.activeDocument().addObject(\"Part::Compound\",\"Compound\")\n App.activeDocument().Compound.Links = [theObject,]\n App.activeDocument().Compound.Label = theObjectName\n App.ActiveDocument.recompute()\n return newObject\n \n \ndef toSolid(theObjectName):\n \n theObject = App.activeDocument().getObject(theObjectName)\n App.activeDocument().addObject(\"Part::Feature\",\"Solid\")\n App.activeDocument().Solid.Shape = Part.Solid(Part.Shell(theObject.Shape.Faces))\n App.activeDocument().Solid.Label = theObjectName\n App.ActiveDocument.recompute()\n \n \nfred = makeSpiderLegSegment(name=\"fred\")\nbetty = makeSpiderLegSegment(name=\"betty\")\n\nfred.Placement = App.Placement(App.Vector(0,0,0), App.Rotation(App.Vector(0,0,1), 90))\n\nfred = toCompound(\"fred\")\ntoSolid(\"fred\")\n"},{"alpha_fraction":0.6784896850585938,"alphanum_fraction":0.7437071204185486,"avg_line_length":23.22222137451172,"blob_id":"b391054ef324a653428fb350457f90eba4aeb226","content_id":"db69ed711688746f443f16cfac1dbba5128ea13e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":874,"license_type":"no_license","max_line_length":61,"num_lines":36,"path":"/spider_dev_legsegment_00.py","repo_name":"neuralian/Spider","src_encoding":"UTF-8","text":"import Part\nfrom FreeCAD import Base\n\n\n\n\n\nellipse1 = Part.Ellipse(Base.Vector(0,0,0), 1.0, 0.8)\nbase1=Part.Wire(Part.Edge(ellipse1))\n\n\nellipse2 = Part.Ellipse(Base.Vector(0,0,0), 0.8, 0.6)\nellipse2.translate(Base.Vector(0,0,5))\ntip1 = Part.Wire(Part.Edge(ellipse2))\n\nside = Part.makeLoft([base1, tip1], True)\n\nellipse3 = Part.Ellipse(Base.Vector(0,0,0), .8, 0.6)\nbase2=Part.Wire(Part.Edge(ellipse3))\n\n\nellipse4 = Part.Ellipse(Base.Vector(0,0,0), 0.6, 0.4)\nellipse4.translate(Base.Vector(0,0,5))\ntip2 = Part.Wire(Part.Edge(ellipse4))\n\ncore = Part.makeLoft([base2, tip2], True)\n\nPart.show(side)\nPart.show(core)\n\nApp.ActiveDocument.addObject(\"Part::Cut\", \"Segment\")\nApp.ActiveDocument.Segment.Base = App.ActiveDocument.Shape\nApp.ActiveDocument.Segment.Tool = App.ActiveDocument.Shape001\n\nGui.activeDocument.Shape.Visibility=False\nGui.activeDocument.Shape001.Visibility=False\n\n\n"},{"alpha_fraction":0.5628291964530945,"alphanum_fraction":0.614747941493988,"avg_line_length":30.619047164916992,"blob_id":"e037ba7f18b6be589a9b2999dbeebb88efeb2a08","content_id":"4bd7e7c43dc5aca02573ae706ba8c9652fc1dd53","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1329,"license_type":"no_license","max_line_length":74,"num_lines":42,"path":"/spider_dev_joint.py","repo_name":"neuralian/Spider","src_encoding":"UTF-8","text":"import Part\nfrom FreeCAD import Base\n\n\ndef makeJoint(radius = 1.0, height = 1.25, width = 1.75, thickness = 0.1):\n\n\n # vertices on outer edge \n V1 = Base.Vector(-radius, -height, -width/2) # lower left\n V2 = Base.Vector(-radius, 0, -width/2)\n V3 = Base.Vector(0, radius, -width/2) # top \n V4 = Base.Vector(radius, 0, -width/2)\n V5 = Base.Vector(radius, -height, -width/2) # lower right\n\n # vertices on inner edge \n # keep numbering in same direction around loop\n V6 = Base.Vector(radius-thickness, -height, -width/2) # lower left\n V7 = Base.Vector(radius-thickness, 0, -width/2)\n V8 = Base.Vector(0, radius-thickness, -width/2) # top \n V9 = Base.Vector(-radius+thickness, 0, -width/2)\n VA = Base.Vector(-radius+thickness, -height, -width/2) # lower right\n\n # create edge segments\n L1 = Part.LineSegment(V1, V2)\n C1 = Part.Arc(V2, V3, V4)\n L2 = Part.LineSegment(V4, V5)\n L3 = Part.LineSegment(V5, V6)\n L4 = Part.LineSegment(V6, V7)\n C2 = Part.Arc(V7, V8, V9)\n L5 = Part.LineSegment(V9, VA)\n L6 = Part.LineSegment(VA, V1)\n\n thickness = Part.Shape([L1, C1, L2, L3, L4, C2, L5, L6])\n W = Part.Wire(thickness.Edges)\n\n F = Part.Face(W)\n P = F.extrude(Base.Vector(0, 0, width))\n \n return P\n \naJoint = makeJoint()\nPart.show(aJoint)\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6552927494049072,\n \"alphanum_fraction\": 0.6766188740730286,\n \"avg_line_length\": 34.81944274902344,\n \"blob_id\": \"943ce2e8d6232189df146542fa6a1547d50e3671\",\n \"content_id\": \"d33ccdc739373c749a1640403d14bcab82f08f28\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2579,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 72,\n \"path\": \"/spider_dev_legsegment_02.py\",\n \"repo_name\": \"neuralian/Spider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import Part\\nfrom FreeCAD import Base\\n\\n\\n# tapered tube with elliptical cross section\\n# major axis in x-direction (because Part.Ellipse works that way)\\ndef makeSpiderLegSegment (baseDiamX = 1.0, baseDiamY = 0.8, \\n tipDiamX = 0.8, tipDiamY = 0.6,\\n segLength = 5.0, skinThick = 0.25,\\n name = \\\"segment\\\"):\\n \\n # outer shell is a tapered ellipsoid loft \\n ellipse1 = Part.Ellipse(Base.Vector(0,0,0), baseDiamX, baseDiamY)\\n base1=Part.Wire(Part.Edge(ellipse1))\\n\\n ellipse2 = Part.Ellipse(Base.Vector(0,0,0), tipDiamX, tipDiamY)\\n ellipse2.translate(Base.Vector(0,0,segLength))\\n tip1 = Part.Wire(Part.Edge(ellipse2))\\n\\n shell = App.ActiveDocument.addObject(\\\"Part::Feature\\\", \\\"shell\\\")\\n shell.Shape = Part.makeLoft([base1, tip1], True)\\n\\n # hollow core is a tapered ellipsoid loft\\n ellipse3 = Part.Ellipse(Base.Vector(0,0,0), \\n baseDiamX-skinThick, baseDiamY-skinThick)\\n base2=Part.Wire(Part.Edge(ellipse3))\\n\\n ellipse4 = Part.Ellipse(Base.Vector(0,0,0), \\n tipDiamX-skinThick, tipDiamY-skinThick)\\n ellipse4.translate(Base.Vector(0,0,segLength))\\n tip2 = Part.Wire(Part.Edge(ellipse4))\\n\\n core = App.ActiveDocument.addObject(\\\"Part::Feature\\\", \\\"core\\\")\\n core.Shape = Part.makeLoft([base2, tip2], True)\\n\\n # Make tube by cutting core from shell\\n legSegment = App.ActiveDocument.addObject(\\\"Part::Cut\\\", name)\\n App.ActiveDocument.ActiveObject.Base = shell \\n App.ActiveDocument.ActiveObject.Tool = core \\n \\n \\n # core.Document.removeObject(\\\"core\\\")\\n # shell.Document.removeObject(\\\"shell\\\")\\n \\n return legSegment\\n \\ndef toCompound(theObjectName):\\n \\n theObject = App.activeDocument().getObject(theObjectName)\\n newObject = App.activeDocument().addObject(\\\"Part::Compound\\\",\\\"Compound\\\")\\n App.activeDocument().Compound.Links = [theObject,]\\n App.activeDocument().Compound.Label = theObjectName\\n App.ActiveDocument.recompute()\\n return newObject\\n \\n \\ndef toSolid(theObjectName):\\n \\n theObject = App.activeDocument().getObject(theObjectName)\\n App.activeDocument().addObject(\\\"Part::Feature\\\",\\\"Solid\\\")\\n App.activeDocument().Solid.Shape = Part.Solid(Part.Shell(theObject.Shape.Faces))\\n App.activeDocument().Solid.Label = theObjectName\\n App.ActiveDocument.recompute()\\n \\n \\nfred = makeSpiderLegSegment(name=\\\"fred\\\")\\nbetty = makeSpiderLegSegment(name=\\\"betty\\\")\\n\\nfred.Placement = App.Placement(App.Vector(0,0,0), App.Rotation(App.Vector(0,0,1), 90))\\n\\nfred = toCompound(\\\"fred\\\")\\ntoSolid(\\\"fred\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.6784896850585938,\n \"alphanum_fraction\": 0.7437071204185486,\n \"avg_line_length\": 23.22222137451172,\n \"blob_id\": \"b391054ef324a653428fb350457f90eba4aeb226\",\n \"content_id\": \"db69ed711688746f443f16cfac1dbba5128ea13e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 874,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 36,\n \"path\": \"/spider_dev_legsegment_00.py\",\n \"repo_name\": \"neuralian/Spider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import Part\\nfrom FreeCAD import Base\\n\\n\\n\\n\\n\\nellipse1 = Part.Ellipse(Base.Vector(0,0,0), 1.0, 0.8)\\nbase1=Part.Wire(Part.Edge(ellipse1))\\n\\n\\nellipse2 = Part.Ellipse(Base.Vector(0,0,0), 0.8, 0.6)\\nellipse2.translate(Base.Vector(0,0,5))\\ntip1 = Part.Wire(Part.Edge(ellipse2))\\n\\nside = Part.makeLoft([base1, tip1], True)\\n\\nellipse3 = Part.Ellipse(Base.Vector(0,0,0), .8, 0.6)\\nbase2=Part.Wire(Part.Edge(ellipse3))\\n\\n\\nellipse4 = Part.Ellipse(Base.Vector(0,0,0), 0.6, 0.4)\\nellipse4.translate(Base.Vector(0,0,5))\\ntip2 = Part.Wire(Part.Edge(ellipse4))\\n\\ncore = Part.makeLoft([base2, tip2], True)\\n\\nPart.show(side)\\nPart.show(core)\\n\\nApp.ActiveDocument.addObject(\\\"Part::Cut\\\", \\\"Segment\\\")\\nApp.ActiveDocument.Segment.Base = App.ActiveDocument.Shape\\nApp.ActiveDocument.Segment.Tool = App.ActiveDocument.Shape001\\n\\nGui.activeDocument.Shape.Visibility=False\\nGui.activeDocument.Shape001.Visibility=False\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5628291964530945,\n \"alphanum_fraction\": 0.614747941493988,\n \"avg_line_length\": 30.619047164916992,\n \"blob_id\": \"e037ba7f18b6be589a9b2999dbeebb88efeb2a08\",\n \"content_id\": \"4bd7e7c43dc5aca02573ae706ba8c9652fc1dd53\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1329,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 42,\n \"path\": \"/spider_dev_joint.py\",\n \"repo_name\": \"neuralian/Spider\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import Part\\nfrom FreeCAD import Base\\n\\n\\ndef makeJoint(radius = 1.0, height = 1.25, width = 1.75, thickness = 0.1):\\n\\n\\n # vertices on outer edge \\n V1 = Base.Vector(-radius, -height, -width/2) # lower left\\n V2 = Base.Vector(-radius, 0, -width/2)\\n V3 = Base.Vector(0, radius, -width/2) # top \\n V4 = Base.Vector(radius, 0, -width/2)\\n V5 = Base.Vector(radius, -height, -width/2) # lower right\\n\\n # vertices on inner edge \\n # keep numbering in same direction around loop\\n V6 = Base.Vector(radius-thickness, -height, -width/2) # lower left\\n V7 = Base.Vector(radius-thickness, 0, -width/2)\\n V8 = Base.Vector(0, radius-thickness, -width/2) # top \\n V9 = Base.Vector(-radius+thickness, 0, -width/2)\\n VA = Base.Vector(-radius+thickness, -height, -width/2) # lower right\\n\\n # create edge segments\\n L1 = Part.LineSegment(V1, V2)\\n C1 = Part.Arc(V2, V3, V4)\\n L2 = Part.LineSegment(V4, V5)\\n L3 = Part.LineSegment(V5, V6)\\n L4 = Part.LineSegment(V6, V7)\\n C2 = Part.Arc(V7, V8, V9)\\n L5 = Part.LineSegment(V9, VA)\\n L6 = Part.LineSegment(VA, V1)\\n\\n thickness = Part.Shape([L1, C1, L2, L3, L4, C2, L5, L6])\\n W = Part.Wire(thickness.Edges)\\n\\n F = Part.Face(W)\\n P = F.extrude(Base.Vector(0, 0, width))\\n \\n return P\\n \\naJoint = makeJoint()\\nPart.show(aJoint)\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979147,"cells":{"repo_name":{"kind":"string","value":"jnphilipp/infohub"},"repo_url":{"kind":"string","value":"https://github.com/jnphilipp/infohub"},"snapshot_id":{"kind":"string","value":"99c8bd5a0d26aa94c4dc2402d411ae2932987e49"},"revision_id":{"kind":"string","value":"0ac449a31073fa437ec9a8fc1f1b0a88eda33ad7"},"directory_id":{"kind":"string","value":"d056134245558897ad4412f1b8f99e4ffa3b38b2"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-22T09:48:24.353060","string":"2021-01-22T09:48:24.353060"},"revision_date":{"kind":"timestamp","value":"2018-10-15T12:23:47","string":"2018-10-15T12:23:47"},"committer_date":{"kind":"timestamp","value":"2018-10-15T12:23:47","string":"2018-10-15T12:23:47"},"github_id":{"kind":"number","value":19138723,"string":"19,138,723"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7346368432044983,"alphanum_fraction":0.7402234673500061,"avg_line_length":27.68000030517578,"blob_id":"fea38995c2e0d9c6ee3b9f7954dde25872199dd4","content_id":"42d536f40c64408d486d1a5c54a3f11061aabae7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":716,"license_type":"no_license","max_line_length":70,"num_lines":25,"path":"/parsers/models.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.db import models\nfrom django.template.defaultfilters import slugify\nfrom south.modelsinspector import add_introspection_rules\nadd_introspection_rules([], [\"^parsers\\.models\\.TextFieldSingleLine\"])\n\nclass TextFieldSingleLine(models.TextField):\n\tpass\n\nclass Parser(models.Model):\n\tcreated_at = models.DateTimeField(auto_now_add=True)\n\tupdated_at = models.DateTimeField(auto_now=True)\n\n\tslug = models.SlugField(max_length=4096, unique=True)\n\tname = TextFieldSingleLine(unique=True)\n\n\tdef save(self, *args, **kwargs):\n\t\tif not self.id:\n\t\t\tself.slug = slugify(self.name).replace('-', '_')\n\t\tsuper(Parser, self).save(*args, **kwargs)\n\n\tdef __str__(self):\n\t\treturn self.name\n\n\tclass Meta:\n\t\tordering = ('name',)"},{"alpha_fraction":0.7369862794876099,"alphanum_fraction":0.7431507110595703,"avg_line_length":34.63414764404297,"blob_id":"3acda72260957254de5a0658a08d54824f349830","content_id":"c25619b35e638db906d90e323c7d65e38384d00c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1460,"license_type":"no_license","max_line_length":92,"num_lines":41,"path":"/feeds/views.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import get_object_or_404, render\nfrom documents.models import Document\nfrom feeds.models import Feed, Report\nfrom datetime import datetime, date, time\nfrom pytz import utc\n\n@login_required(login_url='/admin/')\ndef feeds(request):\n\tfeeds = Feed.objects.order_by('alive', '-updated_at')\n\treturn render(request, 'feeds/feeds.html', locals())\n\n@login_required(login_url='/admin/')\ndef feed(request, slug):\n\tfeed = get_object_or_404(Feed, slug=slug)\n\treturn render(request, 'feeds/feed.html', locals())\n\n@login_required(login_url='/admin/')\ndef reports(request):\n\treports = Report.objects.order_by('-created_at')\n\treturn render(request, 'feeds/reports.html', locals())\n\n@login_required(login_url='/admin/')\ndef report(request, slug):\n\treport = get_object_or_404(Report, slug=slug)\n\treturn render(request, 'feeds/report.html', locals())\n\n@login_required(login_url='/admin/')\ndef statistics(request):\n\ttoday_min = datetime.combine(date.today(), time.min).replace(tzinfo=utc)\n\ttoday_max = datetime.combine(date.today(), time.max).replace(tzinfo=utc)\n\n\tfeeds = Feed.objects.count()\n\n\tdocuments = Document.objects.count()\n\ttoday_documents = Document.objects.filter(created_at__range=(today_min, today_max)).count()\n\n\treports = Report.objects.count()\n\ttoday_reports = Report.objects.filter(created_at__range=(today_min, today_max))\n\n\treturn render(request, 'feeds/statistics.html', locals())"},{"alpha_fraction":0.5888411998748779,"alphanum_fraction":0.5939913988113403,"avg_line_length":39.17241287231445,"blob_id":"90f4e5b3d6f7cbafa20ae19d6a30eadba22073e5","content_id":"dbac26451b780eb24c92f00a83e9eb33f10f7e37","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1165,"license_type":"no_license","max_line_length":115,"num_lines":29,"path":"/parsers/migrations/0002_add_default_parser.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom south.utils import datetime_utils as datetime\nfrom south.db import db\nfrom south.v2 import DataMigration\nfrom django.db import models\n\nclass Migration(DataMigration):\n\n def forwards(self, orm):\n \"Write your forwards methods here.\"\n orm.Parser.objects.create(name='Default Parser', slug='default')\n\n def backwards(self, orm):\n \"Write your backwards methods here.\"\n orm.Parser.objects.get(slug='default').delete()\n\n models = {\n 'parsers.parser': {\n 'Meta': {'object_name': 'Parser', 'ordering': \"('name',)\"},\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now_add': 'True'}),\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\n 'name': ('parsers.models.TextFieldSingleLine', [], {'unique': 'True'}),\n 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '4096', 'unique': 'True'}),\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})\n }\n }\n\n complete_apps = ['parsers']\n symmetrical = True\n"},{"alpha_fraction":0.553529679775238,"alphanum_fraction":0.5596072673797607,"avg_line_length":58.43055725097656,"blob_id":"0e4775e358e25f331dcfec4fda2d69625161dd9a","content_id":"8c22d6e488087f11a37ca63876572854aa73b069","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4278,"license_type":"no_license","max_line_length":139,"num_lines":72,"path":"/documents/migrations/0001_initial.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom south.utils import datetime_utils as datetime\nfrom south.db import db\nfrom south.v2 import SchemaMigration\nfrom django.db import models\n\n\nclass Migration(SchemaMigration):\n depends_on = (\n (\"feeds\", \"0001_initial\"),\n )\n\n def forwards(self, orm):\n # Adding model 'Document'\n db.create_table('documents_document', (\n ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),\n ('created_at', self.gf('django.db.models.fields.DateTimeField')(blank=True, auto_now_add=True)),\n ('updated_at', self.gf('django.db.models.fields.DateTimeField')(blank=True, auto_now=True)),\n ('slug', self.gf('django.db.models.fields.SlugField')(unique=True, max_length=4096)),\n ('url', self.gf('documents.models.TextFieldSingleLine')(unique=True)),\n ('title', self.gf('documents.models.TextFieldSingleLine')(null=True, blank=True)),\n ('meta', self.gf('django.db.models.fields.TextField')()),\n ('content', self.gf('django.db.models.fields.TextField')()),\n ('text', self.gf('django.db.models.fields.TextField')(null=True, blank=True)),\n ('feed', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['feeds.Feed'])),\n ('state', self.gf('django_fsm.FSMField')(max_length=50, default='new')),\n ))\n db.send_create_signal('documents', ['Document'])\n\n\n def backwards(self, orm):\n # Deleting model 'Document'\n db.delete_table('documents_document')\n\n\n models = {\n 'documents.document': {\n 'Meta': {'ordering': \"('-created_at',)\", 'object_name': 'Document'},\n 'content': ('django.db.models.fields.TextField', [], {}),\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now_add': 'True'}),\n 'feed': ('django.db.models.fields.related.ForeignKey', [], {'to': \"orm['feeds.Feed']\"}),\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\n 'meta': ('django.db.models.fields.TextField', [], {}),\n 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '4096'}),\n 'state': ('django_fsm.FSMField', [], {'max_length': '50', 'default': \"'new'\"}),\n 'text': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),\n 'title': ('documents.models.TextFieldSingleLine', [], {'null': 'True', 'blank': 'True'}),\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now': 'True'}),\n 'url': ('documents.models.TextFieldSingleLine', [], {'unique': 'True'})\n },\n 'feeds.feed': {\n 'Meta': {'object_name': 'Feed'},\n 'alive': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now_add': 'True'}),\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\n 'parser': ('django.db.models.fields.related.ForeignKey', [], {'null': 'True', 'blank': 'True', 'to': \"orm['parsers.Parser']\"}),\n 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '4096'}),\n 'title': ('feeds.models.TextFieldSingleLine', [], {'null': 'True', 'blank': 'True'}),\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now': 'True'}),\n 'url': ('feeds.models.TextFieldSingleLine', [], {'unique': 'True'})\n },\n 'parsers.parser': {\n 'Meta': {'ordering': \"('name',)\", 'object_name': 'Parser'},\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now_add': 'True'}),\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\n 'name': ('parsers.models.TextFieldSingleLine', [], {'unique': 'True'}),\n 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '4096'}),\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now': 'True'})\n }\n }\n\n complete_apps = ['documents']"},{"alpha_fraction":0.7037037014961243,"alphanum_fraction":0.7037037014961243,"avg_line_length":12.5,"blob_id":"e632b62a0e85d5722a70079488dc357f6789cbfc","content_id":"e517f2c91f636f4d6fc088e0847accebcbc26650","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":54,"license_type":"no_license","max_line_length":36,"num_lines":4,"path":"/README.md","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"infohub\n=======\n\nFeedparser and text extraction tool.\n"},{"alpha_fraction":0.6341724395751953,"alphanum_fraction":0.6411355137825012,"avg_line_length":23.906665802001953,"blob_id":"d48b15c54f7f7eebcd481a59db1b5e5781c648bd","content_id":"f07c96f2f457017b06e748fe1e4886a592fcc47e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1867,"license_type":"no_license","max_line_length":127,"num_lines":75,"path":"/feeds/templatetags/feeds_tags.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.core.urlresolvers import reverse\nfrom django.db.models import Count, Max, Min\nfrom django.template import Library\nfrom django.utils.safestring import mark_safe\nfrom feeds.models import Report, Feed\n\nimport json\n\nregister = Library()\n\n@register.filter\ndef startswith(value, start):\n\treturn value.startswith(start)\n\n@register.filter\ndef num_new_entries(report):\n\tnew_entries = json.loads(report.text)\n\n\tcount = 0\n\tfor v in new_entries.values():\n\t\ttry:\n\t\t\tcount += v\n\t\texcept:\n\t\t\tpass\n\n\treturn count\n\n@register.filter(needs_autoescape=True)\ndef report_text(report, autoescape=None):\n\ts = '<table>'\n\ts += '<tr><th>feed</th><th>number of new documents</th></tr>'\n\ti = 0\n\tfor key, val in json.loads(report.text).items():\n\t\tfeed = ''\n\t\ttry:\n\t\t\tf = Feed.objects.get(slug=key)\n\t\t\tfeed = '<a href=\"%s\">%s</a>' % (reverse('feed', args=[f.slug]), f.title)\n\t\texcept Feed.DoesNotExist:\n\t\t\tfeed = key\n\n\t\ts += '<tr class=\"%s\"><td>%s</td><td style=\"text-align: center;\">%s</td></tr>' % ('row1' if i % 2 == 0 else 'row2', feed, val)\n\t\ti += 1\n\n\ts += '</table>'\n\n\treturn mark_safe(s)\n\n@register.filter(needs_autoescape=True)\ndef statistics_report_text(reports, autoescape=None):\n\ts = '<table>'\n\ts += '<tr><th>feed</th><th>number of new documents today</th></tr>'\n\ti = 0\n\n\tdaily_report = dict()\n\tfor report in reports:\n\t\tfor key, val in json.loads(report.text).items():\n\t\t\ttry:\n\t\t\t\tdaily_report[key] = daily_report[key] + int(val) if key in daily_report else int(val)\n\t\t\texcept:\n\t\t\t\tpass\n\n\tfor key, val in daily_report.items():\n\t\tfeed = ''\n\t\ttry:\n\t\t\tf = Feed.objects.get(slug=key)\n\t\t\tfeed = '<a href=\"%s\">%s</a>' % (reverse('feed', args=[f.slug]), f.title)\n\t\texcept Feed.DoesNotExist:\n\t\t\tfeed = key\n\n\t\ts += '<tr class=\"%s\"><td>%s</td><td style=\"text-align: center;\">%s</td></tr>' % ('row1' if i % 2 == 0 else 'row2', feed, val)\n\t\ti += 1\n\n\ts += '</table>'\n\n\treturn mark_safe(s)"},{"alpha_fraction":0.651294469833374,"alphanum_fraction":0.6577669978141785,"avg_line_length":23.254901885986328,"blob_id":"028538a35a203b06b5ca0afb4aa87189d489becf","content_id":"a77befe2c2c5f0eb63e038ec60f7b91571b54ecd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1236,"license_type":"no_license","max_line_length":114,"num_lines":51,"path":"/parsers/parsers/base.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\nfrom abc import ABCMeta, abstractmethod\nfrom html import unescape\nimport re\nimport urllib\n\nclass BaseParser(object):\n\t__metaclass__ = ABCMeta\n\n\t@abstractmethod\n\tdef get_text(self, document):\n\t\tpass\n\nclass HTMLParser(BaseParser):\n\tuser_agent = 'Mozilla/4.0'\n\n\tdef fetch(self, url):\n\t\tresponse = urllib.request.urlopen(url)\n\t\ttext = response.read()\n\t\tencoding = str(text).lower().split('charset=', 1)[-1].split('\"', 1)[0]\n\n\t\tif encoding:\n\t\t\ttext = text.decode(encoding)\n\t\telse:\n\t\t\ttext = str(text)\n\n\t\tresponse.close()\n\n\t\treturn text\n\n\tdef fetch_with_data(self, url, values):\n\t\theaders = {'User-Agent': self.user_agent}\n\t\tdata = urllib.parse.urlencode(values)\n\t\treq = urllib.Request(url, data, headers)\n\t\tresponse = urllib.reques.request.urlopen(req)\n\t\treturn response.read()\n\n\tdef get_redirect_url(self, url):\n\t\tresponse = urllib.request.urlopen(url)\n\t\treturn response.url\n\n\tdef clean_unescape(self, text):\n\t\treturn unescape(re.sub(r'\\s\\s+', ' ', re.sub(r'<[^>]+>', '', text.replace('\\n', ' ')))).strip()\n\n\tdef get_print_link(self, text):\n\t\tmatch = re.search(r'<a[^>]*href=\"([^\"]+)\"[^>]*>\\s*([Dd]rucken|[Pp]rint)\\s*</a>', text, re.DOTALL | re.MULTILINE)\n\t\tif match:\n\t\t\treturn match.group(1)\n\t\telse:\n\t\t\treturn None"},{"alpha_fraction":0.5886939764022827,"alphanum_fraction":0.5925925970077515,"avg_line_length":32.129032135009766,"blob_id":"83bc7740aa86a518dbfcd8dd082b8cc21ca94053","content_id":"67f298dfd98f58ff3acc1759111b150fdb8bfbd3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1026,"license_type":"no_license","max_line_length":104,"num_lines":31,"path":"/parsers/parsers/spiegel.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from parsers.parsers.base import HTMLParser\nimport re\nimport urllib\n\nclass SpiegelParser(HTMLParser):\n\tdef get_text(self, document):\n\t\ttry:\n\t\t\ttext = super().fetch('http://www.spiegel.de%s' % super().get_print_link(super().fetch(document.url)))\n\t\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\n\t\t\treturn\n\n\t\ts = ''\n\t\tmatch = re.search(r'(<p>.+</p>).*?(?=<p><i>.+?</i></p>|URL)', text, re.DOTALL | re.MULTILINE)\n\t\tif match:\n\t\t\tmatchs = re.finditer(r'<p>(.+?)</p>', match.group(1), re.DOTALL | re.MULTILINE)\n\t\t\tfor m in matchs:\n\t\t\t\tend = re.search(r'<p><i>.+?</i></p>', m.group(0))\n\t\t\t\tif end:\n\t\t\t\t\tcontinue\n\n\t\t\t\ts += ' ' + super().clean_unescape(re.sub('\\s\\s+', ' ', re.sub('\\n', ' ', m.group(1)))).strip()\n\t\telse:\n\t\t\treturn\n\n\t\tmatch = re.search('name=\"keywords\" content=\"([^\"]+)\"', text)\n\t\tif match:\n\t\t\ttopics = set(match.group(1).split(', '))\n\t\t\tif '' in topics: topics.remove('')\n\t\t\tdocument.meta += '<topics>%s</topics>' % ''.join(['<topic>%s</topic>' % topic for topic in topics])\n\n\t\tdocument.text = s.strip()"},{"alpha_fraction":0.6610738039016724,"alphanum_fraction":0.6610738039016724,"avg_line_length":41.619049072265625,"blob_id":"6d735d39ee64d5a25cb73b853e712c99f8940e96","content_id":"5f46d199820d093fa1afcf9feaa96b1821a68d20","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":894,"license_type":"no_license","max_line_length":100,"num_lines":21,"path":"/infohub/urls.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.conf import settings\nfrom django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n\turl(r'^$', 'feeds.views.feeds', name='home'),\n\turl(r'^feeds/$', 'feeds.views.feeds', name='feeds'),\n\turl(r'^feeds/(?P<slug>[\\w-]+)/$', 'feeds.views.feed', name='feed'),\n\turl(r'^documents/$', 'documents.views.documents', name='documents'),\n\turl(r'^documents/(?P<slug>[\\w-]+)/$', 'documents.views.document', name='document'),\n\turl(r'^statistics/$', 'feeds.views.statistics', name='statistics'),\n\turl(r'^reports/$', 'feeds.views.reports', name='reports'),\n\turl(r'^reports/(?P<slug>[\\w-]+)/$', 'feeds.views.report', name='report'),\n\turl(r'^admin/', include(admin.site.urls)),\n)\n\nif settings.DEBUG:\n\turlpatterns += patterns('',\n\t\turl(r'^media/(?P<path>.*)$', 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT}),\n\t)"},{"alpha_fraction":0.7613412141799927,"alphanum_fraction":0.7731755375862122,"avg_line_length":38.07692337036133,"blob_id":"e2c51456c4fa6c4a96cff5b3b13e2d242a7a00cc","content_id":"834d69cdc7258e608a0595fe2b164275467b8e1f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":507,"license_type":"no_license","max_line_length":61,"num_lines":13,"path":"/documents/views.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import get_object_or_404, render\nfrom documents.models import Document\n\n@login_required(login_url='/admin/')\ndef documents(request):\n\tdocuments = Document.objects.order_by('-created_at')\n\treturn render(request, 'documents/documents.html', locals())\n\n@login_required(login_url='/admin/')\ndef document(request, slug):\n\tdocument = get_object_or_404(Document, slug=slug)\n\treturn render(request, 'documents/document.html', locals())"},{"alpha_fraction":0.715166449546814,"alphanum_fraction":0.715166449546814,"avg_line_length":27,"blob_id":"b02709391ed849301bbd4ecf4c507ce15f1466da","content_id":"8d29bc013ed616daf38a57dc32c9717d7aa67cde","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":811,"license_type":"no_license","max_line_length":75,"num_lines":29,"path":"/parsers/admin.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.contrib import admin\nfrom django.db.models import Count\nfrom django.forms import TextInput\nfrom parsers.models import Parser, TextFieldSingleLine\n\nclass ParserAdmin(admin.ModelAdmin):\n\tdef queryset(self, request):\n\t\treturn Parser.objects.annotate(feed_count=Count('feed'))\n\n\tdef show_feed_count(self, inst):\n\t\treturn inst.feed_count\n\n\tlist_display = ('name', 'slug', 'show_feed_count')\n\tlist_filter = ('feed',)\n\treadonly_fields = ('slug',)\n\tsearch_fields = ('name',)\n\tordering = ('name',)\n\tshow_feed_count.admin_order_field = 'feed_count'\n\tshow_feed_count.short_description = 'Number of Feeds'\n\n\tformfield_overrides = {\n\t\tTextFieldSingleLine: {'widget': TextInput(attrs={'autocomplete':'off'})},\n\t}\n\n\tfieldsets = [\n\t\t(None, {'fields': ['slug', 'name']}),\n\t]\n\nadmin.site.register(Parser, ParserAdmin)"},{"alpha_fraction":0.7416236996650696,"alphanum_fraction":0.7442010045051575,"avg_line_length":28.865385055541992,"blob_id":"daea5848487bd0ba2a912b21a1838197d68dfaff","content_id":"9aa9fd28f4176e44151ff937a7d815041b4a7ec0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1552,"license_type":"no_license","max_line_length":72,"num_lines":52,"path":"/documents/models.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.core.urlresolvers import reverse\nfrom django.db import models\nfrom django.template.defaultfilters import slugify\nfrom django_fsm import FSMField, transition\nfrom feeds.models import Feed\nfrom importlib import import_module\nfrom south.modelsinspector import add_introspection_rules\nadd_introspection_rules([], [\"^documents\\.models\\.TextFieldSingleLine\"])\n\nclass TextFieldSingleLine(models.TextField):\n\tpass\n\nclass Document(models.Model):\n\tcreated_at = models.DateTimeField(auto_now_add=True)\n\tupdated_at = models.DateTimeField(auto_now=True)\n\n\tslug = models.SlugField(max_length=4096, unique=True)\n\turl = TextFieldSingleLine(unique=True)\n\ttitle = TextFieldSingleLine(null=True, blank=True)\n\tmeta = models.TextField()\n\tcontent = models.TextField()\n\ttext = models.TextField(null=True, blank=True)\n\tfeed = models.ForeignKey(Feed)\n\n\tstate = FSMField(default='new', protected=True)\n\n\t@transition(field=state, source='new', target='parsed')\n\tdef parse(self):\n\t\tif not self.feed.parser:\n\t\t\traise Exception('No parser defined.')\n\n\t\tmodule = import_module('parsers.parsers')\n\t\tc = getattr(module, self.feed.parser.slug)\n\t\tparser = c()\n\t\tparser.get_text(self)\n\n\t\tif not self.text:\n\t\t\traise Exception('Could not parse text.')\n\n\tdef get_absolute_url(self):\n\t\treturn reverse('document', args=[str(self.slug)])\n\n\tdef save(self, *args, **kwargs):\n\t\tif not self.id:\n\t\t\tself.slug = slugify(self.url)\n\t\tsuper(Document, self).save(*args, **kwargs)\n\n\tdef __str__(self):\n\t\treturn self.title if self.title else self.url\n\n\tclass Meta:\n\t\tordering = ('-created_at',)"},{"alpha_fraction":0.6138995885848999,"alphanum_fraction":0.6206563711166382,"avg_line_length":33.56666564941406,"blob_id":"96e33ac96ce49694bbd05d6f42c0b6b68152f4d3","content_id":"0690b05251867b640635061921405ab2316bbfcd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1036,"license_type":"no_license","max_line_length":102,"num_lines":30,"path":"/parsers/parsers/focus.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from parsers.parsers.base import HTMLParser\nimport re\nimport urllib\n\nclass FocusParser(HTMLParser):\n\tdef get_text(self, document):\n\t\ttry:\n\t\t\ttext = super().fetch('%s?drucken=1' % document.url)\n\t\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\n\t\t\treturn\n\n\t\ts = ''\n\t\tmatch = re.search(r'<div class=\"leadIn\">\\s*?<p>(.+?)</p>\\s*?</div>', text, re.DOTALL | re.MULTILINE)\n\t\tif match:\n\t\t\ts = super().clean_unescape(match.group(1))\n\n\t\tmatchs = re.finditer(r'<div class=\"textBlock\">(.+?)</div>', text, re.DOTALL | re.MULTILINE)\n\t\tfor match in matchs:\n\t\t\tps = re.finditer(r'(<p>(.+?)</p>|<h2>(.+?)</h2>)', match.group(1), re.DOTALL | re.MULTILINE)\n\t\t\tfor m in ps:\n\t\t\t\ts += ' ' + super().clean_unescape(m.group(1))\n\n\n\t\tmatch = re.search('name=\"news_keywords\" content=\"([^\"]+)\"', text)\n\t\tif match:\n\t\t\ttopics = set(super().clean_unescape(match.group(1)).split(', '))\n\t\t\tif '' in topics: topics.remove('')\n\t\t\tdocument.meta += '<topics>%s</topics>' % ''.join(['<topic>%s</topic>' % topic for topic in topics])\n\n\t\tdocument.text = s.strip()"},{"alpha_fraction":0.7663817405700684,"alphanum_fraction":0.7720797657966614,"avg_line_length":26.076923370361328,"blob_id":"af538a78b337102f259b92b65b0f565b969353d0","content_id":"cf215fbaa3fd260fdae8f76401d2c833d6a90553","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":351,"license_type":"no_license","max_line_length":51,"num_lines":13,"path":"/feeds/crons.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.core import management\nfrom django_cron import CronJobBase, Schedule\n\nclass ParseFeedsCronJob(CronJobBase):\n\tRUN_EVERY_MINS = 10\n\tALLOW_PARALLEL_RUNS = False\n\n\tschedule = Schedule(run_every_mins=RUN_EVERY_MINS)\n\tcode = 'feeds.parse_feeds_cron_job'\n\n\tdef do(self):\n\t\tprint('Start ParseFeedsCronJob.')\n\t\tmanagement.call_command('parsefeeds')"},{"alpha_fraction":0.7836411595344543,"alphanum_fraction":0.7889181971549988,"avg_line_length":28.230770111083984,"blob_id":"16580c7b2a81c4be1821fa816de30d1c2a6fca4f","content_id":"61ac63edea20ecaa0baad4863b6edab050e11b79","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":379,"license_type":"no_license","max_line_length":51,"num_lines":13,"path":"/documents/crons.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.core import management\nfrom django_cron import CronJobBase, Schedule\n\nclass ProcessDocumentsCronJob(CronJobBase):\n\tRUN_EVERY_MINS = 30\n\tALLOW_PARALLEL_RUNS = False\n\n\tschedule = Schedule(run_every_mins=RUN_EVERY_MINS)\n\tcode = 'documents.process_documents_cron_job'\n\n\tdef do(self):\n\t\tprint('Start ProcessDocumentsCronJob.')\n\t\tmanagement.call_command('processdocuments')"},{"alpha_fraction":0.6093617081642151,"alphanum_fraction":0.6144680976867676,"avg_line_length":42.55555725097656,"blob_id":"83f7800d67d706a9cf686bf3280ff13b501845af","content_id":"69a1d37e38805130f3464e541dbe352b907703a5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1175,"license_type":"no_license","max_line_length":120,"num_lines":27,"path":"/parsers/parsers/faz.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from parsers.parsers.base import HTMLParser\nimport re\nimport urllib\n\nclass FAZParser(HTMLParser):\n\tdef get_text(self, document):\n\t\ttry:\n\t\t\torg = super().fetch('%s?printPagedArticle=true' % document.url)\n\t\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\n\t\t\treturn\n\n\t\ts = ''\n\t\tm = re.search(r'<div[^>]*class=\"FAZArtikelText\"[^>]*>(.+?)<div class=\"ArtikelFooter\">', org, re.DOTALL | re.MULTILINE)\n\t\tif m:\n\t\t\ttext = re.sub(r'<span class=\"Bildunterschrift[^>]*>.+?</span>', ' ', m.group(1), 0, re.DOTALL | re.MULTILINE)\n\t\t\ttext = re.sub(r'<p class=\"Bildunterschrift[^>]*>.+?</p>', ' ', text, 0, re.DOTALL | re.MULTILINE)\n\t\t\tmatchs = re.finditer(r'<p[^>]*>(.+?)</p>', text, re.DOTALL | re.MULTILINE)\n\t\t\tfor match in matchs:\n\t\t\t\ts += ' ' + super().clean_unescape(re.sub(r'<[^>]*>.+<[^>]*>', '', match.group(1), 0, re.DOTALL | re.MULTILINE))\n\n\t\tmatch = re.search('name=\"keywords\" content=\"([^\"]+)\"', org)\n\t\tif match:\n\t\t\ttopics = set(super().clean_unescape(match.group(1)).split(', '))\n\t\t\tif '' in topics: topics.remove('')\n\t\t\tdocument.meta += '<topics>%s</topics>' % ''.join(['<topic>%s</topic>' % topic for topic in topics])\n\n\t\tdocument.text = super().clean_unescape(s)"},{"alpha_fraction":0.680232584476471,"alphanum_fraction":0.6860465407371521,"avg_line_length":35.89285659790039,"blob_id":"9d1a9f88bada79739867d9e7ff7dd260534373f6","content_id":"e96b5408c4005515e5422dd0a15ba1adb172cc63","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1032,"license_type":"no_license","max_line_length":107,"num_lines":28,"path":"/documents/admin.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.contrib import admin\nfrom django.forms import TextInput\nfrom django.db import models\nfrom documents.models import Document, TextFieldSingleLine\nfrom suit.widgets import AutosizedTextarea\n\nclass DocumentAdmin(admin.ModelAdmin):\n\tdef show_link(self, obj):\n\t\treturn '<a href=\"%s\"><i class=\"icon-eye-open icon-alpha75\"></i>View on site</a>' % obj.get_absolute_url()\n\n\tlist_display = ('url', 'title', 'updated_at', 'feed', 'show_link')\n\tlist_filter = ('feed', 'state')\n\treadonly_fields = ('slug', 'state')\n\tsearch_fields = ('slug', 'url', 'title', 'feed__title')\n\tordering = ('-updated_at', 'feed')\n\tshow_link.allow_tags = True\n\tshow_link.short_description = 'View on site'\n\n\tformfield_overrides = {\n\t\tTextFieldSingleLine: {'widget': TextInput(attrs={'autocomplete':'off'})},\n\t\tmodels.TextField: {'widget': AutosizedTextarea(attrs={'rows': 10, 'class': 'span12'})},\n\t}\n\n\tfieldsets = [\n\t\t(None, {'fields': ['slug', 'state', 'url', 'title', 'feed', 'meta', 'content', 'text']}),\n\t]\n\nadmin.site.register(Document, DocumentAdmin)"},{"alpha_fraction":0.7276808023452759,"alphanum_fraction":0.7301745414733887,"avg_line_length":26.85416603088379,"blob_id":"9df8f3fabaf1ed52020106a9bebdc7d4b1b8de33","content_id":"94c270ef763e10a8b865570ef159284e10bc676d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4010,"license_type":"no_license","max_line_length":96,"num_lines":144,"path":"/infohub/settings.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"\"\"\"\nDjango settings for infohub project.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/1.6/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/1.6/ref/settings/\n\"\"\"\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nfrom configparser import RawConfigParser\nfrom django.conf.global_settings import TEMPLATE_CONTEXT_PROCESSORS as TCP\nimport os\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\n\nconfig = RawConfigParser()\nconfig.optionxform = str\nconfig.read(BASE_DIR + '/infohub/settings.ini')\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = config.get('secrets','SECRET_KEY')\nSESSION_COOKIE_SECURE = config.getboolean('secrets','SESSION_COOKIE_SECURE')\nCSRF_COOKIE_SECURE = config.getboolean('secrets','CSRF_COOKIE_SECURE')\nSESSION_EXPIRE_AT_BROWSER_CLOSE = config.getboolean('secrets','SESSION_EXPIRE_AT_BROWSER_CLOSE')\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = config.getboolean('debug','DEBUG')\nTEMPLATE_DEBUG = config.getboolean('debug','TEMPLATE_DEBUG')\n\nALLOWED_HOSTS = config.get('host','ALLOWED_HOSTS').split()\nADMINS = tuple(config.items('admins'))\n\nEMAIL_USE_TLS = config.getboolean('host_email','EMAIL_USE_TLS')\nDEFAULT_FROM_EMAIL = config.get('host_email','DEFAULT_FROM_EMAIL')\nSERVER_EMAIL = config.get('host_email','SERVER_EMAIL')\nEMAIL_HOST = config.get('host_email','EMAIL_HOST')\nEMAIL_PORT = config.getint('host_email','EMAIL_PORT')\nEMAIL_HOST_USER = config.get('host_email','EMAIL_HOST_USER')\nEMAIL_HOST_PASSWORD = config.get('host_email','EMAIL_HOST_PASSWORD')\n\n\n# Application definition\nINSTALLED_APPS = (\n\t'suit',\n\t'suit_redactor',\n\t'django.contrib.admin',\n\t'django.contrib.auth',\n\t'django.contrib.contenttypes',\n\t'django.contrib.sessions',\n\t'django.contrib.messages',\n\t'django.contrib.staticfiles',\n\t'south',\n\t'django_cron',\n\t'parsers',\n\t'feeds',\n\t'documents',\n)\n\nMIDDLEWARE_CLASSES = (\n\t'django.contrib.sessions.middleware.SessionMiddleware',\n\t'django.middleware.common.CommonMiddleware',\n\t'django.middleware.csrf.CsrfViewMiddleware',\n\t'django.contrib.auth.middleware.AuthenticationMiddleware',\n\t'django.contrib.messages.middleware.MessageMiddleware',\n\t'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\n\nROOT_URLCONF = 'infohub.urls'\nWSGI_APPLICATION = 'infohub.wsgi.application'\n\n\n# Database\nDATABASES = {\n\t'default': {\n\t\t'ENGINE': config.get('database', 'DATABASE_ENGINE'),\n\t\t'NAME': config.get('database', 'DATABASE_NAME'),\n\t\t'USER': config.get('database', 'DATABASE_USER'),\n\t\t'PASSWORD': config.get('database', 'DATABASE_PASSWORD'),\n\t\t'HOST': config.get('database', 'DATABASE_HOST'),\n\t\t'PORT': config.get('database', 'DATABASE_PORT'),\n\t}\n}\n\n# Internationalization\nLANGUAGE_CODE = 'en-us'\nTIME_ZONE = 'Europe/Berlin'\n\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = True\n\n# Static files (CSS, JavaScript, Images)\nSTATIC_URL = '/static/'\nSTATIC_ROOT = os.path.join(BASE_DIR, 'static')\nSTATICFILES_DIRS = (\n\tos.path.join(BASE_DIR, 'assets'),\n)\nSTATICFILES_FINDERS = (\n\t'django.contrib.staticfiles.finders.FileSystemFinder',\n\t'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n)\n\n\n# Media files\nMEDIA_URL = '/media/'\nMEDIA_ROOT = os.path.join(BASE_DIR, 'media')\n\n\n# Templates\nTEMPLATE_DIRS = (\n\tos.path.join(BASE_DIR, 'templates'),\n)\nTEMPLATE_LOADERS = (\n\t'django.template.loaders.filesystem.Loader',\n\t'django.template.loaders.app_directories.Loader',\n)\nTEMPLATE_CONTEXT_PROCESSORS = TCP + (\n\t'django.core.context_processors.request',\n\t'django.contrib.messages.context_processors.messages',\n)\n\n\nFAILED_RUNS_CRONJOB_EMAIL_PREFIX = \"[infohub CronJob]: \"\nCRON_CLASSES = (\n\t'feeds.crons.ParseFeedsCronJob',\n\t'documents.crons.ProcessDocumentsCronJob',\n\t'django_cron.cron.FailedRunsNotificationCronJob',\n)\n\n\nSUIT_CONFIG = {\n\t'ADMIN_NAME':'infohub',\n\t'LIST_PER_PAGE': 50,\n\t'MENU': (\n\t\t{'label': 'infohub', 'icon':'icon-home', 'url': '/'},\n\t\t'auth',\n\t\t'django_cron',\n\t\t'parsers',\n\t\t'feeds',\n\t\t'documents',\n\t),\n}"},{"alpha_fraction":0.6169354915618896,"alphanum_fraction":0.6199596524238586,"avg_line_length":37.19230651855469,"blob_id":"4452de13223a1d376925151c05c731f3e041b370","content_id":"704c5147759997a6c94aae0d466c7e33bd512990","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":992,"license_type":"no_license","max_line_length":157,"num_lines":26,"path":"/parsers/parsers/suddeutsche.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from parsers.parsers.base import HTMLParser\nimport re\nimport urllib\n\nclass SuddeutscheParser(HTMLParser):\n\tdef get_text(self, document):\n\t\ttry:\n\t\t\torg = super().fetch(document.url)\n\t\t\ttext = super().fetch(super().get_print_link(org))\n\t\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\n\t\t\treturn\n\n\t\ts = ''\n\t\tm = re.search(r'<div[^>]*class=\"body\"[^>]*>(.+)<div[^>]*class=\"footer\"[^>]*>', text, re.DOTALL | re.MULTILINE)\n\t\tif m:\n\t\t\tmatchs = re.finditer(r'<p>(.+?)</p>', m.group(1), re.DOTALL | re.MULTILINE)\n\t\t\tfor match in matchs:\n\t\t\t\ts += ' ' + super().clean_unescape(re.sub('\\s\\s+', ' ', re.sub('\\n', ' ', match.group(1)))).strip()\n\n\t\tdocument.text = s.strip()\n\n\t\tmatch = re.search('name=\"news_keywords\" content=\"([^\"]+)\"', org)\n\t\tif match:\n\t\t\ttopics = set(match.group(1).split(', '))\n\t\t\tif '' in topics: topics.remove('')\n\t\t\tdocument.meta += '<topics>%s</topics>' % ''.join(['<topic>%s</topic>' % super(SuddeutscheParser, self).clean_unescape(topic).strip() for topic in topics])"},{"alpha_fraction":0.68359375,"alphanum_fraction":0.68359375,"avg_line_length":35.57143020629883,"blob_id":"d7da3523f82c8bf4a685fdaeaa18d504c72253eb","content_id":"ff1dbf53ed8c35d6cf645134f40cb76d90ec26c8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":256,"license_type":"no_license","max_line_length":110,"num_lines":7,"path":"/parsers/parsers/default.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from parsers.parsers.base import BaseParser\nfrom html import unescape\nimport re\n\nclass DefaultParser(BaseParser):\n\tdef get_text(self, document):\n\t\tdocument.text = unescape(re.sub(r'\\s\\s+', ' ', re.sub(r'<[^>]+>', '', document.content.replace('\\n', ' '))))\n"},{"alpha_fraction":0.6906198859214783,"alphanum_fraction":0.6950082182884216,"avg_line_length":31.571428298950195,"blob_id":"4aff0088c4d8ac033198243a47e06ad4efc8e2e3","content_id":"9d9cd924f95a8da5b908977845c3e0852612c9a4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1823,"license_type":"no_license","max_line_length":107,"num_lines":56,"path":"/feeds/admin.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.contrib import admin\nfrom django.db.models import Count\nfrom django.forms import TextInput\nfrom django.db import models\nfrom feeds.models import Feed, Report, TextFieldSingleLine\nfrom suit.widgets import AutosizedTextarea\n\nclass FeedAdmin(admin.ModelAdmin):\n\tdef show_link(self, obj):\n\t\treturn '<a href=\"%s\"><i class=\"icon-eye-open icon-alpha75\"></i>View on site</a>' % obj.get_absolute_url()\n\n\tdef queryset(self, request):\n\t\treturn Feed.objects.annotate(document_count=Count('document'))\n\n\tdef show_document_count(self, inst):\n\t\treturn inst.document_count\n\n\tlist_display = ('url', 'title', 'updated_at', 'show_document_count', 'parser', 'show_link')\n\tlist_filter = ('alive', 'parser')\n\treadonly_fields = ('slug',)\n\tsearch_fields = ('url', 'title')\n\tordering = ('-updated_at', 'title')\n\tshow_document_count.admin_order_field = 'document_count'\n\tshow_document_count.short_description = 'Number of Documents'\n\tshow_link.allow_tags = True\n\tshow_link.short_description = 'View on site'\n\n\tformfield_overrides = {\n\t\tTextFieldSingleLine: {'widget': TextInput(attrs={'autocomplete':'off'})},\n\t}\n\n\tfieldsets = [\n\t\t(None, {'fields': ['slug', 'url', 'title', 'alive', 'parser']}),\n\t]\n\nclass ReportAdmin(admin.ModelAdmin):\n\tdef show_link(self, obj):\n\t\treturn '<a href=\"%s\"><i class=\"icon-eye-open icon-alpha75\"></i>View on site</a>' % obj.get_absolute_url()\n\n\tlist_display = ('created_at', 'show_link')\n\tlist_filter = ('created_at',)\n\treadonly_fields = ('slug',)\n\tordering = ('-created_at',)\n\tshow_link.allow_tags = True\n\tshow_link.short_description = 'View on site'\n\n\tformfield_overrides = {\n\t\tmodels.TextField: {'widget': AutosizedTextarea(attrs={'rows': 10, 'class': 'span12'})},\n\t}\n\n\tfieldsets = [\n\t\t(None, {'fields': ['slug', 'text']}),\n\t]\n\nadmin.site.register(Feed, FeedAdmin)\nadmin.site.register(Report, ReportAdmin)"},{"alpha_fraction":0.6372881531715393,"alphanum_fraction":0.6389830708503723,"avg_line_length":33.764705657958984,"blob_id":"34e5551d8e07b2507c4af537c29ba99b3c2b6e9e","content_id":"9a589576a52c0d99918da73d33995cb68be36b01","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":590,"license_type":"no_license","max_line_length":172,"num_lines":17,"path":"/parsers/parsers/welt.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from parsers.parsers.base import HTMLParser\nimport re\nimport urllib\n\nclass DieWeltParser(HTMLParser):\n\tdef get_text(self, document):\n\t\ttry:\n\t\t\ttext = super().fetch('%s?config=print' % document.url)\n\t\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\n\t\t\treturn\n\n\t\ts = ''\n\t\tmatchs = re.finditer(r'<p class=\"text\">(.+?)</p>', text, re.DOTALL | re.MULTILINE)\n\t\tfor match in matchs:\n\t\t\ts += ' ' + super().clean_unescape(re.sub('\\s\\s+', ' ', re.sub('\\n', ' ', re.sub(r'<span class=\"url\">.+?</span>', '', match.group(1), re.DOTALL | re.MULTILINE)))).strip()\n\n\t\tdocument.text = s.strip()"},{"alpha_fraction":0.5647955536842346,"alphanum_fraction":0.5710325837135315,"avg_line_length":51.490909576416016,"blob_id":"a82480a79a42cdef63534c7da67598f90540f5a5","content_id":"07c6ee7c3bb6f07d925f45c5e203ec3d2a20595c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2886,"license_type":"no_license","max_line_length":139,"num_lines":55,"path":"/feeds/migrations/0001_initial.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom south.utils import datetime_utils as datetime\nfrom south.db import db\nfrom south.v2 import SchemaMigration\nfrom django.db import models\n\n\nclass Migration(SchemaMigration):\n depends_on = (\n (\"parsers\", \"0002_add_default_parser\"),\n )\n\n def forwards(self, orm):\n # Adding model 'Feed'\n db.create_table('feeds_feed', (\n ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),\n ('created_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)),\n ('updated_at', self.gf('django.db.models.fields.DateTimeField')(blank=True, auto_now=True)),\n ('slug', self.gf('django.db.models.fields.SlugField')(max_length=4096, unique=True)),\n ('url', self.gf('feeds.models.TextFieldSingleLine')(unique=True)),\n ('title', self.gf('feeds.models.TextFieldSingleLine')(null=True, blank=True)),\n ('alive', self.gf('django.db.models.fields.BooleanField')(default=True)),\n ('parser', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['parsers.Parser'], blank=True, null=True)),\n ))\n db.send_create_signal('feeds', ['Feed'])\n\n\n def backwards(self, orm):\n # Deleting model 'Feed'\n db.delete_table('feeds_feed')\n\n\n models = {\n 'feeds.feed': {\n 'Meta': {'object_name': 'Feed'},\n 'alive': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\n 'parser': ('django.db.models.fields.related.ForeignKey', [], {'to': \"orm['parsers.Parser']\", 'blank': 'True', 'null': 'True'}),\n 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '4096', 'unique': 'True'}),\n 'title': ('feeds.models.TextFieldSingleLine', [], {'null': 'True', 'blank': 'True'}),\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now': 'True'}),\n 'url': ('feeds.models.TextFieldSingleLine', [], {'unique': 'True'})\n },\n 'parsers.parser': {\n 'Meta': {'object_name': 'Parser', 'ordering': \"('name',)\"},\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\n 'name': ('parsers.models.TextFieldSingleLine', [], {'unique': 'True'}),\n 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '4096', 'unique': 'True'}),\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now': 'True'})\n }\n }\n\n complete_apps = ['feeds']"},{"alpha_fraction":0.7025599479675293,"alphanum_fraction":0.7062169909477234,"avg_line_length":28.309524536132812,"blob_id":"2ccdf787a09f992598a84afaa3c5eac8009f4b36","content_id":"7a85c296e07ff2530e024c028739b21eefc735bd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2461,"license_type":"no_license","max_line_length":99,"num_lines":84,"path":"/feeds/models.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from datetime import datetime\nfrom django.core.urlresolvers import reverse\nfrom django.db import models\nfrom django.template.defaultfilters import slugify\nfrom parsers.models import Parser\nfrom south.modelsinspector import add_introspection_rules\n\nimport feedparser\nimport re\n\nadd_introspection_rules([], [\"^feeds\\.models\\.TextFieldSingleLine\"])\n\nclass TextFieldSingleLine(models.TextField):\n\tpass\n\nclass Feed(models.Model):\n\tcreated_at = models.DateTimeField(auto_now_add=True)\n\tupdated_at = models.DateTimeField(auto_now=True)\n\n\tslug = models.SlugField(max_length=4096, unique=True)\n\turl = TextFieldSingleLine(unique=True)\n\ttitle = TextFieldSingleLine(blank=True, null=True)\n\talive = models.BooleanField(default=True)\n\n\tparser = models.ForeignKey(Parser, null=True, blank=True)\n\n\tdef get_absolute_url(self):\n\t\treturn reverse('feed', args=[str(self.slug)])\n\n\tdef parse(self):\n\t\tfrom documents.models import Document\n\t\trssfeed = feedparser.parse(self.url)\n\t\tif not self.title and rssfeed.feed.title:\n\t\t\tself.title = rssfeed.feed.title\n\t\t\tself.save()\n\n\t\tfor entry in rssfeed.entries:\n\t\t\tif not Document.objects.filter(url=entry.link).exists():\n\t\t\t\ttitle = re.sub(r'\\s\\s+', ' ', entry.title.replace('\\n', ' '))\n\t\t\t\tsummary = re.sub(r'\\s\\s+', ' ', entry.summary.replace('\\n', ' ')) if 'summary' in entry else ''\n\n\t\t\t\tdate = None\n\t\t\t\tif 'published' in entry:\n\t\t\t\t\tdate = entry.published_parsed\n\t\t\t\telif 'updated' in entry:\n\t\t\t\t\tdate = entry.updated_parsed\n\t\t\t\telif 'created' in entry:\n\t\t\t\t\tdate = entry.created_parsed\n\n\t\t\t\tmeta = ''\n\t\t\t\tif date:\n\t\t\t\t\tmeta += '<date>%s</date>' % datetime(*date[:6]).isoformat()\n\n\t\t\t\tDocument.objects.create(url=entry.link, title=title, feed=self, content=summary, meta=meta)\n\n\tdef save(self, *args, **kwargs):\n\t\tif not self.id:\n\t\t\tself.slug = slugify(self.url)\n\t\tsuper(Feed, self).save(*args, **kwargs)\n\n\tdef __str__(self):\n\t\treturn self.title if self.title else self.url\n\nclass Report(models.Model):\n\tcreated_at = models.DateTimeField(auto_now_add=True)\n\tupdated_at = models.DateTimeField(auto_now=True)\n\n\tslug = models.SlugField(max_length=4096, unique=True)\n\ttext = models.TextField()\n\n\tdef get_absolute_url(self):\n\t\treturn reverse('report', args=[str(self.slug)])\n\n\tdef save(self, *args, **kwargs):\n\t\tsuper(Report, self).save(*args, **kwargs)\n\t\tif not self.slug:\n\t\t\tself.slug = slugify(self.created_at.isoformat())\n\t\t\tsuper(Report, self).save()\n\n\tdef __str__(self):\n\t\treturn self.slug\n\n\tclass Meta:\n\t\tordering = ('-created_at',)"},{"alpha_fraction":0.6073018908500671,"alphanum_fraction":0.6108637452125549,"avg_line_length":36.46666717529297,"blob_id":"5856b97f04d688a161781b42e26f80a1780fb4d7","content_id":"8118403c5a8503a8fbeca90b58feda491c73ca90","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1123,"license_type":"no_license","max_line_length":177,"num_lines":30,"path":"/parsers/parsers/zeit.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from parsers.parsers.base import HTMLParser\nimport re\nimport urllib\n\nclass ZeitOnlineParser(HTMLParser):\n\tdef get_text(self, document):\n\t\ttry:\n\t\t\ttext = super().fetch('%s?print=true' % document.url)\n\t\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\n\t\t\treturn\n\n\t\ts = ''\n\t\tm = re.search(r'<!--start: content-->(.+?)<div[^>]*class=\"[^\"]*articlefooter[^\"]*\">', text, re.DOTALL | re.MULTILINE)\n\t\tif m:\n\t\t\tmatchs = re.finditer(r'<p>(.+?)</p>', re.sub(r'<div[^>]*class=\"block infobox\"[^>]*><dl>.+</dl></div>', '', m.group(1), 1, re.DOTALL | re.MULTILINE), re.DOTALL | re.MULTILINE)\n\t\t\tfor match in matchs:\n\t\t\t\ts += ' ' + super().clean_unescape(re.sub('\\s\\s+', ' ', re.sub('\\n', ' ', match.group(1)))).strip()\n\n\t\tdocument.text = s.strip()\n\n\t\ttry:\n\t\t\ttext = super().fetch(document.url)\n\t\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\n\t\t\treturn\n\n\t\tmatch = re.search('name=\"keywords\" content=\"([^\"]+)\"', text)\n\t\tif match:\n\t\t\ttopics = set(match.group(1).split(', '))\n\t\t\tif '' in topics: topics.remove('')\n\t\t\tdocument.meta += '<topics>%s</topics>' % ''.join(['<topic>%s</topic>' % topic for topic in topics])"},{"alpha_fraction":0.7324324250221252,"alphanum_fraction":0.7324324250221252,"avg_line_length":27.538461685180664,"blob_id":"d27ce7743af0a85055bde4a7fc97269494c4929c","content_id":"1b0485134f6178a9e6cf55ffd33124fbf10e1ae5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":370,"license_type":"no_license","max_line_length":90,"num_lines":13,"path":"/documents/management/commands/processdocuments.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.core.management.base import BaseCommand\nfrom documents.models import Document\n\nclass Command(BaseCommand):\n\thelp = 'Processes the documents.'\n\n\tdef handle(self, *args, **options):\n\t\tfor document in Document.objects.filter(feed__parser__isnull=False).filter(state='new'):\n\t\t\ttry:\n\t\t\t\tdocument.parse()\n\t\t\t\tdocument.save()\n\t\t\texcept Exception as e:\n\t\t\t\tprint(e)"},{"alpha_fraction":0.8605149984359741,"alphanum_fraction":0.8605149984359741,"avg_line_length":65.71428680419922,"blob_id":"f2e0cd4898f597c34f44327a37b969b1785309b9","content_id":"580125e9cb25b01bf01b7beb91a99cf76c346ac4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":466,"license_type":"no_license","max_line_length":79,"num_lines":7,"path":"/parsers/parsers/__init__.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from parsers.parsers.default import DefaultParser as default\nfrom parsers.parsers.faz import FAZParser as faz_parser\nfrom parsers.parsers.focus import FocusParser as focus_parser\nfrom parsers.parsers.spiegel import SpiegelParser as spiegel_parser\nfrom parsers.parsers.suddeutsche import SuddeutscheParser as suddeutsche_parser\nfrom parsers.parsers.welt import DieWeltParser as die_welt_parser\nfrom parsers.parsers.zeit import ZeitOnlineParser as zeit_online_parser"},{"alpha_fraction":0.6892561912536621,"alphanum_fraction":0.6892561912536621,"avg_line_length":27.85714340209961,"blob_id":"0560a5fa308d853c59600f1547a63aa2abbb36e9","content_id":"9335247c9b8ddbfd6c82bf3f2c1dd639bd8cb3a9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":605,"license_type":"no_license","max_line_length":75,"num_lines":21,"path":"/feeds/management/commands/parsefeeds.py","repo_name":"jnphilipp/infohub","src_encoding":"UTF-8","text":"from django.core.management.base import BaseCommand\nfrom feeds.models import Feed, Report\n\nimport json\n\nclass Command(BaseCommand):\n\thelp = 'Parses the new feeds.'\n\n\tdef handle(self, *args, **options):\n\t\tnew_entries = dict()\n\t\tfor feed in Feed.objects.all():\n\t\t\told = feed.document_set.count()\n\t\t\ttry:\n\t\t\t\tfeed.parse()\n\t\t\t\tfeed.save()\n\t\t\t\tnew_entries[feed.slug] = feed.document_set.count() - old\n\t\t\texcept Exception as e:\n\t\t\t\tnew_entries[feed.slug] = str(e).strip()\n\n\t\treport = Report.objects.create(text=json.dumps(new_entries))\n\t\tself.stdout.write('Report: (%s)\\n %s' % (report.created_at, report.text))"}],"string":"[\n {\n \"alpha_fraction\": 0.7346368432044983,\n \"alphanum_fraction\": 0.7402234673500061,\n \"avg_line_length\": 27.68000030517578,\n \"blob_id\": \"fea38995c2e0d9c6ee3b9f7954dde25872199dd4\",\n \"content_id\": \"42d536f40c64408d486d1a5c54a3f11061aabae7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 716,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 25,\n \"path\": \"/parsers/models.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.db import models\\nfrom django.template.defaultfilters import slugify\\nfrom south.modelsinspector import add_introspection_rules\\nadd_introspection_rules([], [\\\"^parsers\\\\.models\\\\.TextFieldSingleLine\\\"])\\n\\nclass TextFieldSingleLine(models.TextField):\\n\\tpass\\n\\nclass Parser(models.Model):\\n\\tcreated_at = models.DateTimeField(auto_now_add=True)\\n\\tupdated_at = models.DateTimeField(auto_now=True)\\n\\n\\tslug = models.SlugField(max_length=4096, unique=True)\\n\\tname = TextFieldSingleLine(unique=True)\\n\\n\\tdef save(self, *args, **kwargs):\\n\\t\\tif not self.id:\\n\\t\\t\\tself.slug = slugify(self.name).replace('-', '_')\\n\\t\\tsuper(Parser, self).save(*args, **kwargs)\\n\\n\\tdef __str__(self):\\n\\t\\treturn self.name\\n\\n\\tclass Meta:\\n\\t\\tordering = ('name',)\"\n },\n {\n \"alpha_fraction\": 0.7369862794876099,\n \"alphanum_fraction\": 0.7431507110595703,\n \"avg_line_length\": 34.63414764404297,\n \"blob_id\": \"3acda72260957254de5a0658a08d54824f349830\",\n \"content_id\": \"c25619b35e638db906d90e323c7d65e38384d00c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1460,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 41,\n \"path\": \"/feeds/views.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib.auth.decorators import login_required\\nfrom django.shortcuts import get_object_or_404, render\\nfrom documents.models import Document\\nfrom feeds.models import Feed, Report\\nfrom datetime import datetime, date, time\\nfrom pytz import utc\\n\\n@login_required(login_url='/admin/')\\ndef feeds(request):\\n\\tfeeds = Feed.objects.order_by('alive', '-updated_at')\\n\\treturn render(request, 'feeds/feeds.html', locals())\\n\\n@login_required(login_url='/admin/')\\ndef feed(request, slug):\\n\\tfeed = get_object_or_404(Feed, slug=slug)\\n\\treturn render(request, 'feeds/feed.html', locals())\\n\\n@login_required(login_url='/admin/')\\ndef reports(request):\\n\\treports = Report.objects.order_by('-created_at')\\n\\treturn render(request, 'feeds/reports.html', locals())\\n\\n@login_required(login_url='/admin/')\\ndef report(request, slug):\\n\\treport = get_object_or_404(Report, slug=slug)\\n\\treturn render(request, 'feeds/report.html', locals())\\n\\n@login_required(login_url='/admin/')\\ndef statistics(request):\\n\\ttoday_min = datetime.combine(date.today(), time.min).replace(tzinfo=utc)\\n\\ttoday_max = datetime.combine(date.today(), time.max).replace(tzinfo=utc)\\n\\n\\tfeeds = Feed.objects.count()\\n\\n\\tdocuments = Document.objects.count()\\n\\ttoday_documents = Document.objects.filter(created_at__range=(today_min, today_max)).count()\\n\\n\\treports = Report.objects.count()\\n\\ttoday_reports = Report.objects.filter(created_at__range=(today_min, today_max))\\n\\n\\treturn render(request, 'feeds/statistics.html', locals())\"\n },\n {\n \"alpha_fraction\": 0.5888411998748779,\n \"alphanum_fraction\": 0.5939913988113403,\n \"avg_line_length\": 39.17241287231445,\n \"blob_id\": \"90f4e5b3d6f7cbafa20ae19d6a30eadba22073e5\",\n \"content_id\": \"dbac26451b780eb24c92f00a83e9eb33f10f7e37\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1165,\n \"license_type\": \"no_license\",\n \"max_line_length\": 115,\n \"num_lines\": 29,\n \"path\": \"/parsers/migrations/0002_add_default_parser.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom south.utils import datetime_utils as datetime\\nfrom south.db import db\\nfrom south.v2 import DataMigration\\nfrom django.db import models\\n\\nclass Migration(DataMigration):\\n\\n def forwards(self, orm):\\n \\\"Write your forwards methods here.\\\"\\n orm.Parser.objects.create(name='Default Parser', slug='default')\\n\\n def backwards(self, orm):\\n \\\"Write your backwards methods here.\\\"\\n orm.Parser.objects.get(slug='default').delete()\\n\\n models = {\\n 'parsers.parser': {\\n 'Meta': {'object_name': 'Parser', 'ordering': \\\"('name',)\\\"},\\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now_add': 'True'}),\\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\\n 'name': ('parsers.models.TextFieldSingleLine', [], {'unique': 'True'}),\\n 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '4096', 'unique': 'True'}),\\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})\\n }\\n }\\n\\n complete_apps = ['parsers']\\n symmetrical = True\\n\"\n },\n {\n \"alpha_fraction\": 0.553529679775238,\n \"alphanum_fraction\": 0.5596072673797607,\n \"avg_line_length\": 58.43055725097656,\n \"blob_id\": \"0e4775e358e25f331dcfec4fda2d69625161dd9a\",\n \"content_id\": \"8c22d6e488087f11a37ca63876572854aa73b069\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4278,\n \"license_type\": \"no_license\",\n \"max_line_length\": 139,\n \"num_lines\": 72,\n \"path\": \"/documents/migrations/0001_initial.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom south.utils import datetime_utils as datetime\\nfrom south.db import db\\nfrom south.v2 import SchemaMigration\\nfrom django.db import models\\n\\n\\nclass Migration(SchemaMigration):\\n depends_on = (\\n (\\\"feeds\\\", \\\"0001_initial\\\"),\\n )\\n\\n def forwards(self, orm):\\n # Adding model 'Document'\\n db.create_table('documents_document', (\\n ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),\\n ('created_at', self.gf('django.db.models.fields.DateTimeField')(blank=True, auto_now_add=True)),\\n ('updated_at', self.gf('django.db.models.fields.DateTimeField')(blank=True, auto_now=True)),\\n ('slug', self.gf('django.db.models.fields.SlugField')(unique=True, max_length=4096)),\\n ('url', self.gf('documents.models.TextFieldSingleLine')(unique=True)),\\n ('title', self.gf('documents.models.TextFieldSingleLine')(null=True, blank=True)),\\n ('meta', self.gf('django.db.models.fields.TextField')()),\\n ('content', self.gf('django.db.models.fields.TextField')()),\\n ('text', self.gf('django.db.models.fields.TextField')(null=True, blank=True)),\\n ('feed', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['feeds.Feed'])),\\n ('state', self.gf('django_fsm.FSMField')(max_length=50, default='new')),\\n ))\\n db.send_create_signal('documents', ['Document'])\\n\\n\\n def backwards(self, orm):\\n # Deleting model 'Document'\\n db.delete_table('documents_document')\\n\\n\\n models = {\\n 'documents.document': {\\n 'Meta': {'ordering': \\\"('-created_at',)\\\", 'object_name': 'Document'},\\n 'content': ('django.db.models.fields.TextField', [], {}),\\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now_add': 'True'}),\\n 'feed': ('django.db.models.fields.related.ForeignKey', [], {'to': \\\"orm['feeds.Feed']\\\"}),\\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\\n 'meta': ('django.db.models.fields.TextField', [], {}),\\n 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '4096'}),\\n 'state': ('django_fsm.FSMField', [], {'max_length': '50', 'default': \\\"'new'\\\"}),\\n 'text': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),\\n 'title': ('documents.models.TextFieldSingleLine', [], {'null': 'True', 'blank': 'True'}),\\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now': 'True'}),\\n 'url': ('documents.models.TextFieldSingleLine', [], {'unique': 'True'})\\n },\\n 'feeds.feed': {\\n 'Meta': {'object_name': 'Feed'},\\n 'alive': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),\\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now_add': 'True'}),\\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\\n 'parser': ('django.db.models.fields.related.ForeignKey', [], {'null': 'True', 'blank': 'True', 'to': \\\"orm['parsers.Parser']\\\"}),\\n 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '4096'}),\\n 'title': ('feeds.models.TextFieldSingleLine', [], {'null': 'True', 'blank': 'True'}),\\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now': 'True'}),\\n 'url': ('feeds.models.TextFieldSingleLine', [], {'unique': 'True'})\\n },\\n 'parsers.parser': {\\n 'Meta': {'ordering': \\\"('name',)\\\", 'object_name': 'Parser'},\\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now_add': 'True'}),\\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\\n 'name': ('parsers.models.TextFieldSingleLine', [], {'unique': 'True'}),\\n 'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '4096'}),\\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now': 'True'})\\n }\\n }\\n\\n complete_apps = ['documents']\"\n },\n {\n \"alpha_fraction\": 0.7037037014961243,\n \"alphanum_fraction\": 0.7037037014961243,\n \"avg_line_length\": 12.5,\n \"blob_id\": \"e632b62a0e85d5722a70079488dc357f6789cbfc\",\n \"content_id\": \"e517f2c91f636f4d6fc088e0847accebcbc26650\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 54,\n \"license_type\": \"no_license\",\n \"max_line_length\": 36,\n \"num_lines\": 4,\n \"path\": \"/README.md\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"infohub\\n=======\\n\\nFeedparser and text extraction tool.\\n\"\n },\n {\n \"alpha_fraction\": 0.6341724395751953,\n \"alphanum_fraction\": 0.6411355137825012,\n \"avg_line_length\": 23.906665802001953,\n \"blob_id\": \"d48b15c54f7f7eebcd481a59db1b5e5781c648bd\",\n \"content_id\": \"f07c96f2f457017b06e748fe1e4886a592fcc47e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1867,\n \"license_type\": \"no_license\",\n \"max_line_length\": 127,\n \"num_lines\": 75,\n \"path\": \"/feeds/templatetags/feeds_tags.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.core.urlresolvers import reverse\\nfrom django.db.models import Count, Max, Min\\nfrom django.template import Library\\nfrom django.utils.safestring import mark_safe\\nfrom feeds.models import Report, Feed\\n\\nimport json\\n\\nregister = Library()\\n\\n@register.filter\\ndef startswith(value, start):\\n\\treturn value.startswith(start)\\n\\n@register.filter\\ndef num_new_entries(report):\\n\\tnew_entries = json.loads(report.text)\\n\\n\\tcount = 0\\n\\tfor v in new_entries.values():\\n\\t\\ttry:\\n\\t\\t\\tcount += v\\n\\t\\texcept:\\n\\t\\t\\tpass\\n\\n\\treturn count\\n\\n@register.filter(needs_autoescape=True)\\ndef report_text(report, autoescape=None):\\n\\ts = '<table>'\\n\\ts += '<tr><th>feed</th><th>number of new documents</th></tr>'\\n\\ti = 0\\n\\tfor key, val in json.loads(report.text).items():\\n\\t\\tfeed = ''\\n\\t\\ttry:\\n\\t\\t\\tf = Feed.objects.get(slug=key)\\n\\t\\t\\tfeed = '<a href=\\\"%s\\\">%s</a>' % (reverse('feed', args=[f.slug]), f.title)\\n\\t\\texcept Feed.DoesNotExist:\\n\\t\\t\\tfeed = key\\n\\n\\t\\ts += '<tr class=\\\"%s\\\"><td>%s</td><td style=\\\"text-align: center;\\\">%s</td></tr>' % ('row1' if i % 2 == 0 else 'row2', feed, val)\\n\\t\\ti += 1\\n\\n\\ts += '</table>'\\n\\n\\treturn mark_safe(s)\\n\\n@register.filter(needs_autoescape=True)\\ndef statistics_report_text(reports, autoescape=None):\\n\\ts = '<table>'\\n\\ts += '<tr><th>feed</th><th>number of new documents today</th></tr>'\\n\\ti = 0\\n\\n\\tdaily_report = dict()\\n\\tfor report in reports:\\n\\t\\tfor key, val in json.loads(report.text).items():\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tdaily_report[key] = daily_report[key] + int(val) if key in daily_report else int(val)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tpass\\n\\n\\tfor key, val in daily_report.items():\\n\\t\\tfeed = ''\\n\\t\\ttry:\\n\\t\\t\\tf = Feed.objects.get(slug=key)\\n\\t\\t\\tfeed = '<a href=\\\"%s\\\">%s</a>' % (reverse('feed', args=[f.slug]), f.title)\\n\\t\\texcept Feed.DoesNotExist:\\n\\t\\t\\tfeed = key\\n\\n\\t\\ts += '<tr class=\\\"%s\\\"><td>%s</td><td style=\\\"text-align: center;\\\">%s</td></tr>' % ('row1' if i % 2 == 0 else 'row2', feed, val)\\n\\t\\ti += 1\\n\\n\\ts += '</table>'\\n\\n\\treturn mark_safe(s)\"\n },\n {\n \"alpha_fraction\": 0.651294469833374,\n \"alphanum_fraction\": 0.6577669978141785,\n \"avg_line_length\": 23.254901885986328,\n \"blob_id\": \"028538a35a203b06b5ca0afb4aa87189d489becf\",\n \"content_id\": \"a77befe2c2c5f0eb63e038ec60f7b91571b54ecd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1236,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 51,\n \"path\": \"/parsers/parsers/base.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\nfrom abc import ABCMeta, abstractmethod\\nfrom html import unescape\\nimport re\\nimport urllib\\n\\nclass BaseParser(object):\\n\\t__metaclass__ = ABCMeta\\n\\n\\t@abstractmethod\\n\\tdef get_text(self, document):\\n\\t\\tpass\\n\\nclass HTMLParser(BaseParser):\\n\\tuser_agent = 'Mozilla/4.0'\\n\\n\\tdef fetch(self, url):\\n\\t\\tresponse = urllib.request.urlopen(url)\\n\\t\\ttext = response.read()\\n\\t\\tencoding = str(text).lower().split('charset=', 1)[-1].split('\\\"', 1)[0]\\n\\n\\t\\tif encoding:\\n\\t\\t\\ttext = text.decode(encoding)\\n\\t\\telse:\\n\\t\\t\\ttext = str(text)\\n\\n\\t\\tresponse.close()\\n\\n\\t\\treturn text\\n\\n\\tdef fetch_with_data(self, url, values):\\n\\t\\theaders = {'User-Agent': self.user_agent}\\n\\t\\tdata = urllib.parse.urlencode(values)\\n\\t\\treq = urllib.Request(url, data, headers)\\n\\t\\tresponse = urllib.reques.request.urlopen(req)\\n\\t\\treturn response.read()\\n\\n\\tdef get_redirect_url(self, url):\\n\\t\\tresponse = urllib.request.urlopen(url)\\n\\t\\treturn response.url\\n\\n\\tdef clean_unescape(self, text):\\n\\t\\treturn unescape(re.sub(r'\\\\s\\\\s+', ' ', re.sub(r'<[^>]+>', '', text.replace('\\\\n', ' ')))).strip()\\n\\n\\tdef get_print_link(self, text):\\n\\t\\tmatch = re.search(r'<a[^>]*href=\\\"([^\\\"]+)\\\"[^>]*>\\\\s*([Dd]rucken|[Pp]rint)\\\\s*</a>', text, re.DOTALL | re.MULTILINE)\\n\\t\\tif match:\\n\\t\\t\\treturn match.group(1)\\n\\t\\telse:\\n\\t\\t\\treturn None\"\n },\n {\n \"alpha_fraction\": 0.5886939764022827,\n \"alphanum_fraction\": 0.5925925970077515,\n \"avg_line_length\": 32.129032135009766,\n \"blob_id\": \"83bc7740aa86a518dbfcd8dd082b8cc21ca94053\",\n \"content_id\": \"67f298dfd98f58ff3acc1759111b150fdb8bfbd3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1026,\n \"license_type\": \"no_license\",\n \"max_line_length\": 104,\n \"num_lines\": 31,\n \"path\": \"/parsers/parsers/spiegel.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from parsers.parsers.base import HTMLParser\\nimport re\\nimport urllib\\n\\nclass SpiegelParser(HTMLParser):\\n\\tdef get_text(self, document):\\n\\t\\ttry:\\n\\t\\t\\ttext = super().fetch('http://www.spiegel.de%s' % super().get_print_link(super().fetch(document.url)))\\n\\t\\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\\n\\t\\t\\treturn\\n\\n\\t\\ts = ''\\n\\t\\tmatch = re.search(r'(<p>.+</p>).*?(?=<p><i>.+?</i></p>|URL)', text, re.DOTALL | re.MULTILINE)\\n\\t\\tif match:\\n\\t\\t\\tmatchs = re.finditer(r'<p>(.+?)</p>', match.group(1), re.DOTALL | re.MULTILINE)\\n\\t\\t\\tfor m in matchs:\\n\\t\\t\\t\\tend = re.search(r'<p><i>.+?</i></p>', m.group(0))\\n\\t\\t\\t\\tif end:\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\ts += ' ' + super().clean_unescape(re.sub('\\\\s\\\\s+', ' ', re.sub('\\\\n', ' ', m.group(1)))).strip()\\n\\t\\telse:\\n\\t\\t\\treturn\\n\\n\\t\\tmatch = re.search('name=\\\"keywords\\\" content=\\\"([^\\\"]+)\\\"', text)\\n\\t\\tif match:\\n\\t\\t\\ttopics = set(match.group(1).split(', '))\\n\\t\\t\\tif '' in topics: topics.remove('')\\n\\t\\t\\tdocument.meta += '<topics>%s</topics>' % ''.join(['<topic>%s</topic>' % topic for topic in topics])\\n\\n\\t\\tdocument.text = s.strip()\"\n },\n {\n \"alpha_fraction\": 0.6610738039016724,\n \"alphanum_fraction\": 0.6610738039016724,\n \"avg_line_length\": 41.619049072265625,\n \"blob_id\": \"6d735d39ee64d5a25cb73b853e712c99f8940e96\",\n \"content_id\": \"5f46d199820d093fa1afcf9feaa96b1821a68d20\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 894,\n \"license_type\": \"no_license\",\n \"max_line_length\": 100,\n \"num_lines\": 21,\n \"path\": \"/infohub/urls.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.conf import settings\\nfrom django.conf.urls import patterns, include, url\\nfrom django.contrib import admin\\nadmin.autodiscover()\\n\\nurlpatterns = patterns('',\\n\\turl(r'^$', 'feeds.views.feeds', name='home'),\\n\\turl(r'^feeds/$', 'feeds.views.feeds', name='feeds'),\\n\\turl(r'^feeds/(?P<slug>[\\\\w-]+)/$', 'feeds.views.feed', name='feed'),\\n\\turl(r'^documents/$', 'documents.views.documents', name='documents'),\\n\\turl(r'^documents/(?P<slug>[\\\\w-]+)/$', 'documents.views.document', name='document'),\\n\\turl(r'^statistics/$', 'feeds.views.statistics', name='statistics'),\\n\\turl(r'^reports/$', 'feeds.views.reports', name='reports'),\\n\\turl(r'^reports/(?P<slug>[\\\\w-]+)/$', 'feeds.views.report', name='report'),\\n\\turl(r'^admin/', include(admin.site.urls)),\\n)\\n\\nif settings.DEBUG:\\n\\turlpatterns += patterns('',\\n\\t\\turl(r'^media/(?P<path>.*)$', 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT}),\\n\\t)\"\n },\n {\n \"alpha_fraction\": 0.7613412141799927,\n \"alphanum_fraction\": 0.7731755375862122,\n \"avg_line_length\": 38.07692337036133,\n \"blob_id\": \"e2c51456c4fa6c4a96cff5b3b13e2d242a7a00cc\",\n \"content_id\": \"834d69cdc7258e608a0595fe2b164275467b8e1f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 507,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 13,\n \"path\": \"/documents/views.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib.auth.decorators import login_required\\nfrom django.shortcuts import get_object_or_404, render\\nfrom documents.models import Document\\n\\n@login_required(login_url='/admin/')\\ndef documents(request):\\n\\tdocuments = Document.objects.order_by('-created_at')\\n\\treturn render(request, 'documents/documents.html', locals())\\n\\n@login_required(login_url='/admin/')\\ndef document(request, slug):\\n\\tdocument = get_object_or_404(Document, slug=slug)\\n\\treturn render(request, 'documents/document.html', locals())\"\n },\n {\n \"alpha_fraction\": 0.715166449546814,\n \"alphanum_fraction\": 0.715166449546814,\n \"avg_line_length\": 27,\n \"blob_id\": \"b02709391ed849301bbd4ecf4c507ce15f1466da\",\n \"content_id\": \"8d29bc013ed616daf38a57dc32c9717d7aa67cde\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 811,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 29,\n \"path\": \"/parsers/admin.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib import admin\\nfrom django.db.models import Count\\nfrom django.forms import TextInput\\nfrom parsers.models import Parser, TextFieldSingleLine\\n\\nclass ParserAdmin(admin.ModelAdmin):\\n\\tdef queryset(self, request):\\n\\t\\treturn Parser.objects.annotate(feed_count=Count('feed'))\\n\\n\\tdef show_feed_count(self, inst):\\n\\t\\treturn inst.feed_count\\n\\n\\tlist_display = ('name', 'slug', 'show_feed_count')\\n\\tlist_filter = ('feed',)\\n\\treadonly_fields = ('slug',)\\n\\tsearch_fields = ('name',)\\n\\tordering = ('name',)\\n\\tshow_feed_count.admin_order_field = 'feed_count'\\n\\tshow_feed_count.short_description = 'Number of Feeds'\\n\\n\\tformfield_overrides = {\\n\\t\\tTextFieldSingleLine: {'widget': TextInput(attrs={'autocomplete':'off'})},\\n\\t}\\n\\n\\tfieldsets = [\\n\\t\\t(None, {'fields': ['slug', 'name']}),\\n\\t]\\n\\nadmin.site.register(Parser, ParserAdmin)\"\n },\n {\n \"alpha_fraction\": 0.7416236996650696,\n \"alphanum_fraction\": 0.7442010045051575,\n \"avg_line_length\": 28.865385055541992,\n \"blob_id\": \"daea5848487bd0ba2a912b21a1838197d68dfaff\",\n \"content_id\": \"9aa9fd28f4176e44151ff937a7d815041b4a7ec0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1552,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 52,\n \"path\": \"/documents/models.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.core.urlresolvers import reverse\\nfrom django.db import models\\nfrom django.template.defaultfilters import slugify\\nfrom django_fsm import FSMField, transition\\nfrom feeds.models import Feed\\nfrom importlib import import_module\\nfrom south.modelsinspector import add_introspection_rules\\nadd_introspection_rules([], [\\\"^documents\\\\.models\\\\.TextFieldSingleLine\\\"])\\n\\nclass TextFieldSingleLine(models.TextField):\\n\\tpass\\n\\nclass Document(models.Model):\\n\\tcreated_at = models.DateTimeField(auto_now_add=True)\\n\\tupdated_at = models.DateTimeField(auto_now=True)\\n\\n\\tslug = models.SlugField(max_length=4096, unique=True)\\n\\turl = TextFieldSingleLine(unique=True)\\n\\ttitle = TextFieldSingleLine(null=True, blank=True)\\n\\tmeta = models.TextField()\\n\\tcontent = models.TextField()\\n\\ttext = models.TextField(null=True, blank=True)\\n\\tfeed = models.ForeignKey(Feed)\\n\\n\\tstate = FSMField(default='new', protected=True)\\n\\n\\t@transition(field=state, source='new', target='parsed')\\n\\tdef parse(self):\\n\\t\\tif not self.feed.parser:\\n\\t\\t\\traise Exception('No parser defined.')\\n\\n\\t\\tmodule = import_module('parsers.parsers')\\n\\t\\tc = getattr(module, self.feed.parser.slug)\\n\\t\\tparser = c()\\n\\t\\tparser.get_text(self)\\n\\n\\t\\tif not self.text:\\n\\t\\t\\traise Exception('Could not parse text.')\\n\\n\\tdef get_absolute_url(self):\\n\\t\\treturn reverse('document', args=[str(self.slug)])\\n\\n\\tdef save(self, *args, **kwargs):\\n\\t\\tif not self.id:\\n\\t\\t\\tself.slug = slugify(self.url)\\n\\t\\tsuper(Document, self).save(*args, **kwargs)\\n\\n\\tdef __str__(self):\\n\\t\\treturn self.title if self.title else self.url\\n\\n\\tclass Meta:\\n\\t\\tordering = ('-created_at',)\"\n },\n {\n \"alpha_fraction\": 0.6138995885848999,\n \"alphanum_fraction\": 0.6206563711166382,\n \"avg_line_length\": 33.56666564941406,\n \"blob_id\": \"96e33ac96ce49694bbd05d6f42c0b6b68152f4d3\",\n \"content_id\": \"0690b05251867b640635061921405ab2316bbfcd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1036,\n \"license_type\": \"no_license\",\n \"max_line_length\": 102,\n \"num_lines\": 30,\n \"path\": \"/parsers/parsers/focus.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from parsers.parsers.base import HTMLParser\\nimport re\\nimport urllib\\n\\nclass FocusParser(HTMLParser):\\n\\tdef get_text(self, document):\\n\\t\\ttry:\\n\\t\\t\\ttext = super().fetch('%s?drucken=1' % document.url)\\n\\t\\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\\n\\t\\t\\treturn\\n\\n\\t\\ts = ''\\n\\t\\tmatch = re.search(r'<div class=\\\"leadIn\\\">\\\\s*?<p>(.+?)</p>\\\\s*?</div>', text, re.DOTALL | re.MULTILINE)\\n\\t\\tif match:\\n\\t\\t\\ts = super().clean_unescape(match.group(1))\\n\\n\\t\\tmatchs = re.finditer(r'<div class=\\\"textBlock\\\">(.+?)</div>', text, re.DOTALL | re.MULTILINE)\\n\\t\\tfor match in matchs:\\n\\t\\t\\tps = re.finditer(r'(<p>(.+?)</p>|<h2>(.+?)</h2>)', match.group(1), re.DOTALL | re.MULTILINE)\\n\\t\\t\\tfor m in ps:\\n\\t\\t\\t\\ts += ' ' + super().clean_unescape(m.group(1))\\n\\n\\n\\t\\tmatch = re.search('name=\\\"news_keywords\\\" content=\\\"([^\\\"]+)\\\"', text)\\n\\t\\tif match:\\n\\t\\t\\ttopics = set(super().clean_unescape(match.group(1)).split(', '))\\n\\t\\t\\tif '' in topics: topics.remove('')\\n\\t\\t\\tdocument.meta += '<topics>%s</topics>' % ''.join(['<topic>%s</topic>' % topic for topic in topics])\\n\\n\\t\\tdocument.text = s.strip()\"\n },\n {\n \"alpha_fraction\": 0.7663817405700684,\n \"alphanum_fraction\": 0.7720797657966614,\n \"avg_line_length\": 26.076923370361328,\n \"blob_id\": \"af538a78b337102f259b92b65b0f565b969353d0\",\n \"content_id\": \"cf215fbaa3fd260fdae8f76401d2c833d6a90553\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 351,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 13,\n \"path\": \"/feeds/crons.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.core import management\\nfrom django_cron import CronJobBase, Schedule\\n\\nclass ParseFeedsCronJob(CronJobBase):\\n\\tRUN_EVERY_MINS = 10\\n\\tALLOW_PARALLEL_RUNS = False\\n\\n\\tschedule = Schedule(run_every_mins=RUN_EVERY_MINS)\\n\\tcode = 'feeds.parse_feeds_cron_job'\\n\\n\\tdef do(self):\\n\\t\\tprint('Start ParseFeedsCronJob.')\\n\\t\\tmanagement.call_command('parsefeeds')\"\n },\n {\n \"alpha_fraction\": 0.7836411595344543,\n \"alphanum_fraction\": 0.7889181971549988,\n \"avg_line_length\": 28.230770111083984,\n \"blob_id\": \"16580c7b2a81c4be1821fa816de30d1c2a6fca4f\",\n \"content_id\": \"61ac63edea20ecaa0baad4863b6edab050e11b79\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 379,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 13,\n \"path\": \"/documents/crons.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.core import management\\nfrom django_cron import CronJobBase, Schedule\\n\\nclass ProcessDocumentsCronJob(CronJobBase):\\n\\tRUN_EVERY_MINS = 30\\n\\tALLOW_PARALLEL_RUNS = False\\n\\n\\tschedule = Schedule(run_every_mins=RUN_EVERY_MINS)\\n\\tcode = 'documents.process_documents_cron_job'\\n\\n\\tdef do(self):\\n\\t\\tprint('Start ProcessDocumentsCronJob.')\\n\\t\\tmanagement.call_command('processdocuments')\"\n },\n {\n \"alpha_fraction\": 0.6093617081642151,\n \"alphanum_fraction\": 0.6144680976867676,\n \"avg_line_length\": 42.55555725097656,\n \"blob_id\": \"83f7800d67d706a9cf686bf3280ff13b501845af\",\n \"content_id\": \"69a1d37e38805130f3464e541dbe352b907703a5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1175,\n \"license_type\": \"no_license\",\n \"max_line_length\": 120,\n \"num_lines\": 27,\n \"path\": \"/parsers/parsers/faz.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from parsers.parsers.base import HTMLParser\\nimport re\\nimport urllib\\n\\nclass FAZParser(HTMLParser):\\n\\tdef get_text(self, document):\\n\\t\\ttry:\\n\\t\\t\\torg = super().fetch('%s?printPagedArticle=true' % document.url)\\n\\t\\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\\n\\t\\t\\treturn\\n\\n\\t\\ts = ''\\n\\t\\tm = re.search(r'<div[^>]*class=\\\"FAZArtikelText\\\"[^>]*>(.+?)<div class=\\\"ArtikelFooter\\\">', org, re.DOTALL | re.MULTILINE)\\n\\t\\tif m:\\n\\t\\t\\ttext = re.sub(r'<span class=\\\"Bildunterschrift[^>]*>.+?</span>', ' ', m.group(1), 0, re.DOTALL | re.MULTILINE)\\n\\t\\t\\ttext = re.sub(r'<p class=\\\"Bildunterschrift[^>]*>.+?</p>', ' ', text, 0, re.DOTALL | re.MULTILINE)\\n\\t\\t\\tmatchs = re.finditer(r'<p[^>]*>(.+?)</p>', text, re.DOTALL | re.MULTILINE)\\n\\t\\t\\tfor match in matchs:\\n\\t\\t\\t\\ts += ' ' + super().clean_unescape(re.sub(r'<[^>]*>.+<[^>]*>', '', match.group(1), 0, re.DOTALL | re.MULTILINE))\\n\\n\\t\\tmatch = re.search('name=\\\"keywords\\\" content=\\\"([^\\\"]+)\\\"', org)\\n\\t\\tif match:\\n\\t\\t\\ttopics = set(super().clean_unescape(match.group(1)).split(', '))\\n\\t\\t\\tif '' in topics: topics.remove('')\\n\\t\\t\\tdocument.meta += '<topics>%s</topics>' % ''.join(['<topic>%s</topic>' % topic for topic in topics])\\n\\n\\t\\tdocument.text = super().clean_unescape(s)\"\n },\n {\n \"alpha_fraction\": 0.680232584476471,\n \"alphanum_fraction\": 0.6860465407371521,\n \"avg_line_length\": 35.89285659790039,\n \"blob_id\": \"9d1a9f88bada79739867d9e7ff7dd260534373f6\",\n \"content_id\": \"e96b5408c4005515e5422dd0a15ba1adb172cc63\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1032,\n \"license_type\": \"no_license\",\n \"max_line_length\": 107,\n \"num_lines\": 28,\n \"path\": \"/documents/admin.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib import admin\\nfrom django.forms import TextInput\\nfrom django.db import models\\nfrom documents.models import Document, TextFieldSingleLine\\nfrom suit.widgets import AutosizedTextarea\\n\\nclass DocumentAdmin(admin.ModelAdmin):\\n\\tdef show_link(self, obj):\\n\\t\\treturn '<a href=\\\"%s\\\"><i class=\\\"icon-eye-open icon-alpha75\\\"></i>View on site</a>' % obj.get_absolute_url()\\n\\n\\tlist_display = ('url', 'title', 'updated_at', 'feed', 'show_link')\\n\\tlist_filter = ('feed', 'state')\\n\\treadonly_fields = ('slug', 'state')\\n\\tsearch_fields = ('slug', 'url', 'title', 'feed__title')\\n\\tordering = ('-updated_at', 'feed')\\n\\tshow_link.allow_tags = True\\n\\tshow_link.short_description = 'View on site'\\n\\n\\tformfield_overrides = {\\n\\t\\tTextFieldSingleLine: {'widget': TextInput(attrs={'autocomplete':'off'})},\\n\\t\\tmodels.TextField: {'widget': AutosizedTextarea(attrs={'rows': 10, 'class': 'span12'})},\\n\\t}\\n\\n\\tfieldsets = [\\n\\t\\t(None, {'fields': ['slug', 'state', 'url', 'title', 'feed', 'meta', 'content', 'text']}),\\n\\t]\\n\\nadmin.site.register(Document, DocumentAdmin)\"\n },\n {\n \"alpha_fraction\": 0.7276808023452759,\n \"alphanum_fraction\": 0.7301745414733887,\n \"avg_line_length\": 26.85416603088379,\n \"blob_id\": \"9df8f3fabaf1ed52020106a9bebdc7d4b1b8de33\",\n \"content_id\": \"94c270ef763e10a8b865570ef159284e10bc676d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4010,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 144,\n \"path\": \"/infohub/settings.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nDjango settings for infohub project.\\n\\nFor more information on this file, see\\nhttps://docs.djangoproject.com/en/1.6/topics/settings/\\n\\nFor the full list of settings and their values, see\\nhttps://docs.djangoproject.com/en/1.6/ref/settings/\\n\\\"\\\"\\\"\\n\\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\\nfrom configparser import RawConfigParser\\nfrom django.conf.global_settings import TEMPLATE_CONTEXT_PROCESSORS as TCP\\nimport os\\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\\n\\nconfig = RawConfigParser()\\nconfig.optionxform = str\\nconfig.read(BASE_DIR + '/infohub/settings.ini')\\n\\n# SECURITY WARNING: keep the secret key used in production secret!\\nSECRET_KEY = config.get('secrets','SECRET_KEY')\\nSESSION_COOKIE_SECURE = config.getboolean('secrets','SESSION_COOKIE_SECURE')\\nCSRF_COOKIE_SECURE = config.getboolean('secrets','CSRF_COOKIE_SECURE')\\nSESSION_EXPIRE_AT_BROWSER_CLOSE = config.getboolean('secrets','SESSION_EXPIRE_AT_BROWSER_CLOSE')\\n\\n# SECURITY WARNING: don't run with debug turned on in production!\\nDEBUG = config.getboolean('debug','DEBUG')\\nTEMPLATE_DEBUG = config.getboolean('debug','TEMPLATE_DEBUG')\\n\\nALLOWED_HOSTS = config.get('host','ALLOWED_HOSTS').split()\\nADMINS = tuple(config.items('admins'))\\n\\nEMAIL_USE_TLS = config.getboolean('host_email','EMAIL_USE_TLS')\\nDEFAULT_FROM_EMAIL = config.get('host_email','DEFAULT_FROM_EMAIL')\\nSERVER_EMAIL = config.get('host_email','SERVER_EMAIL')\\nEMAIL_HOST = config.get('host_email','EMAIL_HOST')\\nEMAIL_PORT = config.getint('host_email','EMAIL_PORT')\\nEMAIL_HOST_USER = config.get('host_email','EMAIL_HOST_USER')\\nEMAIL_HOST_PASSWORD = config.get('host_email','EMAIL_HOST_PASSWORD')\\n\\n\\n# Application definition\\nINSTALLED_APPS = (\\n\\t'suit',\\n\\t'suit_redactor',\\n\\t'django.contrib.admin',\\n\\t'django.contrib.auth',\\n\\t'django.contrib.contenttypes',\\n\\t'django.contrib.sessions',\\n\\t'django.contrib.messages',\\n\\t'django.contrib.staticfiles',\\n\\t'south',\\n\\t'django_cron',\\n\\t'parsers',\\n\\t'feeds',\\n\\t'documents',\\n)\\n\\nMIDDLEWARE_CLASSES = (\\n\\t'django.contrib.sessions.middleware.SessionMiddleware',\\n\\t'django.middleware.common.CommonMiddleware',\\n\\t'django.middleware.csrf.CsrfViewMiddleware',\\n\\t'django.contrib.auth.middleware.AuthenticationMiddleware',\\n\\t'django.contrib.messages.middleware.MessageMiddleware',\\n\\t'django.middleware.clickjacking.XFrameOptionsMiddleware',\\n)\\n\\n\\nROOT_URLCONF = 'infohub.urls'\\nWSGI_APPLICATION = 'infohub.wsgi.application'\\n\\n\\n# Database\\nDATABASES = {\\n\\t'default': {\\n\\t\\t'ENGINE': config.get('database', 'DATABASE_ENGINE'),\\n\\t\\t'NAME': config.get('database', 'DATABASE_NAME'),\\n\\t\\t'USER': config.get('database', 'DATABASE_USER'),\\n\\t\\t'PASSWORD': config.get('database', 'DATABASE_PASSWORD'),\\n\\t\\t'HOST': config.get('database', 'DATABASE_HOST'),\\n\\t\\t'PORT': config.get('database', 'DATABASE_PORT'),\\n\\t}\\n}\\n\\n# Internationalization\\nLANGUAGE_CODE = 'en-us'\\nTIME_ZONE = 'Europe/Berlin'\\n\\nUSE_I18N = True\\nUSE_L10N = True\\nUSE_TZ = True\\n\\n# Static files (CSS, JavaScript, Images)\\nSTATIC_URL = '/static/'\\nSTATIC_ROOT = os.path.join(BASE_DIR, 'static')\\nSTATICFILES_DIRS = (\\n\\tos.path.join(BASE_DIR, 'assets'),\\n)\\nSTATICFILES_FINDERS = (\\n\\t'django.contrib.staticfiles.finders.FileSystemFinder',\\n\\t'django.contrib.staticfiles.finders.AppDirectoriesFinder',\\n)\\n\\n\\n# Media files\\nMEDIA_URL = '/media/'\\nMEDIA_ROOT = os.path.join(BASE_DIR, 'media')\\n\\n\\n# Templates\\nTEMPLATE_DIRS = (\\n\\tos.path.join(BASE_DIR, 'templates'),\\n)\\nTEMPLATE_LOADERS = (\\n\\t'django.template.loaders.filesystem.Loader',\\n\\t'django.template.loaders.app_directories.Loader',\\n)\\nTEMPLATE_CONTEXT_PROCESSORS = TCP + (\\n\\t'django.core.context_processors.request',\\n\\t'django.contrib.messages.context_processors.messages',\\n)\\n\\n\\nFAILED_RUNS_CRONJOB_EMAIL_PREFIX = \\\"[infohub CronJob]: \\\"\\nCRON_CLASSES = (\\n\\t'feeds.crons.ParseFeedsCronJob',\\n\\t'documents.crons.ProcessDocumentsCronJob',\\n\\t'django_cron.cron.FailedRunsNotificationCronJob',\\n)\\n\\n\\nSUIT_CONFIG = {\\n\\t'ADMIN_NAME':'infohub',\\n\\t'LIST_PER_PAGE': 50,\\n\\t'MENU': (\\n\\t\\t{'label': 'infohub', 'icon':'icon-home', 'url': '/'},\\n\\t\\t'auth',\\n\\t\\t'django_cron',\\n\\t\\t'parsers',\\n\\t\\t'feeds',\\n\\t\\t'documents',\\n\\t),\\n}\"\n },\n {\n \"alpha_fraction\": 0.6169354915618896,\n \"alphanum_fraction\": 0.6199596524238586,\n \"avg_line_length\": 37.19230651855469,\n \"blob_id\": \"4452de13223a1d376925151c05c731f3e041b370\",\n \"content_id\": \"704c5147759997a6c94aae0d466c7e33bd512990\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 992,\n \"license_type\": \"no_license\",\n \"max_line_length\": 157,\n \"num_lines\": 26,\n \"path\": \"/parsers/parsers/suddeutsche.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from parsers.parsers.base import HTMLParser\\nimport re\\nimport urllib\\n\\nclass SuddeutscheParser(HTMLParser):\\n\\tdef get_text(self, document):\\n\\t\\ttry:\\n\\t\\t\\torg = super().fetch(document.url)\\n\\t\\t\\ttext = super().fetch(super().get_print_link(org))\\n\\t\\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\\n\\t\\t\\treturn\\n\\n\\t\\ts = ''\\n\\t\\tm = re.search(r'<div[^>]*class=\\\"body\\\"[^>]*>(.+)<div[^>]*class=\\\"footer\\\"[^>]*>', text, re.DOTALL | re.MULTILINE)\\n\\t\\tif m:\\n\\t\\t\\tmatchs = re.finditer(r'<p>(.+?)</p>', m.group(1), re.DOTALL | re.MULTILINE)\\n\\t\\t\\tfor match in matchs:\\n\\t\\t\\t\\ts += ' ' + super().clean_unescape(re.sub('\\\\s\\\\s+', ' ', re.sub('\\\\n', ' ', match.group(1)))).strip()\\n\\n\\t\\tdocument.text = s.strip()\\n\\n\\t\\tmatch = re.search('name=\\\"news_keywords\\\" content=\\\"([^\\\"]+)\\\"', org)\\n\\t\\tif match:\\n\\t\\t\\ttopics = set(match.group(1).split(', '))\\n\\t\\t\\tif '' in topics: topics.remove('')\\n\\t\\t\\tdocument.meta += '<topics>%s</topics>' % ''.join(['<topic>%s</topic>' % super(SuddeutscheParser, self).clean_unescape(topic).strip() for topic in topics])\"\n },\n {\n \"alpha_fraction\": 0.68359375,\n \"alphanum_fraction\": 0.68359375,\n \"avg_line_length\": 35.57143020629883,\n \"blob_id\": \"d7da3523f82c8bf4a685fdaeaa18d504c72253eb\",\n \"content_id\": \"ff1dbf53ed8c35d6cf645134f40cb76d90ec26c8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 256,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 7,\n \"path\": \"/parsers/parsers/default.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from parsers.parsers.base import BaseParser\\nfrom html import unescape\\nimport re\\n\\nclass DefaultParser(BaseParser):\\n\\tdef get_text(self, document):\\n\\t\\tdocument.text = unescape(re.sub(r'\\\\s\\\\s+', ' ', re.sub(r'<[^>]+>', '', document.content.replace('\\\\n', ' '))))\\n\"\n },\n {\n \"alpha_fraction\": 0.6906198859214783,\n \"alphanum_fraction\": 0.6950082182884216,\n \"avg_line_length\": 31.571428298950195,\n \"blob_id\": \"4aff0088c4d8ac033198243a47e06ad4efc8e2e3\",\n \"content_id\": \"9d9cd924f95a8da5b908977845c3e0852612c9a4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1823,\n \"license_type\": \"no_license\",\n \"max_line_length\": 107,\n \"num_lines\": 56,\n \"path\": \"/feeds/admin.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib import admin\\nfrom django.db.models import Count\\nfrom django.forms import TextInput\\nfrom django.db import models\\nfrom feeds.models import Feed, Report, TextFieldSingleLine\\nfrom suit.widgets import AutosizedTextarea\\n\\nclass FeedAdmin(admin.ModelAdmin):\\n\\tdef show_link(self, obj):\\n\\t\\treturn '<a href=\\\"%s\\\"><i class=\\\"icon-eye-open icon-alpha75\\\"></i>View on site</a>' % obj.get_absolute_url()\\n\\n\\tdef queryset(self, request):\\n\\t\\treturn Feed.objects.annotate(document_count=Count('document'))\\n\\n\\tdef show_document_count(self, inst):\\n\\t\\treturn inst.document_count\\n\\n\\tlist_display = ('url', 'title', 'updated_at', 'show_document_count', 'parser', 'show_link')\\n\\tlist_filter = ('alive', 'parser')\\n\\treadonly_fields = ('slug',)\\n\\tsearch_fields = ('url', 'title')\\n\\tordering = ('-updated_at', 'title')\\n\\tshow_document_count.admin_order_field = 'document_count'\\n\\tshow_document_count.short_description = 'Number of Documents'\\n\\tshow_link.allow_tags = True\\n\\tshow_link.short_description = 'View on site'\\n\\n\\tformfield_overrides = {\\n\\t\\tTextFieldSingleLine: {'widget': TextInput(attrs={'autocomplete':'off'})},\\n\\t}\\n\\n\\tfieldsets = [\\n\\t\\t(None, {'fields': ['slug', 'url', 'title', 'alive', 'parser']}),\\n\\t]\\n\\nclass ReportAdmin(admin.ModelAdmin):\\n\\tdef show_link(self, obj):\\n\\t\\treturn '<a href=\\\"%s\\\"><i class=\\\"icon-eye-open icon-alpha75\\\"></i>View on site</a>' % obj.get_absolute_url()\\n\\n\\tlist_display = ('created_at', 'show_link')\\n\\tlist_filter = ('created_at',)\\n\\treadonly_fields = ('slug',)\\n\\tordering = ('-created_at',)\\n\\tshow_link.allow_tags = True\\n\\tshow_link.short_description = 'View on site'\\n\\n\\tformfield_overrides = {\\n\\t\\tmodels.TextField: {'widget': AutosizedTextarea(attrs={'rows': 10, 'class': 'span12'})},\\n\\t}\\n\\n\\tfieldsets = [\\n\\t\\t(None, {'fields': ['slug', 'text']}),\\n\\t]\\n\\nadmin.site.register(Feed, FeedAdmin)\\nadmin.site.register(Report, ReportAdmin)\"\n },\n {\n \"alpha_fraction\": 0.6372881531715393,\n \"alphanum_fraction\": 0.6389830708503723,\n \"avg_line_length\": 33.764705657958984,\n \"blob_id\": \"34e5551d8e07b2507c4af537c29ba99b3c2b6e9e\",\n \"content_id\": \"9a589576a52c0d99918da73d33995cb68be36b01\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 590,\n \"license_type\": \"no_license\",\n \"max_line_length\": 172,\n \"num_lines\": 17,\n \"path\": \"/parsers/parsers/welt.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from parsers.parsers.base import HTMLParser\\nimport re\\nimport urllib\\n\\nclass DieWeltParser(HTMLParser):\\n\\tdef get_text(self, document):\\n\\t\\ttry:\\n\\t\\t\\ttext = super().fetch('%s?config=print' % document.url)\\n\\t\\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\\n\\t\\t\\treturn\\n\\n\\t\\ts = ''\\n\\t\\tmatchs = re.finditer(r'<p class=\\\"text\\\">(.+?)</p>', text, re.DOTALL | re.MULTILINE)\\n\\t\\tfor match in matchs:\\n\\t\\t\\ts += ' ' + super().clean_unescape(re.sub('\\\\s\\\\s+', ' ', re.sub('\\\\n', ' ', re.sub(r'<span class=\\\"url\\\">.+?</span>', '', match.group(1), re.DOTALL | re.MULTILINE)))).strip()\\n\\n\\t\\tdocument.text = s.strip()\"\n },\n {\n \"alpha_fraction\": 0.5647955536842346,\n \"alphanum_fraction\": 0.5710325837135315,\n \"avg_line_length\": 51.490909576416016,\n \"blob_id\": \"a82480a79a42cdef63534c7da67598f90540f5a5\",\n \"content_id\": \"07c6ee7c3bb6f07d925f45c5e203ec3d2a20595c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2886,\n \"license_type\": \"no_license\",\n \"max_line_length\": 139,\n \"num_lines\": 55,\n \"path\": \"/feeds/migrations/0001_initial.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom south.utils import datetime_utils as datetime\\nfrom south.db import db\\nfrom south.v2 import SchemaMigration\\nfrom django.db import models\\n\\n\\nclass Migration(SchemaMigration):\\n depends_on = (\\n (\\\"parsers\\\", \\\"0002_add_default_parser\\\"),\\n )\\n\\n def forwards(self, orm):\\n # Adding model 'Feed'\\n db.create_table('feeds_feed', (\\n ('id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),\\n ('created_at', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)),\\n ('updated_at', self.gf('django.db.models.fields.DateTimeField')(blank=True, auto_now=True)),\\n ('slug', self.gf('django.db.models.fields.SlugField')(max_length=4096, unique=True)),\\n ('url', self.gf('feeds.models.TextFieldSingleLine')(unique=True)),\\n ('title', self.gf('feeds.models.TextFieldSingleLine')(null=True, blank=True)),\\n ('alive', self.gf('django.db.models.fields.BooleanField')(default=True)),\\n ('parser', self.gf('django.db.models.fields.related.ForeignKey')(to=orm['parsers.Parser'], blank=True, null=True)),\\n ))\\n db.send_create_signal('feeds', ['Feed'])\\n\\n\\n def backwards(self, orm):\\n # Deleting model 'Feed'\\n db.delete_table('feeds_feed')\\n\\n\\n models = {\\n 'feeds.feed': {\\n 'Meta': {'object_name': 'Feed'},\\n 'alive': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),\\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),\\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\\n 'parser': ('django.db.models.fields.related.ForeignKey', [], {'to': \\\"orm['parsers.Parser']\\\", 'blank': 'True', 'null': 'True'}),\\n 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '4096', 'unique': 'True'}),\\n 'title': ('feeds.models.TextFieldSingleLine', [], {'null': 'True', 'blank': 'True'}),\\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now': 'True'}),\\n 'url': ('feeds.models.TextFieldSingleLine', [], {'unique': 'True'})\\n },\\n 'parsers.parser': {\\n 'Meta': {'object_name': 'Parser', 'ordering': \\\"('name',)\\\"},\\n 'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),\\n 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),\\n 'name': ('parsers.models.TextFieldSingleLine', [], {'unique': 'True'}),\\n 'slug': ('django.db.models.fields.SlugField', [], {'max_length': '4096', 'unique': 'True'}),\\n 'updated_at': ('django.db.models.fields.DateTimeField', [], {'blank': 'True', 'auto_now': 'True'})\\n }\\n }\\n\\n complete_apps = ['feeds']\"\n },\n {\n \"alpha_fraction\": 0.7025599479675293,\n \"alphanum_fraction\": 0.7062169909477234,\n \"avg_line_length\": 28.309524536132812,\n \"blob_id\": \"2ccdf787a09f992598a84afaa3c5eac8009f4b36\",\n \"content_id\": \"7a85c296e07ff2530e024c028739b21eefc735bd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2461,\n \"license_type\": \"no_license\",\n \"max_line_length\": 99,\n \"num_lines\": 84,\n \"path\": \"/feeds/models.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\nfrom django.core.urlresolvers import reverse\\nfrom django.db import models\\nfrom django.template.defaultfilters import slugify\\nfrom parsers.models import Parser\\nfrom south.modelsinspector import add_introspection_rules\\n\\nimport feedparser\\nimport re\\n\\nadd_introspection_rules([], [\\\"^feeds\\\\.models\\\\.TextFieldSingleLine\\\"])\\n\\nclass TextFieldSingleLine(models.TextField):\\n\\tpass\\n\\nclass Feed(models.Model):\\n\\tcreated_at = models.DateTimeField(auto_now_add=True)\\n\\tupdated_at = models.DateTimeField(auto_now=True)\\n\\n\\tslug = models.SlugField(max_length=4096, unique=True)\\n\\turl = TextFieldSingleLine(unique=True)\\n\\ttitle = TextFieldSingleLine(blank=True, null=True)\\n\\talive = models.BooleanField(default=True)\\n\\n\\tparser = models.ForeignKey(Parser, null=True, blank=True)\\n\\n\\tdef get_absolute_url(self):\\n\\t\\treturn reverse('feed', args=[str(self.slug)])\\n\\n\\tdef parse(self):\\n\\t\\tfrom documents.models import Document\\n\\t\\trssfeed = feedparser.parse(self.url)\\n\\t\\tif not self.title and rssfeed.feed.title:\\n\\t\\t\\tself.title = rssfeed.feed.title\\n\\t\\t\\tself.save()\\n\\n\\t\\tfor entry in rssfeed.entries:\\n\\t\\t\\tif not Document.objects.filter(url=entry.link).exists():\\n\\t\\t\\t\\ttitle = re.sub(r'\\\\s\\\\s+', ' ', entry.title.replace('\\\\n', ' '))\\n\\t\\t\\t\\tsummary = re.sub(r'\\\\s\\\\s+', ' ', entry.summary.replace('\\\\n', ' ')) if 'summary' in entry else ''\\n\\n\\t\\t\\t\\tdate = None\\n\\t\\t\\t\\tif 'published' in entry:\\n\\t\\t\\t\\t\\tdate = entry.published_parsed\\n\\t\\t\\t\\telif 'updated' in entry:\\n\\t\\t\\t\\t\\tdate = entry.updated_parsed\\n\\t\\t\\t\\telif 'created' in entry:\\n\\t\\t\\t\\t\\tdate = entry.created_parsed\\n\\n\\t\\t\\t\\tmeta = ''\\n\\t\\t\\t\\tif date:\\n\\t\\t\\t\\t\\tmeta += '<date>%s</date>' % datetime(*date[:6]).isoformat()\\n\\n\\t\\t\\t\\tDocument.objects.create(url=entry.link, title=title, feed=self, content=summary, meta=meta)\\n\\n\\tdef save(self, *args, **kwargs):\\n\\t\\tif not self.id:\\n\\t\\t\\tself.slug = slugify(self.url)\\n\\t\\tsuper(Feed, self).save(*args, **kwargs)\\n\\n\\tdef __str__(self):\\n\\t\\treturn self.title if self.title else self.url\\n\\nclass Report(models.Model):\\n\\tcreated_at = models.DateTimeField(auto_now_add=True)\\n\\tupdated_at = models.DateTimeField(auto_now=True)\\n\\n\\tslug = models.SlugField(max_length=4096, unique=True)\\n\\ttext = models.TextField()\\n\\n\\tdef get_absolute_url(self):\\n\\t\\treturn reverse('report', args=[str(self.slug)])\\n\\n\\tdef save(self, *args, **kwargs):\\n\\t\\tsuper(Report, self).save(*args, **kwargs)\\n\\t\\tif not self.slug:\\n\\t\\t\\tself.slug = slugify(self.created_at.isoformat())\\n\\t\\t\\tsuper(Report, self).save()\\n\\n\\tdef __str__(self):\\n\\t\\treturn self.slug\\n\\n\\tclass Meta:\\n\\t\\tordering = ('-created_at',)\"\n },\n {\n \"alpha_fraction\": 0.6073018908500671,\n \"alphanum_fraction\": 0.6108637452125549,\n \"avg_line_length\": 36.46666717529297,\n \"blob_id\": \"5856b97f04d688a161781b42e26f80a1780fb4d7\",\n \"content_id\": \"8118403c5a8503a8fbeca90b58feda491c73ca90\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1123,\n \"license_type\": \"no_license\",\n \"max_line_length\": 177,\n \"num_lines\": 30,\n \"path\": \"/parsers/parsers/zeit.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from parsers.parsers.base import HTMLParser\\nimport re\\nimport urllib\\n\\nclass ZeitOnlineParser(HTMLParser):\\n\\tdef get_text(self, document):\\n\\t\\ttry:\\n\\t\\t\\ttext = super().fetch('%s?print=true' % document.url)\\n\\t\\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\\n\\t\\t\\treturn\\n\\n\\t\\ts = ''\\n\\t\\tm = re.search(r'<!--start: content-->(.+?)<div[^>]*class=\\\"[^\\\"]*articlefooter[^\\\"]*\\\">', text, re.DOTALL | re.MULTILINE)\\n\\t\\tif m:\\n\\t\\t\\tmatchs = re.finditer(r'<p>(.+?)</p>', re.sub(r'<div[^>]*class=\\\"block infobox\\\"[^>]*><dl>.+</dl></div>', '', m.group(1), 1, re.DOTALL | re.MULTILINE), re.DOTALL | re.MULTILINE)\\n\\t\\t\\tfor match in matchs:\\n\\t\\t\\t\\ts += ' ' + super().clean_unescape(re.sub('\\\\s\\\\s+', ' ', re.sub('\\\\n', ' ', match.group(1)))).strip()\\n\\n\\t\\tdocument.text = s.strip()\\n\\n\\t\\ttry:\\n\\t\\t\\ttext = super().fetch(document.url)\\n\\t\\texcept (urllib.error.HTTPError, urllib.error.URLError) as e:\\n\\t\\t\\treturn\\n\\n\\t\\tmatch = re.search('name=\\\"keywords\\\" content=\\\"([^\\\"]+)\\\"', text)\\n\\t\\tif match:\\n\\t\\t\\ttopics = set(match.group(1).split(', '))\\n\\t\\t\\tif '' in topics: topics.remove('')\\n\\t\\t\\tdocument.meta += '<topics>%s</topics>' % ''.join(['<topic>%s</topic>' % topic for topic in topics])\"\n },\n {\n \"alpha_fraction\": 0.7324324250221252,\n \"alphanum_fraction\": 0.7324324250221252,\n \"avg_line_length\": 27.538461685180664,\n \"blob_id\": \"d27ce7743af0a85055bde4a7fc97269494c4929c\",\n \"content_id\": \"1b0485134f6178a9e6cf55ffd33124fbf10e1ae5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 370,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 13,\n \"path\": \"/documents/management/commands/processdocuments.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.core.management.base import BaseCommand\\nfrom documents.models import Document\\n\\nclass Command(BaseCommand):\\n\\thelp = 'Processes the documents.'\\n\\n\\tdef handle(self, *args, **options):\\n\\t\\tfor document in Document.objects.filter(feed__parser__isnull=False).filter(state='new'):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tdocument.parse()\\n\\t\\t\\t\\tdocument.save()\\n\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\tprint(e)\"\n },\n {\n \"alpha_fraction\": 0.8605149984359741,\n \"alphanum_fraction\": 0.8605149984359741,\n \"avg_line_length\": 65.71428680419922,\n \"blob_id\": \"f2e0cd4898f597c34f44327a37b969b1785309b9\",\n \"content_id\": \"580125e9cb25b01bf01b7beb91a99cf76c346ac4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 466,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 7,\n \"path\": \"/parsers/parsers/__init__.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from parsers.parsers.default import DefaultParser as default\\nfrom parsers.parsers.faz import FAZParser as faz_parser\\nfrom parsers.parsers.focus import FocusParser as focus_parser\\nfrom parsers.parsers.spiegel import SpiegelParser as spiegel_parser\\nfrom parsers.parsers.suddeutsche import SuddeutscheParser as suddeutsche_parser\\nfrom parsers.parsers.welt import DieWeltParser as die_welt_parser\\nfrom parsers.parsers.zeit import ZeitOnlineParser as zeit_online_parser\"\n },\n {\n \"alpha_fraction\": 0.6892561912536621,\n \"alphanum_fraction\": 0.6892561912536621,\n \"avg_line_length\": 27.85714340209961,\n \"blob_id\": \"0560a5fa308d853c59600f1547a63aa2abbb36e9\",\n \"content_id\": \"9335247c9b8ddbfd6c82bf3f2c1dd639bd8cb3a9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 605,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 21,\n \"path\": \"/feeds/management/commands/parsefeeds.py\",\n \"repo_name\": \"jnphilipp/infohub\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.core.management.base import BaseCommand\\nfrom feeds.models import Feed, Report\\n\\nimport json\\n\\nclass Command(BaseCommand):\\n\\thelp = 'Parses the new feeds.'\\n\\n\\tdef handle(self, *args, **options):\\n\\t\\tnew_entries = dict()\\n\\t\\tfor feed in Feed.objects.all():\\n\\t\\t\\told = feed.document_set.count()\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tfeed.parse()\\n\\t\\t\\t\\tfeed.save()\\n\\t\\t\\t\\tnew_entries[feed.slug] = feed.document_set.count() - old\\n\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\tnew_entries[feed.slug] = str(e).strip()\\n\\n\\t\\treport = Report.objects.create(text=json.dumps(new_entries))\\n\\t\\tself.stdout.write('Report: (%s)\\\\n %s' % (report.created_at, report.text))\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":28,"string":"28"}}},{"rowIdx":2979148,"cells":{"repo_name":{"kind":"string","value":"pdillon222/FretPositions"},"repo_url":{"kind":"string","value":"https://github.com/pdillon222/FretPositions"},"snapshot_id":{"kind":"string","value":"17a6afca41f3c98f032466725501d56351405f6b"},"revision_id":{"kind":"string","value":"dd6f17d7e0cacebc6c9d9fb36b72816514f147c6"},"directory_id":{"kind":"string","value":"88461806ea62c38e6792bc2e4ada7c06f0f352ac"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2016-08-31T06:23:13.151726","string":"2016-08-31T06:23:13.151726"},"revision_date":{"kind":"timestamp","value":"2016-07-20T20:17:36","string":"2016-07-20T20:17:36"},"committer_date":{"kind":"timestamp","value":"2016-07-20T20:17:36","string":"2016-07-20T20:17:36"},"github_id":{"kind":"number","value":50479268,"string":"50,479,268"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.84375,"alphanum_fraction":0.84375,"avg_line_length":47,"blob_id":"dcb6cee9849ac7d4a74ae8121040a6ff27ea377c","content_id":"ad319584c8a0b69532eea283acdedaf0d3151d1b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":96,"license_type":"no_license","max_line_length":77,"num_lines":2,"path":"/README.md","repo_name":"pdillon222/FretPositions","src_encoding":"UTF-8","text":"###Fret Positions\nPython script that determines guitar notes and corresponding available scales\n"},{"alpha_fraction":0.532750129699707,"alphanum_fraction":0.5558546185493469,"avg_line_length":28.523178100585938,"blob_id":"9761a910cd5cc8eae8f3ffda96fcfaec2da5fcfc","content_id":"b3586afcf2de9aedfe34dd7c67f8387bac8edee3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4458,"license_type":"no_license","max_line_length":71,"num_lines":151,"path":"/guitar_note.py","repo_name":"pdillon222/FretPositions","src_encoding":"UTF-8","text":"one = [\"A\",\"A#\",\"B\",\"C\",\"C#\",\"D\",\"D#\",\"E\",\"F\",\"F#\",\"G\",\"G#\"]*5\nA = one[0:27]\nAsharp = one[1:28]\nB = one[2:29]\nC = one[3:30]\nCsharp = one[4:31]\nD = one[5:32]\nDsharp = one[6:33]\nE = one[7:34]\nF = one[8:35]\nFsharp = one[9:36]\nG = one[10:37]\nGsharp = one[11:38]\n\nmajor = [0,2,4,5,7,9,11]\nminor = [0,2,4,5,7,8,10]\nlydian = [0,2,4,6,7,9,11]\n\ndef find_scale(note,scale):\n lst = []\n for i in scale:\n place = note[i]\n lst.append(place)\n print lst\n\n\ncord = raw_input(\"Please enter a string number(6-1):\")\n_string = int(cord)\n\nif _string > 6:\n print \"String # is too high\"\nelif _string < 1:\n print \"String # is too low\"\nelse: \n pass\n\nnote = raw_input(\"Please enter the fret position(0-27):\")\n_note = int(note)\n\nif _note > 27:\n print \"Fretboard position is too high\"\nelif _note < 0:\n print \"Fretboard position is too low\"\nelse:\n pass\n\nmajor = [0,2,4,5,7,9,11]\nminor = [0,2,3,5,7,8,10]\nlydian = [0,2,4,6,7,9,11]\n\ndef scale_find(note,scale):\n lst = []\n for i in scale:\n place = note[i]\n lst.append(place)\n return lst\n\n\ndef guitar_note(_string, _note):\n if _string == 6:\n pos = E[_note]\n print \"The note is:\", pos\n elif _string == 5:\n pos = A[_note]\n print \"The note is:\", pos\n elif _string == 4:\n pos = D[_note]\n print \"The note is:\", pos\n elif _string == 3:\n pos = G[_note]\n print \"The note is:\", pos\n elif _string == 2:\n pos = B[_note]\n print \"The note is:\", pos\n elif _string == 1:\n pos = E[_note]\n print \"The note is:\", pos\n else:\n print \"That is not a real guitar note!!\"\n if pos == \"E\":\n noted = E\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian)\n elif pos == \"F\":\n noted = F\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian)\n \n elif pos == \"F#\":\n noted = Fsharp\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian)\n elif pos == \"G\":\n noted = G\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian)\n elif pos == \"G#\":\n noted = Gsharp\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian) \n elif pos == \"A\":\n noted = A\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian) \n elif pos == \"A#\":\n noted = Asharp\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian)\n elif pos == \"B\":\n noted = B\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian) \n elif pos == \"C\":\n noted = C\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian) \n elif pos == \"C#\":\n noted = Csharp\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian) \n elif pos == \"D\":\n noted = D\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian) \n elif pos == \"D#\":\n noted = Dsharp\n print \"Major Scale = \", scale_find(noted,major)\n print \"Minor Scale = \", scale_find(noted,minor)\n print \"Lydian Scale = \", scale_find(noted,lydian) \n\nprint \"String # is\",_string\nprint \"Fretboard position is\",_note\n \nguitar_note(_string, _note)\nnext = raw_input(\"Would you like to find some more scales?(yes or no)\")\nif next =='yes':\n\timport note.py\n\treload(note.py)\nelse:\n\tprint \"See you later\"\n"}],"string":"[\n {\n \"alpha_fraction\": 0.84375,\n \"alphanum_fraction\": 0.84375,\n \"avg_line_length\": 47,\n \"blob_id\": \"dcb6cee9849ac7d4a74ae8121040a6ff27ea377c\",\n \"content_id\": \"ad319584c8a0b69532eea283acdedaf0d3151d1b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 96,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"pdillon222/FretPositions\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"###Fret Positions\\nPython script that determines guitar notes and corresponding available scales\\n\"\n },\n {\n \"alpha_fraction\": 0.532750129699707,\n \"alphanum_fraction\": 0.5558546185493469,\n \"avg_line_length\": 28.523178100585938,\n \"blob_id\": \"9761a910cd5cc8eae8f3ffda96fcfaec2da5fcfc\",\n \"content_id\": \"b3586afcf2de9aedfe34dd7c67f8387bac8edee3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4458,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 151,\n \"path\": \"/guitar_note.py\",\n \"repo_name\": \"pdillon222/FretPositions\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"one = [\\\"A\\\",\\\"A#\\\",\\\"B\\\",\\\"C\\\",\\\"C#\\\",\\\"D\\\",\\\"D#\\\",\\\"E\\\",\\\"F\\\",\\\"F#\\\",\\\"G\\\",\\\"G#\\\"]*5\\nA = one[0:27]\\nAsharp = one[1:28]\\nB = one[2:29]\\nC = one[3:30]\\nCsharp = one[4:31]\\nD = one[5:32]\\nDsharp = one[6:33]\\nE = one[7:34]\\nF = one[8:35]\\nFsharp = one[9:36]\\nG = one[10:37]\\nGsharp = one[11:38]\\n\\nmajor = [0,2,4,5,7,9,11]\\nminor = [0,2,4,5,7,8,10]\\nlydian = [0,2,4,6,7,9,11]\\n\\ndef find_scale(note,scale):\\n lst = []\\n for i in scale:\\n place = note[i]\\n lst.append(place)\\n print lst\\n\\n\\ncord = raw_input(\\\"Please enter a string number(6-1):\\\")\\n_string = int(cord)\\n\\nif _string > 6:\\n print \\\"String # is too high\\\"\\nelif _string < 1:\\n print \\\"String # is too low\\\"\\nelse: \\n pass\\n\\nnote = raw_input(\\\"Please enter the fret position(0-27):\\\")\\n_note = int(note)\\n\\nif _note > 27:\\n print \\\"Fretboard position is too high\\\"\\nelif _note < 0:\\n print \\\"Fretboard position is too low\\\"\\nelse:\\n pass\\n\\nmajor = [0,2,4,5,7,9,11]\\nminor = [0,2,3,5,7,8,10]\\nlydian = [0,2,4,6,7,9,11]\\n\\ndef scale_find(note,scale):\\n lst = []\\n for i in scale:\\n place = note[i]\\n lst.append(place)\\n return lst\\n\\n\\ndef guitar_note(_string, _note):\\n if _string == 6:\\n pos = E[_note]\\n print \\\"The note is:\\\", pos\\n elif _string == 5:\\n pos = A[_note]\\n print \\\"The note is:\\\", pos\\n elif _string == 4:\\n pos = D[_note]\\n print \\\"The note is:\\\", pos\\n elif _string == 3:\\n pos = G[_note]\\n print \\\"The note is:\\\", pos\\n elif _string == 2:\\n pos = B[_note]\\n print \\\"The note is:\\\", pos\\n elif _string == 1:\\n pos = E[_note]\\n print \\\"The note is:\\\", pos\\n else:\\n print \\\"That is not a real guitar note!!\\\"\\n if pos == \\\"E\\\":\\n noted = E\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian)\\n elif pos == \\\"F\\\":\\n noted = F\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian)\\n \\n elif pos == \\\"F#\\\":\\n noted = Fsharp\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian)\\n elif pos == \\\"G\\\":\\n noted = G\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian)\\n elif pos == \\\"G#\\\":\\n noted = Gsharp\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian) \\n elif pos == \\\"A\\\":\\n noted = A\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian) \\n elif pos == \\\"A#\\\":\\n noted = Asharp\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian)\\n elif pos == \\\"B\\\":\\n noted = B\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian) \\n elif pos == \\\"C\\\":\\n noted = C\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian) \\n elif pos == \\\"C#\\\":\\n noted = Csharp\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian) \\n elif pos == \\\"D\\\":\\n noted = D\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian) \\n elif pos == \\\"D#\\\":\\n noted = Dsharp\\n print \\\"Major Scale = \\\", scale_find(noted,major)\\n print \\\"Minor Scale = \\\", scale_find(noted,minor)\\n print \\\"Lydian Scale = \\\", scale_find(noted,lydian) \\n\\nprint \\\"String # is\\\",_string\\nprint \\\"Fretboard position is\\\",_note\\n \\nguitar_note(_string, _note)\\nnext = raw_input(\\\"Would you like to find some more scales?(yes or no)\\\")\\nif next =='yes':\\n\\timport note.py\\n\\treload(note.py)\\nelse:\\n\\tprint \\\"See you later\\\"\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979149,"cells":{"repo_name":{"kind":"string","value":"huisso97/baekjoon_learning"},"repo_url":{"kind":"string","value":"https://github.com/huisso97/baekjoon_learning"},"snapshot_id":{"kind":"string","value":"c67377f05bf6598f9e317b616dd5e8812c3819ea"},"revision_id":{"kind":"string","value":"c7c5d3c76b3d8661aa9327a98be2cb8ced35a55c"},"directory_id":{"kind":"string","value":"fa89bb8764dfd5ece61505ed98fbd9bc7a41a6a8"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-07-14T16:56:03.705297","string":"2023-07-14T16:56:03.705297"},"revision_date":{"kind":"timestamp","value":"2023-02-26T13:17:29","string":"2023-02-26T13:17:29"},"committer_date":{"kind":"timestamp","value":"2023-02-26T13:17:29","string":"2023-02-26T13:17:29"},"github_id":{"kind":"number","value":397442123,"string":"397,442,123"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.4962962865829468,"alphanum_fraction":0.529629647731781,"avg_line_length":24.761905670166016,"blob_id":"a3b0cac3c1333df548d433ae6a2d31774bf1bccd","content_id":"e329e9821006df1191e02af75c0b47fd42f3e410","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":540,"license_type":"no_license","max_line_length":48,"num_lines":21,"path":"/programmers/체육복/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(n, lost, reserve):\n answer = 0\n new_lost = list(set(lost) - set(reserve))\n new_reserve = list(set(reserve) - set(lost))\n\n student = [1] * (n + 1)\n\n for num in new_lost:\n student[num] = 0\n\n for num in new_lost:\n if num - 1 in new_reserve:\n new_reserve.remove(num - 1)\n student[num] += 1\n elif num + 1 in new_reserve:\n new_reserve.remove(num + 1)\n student[num] += 1\n answer = student.count(1) - 1\n return answer\n\nsolution(5, [2,4], [1,3,5])"},{"alpha_fraction":0.5763688683509827,"alphanum_fraction":0.6224783658981323,"avg_line_length":17.3157901763916,"blob_id":"19526b656b794b0df9a3efaab98404525ce1aea1","content_id":"5fa8a69c84c86e01aca201f0a28b38dd39f19df4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":513,"license_type":"no_license","max_line_length":45,"num_lines":19,"path":"/algorithm_base/파이썬 기초/구간 합 빠르게 계산하기/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# 특정 인덱스 a 에서 b까지의 합을 구할 때, 각 인덱스까지의 합을 저장한\n# 배열을 만들어서,\n# 구간합을 구하는 방식\n\n# 데이터의 개수 n과 데이터 입력받기\nn = 5\ndata = [10, 20, 30, 40, 50]\n\n# 접두사의 합(Prefix Sum) 배열 계산\nsum_value = 0\nprefix_sum = [0]\nfor i in data:\n sum_value += i\n prefix_sum.append(sum_value)\n\n# 구간 합 계산(세 번째 수부터 네 번째 수까지)\nleft = 3\nright = 4\nprint(prefix_sum[right] - prefix_sum[left-1])"},{"alpha_fraction":0.5089605450630188,"alphanum_fraction":0.5483871102333069,"avg_line_length":21.360000610351562,"blob_id":"5a3436a45ceceed5253ffcb39328e04ac9daffd0","content_id":"214f1b496ed3a8541b5857a484d592328023ff2d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":654,"license_type":"no_license","max_line_length":54,"num_lines":25,"path":"/programmers_school/더 맵게/더 맵게.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import heapq\n\n\ndef solution(scoville, K) :\n answer = 0\n heapq.heapify(scoville)\n\n # 모든 음식의 스코빌 지수가 K이상 혹은 남은 음식 갯수가 0개인데 K미만일때 break\n while True:\n # 1번째로 덜 매운 음식(최솟값)\n min1 = heapq.heappop(scoville)\n if min1 >= K:\n break\n elif len(scoville) ==0:\n answer = -1\n break\n # 2번째로 덜 매운 음식\n min2 = heapq.heappop(scoville)\n new_scoville = min1 + min2*2\n heapq.heappush(scoville,new_scoville)\n answer += 1\n return answer\n\n\nprint(solution([1,2,3,9,10,12],7))"},{"alpha_fraction":0.4070175290107727,"alphanum_fraction":0.46666666865348816,"avg_line_length":13.894737243652344,"blob_id":"b11ffa42cf38092931b025459c0efb83a9ce3e46","content_id":"a74e9d1108abf04abcd0a72e2b3a3a82d33884d7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":387,"license_type":"no_license","max_line_length":31,"num_lines":19,"path":"/baekjoon/설탕배달.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"N = int(input())\n\n#큰값부터 나누고, 나머지에서 계속 나눈다\n#모든 if문에 해당하지 않으면 -1출력\n\ncnt = 0\nsub = 0\n#나눌 수 없으면\nif N // 5 ==0 or N // 3 == 0:\n print(-1)\nelse:\n cnt += N//5\n sub = N % 5\n cnt += sub//3\n sub %= 3\n #3으로 나눈 후, 1,2 남아있으면 한봉지 추가\n if sub>0:\n cnt += 1\n print(cnt)\n\n\n"},{"alpha_fraction":0.4792899489402771,"alphanum_fraction":0.5164835453033447,"avg_line_length":26.488372802734375,"blob_id":"7fdcc6a2a33ea12312ca2afcce5022bb443dbbf7","content_id":"86f9629ae5f5d2d613c217da2189c6a1e81141cd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1799,"license_type":"no_license","max_line_length":69,"num_lines":43,"path":"/programmers_school/체육복.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# 그리디\n\n# 방법 1 (적은 수의 학생 배열에 대해서 처리 -> 모든 학생들을 고려)\n# u : 체육복을 가지고 온 학생과 없는 학생이 1 과 0, 여벌을 가지고 온 학생 2로 구분됨\n\ndef solution(n, lost, reserve):\n # 바운더리를 체크하는 과정에서의 편의를 위해 맨 앞, 뒤에 여분의 1 추가\n u = [1] * (n + 2)\n for i in reserve:\n u[i] += 1\n for i in lost:\n u[i] -= 1\n for i in (range(1,n+1)):\n if (u[i-1]) ==0 and u[i]==2:\n # 리스트 슬라이스로 두명에게 1개씩 부여\n u[i-1:i+1] = [1,1]\n elif u[i] == 2 and u[i+1] == 0:\n u[i:i+2] = [1,1] \n # u의 1번째부터 -1 전까지 에서 원소들을 꺼내는데, 단 0보다 큰 것들만 꺼내어서 길이로 반환\n return len([x for x in u[1:-1] if x > 0])\n\n# 방법 2 (학생 수는 많지만, 여벌을 가지고 있는 학생 수가 적을때, 여벌을 가지고 있는 학생들만 고려)\n# 데이터타입 set(집합): 해시테이블로 구현되어있기 때문에, key로 접근하는 시간이 상수시간임\n# 딕셔너리는 키에 대해서 map되어있지만, set은 value는 없고 특정 데이터가 이 집합에 속해있는지 여부만 판단 가능\n\ndef solution2(n,lost,reserve):\n # 두 집합의 교집합을 나타냄\n s = set(lost) & set(reserve)\n # 차집합 (체육복을 빌려야하는 학생들)\n l = set(lost) - s\n # 여벌이 있는데 도난을 안당한 학생들\n r = set(reserve) - s\n\n for x in sorted(r):\n if x - 1 in l:\n l.remove(x-1)\n elif x +1 in l:\n l.remove(x+1)\n\n # 빌려야하는데 빌리지 못한 학생들만 l에 남음\n\n return n - len(l)\nprint(solution2(5,[2, 4],[1, 3, 5]))\n\n"},{"alpha_fraction":0.5102599263191223,"alphanum_fraction":0.5253077745437622,"avg_line_length":26.11111068725586,"blob_id":"77add4a0ecb7e436fd549ce067c9cbf4ecb3a9cd","content_id":"b8c44f0f7b7ece46d9561e423f7c9ac608ac3103","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":747,"license_type":"no_license","max_line_length":60,"num_lines":27,"path":"/programmers/신고결과받기/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(id_list, report, k):\n report = list(set(report))\n res = {x: 0 for x in id_list}\n\n for i in report:\n res[i.split()[1]] += 1\n print(res)\n answer = [0] * len(id_list)\n for i in range(len(report)):\n if res[report[i].split()[1]] >= k:\n answer[id_list.index(report[i].split()[0])] += 1\n return answer\n\n\ndef solution(id_list, report, k):\n answer = [0] * len(id_list)\n dic_report = {id: [] for id in id_list} # 해당 유저를 신고한 ID\n for i in set(report):\n i = i.split()\n dic_report[i[1]].append(i[0])\n\n for key, value in dic_report.items():\n if len(value) >= k:\n for j in value:\n answer[id_list.index(j)] += 1\n\n return answer"},{"alpha_fraction":0.5072463750839233,"alphanum_fraction":0.5314009785652161,"avg_line_length":13.857142448425293,"blob_id":"a81eb6c531456ae2e426b3fdcea23ba0d3f577d4","content_id":"6f5b7eaaf378fd0ede46f891f4529cc750c575d0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":207,"license_type":"no_license","max_line_length":35,"num_lines":14,"path":"/algorithm_base/코드 구현력 기르기/K번째 약수/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open(\"input.txt\", \"rt\")\nN,K = map(int, input().split())\n\nres = []\n\nfor i in range(1,N+1):\n if N%i==0:\n res.append(i)\n\nif len(res) < K:\n print(-1)\nelse:\n print(res[K-1])"},{"alpha_fraction":0.44186046719551086,"alphanum_fraction":0.45877379179000854,"avg_line_length":16.518518447875977,"blob_id":"fa42d3be8f7b52ff995c4182a81c381ca2459359","content_id":"8ce66a8fea6b5d2372722fb9a1579d2c872523ea","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":533,"license_type":"no_license","max_line_length":32,"num_lines":27,"path":"/algorithm_base/그리디 알고리즘/랜선자르기/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open(\"input.txt\")\n\nK, N = map(int, input().split())\n# N개 이상도 N개 만드는 것에 포함\n# N개를 만들 수 있는 랜선의 초대 길이 단위 출력\narr = []\nfor i in range(K):\n arr.append(int(input()))\ns = 0\ne = max(arr)\nlength = 0\nres = 0\nwhile True:\n m = (s+e) // 2\n for num in arr:\n res += num // m\n if res >= N :\n length = m\n s = m + 1\n if s > e:\n print(length)\n break\n else:\n e = m - 1\n length = 0\n res = 0\n"},{"alpha_fraction":0.5392670035362244,"alphanum_fraction":0.5575916171073914,"avg_line_length":18.100000381469727,"blob_id":"e45d10b50ac1e5801cc3588a45c25af08e646f4b","content_id":"2052c6d27f75f2f31ad7f057780e6f7d0687e92f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":382,"license_type":"no_license","max_line_length":37,"num_lines":20,"path":"/algorithm_base/코드 구현력 기르기/정다면체/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open(\"input.txt\", \"rt\")\nN, M = map(int, input().split())\n\nres = []\nnums = []\n\nfor i in range(1, N+1):\n for j in range(1, M+1):\n nums.append(i+j)\nnums.sort()\nsmall_nums = set(nums)\nres = [0]*40\n\nfor num in small_nums:\n res[num] = nums.count(num)\nmaximum = max(res)\nfor i in range(len(res)):\n if res[i] == maximum:\n print(i, end=' ')\n"},{"alpha_fraction":0.3978102207183838,"alphanum_fraction":0.45255473256111145,"avg_line_length":12.5,"blob_id":"a3c876ecf7fe079dc8b54d84e4df4cff482a5781","content_id":"10f1bc830dbd8c09a5b16aeacfe1db6d36992672","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":358,"license_type":"no_license","max_line_length":26,"num_lines":20,"path":"/algorithm_base/파이썬 기초/반복문/problem.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"'''\n반복문을 이용한 문제풀이\n1) 1부터 N까지 홀수출력하기\n2) 1부터 N까지의 합 구하기\n3) N의 약수출력하기\n'''\n\nn = int(input())\n# 2\n# res = 0\n# for i in range(1, n+1) :\n# if i%2 == 1 :\n# res += i\n# print(res)\n\n# 3\nfor i in range(1, n+1) :\n if n%i==0:\n # 줄바꿈 안함\n print(i, end=\" \") "},{"alpha_fraction":0.4383954107761383,"alphanum_fraction":0.4498567283153534,"avg_line_length":22.33333396911621,"blob_id":"6d8e3ef7b2f7dea2b071dfc6ba767254abf3f4e9","content_id":"0d2049e1e1a92f0f03a4e4f2484dbc079878287e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":349,"license_type":"no_license","max_line_length":42,"num_lines":15,"path":"/algorithm_base/탐색&시뮬레이션/회문 문자열 검사/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open(\"input.txt\", \"rt\")\nn = int(input())\nfor i in range(1, n+1):\n word = input().upper()\n res = ''\n for j in range(len(word)//2):\n if word[j] == word[len(word)-1-j]:\n continue\n else:\n res = \"NO\"\n break\n else:\n res = \"YES\"\n print('#{} {}'.format(i, res))"},{"alpha_fraction":0.4469820559024811,"alphanum_fraction":0.4632952809333801,"avg_line_length":26.909090042114258,"blob_id":"6cd819526e1d38cea1b85383a01e699c18da8d34","content_id":"8b63d615d8539f3f0ccf560018305c3acc511e6c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":613,"license_type":"no_license","max_line_length":103,"num_lines":22,"path":"/programmers/숫자 문자열과 영단어/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(s):\n words = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']\n\n for i in range(len(words)):\n s = s.replace(words[i], str(i))\n\n return int(s)\n\ndef solution(s):\n answer = ''\n chart = {\"zero\":0,\"one\":1,\"two\":2,\"three\":3,\"four\":4,\"five\":5,\"six\":6,\"seven\":7,\"eight\":8,\"nine\":9}\n eng = ''\n for i in range(len(s)):\n if s[i].isdecimal():\n answer+= s[i]\n else:\n eng += s[i]\n if eng in chart:\n answer+= str(chart[eng])\n eng = ''\n print(answer)\n return int(answer)"},{"alpha_fraction":0.37759336829185486,"alphanum_fraction":0.4522821605205536,"avg_line_length":21,"blob_id":"680d677da92eb949bbb8d6e175ed71e5e1ea31ed","content_id":"51963ff5d797447415d18e92fdc5280779fe0c6a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":241,"license_type":"no_license","max_line_length":37,"num_lines":11,"path":"/programmers/소수 찾기/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(n):\n answer = 0\n tmp = [0]*10000000\n for i in range(2, n+1):\n if tmp[i] == 0:\n answer += 1\n for j in range(i, n+1,i):\n tmp[j] = 1\n print(tmp)\n return answer\nsolution(10)"},{"alpha_fraction":0.4606741666793823,"alphanum_fraction":0.47191011905670166,"avg_line_length":11.714285850524902,"blob_id":"ef6a5be4c540e3c9fbef8782bfe43c33250d5721","content_id":"b3e02b9c3b79f78c1c8d87f5fa29a3e27cd6bbb3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":99,"license_type":"no_license","max_line_length":18,"num_lines":7,"path":"/algorithm_base/이론 정리/최소공배수.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"\n# 최대공약수\nA = int(input())\nB = int(input())\nwhile A % B != 0:\n A, B = B, A% B\n\nprint(B)"},{"alpha_fraction":0.4254143536090851,"alphanum_fraction":0.511049747467041,"avg_line_length":19.16666603088379,"blob_id":"1f68100df59ccd9ee054a1d99ce1a213f192c4df","content_id":"6b77f0029ed268005897d842c03c2a0227189df7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":362,"license_type":"no_license","max_line_length":57,"num_lines":18,"path":"/baekjoon/7568 덩치/solution.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open('input.txt')\n\nN = int(input())\n\n\narr = [list(map(int,input().split())) for _ in range(N)]\n# print(arr)\n# [[55, 185], [58, 183], [88, 186], [60, 175], [46, 155]]\nres = [1]*N\n\n# print(arr)\nfor i in range(N):\n tmp = arr[i]\n for j in range(N):\n if tmp[0] < arr[j][0] and tmp[1] <arr[j][1]:\n res[i] += 1\nprint(*res)"},{"alpha_fraction":0.5897436141967773,"alphanum_fraction":0.5897436141967773,"avg_line_length":25,"blob_id":"9651b587f98ae3c0fb58138194e262e828ea58ea","content_id":"4559fba06d0edf6598cd2a2945cee59fdeafe392","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":188,"license_type":"no_license","max_line_length":41,"num_lines":6,"path":"/programmers/문자열 내 마음대로 정렬하기/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(strings, n):\n answer = []\n # n번째 값 기준 정렬 후, x 전체값 기준 재정렬\n strings.sort(key=lambda x: (x[n], x))\n print(strings)\n return strings\n"},{"alpha_fraction":0.4481707215309143,"alphanum_fraction":0.47560974955558777,"avg_line_length":17.27777862548828,"blob_id":"8f4f4fe3aaf831adba424322e8d747bee79cd701","content_id":"53e2dccfd115ab45fe3d7234fbf9fae1f586fb20","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":328,"license_type":"no_license","max_line_length":37,"num_lines":18,"path":"/programmers_school/tshirt.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(people, tshirts):\n answer = 0 \n people.sort()\n tshirts.sort()\n \n idx = 0\n \n for i in range(len(tshirts)):\n if idx == len(people) :\n break\n \n if tshirts[i] >= people[idx]:\n answer+=1\n idx+=1\n \n return answer\n\nprint(solution([2,3],[1,2,3]))"},{"alpha_fraction":0.49504950642585754,"alphanum_fraction":0.5297029614448547,"avg_line_length":12.533333778381348,"blob_id":"ac2cb76f7fa9e8e81426869eaa48d0296c4d6b4e","content_id":"e3de6ea4d2b991285b01c69afcdf7a16bf988a82","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":202,"license_type":"no_license","max_line_length":29,"num_lines":15,"path":"/baekjoon/1436 영화감독 숌/solution.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open('input.txt')\n\nN = int(input())\n\ncnt = 0\n\nnumber = 0\nwhile cnt != N:\n if '666' in str(number):\n cnt += 1\n if cnt == N:\n break\n number += 1\nprint(number)"},{"alpha_fraction":0.46052631735801697,"alphanum_fraction":0.4842105209827423,"avg_line_length":17.14285659790039,"blob_id":"b37735ff18d49568115367306f468eaefc47d17c","content_id":"8b9e754eb7266e0529851fc1e2f754d4602f6731","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":380,"license_type":"no_license","max_line_length":38,"num_lines":21,"path":"/algorithm_base/탐색&시뮬레이션/수의 합/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open(\"input.txt\", \"rt\")\n\nN, M = map(int,input().split())\nnums = list(map(int, input().split()))\n\nlt = 0\ncnt = 1\nres = 0\nwhile lt+cnt <= N and lt < N:\n summation = sum(nums[lt:lt+cnt])\n if summation > M :\n lt += 1\n cnt = 1\n elif summation == M:\n lt += 1\n cnt = 1\n res += 1\n else:\n cnt += 1\nprint(res)"},{"alpha_fraction":0.45783132314682007,"alphanum_fraction":0.4626505970954895,"avg_line_length":23.47058868408203,"blob_id":"b22f998be1dbf0f42d1d4159bd9719da8c942e79","content_id":"ba82786fb8cf5898c522b10fd614d470786624ee","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":471,"license_type":"no_license","max_line_length":52,"num_lines":17,"path":"/programmers/이상한 문자 만들기/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(s):\n s = s.split(\" \")\n answer = []\n for word in s:\n w = \"\"\n for i in range(len(word)):\n if i % 2== 0:\n w += word[i].upper()\n else:\n w += word[i].lower()\n answer.append(w)\n return ' '.join(answer)\nres = solution(\"try hello world\")\nprint(res)\n\n# s.split() 은 모든 공백 없앰\n# ''.join(list) list의 각 항목을 ''로 묶음 즉 뛰어쓰기가 있는 문자열 생성"},{"alpha_fraction":0.37566137313842773,"alphanum_fraction":0.4444444477558136,"avg_line_length":23.933332443237305,"blob_id":"4d0c80d2ebd0ed136dd1ba3bd87cc1943f044943","content_id":"33871ee011b4d3200768ed2b6bb382388807a88e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":378,"license_type":"no_license","max_line_length":68,"num_lines":15,"path":"/baekjoon/적어도 대부분의 배수.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"arr = list(map(int, input().split()))\n\nfor i in range(1, 9999999):\n for j in range(len(arr)-2):\n if i % arr[j] ==0 and i % arr[j+1] ==0 and i % arr[j+2] ==0:\n print(i) \n break\nfor i in range(1, 9999999):\n cnt = 0\n for j in range(len(arr)):\n if i % arr[j] ==0:\n cnt += 1\n if cnt >= 3:\n print(i)\n break\n "},{"alpha_fraction":0.4664948582649231,"alphanum_fraction":0.5154638886451721,"avg_line_length":24.933332443237305,"blob_id":"b702e603ed848a60fef8281bcbdf3aaa9d0b3f16","content_id":"40ec633cf574d02d183d884fe55b1633345751ee","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":388,"license_type":"no_license","max_line_length":47,"num_lines":15,"path":"/programmers/로또의 최고 순위와 최저 순위/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(lottos, win_nums):\n answer = []\n max_win = 0\n min_win = 0\n for num in lottos:\n if num in win_nums:\n min_win += 1\n elif num == 0:\n max_win += 1\n max_win += min_win\n print(max_win, min_win)\n ranks = {6:1, 5:2, 4:3, 3:4, 2:5, 1:6, 0:6}\n answer.append(ranks[max_win])\n answer.append(ranks[min_win])\n return answer"},{"alpha_fraction":0.4343675374984741,"alphanum_fraction":0.4677804410457611,"avg_line_length":25.25,"blob_id":"3657ad2dbb73818ecdea864e7e4d5062d2397fa3","content_id":"17048819f0b89118cfd52e1741c4cc12734d3ab3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":473,"license_type":"no_license","max_line_length":63,"num_lines":16,"path":"/programmers/최소직사각형/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(sizes):\n answer = 0\n # 둘 중 높은 값들을 1인덱스로 옮김\n # 각 인덱스의 맥스값을 구해서 곱합\n print(sizes)\n max_w = 0\n max_h = 0\n for i in range(len(sizes)):\n if sizes[i][0] < sizes[i][1]:\n sizes[i][0], sizes[i][1] = sizes[i][1], sizes[i][0]\n if sizes[i][1] > max_w:\n max_w = sizes[i][1]\n if sizes[i][0] > max_h:\n max_h = sizes[i][0]\n\n return max_w * max_h"},{"alpha_fraction":0.5066313147544861,"alphanum_fraction":0.5278514623641968,"avg_line_length":22.625,"blob_id":"6cf1dba634ad269882340ab2da3fa179382de2cc","content_id":"79bc9df6a1de26a710cdde5f36018eb487abdf85","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":393,"license_type":"no_license","max_line_length":47,"num_lines":16,"path":"/algorithm_base/스터디 테스트/버블정렬/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open(\"input.txt\", \"rt\")\narr = list(map(int, input().split()))\nprint(arr)\nfor i in range(len(arr)):\n exchange = 0\n for j in range(len(arr)-1, i, -1):\n if arr[j-1] > arr[j]:\n arr[j-1], arr[j] = arr[j], arr[j-1]\n exchange += 1\n print(arr)\n print(exchange)\n if exchange == 0: # 정렬할 것이 없다면\n break\n\nprint(arr)"},{"alpha_fraction":0.46449702978134155,"alphanum_fraction":0.5059171319007874,"avg_line_length":23.214284896850586,"blob_id":"b095ef591a3ec2299512644ffc85399642b83b14","content_id":"58ab9c8351e957093a22fc647cd9ca0365cb1b3e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":338,"license_type":"no_license","max_line_length":59,"num_lines":14,"path":"/algorithm_base/탐색&시뮬레이션/사과나무/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open(\"input.txt\", \"rt\")\n\nN = int(input())\nboard = [list(map(int, input().split())) for _ in range(N)]\n\nres = 0\nfor i in range(N//2+1):\n if i == N//2:\n res += sum(board[i][N//2-i:N//2+i+1])\n break\n res += sum(board[i][N//2-i:N//2+i+1])\n res += sum(board[N-i-1][N//2-i:N//2+i+1])\nprint(res)"},{"alpha_fraction":0.5039370059967041,"alphanum_fraction":0.5236220359802246,"avg_line_length":14.9375,"blob_id":"7c39eed0316217eebdd0ced2cd7d3d2d6be05cd1","content_id":"c8ca3bc5166887fcfb950abe33b3df6b190e56d3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":254,"license_type":"no_license","max_line_length":40,"num_lines":16,"path":"/algorithm_base/코드 구현력 기르기/점수 계산/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open('input.txt')\n\nN = int(input())\nresult = list(map(int, input().split()))\n\ncnt = 0\nscore = 0\n\nfor i in range(N):\n if result[i] == 1:\n cnt += 1\n score += cnt * result[i]\n else:\n cnt = 0\nprint(score)"},{"alpha_fraction":0.48262548446655273,"alphanum_fraction":0.5135135054588318,"avg_line_length":22.454545974731445,"blob_id":"cd9b511b41bd80a02ffcbcc5c6c2f1b8f0810f37","content_id":"c4d55dccec845b43e80b6cd7d5cfdb294e65b976","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":259,"license_type":"no_license","max_line_length":51,"num_lines":11,"path":"/algorithm_base/탐색&시뮬레이션/카드 역배치/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open(\"input.txt\", \"rt\")\n\narr = list(range(1,21))\nfor i in range(10):\n a, b = map(int, input().split())\n lt = a-1\n rt = b-1\n for j in range((lt+rt)//2):\n arr[lt+j], arr[rt-j] = arr[rt-j], arr[lt+j]\n print(arr)\n\n"},{"alpha_fraction":0.3875890076160431,"alphanum_fraction":0.43234995007514954,"avg_line_length":19.893617630004883,"blob_id":"a2ffffa94f3017e4cae1c213d2f8320473612628","content_id":"017114547f94b059bbe60d1f799ee053cbcc0e7d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1199,"license_type":"no_license","max_line_length":55,"num_lines":47,"path":"/swea/2005.파스칼의 삼각형/2005.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open('input.txt')\n\nT = int(input())\nfor tc in range(1, T+1):\n n = int(input())+1\n arr = [[0]*n for _ in range(n)]\n # print(arr)\n # 규칙을 찾아랏\n # 이게 5바이5 행렬이라서 첫 행과 열은 다 0이라 1인덱스부터 시작\n for i in range(1, n):\n for k in range(1,i+1):\n if i <= 2:\n arr[i][k] = 1\n else:\n arr[i][k] = arr[i-1][k-1] + arr[i-1][k]\n result = []\n #5바이5에서 4바이4만 탐색한다!\n for i in range(1, n):\n #리스트마다 갱신될 때, result에 넣을 sub\n sub = []\n for j in range(1, n):\n if arr[i][j] != 0:\n #i행에서 0이 아닌 모든값들이 sub에 들어간다\n sub.append(arr[i][j])\n #i가 바뀌기 전에 result에 더한다\n result.append(sub)\n\n print('#{}'.format(tc))\n for i in result:\n # print(*i)\n # int는 조인함수 없어서 스트링으로 변환해서 출력\n print(' '.join(map(str, i)))\n #진짜 너무 똑ㄸ고하시네....\n\n\n\"\"\"\n 1\n 1 1\n 1 2 1\n 1 3 3 1 \n 1\n 1 1 \n 1 2 1\n 1 3 3 1\n\n\"\"\"\n\n"},{"alpha_fraction":0.48695650696754456,"alphanum_fraction":0.49130433797836304,"avg_line_length":11.1578950881958,"blob_id":"78e766eab6eb1052171b583cc081c9ac0793c49f","content_id":"b4f1aea4afa598732b7cf07fe577d62fd17e60c2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":244,"license_type":"no_license","max_line_length":29,"num_lines":19,"path":"/baekjoon/분해합/solution.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open('input.txt')\n\nN = int(input())\n\n# 생성자 변수 선언\nres = 0\n\n\nfor i in range(N):\n tmp = i\n num = str(tmp)\n for j in num:\n tmp += int(j)\n\n if tmp == N:\n res = i\n break\nprint(res)"},{"alpha_fraction":0.38768115639686584,"alphanum_fraction":0.4166666567325592,"avg_line_length":24.18181800842285,"blob_id":"e1d664cd9ad7826bef0bb8d0664a5f7ff4959379","content_id":"af0e29dae9ae3f090a1ab7af226026b053fd6ee7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":276,"license_type":"no_license","max_line_length":41,"num_lines":11,"path":"/baekjoon/Doubles.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"for tc in range(3):\n arr = list(map(int, input().split()))\n cnt = 0\n if arr[0] == -1:\n break\n else:\n for i in range(len(arr)-1):\n for j in range(len(arr)-1):\n if arr[i]*2 == arr[j]:\n cnt +=1\n print(cnt)"},{"alpha_fraction":0.5255101919174194,"alphanum_fraction":0.579081654548645,"avg_line_length":27.071428298950195,"blob_id":"013e89a172058d71ba91d33aa77f32457b8d1611","content_id":"08a0bc68f350b5bfcdf2b11248d47552f1e11581","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":488,"license_type":"no_license","max_line_length":56,"num_lines":14,"path":"/programmers_school/가장 큰 수.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(numbers):\n numbers = [str(x) for x in numbers]\n # x라는 원소가 주어진 x를 4번반복하고 앞에서 4개까지 슬라이스한 것을 기준\n # 내림차순 정렬\n numbers.sort(key=lambda x : (x*4)[:4], reverse=True)\n # numbers의 원소가 0만 있을 때에 대한 처리 [0,0,0]\n if numbers[0] == \"0\" :\n answer = \"0\"\n else :\n answer = \"\".join(numbers)\n return answer\n\nprint(solution([3,30,34,5,9]))\nprint(solution([0,0,0]))"},{"alpha_fraction":0.479892760515213,"alphanum_fraction":0.5093833804130554,"avg_line_length":22.375,"blob_id":"1a77b69902c0641d4b6ddadbd78984c88ebf81b4","content_id":"2f7119972dec5732bb6814ae7037e46e2d6eb93a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":373,"license_type":"no_license","max_line_length":61,"num_lines":16,"path":"/baekjoon/2167 2차원 배열의 합/solution.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open('input.txt')\n\nN,M = map(int,input().split())\n\n# [[1, 2, 4], [8, 16, 32]]\narr = list(list(map(int, input().split())) for _ in range(N))\nK = int(input())\nfor _ in range(K):\n i,j,x,y = map(int, input().split())\n # print(i,j,x,y)\n res = 0\n for a in range(i-1,x):\n for b in range(j-1,y):\n res += arr[a][b]\n print(res)"},{"alpha_fraction":0.43971630930900574,"alphanum_fraction":0.4929077923297882,"avg_line_length":17.866666793823242,"blob_id":"6bac3315444f8d1c492f7a2b0c714ebd061828dd","content_id":"37ee67f4a041fde4648c1576625c91ab9daa9700","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":282,"license_type":"no_license","max_line_length":57,"num_lines":15,"path":"/algorithm_base/탐색&시뮬레이션/숫자만 추출/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open(\"input.txt\", \"rt\")\nwords = input()\nres = ''\nfor word in words:\n if word in ['0','1','2','3','4','5','6','7','8','9']:\n res += word\n\nres = int(res)\ncnt = 0\nfor i in range(1, res+1):\n if res % i == 0 :\n cnt += 1\nprint(res)\nprint(cnt)"},{"alpha_fraction":0.4153645932674408,"alphanum_fraction":0.4817708432674408,"avg_line_length":16.86046600341797,"blob_id":"49abccf76de2c96bec4c550967986558e20b311a","content_id":"8d741729e3c92228dfa822e1db34311432881278","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1270,"license_type":"no_license","max_line_length":73,"num_lines":43,"path":"/programmers_school/N으로 표현/README.md","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# 지문 이해 및 풀이 계획 세우기\n## 동적계획법(Dynamic Programming)\n주어진 최적화 문제를 재귀적인 방식으로 보다 작은 문제로 나누어 부분 문제를 풀어,이 해를 조합하여 전체 문제의 해답에 이르는 방식\n\n알고리즘의 진행에 따라 **탐색해야 할 범위를 동적으로 결정**함으로써 탐색 범위를 한정할 수 있음\n\n## 동저계획법의 적용 예\n### 피보나치 수열\n#### 재귀함수로 구현\nf(4) = f(3) +f(2)\n\nf(4) = f(2) +f(1) +f(1) +f(0) \n\nf(4) = f(1) + f(0) +(f1) +f(1) +f(0)\n\n#### 동적계획법을 적용\nf(0) = 0\n\nf(1) = 1\n\nf(2) = f(1) + f(0) = 1\n\nf(3) = f(2) + f(1) = 2\n\n복잡도 : 선형함수의 형태\n\n## 문제의 해결 - 예제\nN = 5\n\n1. 1번 사용한 경우\n 5\n2. 2번 사용한 경우\n 55 => + | - | x | / => 10 | 0 | 25 | 1\n 3. 3번 사용한 경우\n 555 => (1번 사용한 수)에 (2번 사용한 수)를 + | - | x | /\n 혹은 => (2번 사용한 수)에 (1번 사용한 수)를 + | - | x | / \n \n\n## 문제의 해결 - 일반화\n\n\n## 요약\n문제의 성질에 따라, 동적계획법으로 풀어냄으로써 탐색해야하는 범위를 효과적으로 줄일 수 있음\n"},{"alpha_fraction":0.5,"alphanum_fraction":0.5174418687820435,"avg_line_length":10.5,"blob_id":"082006a0f37ad16a0e01cced8c9efd4f5372ce1a","content_id":"fb16e4884f2b2be5fd2301df18aebdb7e2774b12","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":344,"license_type":"no_license","max_line_length":37,"num_lines":30,"path":"/algorithm_base/코드 구현력 기르기/자릿수의 합/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open(\"input.txt\", \"rt\")\n#\n\ndef digit_sum(x):\n res = 0\n for i in x:\n res+= int(i)\n return res\n\nn = int(input())\n\nnums = list((input().split()))\n\nmaximum = 0\nres = 0\nfor i in range(n):\n hap = digit_sum(nums[i])\n if hap > maximum:\n maximum = hap\n res = nums[i]\nprint(res)\n\n\n\n\n\n\n\n# 137"},{"alpha_fraction":0.4404761791229248,"alphanum_fraction":0.4672619104385376,"avg_line_length":21.46666717529297,"blob_id":"3fe0fddae3ae0c927fb9164b01b5b77bccac3d74","content_id":"36a6e1390b3a12783858828641f5563f62fde969","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":336,"license_type":"no_license","max_line_length":37,"num_lines":15,"path":"/programmers/문자열 다루기 기본/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(s):\n answer = True\n if len(s) != 4 and len(s) != 6 :\n print(len(s) != 4 )\n print(len(s) != 6 )\n answer = False\n return answer\n for i in s:\n if (i.isdecimal() == False):\n answer = False\n print(answer)\n break\n return answer\n\nsolution(\"a12234\")"},{"alpha_fraction":0.4713114798069,"alphanum_fraction":0.4754098355770111,"avg_line_length":19.41666603088379,"blob_id":"04139219339ebd11cb054f302900b749d93d2393","content_id":"8319b511c5ccaa7fdc00f2f52d2d380a1b90eeb5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":278,"license_type":"no_license","max_line_length":38,"num_lines":12,"path":"/programmers/최대공약수와 최소공배수/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(n, m):\n answer = []\n\n # 임의의 n, m 중 max, min의 값을 찾는다.\n _max, _min = max(n, m), min(n, m)\n # min의 값이 0보다 크면\n while _min:\n _max, _min = _min, _max % _min\n\n answer = [_max, int(n * m / _max)]\n\n return answer"},{"alpha_fraction":0.5456790328025818,"alphanum_fraction":0.5604938268661499,"avg_line_length":20.3157901763916,"blob_id":"c2b03d1a6d372b767aac647390eac158ca994ed6","content_id":"32e045a69e82f876f573ffbacd157ce1ccc389bd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":405,"license_type":"no_license","max_line_length":41,"num_lines":19,"path":"/programmers/정수 내림차순 배치하기/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# def solution(n):\n# answer = ''\n# numbers = list(str(n))\n# print(numbers)\n# numbers = [int(i) for i in numbers]\n# numbers.sort(reverse=True)\n# for i in numbers:\n# answer += str(i)\n# answer = int(answer)\n# return answer\n\ndef solution(n):\n ls = list(str(n))\n ls.sort(reverse = True)\n print(ls)\n return int(\"\".join(ls))\n\nres = solution(118372)\nprint(res)\n"},{"alpha_fraction":0.46915167570114136,"alphanum_fraction":0.5051413774490356,"avg_line_length":24.112903594970703,"blob_id":"184101f729a23baef1c632fd887a32e87c7cab4a","content_id":"d0739a6996b0ce288c0937955a1206766741adce","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2114,"license_type":"no_license","max_line_length":51,"num_lines":62,"path":"/swea/practice3/practice3.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open('input.txt')\n\n#지나간 경로 저장 함수\ndef dfs(s, V):\n visited = [0]*(V+1)\n stack = [] #1,2,4,6,5,7,3 경로 저장\n i = s #현재 방문한 접점 i\n visited[i] = 1 #현재 시작점 == True(1)\n #node[i] = 1 i는 1\n print(node[i])\n while i != 0: #True\n for w in range(1, V+1):\n #인접하고 아직 방문 x\n if adj[i][w] == 1 and visited[w] == 0 :\n stack.append(i) #방문경로저장\n i = w\n visited[w] = 1 #현 정점 업데이트\n print(node[i])\n break #방문 1회 했으니 break\n #for문 다 끝나고\n else:\n #stack에 값이 있으면\n if stack:\n i = stack.pop()\n else:\n i = 0 #break i가 0이면 while문 끝남\nV = 7 #접점 갯수 1~7\nE = 8 #엣지 갯수\nconnect = list(map(int, input().split()))\n#점\nconnect_pt = []\nfor i in range(0, len(connect), 2):\n #1,2 1,3 2,4 이렇게 점 좌표들을 넣는다\n sub = [connect[i], connect[i+1]]\n connect_pt.append(sub)\n\n# print(connect_pt)\n#connect_pt의 좌표들에 1을 넣어라\n\nadj = [[0]*(V+1) for _ in range(V+1)] #행,열이 0~7까지\n\n#connect_pt에 해당하는 값들마다 바꿔줄거니까\nfor i in range(len(connect_pt)):\n x = connect_pt[i][0] # connect_pt에서의 각 리스트 x좌표\n y = connect_pt[i][1] # connect_pt에서의 각 리스트 y좌표\n adj[x][y] = 1\n adj[y][x] = 1\n# print(adj)\n\nnode = ['',1,2,3,4,5,6,7]\n#1은 시작점, 7은 숫자의 개수다\ndfs(1,7)\n\n#함수 내에 프린트가 있는 경우 함수 호출만 해줘도 결과가 나온다\n#함수 내에 프린트가 있는 경우, 프린트로 함수를 다시 불러오면 결과와 함께 마지막에\n#None을 반환한다\n\n#이를 방지하기 위해, 함수 내에 프린트 대신, return을 써준다.!~!~!~!!~\n#이 경우에는 무조건 함수를 프린트로 불러와야한다. 오~!!!넹! ㅎㅎ\n\n#위의 두가지 방법은 틀린 것이 아니라, 다른 것이다!!!!"},{"alpha_fraction":0.41911765933036804,"alphanum_fraction":0.5220588445663452,"avg_line_length":18.571428298950195,"blob_id":"0ce439dee49723734133832573e9cbf804c98fba","content_id":"c135e5905245650823e028a150ce0ba52a168320","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":136,"license_type":"no_license","max_line_length":33,"num_lines":7,"path":"/baekjoon/통학의신.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"A, B = map(int, input().split())\n\nresult = 0\nfor i in range(1, 999999999):\n if 2*-i == 2*A and i**2 == B:\n result = i\nprint(i)"},{"alpha_fraction":0.5072992444038391,"alphanum_fraction":0.5218977928161621,"avg_line_length":25.047618865966797,"blob_id":"85c583c1bfd55baa162088d9636b1e563e2a3a8a","content_id":"7bedbbf91d2568e756df8d18fa14b744be108e10","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":640,"license_type":"no_license","max_line_length":46,"num_lines":21,"path":"/programmers/완주하지 못한 선수/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(participant, completion):\n answer = ''\n tmp = {}\n for person in (participant):\n # participant을 하나씩 순회하면서\n # tmp에 키:밸류로 1씩 더해줌\n if tmp.get(person):\n tmp[person] += 1\n else:\n tmp[person] = 1\n\n # completion를 순회하면서\n for person in completion:\n # tmp의 키값에 해당되고, 그 벨류 1이상이면\n if person in tmp and tmp[person] >= 1:\n # participant의 벨류를 1씩 감소\n tmp[person] -= 1\n for val in tmp :\n if tmp[val] >= 1:\n answer = val\n return answer\n\n"},{"alpha_fraction":0.31991052627563477,"alphanum_fraction":0.35346755385398865,"avg_line_length":20.238094329833984,"blob_id":"559cab2fdf347e8bab07c22b1f02c5d3e694dbd3","content_id":"4747e687ff405b1ef0393976de970ea0551ccd9a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":489,"license_type":"no_license","max_line_length":43,"num_lines":21,"path":"/baekjoon/집주소.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open('집주소.txt')\n\n\nwhile True:\n arr = [input()]\n # 1*(숫자길이 + 1) == 여백\n # 2*('1'의 개수), 4*('0'의 개수), 3*(나머지의 개수)\n if len(arr) == 1:\n break\n else:\n cnt = 0\n for i in range(len(arr)):\n cnt += len(arr)+1\n if arr[i] == '1':\n cnt += 2\n elif arr[i] == '0':\n cnt += 4\n else:\n cnt += 3\n print(cnt)\n\n"},{"alpha_fraction":0.43843844532966614,"alphanum_fraction":0.4954954981803894,"avg_line_length":18.647058486938477,"blob_id":"62a2f88b926f46b5368dc03991c6fe835de9a89b","content_id":"724e73efa064ad14c472ad06a3218b3cedbf53e4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":333,"license_type":"no_license","max_line_length":45,"num_lines":17,"path":"/algorithm_base/그리디 알고리즘/마구간 정하기/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(arr1, arr2):\n answer = [[]]\n for i in range(len(arr1)):\n tmp = []\n for j in range(len(arr1[0])):\n tmp.append(arr1[i][j]+arr2[i][j])\n answer.append(tmp)\n return answer\n\narr1 = [[1],[2]]\narr2 = [[3],[4]]\nres = solution(arr1, arr2)\nprint(res)\n\n# zip(a,b)\n# a[0] + b[0]\n# a[1] + b[1]"},{"alpha_fraction":0.37784522771835327,"alphanum_fraction":0.40212443470954895,"avg_line_length":27.69565200805664,"blob_id":"7bf2667d03e6b0b2d0b9c8a159e936cf3c0ffa87","content_id":"c3bbbc21be4d985027034c0df6c2078455a97e13","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":717,"license_type":"no_license","max_line_length":51,"num_lines":23,"path":"/programmers/문자열 압축/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(s):\n answer = len(s)\n # aabbccdd 압축의 범위는 최대 문자열 길이 / 2 -> 그 위로는 압축 불가\n for i in range(1, int(len(s)/2)+1):\n comp = ''\n cnt = 1\n summation = 0\n for j in range(0, len(s), i):\n tmp = s[j:j+i]\n if comp == tmp:\n cnt += 1\n elif comp != tmp:\n summation += len(tmp)\n if cnt > 1:\n # cnt가 10, 100 단위일 수도 있음\n summation += len(str(cnt))\n cnt = 1\n comp = tmp\n\n if cnt > 1:\n summation += len(str(cnt))\n answer = min(answer, summation)\n return answer"},{"alpha_fraction":0.517110288143158,"alphanum_fraction":0.5513308048248291,"avg_line_length":12.100000381469727,"blob_id":"ea252dd55713948453b9c4fd04ce960b67da3df9","content_id":"f0a02f8d593c7d5039997e9a948f2f76348f2e26","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":263,"license_type":"no_license","max_line_length":37,"num_lines":20,"path":"/baekjoon/1157 단어공부/solution.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open('input.txt')\n\n# T= int(input())\n#\n# for tc in range(T):\n#\n\n# 65~96\nword = input().upper()\n\nres = [0]*97\n\nfor val in word:\n res[ord(val)] += 1\n\nif res.count(max(res)) >= 2:\n print(\"?\")\nelse:\n print(chr(res.index((max(res)))))\n\n"},{"alpha_fraction":0.39732494950294495,"alphanum_fraction":0.4484657645225525,"avg_line_length":32.47368240356445,"blob_id":"bc3a3b8917c914dcf08d9d76142cd3c8376508ae","content_id":"098d161f1e23ce74f2617f03987edd8ba9b67c1a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1271,"license_type":"no_license","max_line_length":92,"num_lines":38,"path":"/programmers/키패드 누르기/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(numbers, hand):\n answer = ''\n position = ['*', '#']\n left = [1, 4, 7]\n right = [3, 6, 9]\n keypad = {\n 1: (0, 0), 2: (0, 1), 3: (0, 2),\n 4: (1, 0), 5: (1, 1), 6: (1, 2),\n 7: (2, 0), 8: (2, 1), 9: (2, 2),\n '*': (3, 0), 0: (3, 1), '#': (3, 2)}\n for num in numbers:\n if num in left:\n answer += 'L'\n position[0] = num\n elif num in right:\n answer += 'R'\n position[1] = num\n else:\n near_hand = get_near_hand(position[0], position[1], keypad, num, hand)\n print(near_hand)\n if near_hand == 'L':\n answer+= 'L'\n position[0] = num\n else:\n answer += 'R'\n position[1] = num\n\n return answer\n\n\ndef get_near_hand(l, r, keypad, num, hand):\n left_distance = abs(keypad[num][0] - keypad[l][0]) + abs(keypad[num][1] - keypad[l][1])\n right_distance = abs(keypad[num][0] - keypad[r][0]) + abs(keypad[num][1] - keypad[r][1])\n near_hand = 'L' if left_distance > right_distance else 'R'\n if left_distance == right_distance:\n near_hand = 'R' if hand == \"right\" else 'L'\n return near_hand\nsolution([1, 3, 4, 5, 8, 2, 1, 4, 5, 9, 5]\t, \"right\")"},{"alpha_fraction":0.5194174647331238,"alphanum_fraction":0.5291262269020081,"avg_line_length":22,"blob_id":"c324a2f6614494d5e7db6ec74d847dc69c919a47","content_id":"1467e225143a7601bd42ab50ba3ee679f9ecf60e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":206,"license_type":"no_license","max_line_length":47,"num_lines":9,"path":"/programmers/폰켓몬/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(nums):\n answer = 0\n catch = len(nums) // 2\n tmp = []\n for num in nums:\n if num not in tmp and len(tmp) < catch:\n tmp.append(num)\n print(tmp)\n return len(tmp)"},{"alpha_fraction":0.5154638886451721,"alphanum_fraction":0.5726335644721985,"avg_line_length":18.363636016845703,"blob_id":"e7726554a838479ee602f4f40a3c00e94c1dfce8","content_id":"73b11952b520a223f465c51564fbb5d5cf9a97bd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1741,"license_type":"no_license","max_line_length":42,"num_lines":55,"path":"/programmers_school/더 맵게/README.md","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# 지문 이해 및 풀이 계획 세우기\n## 문제의 해결 - 예제\narr = 1 2 3 9 10 12\n\nk = 7\n\n1. 1 2 3 9 10 12 | 1+2*2 = 5 | 5 3 9 10 12\n2. 3 5 9 10 12 | 3 + 5*2 = 13 | 13 9 10 12\n3. 9 10 12 13 | K <= 9 | ==> return \n\n## 알고리즘 복잡도\n- 최악의 경우:\n - 수가 하나 남을때까지 섞어야 하는 경우(n-1회)\n- 각 단계(\"섞는 일\")에서 요구되는 계산량:\n - 정렬된 리스트에 순서 맞추어 원소 삽입\n - O(n)\n- 전체 문제 풀이의 복잡도:\n - O(n^2)\n - 지나치게 높다.\n\n## 보다 나은 방법\n- 최소/최대 원소를 빠르게 꺼낼 수 있으면 좋은데..!\n- 힙(heap)\n - max heap (최대 원소를 빠르게 꺼낼 수 있는 자료구조)\n - min heap (최소 원소를 빠르게 꺼낼 수 있는 자료구조)\n## 힙(Heaps)\n- 성질 : 최대/최소 원소를 빠르게 찾을 수 있음\n - 상수시간에 최대/최소 원소를 찾을 수 있다.\n- 연산\n - 힙 구성(heapify) => O(NlogN)\n - 삽입(insert) => O(logN)\n - 삭제(remove) => O(logN)\n## 힙의 구현 \n- 완전 이진 트리(complete binary tree)\n - 배열을 이용해서 구현 가능! \n - 인덱스로 자식 노드 찾아나가기 쉽다 -> 공간 효율성이 좋다\n## 힙의 응용\n- 정렬(heapsort)\n- 우선 순위 큐(priority queue)\n# 풀이 엿보기\n## Python에서 힙 적용\n### import heapq\n### heapq.heapify(L)\n리스트 L로부터 min heap 구성\n### m = heapq.heappop(L)\nmin heap L 에서 최소값 삭제(반환)\n\nm에는 L에서 최소값이 담겨져있음\n### heapq.heappush(L, x)\nmin heap L에 원소 x 삽입\n\nL은 여전히 min heap 구조 유지(log(N)이 걸림)\n\n만약 리스트, 배열로 정렬하고 끼워넣는식으로 진행한다면\nO(N2)이 될 것\n\n\n"},{"alpha_fraction":0.4438502788543701,"alphanum_fraction":0.4634581208229065,"avg_line_length":19.035715103149414,"blob_id":"d9ef52dd893d886262819b4b3402161dc59e37a1","content_id":"cf06b3c425b53404d2bd602af2c55190acf189ce","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":561,"license_type":"no_license","max_line_length":37,"num_lines":28,"path":"/algorithm_base/코드 구현력 기르기/뒤집은 소수/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"\ndef reverse(x):\n num = 0\n i = 1\n while len(str(x))-i >= 0:\n if len(str(x))-i < 0 :\n break\n base = 10 ** (len(str(x))-i)\n num += int(str(x)[-i]) * base\n i += 1\n\n return num\ndef isPrime(x):\n num = reverse(x)\n for i in range(2, num):\n if num % i == 0:\n break\n return\n else:\n return num\nx = isPrime(32)\nn = int(input())\nres = []\narr = list(map(int, input().split()))\nfor i in range(n):\n result = isPrime(arr[i])\n if result:\n res.append(result)\nprint(*res)"},{"alpha_fraction":0.5203251838684082,"alphanum_fraction":0.5487805008888245,"avg_line_length":24.842105865478516,"blob_id":"6a280728fd74255202e58f14fb254ee14cc1e66b","content_id":"b72aa3aa7e5a6e0282b2b1ef94d209e1237c0b54","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":492,"license_type":"no_license","max_line_length":59,"num_lines":19,"path":"/algorithm_base/탐색&시뮬레이션/격자판 최대합/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"\nN = int(input())\nboard = [list(map(int, input().split())) for _ in range(N)]\n\nres = []\nfor i in range(N):\n summation_0 = 0\n summation = 0\n summation_1 = 0\n summation_2 = 0\n for j in range(N):\n summation += board[j][i]\n summation_0 += board[i][j]\n summation_1 += board[j][j]\n summation_2 += board[j][N-1-j]\n res.append(summation)\n res.append(summation_0)\n res.append(summation_1)\n res.append(summation_2)\nprint(max(res))\n"},{"alpha_fraction":0.3840579688549042,"alphanum_fraction":0.4251207709312439,"avg_line_length":22.685714721679688,"blob_id":"4516a8c89c72fbb3b6ab09b6e769a40cc478bb24","content_id":"28cc2bf15d1737a4672dedd2923ae559d1f8163d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":828,"license_type":"no_license","max_line_length":53,"num_lines":35,"path":"/programmers/[1차] 비밀지도/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(n, arr1, arr2):\n answer = []\n\n new_arr = []\n for i in range(n):\n num = list(bin(arr1[i] | arr2[i]))[2:]\n if len(num) < n:\n while len(num) < n:\n num.insert(0, '0')\n new_arr.append(num)\n\n for i in range(len(new_arr)):\n tmp = ''\n for j in range(len(new_arr)):\n if new_arr[i][j] == '1':\n tmp += '#'\n else:\n tmp += ' '\n answer.append(tmp)\n\n return answer\n\nsolution(5, [9, 20, 28, 18, 11], [30, 1, 21, 17, 28])\n\n# def solution(n, arr1, arr2):\n# answer = []\n# for i in range(n):\n# tmp = bin(arr1[i] | arr2[i])\n#\n# tmp = tmp[2:].zfill(n)\n# tmp = tmp.replace('1', '#')\n# tmp = tmp.replace('0', ' ')\n# answer.append(tmp)\n#\n# return answer"},{"alpha_fraction":0.43923866748809814,"alphanum_fraction":0.4538799524307251,"avg_line_length":20.375,"blob_id":"2b7c3e4095513b20c43c7815130442a84aa4564c","content_id":"53f5d30496de44261ce5efd1beaeb389d0f2e4cc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":683,"license_type":"no_license","max_line_length":37,"num_lines":32,"path":"/algorithm_base/스터디 테스트/병합정렬/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open(\"input.txt\", \"rt\")\n\narr = list(map(int, input().split()))\n\ndef merge(arr, lt, mid, rt):\n tmp = []\n i = lt\n j = mid + 1\n while i <= mid and j <= rt:\n if arr[i] < arr[j]:\n tmp.append(arr[i])\n i += 1\n else:\n tmp.append(arr[j])\n j += 1\n if i <= mid :\n tmp += arr[i:mid+1]\n else:\n tmp += arr[j:rt+1]\n for k in range(lt, rt+1):\n arr[k] = tmp[k-lt]\n\ndef merge_sort(arr,lt,rt):\n if lt < rt :\n mid = (lt+rt)// 2\n merge_sort(arr, lt, mid)\n merge_sort(arr, mid+1, rt)\n merge(arr, lt, mid, rt)\n\nmerge_sort(arr, 0, len(arr)-1)\nprint(arr)"},{"alpha_fraction":0.4334365427494049,"alphanum_fraction":0.47523218393325806,"avg_line_length":22.10714340209961,"blob_id":"2572acd20ef169b0c0122b8a9de916e66feeb72a","content_id":"22215d7b6be0f076768861de544e4527fd53fe62","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":822,"license_type":"no_license","max_line_length":71,"num_lines":28,"path":"/algorithm_base/이론 정리/버블정렬.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# 1\n\ndef bubble_sort(a):\n n = len(a)\n for i in range(n-1):\n for j in range(n-1, i, -1):\n if a[j-1] > a[j]:\n a[j-1], a[j] = a[j], a[j-1]\narr = [8,5,3,9,1,7,0]\nbubble_sort(arr)\nprint(arr)\n\n#2\n# 어떤 패스의 원소 교환 횟수가 0이면 모든 원소가 정렬을 완료한 경우이므로 그 이후의 패스는 불필요하다고 판단하여 정렬 중단\n# 정렬을 모두 마쳤거나 정렬이 거의 다된 배열에서는 비교 연산이 크게 줄어 실행 시간을 단축\ndef bubble_sort2(a):\n n = len(a)\n for i in range(n-1):\n exchange=0\n for j in range(n-1, i, -1):\n if a[j-1] > a[j]:\n a[j-1],a[j] = a[j], a[j-1]\n exchange += 1\n if exchange == 0:\n break\n\nbubble_sort2(arr)\nprint(arr)"},{"alpha_fraction":0.3896103799343109,"alphanum_fraction":0.4318181872367859,"avg_line_length":18.3125,"blob_id":"05939053a5447f6c58133c51284d743037ffbcef","content_id":"33dc1d6ecf0a1d2e482d404243fca65616c1dbda","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":334,"license_type":"no_license","max_line_length":41,"num_lines":16,"path":"/programmers/예산/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(d, budget):\n answer = 0\n # 예산에서 제일 작은 값부터 뺀다\n d.sort()\n i = 0\n while i <= len(d) - 1 and budget > 0:\n if budget <= 0:\n break\n if d[i] <= budget:\n budget -= d[i]\n answer += 1\n i += 1\n\n return answer\n\nsolution([1,3,2,5,4], 9)"},{"alpha_fraction":0.5090909004211426,"alphanum_fraction":0.5136363506317139,"avg_line_length":12.8125,"blob_id":"32864e69b459cc83df96e70d4c3e86e00d0d5e71","content_id":"73640e4740207d9117cf67fa41f07e64af67c81d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":220,"license_type":"no_license","max_line_length":32,"num_lines":16,"path":"/baekjoon/solution.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open('input.txt')\n\nN = int(input())\n\n\nres = 0\n\nfor i in range(N) :\n tmp = i\n for j in range(len(str(N))):\n tmp += int(N[j])\n if tmp == N and tmp < res:\n res = tmp\n\nprint(res)"},{"alpha_fraction":0.45192307233810425,"alphanum_fraction":0.4951923191547394,"avg_line_length":16.41666603088379,"blob_id":"8dbff70b4dddc29ac8b0e314918a20853e3c8894","content_id":"ae231f54d50c7d86d4fdd6250eedfa6f239e97d1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":224,"license_type":"no_license","max_line_length":34,"num_lines":12,"path":"/programmers/정수제곱근 판별/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(n):\n answer = 0\n num = n ** (1/2)\n # 양의 정수인지 확인\n if num == int(num):\n answer = int((num+1) ** 2)\n else:\n answer = -1\n return answer\n\nres = solution(121)\nprint(res)"},{"alpha_fraction":0.343478262424469,"alphanum_fraction":0.3637681305408478,"avg_line_length":25.576923370361328,"blob_id":"ce29226b16078980cb512aa59092eabb1b059d87","content_id":"9398cd1ce11da0f0a607f796f371e9339328e1ba","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":690,"license_type":"no_license","max_line_length":49,"num_lines":26,"path":"/programmers/다트 게임/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(dartResult):\n num = ''\n answer = []\n score = {'S':1, 'D':2, 'T':3}\n for i in dartResult:\n if i.isdecimal():\n if num:\n num = int(num + str(i))\n else:\n num = str(i)\n elif i.isalpha():\n # print(answer[-1])\n # print(score[dartResult[i][j]])\n answer.append(int(num))\n num = ''\n answer[-1] = (answer[-1]) ** score[i]\n elif i == \"*\":\n try:\n answer[-2] *= 2\n answer[-1] *= 2\n except:\n answer[-1] *= 2\n elif i == \"#\":\n answer[-1] *= (-1)\n\n return sum(answer)"},{"alpha_fraction":0.5541922450065613,"alphanum_fraction":0.5705521702766418,"avg_line_length":27.823530197143555,"blob_id":"9717f9d8e33ff5e3f5034f1dd49aeab43df5f94f","content_id":"01bf6955afd5fea87d1a98ab861821fee7d561cc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":489,"license_type":"no_license","max_line_length":68,"num_lines":17,"path":"/programmers/실패율/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(N, stages):\n People = len(stages)\n faillist = {}\n for i in range(1, N + 1):\n if People != 0:\n faillist[i] = stages.count(i) / People\n People -= stages.count(i)\n print(faillist)\n else:\n faillist[i] = 0\n print(faillist)\n sorted(faillist, key=lambda x: faillist[x], reverse=True)\n print(faillist)\n return sorted(faillist, key=lambda x: faillist[x], reverse=True)\n\nres = solution(5,[2,3,4])\nprint(res)"},{"alpha_fraction":0.4887387454509735,"alphanum_fraction":0.545045018196106,"avg_line_length":21.25,"blob_id":"862636356d8474d0bd11553d313c8f2e94cf8be0","content_id":"8a25e33a42234ebea1db0317c3fd9bcd3546c78d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":444,"license_type":"no_license","max_line_length":44,"num_lines":20,"path":"/algorithm_base/코드 구현력 기르기/주사위 게임/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open('input.txt')\n\nN = int(input())\n\nres = []\nfor _ in range(N):\n person = list(map(int, input().split()))\n tmp = [0]*7\n for i in range(3):\n tmp[person[i]] += 1\n idx = tmp.index(max(tmp))\n tmp.sort(reverse=True)\n if max(tmp) == 3:\n res.append(10000+1000*idx)\n elif max(tmp) == 2:\n res.append(1000+idx*100)\n else:\n res.append(100*tmp.index(max(tmp)))\nprint(max(res))"},{"alpha_fraction":0.5639534592628479,"alphanum_fraction":0.5717054009437561,"avg_line_length":20.54166603088379,"blob_id":"af5dfa096fb3a0673e0eabf9eab68c7c22915c2a","content_id":"93853aa1ca1e65df99d4ac2290fcaf4f51abd97e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":516,"license_type":"no_license","max_line_length":45,"num_lines":24,"path":"/algorithm_base/탐색&시뮬레이션/두 리스트 합치기/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open(\"input.txt\", \"rt\")\n\nN = int(input())\nN_list = list(map(int, input().split()))\nM = int(input())\nM_list = list(map(int, input().split()))\n\nN_pointer = 0\nM_pointer = 0\nres = []\nwhile N_pointer < N and M_pointer < M:\n if N_list[N_pointer] < M_list[M_pointer]:\n res.append(N_list[N_pointer])\n N_pointer += 1\n else:\n res.append(M_list[M_pointer])\n M_pointer += 1\n\nif N_pointer < N :\n res+=N_list[N_pointer:]\nelse:\n res+=M_list[M_pointer:]\nprint(*res)"},{"alpha_fraction":0.6194690465927124,"alphanum_fraction":0.6194690465927124,"avg_line_length":21.600000381469727,"blob_id":"72a2313fb5ec7cee1ebeece975391d6273c564b1","content_id":"9db2222d7cc4bddf008d50d1a3da4f4522231315","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":113,"license_type":"no_license","max_line_length":37,"num_lines":5,"path":"/algorithm_base/그리디 알고리즘/뮤직비디오/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open(\"input.txt\")\n\nN, M = map(int, input().split())\narr = list(map(int, input().split()))\n"},{"alpha_fraction":0.4707792103290558,"alphanum_fraction":0.48701298236846924,"avg_line_length":16.11111068725586,"blob_id":"421b9fc898a9dc7215af1f1ad70a23d2e4eb4f1a","content_id":"2c3ea49951312b889ee054f179c5d0b4793fba2a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":308,"license_type":"no_license","max_line_length":40,"num_lines":18,"path":"/algorithm_base/스터디 테스트/이진탐/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open(\"input.txt\", \"rt\")\n\narr = list(map(int, input().split(',')))\ngoal = int(input())\n\nlt = 0\nrt = len(arr)-1\n\nwhile lt <= rt:\n mid = (lt+rt)//2\n if arr[mid] == goal:\n print(mid)\n break\n elif arr[mid] < goal:\n lt = mid + 1\n else :\n rt = mid - 1\n"},{"alpha_fraction":0.4010600745677948,"alphanum_fraction":0.416961133480072,"avg_line_length":16.6875,"blob_id":"7c6d117a066e862abb838806efcc60d77c718a1b","content_id":"28d652add1e2faba6880340a9a3408427e84cb06","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":582,"license_type":"no_license","max_line_length":38,"num_lines":32,"path":"/swea/practice2/practice2.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open('input.txt')\n\ndef Checking(arr):\n stack = []\n\n top = -1\n\n for i in range(len(arr)):\n if arr[i] == '(':\n stack.append('(')\n top += 1\n else:\n # 여기서 막힌다 유유\n if len(stack) == 0:\n return -1\n\n else:\n stack.pop()\n top -= 1\n\n if len(stack):\n return -1\n else:\n return 1\n\nT = int(input())\nfor tc in range(1, T+1):\n arr = list(input())\n result = Checking(arr)\n\n print('#{} {}'.format(tc, result))\n"},{"alpha_fraction":0.42152467370033264,"alphanum_fraction":0.46188339591026306,"avg_line_length":16.230770111083984,"blob_id":"af86781c86936841c64cae5de7ffc511448ef19c","content_id":"34c5e03bd42cda841e13afe7efefbf867c393976","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":223,"license_type":"no_license","max_line_length":37,"num_lines":13,"path":"/algorithm_base/코드 구현력 기르기/소수(에라토스테네스 체)/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open(\"input.txt\", \"rt\")\nn = int(input())\nres = [0]*(n+1)\n\ncnt = 0\n\nfor i in range(2, n+1):\n if res[i] == 0:\n cnt += 1\n for j in range(i, n+1, i):\n res[j] = 1\nprint(cnt)"},{"alpha_fraction":0.5131086111068726,"alphanum_fraction":0.5280898809432983,"avg_line_length":19.615385055541992,"blob_id":"40e0048f7a5209e238bc311d7f163a7e6c1c49e7","content_id":"affd7aa8ecc2fbd4b9a2db1f89f99acb102838c8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":267,"license_type":"no_license","max_line_length":41,"num_lines":13,"path":"/algorithm_base/코드 구현력 기르기/K번째 수/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open(\"input.txt\", \"rt\")\nT = int(input())\n\nfor tc in range(1,T+1):\n N,s,e,k = map(int, input().split())\n nums = list(map(int,input().split()))\n\n res = nums[s-1:e]\n res.sort()\n print('#',tc, end=\" \")\n print(res[k-1])\n print()"},{"alpha_fraction":0.5615550875663757,"alphanum_fraction":0.5680345296859741,"avg_line_length":28,"blob_id":"68ae8997a13c94ca5d4cd69f315eb03282f0afa5","content_id":"66824e8036fb22ed388ea1c9c19b01f003bb7cdb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":463,"license_type":"no_license","max_line_length":49,"num_lines":16,"path":"/algorithm_base/코드 구현력 기르기/K번째 큰 수/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\n# sys.stdin = open(\"input.tx/t\", \"rt\")\nN, K = map(int, input().split())\ncards = list(map(int,input().split()))\nres = []\nfor i in range(len(cards)):\n for j in range(i+1, len(cards)):\n for k in range(j+1, len(cards)):\n nums = cards[i] + cards[j] + cards[k]\n if nums not in res:\n res.append(nums)\nres.sort(reverse=True)\nprint(res[K-1])\n# print(cards.sort())\n# cards.sort(reverse=True)\n# print(sum(cards[:K]))"},{"alpha_fraction":0.3530997335910797,"alphanum_fraction":0.4002695381641388,"avg_line_length":27.538461685180664,"blob_id":"a96f03bc021b6d309845a600b76502d229a2d1ad","content_id":"2e985941b25d07dfc1fff1a561513e0b6143822f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":814,"license_type":"no_license","max_line_length":52,"num_lines":26,"path":"/programmers_school/N으로 표현/N으로 표현.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(N, number):\n answer = 0\n # 최대 8개까지 연산 가능\n s = [set() for x in range(8)]\n\n # s를 enumerate로 꺼내는데 i의 시작값은 1\n for i, x in enumerate(s, start=1):\n # set인 x에 각 수들을 i번 반복한 문자열을 넣음 5, 55, 555...\n x.add(int(str(N)*i))\n for i in range(1,len(s)):\n for j in range(i):\n # j= 0,1,2 = op1\n # op2 = 2, 1, 0\n for op1 in s[j]:\n for op2 in s[i-j-1]:\n s[i].add(op1+op2)\n s[i].add(op1-op2)\n s[i].add(op1 *op2)\n if op2 != 0:\n s[i].add(op1 // op2)\n if number in s[i]:\n answer = i+1\n break\n else :\n answer -=1\n return answer\n"},{"alpha_fraction":0.4063205420970917,"alphanum_fraction":0.4706546366214752,"avg_line_length":30.678571701049805,"blob_id":"68e81ae26f495563dcef0149190b009c148aef96","content_id":"25038eda0b31c7e5e4d8590ca513f9d2e38d40e5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":886,"license_type":"no_license","max_line_length":91,"num_lines":28,"path":"/programmers_school/상담예약제.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def change_time(time):\n h, m = time.split(\":\")\n return int(h) * 60 + int(m)\n\n\ndef solution(booked, unbooked):\n answer = []\n booked = [(change_time(t), name) for t, name in booked] + [(24 * 60, None)]\n unbooked = [(change_time(t), name) for t, name in unbooked] + [(24 * 60, None)]\n b, u, time = 0, 0, 0\n\n while b < len(booked) and u < len(unbooked):\n t1, t2 = booked[b][0], unbooked[u][0]\n \n if t1 <= time :\n answer.append(booked[b][1])\n b += 1\n time += 10\n elif t2 <= time :\n answer.append(unbooked[u][1])\n u += 1\n time += 10\n else:\n time = min(t1, t2)\n answer.pop()\n return answer\nprint(solution([[\"09:10\", \"lee\"]],\t[[\"09:00\", \"kim\"], [\"09:05\", \"bae\"]]))\nprint(solution([[\"09:55\", \"hae\"], [\"10:05\", \"jee\"]],\t[[\"10:04\", \"hee\"], [\"14:07\", \"eom\"]]))"},{"alpha_fraction":0.5149253606796265,"alphanum_fraction":0.5223880410194397,"avg_line_length":13.851851463317871,"blob_id":"520fb784a5a309fe012fb05558320fb7b248ec49","content_id":"5107e8e905c2b5e580c9b3a039fc0314694e25f5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":416,"license_type":"no_license","max_line_length":43,"num_lines":27,"path":"/baekjoon/2750 수 정렬하기/solution.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open('input.txt')\n\nN = int(input())\n\n\narr = list(int(input()) for _ in range(N) )\n# sort 쓴 방법\n# arr.sort()\n# for i in arr:\n# print(i)\n\n# sort 안 쓴 방법\n\nres = [0]*N\nfor i in range(N):\n base = arr[i]\n for j in range(N):\n if base > arr[j] :\n res[i] += 1\n\nresult = [0]*N\n\nfor i in range(len(res)):\n result[res[i]] = arr[i]\nfor i in result:\n print(i)\n\n"},{"alpha_fraction":0.4518950581550598,"alphanum_fraction":0.4693877696990967,"avg_line_length":15.333333015441895,"blob_id":"1882be7e81d05378172d8346cdeaffe19b0bfec8","content_id":"fffcd6ff41ce30d5be979892176605014146452f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":343,"license_type":"no_license","max_line_length":37,"num_lines":21,"path":"/algorithm_base/그리디 알고리즘/이분 검색/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open(\"input.txt\")\n\nN, M = map(int, input().split())\narr = list(map(int, input().split()))\narr.sort()\n\nlt = 0\nrt = len(arr)-1\n\nwhile lt<=rt:\n if lt > rt:\n break\n md = (lt+rt) // 2\n if M > arr[md] :\n lt = md+1\n elif M < arr[md] :\n rt = md-1\n else:\n print(md+1)\n break\n"},{"alpha_fraction":0.4776536226272583,"alphanum_fraction":0.4944134056568146,"avg_line_length":18.77777862548828,"blob_id":"9d1a60d40897f6f97cbb6d9695f315fc5a9429b9","content_id":"9648c71df3df1df5b67e2f97b0f52775aaf79eb4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":358,"license_type":"no_license","max_line_length":38,"num_lines":18,"path":"/algorithm_base/이분 탐색/이분검색/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open(\"input.txt\", \"rt\")\nN,M = map(int, input().split())\nnums = list(map(int, input().split()))\nnums.sort()\n\nlt = 0\nrt = N-1\nwhile lt < rt:\n mid = (lt+rt) // 2\n print(lt, mid, rt)\n if nums[mid] == M:\n print(mid+1)\n break\n elif nums[mid] < M:\n lt = mid + 1\n elif nums[mid] > M:\n rt = mid - 1\n\n\n"},{"alpha_fraction":0.5660377144813538,"alphanum_fraction":0.6084905862808228,"avg_line_length":20,"blob_id":"980b296cc0eaccdbbb6b7637f591565916355c76","content_id":"ce525cdf9bec15fd56c1be3e2082aeb34b8329db","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":304,"license_type":"no_license","max_line_length":32,"num_lines":10,"path":"/algorithm_base/파이썬 기초/람다함수/problem.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# map(함수, 인자)\n# map(int, [1,2,3])\n# 각 배열의 요소들을 int로 전환해라\ndef plus_one(x):\n return x+1\na= [1,2,3]\nprint(list(map(plus_one, a)))\n# 람다는 익명함수 x를 x+1로 바꿔라\n# 내장함수의 인자로 쓰기에 람다는 편하다\nprint(list(map(lambda x:x+1,a)))\n\n\n"},{"alpha_fraction":0.47547170519828796,"alphanum_fraction":0.498113214969635,"avg_line_length":23.18181800842285,"blob_id":"3c8173c0eff2f2c9a10735f722c18a3bdf5d4924","content_id":"52051359d8dbd3d397c32e997d1c26ff85bcb2a0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":265,"license_type":"no_license","max_line_length":47,"num_lines":11,"path":"/algorithm_base/스터디 테스트/삽입정렬/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"import sys\nsys.stdin = open(\"input.txt\", \"rt\")\narr = list(map(int,input().split()))\n\nfor i in range(1, len(arr)):\n for j in range(i, 0, -1):\n if arr[j-1] > arr[j] :\n arr[j-1], arr[j] = arr[j], arr[j-1]\n else:\n break\nprint(arr)"},{"alpha_fraction":0.5570470094680786,"alphanum_fraction":0.5570470094680786,"avg_line_length":15.666666984558105,"blob_id":"b231aa02b3f31293d52da443fb04f8ecdbeaab4d","content_id":"08dd05740b04ca0600bd86ba12517a33879cfdaf","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":179,"license_type":"no_license","max_line_length":31,"num_lines":9,"path":"/programmers/자연수뒤집어배열로만들기/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(n):\n answer = []\n num = list(str(n))\n num.reverse()\n num = [int(i) for i in num]\n\n return num\n\n# 문제를 잘 읽자 sort가 아니라 뒤집는거임"},{"alpha_fraction":0.5368525981903076,"alphanum_fraction":0.5607569813728333,"avg_line_length":22.904762268066406,"blob_id":"efea5cd179eabf6e9c653958287fcb0e3ec5cd9f","content_id":"9f410a0082f1e19381d94f10785377c33b612024","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1764,"license_type":"no_license","max_line_length":72,"num_lines":42,"path":"/algorithm_base/이론 정리/재귀_하노이탑.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# 하노이의 탑이란 세 개의 기둥과 이 기둥에 꽂을 수 있는 크기가 다양한 원판들이 있고, 퍼즐을 시작하기 전에는\n# 한 기둥에 원판들이 작은 것이 위에 있도록 순서대로 쌓여 있다.\n# 게임의 목적은 다음 두 가지 조건을 만족시키면서, 한 기둥에 꽂힌 원판들을 그 순서 그대로 다른 기둥으로 옮겨서 다시 쌓는 것\n# 한 번에 하나의 원판만 옮길 수 있다.\n# 큰 원판이 작은 원판 위에 있어서는 안 된다.\n# => 즉 이 조건은 처음 원판을 움직일 때 목적지에 제일 큰 원판이 있어야 하고\n# 그 위에 올라가야할 하노이 탑이 다른 기둥에 완성이 돼야 한다.\n# N개의 원반을 옮기기 위해서는 N-1개의 원반을 이웃한 기둥으로 옮기고 가장 큰 원판을 목적 기둥으로 옮긴다.\n# 이후 N-1개의 원반을 최종 목적 기둥으로 옮기면 되는 과정을 반복\n\n# 옮기려는 원반의 개수 n\n# 옮길 원반이 현재 있는 출발점 기둥 f\n# 원반을 옮길 도착점 기둥 t\n# 옮기는 과정에서 사용할 보조 기둥 o\n\n# 1\ndef hanoi(n, f, t, o):\n if n == 1:\n # 원반을 옮기는 순서 출력\n print(f, '->', t)\n return\n hanoi(n-1, f, o, t)\n # 원반을 옮기는 순서 출력\n print(f, '->', t)\n hanoi(n-1, o, t, f)\n\nprint('n = 1')\nhanoi(1, 1, 3, 2)\n\nprint('n = 2')\nhanoi(2, 1, 3, 2)\n\nprint('n = 3')\nhanoi(3, 1, 3, 2)\n\ndef hanoi_definition(원반개수, 시작, 목표, 보조):\n if 원반개수 == 1:\n print(시작, '->', 목표)\n return\n hanoi_definition(원반개수-1, 시작, 보조, 목표)\n print(시작,'->',목표)\n hanoi_definition(원반개수-1, 보조, 목표, 시작)\n"},{"alpha_fraction":0.572062075138092,"alphanum_fraction":0.5787139534950256,"avg_line_length":25.41176414489746,"blob_id":"fcace77c4439a1198f5f25822be4a4a7c366af89","content_id":"c99f59a85f5d25e47796a2b3a9b62b06a8a34847","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":451,"license_type":"no_license","max_line_length":66,"num_lines":17,"path":"/algorithm_base/코드 구현력 기르기/대표값/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# import sys\n# sys.stdin = open(\"input.txt\", \"rt\")\nn = int(input())\nscores = list(map(int, input().split()))\n\nmiddle = round(sum(scores) / n)\n\nres = 0\ngap = abs(middle-scores[res])\nfor i in range(1, len(scores)):\n if abs(middle-scores[i]) < gap:\n res = i\n gap = abs(middle - scores[res])\n elif abs(middle-scores[i]) == gap and scores[res] < scores[i]:\n res = i\n gap = abs(middle - scores[res])\nprint(middle, res+1)\n\n\n"},{"alpha_fraction":0.3722584545612335,"alphanum_fraction":0.3918198049068451,"avg_line_length":26.225807189941406,"blob_id":"c78d91fcb9c28ed899ae7090528a09c03d029a68","content_id":"71eb11698ed2bf96f6b5c2578c41a425606badc1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1875,"license_type":"no_license","max_line_length":86,"num_lines":62,"path":"/programmers/신규 아이디 추천/AA.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"def solution(new_id):\n\n new_id = list(new_id)\n # 1단계 대문자를 소문자로 치환\n for i in range(len(new_id)):\n if new_id[i].isalpha():\n new_id[i] = new_id[i].lower()\n\n # 2단계 소문자, 숫자, 빼기, 밑줄, 마침표 제외한 모든 문자 제거\n for i in range(len(new_id)):\n if new_id[i].isalpha() or new_id[i].isdecimal() or new_id[i] in ['-','_','.']:\n continue\n else:\n new_id[i] = ''\n new_id = list(''.join(new_id))\n # 3단계 . 2번 이상 연속이면 1개로 치환\n for i in range(len(new_id)):\n if new_id[i] == '.':\n for j in range(i, len(new_id)):\n if new_id[j] == '.':\n # 마지막 차례면\n if j == len(new_id)-1:\n for k in range(i, len(new_id)):\n new_id[k] = ''\n\n else:\n for k in range(i+1, j):\n new_id[k] = ''\n break\n\n\n new_id = list(''.join(new_id))\n\n # 4단계 . 처음 혹은 끝 제거\n while True:\n if len(new_id) < 1:\n break\n if new_id[0] != '.' and new_id[-1] != '.':\n break\n if new_id[0] == '.':\n new_id[0] = ''\n if new_id[-1] == '.':\n new_id[-1] = ''\n new_id = list(''.join(new_id))\n # 5단계 빈문자열이면 'a'러\n new_id = list(''.join(new_id))\n if len(new_id) == 0:\n new_id = ['a']\n # 6단계 16자 이상이면 0~15까지만 살리고, 마지막에 . 있으면 제거\n if len(new_id) >= 16:\n new_id = new_id[0:15]\n if new_id[-1] == '.':\n new_id[-1] = ''\n\n if len(new_id) <= 2:\n\n while True:\n if len(new_id) == 3:\n break\n new_id.append(new_id[-1])\n\n return ''.join(new_id)"},{"alpha_fraction":0.354938268661499,"alphanum_fraction":0.4197530746459961,"avg_line_length":13.043478012084961,"blob_id":"6f972f86839290805ef4cee8a436d3e390ab9331","content_id":"3fbafd83514791fd2abaec32f8263bd2ed0aa171","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":420,"license_type":"no_license","max_line_length":40,"num_lines":23,"path":"/algorithm_base/이론 정리/재귀_피보나치수열.py","repo_name":"huisso97/baekjoon_learning","src_encoding":"UTF-8","text":"# 재귀에서는 문제 정의와 종료조건이 굉장히 중요하다\n# 피보나치 수열이란 : 어떤 수열의 항이, 앞의 두항의 합과 같은 수열\n# f(1) = 1\n# f(2) = 1\n# f(3) = 2\n# f(4) = 3\n# f(5) = 5\n# f(6) = 8\n# ...\n# f(n-2)+f(n-1) = f(n)\n\ndef fibo(n):\n a = 1\n b = 1\n if n == 1 or n == 2:\n return 1\n for i in range(1, n):\n a, b = b, b + a\n\n return a\n\n\nprint(fibo(5))\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.4962962865829468,\n \"alphanum_fraction\": 0.529629647731781,\n \"avg_line_length\": 24.761905670166016,\n \"blob_id\": \"a3b0cac3c1333df548d433ae6a2d31774bf1bccd\",\n \"content_id\": \"e329e9821006df1191e02af75c0b47fd42f3e410\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 540,\n \"license_type\": \"no_license\",\n \"max_line_length\": 48,\n \"num_lines\": 21,\n \"path\": \"/programmers/체육복/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(n, lost, reserve):\\n answer = 0\\n new_lost = list(set(lost) - set(reserve))\\n new_reserve = list(set(reserve) - set(lost))\\n\\n student = [1] * (n + 1)\\n\\n for num in new_lost:\\n student[num] = 0\\n\\n for num in new_lost:\\n if num - 1 in new_reserve:\\n new_reserve.remove(num - 1)\\n student[num] += 1\\n elif num + 1 in new_reserve:\\n new_reserve.remove(num + 1)\\n student[num] += 1\\n answer = student.count(1) - 1\\n return answer\\n\\nsolution(5, [2,4], [1,3,5])\"\n },\n {\n \"alpha_fraction\": 0.5763688683509827,\n \"alphanum_fraction\": 0.6224783658981323,\n \"avg_line_length\": 17.3157901763916,\n \"blob_id\": \"19526b656b794b0df9a3efaab98404525ce1aea1\",\n \"content_id\": \"5fa8a69c84c86e01aca201f0a28b38dd39f19df4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 513,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 19,\n \"path\": \"/algorithm_base/파이썬 기초/구간 합 빠르게 계산하기/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 특정 인덱스 a 에서 b까지의 합을 구할 때, 각 인덱스까지의 합을 저장한\\n# 배열을 만들어서,\\n# 구간합을 구하는 방식\\n\\n# 데이터의 개수 n과 데이터 입력받기\\nn = 5\\ndata = [10, 20, 30, 40, 50]\\n\\n# 접두사의 합(Prefix Sum) 배열 계산\\nsum_value = 0\\nprefix_sum = [0]\\nfor i in data:\\n sum_value += i\\n prefix_sum.append(sum_value)\\n\\n# 구간 합 계산(세 번째 수부터 네 번째 수까지)\\nleft = 3\\nright = 4\\nprint(prefix_sum[right] - prefix_sum[left-1])\"\n },\n {\n \"alpha_fraction\": 0.5089605450630188,\n \"alphanum_fraction\": 0.5483871102333069,\n \"avg_line_length\": 21.360000610351562,\n \"blob_id\": \"5a3436a45ceceed5253ffcb39328e04ac9daffd0\",\n \"content_id\": \"214f1b496ed3a8541b5857a484d592328023ff2d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 654,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 25,\n \"path\": \"/programmers_school/더 맵게/더 맵게.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import heapq\\n\\n\\ndef solution(scoville, K) :\\n answer = 0\\n heapq.heapify(scoville)\\n\\n # 모든 음식의 스코빌 지수가 K이상 혹은 남은 음식 갯수가 0개인데 K미만일때 break\\n while True:\\n # 1번째로 덜 매운 음식(최솟값)\\n min1 = heapq.heappop(scoville)\\n if min1 >= K:\\n break\\n elif len(scoville) ==0:\\n answer = -1\\n break\\n # 2번째로 덜 매운 음식\\n min2 = heapq.heappop(scoville)\\n new_scoville = min1 + min2*2\\n heapq.heappush(scoville,new_scoville)\\n answer += 1\\n return answer\\n\\n\\nprint(solution([1,2,3,9,10,12],7))\"\n },\n {\n \"alpha_fraction\": 0.4070175290107727,\n \"alphanum_fraction\": 0.46666666865348816,\n \"avg_line_length\": 13.894737243652344,\n \"blob_id\": \"b11ffa42cf38092931b025459c0efb83a9ce3e46\",\n \"content_id\": \"a74e9d1108abf04abcd0a72e2b3a3a82d33884d7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 387,\n \"license_type\": \"no_license\",\n \"max_line_length\": 31,\n \"num_lines\": 19,\n \"path\": \"/baekjoon/설탕배달.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"N = int(input())\\n\\n#큰값부터 나누고, 나머지에서 계속 나눈다\\n#모든 if문에 해당하지 않으면 -1출력\\n\\ncnt = 0\\nsub = 0\\n#나눌 수 없으면\\nif N // 5 ==0 or N // 3 == 0:\\n print(-1)\\nelse:\\n cnt += N//5\\n sub = N % 5\\n cnt += sub//3\\n sub %= 3\\n #3으로 나눈 후, 1,2 남아있으면 한봉지 추가\\n if sub>0:\\n cnt += 1\\n print(cnt)\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.4792899489402771,\n \"alphanum_fraction\": 0.5164835453033447,\n \"avg_line_length\": 26.488372802734375,\n \"blob_id\": \"7fdcc6a2a33ea12312ca2afcce5022bb443dbbf7\",\n \"content_id\": \"86f9629ae5f5d2d613c217da2189c6a1e81141cd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1799,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 43,\n \"path\": \"/programmers_school/체육복.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 그리디\\n\\n# 방법 1 (적은 수의 학생 배열에 대해서 처리 -> 모든 학생들을 고려)\\n# u : 체육복을 가지고 온 학생과 없는 학생이 1 과 0, 여벌을 가지고 온 학생 2로 구분됨\\n\\ndef solution(n, lost, reserve):\\n # 바운더리를 체크하는 과정에서의 편의를 위해 맨 앞, 뒤에 여분의 1 추가\\n u = [1] * (n + 2)\\n for i in reserve:\\n u[i] += 1\\n for i in lost:\\n u[i] -= 1\\n for i in (range(1,n+1)):\\n if (u[i-1]) ==0 and u[i]==2:\\n # 리스트 슬라이스로 두명에게 1개씩 부여\\n u[i-1:i+1] = [1,1]\\n elif u[i] == 2 and u[i+1] == 0:\\n u[i:i+2] = [1,1] \\n # u의 1번째부터 -1 전까지 에서 원소들을 꺼내는데, 단 0보다 큰 것들만 꺼내어서 길이로 반환\\n return len([x for x in u[1:-1] if x > 0])\\n\\n# 방법 2 (학생 수는 많지만, 여벌을 가지고 있는 학생 수가 적을때, 여벌을 가지고 있는 학생들만 고려)\\n# 데이터타입 set(집합): 해시테이블로 구현되어있기 때문에, key로 접근하는 시간이 상수시간임\\n# 딕셔너리는 키에 대해서 map되어있지만, set은 value는 없고 특정 데이터가 이 집합에 속해있는지 여부만 판단 가능\\n\\ndef solution2(n,lost,reserve):\\n # 두 집합의 교집합을 나타냄\\n s = set(lost) & set(reserve)\\n # 차집합 (체육복을 빌려야하는 학생들)\\n l = set(lost) - s\\n # 여벌이 있는데 도난을 안당한 학생들\\n r = set(reserve) - s\\n\\n for x in sorted(r):\\n if x - 1 in l:\\n l.remove(x-1)\\n elif x +1 in l:\\n l.remove(x+1)\\n\\n # 빌려야하는데 빌리지 못한 학생들만 l에 남음\\n\\n return n - len(l)\\nprint(solution2(5,[2, 4],[1, 3, 5]))\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5102599263191223,\n \"alphanum_fraction\": 0.5253077745437622,\n \"avg_line_length\": 26.11111068725586,\n \"blob_id\": \"77add4a0ecb7e436fd549ce067c9cbf4ecb3a9cd\",\n \"content_id\": \"b8c44f0f7b7ece46d9561e423f7c9ac608ac3103\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 747,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 27,\n \"path\": \"/programmers/신고결과받기/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(id_list, report, k):\\n report = list(set(report))\\n res = {x: 0 for x in id_list}\\n\\n for i in report:\\n res[i.split()[1]] += 1\\n print(res)\\n answer = [0] * len(id_list)\\n for i in range(len(report)):\\n if res[report[i].split()[1]] >= k:\\n answer[id_list.index(report[i].split()[0])] += 1\\n return answer\\n\\n\\ndef solution(id_list, report, k):\\n answer = [0] * len(id_list)\\n dic_report = {id: [] for id in id_list} # 해당 유저를 신고한 ID\\n for i in set(report):\\n i = i.split()\\n dic_report[i[1]].append(i[0])\\n\\n for key, value in dic_report.items():\\n if len(value) >= k:\\n for j in value:\\n answer[id_list.index(j)] += 1\\n\\n return answer\"\n },\n {\n \"alpha_fraction\": 0.5072463750839233,\n \"alphanum_fraction\": 0.5314009785652161,\n \"avg_line_length\": 13.857142448425293,\n \"blob_id\": \"a81eb6c531456ae2e426b3fdcea23ba0d3f577d4\",\n \"content_id\": \"6f5b7eaaf378fd0ede46f891f4529cc750c575d0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 207,\n \"license_type\": \"no_license\",\n \"max_line_length\": 35,\n \"num_lines\": 14,\n \"path\": \"/algorithm_base/코드 구현력 기르기/K번째 약수/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\nN,K = map(int, input().split())\\n\\nres = []\\n\\nfor i in range(1,N+1):\\n if N%i==0:\\n res.append(i)\\n\\nif len(res) < K:\\n print(-1)\\nelse:\\n print(res[K-1])\"\n },\n {\n \"alpha_fraction\": 0.44186046719551086,\n \"alphanum_fraction\": 0.45877379179000854,\n \"avg_line_length\": 16.518518447875977,\n \"blob_id\": \"fa42d3be8f7b52ff995c4182a81c381ca2459359\",\n \"content_id\": \"8ce66a8fea6b5d2372722fb9a1579d2c872523ea\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 533,\n \"license_type\": \"no_license\",\n \"max_line_length\": 32,\n \"num_lines\": 27,\n \"path\": \"/algorithm_base/그리디 알고리즘/랜선자르기/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open(\\\"input.txt\\\")\\n\\nK, N = map(int, input().split())\\n# N개 이상도 N개 만드는 것에 포함\\n# N개를 만들 수 있는 랜선의 초대 길이 단위 출력\\narr = []\\nfor i in range(K):\\n arr.append(int(input()))\\ns = 0\\ne = max(arr)\\nlength = 0\\nres = 0\\nwhile True:\\n m = (s+e) // 2\\n for num in arr:\\n res += num // m\\n if res >= N :\\n length = m\\n s = m + 1\\n if s > e:\\n print(length)\\n break\\n else:\\n e = m - 1\\n length = 0\\n res = 0\\n\"\n },\n {\n \"alpha_fraction\": 0.5392670035362244,\n \"alphanum_fraction\": 0.5575916171073914,\n \"avg_line_length\": 18.100000381469727,\n \"blob_id\": \"e45d10b50ac1e5801cc3588a45c25af08e646f4b\",\n \"content_id\": \"2052c6d27f75f2f31ad7f057780e6f7d0687e92f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 382,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 20,\n \"path\": \"/algorithm_base/코드 구현력 기르기/정다면체/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\nN, M = map(int, input().split())\\n\\nres = []\\nnums = []\\n\\nfor i in range(1, N+1):\\n for j in range(1, M+1):\\n nums.append(i+j)\\nnums.sort()\\nsmall_nums = set(nums)\\nres = [0]*40\\n\\nfor num in small_nums:\\n res[num] = nums.count(num)\\nmaximum = max(res)\\nfor i in range(len(res)):\\n if res[i] == maximum:\\n print(i, end=' ')\\n\"\n },\n {\n \"alpha_fraction\": 0.3978102207183838,\n \"alphanum_fraction\": 0.45255473256111145,\n \"avg_line_length\": 12.5,\n \"blob_id\": \"a3c876ecf7fe079dc8b54d84e4df4cff482a5781\",\n \"content_id\": \"10f1bc830dbd8c09a5b16aeacfe1db6d36992672\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 358,\n \"license_type\": \"no_license\",\n \"max_line_length\": 26,\n \"num_lines\": 20,\n \"path\": \"/algorithm_base/파이썬 기초/반복문/problem.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"'''\\n반복문을 이용한 문제풀이\\n1) 1부터 N까지 홀수출력하기\\n2) 1부터 N까지의 합 구하기\\n3) N의 약수출력하기\\n'''\\n\\nn = int(input())\\n# 2\\n# res = 0\\n# for i in range(1, n+1) :\\n# if i%2 == 1 :\\n# res += i\\n# print(res)\\n\\n# 3\\nfor i in range(1, n+1) :\\n if n%i==0:\\n # 줄바꿈 안함\\n print(i, end=\\\" \\\") \"\n },\n {\n \"alpha_fraction\": 0.4383954107761383,\n \"alphanum_fraction\": 0.4498567283153534,\n \"avg_line_length\": 22.33333396911621,\n \"blob_id\": \"6d8e3ef7b2f7dea2b071dfc6ba767254abf3f4e9\",\n \"content_id\": \"0d2049e1e1a92f0f03a4e4f2484dbc079878287e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 349,\n \"license_type\": \"no_license\",\n \"max_line_length\": 42,\n \"num_lines\": 15,\n \"path\": \"/algorithm_base/탐색&시뮬레이션/회문 문자열 검사/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\nn = int(input())\\nfor i in range(1, n+1):\\n word = input().upper()\\n res = ''\\n for j in range(len(word)//2):\\n if word[j] == word[len(word)-1-j]:\\n continue\\n else:\\n res = \\\"NO\\\"\\n break\\n else:\\n res = \\\"YES\\\"\\n print('#{} {}'.format(i, res))\"\n },\n {\n \"alpha_fraction\": 0.4469820559024811,\n \"alphanum_fraction\": 0.4632952809333801,\n \"avg_line_length\": 26.909090042114258,\n \"blob_id\": \"6cd819526e1d38cea1b85383a01e699c18da8d34\",\n \"content_id\": \"8b63d615d8539f3f0ccf560018305c3acc511e6c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 613,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 22,\n \"path\": \"/programmers/숫자 문자열과 영단어/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(s):\\n words = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']\\n\\n for i in range(len(words)):\\n s = s.replace(words[i], str(i))\\n\\n return int(s)\\n\\ndef solution(s):\\n answer = ''\\n chart = {\\\"zero\\\":0,\\\"one\\\":1,\\\"two\\\":2,\\\"three\\\":3,\\\"four\\\":4,\\\"five\\\":5,\\\"six\\\":6,\\\"seven\\\":7,\\\"eight\\\":8,\\\"nine\\\":9}\\n eng = ''\\n for i in range(len(s)):\\n if s[i].isdecimal():\\n answer+= s[i]\\n else:\\n eng += s[i]\\n if eng in chart:\\n answer+= str(chart[eng])\\n eng = ''\\n print(answer)\\n return int(answer)\"\n },\n {\n \"alpha_fraction\": 0.37759336829185486,\n \"alphanum_fraction\": 0.4522821605205536,\n \"avg_line_length\": 21,\n \"blob_id\": \"680d677da92eb949bbb8d6e175ed71e5e1ea31ed\",\n \"content_id\": \"51963ff5d797447415d18e92fdc5280779fe0c6a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 241,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 11,\n \"path\": \"/programmers/소수 찾기/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(n):\\n answer = 0\\n tmp = [0]*10000000\\n for i in range(2, n+1):\\n if tmp[i] == 0:\\n answer += 1\\n for j in range(i, n+1,i):\\n tmp[j] = 1\\n print(tmp)\\n return answer\\nsolution(10)\"\n },\n {\n \"alpha_fraction\": 0.4606741666793823,\n \"alphanum_fraction\": 0.47191011905670166,\n \"avg_line_length\": 11.714285850524902,\n \"blob_id\": \"ef6a5be4c540e3c9fbef8782bfe43c33250d5721\",\n \"content_id\": \"b3e02b9c3b79f78c1c8d87f5fa29a3e27cd6bbb3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 99,\n \"license_type\": \"no_license\",\n \"max_line_length\": 18,\n \"num_lines\": 7,\n \"path\": \"/algorithm_base/이론 정리/최소공배수.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n# 최대공약수\\nA = int(input())\\nB = int(input())\\nwhile A % B != 0:\\n A, B = B, A% B\\n\\nprint(B)\"\n },\n {\n \"alpha_fraction\": 0.4254143536090851,\n \"alphanum_fraction\": 0.511049747467041,\n \"avg_line_length\": 19.16666603088379,\n \"blob_id\": \"1f68100df59ccd9ee054a1d99ce1a213f192c4df\",\n \"content_id\": \"6b77f0029ed268005897d842c03c2a0227189df7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 362,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 18,\n \"path\": \"/baekjoon/7568 덩치/solution.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open('input.txt')\\n\\nN = int(input())\\n\\n\\narr = [list(map(int,input().split())) for _ in range(N)]\\n# print(arr)\\n# [[55, 185], [58, 183], [88, 186], [60, 175], [46, 155]]\\nres = [1]*N\\n\\n# print(arr)\\nfor i in range(N):\\n tmp = arr[i]\\n for j in range(N):\\n if tmp[0] < arr[j][0] and tmp[1] <arr[j][1]:\\n res[i] += 1\\nprint(*res)\"\n },\n {\n \"alpha_fraction\": 0.5897436141967773,\n \"alphanum_fraction\": 0.5897436141967773,\n \"avg_line_length\": 25,\n \"blob_id\": \"9651b587f98ae3c0fb58138194e262e828ea58ea\",\n \"content_id\": \"4559fba06d0edf6598cd2a2945cee59fdeafe392\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 188,\n \"license_type\": \"no_license\",\n \"max_line_length\": 41,\n \"num_lines\": 6,\n \"path\": \"/programmers/문자열 내 마음대로 정렬하기/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(strings, n):\\n answer = []\\n # n번째 값 기준 정렬 후, x 전체값 기준 재정렬\\n strings.sort(key=lambda x: (x[n], x))\\n print(strings)\\n return strings\\n\"\n },\n {\n \"alpha_fraction\": 0.4481707215309143,\n \"alphanum_fraction\": 0.47560974955558777,\n \"avg_line_length\": 17.27777862548828,\n \"blob_id\": \"8f4f4fe3aaf831adba424322e8d747bee79cd701\",\n \"content_id\": \"53e2dccfd115ab45fe3d7234fbf9fae1f586fb20\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 328,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 18,\n \"path\": \"/programmers_school/tshirt.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(people, tshirts):\\n answer = 0 \\n people.sort()\\n tshirts.sort()\\n \\n idx = 0\\n \\n for i in range(len(tshirts)):\\n if idx == len(people) :\\n break\\n \\n if tshirts[i] >= people[idx]:\\n answer+=1\\n idx+=1\\n \\n return answer\\n\\nprint(solution([2,3],[1,2,3]))\"\n },\n {\n \"alpha_fraction\": 0.49504950642585754,\n \"alphanum_fraction\": 0.5297029614448547,\n \"avg_line_length\": 12.533333778381348,\n \"blob_id\": \"ac2cb76f7fa9e8e81426869eaa48d0296c4d6b4e\",\n \"content_id\": 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\"length_bytes\": 380,\n \"license_type\": \"no_license\",\n \"max_line_length\": 38,\n \"num_lines\": 21,\n \"path\": \"/algorithm_base/탐색&시뮬레이션/수의 합/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\n\\nN, M = map(int,input().split())\\nnums = list(map(int, input().split()))\\n\\nlt = 0\\ncnt = 1\\nres = 0\\nwhile lt+cnt <= N and lt < N:\\n summation = sum(nums[lt:lt+cnt])\\n if summation > M :\\n lt += 1\\n cnt = 1\\n elif summation == M:\\n lt += 1\\n cnt = 1\\n res += 1\\n else:\\n cnt += 1\\nprint(res)\"\n },\n {\n \"alpha_fraction\": 0.45783132314682007,\n \"alphanum_fraction\": 0.4626505970954895,\n \"avg_line_length\": 23.47058868408203,\n \"blob_id\": \"b22f998be1dbf0f42d1d4159bd9719da8c942e79\",\n \"content_id\": \"ba82786fb8cf5898c522b10fd614d470786624ee\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 471,\n \"license_type\": \"no_license\",\n \"max_line_length\": 52,\n \"num_lines\": 17,\n \"path\": \"/programmers/이상한 문자 만들기/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(s):\\n s = s.split(\\\" \\\")\\n answer = []\\n for word in s:\\n w = \\\"\\\"\\n for i in range(len(word)):\\n if i % 2== 0:\\n w += word[i].upper()\\n else:\\n w += word[i].lower()\\n answer.append(w)\\n return ' '.join(answer)\\nres = solution(\\\"try hello world\\\")\\nprint(res)\\n\\n# s.split() 은 모든 공백 없앰\\n# ''.join(list) list의 각 항목을 ''로 묶음 즉 뛰어쓰기가 있는 문자열 생성\"\n },\n {\n \"alpha_fraction\": 0.37566137313842773,\n \"alphanum_fraction\": 0.4444444477558136,\n \"avg_line_length\": 23.933332443237305,\n \"blob_id\": \"4d0c80d2ebd0ed136dd1ba3bd87cc1943f044943\",\n \"content_id\": \"33871ee011b4d3200768ed2b6bb382388807a88e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 378,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 15,\n \"path\": \"/baekjoon/적어도 대부분의 배수.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"arr = list(map(int, input().split()))\\n\\nfor i in range(1, 9999999):\\n for j in range(len(arr)-2):\\n if i % arr[j] ==0 and i % arr[j+1] ==0 and i % arr[j+2] ==0:\\n print(i) \\n break\\nfor i in range(1, 9999999):\\n cnt = 0\\n for j in range(len(arr)):\\n if i % arr[j] ==0:\\n cnt += 1\\n if cnt >= 3:\\n print(i)\\n break\\n \"\n },\n {\n \"alpha_fraction\": 0.4664948582649231,\n \"alphanum_fraction\": 0.5154638886451721,\n \"avg_line_length\": 24.933332443237305,\n \"blob_id\": \"b702e603ed848a60fef8281bcbdf3aaa9d0b3f16\",\n \"content_id\": \"40ec633cf574d02d183d884fe55b1633345751ee\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 388,\n \"license_type\": \"no_license\",\n \"max_line_length\": 47,\n \"num_lines\": 15,\n \"path\": \"/programmers/로또의 최고 순위와 최저 순위/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(lottos, win_nums):\\n answer = []\\n max_win = 0\\n min_win = 0\\n for num in lottos:\\n if num in win_nums:\\n min_win += 1\\n elif num == 0:\\n max_win += 1\\n max_win += min_win\\n print(max_win, min_win)\\n ranks = {6:1, 5:2, 4:3, 3:4, 2:5, 1:6, 0:6}\\n answer.append(ranks[max_win])\\n answer.append(ranks[min_win])\\n return answer\"\n },\n {\n \"alpha_fraction\": 0.4343675374984741,\n \"alphanum_fraction\": 0.4677804410457611,\n \"avg_line_length\": 25.25,\n \"blob_id\": \"3657ad2dbb73818ecdea864e7e4d5062d2397fa3\",\n \"content_id\": \"17048819f0b89118cfd52e1741c4cc12734d3ab3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 473,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 16,\n \"path\": \"/programmers/최소직사각형/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(sizes):\\n answer = 0\\n # 둘 중 높은 값들을 1인덱스로 옮김\\n # 각 인덱스의 맥스값을 구해서 곱합\\n print(sizes)\\n max_w = 0\\n max_h = 0\\n for i in range(len(sizes)):\\n if sizes[i][0] < sizes[i][1]:\\n sizes[i][0], sizes[i][1] = sizes[i][1], sizes[i][0]\\n if sizes[i][1] > max_w:\\n max_w = sizes[i][1]\\n if sizes[i][0] > max_h:\\n max_h = sizes[i][0]\\n\\n return max_w * max_h\"\n },\n {\n \"alpha_fraction\": 0.5066313147544861,\n \"alphanum_fraction\": 0.5278514623641968,\n \"avg_line_length\": 22.625,\n \"blob_id\": \"6cf1dba634ad269882340ab2da3fa179382de2cc\",\n \"content_id\": \"79bc9df6a1de26a710cdde5f36018eb487abdf85\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 393,\n \"license_type\": \"no_license\",\n \"max_line_length\": 47,\n \"num_lines\": 16,\n \"path\": \"/algorithm_base/스터디 테스트/버블정렬/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\narr = list(map(int, input().split()))\\nprint(arr)\\nfor i in range(len(arr)):\\n exchange = 0\\n for j in range(len(arr)-1, i, -1):\\n if arr[j-1] > arr[j]:\\n arr[j-1], arr[j] = arr[j], arr[j-1]\\n exchange += 1\\n print(arr)\\n print(exchange)\\n if exchange == 0: # 정렬할 것이 없다면\\n break\\n\\nprint(arr)\"\n },\n {\n \"alpha_fraction\": 0.46449702978134155,\n \"alphanum_fraction\": 0.5059171319007874,\n \"avg_line_length\": 23.214284896850586,\n \"blob_id\": \"b095ef591a3ec2299512644ffc85399642b83b14\",\n \"content_id\": \"58ab9c8351e957093a22fc647cd9ca0365cb1b3e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 338,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 14,\n \"path\": \"/algorithm_base/탐색&시뮬레이션/사과나무/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\n\\nN = int(input())\\nboard = [list(map(int, input().split())) for _ in range(N)]\\n\\nres = 0\\nfor i in range(N//2+1):\\n if i == N//2:\\n res += sum(board[i][N//2-i:N//2+i+1])\\n break\\n res += sum(board[i][N//2-i:N//2+i+1])\\n res += sum(board[N-i-1][N//2-i:N//2+i+1])\\nprint(res)\"\n },\n {\n \"alpha_fraction\": 0.5039370059967041,\n \"alphanum_fraction\": 0.5236220359802246,\n \"avg_line_length\": 14.9375,\n \"blob_id\": \"7c39eed0316217eebdd0ced2cd7d3d2d6be05cd1\",\n \"content_id\": \"c8ca3bc5166887fcfb950abe33b3df6b190e56d3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 254,\n \"license_type\": \"no_license\",\n \"max_line_length\": 40,\n \"num_lines\": 16,\n \"path\": \"/algorithm_base/코드 구현력 기르기/점수 계산/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open('input.txt')\\n\\nN = int(input())\\nresult = list(map(int, input().split()))\\n\\ncnt = 0\\nscore = 0\\n\\nfor i in range(N):\\n if result[i] == 1:\\n cnt += 1\\n score += cnt * result[i]\\n else:\\n cnt = 0\\nprint(score)\"\n },\n {\n \"alpha_fraction\": 0.48262548446655273,\n \"alphanum_fraction\": 0.5135135054588318,\n \"avg_line_length\": 22.454545974731445,\n \"blob_id\": \"cd9b511b41bd80a02ffcbcc5c6c2f1b8f0810f37\",\n \"content_id\": \"c4d55dccec845b43e80b6cd7d5cfdb294e65b976\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 259,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 11,\n \"path\": \"/algorithm_base/탐색&시뮬레이션/카드 역배치/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\n\\narr = list(range(1,21))\\nfor i in range(10):\\n a, b = map(int, input().split())\\n lt = a-1\\n rt = b-1\\n for j in range((lt+rt)//2):\\n arr[lt+j], arr[rt-j] = arr[rt-j], arr[lt+j]\\n print(arr)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.3875890076160431,\n \"alphanum_fraction\": 0.43234995007514954,\n \"avg_line_length\": 19.893617630004883,\n \"blob_id\": \"a2ffffa94f3017e4cae1c213d2f8320473612628\",\n \"content_id\": \"017114547f94b059bbe60d1f799ee053cbcc0e7d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1199,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 47,\n \"path\": \"/swea/2005.파스칼의 삼각형/2005.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open('input.txt')\\n\\nT = int(input())\\nfor tc in range(1, T+1):\\n n = int(input())+1\\n arr = [[0]*n for _ in range(n)]\\n # print(arr)\\n # 규칙을 찾아랏\\n # 이게 5바이5 행렬이라서 첫 행과 열은 다 0이라 1인덱스부터 시작\\n for i in range(1, n):\\n for k in range(1,i+1):\\n if i <= 2:\\n arr[i][k] = 1\\n else:\\n arr[i][k] = arr[i-1][k-1] + arr[i-1][k]\\n result = []\\n #5바이5에서 4바이4만 탐색한다!\\n for i in range(1, n):\\n #리스트마다 갱신될 때, result에 넣을 sub\\n sub = []\\n for j in range(1, n):\\n if arr[i][j] != 0:\\n #i행에서 0이 아닌 모든값들이 sub에 들어간다\\n sub.append(arr[i][j])\\n #i가 바뀌기 전에 result에 더한다\\n result.append(sub)\\n\\n print('#{}'.format(tc))\\n for i in result:\\n # print(*i)\\n # int는 조인함수 없어서 스트링으로 변환해서 출력\\n print(' '.join(map(str, i)))\\n #진짜 너무 똑ㄸ고하시네....\\n\\n\\n\\\"\\\"\\\"\\n 1\\n 1 1\\n 1 2 1\\n 1 3 3 1 \\n 1\\n 1 1 \\n 1 2 1\\n 1 3 3 1\\n\\n\\\"\\\"\\\"\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.48695650696754456,\n \"alphanum_fraction\": 0.49130433797836304,\n \"avg_line_length\": 11.1578950881958,\n \"blob_id\": \"78e766eab6eb1052171b583cc081c9ac0793c49f\",\n \"content_id\": \"b4f1aea4afa598732b7cf07fe577d62fd17e60c2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 244,\n \"license_type\": \"no_license\",\n \"max_line_length\": 29,\n \"num_lines\": 19,\n \"path\": \"/baekjoon/분해합/solution.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open('input.txt')\\n\\nN = int(input())\\n\\n# 생성자 변수 선언\\nres = 0\\n\\n\\nfor i in range(N):\\n tmp = i\\n num = str(tmp)\\n for j in num:\\n tmp += int(j)\\n\\n if tmp == N:\\n res = i\\n break\\nprint(res)\"\n },\n {\n \"alpha_fraction\": 0.38768115639686584,\n \"alphanum_fraction\": 0.4166666567325592,\n \"avg_line_length\": 24.18181800842285,\n \"blob_id\": \"e1d664cd9ad7826bef0bb8d0664a5f7ff4959379\",\n \"content_id\": \"af0e29dae9ae3f090a1ab7af226026b053fd6ee7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 276,\n \"license_type\": \"no_license\",\n \"max_line_length\": 41,\n \"num_lines\": 11,\n \"path\": \"/baekjoon/Doubles.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"for tc in range(3):\\n arr = list(map(int, input().split()))\\n cnt = 0\\n if arr[0] == -1:\\n break\\n else:\\n for i in range(len(arr)-1):\\n for j in range(len(arr)-1):\\n if arr[i]*2 == arr[j]:\\n cnt +=1\\n print(cnt)\"\n },\n {\n \"alpha_fraction\": 0.5255101919174194,\n \"alphanum_fraction\": 0.579081654548645,\n \"avg_line_length\": 27.071428298950195,\n \"blob_id\": \"013e89a172058d71ba91d33aa77f32457b8d1611\",\n \"content_id\": \"08a0bc68f350b5bfcdf2b11248d47552f1e11581\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 488,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 14,\n \"path\": \"/programmers_school/가장 큰 수.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(numbers):\\n numbers = [str(x) for x in numbers]\\n # x라는 원소가 주어진 x를 4번반복하고 앞에서 4개까지 슬라이스한 것을 기준\\n # 내림차순 정렬\\n numbers.sort(key=lambda x : (x*4)[:4], reverse=True)\\n # numbers의 원소가 0만 있을 때에 대한 처리 [0,0,0]\\n if numbers[0] == \\\"0\\\" :\\n answer = \\\"0\\\"\\n else :\\n answer = \\\"\\\".join(numbers)\\n return answer\\n\\nprint(solution([3,30,34,5,9]))\\nprint(solution([0,0,0]))\"\n },\n {\n \"alpha_fraction\": 0.479892760515213,\n \"alphanum_fraction\": 0.5093833804130554,\n \"avg_line_length\": 22.375,\n \"blob_id\": \"1a77b69902c0641d4b6ddadbd78984c88ebf81b4\",\n \"content_id\": \"2f7119972dec5732bb6814ae7037e46e2d6eb93a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 373,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 16,\n \"path\": \"/baekjoon/2167 2차원 배열의 합/solution.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open('input.txt')\\n\\nN,M = map(int,input().split())\\n\\n# [[1, 2, 4], [8, 16, 32]]\\narr = list(list(map(int, input().split())) for _ in range(N))\\nK = int(input())\\nfor _ in range(K):\\n i,j,x,y = map(int, input().split())\\n # print(i,j,x,y)\\n res = 0\\n for a in range(i-1,x):\\n for b in range(j-1,y):\\n res += arr[a][b]\\n print(res)\"\n },\n {\n \"alpha_fraction\": 0.43971630930900574,\n \"alphanum_fraction\": 0.4929077923297882,\n \"avg_line_length\": 17.866666793823242,\n \"blob_id\": \"6bac3315444f8d1c492f7a2b0c714ebd061828dd\",\n \"content_id\": \"37ee67f4a041fde4648c1576625c91ab9daa9700\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 282,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 15,\n \"path\": \"/algorithm_base/탐색&시뮬레이션/숫자만 추출/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\nwords = input()\\nres = ''\\nfor word in words:\\n if word in ['0','1','2','3','4','5','6','7','8','9']:\\n res += word\\n\\nres = int(res)\\ncnt = 0\\nfor i in range(1, res+1):\\n if res % i == 0 :\\n cnt += 1\\nprint(res)\\nprint(cnt)\"\n },\n {\n \"alpha_fraction\": 0.4153645932674408,\n \"alphanum_fraction\": 0.4817708432674408,\n \"avg_line_length\": 16.86046600341797,\n \"blob_id\": \"49abccf76de2c96bec4c550967986558e20b311a\",\n \"content_id\": \"8d741729e3c92228dfa822e1db34311432881278\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1270,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 43,\n \"path\": \"/programmers_school/N으로 표현/README.md\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 지문 이해 및 풀이 계획 세우기\\n## 동적계획법(Dynamic Programming)\\n주어진 최적화 문제를 재귀적인 방식으로 보다 작은 문제로 나누어 부분 문제를 풀어,이 해를 조합하여 전체 문제의 해답에 이르는 방식\\n\\n알고리즘의 진행에 따라 **탐색해야 할 범위를 동적으로 결정**함으로써 탐색 범위를 한정할 수 있음\\n\\n## 동저계획법의 적용 예\\n### 피보나치 수열\\n#### 재귀함수로 구현\\nf(4) = f(3) +f(2)\\n\\nf(4) = f(2) +f(1) +f(1) +f(0) \\n\\nf(4) = f(1) + f(0) +(f1) +f(1) +f(0)\\n\\n#### 동적계획법을 적용\\nf(0) = 0\\n\\nf(1) = 1\\n\\nf(2) = f(1) + f(0) = 1\\n\\nf(3) = f(2) + f(1) = 2\\n\\n복잡도 : 선형함수의 형태\\n\\n## 문제의 해결 - 예제\\nN = 5\\n\\n1. 1번 사용한 경우\\n 5\\n2. 2번 사용한 경우\\n 55 => + | - | x | / => 10 | 0 | 25 | 1\\n 3. 3번 사용한 경우\\n 555 => (1번 사용한 수)에 (2번 사용한 수)를 + | - | x | /\\n 혹은 => (2번 사용한 수)에 (1번 사용한 수)를 + | - | x | / \\n \\n\\n## 문제의 해결 - 일반화\\n\\n\\n## 요약\\n문제의 성질에 따라, 동적계획법으로 풀어냄으로써 탐색해야하는 범위를 효과적으로 줄일 수 있음\\n\"\n },\n {\n \"alpha_fraction\": 0.5,\n \"alphanum_fraction\": 0.5174418687820435,\n \"avg_line_length\": 10.5,\n \"blob_id\": \"082006a0f37ad16a0e01cced8c9efd4f5372ce1a\",\n \"content_id\": \"fb16e4884f2b2be5fd2301df18aebdb7e2774b12\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 344,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 30,\n \"path\": \"/algorithm_base/코드 구현력 기르기/자릿수의 합/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\n#\\n\\ndef digit_sum(x):\\n res = 0\\n for i in x:\\n res+= int(i)\\n return res\\n\\nn = int(input())\\n\\nnums = list((input().split()))\\n\\nmaximum = 0\\nres = 0\\nfor i in range(n):\\n hap = digit_sum(nums[i])\\n if hap > maximum:\\n maximum = hap\\n res = nums[i]\\nprint(res)\\n\\n\\n\\n\\n\\n\\n\\n# 137\"\n },\n {\n \"alpha_fraction\": 0.4404761791229248,\n \"alphanum_fraction\": 0.4672619104385376,\n \"avg_line_length\": 21.46666717529297,\n \"blob_id\": \"3fe0fddae3ae0c927fb9164b01b5b77bccac3d74\",\n \"content_id\": \"36a6e1390b3a12783858828641f5563f62fde969\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 336,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 15,\n \"path\": \"/programmers/문자열 다루기 기본/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(s):\\n answer = True\\n if len(s) != 4 and len(s) != 6 :\\n print(len(s) != 4 )\\n print(len(s) != 6 )\\n answer = False\\n return answer\\n for i in s:\\n if (i.isdecimal() == False):\\n answer = False\\n print(answer)\\n break\\n return answer\\n\\nsolution(\\\"a12234\\\")\"\n },\n {\n \"alpha_fraction\": 0.4713114798069,\n \"alphanum_fraction\": 0.4754098355770111,\n \"avg_line_length\": 19.41666603088379,\n \"blob_id\": \"04139219339ebd11cb054f302900b749d93d2393\",\n \"content_id\": \"8319b511c5ccaa7fdc00f2f52d2d380a1b90eeb5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 278,\n \"license_type\": \"no_license\",\n \"max_line_length\": 38,\n \"num_lines\": 12,\n \"path\": \"/programmers/최대공약수와 최소공배수/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(n, m):\\n answer = []\\n\\n # 임의의 n, m 중 max, min의 값을 찾는다.\\n _max, _min = max(n, m), min(n, m)\\n # min의 값이 0보다 크면\\n while _min:\\n _max, _min = _min, _max % _min\\n\\n answer = [_max, int(n * m / _max)]\\n\\n return answer\"\n },\n {\n \"alpha_fraction\": 0.5456790328025818,\n \"alphanum_fraction\": 0.5604938268661499,\n \"avg_line_length\": 20.3157901763916,\n \"blob_id\": \"c2b03d1a6d372b767aac647390eac158ca994ed6\",\n \"content_id\": \"32e045a69e82f876f573ffbacd157ce1ccc389bd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 405,\n \"license_type\": \"no_license\",\n \"max_line_length\": 41,\n \"num_lines\": 19,\n \"path\": \"/programmers/정수 내림차순 배치하기/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# def solution(n):\\n# answer = ''\\n# numbers = list(str(n))\\n# print(numbers)\\n# numbers = [int(i) for i in numbers]\\n# numbers.sort(reverse=True)\\n# for i in numbers:\\n# answer += str(i)\\n# answer = int(answer)\\n# return answer\\n\\ndef solution(n):\\n ls = list(str(n))\\n ls.sort(reverse = True)\\n print(ls)\\n return int(\\\"\\\".join(ls))\\n\\nres = solution(118372)\\nprint(res)\\n\"\n },\n {\n \"alpha_fraction\": 0.46915167570114136,\n \"alphanum_fraction\": 0.5051413774490356,\n \"avg_line_length\": 24.112903594970703,\n \"blob_id\": \"184101f729a23baef1c632fd887a32e87c7cab4a\",\n \"content_id\": \"d0739a6996b0ce288c0937955a1206766741adce\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2114,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 62,\n \"path\": \"/swea/practice3/practice3.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open('input.txt')\\n\\n#지나간 경로 저장 함수\\ndef dfs(s, V):\\n visited = [0]*(V+1)\\n stack = [] #1,2,4,6,5,7,3 경로 저장\\n i = s #현재 방문한 접점 i\\n visited[i] = 1 #현재 시작점 == True(1)\\n #node[i] = 1 i는 1\\n print(node[i])\\n while i != 0: #True\\n for w in range(1, V+1):\\n #인접하고 아직 방문 x\\n if adj[i][w] == 1 and visited[w] == 0 :\\n stack.append(i) #방문경로저장\\n i = w\\n visited[w] = 1 #현 정점 업데이트\\n print(node[i])\\n break #방문 1회 했으니 break\\n #for문 다 끝나고\\n else:\\n #stack에 값이 있으면\\n if stack:\\n i = stack.pop()\\n else:\\n i = 0 #break i가 0이면 while문 끝남\\nV = 7 #접점 갯수 1~7\\nE = 8 #엣지 갯수\\nconnect = list(map(int, input().split()))\\n#점\\nconnect_pt = []\\nfor i in range(0, len(connect), 2):\\n #1,2 1,3 2,4 이렇게 점 좌표들을 넣는다\\n sub = [connect[i], connect[i+1]]\\n connect_pt.append(sub)\\n\\n# print(connect_pt)\\n#connect_pt의 좌표들에 1을 넣어라\\n\\nadj = [[0]*(V+1) for _ in range(V+1)] #행,열이 0~7까지\\n\\n#connect_pt에 해당하는 값들마다 바꿔줄거니까\\nfor i in range(len(connect_pt)):\\n x = connect_pt[i][0] # connect_pt에서의 각 리스트 x좌표\\n y = connect_pt[i][1] # connect_pt에서의 각 리스트 y좌표\\n adj[x][y] = 1\\n adj[y][x] = 1\\n# print(adj)\\n\\nnode = ['',1,2,3,4,5,6,7]\\n#1은 시작점, 7은 숫자의 개수다\\ndfs(1,7)\\n\\n#함수 내에 프린트가 있는 경우 함수 호출만 해줘도 결과가 나온다\\n#함수 내에 프린트가 있는 경우, 프린트로 함수를 다시 불러오면 결과와 함께 마지막에\\n#None을 반환한다\\n\\n#이를 방지하기 위해, 함수 내에 프린트 대신, return을 써준다.!~!~!~!!~\\n#이 경우에는 무조건 함수를 프린트로 불러와야한다. 오~!!!넹! ㅎㅎ\\n\\n#위의 두가지 방법은 틀린 것이 아니라, 다른 것이다!!!!\"\n },\n {\n \"alpha_fraction\": 0.41911765933036804,\n \"alphanum_fraction\": 0.5220588445663452,\n \"avg_line_length\": 18.571428298950195,\n \"blob_id\": \"0ce439dee49723734133832573e9cbf804c98fba\",\n \"content_id\": \"c135e5905245650823e028a150ce0ba52a168320\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 136,\n \"license_type\": \"no_license\",\n \"max_line_length\": 33,\n \"num_lines\": 7,\n \"path\": \"/baekjoon/통학의신.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"A, B = map(int, input().split())\\n\\nresult = 0\\nfor i in range(1, 999999999):\\n if 2*-i == 2*A and i**2 == B:\\n result = i\\nprint(i)\"\n },\n {\n \"alpha_fraction\": 0.5072992444038391,\n \"alphanum_fraction\": 0.5218977928161621,\n \"avg_line_length\": 25.047618865966797,\n \"blob_id\": \"85c583c1bfd55baa162088d9636b1e563e2a3a8a\",\n \"content_id\": \"7bedbbf91d2568e756df8d18fa14b744be108e10\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 640,\n \"license_type\": \"no_license\",\n \"max_line_length\": 46,\n \"num_lines\": 21,\n \"path\": \"/programmers/완주하지 못한 선수/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(participant, completion):\\n answer = ''\\n tmp = {}\\n for person in (participant):\\n # participant을 하나씩 순회하면서\\n # tmp에 키:밸류로 1씩 더해줌\\n if tmp.get(person):\\n tmp[person] += 1\\n else:\\n tmp[person] = 1\\n\\n # completion를 순회하면서\\n for person in completion:\\n # tmp의 키값에 해당되고, 그 벨류 1이상이면\\n if person in tmp and tmp[person] >= 1:\\n # participant의 벨류를 1씩 감소\\n tmp[person] -= 1\\n for val in tmp :\\n if tmp[val] >= 1:\\n answer = val\\n return answer\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.31991052627563477,\n \"alphanum_fraction\": 0.35346755385398865,\n \"avg_line_length\": 20.238094329833984,\n \"blob_id\": \"559cab2fdf347e8bab07c22b1f02c5d3e694dbd3\",\n \"content_id\": \"4747e687ff405b1ef0393976de970ea0551ccd9a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 489,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 21,\n \"path\": \"/baekjoon/집주소.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open('집주소.txt')\\n\\n\\nwhile True:\\n arr = [input()]\\n # 1*(숫자길이 + 1) == 여백\\n # 2*('1'의 개수), 4*('0'의 개수), 3*(나머지의 개수)\\n if len(arr) == 1:\\n break\\n else:\\n cnt = 0\\n for i in range(len(arr)):\\n cnt += len(arr)+1\\n if arr[i] == '1':\\n cnt += 2\\n elif arr[i] == '0':\\n cnt += 4\\n else:\\n cnt += 3\\n print(cnt)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.43843844532966614,\n \"alphanum_fraction\": 0.4954954981803894,\n \"avg_line_length\": 18.647058486938477,\n \"blob_id\": \"62a2f88b926f46b5368dc03991c6fe835de9a89b\",\n \"content_id\": \"724e73efa064ad14c472ad06a3218b3cedbf53e4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 333,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 17,\n \"path\": \"/algorithm_base/그리디 알고리즘/마구간 정하기/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(arr1, arr2):\\n answer = [[]]\\n for i in range(len(arr1)):\\n tmp = []\\n for j in range(len(arr1[0])):\\n tmp.append(arr1[i][j]+arr2[i][j])\\n answer.append(tmp)\\n return answer\\n\\narr1 = [[1],[2]]\\narr2 = [[3],[4]]\\nres = solution(arr1, arr2)\\nprint(res)\\n\\n# zip(a,b)\\n# a[0] + b[0]\\n# a[1] + b[1]\"\n },\n {\n \"alpha_fraction\": 0.37784522771835327,\n \"alphanum_fraction\": 0.40212443470954895,\n \"avg_line_length\": 27.69565200805664,\n \"blob_id\": \"7bf2667d03e6b0b2d0b9c8a159e936cf3c0ffa87\",\n \"content_id\": \"c3bbbc21be4d985027034c0df6c2078455a97e13\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 717,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 23,\n \"path\": \"/programmers/문자열 압축/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(s):\\n answer = len(s)\\n # aabbccdd 압축의 범위는 최대 문자열 길이 / 2 -> 그 위로는 압축 불가\\n for i in range(1, int(len(s)/2)+1):\\n comp = ''\\n cnt = 1\\n summation = 0\\n for j in range(0, len(s), i):\\n tmp = s[j:j+i]\\n if comp == tmp:\\n cnt += 1\\n elif comp != tmp:\\n summation += len(tmp)\\n if cnt > 1:\\n # cnt가 10, 100 단위일 수도 있음\\n summation += len(str(cnt))\\n cnt = 1\\n comp = tmp\\n\\n if cnt > 1:\\n summation += len(str(cnt))\\n answer = min(answer, summation)\\n return answer\"\n },\n {\n \"alpha_fraction\": 0.517110288143158,\n \"alphanum_fraction\": 0.5513308048248291,\n \"avg_line_length\": 12.100000381469727,\n \"blob_id\": \"ea252dd55713948453b9c4fd04ce960b67da3df9\",\n \"content_id\": \"f0a02f8d593c7d5039997e9a948f2f76348f2e26\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 263,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 20,\n \"path\": \"/baekjoon/1157 단어공부/solution.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open('input.txt')\\n\\n# T= int(input())\\n#\\n# for tc in range(T):\\n#\\n\\n# 65~96\\nword = input().upper()\\n\\nres = [0]*97\\n\\nfor val in word:\\n res[ord(val)] += 1\\n\\nif res.count(max(res)) >= 2:\\n print(\\\"?\\\")\\nelse:\\n print(chr(res.index((max(res)))))\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.39732494950294495,\n \"alphanum_fraction\": 0.4484657645225525,\n \"avg_line_length\": 32.47368240356445,\n \"blob_id\": \"bc3a3b8917c914dcf08d9d76142cd3c8376508ae\",\n \"content_id\": \"098d161f1e23ce74f2617f03987edd8ba9b67c1a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1271,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 38,\n \"path\": \"/programmers/키패드 누르기/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(numbers, hand):\\n answer = ''\\n position = ['*', '#']\\n left = [1, 4, 7]\\n right = [3, 6, 9]\\n keypad = {\\n 1: (0, 0), 2: (0, 1), 3: (0, 2),\\n 4: (1, 0), 5: (1, 1), 6: (1, 2),\\n 7: (2, 0), 8: (2, 1), 9: (2, 2),\\n '*': (3, 0), 0: (3, 1), '#': (3, 2)}\\n for num in numbers:\\n if num in left:\\n answer += 'L'\\n position[0] = num\\n elif num in right:\\n answer += 'R'\\n position[1] = num\\n else:\\n near_hand = get_near_hand(position[0], position[1], keypad, num, hand)\\n print(near_hand)\\n if near_hand == 'L':\\n answer+= 'L'\\n position[0] = num\\n else:\\n answer += 'R'\\n position[1] = num\\n\\n return answer\\n\\n\\ndef get_near_hand(l, r, keypad, num, hand):\\n left_distance = abs(keypad[num][0] - keypad[l][0]) + abs(keypad[num][1] - keypad[l][1])\\n right_distance = abs(keypad[num][0] - keypad[r][0]) + abs(keypad[num][1] - keypad[r][1])\\n near_hand = 'L' if left_distance > right_distance else 'R'\\n if left_distance == right_distance:\\n near_hand = 'R' if hand == \\\"right\\\" else 'L'\\n return near_hand\\nsolution([1, 3, 4, 5, 8, 2, 1, 4, 5, 9, 5]\\t, \\\"right\\\")\"\n },\n {\n \"alpha_fraction\": 0.5194174647331238,\n \"alphanum_fraction\": 0.5291262269020081,\n \"avg_line_length\": 22,\n \"blob_id\": \"c324a2f6614494d5e7db6ec74d847dc69c919a47\",\n \"content_id\": \"1467e225143a7601bd42ab50ba3ee679f9ecf60e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 206,\n \"license_type\": \"no_license\",\n \"max_line_length\": 47,\n \"num_lines\": 9,\n \"path\": \"/programmers/폰켓몬/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(nums):\\n answer = 0\\n catch = len(nums) // 2\\n tmp = []\\n for num in nums:\\n if num not in tmp and len(tmp) < catch:\\n tmp.append(num)\\n print(tmp)\\n return len(tmp)\"\n },\n {\n \"alpha_fraction\": 0.5154638886451721,\n \"alphanum_fraction\": 0.5726335644721985,\n \"avg_line_length\": 18.363636016845703,\n \"blob_id\": \"e7726554a838479ee602f4f40a3c00e94c1dfce8\",\n \"content_id\": \"73b11952b520a223f465c51564fbb5d5cf9a97bd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1741,\n \"license_type\": \"no_license\",\n \"max_line_length\": 42,\n \"num_lines\": 55,\n \"path\": \"/programmers_school/더 맵게/README.md\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 지문 이해 및 풀이 계획 세우기\\n## 문제의 해결 - 예제\\narr = 1 2 3 9 10 12\\n\\nk = 7\\n\\n1. 1 2 3 9 10 12 | 1+2*2 = 5 | 5 3 9 10 12\\n2. 3 5 9 10 12 | 3 + 5*2 = 13 | 13 9 10 12\\n3. 9 10 12 13 | K <= 9 | ==> return \\n\\n## 알고리즘 복잡도\\n- 최악의 경우:\\n - 수가 하나 남을때까지 섞어야 하는 경우(n-1회)\\n- 각 단계(\\\"섞는 일\\\")에서 요구되는 계산량:\\n - 정렬된 리스트에 순서 맞추어 원소 삽입\\n - O(n)\\n- 전체 문제 풀이의 복잡도:\\n - O(n^2)\\n - 지나치게 높다.\\n\\n## 보다 나은 방법\\n- 최소/최대 원소를 빠르게 꺼낼 수 있으면 좋은데..!\\n- 힙(heap)\\n - max heap (최대 원소를 빠르게 꺼낼 수 있는 자료구조)\\n - min heap (최소 원소를 빠르게 꺼낼 수 있는 자료구조)\\n## 힙(Heaps)\\n- 성질 : 최대/최소 원소를 빠르게 찾을 수 있음\\n - 상수시간에 최대/최소 원소를 찾을 수 있다.\\n- 연산\\n - 힙 구성(heapify) => O(NlogN)\\n - 삽입(insert) => O(logN)\\n - 삭제(remove) => O(logN)\\n## 힙의 구현 \\n- 완전 이진 트리(complete binary tree)\\n - 배열을 이용해서 구현 가능! \\n - 인덱스로 자식 노드 찾아나가기 쉽다 -> 공간 효율성이 좋다\\n## 힙의 응용\\n- 정렬(heapsort)\\n- 우선 순위 큐(priority queue)\\n# 풀이 엿보기\\n## Python에서 힙 적용\\n### import heapq\\n### heapq.heapify(L)\\n리스트 L로부터 min heap 구성\\n### m = heapq.heappop(L)\\nmin heap L 에서 최소값 삭제(반환)\\n\\nm에는 L에서 최소값이 담겨져있음\\n### heapq.heappush(L, x)\\nmin heap L에 원소 x 삽입\\n\\nL은 여전히 min heap 구조 유지(log(N)이 걸림)\\n\\n만약 리스트, 배열로 정렬하고 끼워넣는식으로 진행한다면\\nO(N2)이 될 것\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.4438502788543701,\n \"alphanum_fraction\": 0.4634581208229065,\n \"avg_line_length\": 19.035715103149414,\n \"blob_id\": \"d9ef52dd893d886262819b4b3402161dc59e37a1\",\n \"content_id\": \"cf06b3c425b53404d2bd602af2c55190acf189ce\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 561,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 28,\n \"path\": \"/algorithm_base/코드 구현력 기르기/뒤집은 소수/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\ndef reverse(x):\\n num = 0\\n i = 1\\n while len(str(x))-i >= 0:\\n if len(str(x))-i < 0 :\\n break\\n base = 10 ** (len(str(x))-i)\\n num += int(str(x)[-i]) * base\\n i += 1\\n\\n return num\\ndef isPrime(x):\\n num = reverse(x)\\n for i in range(2, num):\\n if num % i == 0:\\n break\\n return\\n else:\\n return num\\nx = isPrime(32)\\nn = int(input())\\nres = []\\narr = list(map(int, input().split()))\\nfor i in range(n):\\n result = isPrime(arr[i])\\n if result:\\n res.append(result)\\nprint(*res)\"\n },\n {\n \"alpha_fraction\": 0.5203251838684082,\n \"alphanum_fraction\": 0.5487805008888245,\n \"avg_line_length\": 24.842105865478516,\n \"blob_id\": \"6a280728fd74255202e58f14fb254ee14cc1e66b\",\n \"content_id\": \"b72aa3aa7e5a6e0282b2b1ef94d209e1237c0b54\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 492,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 19,\n \"path\": \"/algorithm_base/탐색&시뮬레이션/격자판 최대합/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nN = int(input())\\nboard = [list(map(int, input().split())) for _ in range(N)]\\n\\nres = []\\nfor i in range(N):\\n summation_0 = 0\\n summation = 0\\n summation_1 = 0\\n summation_2 = 0\\n for j in range(N):\\n summation += board[j][i]\\n summation_0 += board[i][j]\\n summation_1 += board[j][j]\\n summation_2 += board[j][N-1-j]\\n res.append(summation)\\n res.append(summation_0)\\n res.append(summation_1)\\n res.append(summation_2)\\nprint(max(res))\\n\"\n },\n {\n \"alpha_fraction\": 0.3840579688549042,\n \"alphanum_fraction\": 0.4251207709312439,\n \"avg_line_length\": 22.685714721679688,\n \"blob_id\": \"4516a8c89c72fbb3b6ab09b6e769a40cc478bb24\",\n \"content_id\": \"28cc2bf15d1737a4672dedd2923ae559d1f8163d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 828,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 35,\n \"path\": \"/programmers/[1차] 비밀지도/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(n, arr1, arr2):\\n answer = []\\n\\n new_arr = []\\n for i in range(n):\\n num = list(bin(arr1[i] | arr2[i]))[2:]\\n if len(num) < n:\\n while len(num) < n:\\n num.insert(0, '0')\\n new_arr.append(num)\\n\\n for i in range(len(new_arr)):\\n tmp = ''\\n for j in range(len(new_arr)):\\n if new_arr[i][j] == '1':\\n tmp += '#'\\n else:\\n tmp += ' '\\n answer.append(tmp)\\n\\n return answer\\n\\nsolution(5, [9, 20, 28, 18, 11], [30, 1, 21, 17, 28])\\n\\n# def solution(n, arr1, arr2):\\n# answer = []\\n# for i in range(n):\\n# tmp = bin(arr1[i] | arr2[i])\\n#\\n# tmp = tmp[2:].zfill(n)\\n# tmp = tmp.replace('1', '#')\\n# tmp = tmp.replace('0', ' ')\\n# answer.append(tmp)\\n#\\n# return answer\"\n },\n {\n \"alpha_fraction\": 0.43923866748809814,\n \"alphanum_fraction\": 0.4538799524307251,\n \"avg_line_length\": 20.375,\n \"blob_id\": \"2b7c3e4095513b20c43c7815130442a84aa4564c\",\n \"content_id\": \"53f5d30496de44261ce5efd1beaeb389d0f2e4cc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 683,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 32,\n \"path\": \"/algorithm_base/스터디 테스트/병합정렬/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\n\\narr = list(map(int, input().split()))\\n\\ndef merge(arr, lt, mid, rt):\\n tmp = []\\n i = lt\\n j = mid + 1\\n while i <= mid and j <= rt:\\n if arr[i] < arr[j]:\\n tmp.append(arr[i])\\n i += 1\\n else:\\n tmp.append(arr[j])\\n j += 1\\n if i <= mid :\\n tmp += arr[i:mid+1]\\n else:\\n tmp += arr[j:rt+1]\\n for k in range(lt, rt+1):\\n arr[k] = tmp[k-lt]\\n\\ndef merge_sort(arr,lt,rt):\\n if lt < rt :\\n mid = (lt+rt)// 2\\n merge_sort(arr, lt, mid)\\n merge_sort(arr, mid+1, rt)\\n merge(arr, lt, mid, rt)\\n\\nmerge_sort(arr, 0, len(arr)-1)\\nprint(arr)\"\n },\n {\n \"alpha_fraction\": 0.4334365427494049,\n \"alphanum_fraction\": 0.47523218393325806,\n \"avg_line_length\": 22.10714340209961,\n \"blob_id\": \"2572acd20ef169b0c0122b8a9de916e66feeb72a\",\n \"content_id\": \"22215d7b6be0f076768861de544e4527fd53fe62\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 822,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 28,\n \"path\": \"/algorithm_base/이론 정리/버블정렬.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 1\\n\\ndef bubble_sort(a):\\n n = len(a)\\n for i in range(n-1):\\n for j in range(n-1, i, -1):\\n if a[j-1] > a[j]:\\n a[j-1], a[j] = a[j], a[j-1]\\narr = [8,5,3,9,1,7,0]\\nbubble_sort(arr)\\nprint(arr)\\n\\n#2\\n# 어떤 패스의 원소 교환 횟수가 0이면 모든 원소가 정렬을 완료한 경우이므로 그 이후의 패스는 불필요하다고 판단하여 정렬 중단\\n# 정렬을 모두 마쳤거나 정렬이 거의 다된 배열에서는 비교 연산이 크게 줄어 실행 시간을 단축\\ndef bubble_sort2(a):\\n n = len(a)\\n for i in range(n-1):\\n exchange=0\\n for j in range(n-1, i, -1):\\n if a[j-1] > a[j]:\\n a[j-1],a[j] = a[j], a[j-1]\\n exchange += 1\\n if exchange == 0:\\n break\\n\\nbubble_sort2(arr)\\nprint(arr)\"\n },\n {\n \"alpha_fraction\": 0.3896103799343109,\n \"alphanum_fraction\": 0.4318181872367859,\n \"avg_line_length\": 18.3125,\n \"blob_id\": \"05939053a5447f6c58133c51284d743037ffbcef\",\n \"content_id\": \"33dc1d6ecf0a1d2e482d404243fca65616c1dbda\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 334,\n \"license_type\": \"no_license\",\n \"max_line_length\": 41,\n \"num_lines\": 16,\n \"path\": \"/programmers/예산/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(d, budget):\\n answer = 0\\n # 예산에서 제일 작은 값부터 뺀다\\n d.sort()\\n i = 0\\n while i <= len(d) - 1 and budget > 0:\\n if budget <= 0:\\n break\\n if d[i] <= budget:\\n budget -= d[i]\\n answer += 1\\n i += 1\\n\\n return answer\\n\\nsolution([1,3,2,5,4], 9)\"\n },\n {\n \"alpha_fraction\": 0.5090909004211426,\n \"alphanum_fraction\": 0.5136363506317139,\n \"avg_line_length\": 12.8125,\n \"blob_id\": \"32864e69b459cc83df96e70d4c3e86e00d0d5e71\",\n \"content_id\": \"73640e4740207d9117cf67fa41f07e64af67c81d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 220,\n \"license_type\": \"no_license\",\n \"max_line_length\": 32,\n \"num_lines\": 16,\n \"path\": \"/baekjoon/solution.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open('input.txt')\\n\\nN = int(input())\\n\\n\\nres = 0\\n\\nfor i in range(N) :\\n tmp = i\\n for j in range(len(str(N))):\\n tmp += int(N[j])\\n if tmp == N and tmp < res:\\n res = tmp\\n\\nprint(res)\"\n },\n {\n \"alpha_fraction\": 0.45192307233810425,\n \"alphanum_fraction\": 0.4951923191547394,\n \"avg_line_length\": 16.41666603088379,\n \"blob_id\": \"8dbff70b4dddc29ac8b0e314918a20853e3c8894\",\n \"content_id\": \"ae231f54d50c7d86d4fdd6250eedfa6f239e97d1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 224,\n \"license_type\": \"no_license\",\n \"max_line_length\": 34,\n \"num_lines\": 12,\n \"path\": \"/programmers/정수제곱근 판별/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(n):\\n answer = 0\\n num = n ** (1/2)\\n # 양의 정수인지 확인\\n if num == int(num):\\n answer = int((num+1) ** 2)\\n else:\\n answer = -1\\n return answer\\n\\nres = solution(121)\\nprint(res)\"\n },\n {\n \"alpha_fraction\": 0.343478262424469,\n \"alphanum_fraction\": 0.3637681305408478,\n \"avg_line_length\": 25.576923370361328,\n \"blob_id\": \"ce29226b16078980cb512aa59092eabb1b059d87\",\n \"content_id\": \"9398cd1ce11da0f0a607f796f371e9339328e1ba\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 690,\n \"license_type\": \"no_license\",\n \"max_line_length\": 49,\n \"num_lines\": 26,\n \"path\": \"/programmers/다트 게임/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(dartResult):\\n num = ''\\n answer = []\\n score = {'S':1, 'D':2, 'T':3}\\n for i in dartResult:\\n if i.isdecimal():\\n if num:\\n num = int(num + str(i))\\n else:\\n num = str(i)\\n elif i.isalpha():\\n # print(answer[-1])\\n # print(score[dartResult[i][j]])\\n answer.append(int(num))\\n num = ''\\n answer[-1] = (answer[-1]) ** score[i]\\n elif i == \\\"*\\\":\\n try:\\n answer[-2] *= 2\\n answer[-1] *= 2\\n except:\\n answer[-1] *= 2\\n elif i == \\\"#\\\":\\n answer[-1] *= (-1)\\n\\n return sum(answer)\"\n },\n {\n \"alpha_fraction\": 0.5541922450065613,\n \"alphanum_fraction\": 0.5705521702766418,\n \"avg_line_length\": 27.823530197143555,\n \"blob_id\": \"9717f9d8e33ff5e3f5034f1dd49aeab43df5f94f\",\n \"content_id\": \"01bf6955afd5fea87d1a98ab861821fee7d561cc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 489,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 17,\n \"path\": \"/programmers/실패율/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(N, stages):\\n People = len(stages)\\n faillist = {}\\n for i in range(1, N + 1):\\n if People != 0:\\n faillist[i] = stages.count(i) / People\\n People -= stages.count(i)\\n print(faillist)\\n else:\\n faillist[i] = 0\\n print(faillist)\\n sorted(faillist, key=lambda x: faillist[x], reverse=True)\\n print(faillist)\\n return sorted(faillist, key=lambda x: faillist[x], reverse=True)\\n\\nres = solution(5,[2,3,4])\\nprint(res)\"\n },\n {\n \"alpha_fraction\": 0.4887387454509735,\n \"alphanum_fraction\": 0.545045018196106,\n \"avg_line_length\": 21.25,\n \"blob_id\": \"862636356d8474d0bd11553d313c8f2e94cf8be0\",\n \"content_id\": \"8a25e33a42234ebea1db0317c3fd9bcd3546c78d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 444,\n \"license_type\": \"no_license\",\n \"max_line_length\": 44,\n \"num_lines\": 20,\n \"path\": \"/algorithm_base/코드 구현력 기르기/주사위 게임/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open('input.txt')\\n\\nN = int(input())\\n\\nres = []\\nfor _ in range(N):\\n person = list(map(int, input().split()))\\n tmp = [0]*7\\n for i in range(3):\\n tmp[person[i]] += 1\\n idx = tmp.index(max(tmp))\\n tmp.sort(reverse=True)\\n if max(tmp) == 3:\\n res.append(10000+1000*idx)\\n elif max(tmp) == 2:\\n res.append(1000+idx*100)\\n else:\\n res.append(100*tmp.index(max(tmp)))\\nprint(max(res))\"\n },\n {\n \"alpha_fraction\": 0.5639534592628479,\n \"alphanum_fraction\": 0.5717054009437561,\n \"avg_line_length\": 20.54166603088379,\n \"blob_id\": \"af5dfa096fb3a0673e0eabf9eab68c7c22915c2a\",\n \"content_id\": \"93853aa1ca1e65df99d4ac2290fcaf4f51abd97e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 516,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 24,\n \"path\": \"/algorithm_base/탐색&시뮬레이션/두 리스트 합치기/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\n\\nN = int(input())\\nN_list = list(map(int, input().split()))\\nM = int(input())\\nM_list = list(map(int, input().split()))\\n\\nN_pointer = 0\\nM_pointer = 0\\nres = []\\nwhile N_pointer < N and M_pointer < M:\\n if N_list[N_pointer] < M_list[M_pointer]:\\n res.append(N_list[N_pointer])\\n N_pointer += 1\\n else:\\n res.append(M_list[M_pointer])\\n M_pointer += 1\\n\\nif N_pointer < N :\\n res+=N_list[N_pointer:]\\nelse:\\n res+=M_list[M_pointer:]\\nprint(*res)\"\n },\n {\n \"alpha_fraction\": 0.6194690465927124,\n \"alphanum_fraction\": 0.6194690465927124,\n \"avg_line_length\": 21.600000381469727,\n \"blob_id\": \"72a2313fb5ec7cee1ebeece975391d6273c564b1\",\n \"content_id\": \"9db2222d7cc4bddf008d50d1a3da4f4522231315\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 113,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 5,\n \"path\": \"/algorithm_base/그리디 알고리즘/뮤직비디오/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open(\\\"input.txt\\\")\\n\\nN, M = map(int, input().split())\\narr = list(map(int, input().split()))\\n\"\n },\n {\n \"alpha_fraction\": 0.4707792103290558,\n \"alphanum_fraction\": 0.48701298236846924,\n \"avg_line_length\": 16.11111068725586,\n \"blob_id\": \"421b9fc898a9dc7215af1f1ad70a23d2e4eb4f1a\",\n \"content_id\": \"2c3ea49951312b889ee054f179c5d0b4793fba2a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 308,\n \"license_type\": \"no_license\",\n \"max_line_length\": 40,\n \"num_lines\": 18,\n \"path\": \"/algorithm_base/스터디 테스트/이진탐/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\n\\narr = list(map(int, input().split(',')))\\ngoal = int(input())\\n\\nlt = 0\\nrt = len(arr)-1\\n\\nwhile lt <= rt:\\n mid = (lt+rt)//2\\n if arr[mid] == goal:\\n print(mid)\\n break\\n elif arr[mid] < goal:\\n lt = mid + 1\\n else :\\n rt = mid - 1\\n\"\n },\n {\n \"alpha_fraction\": 0.4010600745677948,\n \"alphanum_fraction\": 0.416961133480072,\n \"avg_line_length\": 16.6875,\n \"blob_id\": \"7c6d117a066e862abb838806efcc60d77c718a1b\",\n \"content_id\": \"28d652add1e2faba6880340a9a3408427e84cb06\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 582,\n \"license_type\": \"no_license\",\n \"max_line_length\": 38,\n \"num_lines\": 32,\n \"path\": \"/swea/practice2/practice2.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open('input.txt')\\n\\ndef Checking(arr):\\n stack = []\\n\\n top = -1\\n\\n for i in range(len(arr)):\\n if arr[i] == '(':\\n stack.append('(')\\n top += 1\\n else:\\n # 여기서 막힌다 유유\\n if len(stack) == 0:\\n return -1\\n\\n else:\\n stack.pop()\\n top -= 1\\n\\n if len(stack):\\n return -1\\n else:\\n return 1\\n\\nT = int(input())\\nfor tc in range(1, T+1):\\n arr = list(input())\\n result = Checking(arr)\\n\\n print('#{} {}'.format(tc, result))\\n\"\n },\n {\n \"alpha_fraction\": 0.42152467370033264,\n \"alphanum_fraction\": 0.46188339591026306,\n \"avg_line_length\": 16.230770111083984,\n \"blob_id\": \"af86781c86936841c64cae5de7ffc511448ef19c\",\n \"content_id\": \"34c5e03bd42cda841e13afe7efefbf867c393976\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 223,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 13,\n \"path\": \"/algorithm_base/코드 구현력 기르기/소수(에라토스테네스 체)/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\nn = int(input())\\nres = [0]*(n+1)\\n\\ncnt = 0\\n\\nfor i in range(2, n+1):\\n if res[i] == 0:\\n cnt += 1\\n for j in range(i, n+1, i):\\n res[j] = 1\\nprint(cnt)\"\n },\n {\n \"alpha_fraction\": 0.5131086111068726,\n \"alphanum_fraction\": 0.5280898809432983,\n \"avg_line_length\": 19.615385055541992,\n \"blob_id\": \"40e0048f7a5209e238bc311d7f163a7e6c1c49e7\",\n \"content_id\": \"affd7aa8ecc2fbd4b9a2db1f89f99acb102838c8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 267,\n \"license_type\": \"no_license\",\n \"max_line_length\": 41,\n \"num_lines\": 13,\n \"path\": \"/algorithm_base/코드 구현력 기르기/K번째 수/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\nT = int(input())\\n\\nfor tc in range(1,T+1):\\n N,s,e,k = map(int, input().split())\\n nums = list(map(int,input().split()))\\n\\n res = nums[s-1:e]\\n res.sort()\\n print('#',tc, end=\\\" \\\")\\n print(res[k-1])\\n print()\"\n },\n {\n \"alpha_fraction\": 0.5615550875663757,\n \"alphanum_fraction\": 0.5680345296859741,\n \"avg_line_length\": 28,\n \"blob_id\": \"68ae8997a13c94ca5d4cd69f315eb03282f0afa5\",\n \"content_id\": \"66824e8036fb22ed388ea1c9c19b01f003bb7cdb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 463,\n \"license_type\": \"no_license\",\n \"max_line_length\": 49,\n \"num_lines\": 16,\n \"path\": \"/algorithm_base/코드 구현력 기르기/K번째 큰 수/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\n# sys.stdin = open(\\\"input.tx/t\\\", \\\"rt\\\")\\nN, K = map(int, input().split())\\ncards = list(map(int,input().split()))\\nres = []\\nfor i in range(len(cards)):\\n for j in range(i+1, len(cards)):\\n for k in range(j+1, len(cards)):\\n nums = cards[i] + cards[j] + cards[k]\\n if nums not in res:\\n res.append(nums)\\nres.sort(reverse=True)\\nprint(res[K-1])\\n# print(cards.sort())\\n# cards.sort(reverse=True)\\n# print(sum(cards[:K]))\"\n },\n {\n \"alpha_fraction\": 0.3530997335910797,\n \"alphanum_fraction\": 0.4002695381641388,\n \"avg_line_length\": 27.538461685180664,\n \"blob_id\": \"a96f03bc021b6d309845a600b76502d229a2d1ad\",\n \"content_id\": \"2e985941b25d07dfc1fff1a561513e0b6143822f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 814,\n \"license_type\": \"no_license\",\n \"max_line_length\": 52,\n \"num_lines\": 26,\n \"path\": \"/programmers_school/N으로 표현/N으로 표현.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(N, number):\\n answer = 0\\n # 최대 8개까지 연산 가능\\n s = [set() for x in range(8)]\\n\\n # s를 enumerate로 꺼내는데 i의 시작값은 1\\n for i, x in enumerate(s, start=1):\\n # set인 x에 각 수들을 i번 반복한 문자열을 넣음 5, 55, 555...\\n x.add(int(str(N)*i))\\n for i in range(1,len(s)):\\n for j in range(i):\\n # j= 0,1,2 = op1\\n # op2 = 2, 1, 0\\n for op1 in s[j]:\\n for op2 in s[i-j-1]:\\n s[i].add(op1+op2)\\n s[i].add(op1-op2)\\n s[i].add(op1 *op2)\\n if op2 != 0:\\n s[i].add(op1 // op2)\\n if number in s[i]:\\n answer = i+1\\n break\\n else :\\n answer -=1\\n return answer\\n\"\n },\n {\n \"alpha_fraction\": 0.4063205420970917,\n \"alphanum_fraction\": 0.4706546366214752,\n \"avg_line_length\": 30.678571701049805,\n \"blob_id\": \"68e81ae26f495563dcef0149190b009c148aef96\",\n \"content_id\": \"25038eda0b31c7e5e4d8590ca513f9d2e38d40e5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 886,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 28,\n \"path\": \"/programmers_school/상담예약제.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def change_time(time):\\n h, m = time.split(\\\":\\\")\\n return int(h) * 60 + int(m)\\n\\n\\ndef solution(booked, unbooked):\\n answer = []\\n booked = [(change_time(t), name) for t, name in booked] + [(24 * 60, None)]\\n unbooked = [(change_time(t), name) for t, name in unbooked] + [(24 * 60, None)]\\n b, u, time = 0, 0, 0\\n\\n while b < len(booked) and u < len(unbooked):\\n t1, t2 = booked[b][0], unbooked[u][0]\\n \\n if t1 <= time :\\n answer.append(booked[b][1])\\n b += 1\\n time += 10\\n elif t2 <= time :\\n answer.append(unbooked[u][1])\\n u += 1\\n time += 10\\n else:\\n time = min(t1, t2)\\n answer.pop()\\n return answer\\nprint(solution([[\\\"09:10\\\", \\\"lee\\\"]],\\t[[\\\"09:00\\\", \\\"kim\\\"], [\\\"09:05\\\", \\\"bae\\\"]]))\\nprint(solution([[\\\"09:55\\\", \\\"hae\\\"], [\\\"10:05\\\", \\\"jee\\\"]],\\t[[\\\"10:04\\\", \\\"hee\\\"], [\\\"14:07\\\", \\\"eom\\\"]]))\"\n },\n {\n \"alpha_fraction\": 0.5149253606796265,\n \"alphanum_fraction\": 0.5223880410194397,\n \"avg_line_length\": 13.851851463317871,\n \"blob_id\": \"520fb784a5a309fe012fb05558320fb7b248ec49\",\n \"content_id\": \"5107e8e905c2b5e580c9b3a039fc0314694e25f5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 416,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 27,\n \"path\": \"/baekjoon/2750 수 정렬하기/solution.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open('input.txt')\\n\\nN = int(input())\\n\\n\\narr = list(int(input()) for _ in range(N) )\\n# sort 쓴 방법\\n# arr.sort()\\n# for i in arr:\\n# print(i)\\n\\n# sort 안 쓴 방법\\n\\nres = [0]*N\\nfor i in range(N):\\n base = arr[i]\\n for j in range(N):\\n if base > arr[j] :\\n res[i] += 1\\n\\nresult = [0]*N\\n\\nfor i in range(len(res)):\\n result[res[i]] = arr[i]\\nfor i in result:\\n print(i)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.4518950581550598,\n \"alphanum_fraction\": 0.4693877696990967,\n \"avg_line_length\": 15.333333015441895,\n \"blob_id\": \"1882be7e81d05378172d8346cdeaffe19b0bfec8\",\n \"content_id\": \"fffcd6ff41ce30d5be979892176605014146452f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 343,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 21,\n \"path\": \"/algorithm_base/그리디 알고리즘/이분 검색/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open(\\\"input.txt\\\")\\n\\nN, M = map(int, input().split())\\narr = list(map(int, input().split()))\\narr.sort()\\n\\nlt = 0\\nrt = len(arr)-1\\n\\nwhile lt<=rt:\\n if lt > rt:\\n break\\n md = (lt+rt) // 2\\n if M > arr[md] :\\n lt = md+1\\n elif M < arr[md] :\\n rt = md-1\\n else:\\n print(md+1)\\n break\\n\"\n },\n {\n \"alpha_fraction\": 0.4776536226272583,\n \"alphanum_fraction\": 0.4944134056568146,\n \"avg_line_length\": 18.77777862548828,\n \"blob_id\": \"9d1a60d40897f6f97cbb6d9695f315fc5a9429b9\",\n \"content_id\": \"9648c71df3df1df5b67e2f97b0f52775aaf79eb4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 358,\n \"license_type\": \"no_license\",\n \"max_line_length\": 38,\n \"num_lines\": 18,\n \"path\": \"/algorithm_base/이분 탐색/이분검색/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\nN,M = map(int, input().split())\\nnums = list(map(int, input().split()))\\nnums.sort()\\n\\nlt = 0\\nrt = N-1\\nwhile lt < rt:\\n mid = (lt+rt) // 2\\n print(lt, mid, rt)\\n if nums[mid] == M:\\n print(mid+1)\\n break\\n elif nums[mid] < M:\\n lt = mid + 1\\n elif nums[mid] > M:\\n rt = mid - 1\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5660377144813538,\n \"alphanum_fraction\": 0.6084905862808228,\n \"avg_line_length\": 20,\n \"blob_id\": \"980b296cc0eaccdbbb6b7637f591565916355c76\",\n \"content_id\": \"ce525cdf9bec15fd56c1be3e2082aeb34b8329db\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 304,\n \"license_type\": \"no_license\",\n \"max_line_length\": 32,\n \"num_lines\": 10,\n \"path\": \"/algorithm_base/파이썬 기초/람다함수/problem.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# map(함수, 인자)\\n# map(int, [1,2,3])\\n# 각 배열의 요소들을 int로 전환해라\\ndef plus_one(x):\\n return x+1\\na= [1,2,3]\\nprint(list(map(plus_one, a)))\\n# 람다는 익명함수 x를 x+1로 바꿔라\\n# 내장함수의 인자로 쓰기에 람다는 편하다\\nprint(list(map(lambda x:x+1,a)))\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.47547170519828796,\n \"alphanum_fraction\": 0.498113214969635,\n \"avg_line_length\": 23.18181800842285,\n \"blob_id\": \"3c8173c0eff2f2c9a10735f722c18a3bdf5d4924\",\n \"content_id\": \"52051359d8dbd3d397c32e997d1c26ff85bcb2a0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 265,\n \"license_type\": \"no_license\",\n \"max_line_length\": 47,\n \"num_lines\": 11,\n \"path\": \"/algorithm_base/스터디 테스트/삽입정렬/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\narr = list(map(int,input().split()))\\n\\nfor i in range(1, len(arr)):\\n for j in range(i, 0, -1):\\n if arr[j-1] > arr[j] :\\n arr[j-1], arr[j] = arr[j], arr[j-1]\\n else:\\n break\\nprint(arr)\"\n },\n {\n \"alpha_fraction\": 0.5570470094680786,\n \"alphanum_fraction\": 0.5570470094680786,\n \"avg_line_length\": 15.666666984558105,\n \"blob_id\": \"b231aa02b3f31293d52da443fb04f8ecdbeaab4d\",\n \"content_id\": \"08dd05740b04ca0600bd86ba12517a33879cfdaf\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 179,\n \"license_type\": \"no_license\",\n \"max_line_length\": 31,\n \"num_lines\": 9,\n \"path\": \"/programmers/자연수뒤집어배열로만들기/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(n):\\n answer = []\\n num = list(str(n))\\n num.reverse()\\n num = [int(i) for i in num]\\n\\n return num\\n\\n# 문제를 잘 읽자 sort가 아니라 뒤집는거임\"\n },\n {\n \"alpha_fraction\": 0.5368525981903076,\n \"alphanum_fraction\": 0.5607569813728333,\n \"avg_line_length\": 22.904762268066406,\n \"blob_id\": \"efea5cd179eabf6e9c653958287fcb0e3ec5cd9f\",\n \"content_id\": \"9f410a0082f1e19381d94f10785377c33b612024\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1764,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 42,\n \"path\": \"/algorithm_base/이론 정리/재귀_하노이탑.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 하노이의 탑이란 세 개의 기둥과 이 기둥에 꽂을 수 있는 크기가 다양한 원판들이 있고, 퍼즐을 시작하기 전에는\\n# 한 기둥에 원판들이 작은 것이 위에 있도록 순서대로 쌓여 있다.\\n# 게임의 목적은 다음 두 가지 조건을 만족시키면서, 한 기둥에 꽂힌 원판들을 그 순서 그대로 다른 기둥으로 옮겨서 다시 쌓는 것\\n# 한 번에 하나의 원판만 옮길 수 있다.\\n# 큰 원판이 작은 원판 위에 있어서는 안 된다.\\n# => 즉 이 조건은 처음 원판을 움직일 때 목적지에 제일 큰 원판이 있어야 하고\\n# 그 위에 올라가야할 하노이 탑이 다른 기둥에 완성이 돼야 한다.\\n# N개의 원반을 옮기기 위해서는 N-1개의 원반을 이웃한 기둥으로 옮기고 가장 큰 원판을 목적 기둥으로 옮긴다.\\n# 이후 N-1개의 원반을 최종 목적 기둥으로 옮기면 되는 과정을 반복\\n\\n# 옮기려는 원반의 개수 n\\n# 옮길 원반이 현재 있는 출발점 기둥 f\\n# 원반을 옮길 도착점 기둥 t\\n# 옮기는 과정에서 사용할 보조 기둥 o\\n\\n# 1\\ndef hanoi(n, f, t, o):\\n if n == 1:\\n # 원반을 옮기는 순서 출력\\n print(f, '->', t)\\n return\\n hanoi(n-1, f, o, t)\\n # 원반을 옮기는 순서 출력\\n print(f, '->', t)\\n hanoi(n-1, o, t, f)\\n\\nprint('n = 1')\\nhanoi(1, 1, 3, 2)\\n\\nprint('n = 2')\\nhanoi(2, 1, 3, 2)\\n\\nprint('n = 3')\\nhanoi(3, 1, 3, 2)\\n\\ndef hanoi_definition(원반개수, 시작, 목표, 보조):\\n if 원반개수 == 1:\\n print(시작, '->', 목표)\\n return\\n hanoi_definition(원반개수-1, 시작, 보조, 목표)\\n print(시작,'->',목표)\\n hanoi_definition(원반개수-1, 보조, 목표, 시작)\\n\"\n },\n {\n \"alpha_fraction\": 0.572062075138092,\n \"alphanum_fraction\": 0.5787139534950256,\n \"avg_line_length\": 25.41176414489746,\n \"blob_id\": \"fcace77c4439a1198f5f25822be4a4a7c366af89\",\n \"content_id\": \"c99f59a85f5d25e47796a2b3a9b62b06a8a34847\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 451,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 17,\n \"path\": \"/algorithm_base/코드 구현력 기르기/대표값/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# import sys\\n# sys.stdin = open(\\\"input.txt\\\", \\\"rt\\\")\\nn = int(input())\\nscores = list(map(int, input().split()))\\n\\nmiddle = round(sum(scores) / n)\\n\\nres = 0\\ngap = abs(middle-scores[res])\\nfor i in range(1, len(scores)):\\n if abs(middle-scores[i]) < gap:\\n res = i\\n gap = abs(middle - scores[res])\\n elif abs(middle-scores[i]) == gap and scores[res] < scores[i]:\\n res = i\\n gap = abs(middle - scores[res])\\nprint(middle, res+1)\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.3722584545612335,\n \"alphanum_fraction\": 0.3918198049068451,\n \"avg_line_length\": 26.225807189941406,\n \"blob_id\": \"c78d91fcb9c28ed899ae7090528a09c03d029a68\",\n \"content_id\": \"71eb11698ed2bf96f6b5c2578c41a425606badc1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1875,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 62,\n \"path\": \"/programmers/신규 아이디 추천/AA.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def solution(new_id):\\n\\n new_id = list(new_id)\\n # 1단계 대문자를 소문자로 치환\\n for i in range(len(new_id)):\\n if new_id[i].isalpha():\\n new_id[i] = new_id[i].lower()\\n\\n # 2단계 소문자, 숫자, 빼기, 밑줄, 마침표 제외한 모든 문자 제거\\n for i in range(len(new_id)):\\n if new_id[i].isalpha() or new_id[i].isdecimal() or new_id[i] in ['-','_','.']:\\n continue\\n else:\\n new_id[i] = ''\\n new_id = list(''.join(new_id))\\n # 3단계 . 2번 이상 연속이면 1개로 치환\\n for i in range(len(new_id)):\\n if new_id[i] == '.':\\n for j in range(i, len(new_id)):\\n if new_id[j] == '.':\\n # 마지막 차례면\\n if j == len(new_id)-1:\\n for k in range(i, len(new_id)):\\n new_id[k] = ''\\n\\n else:\\n for k in range(i+1, j):\\n new_id[k] = ''\\n break\\n\\n\\n new_id = list(''.join(new_id))\\n\\n # 4단계 . 처음 혹은 끝 제거\\n while True:\\n if len(new_id) < 1:\\n break\\n if new_id[0] != '.' and new_id[-1] != '.':\\n break\\n if new_id[0] == '.':\\n new_id[0] = ''\\n if new_id[-1] == '.':\\n new_id[-1] = ''\\n new_id = list(''.join(new_id))\\n # 5단계 빈문자열이면 'a'러\\n new_id = list(''.join(new_id))\\n if len(new_id) == 0:\\n new_id = ['a']\\n # 6단계 16자 이상이면 0~15까지만 살리고, 마지막에 . 있으면 제거\\n if len(new_id) >= 16:\\n new_id = new_id[0:15]\\n if new_id[-1] == '.':\\n new_id[-1] = ''\\n\\n if len(new_id) <= 2:\\n\\n while True:\\n if len(new_id) == 3:\\n break\\n new_id.append(new_id[-1])\\n\\n return ''.join(new_id)\"\n },\n {\n \"alpha_fraction\": 0.354938268661499,\n \"alphanum_fraction\": 0.4197530746459961,\n \"avg_line_length\": 13.043478012084961,\n \"blob_id\": \"6f972f86839290805ef4cee8a436d3e390ab9331\",\n \"content_id\": \"3fbafd83514791fd2abaec32f8263bd2ed0aa171\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 420,\n \"license_type\": \"no_license\",\n \"max_line_length\": 40,\n \"num_lines\": 23,\n \"path\": \"/algorithm_base/이론 정리/재귀_피보나치수열.py\",\n \"repo_name\": \"huisso97/baekjoon_learning\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 재귀에서는 문제 정의와 종료조건이 굉장히 중요하다\\n# 피보나치 수열이란 : 어떤 수열의 항이, 앞의 두항의 합과 같은 수열\\n# f(1) = 1\\n# f(2) = 1\\n# f(3) = 2\\n# f(4) = 3\\n# f(5) = 5\\n# f(6) = 8\\n# ...\\n# f(n-2)+f(n-1) = f(n)\\n\\ndef fibo(n):\\n a = 1\\n b = 1\\n if n == 1 or n == 2:\\n return 1\\n for i in range(1, n):\\n a, b = b, b + a\\n\\n return a\\n\\n\\nprint(fibo(5))\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":78,"string":"78"}}},{"rowIdx":2979150,"cells":{"repo_name":{"kind":"string","value":"gambini16/proy_ventas"},"repo_url":{"kind":"string","value":"https://github.com/gambini16/proy_ventas"},"snapshot_id":{"kind":"string","value":"c440a5ecca97bf2c54feb474a7644249331f15aa"},"revision_id":{"kind":"string","value":"b9d76c9c084e920535e8b413cea51f7a50b210c1"},"directory_id":{"kind":"string","value":"f82e09d09a04bfe504a21c15845e01c4548942db"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-22T21:27:29.431018","string":"2021-01-22T21:27:29.431018"},"revision_date":{"kind":"timestamp","value":"2017-08-31T06:01:12","string":"2017-08-31T06:01:12"},"committer_date":{"kind":"timestamp","value":"2017-08-31T06:01:12","string":"2017-08-31T06:01:12"},"github_id":{"kind":"number","value":100669673,"string":"100,669,673"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.628555178642273,"alphanum_fraction":0.6291239857673645,"avg_line_length":24.867647171020508,"blob_id":"9d426d48e152aa674649112309e28297375993a0","content_id":"122fe211748cf960919803218d231a6d182d4b89","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1759,"license_type":"no_license","max_line_length":184,"num_lines":68,"path":"/proy_ventas/mantenimiento/cliente.py","repo_name":"gambini16/proy_ventas","src_encoding":"UTF-8","text":"import sys\n\nsys.path.append('..')\nfrom data.clsConexion import conexion\n\ndef insertarDatos(nombre_cliente,ruc_cliente,direccion_cliente):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"INSERT INTO cliente(nombre_cliente,ruc_cliente,direccion_cliente,estado_cliente) VALUES ('\" + nombre_cliente + \"','\" + ruc_cliente + \"','\" + direccion_cliente + \"','1')\"\n\n try:\n cursor.execute(valores)\n con.commit()\n print(\"Se han insertado los valores correctamente\")\n except:\n print(\"Eror, no se han insertado los valores correctamente\")\n finally:\n con.close()\n\n\ndef actualizarDatos(id_cliente, nombre_cliente):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"UPDATE cliente SET nombre_cliente='\" + nombre_cliente + \"' WHERE id_cliente=\" + id_cliente + \" \"\n\n try:\n cursor.execute(valores)\n con.commit()\n print(\"Se actualizó el valor correctamente\")\n except:\n print(\"Eror, no se han insertado los valores correctamente\")\n finally:\n cursor.close()\n con.close()\n\n\ndef eliminarDatos(id_cliente):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"DELETE FROM cliente \" + \" WHERE id_cliente=\" + id_cliente + \" \"\n\n try:\n cursor.execute(valores)\n con.commit()\n print(\"Se han eliminado los valores correctamente\")\n except:\n print(\"Eror, no se ha eliminado el producto indicado\")\n finally:\n cursor.close()\n con.close()\n\n\ndef leerDatos():\n con = conexion()\n cursor = con.cursor()\n\n cursor.execute(\"SELECT id_cliente,nombre_cliente,ruc_cliente,direccion_cliente FROM cliente\")\n\n rows = cursor.fetchall()\n\n for row in rows:\n print(row)\n\n cursor.close()\n con.close()"},{"alpha_fraction":0.7861506938934326,"alphanum_fraction":0.7861506938934326,"avg_line_length":31.66666603088379,"blob_id":"a3e1247f6e420fa5e7fc35fcdba23bdcb3e06c87","content_id":"2c6f30dbc02f9090bb29b2de8af3feba1fc09d31","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":491,"license_type":"no_license","max_line_length":80,"num_lines":15,"path":"/proy_ventas/entidades/Producto.py","repo_name":"gambini16/proy_ventas","src_encoding":"UTF-8","text":"class Producto(object):\n\tdef __init__(self,id_producto,nombre_producto,precio_producto,estado_producto):\n\t\tself.id_producto=id_producto\n\t\tself.nombre_producto=nombre_producto\n\t\tself.precio_producto=precio_producto\n\t\tself.estado_producto=estado_producto\n\n\tdef darId_producto(self):\n\t\treturn self.id_producto\n\tdef darNombre_producto(self):\n\t\treturn self.nombre_producto\n\tdef darPrecio_producto(self):\n\t\treturn self.precio_producto\n\tdef darEstado_producto(self):\n\t\treturn self.estado_producto\n\t"},{"alpha_fraction":0.709785521030426,"alphanum_fraction":0.7104557752609253,"avg_line_length":21.621212005615234,"blob_id":"84ff9807dc560225e73186911c52f33995f0ae59","content_id":"6d5c27c369cf569aff58828f964bae86a30071e1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1493,"license_type":"no_license","max_line_length":137,"num_lines":66,"path":"/proy_ventas/mantenimiento/productos.py","repo_name":"gambini16/proy_ventas","src_encoding":"UTF-8","text":"import sys\nsys.path.append('..')\nfrom data.clsConexion import conexion\n\ndef insertarDatos(nombre_producto,precio_producto):\n\tcon=conexion()\n\tcursor = con.cursor()\n\n\tvalores = \"INSERT INTO producto(nombre_producto,precio_producto,estado_producto) VALUES ('\"+nombre_producto+\"',\"+precio_producto+\",'1')\"\n\t\n\ttry:\n\t\tcursor.execute(valores)\n\t\tcon.commit()\n\t\tprint(\"Se han insertado los valores correctamente\")\n\texcept:\n\t\tprint(\"Eror, no se han insertado los valores correctamente\")\n\tfinally:\n\t\tcon.close()\n\ndef actualizarDatos(id_producto,nombre_producto):\n\tcon=conexion()\n\tcursor = con.cursor()\n\t\n\tvalores = \"UPDATE producto SET nombre_producto='\" + nombre_producto + \"' WHERE id_producto=\" + id_producto + \" \" \n\t\n\ttry:\n\t\tcursor.execute(valores)\n\t\tcon.commit()\n\t\tprint(\"Se actualizó el valor correctamente\")\n\texcept:\n\t\tprint(\"Eror, no se han insertado los valores correctamente\")\n\tfinally:\n\t\tcon.close()\n\ndef eliminarDatos(id_producto):\n\tcon=conexion()\n\tcursor = con.cursor()\n\t\n\tvalores = \"DELETE FROM producto \"+\" WHERE id_producto=\" + id_producto + \" \" \n\t\n\ttry:\n\t\tcursor.execute(valores)\n\t\tcon.commit()\n\t\tprint(\"Se han eliminado los valores correctamente\")\n\texcept:\n\t\tprint(\"Eror, no se ha eliminado el producto indicado\")\n\tfinally:\n\t\tcon.close()\n\t\n\t\n\ndef leerDatos():\n\tcon=conexion()\n\tcursor = con.cursor()\n\t\n\tcursor.execute(\"SELECT id_producto,nombre_producto,precio_producto FROM producto\")\t\n\t\n\trows = cursor.fetchall()\n\n\tfor row in rows:\n\t\tid =row\n\n\tcursor.close()\n\tcon.close()\n\n\treturn id"},{"alpha_fraction":0.6259586811065674,"alphanum_fraction":0.6265486478805542,"avg_line_length":23.941177368164062,"blob_id":"66952e772baa56e999c3c14e803841e4d54b86d3","content_id":"c71665f27e855db6b2d023bf68ddfc208fb450f5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1696,"license_type":"no_license","max_line_length":146,"num_lines":68,"path":"/proy_ventas/mantenimiento/usuario.py","repo_name":"gambini16/proy_ventas","src_encoding":"UTF-8","text":"import sys\n\nsys.path.append('..')\nfrom data.clsConexion import conexion\n\ndef insertarDatos(nombre_usuario, password_usuario):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"INSERT INTO usuario(nombre_usuario,password_usuario,estado_usuario) VALUES ('\" + nombre_usuario + \"',\" + password_usuario + \",'1')\"\n\n try:\n cursor.execute(valores)\n con.commit()\n print(\"Se han insertado los valores correctamente\")\n except:\n print(\"Eror, no se han insertado los valores correctamente\")\n finally:\n con.close()\n\n\ndef actualizarDatos(id_usuario, nombre_usuario):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"UPDATE usuario SET nombre_usuario='\" + nombre_usuario + \"' WHERE id_usuario=\" + id_usuario + \" \"\n\n try:\n cursor.execute(valores)\n con.commit()\n print(\"Se actualizó el valor correctamente\")\n except:\n print(\"Eror, no se han insertado los valores correctamente\")\n finally:\n cursor.close()\n con.close()\n\n\ndef eliminarDatos(id_usuario):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"DELETE FROM usuario \" + \" WHERE id_usuario=\" + id_usuario + \" \"\n\n try:\n cursor.execute(valores)\n con.commit()\n print(\"Se han eliminado los valores correctamente\")\n except:\n print(\"Eror, no se ha eliminado el producto indicado\")\n finally:\n cursor.close()\n con.close()\n\n\ndef leerDatos():\n con = conexion()\n cursor = con.cursor()\n\n cursor.execute(\"SELECT id_usuario,nombre_usuario,password_usuario FROM usuario\")\n\n rows = cursor.fetchall()\n\n for row in rows:\n print(row)\n\n cursor.close()\n con.close()"},{"alpha_fraction":0.6133480668067932,"alphanum_fraction":0.6174848079681396,"avg_line_length":25.8592586517334,"blob_id":"ca72104acb838dcbfeac8d0410d383b993a5c667","content_id":"43280a43839e58123cd6cb2901ccce7fb9375e0e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3627,"license_type":"no_license","max_line_length":206,"num_lines":135,"path":"/proy_ventas/ventas/pedido_cabecera.py","repo_name":"gambini16/proy_ventas","src_encoding":"UTF-8","text":"import mysql.connector\nfrom mysql.connector import errorcode\nimport sys\n\nsys.path.append('..')\nfrom data.clsConexion import conexion\n\n \n \ndef insertarDatos(id_cliente, id_usuario, id_vendedor, fecha_venta, id_producto, cantidad, precio_unitario):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"INSERT INTO pedido_cabecera(id_cliente,id_usuario,id_vendedor,fecha_venta) VALUES (\" + id_cliente + \",\" + id_usuario + \",\" + id_vendedor + \",'\" + fecha_venta + \"')\"\n \n try:\n cursor.execute(valores)\n \n \n idPedidoTemporal=str(obtenerIdPedidoCabecera())\n total =str(float(cantidad) * float(precio_unitario))\n \n str_salida=insertarDetalles(idPedidoTemporal,id_producto, cantidad, precio_unitario, total)\n print(str_salida)\n \n if str_salida == \"OK\":\n con.commit()\n print(\"Se han insertado los valores de cabecera correctamente\")\n else:\n con.rollback()\n print(\"Error en el detalle\")\n \n except mysql.connector.Error as err:\n print(\"ERROR PED. CABECERA:\", err)\n con.rollback()\n sys.exit(2)\n except:\n print(\"Error desconocido, no se han insertado los valores cabecera\")\n con.rollback()\n sys.exit(2)\n finally:\n cursor.close()\n con.close()\n\n\n\n \n\n\ndef insertarDetalles(idPedidoTemporal, id_producto, cantidad, precio_unitario, total):\n con2 = conexion()\n cursor2 = con2.cursor()\n \n valores2 = \"INSERT INTO pedido_detalle(id_pedido_cabecera,id_producto,cantidad,precio_unitario,total) VALUES (\"+ idPedidoTemporal + \",\"+ id_producto +\",\"+ cantidad +\",\"+ precio_unitario +\",\"+ total +\")\"\n \n try:\n cursor2.execute(valores2)\n con2.commit()\n print(\"Se han insertado los detalles correctamente\")\n str_exit=\"OK\"\n except mysql.connector.Error as err:\n print(\"ERROR PED. DETALLE:\", err)\n con2.rollback()\n str_exit=\"FALLO\"\n except:\n print(\"Error desconocido, no se han insertado los valores detalles\")\n con2.rollback()\n str_exit=\"FALLO\"\n finally:\n cursor2.close()\n con2.close()\n return str_exit\n\n\ndef obtenerIdPedidoCabecera():\n con = conexion()\n cursor = con.cursor()\n\n cursor.execute(\"SELECT id_pedido_cabecera FROM pedido_cabecera order by id_pedido_cabecera DESC LIMIT 1\")\n\n rows = cursor.fetchall()\n\n for row in rows:\n id = row[0]\n\n cursor.close()\n con.close()\n return id\n\n\ndef actualizarDatos(id_producto, nombre_producto):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"UPDATE producto SET nombre_producto='\" + nombre_producto + \"' WHERE id_producto=\" + id_producto + \" \"\n\n try:\n cursor.execute(valores)\n con.commit()\n print(\"Se actualizó el valor correctamente\")\n except:\n print(\"Eror, no se han insertado los valores correctamente\")\n finally:\n con.close()\n\n\ndef eliminarDatos(id_producto):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"DELETE FROM producto \" + \" WHERE id_producto=\" + id_producto + \" \"\n\n try:\n cursor.execute(valores)\n con.commit()\n print(\"Se han eliminado los valores correctamente\")\n except:\n print(\"Eror, no se ha eliminado el producto indicado\")\n finally:\n con.close()\n\n\ndef leerDatos():\n con = conexion()\n cursor = con.cursor()\n\n cursor.execute(\"SELECT id_producto,nombre_producto,precio_producto FROM producto\")\n\n rows = cursor.fetchall()\n\n for row in rows:\n print(row)\n\n cursor.close()\n con.close()\n"},{"alpha_fraction":0.618396520614624,"alphanum_fraction":0.619018018245697,"avg_line_length":22.676469802856445,"blob_id":"1823860254a9755a9e478e9166392d2e0307764d","content_id":"494977111e001d58bf96ce4d71e49f43c21dd40a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1610,"license_type":"no_license","max_line_length":117,"num_lines":68,"path":"/proy_ventas/mantenimiento/vendedor.py","repo_name":"gambini16/proy_ventas","src_encoding":"UTF-8","text":"import sys\n\nsys.path.append('..')\nfrom data.clsConexion import conexion\n\ndef insertarDatos(nombre_vendedor):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"INSERT INTO vendedor(nombre_vendedor,estado_vendedor) VALUES ('\" + nombre_vendedor + \"','1')\"\n\n try:\n cursor.execute(valores)\n con.commit()\n print(\"Se han insertado los valores correctamente\")\n except:\n print(\"Eror, no se han insertado los valores correctamente\")\n finally:\n con.close()\n\n\ndef actualizarDatos(id_vendedor,nombre_vendedor):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"UPDATE vendedor SET nombre_vendedor='\" + nombre_vendedor + \"' WHERE id_vendedor=\" + id_vendedor + \" \"\n\n try:\n cursor.execute(valores)\n con.commit()\n print(\"Se actualizó el valor correctamente\")\n except:\n print(\"Eror, no se han insertado los valores correctamente\")\n finally:\n cursor.close()\n con.close()\n\n\ndef eliminarDatos(id_usuario):\n con = conexion()\n cursor = con.cursor()\n\n valores = \"DELETE FROM vendedor \" + \" WHERE id_vendedor=\" + id_vendedor + \" \"\n\n try:\n cursor.execute(valores)\n con.commit()\n print(\"Se han eliminado los valores correctamente\")\n except:\n print(\"Eror, no se ha eliminado el producto indicado\")\n finally:\n cursor.close()\n con.close()\n\n\ndef leerDatos():\n con = conexion()\n cursor = con.cursor()\n\n cursor.execute(\"SELECT * FROM vendedor\")\n\n rows = cursor.fetchall()\n\n for row in rows:\n print(row)\n\n cursor.close()\n con.close()"},{"alpha_fraction":0.5972279906272888,"alphanum_fraction":0.5972279906272888,"avg_line_length":26.53816795349121,"blob_id":"152e82b237a4de3b0b46dc21386dac87d689b670","content_id":"e8a2c54a038e94ec0e6324e85723b645dd74a21b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7237,"license_type":"no_license","max_line_length":161,"num_lines":262,"path":"/proy_ventas/index.py","repo_name":"gambini16/proy_ventas","src_encoding":"UTF-8","text":"import mantenimiento.productos\nimport mantenimiento.usuario\nimport mantenimiento.cliente\nimport mantenimiento.vendedor\nimport ventas.pedido_cabecera\nimport datetime\nimport time\n\ndef load():\n menuPrincipal()\n\ndef menuPrincipal():\n print(\"****************************************\")\n print(\"*************SISTEMA VENTAS*************\")\n print(\"****************************************\")\n print(\"MENU\")\n print(\"A=> MANTENIMIENTO\")\n print(\"B=> VENTAS\")\n print(\"C=> REPORTES\")\n print(\"****************************************\")\n\n opcion = input(\"Ingrese una opción:\")\n\n if opcion == \"A\":\n opcionesMantenimiento()\n elif opcion == \"B\":\n opcionesVenta()\n elif opcion == \"C\":\n print(\"Reportes\")\n else:\n print(\"La opción: \" + opcion + \" ingresada no existe\")\n\n\n\n\ndef opcionesMantenimiento():\n print(\"*************MANTENIMIENTO*************\")\n print(\"A=> Productos\")\n print(\"B=> Usuario\")\n print(\"C=> Cliente\")\n print(\"D=> Vendedor\")\n print(\"****************************************\")\n opcion = input(\"Ingrese una opción:\")\n\n if opcion == \"A\":\n opcionesProductos()\n elif opcion == \"B\":\n opcionesUsuarios()\n elif opcion == \"C\":\n opcionesClientes()\n elif opcion == \"D\":\n opcionesVendedores()\n else:\n print(\"La opción: \" + opcion + \" ingresada no existe\")\n\n\n# INICIO-REGIÓN MANTENIMIENTO DE PRODUCTOS\ndef opcionesProductos():\n print(\"*************MANT.PRODUCTOS************\")\n print(\"A=> Registra\")\n print(\"B=> Actualiza\")\n print(\"C=> Elimina\")\n print(\"D=> lista\")\n print(\"****************************************\")\n\n opcion = input(\"Ingrese una opción:\")\n\n if opcion == \"A\":\n registarProducto()\n elif opcion == \"B\":\n actualizarProducto()\n elif opcion == \"C\":\n eliminarProducto()\n elif opcion == \"D\":\n listarProducto()\n else:\n print(\"La opción: \" + opcion + \" ingresada no existe\")\n\ndef registarProducto():\n nombre_producto = input(\"Ingrese el nombre del producto: \")\n precio_producto = input(\"Ingrese el precio del producto: \")\n\n mantenimiento.productos.insertarDatos(nombre_producto, precio_producto)\n\ndef actualizarProducto():\n id_producto = input(\"ingrese un id: \")\n nombre_producto = input(\"ingrese un nombre: \")\n\n mantenimiento.productos.actualizarDatos(id_producto, nombre_producto)\n\ndef eliminarProducto():\n id_producto = input(\"Ingrese id: \")\n mantenimiento.productos.eliminarDatos(id_producto)\n\ndef listarProducto():\n\n mantenimiento.productos.leerDatos()\n\n# FIN-REGIÓN MANTENIMIENTO DE PRODUCTOS\n\n# INICIO-REGIÓN MANTENIMIENTO DE PRODUCTOS\n\n\ndef opcionesUsuarios():\n print(\"*************MANT.USUARIOS************\")\n print(\"A=> Registra\")\n print(\"B=> Actualiza\")\n print(\"C=> Elimina\")\n print(\"D=> lista\")\n print(\"****************************************\")\n\n opcion = input(\"Ingrese una opción:\")\n\n if opcion == \"A\":\n registarUsuario()\n elif opcion == \"B\":\n actualizarUsuario()\n elif opcion == \"C\":\n eliminarUsuario()\n elif opcion == \"D\":\n listarUsuario()\n else:\n print(\"La opción: \" + opcion + \" ingresada no existe\")\n\ndef registarUsuario():\n nombre_usuario = input(\"Ingrese el nombre del usuario: \")\n password_usuario = input(\"Ingrese el password del usuario: \")\n\n mantenimiento.usuario.insertarDatos(nombre_usuario, password_usuario)\n\ndef actualizarUsuario():\n id_usuario=input(\"Ingrese el id del usuario: \")\n nombre_usuario=input(\"Ingrese el nombre del usuario: \")\n\n mantenimiento.usuario.actualizarDatos(id_usuario,nombre_usuario)\n\ndef eliminarUsuario():\n id_usuario=input(\"Ingrese el id del usuario: \")\n\n mantenimiento.usuario.eliminarDatos(id_usuario)\n\ndef listarUsuario():\n mantenimiento.usuario.leerDatos()\n# FIN-REGIÓN MANTENIMIENTO DE PRODUCTOS\n\n#INICIO-REGIÓN VENDEDORES\n\ndef opcionesClientes():\n print(\"*************MANT.CLIENTES************\")\n print(\"A=> Registra\")\n print(\"B=> Actualiza\")\n print(\"C=> Elimina\")\n print(\"D=> lista\")\n print(\"****************************************\")\n\n opcion = input(\"Ingrese una opción:\")\n\n if opcion == \"A\":\n registarCliente()\n elif opcion == \"B\":\n actualizarCliente()\n elif opcion == \"C\":\n eliminarCliente()\n elif opcion == \"D\":\n listarCliente()\n else:\n print(\"La opción: \" + opcion + \" ingresada no existe\")\n\ndef registarCliente():\n nombre_cliente = input(\"Ingrese el nombre del cliente: \")\n ruc_cliente = input(\"Ingrese el ruc del cliente: \")\n direccion_cliente = input(\"Ingrese la dirección del cliente: \")\n\n mantenimiento.cliente.insertarDatos(nombre_cliente,ruc_cliente,direccion_cliente)\n\ndef actualizarCliente():\n id_cliente = input(\"Ingrese el id del cliente: \")\n nombre_cliente = input(\"Ingrese el nombre del cliente: \")\n\n mantenimiento.cliente.actualizarDatos(id_cliente, nombre_cliente)\n\ndef eliminarCliente():\n id_cliente = input(\"Ingrese el id del cliente: \")\n\n mantenimiento.cliente.eliminarDatos(id_cliente)\n\ndef listarCliente():\n mantenimiento.cliente.leerDatos()\n\n#FIN-REGIÓN CLIENTES\n\n#INICIO-REGIÓN VENDEDORES\ndef opcionesVendedores():\n print(\"*************MANT.VENDEDORES************\")\n print(\"A=> Registra\")\n print(\"B=> Actualiza\")\n print(\"C=> Elimina\")\n print(\"D=> lista\")\n print(\"****************************************\")\n\n opcion = input(\"Ingrese una opción:\")\n\n if opcion == \"A\":\n registarVendedor()\n elif opcion == \"B\":\n actualizarVendedor()\n elif opcion == \"C\":\n eliminarVendedor()\n elif opcion == \"D\":\n listarVendedor()\n else:\n print(\"La opción: \" + opcion + \" ingresada no existe\")\n\ndef registarVendedor():\n nombre_vendedor = input(\"Ingrese el nombre del vendedor: \")\n\n mantenimiento.vendedor.insertarDatos(nombre_vendedor)\n\ndef actualizarVendedor():\n id_vendedor=input(\"Ingrese el id del vendedor: \")\n\n mantenimiento.vendedor.actualizarDatos(id_vendedor)\n\ndef eliminarVendedor():\n id_vendedor=input(\"Ingrese el id del vendedor: \")\n\n mantenimiento.vendedor.eliminarDatos(id_vendedor)\n\ndef listarVendedor():\n mantenimiento.vendedor.leerDatos()\n\n#FIN-REGIÓN VENDEDORES\n\ndef opcionesVenta():\n print(\"*************VENTA*************\")\n print(\"A=>REGISTRAR\")\n print(\"B=>LISTAR\")\n print(\"****************************************\")\n opcion = input(\"Ingrese opcion:\")\n\n if opcion == \"A\":\n registraPedido()\n elif opcion == \"B\":\n listaPedido()\n else:\n print(\"La opción: \" + opcion + \" ingresada no existe\")\n\ndef registraPedido():\n \n id_cliente= input(\"Ingrese el id cliente: \")\n id_usuario = input(\"Ingrese el id usuario: \")\n id_vendedor=input(\"Ingrese el id vendedor: \")\n fecha = datetime.datetime.now()\n\n id_producto=input(\"Ingrese el id producto: \")\n cantidad=input(\"Ingrese el cantidad: \")\n precio_unitario=input(\"Ingrese el precio: \")\n \n ventas.pedido_cabecera.insertarDatos(id_cliente,id_usuario,id_vendedor,\"%s/%s/%s\" % (fecha.year,fecha.month, fecha.day),id_producto,cantidad,precio_unitario)\n\n\nload()\n"},{"alpha_fraction":0.6405228972434998,"alphanum_fraction":0.6797385811805725,"avg_line_length":24.5,"blob_id":"19b0cf0cc0405ac298435a232032e070804f4a50","content_id":"2988c61c557b10f9616feeb5d4ef70b18a448774","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":153,"license_type":"no_license","max_line_length":96,"num_lines":6,"path":"/proy_ventas/data/clsConexion.py","repo_name":"gambini16/proy_ventas","src_encoding":"UTF-8","text":"import mysql.connector\n\n\ndef conexion():\n con = mysql.connector.connect(user=\"root\", password=\"\", host=\"127.0.0.1\", database=\"ventas\")\n return con\n"}],"string":"[\n {\n \"alpha_fraction\": 0.628555178642273,\n \"alphanum_fraction\": 0.6291239857673645,\n \"avg_line_length\": 24.867647171020508,\n \"blob_id\": \"9d426d48e152aa674649112309e28297375993a0\",\n \"content_id\": \"122fe211748cf960919803218d231a6d182d4b89\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1759,\n \"license_type\": \"no_license\",\n \"max_line_length\": 184,\n \"num_lines\": 68,\n \"path\": \"/proy_ventas/mantenimiento/cliente.py\",\n \"repo_name\": \"gambini16/proy_ventas\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\n\\nsys.path.append('..')\\nfrom data.clsConexion import conexion\\n\\ndef insertarDatos(nombre_cliente,ruc_cliente,direccion_cliente):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"INSERT INTO cliente(nombre_cliente,ruc_cliente,direccion_cliente,estado_cliente) VALUES ('\\\" + nombre_cliente + \\\"','\\\" + ruc_cliente + \\\"','\\\" + direccion_cliente + \\\"','1')\\\"\\n\\n try:\\n cursor.execute(valores)\\n con.commit()\\n print(\\\"Se han insertado los valores correctamente\\\")\\n except:\\n print(\\\"Eror, no se han insertado los valores correctamente\\\")\\n finally:\\n con.close()\\n\\n\\ndef actualizarDatos(id_cliente, nombre_cliente):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"UPDATE cliente SET nombre_cliente='\\\" + nombre_cliente + \\\"' WHERE id_cliente=\\\" + id_cliente + \\\" \\\"\\n\\n try:\\n cursor.execute(valores)\\n con.commit()\\n print(\\\"Se actualizó el valor correctamente\\\")\\n except:\\n print(\\\"Eror, no se han insertado los valores correctamente\\\")\\n finally:\\n cursor.close()\\n con.close()\\n\\n\\ndef eliminarDatos(id_cliente):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"DELETE FROM cliente \\\" + \\\" WHERE id_cliente=\\\" + id_cliente + \\\" \\\"\\n\\n try:\\n cursor.execute(valores)\\n con.commit()\\n print(\\\"Se han eliminado los valores correctamente\\\")\\n except:\\n print(\\\"Eror, no se ha eliminado el producto indicado\\\")\\n finally:\\n cursor.close()\\n con.close()\\n\\n\\ndef leerDatos():\\n con = conexion()\\n cursor = con.cursor()\\n\\n cursor.execute(\\\"SELECT id_cliente,nombre_cliente,ruc_cliente,direccion_cliente FROM cliente\\\")\\n\\n rows = cursor.fetchall()\\n\\n for row in rows:\\n print(row)\\n\\n cursor.close()\\n con.close()\"\n },\n {\n \"alpha_fraction\": 0.7861506938934326,\n \"alphanum_fraction\": 0.7861506938934326,\n \"avg_line_length\": 31.66666603088379,\n \"blob_id\": \"a3e1247f6e420fa5e7fc35fcdba23bdcb3e06c87\",\n \"content_id\": \"2c6f30dbc02f9090bb29b2de8af3feba1fc09d31\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 491,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 15,\n \"path\": \"/proy_ventas/entidades/Producto.py\",\n \"repo_name\": \"gambini16/proy_ventas\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Producto(object):\\n\\tdef __init__(self,id_producto,nombre_producto,precio_producto,estado_producto):\\n\\t\\tself.id_producto=id_producto\\n\\t\\tself.nombre_producto=nombre_producto\\n\\t\\tself.precio_producto=precio_producto\\n\\t\\tself.estado_producto=estado_producto\\n\\n\\tdef darId_producto(self):\\n\\t\\treturn self.id_producto\\n\\tdef darNombre_producto(self):\\n\\t\\treturn self.nombre_producto\\n\\tdef darPrecio_producto(self):\\n\\t\\treturn self.precio_producto\\n\\tdef darEstado_producto(self):\\n\\t\\treturn self.estado_producto\\n\\t\"\n },\n {\n \"alpha_fraction\": 0.709785521030426,\n \"alphanum_fraction\": 0.7104557752609253,\n \"avg_line_length\": 21.621212005615234,\n \"blob_id\": \"84ff9807dc560225e73186911c52f33995f0ae59\",\n \"content_id\": \"6d5c27c369cf569aff58828f964bae86a30071e1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1493,\n \"license_type\": \"no_license\",\n \"max_line_length\": 137,\n \"num_lines\": 66,\n \"path\": \"/proy_ventas/mantenimiento/productos.py\",\n \"repo_name\": \"gambini16/proy_ventas\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nsys.path.append('..')\\nfrom data.clsConexion import conexion\\n\\ndef insertarDatos(nombre_producto,precio_producto):\\n\\tcon=conexion()\\n\\tcursor = con.cursor()\\n\\n\\tvalores = \\\"INSERT INTO producto(nombre_producto,precio_producto,estado_producto) VALUES ('\\\"+nombre_producto+\\\"',\\\"+precio_producto+\\\",'1')\\\"\\n\\t\\n\\ttry:\\n\\t\\tcursor.execute(valores)\\n\\t\\tcon.commit()\\n\\t\\tprint(\\\"Se han insertado los valores correctamente\\\")\\n\\texcept:\\n\\t\\tprint(\\\"Eror, no se han insertado los valores correctamente\\\")\\n\\tfinally:\\n\\t\\tcon.close()\\n\\ndef actualizarDatos(id_producto,nombre_producto):\\n\\tcon=conexion()\\n\\tcursor = con.cursor()\\n\\t\\n\\tvalores = \\\"UPDATE producto SET nombre_producto='\\\" + nombre_producto + \\\"' WHERE id_producto=\\\" + id_producto + \\\" \\\" \\n\\t\\n\\ttry:\\n\\t\\tcursor.execute(valores)\\n\\t\\tcon.commit()\\n\\t\\tprint(\\\"Se actualizó el valor correctamente\\\")\\n\\texcept:\\n\\t\\tprint(\\\"Eror, no se han insertado los valores correctamente\\\")\\n\\tfinally:\\n\\t\\tcon.close()\\n\\ndef eliminarDatos(id_producto):\\n\\tcon=conexion()\\n\\tcursor = con.cursor()\\n\\t\\n\\tvalores = \\\"DELETE FROM producto \\\"+\\\" WHERE id_producto=\\\" + id_producto + \\\" \\\" \\n\\t\\n\\ttry:\\n\\t\\tcursor.execute(valores)\\n\\t\\tcon.commit()\\n\\t\\tprint(\\\"Se han eliminado los valores correctamente\\\")\\n\\texcept:\\n\\t\\tprint(\\\"Eror, no se ha eliminado el producto indicado\\\")\\n\\tfinally:\\n\\t\\tcon.close()\\n\\t\\n\\t\\n\\ndef leerDatos():\\n\\tcon=conexion()\\n\\tcursor = con.cursor()\\n\\t\\n\\tcursor.execute(\\\"SELECT id_producto,nombre_producto,precio_producto FROM producto\\\")\\t\\n\\t\\n\\trows = cursor.fetchall()\\n\\n\\tfor row in rows:\\n\\t\\tid =row\\n\\n\\tcursor.close()\\n\\tcon.close()\\n\\n\\treturn id\"\n },\n {\n \"alpha_fraction\": 0.6259586811065674,\n \"alphanum_fraction\": 0.6265486478805542,\n \"avg_line_length\": 23.941177368164062,\n \"blob_id\": \"66952e772baa56e999c3c14e803841e4d54b86d3\",\n \"content_id\": \"c71665f27e855db6b2d023bf68ddfc208fb450f5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1696,\n \"license_type\": \"no_license\",\n \"max_line_length\": 146,\n \"num_lines\": 68,\n \"path\": \"/proy_ventas/mantenimiento/usuario.py\",\n \"repo_name\": \"gambini16/proy_ventas\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\n\\nsys.path.append('..')\\nfrom data.clsConexion import conexion\\n\\ndef insertarDatos(nombre_usuario, password_usuario):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"INSERT INTO usuario(nombre_usuario,password_usuario,estado_usuario) VALUES ('\\\" + nombre_usuario + \\\"',\\\" + password_usuario + \\\",'1')\\\"\\n\\n try:\\n cursor.execute(valores)\\n con.commit()\\n print(\\\"Se han insertado los valores correctamente\\\")\\n except:\\n print(\\\"Eror, no se han insertado los valores correctamente\\\")\\n finally:\\n con.close()\\n\\n\\ndef actualizarDatos(id_usuario, nombre_usuario):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"UPDATE usuario SET nombre_usuario='\\\" + nombre_usuario + \\\"' WHERE id_usuario=\\\" + id_usuario + \\\" \\\"\\n\\n try:\\n cursor.execute(valores)\\n con.commit()\\n print(\\\"Se actualizó el valor correctamente\\\")\\n except:\\n print(\\\"Eror, no se han insertado los valores correctamente\\\")\\n finally:\\n cursor.close()\\n con.close()\\n\\n\\ndef eliminarDatos(id_usuario):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"DELETE FROM usuario \\\" + \\\" WHERE id_usuario=\\\" + id_usuario + \\\" \\\"\\n\\n try:\\n cursor.execute(valores)\\n con.commit()\\n print(\\\"Se han eliminado los valores correctamente\\\")\\n except:\\n print(\\\"Eror, no se ha eliminado el producto indicado\\\")\\n finally:\\n cursor.close()\\n con.close()\\n\\n\\ndef leerDatos():\\n con = conexion()\\n cursor = con.cursor()\\n\\n cursor.execute(\\\"SELECT id_usuario,nombre_usuario,password_usuario FROM usuario\\\")\\n\\n rows = cursor.fetchall()\\n\\n for row in rows:\\n print(row)\\n\\n cursor.close()\\n con.close()\"\n },\n {\n \"alpha_fraction\": 0.6133480668067932,\n \"alphanum_fraction\": 0.6174848079681396,\n \"avg_line_length\": 25.8592586517334,\n \"blob_id\": \"ca72104acb838dcbfeac8d0410d383b993a5c667\",\n \"content_id\": \"43280a43839e58123cd6cb2901ccce7fb9375e0e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3627,\n \"license_type\": \"no_license\",\n \"max_line_length\": 206,\n \"num_lines\": 135,\n \"path\": \"/proy_ventas/ventas/pedido_cabecera.py\",\n \"repo_name\": \"gambini16/proy_ventas\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import mysql.connector\\nfrom mysql.connector import errorcode\\nimport sys\\n\\nsys.path.append('..')\\nfrom data.clsConexion import conexion\\n\\n \\n \\ndef insertarDatos(id_cliente, id_usuario, id_vendedor, fecha_venta, id_producto, cantidad, precio_unitario):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"INSERT INTO pedido_cabecera(id_cliente,id_usuario,id_vendedor,fecha_venta) VALUES (\\\" + id_cliente + \\\",\\\" + id_usuario + \\\",\\\" + id_vendedor + \\\",'\\\" + fecha_venta + \\\"')\\\"\\n \\n try:\\n cursor.execute(valores)\\n \\n \\n idPedidoTemporal=str(obtenerIdPedidoCabecera())\\n total =str(float(cantidad) * float(precio_unitario))\\n \\n str_salida=insertarDetalles(idPedidoTemporal,id_producto, cantidad, precio_unitario, total)\\n print(str_salida)\\n \\n if str_salida == \\\"OK\\\":\\n con.commit()\\n print(\\\"Se han insertado los valores de cabecera correctamente\\\")\\n else:\\n con.rollback()\\n print(\\\"Error en el detalle\\\")\\n \\n except mysql.connector.Error as err:\\n print(\\\"ERROR PED. CABECERA:\\\", err)\\n con.rollback()\\n sys.exit(2)\\n except:\\n print(\\\"Error desconocido, no se han insertado los valores cabecera\\\")\\n con.rollback()\\n sys.exit(2)\\n finally:\\n cursor.close()\\n con.close()\\n\\n\\n\\n \\n\\n\\ndef insertarDetalles(idPedidoTemporal, id_producto, cantidad, precio_unitario, total):\\n con2 = conexion()\\n cursor2 = con2.cursor()\\n \\n valores2 = \\\"INSERT INTO pedido_detalle(id_pedido_cabecera,id_producto,cantidad,precio_unitario,total) VALUES (\\\"+ idPedidoTemporal + \\\",\\\"+ id_producto +\\\",\\\"+ cantidad +\\\",\\\"+ precio_unitario +\\\",\\\"+ total +\\\")\\\"\\n \\n try:\\n cursor2.execute(valores2)\\n con2.commit()\\n print(\\\"Se han insertado los detalles correctamente\\\")\\n str_exit=\\\"OK\\\"\\n except mysql.connector.Error as err:\\n print(\\\"ERROR PED. DETALLE:\\\", err)\\n con2.rollback()\\n str_exit=\\\"FALLO\\\"\\n except:\\n print(\\\"Error desconocido, no se han insertado los valores detalles\\\")\\n con2.rollback()\\n str_exit=\\\"FALLO\\\"\\n finally:\\n cursor2.close()\\n con2.close()\\n return str_exit\\n\\n\\ndef obtenerIdPedidoCabecera():\\n con = conexion()\\n cursor = con.cursor()\\n\\n cursor.execute(\\\"SELECT id_pedido_cabecera FROM pedido_cabecera order by id_pedido_cabecera DESC LIMIT 1\\\")\\n\\n rows = cursor.fetchall()\\n\\n for row in rows:\\n id = row[0]\\n\\n cursor.close()\\n con.close()\\n return id\\n\\n\\ndef actualizarDatos(id_producto, nombre_producto):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"UPDATE producto SET nombre_producto='\\\" + nombre_producto + \\\"' WHERE id_producto=\\\" + id_producto + \\\" \\\"\\n\\n try:\\n cursor.execute(valores)\\n con.commit()\\n print(\\\"Se actualizó el valor correctamente\\\")\\n except:\\n print(\\\"Eror, no se han insertado los valores correctamente\\\")\\n finally:\\n con.close()\\n\\n\\ndef eliminarDatos(id_producto):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"DELETE FROM producto \\\" + \\\" WHERE id_producto=\\\" + id_producto + \\\" \\\"\\n\\n try:\\n cursor.execute(valores)\\n con.commit()\\n print(\\\"Se han eliminado los valores correctamente\\\")\\n except:\\n print(\\\"Eror, no se ha eliminado el producto indicado\\\")\\n finally:\\n con.close()\\n\\n\\ndef leerDatos():\\n con = conexion()\\n cursor = con.cursor()\\n\\n cursor.execute(\\\"SELECT id_producto,nombre_producto,precio_producto FROM producto\\\")\\n\\n rows = cursor.fetchall()\\n\\n for row in rows:\\n print(row)\\n\\n cursor.close()\\n con.close()\\n\"\n },\n {\n \"alpha_fraction\": 0.618396520614624,\n \"alphanum_fraction\": 0.619018018245697,\n \"avg_line_length\": 22.676469802856445,\n \"blob_id\": \"1823860254a9755a9e478e9166392d2e0307764d\",\n \"content_id\": \"494977111e001d58bf96ce4d71e49f43c21dd40a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1610,\n \"license_type\": \"no_license\",\n \"max_line_length\": 117,\n \"num_lines\": 68,\n \"path\": \"/proy_ventas/mantenimiento/vendedor.py\",\n \"repo_name\": \"gambini16/proy_ventas\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\n\\nsys.path.append('..')\\nfrom data.clsConexion import conexion\\n\\ndef insertarDatos(nombre_vendedor):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"INSERT INTO vendedor(nombre_vendedor,estado_vendedor) VALUES ('\\\" + nombre_vendedor + \\\"','1')\\\"\\n\\n try:\\n cursor.execute(valores)\\n con.commit()\\n print(\\\"Se han insertado los valores correctamente\\\")\\n except:\\n print(\\\"Eror, no se han insertado los valores correctamente\\\")\\n finally:\\n con.close()\\n\\n\\ndef actualizarDatos(id_vendedor,nombre_vendedor):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"UPDATE vendedor SET nombre_vendedor='\\\" + nombre_vendedor + \\\"' WHERE id_vendedor=\\\" + id_vendedor + \\\" \\\"\\n\\n try:\\n cursor.execute(valores)\\n con.commit()\\n print(\\\"Se actualizó el valor correctamente\\\")\\n except:\\n print(\\\"Eror, no se han insertado los valores correctamente\\\")\\n finally:\\n cursor.close()\\n con.close()\\n\\n\\ndef eliminarDatos(id_usuario):\\n con = conexion()\\n cursor = con.cursor()\\n\\n valores = \\\"DELETE FROM vendedor \\\" + \\\" WHERE id_vendedor=\\\" + id_vendedor + \\\" \\\"\\n\\n try:\\n cursor.execute(valores)\\n con.commit()\\n print(\\\"Se han eliminado los valores correctamente\\\")\\n except:\\n print(\\\"Eror, no se ha eliminado el producto indicado\\\")\\n finally:\\n cursor.close()\\n con.close()\\n\\n\\ndef leerDatos():\\n con = conexion()\\n cursor = con.cursor()\\n\\n cursor.execute(\\\"SELECT * FROM vendedor\\\")\\n\\n rows = cursor.fetchall()\\n\\n for row in rows:\\n print(row)\\n\\n cursor.close()\\n con.close()\"\n },\n {\n \"alpha_fraction\": 0.5972279906272888,\n \"alphanum_fraction\": 0.5972279906272888,\n \"avg_line_length\": 26.53816795349121,\n \"blob_id\": \"152e82b237a4de3b0b46dc21386dac87d689b670\",\n \"content_id\": \"e8a2c54a038e94ec0e6324e85723b645dd74a21b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7237,\n \"license_type\": \"no_license\",\n \"max_line_length\": 161,\n \"num_lines\": 262,\n \"path\": \"/proy_ventas/index.py\",\n \"repo_name\": \"gambini16/proy_ventas\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import mantenimiento.productos\\nimport mantenimiento.usuario\\nimport mantenimiento.cliente\\nimport mantenimiento.vendedor\\nimport ventas.pedido_cabecera\\nimport datetime\\nimport time\\n\\ndef load():\\n menuPrincipal()\\n\\ndef menuPrincipal():\\n print(\\\"****************************************\\\")\\n print(\\\"*************SISTEMA VENTAS*************\\\")\\n print(\\\"****************************************\\\")\\n print(\\\"MENU\\\")\\n print(\\\"A=> MANTENIMIENTO\\\")\\n print(\\\"B=> VENTAS\\\")\\n print(\\\"C=> REPORTES\\\")\\n print(\\\"****************************************\\\")\\n\\n opcion = input(\\\"Ingrese una opción:\\\")\\n\\n if opcion == \\\"A\\\":\\n opcionesMantenimiento()\\n elif opcion == \\\"B\\\":\\n opcionesVenta()\\n elif opcion == \\\"C\\\":\\n print(\\\"Reportes\\\")\\n else:\\n print(\\\"La opción: \\\" + opcion + \\\" ingresada no existe\\\")\\n\\n\\n\\n\\ndef opcionesMantenimiento():\\n print(\\\"*************MANTENIMIENTO*************\\\")\\n print(\\\"A=> Productos\\\")\\n print(\\\"B=> Usuario\\\")\\n print(\\\"C=> Cliente\\\")\\n print(\\\"D=> Vendedor\\\")\\n print(\\\"****************************************\\\")\\n opcion = input(\\\"Ingrese una opción:\\\")\\n\\n if opcion == \\\"A\\\":\\n opcionesProductos()\\n elif opcion == \\\"B\\\":\\n opcionesUsuarios()\\n elif opcion == \\\"C\\\":\\n opcionesClientes()\\n elif opcion == \\\"D\\\":\\n opcionesVendedores()\\n else:\\n print(\\\"La opción: \\\" + opcion + \\\" ingresada no existe\\\")\\n\\n\\n# INICIO-REGIÓN MANTENIMIENTO DE PRODUCTOS\\ndef opcionesProductos():\\n print(\\\"*************MANT.PRODUCTOS************\\\")\\n print(\\\"A=> Registra\\\")\\n print(\\\"B=> Actualiza\\\")\\n print(\\\"C=> Elimina\\\")\\n print(\\\"D=> lista\\\")\\n print(\\\"****************************************\\\")\\n\\n opcion = input(\\\"Ingrese una opción:\\\")\\n\\n if opcion == \\\"A\\\":\\n registarProducto()\\n elif opcion == \\\"B\\\":\\n actualizarProducto()\\n elif opcion == \\\"C\\\":\\n eliminarProducto()\\n elif opcion == \\\"D\\\":\\n listarProducto()\\n else:\\n print(\\\"La opción: \\\" + opcion + \\\" ingresada no existe\\\")\\n\\ndef registarProducto():\\n nombre_producto = input(\\\"Ingrese el nombre del producto: \\\")\\n precio_producto = input(\\\"Ingrese el precio del producto: \\\")\\n\\n mantenimiento.productos.insertarDatos(nombre_producto, precio_producto)\\n\\ndef actualizarProducto():\\n id_producto = input(\\\"ingrese un id: \\\")\\n nombre_producto = input(\\\"ingrese un nombre: \\\")\\n\\n mantenimiento.productos.actualizarDatos(id_producto, nombre_producto)\\n\\ndef eliminarProducto():\\n id_producto = input(\\\"Ingrese id: \\\")\\n mantenimiento.productos.eliminarDatos(id_producto)\\n\\ndef listarProducto():\\n\\n mantenimiento.productos.leerDatos()\\n\\n# FIN-REGIÓN MANTENIMIENTO DE PRODUCTOS\\n\\n# INICIO-REGIÓN MANTENIMIENTO DE PRODUCTOS\\n\\n\\ndef opcionesUsuarios():\\n print(\\\"*************MANT.USUARIOS************\\\")\\n print(\\\"A=> Registra\\\")\\n print(\\\"B=> Actualiza\\\")\\n print(\\\"C=> Elimina\\\")\\n print(\\\"D=> lista\\\")\\n print(\\\"****************************************\\\")\\n\\n opcion = input(\\\"Ingrese una opción:\\\")\\n\\n if opcion == \\\"A\\\":\\n registarUsuario()\\n elif opcion == \\\"B\\\":\\n actualizarUsuario()\\n elif opcion == \\\"C\\\":\\n eliminarUsuario()\\n elif opcion == \\\"D\\\":\\n listarUsuario()\\n else:\\n print(\\\"La opción: \\\" + opcion + \\\" ingresada no existe\\\")\\n\\ndef registarUsuario():\\n nombre_usuario = input(\\\"Ingrese el nombre del usuario: \\\")\\n password_usuario = input(\\\"Ingrese el password del usuario: \\\")\\n\\n mantenimiento.usuario.insertarDatos(nombre_usuario, password_usuario)\\n\\ndef actualizarUsuario():\\n id_usuario=input(\\\"Ingrese el id del usuario: \\\")\\n nombre_usuario=input(\\\"Ingrese el nombre del usuario: \\\")\\n\\n mantenimiento.usuario.actualizarDatos(id_usuario,nombre_usuario)\\n\\ndef eliminarUsuario():\\n id_usuario=input(\\\"Ingrese el id del usuario: \\\")\\n\\n mantenimiento.usuario.eliminarDatos(id_usuario)\\n\\ndef listarUsuario():\\n mantenimiento.usuario.leerDatos()\\n# FIN-REGIÓN MANTENIMIENTO DE PRODUCTOS\\n\\n#INICIO-REGIÓN VENDEDORES\\n\\ndef opcionesClientes():\\n print(\\\"*************MANT.CLIENTES************\\\")\\n print(\\\"A=> Registra\\\")\\n print(\\\"B=> Actualiza\\\")\\n print(\\\"C=> Elimina\\\")\\n print(\\\"D=> lista\\\")\\n print(\\\"****************************************\\\")\\n\\n opcion = input(\\\"Ingrese una opción:\\\")\\n\\n if opcion == \\\"A\\\":\\n registarCliente()\\n elif opcion == \\\"B\\\":\\n actualizarCliente()\\n elif opcion == \\\"C\\\":\\n eliminarCliente()\\n elif opcion == \\\"D\\\":\\n listarCliente()\\n else:\\n print(\\\"La opción: \\\" + opcion + \\\" ingresada no existe\\\")\\n\\ndef registarCliente():\\n nombre_cliente = input(\\\"Ingrese el nombre del cliente: \\\")\\n ruc_cliente = input(\\\"Ingrese el ruc del cliente: \\\")\\n direccion_cliente = input(\\\"Ingrese la dirección del cliente: \\\")\\n\\n mantenimiento.cliente.insertarDatos(nombre_cliente,ruc_cliente,direccion_cliente)\\n\\ndef actualizarCliente():\\n id_cliente = input(\\\"Ingrese el id del cliente: \\\")\\n nombre_cliente = input(\\\"Ingrese el nombre del cliente: \\\")\\n\\n mantenimiento.cliente.actualizarDatos(id_cliente, nombre_cliente)\\n\\ndef eliminarCliente():\\n id_cliente = input(\\\"Ingrese el id del cliente: \\\")\\n\\n mantenimiento.cliente.eliminarDatos(id_cliente)\\n\\ndef listarCliente():\\n mantenimiento.cliente.leerDatos()\\n\\n#FIN-REGIÓN CLIENTES\\n\\n#INICIO-REGIÓN VENDEDORES\\ndef opcionesVendedores():\\n print(\\\"*************MANT.VENDEDORES************\\\")\\n print(\\\"A=> Registra\\\")\\n print(\\\"B=> Actualiza\\\")\\n print(\\\"C=> Elimina\\\")\\n print(\\\"D=> lista\\\")\\n print(\\\"****************************************\\\")\\n\\n opcion = input(\\\"Ingrese una opción:\\\")\\n\\n if opcion == \\\"A\\\":\\n registarVendedor()\\n elif opcion == \\\"B\\\":\\n actualizarVendedor()\\n elif opcion == \\\"C\\\":\\n eliminarVendedor()\\n elif opcion == \\\"D\\\":\\n listarVendedor()\\n else:\\n print(\\\"La opción: \\\" + opcion + \\\" ingresada no existe\\\")\\n\\ndef registarVendedor():\\n nombre_vendedor = input(\\\"Ingrese el nombre del vendedor: \\\")\\n\\n mantenimiento.vendedor.insertarDatos(nombre_vendedor)\\n\\ndef actualizarVendedor():\\n id_vendedor=input(\\\"Ingrese el id del vendedor: \\\")\\n\\n mantenimiento.vendedor.actualizarDatos(id_vendedor)\\n\\ndef eliminarVendedor():\\n id_vendedor=input(\\\"Ingrese el id del vendedor: \\\")\\n\\n mantenimiento.vendedor.eliminarDatos(id_vendedor)\\n\\ndef listarVendedor():\\n mantenimiento.vendedor.leerDatos()\\n\\n#FIN-REGIÓN VENDEDORES\\n\\ndef opcionesVenta():\\n print(\\\"*************VENTA*************\\\")\\n print(\\\"A=>REGISTRAR\\\")\\n print(\\\"B=>LISTAR\\\")\\n print(\\\"****************************************\\\")\\n opcion = input(\\\"Ingrese opcion:\\\")\\n\\n if opcion == \\\"A\\\":\\n registraPedido()\\n elif opcion == \\\"B\\\":\\n listaPedido()\\n else:\\n print(\\\"La opción: \\\" + opcion + \\\" ingresada no existe\\\")\\n\\ndef registraPedido():\\n \\n id_cliente= input(\\\"Ingrese el id cliente: \\\")\\n id_usuario = input(\\\"Ingrese el id usuario: \\\")\\n id_vendedor=input(\\\"Ingrese el id vendedor: \\\")\\n fecha = datetime.datetime.now()\\n\\n id_producto=input(\\\"Ingrese el id producto: \\\")\\n cantidad=input(\\\"Ingrese el cantidad: \\\")\\n precio_unitario=input(\\\"Ingrese el precio: \\\")\\n \\n ventas.pedido_cabecera.insertarDatos(id_cliente,id_usuario,id_vendedor,\\\"%s/%s/%s\\\" % (fecha.year,fecha.month, fecha.day),id_producto,cantidad,precio_unitario)\\n\\n\\nload()\\n\"\n },\n {\n \"alpha_fraction\": 0.6405228972434998,\n \"alphanum_fraction\": 0.6797385811805725,\n \"avg_line_length\": 24.5,\n \"blob_id\": \"19b0cf0cc0405ac298435a232032e070804f4a50\",\n \"content_id\": \"2988c61c557b10f9616feeb5d4ef70b18a448774\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 153,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 6,\n \"path\": \"/proy_ventas/data/clsConexion.py\",\n \"repo_name\": \"gambini16/proy_ventas\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import mysql.connector\\n\\n\\ndef conexion():\\n con = mysql.connector.connect(user=\\\"root\\\", password=\\\"\\\", host=\\\"127.0.0.1\\\", database=\\\"ventas\\\")\\n return con\\n\"\n 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like","value":[{"alpha_fraction":0.8039215803146362,"alphanum_fraction":0.8039215803146362,"avg_line_length":37.25,"blob_id":"43eec295498987d61fe4330d0ce7f794e5dccf35","content_id":"495a718c3da8b7aea577ceaf1fc0339dd5dc32f5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":153,"license_type":"no_license","max_line_length":62,"num_lines":4,"path":"/README.md","repo_name":"adtech-scott/samsara-sfmta","src_encoding":"UTF-8","text":"# SFMTA - Samsara Integration with Route Cheking\n\nModified By Scott for route checking. Original work by Samsara\nPlease contact support@samsara.com for more details\n"},{"alpha_fraction":0.6854981184005737,"alphanum_fraction":0.6936293840408325,"avg_line_length":30.08351707458496,"blob_id":"c05739005b27d577cddb3cb1f5e466ad1c667127","content_id":"61f4bcf823e098452fc228c954e08d1b7792fefc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":14143,"license_type":"no_license","max_line_length":156,"num_lines":455,"path":"/application.py","repo_name":"adtech-scott/samsara-sfmta","src_encoding":"UTF-8","text":"#! /bin/python\n#\n# --- Samsara // SFMTA integration ---\n#\n# Contact support@samsara.com with any issues or questions\n#\n#\n# Notes on logging:\n# - Implemented logging using Python's logging module\n# - Info level messages are logged for most function entrances and exits\n# - Did not implement these messages for functions that are called in the the ThreadPool\n#\n# Notes on exception handling\n# - API requests are wrapped in try/catch blocks\n# - any exception that occurs is logged as a warning\n# - in the event of an exception we retry the request for MAX_RETRIES attempts\n# - in the event we reach MAX_RETRIES, an error email is sent, and we log as an error\n#\n# Notes on error emails\n# - error emails are sent via the parameters set in the following environment variables\n# --- SFMTA_ERROR_FROM_EMAIL, SFMTA_ERROR_TO_EMAIL, SFMTA_ERROR_FROM_PASSWORD\n# - error emails are sent at most once for every ERROR_EMAIL_DELAY\n# --- this is to avoid repeated emails in the case of a persistent failure\n\n##############################\n#\n# Imports\n#\n##############################\n\nimport boto3\nfrom collections import OrderedDict\nfrom flask import Flask, render_template, request, url_for, redirect\nimport itertools\nimport json\nimport logging\nimport math\nfrom math import radians, cos, sin, asin, sqrt, atan2\nfrom multiprocessing.dummy import Pool as ThreadPool \nimport os\nimport requests\nimport smtplib\nimport sys\nimport time\nimport traceback\nimport urllib\n\n##############################\n#\n# Config Variables\n#\n##############################\n\napplication = Flask(__name__)\n\nVEHICLE_SHEETS_JSON_URL = 'https://spreadsheets.google.com/feeds/list/' + os.environ['SFMTA_VEHICLE_GOOGLE_SHEETS_KEY'] + '/od6/public/values?alt=json'\nSAMSARA_LOCATIONS_URL = 'https://api.samsara.com/v1/fleet/locations?access_token=' + os.environ['SAMSARA_SFMTA_API_TOKEN']\nFREQUENCY = 5\t\t\t\t# SFMTA requires GPS ping frequency of once every 5 seconds\nDISTANCE_THRESHOLD = 50\t\t# Consider vehicle is at a SFMTA Allowed Stop if less than 50 meters away from it\nMAX_RETRIES = 10\t\t\t# number of times to retry an action that results in an exception\nLAST_ERROR_EMAIL_TIME = 0\t# initial time value to update when first error email is sent\nERROR_EMAIL_DELAY = 7200\t# send an error email maximum of once every two hours\nSAMSARA_SFMTA_S3 = os.environ['SAMSARA_SFMTA_S3_BUCKET']\n\nif 'SFMTA_DEBUG' in os.environ and os.environ['SFMTA_DEBUG'] == '1':\n\tapplication.debug = True\n\tSFMTA_URL = 'https://stageservices.sfmta.com/shuttle/api'\nelse:\n\tSFMTA_URL = 'https://services.sfmta.com/shuttle/api'\n\n# set the logging level\nlogging.basicConfig(filename = \"sfmta.log\", \n\t\t\t\t\tlevel = logging.DEBUG,\n\t\t\t\t\tformat=\"%(asctime)s:%(levelname)s:%(message)s\")\n\ns3 = boto3.resource('s3', region_name = 'us-west-2')\n\n##############################\n#\n# Global Variables\n#\n##############################\n\nvehicle_ids = set()\nplacards = {}\nlicense_plates = {}\nvehicle_names = {}\n\nvehicle_lat = {}\nvehicle_long = {}\nvehicle_onTrip = {}\nvehicle_timestamp_ms = {}\n\n##############################\n#\n# Helper Functions\n#\n##############################\n\n#Healthcheck URL for AWS\n@application.route('/admin/healthcheck')\ndef healthcheck():\n\treturn \"Hello World!\" \n# end healthcheck\n\n\n\n# Great Circle Distance between two lat/longs\ndef distance(origin_lat, origin_long, dest_lat, dest_long):\n\tradius = 6371 * 1000 # meters\n\n\tlat1 = origin_lat\n\tlon1 = origin_long\n\n\tlat2 = dest_lat\n\tlon2 = dest_long\n\n\tif lat1 is None or lon1 is None or lat2 is None or lon2 is None:\n\t\treturn 99999\n\n\tdlat = radians(lat2-lat1)\n\tdlon = radians(lon2-lon1)\n\ta = sin(dlat/2) * sin(dlat/2) + cos(radians(lat1)) \\\n\t\t* cos(radians(lat2)) * sin(dlon/2) * sin(dlon/2)\n\tc = 2 * atan2(sqrt(a), sqrt(1-a))\n\td = radius * c\n\n\treturn d\n# end distance\n\n\n\n# Gets vehicle info from a Google Sheet, and updates global variables\ndef get_vehicle_details(url):\n\tlogging.info(\"Starting get_vehicle_details\")\n\ttry:\n\t\tresponse = urllib.urlopen(url)\n\n\t\t# there should be a function like r.raise_for_status in the urllib module\n\t\tif response.getcode() != 200:\n\t\t\tprint 'Google Sheet returned error - ' + str(response.getcode())\n\t\t\tprint response.read()\n\t\t\tprint 'Continuing loop'\n\n\t\telse:\n\t\t\tjson_data = json.loads(response.read())\n\n\t\t\tvehicle_ids.clear()\n\n\t\t\tfor entry in json_data['feed']['entry']:\n\t\t\t\tvehicle_id = entry['gsx$samsaradeviceid']['$t']\n\t\t\t\tvehicle_ids.add(vehicle_id)\n\n\t\t\t\tplacards[vehicle_id] = entry['gsx$vehicleplacardnumber']['$t']\n\t\t\t\tlicense_plates[vehicle_id] = entry['gsx$licenseplatenumber']['$t']\n\t\t\t\tvehicle_names[vehicle_id] = entry['gsx$vehicleidname']['$t']\n\t\tlogging.info(\"Finished get_vehicle_details\")\n\t\treturn \"Success\"\n\texcept Exception as e:\n\t\treturn \"Error reading vehicle details from Google Sheet\\n\" + str(e)\n# end get_vehicle_details\n\n\n\n# sends an email with the given body and subject\n# to/from email addresses are defined as environment variables\ndef send_error_email(email_body, email_subject):\n\tlogging.info(\"Starting send_error_email\")\n\tformatted_lines = traceback.format_exc().splitlines()\n\tfor j in formatted_lines:\n\t\temail_body += j\n\tmessage = 'Subject: %s\\n\\n%s' % (email_subject, email_body)\n\n\tfrom_email = os.environ['SFMTA_ERROR_FROM_EMAIL']\n\tto_email = os.environ['SFMTA_ERROR_TO_EMAIL']\n\n\ts = smtplib.SMTP('smtp.gmail.com', 587)\n\ts.ehlo() \n\ts.starttls() \n\ts.login(from_email, os.environ['SFMTA_ERROR_FROM_PASSWORD'])\n\n\t# Send email and close the connection\n\ts.sendmail(from_email, to_email, message)\n\ts.quit()\n\tlogging.info(\"Finished send_error_email\")\n# end send_error_email\n\n\n\n# Pull SFMTA Allowed Stops and store in S3\n@application.route('/get_sfmta_stops', methods=['GET', 'POST'])\ndef get_sfmta_stops():\n\theaders = {'accept': 'application/json', 'content-type': 'application/json'}\n\ttry:\n\t\tr = requests.get(SFMTA_URL+'/AllowedStops', headers = headers)\n\t\ts3.Object(SAMSARA_SFMTA_S3,'allowed_stops.json').put(Body=r.text)\n\t\treturn \"SFMTA Allowed Stops updated\"\n\texcept Exception as e:\n\t\treturn \"Error updating SFMTA Allowed Stops\\n\" + str(e)\n# end get_sfmta_stops\n\n\n\n# Check if a location is near one of the SFMTA AllowedStops - if yes, return the stop ID, else return 9999\ndef find_stop_id(stop_lat, stop_long):\n\ttry:\n\t\tallowed_stops_object = s3.Object(SAMSARA_SFMTA_S3,'allowed_stops.json')\n\t\tallowed_stops_json = json.loads(allowed_stops_object.get()['Body'].read().decode('utf-8'))\n\n\t\tclosest_stop_id = 9999\n\t\tmin_distance = 99999\n\n\t\tfor stop in allowed_stops_json['Stops']['Stop']:\n\t\t\tcurrent_stop_id = stop['StopId']\n\t\t\tcurrent_stop_lat = stop['StopLocationLatitude']\n\t\t\tcurrent_stop_long = stop['StopLocationLongitude']\n\n\t\t\tcurr_distance = distance(current_stop_lat,current_stop_long,stop_lat,stop_long)\n\n\t\t\tif(curr_distance < min_distance):\n\t\t\t\tmin_distance = curr_distance\n\t\t\t\tclosest_stop_id = current_stop_id\n\n\t\tif (min_distance <= DISTANCE_THRESHOLD ):\n\t\t\tstop_id = closest_stop_id\n\t\telse:\n\t\t\tstop_id = 9999\n\n\t\treturn stop_id\n\texcept Exception as e:\n\t\treturn \"Error loading data from S3 bucket\\n\" + str(e)\n# end find_stop_id\n\n\n\n##############################\n#\n# Samsara API Functions\n#\n##############################\n\n# get all vehicle telematics data from Samsara\ndef get_all_vehicle_data():\n\tlogging.info(\"Starting get_all_vehicle_data\")\n\n\tget_vehicle_details(VEHICLE_SHEETS_JSON_URL)\n\n\tgroup_payload = { \"groupId\" : int(os.environ['SAMSARA_SFMTA_GROUP_ID']) }\n\n\t# attempt to pull data from Samsara API for maximum of MAX_RETRIES attempts\n\tlocal_retries = MAX_RETRIES\n\twhile local_retries > 0:\n\t\ttry:\n\t\t\tr = requests.post(SAMSARA_LOCATIONS_URL, data = json.dumps(group_payload))\n\n\t\t\t# raise an exception if we get a bad HTTP response\n\t\t\tr.raise_for_status()\n\n\t\t\tlocations_json = r.json()\n\n\t\t\tfor vehicle in locations_json['vehicles']:\n\t\t\t\tvehicle_id = str(vehicle['id']).decode(\"utf-8\")\n\t\t\t\tvehicle_lat[vehicle_id] = vehicle['latitude']\n\t\t\t\tvehicle_long[vehicle_id] = vehicle['longitude']\n\t\t\t\tvehicle_onTrip[vehicle_id] = vehicle['onTrip']\n\n\t\t\tlogging.info(\"Finished get_all_vehicle_data\")\n\t\t\treturn \"Success\"\n\n\t\texcept Exception as e:\n\t\t\tlogging.warning('Error getting data from Samsara API -- ' + str(e))\n\t\t\tlogging.warning('Retrying request, %s retries remaining', local_retries)\n\t\t\tlocal_retries -= 1\n\n\t# confirm that we have used MAX_RETRIES attempts\n\tif local_retries == 0:\n\t\tcurrent_error_time = time.time()\n\t\tglobal LAST_ERROR_EMAIL_TIME\n\n\t\t# send error email if more than 2 hours has passed since last error email\n\t\tif (current_error_time - LAST_ERROR_EMAIL_TIME) > ERROR_EMAIL_DELAY:\n\t\t\temail_body = 'There was an error pulling data from Samsara API - please check logs\\n\\n'\n\t\t\temail_subject = 'Error sending data to SFMTA'\n\t\t\tsend_error_email(email_body, email_subject)\n\t\t\tLAST_ERROR_EMAIL_TIME = current_error_time\n\t\t\tlogging.error('Error email sent at ' + str(current_error_time))\n\t\treturn \"Failure\"\n# end get_all_vehicle_data\n\n\n\n##############################\n#\n# SFMTA API Functions\n#\n##############################\n\n# build the payload to send to sfmta for the given vehicle and time\ndef build_sfmta_payload(vehicle_id, current_time):\n\n\tsfmta_payload = OrderedDict()\n\n\tsfmta_payload['TechProviderId'] = int(os.environ['SFMTA_TECH_PROVIDER_ID'])\n\tsfmta_payload['ShuttleCompanyId'] = os.environ['SFMTA_SHUTTLE_COMPANY_ID']\n\tsfmta_payload['VehiclePlacardNum'] = placards[vehicle_id]\n\tsfmta_payload['LicensePlateNum'] = license_plates[vehicle_id]\n\n\tif(vehicle_onTrip[vehicle_id] == True):\n\t\tvehicle_status = 1\n\t\tstop_id = 9999\n\telse:\n\t\tvehicle_status = 2\n\t\tstop_id = find_stop_id(vehicle_lat[vehicle_id], vehicle_long[vehicle_id])\n\n\n\tsfmta_payload['StopId'] = stop_id\n\tsfmta_payload['VehicleStatus'] = vehicle_status\n\n\tsfmta_payload['LocationLatitude'] = vehicle_lat[vehicle_id]\n\tsfmta_payload['LocationLongitude'] = vehicle_long[vehicle_id]\n\tsfmta_payload['TimeStampLocal'] = time.strftime('%Y-%m-%dT%H:%M:%S', time.localtime(current_time))\n\n\treturn sfmta_payload\n# end build_sfmta_payload\n\n\n\n# build payload, convert to json, then push to sfmta api\ndef push_vehicle_data(vehicle_id, current_time):\n\tsfmta_payload = build_sfmta_payload(vehicle_id, current_time)\n\tsfmta_payload_json = json.dumps(sfmta_payload)\n\n\tsfmta_telemetry_url = SFMTA_URL + '/Telemetry/'\n\n\theaders = {'content-type': 'application/json'}\n\n\tlocal_retries = MAX_RETRIES\n\twhile local_retries > 0:\n\t\ttry:\n\t\t\tr = requests.post(sfmta_telemetry_url, data = sfmta_payload_json, auth= (os.environ['SFMTA_USERNAME'], os.environ['SFMTA_PASSWORD']), headers = headers )\n\t\t\tr.raise_for_status()\n\t\t\treturn \"Success\"\n\t\texcept Exception as e:\n\t\t\tlogging.warning('Error pushing data to SFMTA API -- ' + str(e))\n\t\t\tlogging.warning('Retrying request, %s retries remaining', local_retries)\n\t\t\tlocal_retries -= 1\n\t# confirm that we have used MAX_RETRIES attempts\n\tif local_retries == 0:\n\t\tcurrent_error_time = time.time()\n\t\tglobal LAST_ERROR_EMAIL_TIME\n\n\t\t# send error email if more than 2 hours has passed since last error email\n\t\tif (current_error_time - LAST_ERROR_EMAIL_TIME) > ERROR_EMAIL_DELAY:\n\t\t\temail_body = 'There was an error pushing data to SFMTA API - please check logs\\n\\n'\n\t\t\temail_subject = 'Error sending data to SFMTA'\n\t\t\tsend_error_email(email_body, email_subject)\n\t\t\tLAST_ERROR_EMAIL_TIME = current_error_time\n\t\t\tlogging.error('Error email sent at ' + str(current_error_time))\n\t\t\treturn \"Failure\"\n# end push_vehicle_data\n\n\n\n# unpacks the list passed to this function to arguments, and calls function\n# Convert `f([1,2])` to `f(1,2)` call\ndef push_vehicle_data_star(vehicle_data):\n\treturn push_vehicle_data(*vehicle_data)\n# end push_vehicle_data_star\n\n\n\n# Push all vehicle data to SFMTA\n#\n# creates one thread for each vehicle, push payload for each vehicle in parallel\ndef push_all_vehicle_data(current_time):\n\tlogging.info('Starting push_all_vehicle_data')\n\n\tnum_vehicles = len(vehicle_ids)\n\tpool = ThreadPool(num_vehicles)\n\n\tlogging.info('Mapping push_vehicle_data_star to each thread in the pool')\n\tpool.map(push_vehicle_data_star, itertools.izip(vehicle_ids,itertools.repeat(current_time)))\n\tlogging.info('All threads finished executing push_vehicle_data')\n\n\tpool.close()\n\tpool.join()\n\n\tlogging.info('Finished push_all_vehicle_data')\n\treturn\n# end push_all_vehicle_data\n\n\n\n##############################\n#\n# Main Application Loop\n#\n##############################\n\n# Infinite loop that pulls & pushes data every 5 seconds - this is called once in a cron job at a specific time (when system is activated)\n# If any errors are generated, emails are sent\n@application.route('/push_sfmta')\ndef push_all_data():\n\tlogging.info('Starting push_all_data')\n\n\ttime.tzset()\n\t\n\twhile True:\n\t\ttry:\n\t\t\tstart_time = time.time()\n\t\t\tcurrent_time = start_time\n\t\t\tlogging.info(\"Processing loop for time = \" + str(current_time))\n\n\t\t\t# if the function fails then skip to next iteration of the loop\n\t\t\tif get_all_vehicle_data() != 'Success':\n\t\t\t\tcontinue\n\n\t\t\tsamsara_api_time = time.time() - start_time\n\t\t\tlogging.info(\"Samsara API time taken = \"+ str(samsara_api_time))\n\n\t\t\tpush_all_vehicle_data(current_time)\n\n\t\t\tend_time = time.time()\n\t\t\ttime_spent = end_time - start_time\n\t\t\tlogging.info(\"Total time taken for this iteration = \" + str(time_spent))\n\t\t\tlogging.info(\"Completed processing loop for time = \" + str(current_time))\n\n\t\t\ttime_to_wait = FREQUENCY - time_spent\n\n\t\t\tif time_to_wait >= 0:\n\t\t\t\ttime.sleep(time_to_wait)\n\n\t\t# handle any other exceptions that may be generated\n\t\t# in the event of an exception continue to the next iteration of the loop\n\t\texcept Exception as e:\n\t\t\tcurrent_error_time = time.time()\n\t\t\tglobal LAST_ERROR_EMAIL_TIME\n\n\t\t\t# send error email if more than 2 hours has passed since last error email\n\t\t\tif (current_error_time - LAST_ERROR_EMAIL_TIME) > ERROR_EMAIL_DELAY:\n\t\t\t\temail_body = \"There was an error sending data to SFMTA - please check logs\\n\\n\"\n\t\t\t\temail_subject = \"Error sending data to SFMTA\"\n\t\t\t\tsend_error_email(email_body, email_subject)\n\t\t\t\tLAST_ERROR_EMAIL_TIME = current_error_time\n\t\t\t\tlogging.error('Error email sent at ' + str(current_error_time))\n\t\t\tcontinue\n# end push_all_data\n\t\t\n\n\nif __name__ == '__main__':\n\tif 'SFMTA_LOCALHOST' in os.environ and os.environ['SFMTA_LOCALHOST'] == '1':\n\t\tapplication.run(use_reloader=False)\n\telse:\n\t\tapplication.run('0.0.0.0')\n"},{"alpha_fraction":0.39534884691238403,"alphanum_fraction":0.6744186282157898,"avg_line_length":13.666666984558105,"blob_id":"1eacf76f495765ff6638f536ec95dc7adbe8f744","content_id":"404426dfab5113196505d3981f06d4de34fcb376","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":43,"license_type":"no_license","max_line_length":16,"num_lines":3,"path":"/requirements.txt","repo_name":"adtech-scott/samsara-sfmta","src_encoding":"UTF-8","text":"boto3==1.4.4\nFlask==0.10.1\nrequests==2.10.0"}],"string":"[\n {\n \"alpha_fraction\": 0.8039215803146362,\n \"alphanum_fraction\": 0.8039215803146362,\n \"avg_line_length\": 37.25,\n \"blob_id\": \"43eec295498987d61fe4330d0ce7f794e5dccf35\",\n \"content_id\": \"495a718c3da8b7aea577ceaf1fc0339dd5dc32f5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 153,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 4,\n \"path\": \"/README.md\",\n \"repo_name\": \"adtech-scott/samsara-sfmta\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# SFMTA - Samsara Integration with Route Cheking\\n\\nModified By Scott for route checking. Original work by Samsara\\nPlease contact support@samsara.com for more details\\n\"\n },\n {\n \"alpha_fraction\": 0.6854981184005737,\n \"alphanum_fraction\": 0.6936293840408325,\n \"avg_line_length\": 30.08351707458496,\n \"blob_id\": \"c05739005b27d577cddb3cb1f5e466ad1c667127\",\n \"content_id\": \"61f4bcf823e098452fc228c954e08d1b7792fefc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 14143,\n \"license_type\": \"no_license\",\n \"max_line_length\": 156,\n \"num_lines\": 455,\n \"path\": \"/application.py\",\n \"repo_name\": \"adtech-scott/samsara-sfmta\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#! /bin/python\\n#\\n# --- Samsara // SFMTA integration ---\\n#\\n# Contact support@samsara.com with any issues or questions\\n#\\n#\\n# Notes on logging:\\n# - Implemented logging using Python's logging module\\n# - Info level messages are logged for most function entrances and exits\\n# - Did not implement these messages for functions that are called in the the ThreadPool\\n#\\n# Notes on exception handling\\n# - API requests are wrapped in try/catch blocks\\n# - any exception that occurs is logged as a warning\\n# - in the event of an exception we retry the request for MAX_RETRIES attempts\\n# - in the event we reach MAX_RETRIES, an error email is sent, and we log as an error\\n#\\n# Notes on error emails\\n# - error emails are sent via the parameters set in the following environment variables\\n# --- SFMTA_ERROR_FROM_EMAIL, SFMTA_ERROR_TO_EMAIL, SFMTA_ERROR_FROM_PASSWORD\\n# - error emails are sent at most once for every ERROR_EMAIL_DELAY\\n# --- this is to avoid repeated emails in the case of a persistent failure\\n\\n##############################\\n#\\n# Imports\\n#\\n##############################\\n\\nimport boto3\\nfrom collections import OrderedDict\\nfrom flask import Flask, render_template, request, url_for, redirect\\nimport itertools\\nimport json\\nimport logging\\nimport math\\nfrom math import radians, cos, sin, asin, sqrt, atan2\\nfrom multiprocessing.dummy import Pool as ThreadPool \\nimport os\\nimport requests\\nimport smtplib\\nimport sys\\nimport time\\nimport traceback\\nimport urllib\\n\\n##############################\\n#\\n# Config Variables\\n#\\n##############################\\n\\napplication = Flask(__name__)\\n\\nVEHICLE_SHEETS_JSON_URL = 'https://spreadsheets.google.com/feeds/list/' + os.environ['SFMTA_VEHICLE_GOOGLE_SHEETS_KEY'] + '/od6/public/values?alt=json'\\nSAMSARA_LOCATIONS_URL = 'https://api.samsara.com/v1/fleet/locations?access_token=' + os.environ['SAMSARA_SFMTA_API_TOKEN']\\nFREQUENCY = 5\\t\\t\\t\\t# SFMTA requires GPS ping frequency of once every 5 seconds\\nDISTANCE_THRESHOLD = 50\\t\\t# Consider vehicle is at a SFMTA Allowed Stop if less than 50 meters away from it\\nMAX_RETRIES = 10\\t\\t\\t# number of times to retry an action that results in an exception\\nLAST_ERROR_EMAIL_TIME = 0\\t# initial time value to update when first error email is sent\\nERROR_EMAIL_DELAY = 7200\\t# send an error email maximum of once every two hours\\nSAMSARA_SFMTA_S3 = os.environ['SAMSARA_SFMTA_S3_BUCKET']\\n\\nif 'SFMTA_DEBUG' in os.environ and os.environ['SFMTA_DEBUG'] == '1':\\n\\tapplication.debug = True\\n\\tSFMTA_URL = 'https://stageservices.sfmta.com/shuttle/api'\\nelse:\\n\\tSFMTA_URL = 'https://services.sfmta.com/shuttle/api'\\n\\n# set the logging level\\nlogging.basicConfig(filename = \\\"sfmta.log\\\", \\n\\t\\t\\t\\t\\tlevel = logging.DEBUG,\\n\\t\\t\\t\\t\\tformat=\\\"%(asctime)s:%(levelname)s:%(message)s\\\")\\n\\ns3 = boto3.resource('s3', region_name = 'us-west-2')\\n\\n##############################\\n#\\n# Global Variables\\n#\\n##############################\\n\\nvehicle_ids = set()\\nplacards = {}\\nlicense_plates = {}\\nvehicle_names = {}\\n\\nvehicle_lat = {}\\nvehicle_long = {}\\nvehicle_onTrip = {}\\nvehicle_timestamp_ms = {}\\n\\n##############################\\n#\\n# Helper Functions\\n#\\n##############################\\n\\n#Healthcheck URL for AWS\\n@application.route('/admin/healthcheck')\\ndef healthcheck():\\n\\treturn \\\"Hello World!\\\" \\n# end healthcheck\\n\\n\\n\\n# Great Circle Distance between two lat/longs\\ndef distance(origin_lat, origin_long, dest_lat, dest_long):\\n\\tradius = 6371 * 1000 # meters\\n\\n\\tlat1 = origin_lat\\n\\tlon1 = origin_long\\n\\n\\tlat2 = dest_lat\\n\\tlon2 = dest_long\\n\\n\\tif lat1 is None or lon1 is None or lat2 is None or lon2 is None:\\n\\t\\treturn 99999\\n\\n\\tdlat = radians(lat2-lat1)\\n\\tdlon = radians(lon2-lon1)\\n\\ta = sin(dlat/2) * sin(dlat/2) + cos(radians(lat1)) \\\\\\n\\t\\t* cos(radians(lat2)) * sin(dlon/2) * sin(dlon/2)\\n\\tc = 2 * atan2(sqrt(a), sqrt(1-a))\\n\\td = radius * c\\n\\n\\treturn d\\n# end distance\\n\\n\\n\\n# Gets vehicle info from a Google Sheet, and updates global variables\\ndef get_vehicle_details(url):\\n\\tlogging.info(\\\"Starting get_vehicle_details\\\")\\n\\ttry:\\n\\t\\tresponse = urllib.urlopen(url)\\n\\n\\t\\t# there should be a function like r.raise_for_status in the urllib module\\n\\t\\tif response.getcode() != 200:\\n\\t\\t\\tprint 'Google Sheet returned error - ' + str(response.getcode())\\n\\t\\t\\tprint response.read()\\n\\t\\t\\tprint 'Continuing loop'\\n\\n\\t\\telse:\\n\\t\\t\\tjson_data = json.loads(response.read())\\n\\n\\t\\t\\tvehicle_ids.clear()\\n\\n\\t\\t\\tfor entry in json_data['feed']['entry']:\\n\\t\\t\\t\\tvehicle_id = entry['gsx$samsaradeviceid']['$t']\\n\\t\\t\\t\\tvehicle_ids.add(vehicle_id)\\n\\n\\t\\t\\t\\tplacards[vehicle_id] = entry['gsx$vehicleplacardnumber']['$t']\\n\\t\\t\\t\\tlicense_plates[vehicle_id] = entry['gsx$licenseplatenumber']['$t']\\n\\t\\t\\t\\tvehicle_names[vehicle_id] = entry['gsx$vehicleidname']['$t']\\n\\t\\tlogging.info(\\\"Finished get_vehicle_details\\\")\\n\\t\\treturn \\\"Success\\\"\\n\\texcept Exception as e:\\n\\t\\treturn \\\"Error reading vehicle details from Google Sheet\\\\n\\\" + str(e)\\n# end get_vehicle_details\\n\\n\\n\\n# sends an email with the given body and subject\\n# to/from email addresses are defined as environment variables\\ndef send_error_email(email_body, email_subject):\\n\\tlogging.info(\\\"Starting send_error_email\\\")\\n\\tformatted_lines = traceback.format_exc().splitlines()\\n\\tfor j in formatted_lines:\\n\\t\\temail_body += j\\n\\tmessage = 'Subject: %s\\\\n\\\\n%s' % (email_subject, email_body)\\n\\n\\tfrom_email = os.environ['SFMTA_ERROR_FROM_EMAIL']\\n\\tto_email = os.environ['SFMTA_ERROR_TO_EMAIL']\\n\\n\\ts = smtplib.SMTP('smtp.gmail.com', 587)\\n\\ts.ehlo() \\n\\ts.starttls() \\n\\ts.login(from_email, os.environ['SFMTA_ERROR_FROM_PASSWORD'])\\n\\n\\t# Send email and close the connection\\n\\ts.sendmail(from_email, to_email, message)\\n\\ts.quit()\\n\\tlogging.info(\\\"Finished send_error_email\\\")\\n# end send_error_email\\n\\n\\n\\n# Pull SFMTA Allowed Stops and store in S3\\n@application.route('/get_sfmta_stops', methods=['GET', 'POST'])\\ndef get_sfmta_stops():\\n\\theaders = {'accept': 'application/json', 'content-type': 'application/json'}\\n\\ttry:\\n\\t\\tr = requests.get(SFMTA_URL+'/AllowedStops', headers = headers)\\n\\t\\ts3.Object(SAMSARA_SFMTA_S3,'allowed_stops.json').put(Body=r.text)\\n\\t\\treturn \\\"SFMTA Allowed Stops updated\\\"\\n\\texcept Exception as e:\\n\\t\\treturn \\\"Error updating SFMTA Allowed Stops\\\\n\\\" + str(e)\\n# end get_sfmta_stops\\n\\n\\n\\n# Check if a location is near one of the SFMTA AllowedStops - if yes, return the stop ID, else return 9999\\ndef find_stop_id(stop_lat, stop_long):\\n\\ttry:\\n\\t\\tallowed_stops_object = s3.Object(SAMSARA_SFMTA_S3,'allowed_stops.json')\\n\\t\\tallowed_stops_json = json.loads(allowed_stops_object.get()['Body'].read().decode('utf-8'))\\n\\n\\t\\tclosest_stop_id = 9999\\n\\t\\tmin_distance = 99999\\n\\n\\t\\tfor stop in allowed_stops_json['Stops']['Stop']:\\n\\t\\t\\tcurrent_stop_id = stop['StopId']\\n\\t\\t\\tcurrent_stop_lat = stop['StopLocationLatitude']\\n\\t\\t\\tcurrent_stop_long = stop['StopLocationLongitude']\\n\\n\\t\\t\\tcurr_distance = distance(current_stop_lat,current_stop_long,stop_lat,stop_long)\\n\\n\\t\\t\\tif(curr_distance < min_distance):\\n\\t\\t\\t\\tmin_distance = curr_distance\\n\\t\\t\\t\\tclosest_stop_id = current_stop_id\\n\\n\\t\\tif (min_distance <= DISTANCE_THRESHOLD ):\\n\\t\\t\\tstop_id = closest_stop_id\\n\\t\\telse:\\n\\t\\t\\tstop_id = 9999\\n\\n\\t\\treturn stop_id\\n\\texcept Exception as e:\\n\\t\\treturn \\\"Error loading data from S3 bucket\\\\n\\\" + str(e)\\n# end find_stop_id\\n\\n\\n\\n##############################\\n#\\n# Samsara API Functions\\n#\\n##############################\\n\\n# get all vehicle telematics data from Samsara\\ndef get_all_vehicle_data():\\n\\tlogging.info(\\\"Starting get_all_vehicle_data\\\")\\n\\n\\tget_vehicle_details(VEHICLE_SHEETS_JSON_URL)\\n\\n\\tgroup_payload = { \\\"groupId\\\" : int(os.environ['SAMSARA_SFMTA_GROUP_ID']) }\\n\\n\\t# attempt to pull data from Samsara API for maximum of MAX_RETRIES attempts\\n\\tlocal_retries = MAX_RETRIES\\n\\twhile local_retries > 0:\\n\\t\\ttry:\\n\\t\\t\\tr = requests.post(SAMSARA_LOCATIONS_URL, data = json.dumps(group_payload))\\n\\n\\t\\t\\t# raise an exception if we get a bad HTTP response\\n\\t\\t\\tr.raise_for_status()\\n\\n\\t\\t\\tlocations_json = r.json()\\n\\n\\t\\t\\tfor vehicle in locations_json['vehicles']:\\n\\t\\t\\t\\tvehicle_id = str(vehicle['id']).decode(\\\"utf-8\\\")\\n\\t\\t\\t\\tvehicle_lat[vehicle_id] = vehicle['latitude']\\n\\t\\t\\t\\tvehicle_long[vehicle_id] = vehicle['longitude']\\n\\t\\t\\t\\tvehicle_onTrip[vehicle_id] = vehicle['onTrip']\\n\\n\\t\\t\\tlogging.info(\\\"Finished get_all_vehicle_data\\\")\\n\\t\\t\\treturn \\\"Success\\\"\\n\\n\\t\\texcept Exception as e:\\n\\t\\t\\tlogging.warning('Error getting data from Samsara API -- ' + str(e))\\n\\t\\t\\tlogging.warning('Retrying request, %s retries remaining', local_retries)\\n\\t\\t\\tlocal_retries -= 1\\n\\n\\t# confirm that we have used MAX_RETRIES attempts\\n\\tif local_retries == 0:\\n\\t\\tcurrent_error_time = time.time()\\n\\t\\tglobal LAST_ERROR_EMAIL_TIME\\n\\n\\t\\t# send error email if more than 2 hours has passed since last error email\\n\\t\\tif (current_error_time - LAST_ERROR_EMAIL_TIME) > ERROR_EMAIL_DELAY:\\n\\t\\t\\temail_body = 'There was an error pulling data from Samsara API - please check logs\\\\n\\\\n'\\n\\t\\t\\temail_subject = 'Error sending data to SFMTA'\\n\\t\\t\\tsend_error_email(email_body, email_subject)\\n\\t\\t\\tLAST_ERROR_EMAIL_TIME = current_error_time\\n\\t\\t\\tlogging.error('Error email sent at ' + str(current_error_time))\\n\\t\\treturn \\\"Failure\\\"\\n# end get_all_vehicle_data\\n\\n\\n\\n##############################\\n#\\n# SFMTA API Functions\\n#\\n##############################\\n\\n# build the payload to send to sfmta for the given vehicle and time\\ndef build_sfmta_payload(vehicle_id, current_time):\\n\\n\\tsfmta_payload = OrderedDict()\\n\\n\\tsfmta_payload['TechProviderId'] = int(os.environ['SFMTA_TECH_PROVIDER_ID'])\\n\\tsfmta_payload['ShuttleCompanyId'] = os.environ['SFMTA_SHUTTLE_COMPANY_ID']\\n\\tsfmta_payload['VehiclePlacardNum'] = placards[vehicle_id]\\n\\tsfmta_payload['LicensePlateNum'] = license_plates[vehicle_id]\\n\\n\\tif(vehicle_onTrip[vehicle_id] == True):\\n\\t\\tvehicle_status = 1\\n\\t\\tstop_id = 9999\\n\\telse:\\n\\t\\tvehicle_status = 2\\n\\t\\tstop_id = find_stop_id(vehicle_lat[vehicle_id], vehicle_long[vehicle_id])\\n\\n\\n\\tsfmta_payload['StopId'] = stop_id\\n\\tsfmta_payload['VehicleStatus'] = vehicle_status\\n\\n\\tsfmta_payload['LocationLatitude'] = vehicle_lat[vehicle_id]\\n\\tsfmta_payload['LocationLongitude'] = vehicle_long[vehicle_id]\\n\\tsfmta_payload['TimeStampLocal'] = time.strftime('%Y-%m-%dT%H:%M:%S', time.localtime(current_time))\\n\\n\\treturn sfmta_payload\\n# end build_sfmta_payload\\n\\n\\n\\n# build payload, convert to json, then push to sfmta api\\ndef push_vehicle_data(vehicle_id, current_time):\\n\\tsfmta_payload = build_sfmta_payload(vehicle_id, current_time)\\n\\tsfmta_payload_json = json.dumps(sfmta_payload)\\n\\n\\tsfmta_telemetry_url = SFMTA_URL + '/Telemetry/'\\n\\n\\theaders = {'content-type': 'application/json'}\\n\\n\\tlocal_retries = MAX_RETRIES\\n\\twhile local_retries > 0:\\n\\t\\ttry:\\n\\t\\t\\tr = requests.post(sfmta_telemetry_url, data = sfmta_payload_json, auth= (os.environ['SFMTA_USERNAME'], os.environ['SFMTA_PASSWORD']), headers = headers )\\n\\t\\t\\tr.raise_for_status()\\n\\t\\t\\treturn \\\"Success\\\"\\n\\t\\texcept Exception as e:\\n\\t\\t\\tlogging.warning('Error pushing data to SFMTA API -- ' + str(e))\\n\\t\\t\\tlogging.warning('Retrying request, %s retries remaining', local_retries)\\n\\t\\t\\tlocal_retries -= 1\\n\\t# confirm that we have used MAX_RETRIES attempts\\n\\tif local_retries == 0:\\n\\t\\tcurrent_error_time = time.time()\\n\\t\\tglobal LAST_ERROR_EMAIL_TIME\\n\\n\\t\\t# send error email if more than 2 hours has passed since last error email\\n\\t\\tif (current_error_time - LAST_ERROR_EMAIL_TIME) > ERROR_EMAIL_DELAY:\\n\\t\\t\\temail_body = 'There was an error pushing data to SFMTA API - please check logs\\\\n\\\\n'\\n\\t\\t\\temail_subject = 'Error sending data to SFMTA'\\n\\t\\t\\tsend_error_email(email_body, email_subject)\\n\\t\\t\\tLAST_ERROR_EMAIL_TIME = current_error_time\\n\\t\\t\\tlogging.error('Error email sent at ' + str(current_error_time))\\n\\t\\t\\treturn \\\"Failure\\\"\\n# end push_vehicle_data\\n\\n\\n\\n# unpacks the list passed to this function to arguments, and calls function\\n# Convert `f([1,2])` to `f(1,2)` call\\ndef push_vehicle_data_star(vehicle_data):\\n\\treturn push_vehicle_data(*vehicle_data)\\n# end push_vehicle_data_star\\n\\n\\n\\n# Push all vehicle data to SFMTA\\n#\\n# creates one thread for each vehicle, push payload for each vehicle in parallel\\ndef push_all_vehicle_data(current_time):\\n\\tlogging.info('Starting push_all_vehicle_data')\\n\\n\\tnum_vehicles = len(vehicle_ids)\\n\\tpool = ThreadPool(num_vehicles)\\n\\n\\tlogging.info('Mapping push_vehicle_data_star to each thread in the pool')\\n\\tpool.map(push_vehicle_data_star, itertools.izip(vehicle_ids,itertools.repeat(current_time)))\\n\\tlogging.info('All threads finished executing push_vehicle_data')\\n\\n\\tpool.close()\\n\\tpool.join()\\n\\n\\tlogging.info('Finished push_all_vehicle_data')\\n\\treturn\\n# end push_all_vehicle_data\\n\\n\\n\\n##############################\\n#\\n# Main Application Loop\\n#\\n##############################\\n\\n# Infinite loop that pulls & pushes data every 5 seconds - this is called once in a cron job at a specific time (when system is activated)\\n# If any errors are generated, emails are sent\\n@application.route('/push_sfmta')\\ndef push_all_data():\\n\\tlogging.info('Starting push_all_data')\\n\\n\\ttime.tzset()\\n\\t\\n\\twhile True:\\n\\t\\ttry:\\n\\t\\t\\tstart_time = time.time()\\n\\t\\t\\tcurrent_time = start_time\\n\\t\\t\\tlogging.info(\\\"Processing loop for time = \\\" + str(current_time))\\n\\n\\t\\t\\t# if the function fails then skip to next iteration of the loop\\n\\t\\t\\tif get_all_vehicle_data() != 'Success':\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tsamsara_api_time = time.time() - start_time\\n\\t\\t\\tlogging.info(\\\"Samsara API time taken = \\\"+ str(samsara_api_time))\\n\\n\\t\\t\\tpush_all_vehicle_data(current_time)\\n\\n\\t\\t\\tend_time = time.time()\\n\\t\\t\\ttime_spent = end_time - start_time\\n\\t\\t\\tlogging.info(\\\"Total time taken for this iteration = \\\" + str(time_spent))\\n\\t\\t\\tlogging.info(\\\"Completed processing loop for time = \\\" + str(current_time))\\n\\n\\t\\t\\ttime_to_wait = FREQUENCY - time_spent\\n\\n\\t\\t\\tif time_to_wait >= 0:\\n\\t\\t\\t\\ttime.sleep(time_to_wait)\\n\\n\\t\\t# handle any other exceptions that may be generated\\n\\t\\t# in the event of an exception continue to the next iteration of the loop\\n\\t\\texcept Exception as e:\\n\\t\\t\\tcurrent_error_time = time.time()\\n\\t\\t\\tglobal LAST_ERROR_EMAIL_TIME\\n\\n\\t\\t\\t# send error email if more than 2 hours has passed since last error email\\n\\t\\t\\tif (current_error_time - LAST_ERROR_EMAIL_TIME) > ERROR_EMAIL_DELAY:\\n\\t\\t\\t\\temail_body = \\\"There was an error sending data to SFMTA - please check logs\\\\n\\\\n\\\"\\n\\t\\t\\t\\temail_subject = \\\"Error sending data to SFMTA\\\"\\n\\t\\t\\t\\tsend_error_email(email_body, email_subject)\\n\\t\\t\\t\\tLAST_ERROR_EMAIL_TIME = current_error_time\\n\\t\\t\\t\\tlogging.error('Error email sent at ' + str(current_error_time))\\n\\t\\t\\tcontinue\\n# end push_all_data\\n\\t\\t\\n\\n\\nif __name__ == '__main__':\\n\\tif 'SFMTA_LOCALHOST' in os.environ and os.environ['SFMTA_LOCALHOST'] == '1':\\n\\t\\tapplication.run(use_reloader=False)\\n\\telse:\\n\\t\\tapplication.run('0.0.0.0')\\n\"\n },\n {\n \"alpha_fraction\": 0.39534884691238403,\n \"alphanum_fraction\": 0.6744186282157898,\n \"avg_line_length\": 13.666666984558105,\n \"blob_id\": \"1eacf76f495765ff6638f536ec95dc7adbe8f744\",\n \"content_id\": \"404426dfab5113196505d3981f06d4de34fcb376\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 43,\n \"license_type\": \"no_license\",\n \"max_line_length\": 16,\n \"num_lines\": 3,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"adtech-scott/samsara-sfmta\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"boto3==1.4.4\\nFlask==0.10.1\\nrequests==2.10.0\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979152,"cells":{"repo_name":{"kind":"string","value":"scottherold/django_login_and_registration"},"repo_url":{"kind":"string","value":"https://github.com/scottherold/django_login_and_registration"},"snapshot_id":{"kind":"string","value":"067ff9d15b35defb1512dc5a4d8863425e5ee839"},"revision_id":{"kind":"string","value":"9f8d081ea6bf995a2fbb1448b0917cb8528b4ee7"},"directory_id":{"kind":"string","value":"4bc876b62d26e4096376677ddc9763e705d81f26"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-04-02T23:19:54.818513","string":"2020-04-02T23:19:54.818513"},"revision_date":{"kind":"timestamp","value":"2018-10-26T16:34:47","string":"2018-10-26T16:34:47"},"committer_date":{"kind":"timestamp","value":"2018-10-26T16:34:47","string":"2018-10-26T16:34:47"},"github_id":{"kind":"number","value":154863721,"string":"154,863,721"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6167247295379639,"alphanum_fraction":0.6167247295379639,"avg_line_length":26.899999618530273,"blob_id":"25951564ee9dac9d06cee5ba239d5e8207fede7e","content_id":"9a9d8cdc0e038268710ea7be2f7344a1f8b629fc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":287,"license_type":"no_license","max_line_length":58,"num_lines":10,"path":"/apps/login_and_registration/urls.py","repo_name":"scottherold/django_login_and_registration","src_encoding":"UTF-8","text":"from django.conf.urls import url\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n url(r'^$', views.index),\r\n url(r'^create/$', views.create),\r\n url(r'^login/validate_login/$', views.validate_login),\r\n url(r'^login/success/$', views.success),\r\n url(r'^logout/$', views.logout),\r\n]"},{"alpha_fraction":0.616814136505127,"alphanum_fraction":0.616814136505127,"avg_line_length":40.86419677734375,"blob_id":"23e6a551754c131b59ce4ddf624066b4a42e0058","content_id":"13b8be4fff18e322ef052a3d04f49cfc45bb62de","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3390,"license_type":"no_license","max_line_length":120,"num_lines":81,"path":"/apps/login_and_registration/views.py","repo_name":"scottherold/django_login_and_registration","src_encoding":"UTF-8","text":"from django.shortcuts import render, HttpResponse, redirect\nfrom django.contrib import messages\nfrom .models import User\nimport bcrypt\n\n# Create your views here.\n\ndef index(request):\n if 'logged_in' in request.session:\n if request.session['logged_in'] == True:\n user = User.objects.get(id=request.session['id'])\n if user.email != request.session['email']:\n request.session.clear()\n request.session.modified = True\n return render(request, 'login_and_registration/index.html')\n else:\n return render(request, 'login_and_registration/index.html', { \"user\": User.objects.get(id=user.id) } )\n else:\n return render(request, 'login_and_registration/index.html')\n else:\n return render(request, 'login_and_registration/index.html')\n\ndef create(request):\n errors = User.objects.basic_validator(request.POST)\n if len(errors):\n for key, value in errors.items():\n messages.error(request, value)\n return redirect('/')\n else:\n user = User.objects.create()\n user.first_name = request.POST['first_name']\n user.last_name = request.POST['last_name']\n user.email = request.POST['email']\n user.password = bcrypt.hashpw(request.POST['password'].encode(), bcrypt.gensalt())\n user.save()\n request.session['new_registration'] = True\n request.session['id'] = user.id\n request.session['email'] = user.email\n return redirect(\"/login/success\")\n\ndef validate_login(request):\n users = User.objects.all()\n try:\n user = users.get(email=request.POST['email'])\n if bcrypt.checkpw(request.POST['password'].encode(), user.password.encode()):\n request.session['logged_in'] = True\n request.session['id'] = user.id\n request.session['email'] = user.email\n if 'new_registration' in request.session:\n del request.session['new_registration']\n request.session.modified = True\n return redirect('/login/success')\n else:\n messages.error(request, \"Invalid password!\")\n return redirect('/')\n except User.DoesNotExist:\n messages.error(request, \"Email does not exist! Please register to continue\")\n return redirect('/')\n\ndef success(request):\n if 'new_registration' in request.session:\n request.session['logged_in'] = True\n user = User.objects.get(id=request.session['id']) \n return render(request, 'login_and_registration/success.html', { \"user\": User.objects.get(id=user.id) })\n elif 'logged_in' in request.session:\n if request.session['logged_in'] == True:\n user = User.objects.get(id=request.session['id'])\n if user.email != request.session['email']:\n request.session.clear()\n return render(request, 'login_and_registration/success.html')\n else:\n return render(request, 'login_and_registration/success.html', { \"user\": User.objects.get(id=user.id) } )\n else:\n return render(request, 'login_and_registration/success.html')\n else:\n return render(request, 'login_and_registration/success.html')\n\ndef logout(request):\n request.session.clear()\n request.session.modified = True\n return redirect('/')"}],"string":"[\n {\n \"alpha_fraction\": 0.6167247295379639,\n \"alphanum_fraction\": 0.6167247295379639,\n \"avg_line_length\": 26.899999618530273,\n \"blob_id\": \"25951564ee9dac9d06cee5ba239d5e8207fede7e\",\n \"content_id\": \"9a9d8cdc0e038268710ea7be2f7344a1f8b629fc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 287,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 10,\n \"path\": \"/apps/login_and_registration/urls.py\",\n \"repo_name\": \"scottherold/django_login_and_registration\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.conf.urls import url\\r\\nfrom . import views\\r\\n\\r\\nurlpatterns = [\\r\\n url(r'^$', views.index),\\r\\n url(r'^create/$', views.create),\\r\\n url(r'^login/validate_login/$', views.validate_login),\\r\\n url(r'^login/success/$', views.success),\\r\\n url(r'^logout/$', views.logout),\\r\\n]\"\n },\n {\n \"alpha_fraction\": 0.616814136505127,\n \"alphanum_fraction\": 0.616814136505127,\n \"avg_line_length\": 40.86419677734375,\n \"blob_id\": \"23e6a551754c131b59ce4ddf624066b4a42e0058\",\n \"content_id\": \"13b8be4fff18e322ef052a3d04f49cfc45bb62de\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3390,\n \"license_type\": \"no_license\",\n \"max_line_length\": 120,\n \"num_lines\": 81,\n \"path\": \"/apps/login_and_registration/views.py\",\n \"repo_name\": \"scottherold/django_login_and_registration\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render, HttpResponse, redirect\\nfrom django.contrib import messages\\nfrom .models import User\\nimport bcrypt\\n\\n# Create your views here.\\n\\ndef index(request):\\n if 'logged_in' in request.session:\\n if request.session['logged_in'] == True:\\n user = User.objects.get(id=request.session['id'])\\n if user.email != request.session['email']:\\n request.session.clear()\\n request.session.modified = True\\n return render(request, 'login_and_registration/index.html')\\n else:\\n return render(request, 'login_and_registration/index.html', { \\\"user\\\": User.objects.get(id=user.id) } )\\n else:\\n return render(request, 'login_and_registration/index.html')\\n else:\\n return render(request, 'login_and_registration/index.html')\\n\\ndef create(request):\\n errors = User.objects.basic_validator(request.POST)\\n if len(errors):\\n for key, value in errors.items():\\n messages.error(request, value)\\n return redirect('/')\\n else:\\n user = User.objects.create()\\n user.first_name = request.POST['first_name']\\n user.last_name = request.POST['last_name']\\n user.email = request.POST['email']\\n user.password = bcrypt.hashpw(request.POST['password'].encode(), bcrypt.gensalt())\\n user.save()\\n request.session['new_registration'] = True\\n request.session['id'] = user.id\\n request.session['email'] = user.email\\n return redirect(\\\"/login/success\\\")\\n\\ndef validate_login(request):\\n users = User.objects.all()\\n try:\\n user = users.get(email=request.POST['email'])\\n if bcrypt.checkpw(request.POST['password'].encode(), user.password.encode()):\\n request.session['logged_in'] = True\\n request.session['id'] = user.id\\n request.session['email'] = user.email\\n if 'new_registration' in request.session:\\n del request.session['new_registration']\\n request.session.modified = True\\n return redirect('/login/success')\\n else:\\n messages.error(request, \\\"Invalid password!\\\")\\n return redirect('/')\\n except User.DoesNotExist:\\n messages.error(request, \\\"Email does not exist! Please register to continue\\\")\\n return redirect('/')\\n\\ndef success(request):\\n if 'new_registration' in request.session:\\n request.session['logged_in'] = True\\n user = User.objects.get(id=request.session['id']) \\n return render(request, 'login_and_registration/success.html', { \\\"user\\\": User.objects.get(id=user.id) })\\n elif 'logged_in' in request.session:\\n if request.session['logged_in'] == True:\\n user = User.objects.get(id=request.session['id'])\\n if user.email != request.session['email']:\\n request.session.clear()\\n return render(request, 'login_and_registration/success.html')\\n else:\\n return render(request, 'login_and_registration/success.html', { \\\"user\\\": User.objects.get(id=user.id) } )\\n else:\\n return render(request, 'login_and_registration/success.html')\\n else:\\n return render(request, 'login_and_registration/success.html')\\n\\ndef logout(request):\\n request.session.clear()\\n request.session.modified = True\\n return redirect('/')\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979153,"cells":{"repo_name":{"kind":"string","value":"samesense/msCentipede_example"},"repo_url":{"kind":"string","value":"https://github.com/samesense/msCentipede_example"},"snapshot_id":{"kind":"string","value":"23efa4884ce1f57ed7b96bb309a8c51d5061e9ca"},"revision_id":{"kind":"string","value":"6eb87504ad9eb0bdb8affbf2d36f945ef8925173"},"directory_id":{"kind":"string","value":"010aa85fc600d8e4c8b2a73940f26b729beedd9c"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2019-05-13T00:18:45.836167","string":"2019-05-13T00:18:45.836167"},"revision_date":{"kind":"timestamp","value":"2016-07-28T18:22:50","string":"2016-07-28T18:22:50"},"committer_date":{"kind":"timestamp","value":"2016-07-28T18:22:50","string":"2016-07-28T18:22:50"},"github_id":{"kind":"number","value":64418353,"string":"64,418,353"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7708333134651184,"alphanum_fraction":0.8055555820465088,"avg_line_length":47.33333206176758,"blob_id":"3feb6458f0a5dad13d7fb654e9dc3d3efd006bd9","content_id":"db2e60076d58c0ce609f39457aa7a75ac5e5ef2f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":144,"license_type":"no_license","max_line_length":65,"num_lines":3,"path":"/README.md","repo_name":"samesense/msCentipede_example","src_encoding":"UTF-8","text":"Run example from\n[msCentipede](http://rajanil.github.io/msCentipede/). You need to\ndownload the GM12878 bam files and install msCentipede first."},{"alpha_fraction":0.5523996353149414,"alphanum_fraction":0.5866796970367432,"avg_line_length":50.04999923706055,"blob_id":"80ad6fe37983951c892f66d7b32137e95a828d27","content_id":"c13e2419e08f7003730f662d7c520d6bf618dc6e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2042,"license_type":"no_license","max_line_length":112,"num_lines":40,"path":"/code/sf.py","repo_name":"samesense/msCentipede_example","src_encoding":"UTF-8","text":"\"\"\"Run msCentipede example.\"\"\"\nimport sys\nsys.path.append('/home/evansj/me/projects/me/tool_dirs/')\nfrom tools import *\n\nPY27 = '/nas/is1/perry/miniconda3/envs/p27/bin/python'\nCENT = '/home/evansj/me/tools/msCentipede/'\nCALL_BINDING = CENT + 'call_binding.py'\n\nrule sort:\n input: '../data/ex/{sample}.bam'\n output: '../data/ex/sort/{sample}.bam'\n shell: 'samtools sort {input} -T {LUSTRE}tmpsamsort.{wildcards.sample} -o {output}'\n\nrule index:\n input: '../data/ex/sort/{sample}.bam'\n output: '../data/ex/sort/{sample}.bai'\n shell: '{JAVA} -jar {PICARD}BuildBamIndex.jar INPUT={input} TMP_DIR={LUSTRE}'\n\nrule exLearn:\n input: ls = expand('{f}', f=(CENT + 'test/Ctcf_chr10_motifs.txt.gz', \\\n '../data/ex/sort/wgEncodeUwDnaseGm12878AlnRep1.bam', \\\n '../data/ex/sort/wgEncodeUwDnaseGm12878AlnRep2.bam')),\n bias = expand('../data/ex/sort/{s}.bai', \\\n s=('wgEncodeUwDnaseGm12878AlnRep1', \\\n 'wgEncodeUwDnaseGm12878AlnRep2') )\n output: log = '../work/CTCF_chr10_motifs_msCentipede_log.txt',\n model = '../work/CTCF_chr10_motifs_msCentipede_model_parameters.pkl'\n shell: '{PY27} {CALL_BINDING} --log_file {output.log} --model_file {output.model} --task learn {input.ls}'\n\nrule exInfer:\n input: ls = expand('{f}', f=(CENT + 'test/Ctcf_chr10_motifs.txt.gz', \\\n '../data/ex/sort/wgEncodeUwDnaseGm12878AlnRep1.bam', \\\n '../data/ex/sort/wgEncodeUwDnaseGm12878AlnRep2.bam')),\n bias = expand('../data/ex/sort/{s}.bai', \\\n s=('wgEncodeUwDnaseGm12878AlnRep1', \\\n 'wgEncodeUwDnaseGm12878AlnRep2') ),\n model = '../work/CTCF_chr10_motifs_msCentipede_model_parameters.pkl'\n output: '../work/CTCF_chr10_motifs_msCentipede_post.gz'\n shell: '{PY27} {CALL_BINDING} --posterior_file {output} --model_file {input.model} --task infer {input.ls}'\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7708333134651184,\n \"alphanum_fraction\": 0.8055555820465088,\n \"avg_line_length\": 47.33333206176758,\n \"blob_id\": \"3feb6458f0a5dad13d7fb654e9dc3d3efd006bd9\",\n \"content_id\": \"db2e60076d58c0ce609f39457aa7a75ac5e5ef2f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 144,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 3,\n \"path\": \"/README.md\",\n \"repo_name\": \"samesense/msCentipede_example\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Run example from\\n[msCentipede](http://rajanil.github.io/msCentipede/). You need to\\ndownload the GM12878 bam files and install msCentipede first.\"\n },\n {\n \"alpha_fraction\": 0.5523996353149414,\n \"alphanum_fraction\": 0.5866796970367432,\n \"avg_line_length\": 50.04999923706055,\n \"blob_id\": \"80ad6fe37983951c892f66d7b32137e95a828d27\",\n \"content_id\": \"c13e2419e08f7003730f662d7c520d6bf618dc6e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2042,\n \"license_type\": \"no_license\",\n \"max_line_length\": 112,\n \"num_lines\": 40,\n \"path\": \"/code/sf.py\",\n \"repo_name\": \"samesense/msCentipede_example\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Run msCentipede example.\\\"\\\"\\\"\\nimport sys\\nsys.path.append('/home/evansj/me/projects/me/tool_dirs/')\\nfrom tools import *\\n\\nPY27 = '/nas/is1/perry/miniconda3/envs/p27/bin/python'\\nCENT = '/home/evansj/me/tools/msCentipede/'\\nCALL_BINDING = CENT + 'call_binding.py'\\n\\nrule sort:\\n input: '../data/ex/{sample}.bam'\\n output: '../data/ex/sort/{sample}.bam'\\n shell: 'samtools sort {input} -T {LUSTRE}tmpsamsort.{wildcards.sample} -o {output}'\\n\\nrule index:\\n input: '../data/ex/sort/{sample}.bam'\\n output: '../data/ex/sort/{sample}.bai'\\n shell: '{JAVA} -jar {PICARD}BuildBamIndex.jar INPUT={input} TMP_DIR={LUSTRE}'\\n\\nrule exLearn:\\n input: ls = expand('{f}', f=(CENT + 'test/Ctcf_chr10_motifs.txt.gz', \\\\\\n '../data/ex/sort/wgEncodeUwDnaseGm12878AlnRep1.bam', \\\\\\n '../data/ex/sort/wgEncodeUwDnaseGm12878AlnRep2.bam')),\\n bias = expand('../data/ex/sort/{s}.bai', \\\\\\n s=('wgEncodeUwDnaseGm12878AlnRep1', \\\\\\n 'wgEncodeUwDnaseGm12878AlnRep2') )\\n output: log = '../work/CTCF_chr10_motifs_msCentipede_log.txt',\\n model = '../work/CTCF_chr10_motifs_msCentipede_model_parameters.pkl'\\n shell: '{PY27} {CALL_BINDING} --log_file {output.log} --model_file {output.model} --task learn {input.ls}'\\n\\nrule exInfer:\\n input: ls = expand('{f}', f=(CENT + 'test/Ctcf_chr10_motifs.txt.gz', \\\\\\n '../data/ex/sort/wgEncodeUwDnaseGm12878AlnRep1.bam', \\\\\\n '../data/ex/sort/wgEncodeUwDnaseGm12878AlnRep2.bam')),\\n bias = expand('../data/ex/sort/{s}.bai', \\\\\\n s=('wgEncodeUwDnaseGm12878AlnRep1', \\\\\\n 'wgEncodeUwDnaseGm12878AlnRep2') ),\\n model = '../work/CTCF_chr10_motifs_msCentipede_model_parameters.pkl'\\n output: '../work/CTCF_chr10_motifs_msCentipede_post.gz'\\n shell: '{PY27} {CALL_BINDING} --posterior_file {output} --model_file {input.model} --task infer {input.ls}'\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979154,"cells":{"repo_name":{"kind":"string","value":"Subik25/Interesting"},"repo_url":{"kind":"string","value":"https://github.com/Subik25/Interesting"},"snapshot_id":{"kind":"string","value":"1f4ac9402ca89bb316d5346b7bf3bd4e7a94d8af"},"revision_id":{"kind":"string","value":"e78aca7b7c92f675cc887413f9908942336b6eea"},"directory_id":{"kind":"string","value":"1fd0edecc3499fef8b342cd984fa38fa3b242899"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-08-24T23:02:21.976790","string":"2023-08-24T23:02:21.976790"},"revision_date":{"kind":"timestamp","value":"2021-10-21T16:06:08","string":"2021-10-21T16:06:08"},"committer_date":{"kind":"timestamp","value":"2021-10-21T16:06:08","string":"2021-10-21T16:06:08"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6660341620445251,"alphanum_fraction":0.6869069933891296,"avg_line_length":39.61538314819336,"blob_id":"2522667da91412c63c580912d4cc549ee352ca1e","content_id":"b10333cf5691625f0c4da8831b92b86308cd9025","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":527,"license_type":"no_license","max_line_length":94,"num_lines":13,"path":"/генератор паролей.py","repo_name":"Subik25/Interesting","src_encoding":"UTF-8","text":"user_name=''\nwhile len(user_name.strip())<1:\n user_name=input('What is your name?\\n')\nprint('It was nice to meet you,' + user_name + '!')\nimport random\ndictionary = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklnopqrstuvwxyz1234567890+-/*!&$#?=@<>(),._'\nnumber = int(input('Please enter the desired number of passwords:\\n'))\nlength = int(input('Please enter password length:\\n'))\nfor n in range(number):\n password =''\n for i in range(length):\n password += random.choice(dictionary)\n print(\"Your password:\",'',password)"}],"string":"[\n {\n \"alpha_fraction\": 0.6660341620445251,\n \"alphanum_fraction\": 0.6869069933891296,\n \"avg_line_length\": 39.61538314819336,\n \"blob_id\": \"2522667da91412c63c580912d4cc549ee352ca1e\",\n \"content_id\": \"b10333cf5691625f0c4da8831b92b86308cd9025\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 527,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 13,\n \"path\": \"/генератор паролей.py\",\n \"repo_name\": \"Subik25/Interesting\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"user_name=''\\nwhile len(user_name.strip())<1:\\n user_name=input('What is your name?\\\\n')\\nprint('It was nice to meet you,' + user_name + '!')\\nimport random\\ndictionary = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklnopqrstuvwxyz1234567890+-/*!&$#?=@<>(),._'\\nnumber = int(input('Please enter the desired number of passwords:\\\\n'))\\nlength = int(input('Please enter password length:\\\\n'))\\nfor n in range(number):\\n password =''\\n for i in range(length):\\n password += random.choice(dictionary)\\n print(\\\"Your password:\\\",'',password)\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979155,"cells":{"repo_name":{"kind":"string","value":"Joao-Maria-Janeiro/SmartTicket"},"repo_url":{"kind":"string","value":"https://github.com/Joao-Maria-Janeiro/SmartTicket"},"snapshot_id":{"kind":"string","value":"01442dece3046cc3105d615157f8908be32e256d"},"revision_id":{"kind":"string","value":"557429278ea94b4b35d9ff15b856da055719854c"},"directory_id":{"kind":"string","value":"c69df28453ad90e050f54e3c3e72e2971b24aa27"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-04-13T19:45:02.407684","string":"2020-04-13T19:45:02.407684"},"revision_date":{"kind":"timestamp","value":"2019-01-17T12:29:00","string":"2019-01-17T12:29:00"},"committer_date":{"kind":"timestamp","value":"2019-01-17T12:29:00","string":"2019-01-17T12:29:00"},"github_id":{"kind":"number","value":163411730,"string":"163,411,730"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7662337422370911,"alphanum_fraction":0.7705627679824829,"avg_line_length":30.5,"blob_id":"431331ea40b01a198f419fca33a5834418d90078","content_id":"723802e132ed861a1aa61ba5c5ed7c4390e5a5ac","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":693,"license_type":"no_license","max_line_length":68,"num_lines":22,"path":"/backend/services/views.py","repo_name":"Joao-Maria-Janeiro/SmartTicket","src_encoding":"UTF-8","text":"from django.shortcuts import render\nfrom rest_framework import viewsets\nfrom .models import Service\nfrom .serializers import ServiceSerializer\nfrom django.http import JsonResponse\n\n\n# Create your views here.\nclass ServiceView(viewsets.ModelViewSet):\n queryset = Service.objects.all()\n serializer_class = ServiceSerializer\n\ndef incrementCurrentTicket(request, name):\n service = Service.objects.get(name=name)\n service.current_ticket += 1\n service.save()\n\ndef incrementLatestRetriviedTicket(request, name):\n service = Service.objects.get(name=name)\n service.latest_retrived_ticket += 1\n service.save()\n return JsonResponse({'ticket':service.latest_retrived_ticket-1})\n"},{"alpha_fraction":0.7585943341255188,"alphanum_fraction":0.7860962748527527,"avg_line_length":55.91304397583008,"blob_id":"68e4f838f23eb5e0261dd415753cce573588d1c0","content_id":"85577335c39102f935b489b7a295154a14987fed","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1309,"license_type":"no_license","max_line_length":400,"num_lines":23,"path":"/README.md","repo_name":"Joao-Maria-Janeiro/SmartTicket","src_encoding":"UTF-8","text":"# SmartTicket\n\n![Flutter](https://flutter.io/assets/flutter-lockup-4cb0ee072ab312e59784d9fbf4fb7ad42688a7fdaea1270ccf6bbf4f34b7e03f.svg \"Flutter\") <img src=\"https://www.django-rest-framework.org/img/logo.png\" width=\"100\">\n\n\nSmartTicket let's you be free of the ticket's waiting times. Now when you go to a supermarket or other ticket dependent services you can retrieve a ticket on your phone\nsimply by scanning a QRCode and keep track of the current ticket being served.\n\n## Technologies Used\n* Flutter\n* Django Rest Framework\n\n### The API's\nIn order to develop this platform I had to develop two API's using django rest framework.\nThis was to show that you can have multiple services/stores all in the same app, in this example I had a supermarket and the citizen card store.\n\n\n### The mobile App\nThis was developed using Flutter and by scaning a QRCode you would acess an API which would give all the ticekt based services available on the store (the owner of the API). After that you can choose all the services you want and you will get a ticket and the current ticket will be displayed in real time so you can go and do your shopping or other affairs without having to worry about the tickets.\n\n### API's:\n* https://smartticket-lojadocidadao.herokuapp.com/services/\n* https://smartticket.herokuapp.com/\n"},{"alpha_fraction":0.7321428656578064,"alphanum_fraction":0.7455357313156128,"avg_line_length":31,"blob_id":"60fdbb00f23394283a2eadc10dc2a93dff65c680","content_id":"762fcfcb6abd56b5661d85285ffbb5ddd62a0a95","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":224,"license_type":"no_license","max_line_length":50,"num_lines":7,"path":"/backend/services/models.py","repo_name":"Joao-Maria-Janeiro/SmartTicket","src_encoding":"UTF-8","text":"from django.db import models\n\n# Create your models here.\nclass Service(models.Model):\n name = models.CharField(max_length=200)\n latest_retrived_ticket = models.IntegerField()\n current_ticket = models.IntegerField()\n"},{"alpha_fraction":0.7231404781341553,"alphanum_fraction":0.7231404781341553,"avg_line_length":33.57143020629883,"blob_id":"053741d19ca95b0ccd685c9bf427cb955e81d92a","content_id":"86ddd210020306a8dbf0be3de98101a9e5916a72","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":242,"license_type":"no_license","max_line_length":75,"num_lines":7,"path":"/backend/services/serializers.py","repo_name":"Joao-Maria-Janeiro/SmartTicket","src_encoding":"UTF-8","text":"from rest_framework.serializers import ModelSerializer\nfrom .models import Service\n\nclass ServiceSerializer(ModelSerializer):\n class Meta:\n model = Service\n fields = ('id', 'name', 'latest_retrived_ticket', 'current_ticket')\n"},{"alpha_fraction":0.7599067687988281,"alphanum_fraction":0.7599067687988281,"avg_line_length":34.75,"blob_id":"53338749be825e6348f8cf99a11552a4bcf09dc1","content_id":"b3e61133e015c3158325ebc01352ad7b38b1b222","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":429,"license_type":"no_license","max_line_length":107,"num_lines":12,"path":"/backend/services/urls.py","repo_name":"Joao-Maria-Janeiro/SmartTicket","src_encoding":"UTF-8","text":"from django.urls import path, include\nfrom . import views\nfrom rest_framework import routers\n\nrouter = routers.DefaultRouter()\nrouter.register('services', views.ServiceView)\n\nurlpatterns = [\n path('', include(router.urls)),\n path('update/<str:name>', views.incrementCurrentTicket, name=\"incrementCurrentTicket\"),\n path('take/<str:name>', views.incrementLatestRetriviedTicket, name = \"incrementLatestRetriviedTicket\"),\n]\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7662337422370911,\n \"alphanum_fraction\": 0.7705627679824829,\n \"avg_line_length\": 30.5,\n \"blob_id\": \"431331ea40b01a198f419fca33a5834418d90078\",\n \"content_id\": \"723802e132ed861a1aa61ba5c5ed7c4390e5a5ac\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 693,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 22,\n \"path\": \"/backend/services/views.py\",\n \"repo_name\": \"Joao-Maria-Janeiro/SmartTicket\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render\\nfrom rest_framework import viewsets\\nfrom .models import Service\\nfrom .serializers import ServiceSerializer\\nfrom django.http import JsonResponse\\n\\n\\n# Create your views here.\\nclass ServiceView(viewsets.ModelViewSet):\\n queryset = Service.objects.all()\\n serializer_class = ServiceSerializer\\n\\ndef incrementCurrentTicket(request, name):\\n service = Service.objects.get(name=name)\\n service.current_ticket += 1\\n service.save()\\n\\ndef incrementLatestRetriviedTicket(request, name):\\n service = Service.objects.get(name=name)\\n service.latest_retrived_ticket += 1\\n service.save()\\n return JsonResponse({'ticket':service.latest_retrived_ticket-1})\\n\"\n },\n {\n \"alpha_fraction\": 0.7585943341255188,\n \"alphanum_fraction\": 0.7860962748527527,\n \"avg_line_length\": 55.91304397583008,\n \"blob_id\": \"68e4f838f23eb5e0261dd415753cce573588d1c0\",\n \"content_id\": \"85577335c39102f935b489b7a295154a14987fed\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1309,\n \"license_type\": \"no_license\",\n \"max_line_length\": 400,\n \"num_lines\": 23,\n \"path\": \"/README.md\",\n \"repo_name\": \"Joao-Maria-Janeiro/SmartTicket\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# SmartTicket\\n\\n![Flutter](https://flutter.io/assets/flutter-lockup-4cb0ee072ab312e59784d9fbf4fb7ad42688a7fdaea1270ccf6bbf4f34b7e03f.svg \\\"Flutter\\\") <img src=\\\"https://www.django-rest-framework.org/img/logo.png\\\" width=\\\"100\\\">\\n\\n\\nSmartTicket let's you be free of the ticket's waiting times. Now when you go to a supermarket or other ticket dependent services you can retrieve a ticket on your phone\\nsimply by scanning a QRCode and keep track of the current ticket being served.\\n\\n## Technologies Used\\n* Flutter\\n* Django Rest Framework\\n\\n### The API's\\nIn order to develop this platform I had to develop two API's using django rest framework.\\nThis was to show that you can have multiple services/stores all in the same app, in this example I had a supermarket and the citizen card store.\\n\\n\\n### The mobile App\\nThis was developed using Flutter and by scaning a QRCode you would acess an API which would give all the ticekt based services available on the store (the owner of the API). After that you can choose all the services you want and you will get a ticket and the current ticket will be displayed in real time so you can go and do your shopping or other affairs without having to worry about the tickets.\\n\\n### API's:\\n* https://smartticket-lojadocidadao.herokuapp.com/services/\\n* https://smartticket.herokuapp.com/\\n\"\n },\n {\n \"alpha_fraction\": 0.7321428656578064,\n \"alphanum_fraction\": 0.7455357313156128,\n \"avg_line_length\": 31,\n \"blob_id\": \"60fdbb00f23394283a2eadc10dc2a93dff65c680\",\n \"content_id\": \"762fcfcb6abd56b5661d85285ffbb5ddd62a0a95\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 224,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 7,\n \"path\": \"/backend/services/models.py\",\n \"repo_name\": \"Joao-Maria-Janeiro/SmartTicket\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.db import models\\n\\n# Create your models here.\\nclass Service(models.Model):\\n name = models.CharField(max_length=200)\\n latest_retrived_ticket = models.IntegerField()\\n current_ticket = models.IntegerField()\\n\"\n },\n {\n \"alpha_fraction\": 0.7231404781341553,\n \"alphanum_fraction\": 0.7231404781341553,\n \"avg_line_length\": 33.57143020629883,\n \"blob_id\": \"053741d19ca95b0ccd685c9bf427cb955e81d92a\",\n \"content_id\": \"86ddd210020306a8dbf0be3de98101a9e5916a72\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 242,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 7,\n \"path\": \"/backend/services/serializers.py\",\n \"repo_name\": \"Joao-Maria-Janeiro/SmartTicket\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from rest_framework.serializers import ModelSerializer\\nfrom .models import Service\\n\\nclass ServiceSerializer(ModelSerializer):\\n class Meta:\\n model = Service\\n fields = ('id', 'name', 'latest_retrived_ticket', 'current_ticket')\\n\"\n },\n {\n \"alpha_fraction\": 0.7599067687988281,\n \"alphanum_fraction\": 0.7599067687988281,\n \"avg_line_length\": 34.75,\n \"blob_id\": \"53338749be825e6348f8cf99a11552a4bcf09dc1\",\n \"content_id\": \"b3e61133e015c3158325ebc01352ad7b38b1b222\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 429,\n \"license_type\": \"no_license\",\n \"max_line_length\": 107,\n \"num_lines\": 12,\n \"path\": \"/backend/services/urls.py\",\n \"repo_name\": \"Joao-Maria-Janeiro/SmartTicket\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.urls import path, include\\nfrom . import views\\nfrom rest_framework import routers\\n\\nrouter = routers.DefaultRouter()\\nrouter.register('services', views.ServiceView)\\n\\nurlpatterns = [\\n path('', include(router.urls)),\\n path('update/<str:name>', views.incrementCurrentTicket, name=\\\"incrementCurrentTicket\\\"),\\n path('take/<str:name>', views.incrementLatestRetriviedTicket, name = \\\"incrementLatestRetriviedTicket\\\"),\\n]\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":5,"string":"5"}}},{"rowIdx":2979156,"cells":{"repo_name":{"kind":"string","value":"guxuan123456/DSG-SEG"},"repo_url":{"kind":"string","value":"https://github.com/guxuan123456/DSG-SEG"},"snapshot_id":{"kind":"string","value":"93810dd94ba5af7fe3d6a07466c5b168f54f8d96"},"revision_id":{"kind":"string","value":"5ddeaedd8575abd0db72db9a41f6b64442203d6c"},"directory_id":{"kind":"string","value":"c27d3112cbff44602cf7a65e9d5bd14599f8846a"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-03-31T10:03:02.069007","string":"2023-03-31T10:03:02.069007"},"revision_date":{"kind":"timestamp","value":"2021-04-01T06:36:15","string":"2021-04-01T06:36:15"},"committer_date":{"kind":"timestamp","value":"2021-04-01T06:36:15","string":"2021-04-01T06:36:15"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5315375328063965,"alphanum_fraction":0.5412371158599854,"avg_line_length":44.102500915527344,"blob_id":"ef19713e9a5582f1a244ca2d55e68ac2107fba68","content_id":"bd5c38a3926b1276e5bd4fc906f46cae6fc7c854","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":18042,"license_type":"no_license","max_line_length":111,"num_lines":400,"path":"/models/Treelstm/models.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\nfrom torch.nn import init\n\nfrom models.Treelstm import basic\nfrom models.Treelstm.utils import *\n\nclass AttnCombiner(nn.Module):\n def __init__(self, bidirectional, hidden_size, attention_size=128, dropout=0):\n super(AttnCombiner, self).__init__()\n self.num_directions = 2 if bidirectional else 1\n self.ws1 = nn.Linear(hidden_size * self.num_directions, attention_size, bias=False)\n self.ws2 = nn.Linear(attention_size, 1, bias=False)\n init.orthogonal(self.ws1.weight.data)\n init.orthogonal(self.ws2.weight.data)\n self.tanh = nn.Tanh()\n self.drop = nn.Dropout(dropout)\n\n def forward(self, hiddens):\n size = hiddens.size() # [bsz, len, in_dim]\n x_flat = hiddens.contiguous().view(-1, size[2]) # [bsz*len, in_dim]\n h_bar = self.tanh(self.ws1(self.drop(x_flat))) # [bsz*len, attn_hid]\n alphas = self.ws2(h_bar).view(size[0], size[1]) # [bsz, len]\n return alphas\n\n def __call__(self, *args, **kwargs):\n return self.forward(*args, **kwargs)\n\n\nclass BinaryTreeLSTMLayer(nn.Module):\n\n def __init__(self, hidden_dim):\n super(BinaryTreeLSTMLayer, self).__init__()\n self.hidden_dim = hidden_dim\n self.comp_linear = nn.Linear(in_features=2 * hidden_dim,\n out_features=5 * hidden_dim)\n self.reset_parameters()\n\n def reset_parameters(self):\n init.kaiming_normal(self.comp_linear.weight.data)\n init.constant(self.comp_linear.bias.data, val=0)\n\n def forward(self, left=None, right=None):\n \"\"\"\n Args:\n left: A (h_l, c_l) tuple, where each value has the size\n (batch_size, max_length, hidden_dim).\n right: A (h_r, c_r) tuple, where each value has the size\n (batch_size, max_length, hidden_dim).\n Returns:\n h, c: The hidden and cell state of the composed parent,\n each of which has the size\n (batch_size, max_length - 1, hidden_dim).\n \"\"\"\n\n hl, cl = left\n hr, cr = right\n hlr_cat = torch.cat([hl, hr], dim=2)\n treelstm_vector = basic.apply_nd(fn=self.comp_linear, input=hlr_cat)\n i, fl, fr, u, o = treelstm_vector.chunk(chunks=5, dim=2)\n c = (cl*(fl + 1).sigmoid() + cr*(fr + 1).sigmoid()\n + u.tanh()*i.sigmoid())\n h = o.sigmoid() * c.tanh()\n return h, c\n\n def __call__(self, *args, **kwargs):\n return self.forward(*args, **kwargs)\n\n\nclass TreeLSTMEncoder(nn.Module):\n\n def __init__(self, word_dim, hidden_dim, use_leaf_rnn, pooling_method, bidirectional):\n super(TreeLSTMEncoder, self).__init__()\n self.word_dim = word_dim\n self.hidden_dim = hidden_dim\n self.use_leaf_rnn = use_leaf_rnn\n self.pooling_method = pooling_method\n self.bidirectional = bidirectional\n\n assert not (self.bidirectional and not self.use_leaf_rnn)\n\n if use_leaf_rnn:\n self.leaf_rnn_cell = nn.LSTMCell(\n input_size=word_dim, hidden_size=hidden_dim)\n if bidirectional:\n self.leaf_rnn_cell_bw = nn.LSTMCell(\n input_size=word_dim, hidden_size=hidden_dim)\n else:\n self.word_linear = nn.Linear(in_features=word_dim,\n out_features=2 * hidden_dim)\n self.treelstm_layer = BinaryTreeLSTMLayer(hidden_dim * 2 if self.bidirectional else hidden_dim)\n\n def reset_parameters(self):\n if self.use_leaf_rnn:\n init.kaiming_normal(self.leaf_rnn_cell.weight_ih.data)\n init.orthogonal(self.leaf_rnn_cell.weight_hh.data)\n init.constant(self.leaf_rnn_cell.bias_ih.data, val=0)\n init.constant(self.leaf_rnn_cell.bias_hh.data, val=0)\n # Set forget bias to 1\n self.leaf_rnn_cell.bias_ih.data.chunk(4)[1].fill_(1)\n if self.bidirectional:\n init.kaiming_normal(self.leaf_rnn_cell_bw.weight_ih.data)\n init.orthogonal(self.leaf_rnn_cell_bw.weight_hh.data)\n init.constant(self.leaf_rnn_cell_bw.bias_ih.data, val=0)\n init.constant(self.leaf_rnn_cell_bw.bias_hh.data, val=0)\n # Set forget bias to 1\n self.leaf_rnn_cell_bw.bias_ih.data.chunk(4)[1].fill_(1)\n else:\n init.kaiming_normal(self.word_linear.weight.data)\n init.constant(self.word_linear.bias.data, val=0)\n self.treelstm_layer.reset_parameters()\n\n @staticmethod\n def update_state(old_state, new_state, done_mask):\n old_h, old_c = old_state\n new_h, new_c = new_state\n done_mask = done_mask.float().unsqueeze(1).unsqueeze(2).expand_as(new_h)\n h = done_mask * new_h + (1 - done_mask) * old_h[:, :-1, :]\n c = done_mask * new_c + (1 - done_mask) * old_c[:, :-1, :]\n return h, c\n\n def forward(self, *inp):\n pass\n\n\nclass GumbelTreeLSTMEncoder(TreeLSTMEncoder):\n\n def __init__(self, word_dim, hidden_dim, use_leaf_rnn, pooling_method, bidirectional, gumbel_temperature):\n super(GumbelTreeLSTMEncoder, self).__init__(\n word_dim, hidden_dim, use_leaf_rnn, pooling_method, bidirectional)\n self.gumbel_temperature = gumbel_temperature\n self.comp_query = nn.Parameter(torch.FloatTensor(hidden_dim * 2 if self.bidirectional else hidden_dim))\n if pooling_method == 'attention':\n self.combiner = AttnCombiner(bidirectional, hidden_dim)\n self.reset_parameters()\n\n def reset_parameters(self):\n super(GumbelTreeLSTMEncoder, self).reset_parameters()\n init.normal(self.comp_query.data, mean=0, std=0.01)\n\n def select_composition(self, old_state, new_state, mask):\n new_h, new_c = new_state\n old_h, old_c = old_state\n old_h_left, old_h_right = old_h[:, :-1, :], old_h[:, 1:, :]\n old_c_left, old_c_right = old_c[:, :-1, :], old_c[:, 1:, :]\n comp_weights = basic.dot_nd(query=self.comp_query, candidates=new_h)\n if self.training:\n select_mask = basic.st_gumbel_softmax(\n logits=comp_weights, temperature=self.gumbel_temperature, mask=mask)\n else:\n select_mask = basic.greedy_select(logits=comp_weights, mask=mask)\n select_mask = select_mask.float()\n select_mask_expand = select_mask.unsqueeze(2).expand_as(new_h)\n select_mask_cumsum = select_mask.cumsum(1)\n left_mask = 1 - select_mask_cumsum\n left_mask_expand = left_mask.unsqueeze(2).expand_as(old_h_left)\n right_mask_leftmost_col = Variable(\n select_mask_cumsum.data.new(new_h.size(0), 1).zero_())\n right_mask = torch.cat(\n [right_mask_leftmost_col, select_mask_cumsum[:, :-1]], dim=1)\n right_mask_expand = right_mask.unsqueeze(2).expand_as(old_h_right)\n new_h = (select_mask_expand * new_h\n + left_mask_expand * old_h_left\n + right_mask_expand * old_h_right)\n new_c = (select_mask_expand * new_c\n + left_mask_expand * old_c_left\n + right_mask_expand * old_c_right)\n selected_h = (select_mask_expand * new_h).sum(1)\n return new_h, new_c, select_mask, selected_h\n\n def forward(self, inp, length, return_select_masks=False):\n max_depth = inp.size(1)\n length_mask = basic.sequence_mask(sequence_length=length, max_length=max_depth)\n select_masks = list()\n features = list()\n\n if self.use_leaf_rnn:\n hs = list()\n cs = list()\n batch_size, max_length, _ = inp.size()\n zero_state = Variable(inp.data.new(batch_size, self.hidden_dim)\n .zero_())\n h_prev = c_prev = zero_state\n for i in range(max_length):\n h, c = self.leaf_rnn_cell(\n input=inp[:, i, :], hx=(h_prev, c_prev))\n hs.append(h)\n cs.append(c)\n h_prev = h\n c_prev = c\n hs = torch.stack(hs, dim=1)\n cs = torch.stack(cs, dim=1)\n\n if self.bidirectional:\n hs_bw = list()\n cs_bw = list()\n h_bw_prev = c_bw_prev = zero_state\n lengths_list = list(length.data)\n input_bw = basic.reverse_padded_sequence(\n inputs=inp, lengths=lengths_list, batch_first=True)\n for i in range(max_length):\n h_bw, c_bw = self.leaf_rnn_cell_bw(\n input=input_bw[:, i, :], hx=(h_bw_prev, c_bw_prev))\n hs_bw.append(h_bw)\n cs_bw.append(c_bw)\n h_bw_prev = h_bw\n c_bw_prev = c_bw\n hs_bw = torch.stack(hs_bw, dim=1)\n cs_bw = torch.stack(cs_bw, dim=1)\n hs_bw = basic.reverse_padded_sequence(\n inputs=hs_bw, lengths=lengths_list, batch_first=True)\n cs_bw = basic.reverse_padded_sequence(\n inputs=cs_bw, lengths=lengths_list, batch_first=True)\n hs = torch.cat([hs, hs_bw], dim=2)\n cs = torch.cat([cs, cs_bw], dim=2)\n state = (hs, cs)\n else:\n state = basic.apply_nd(fn=self.word_linear, input=inp)\n state = state.chunk(chunks=2, dim=2)\n nodes = list()\n if self.pooling_method is not None:\n nodes.append(state[0])\n for i in range(max_depth - 1):\n h, c = state\n left = (h[:, :-1, :], c[:, :-1, :])\n right = (h[:, 1:, :], c[:, 1:, :])\n new_state = self.treelstm_layer(left=left, right=right)\n if i < max_depth - 2:\n # We don't need to greedily select the composition in the\n # last iteration, since it has only one option left.\n new_h, new_c, select_mask, selected_h = self.select_composition(\n old_state=state, new_state=new_state,\n mask=length_mask[:, i+1:])\n new_state = (new_h, new_c)\n select_masks.append(select_mask)\n features.append(selected_h)\n if self.pooling_method is not None:\n nodes.append(selected_h.unsqueeze(1))\n done_mask = length_mask[:, i+1]\n state = self.update_state(old_state=state, new_state=new_state,\n done_mask=done_mask)\n if (self.pooling_method is not None) and i >= max_depth - 2:\n nodes.append(state[0])\n h, c = state\n if self.pooling_method == 'max':\n nodes = torch.cat(nodes, dim=1)\n h = nodes.max(1)[0].unsqueeze(1)\n elif self.pooling_method == 'mean':\n nodes = torch.cat(nodes, dim=1).sum(1)\n lengths = length * 2 - 1\n lengths = lengths.unsqueeze(1).float().expand_as(nodes)\n h = (nodes / lengths).unsqueeze(1)\n elif self.pooling_method == 'attention':\n nodes = torch.cat(nodes, dim=1)\n att_mask = torch.cat([length_mask, length_mask[:, 1:]], dim=1)\n att_mask_expand = att_mask.float().unsqueeze(2).expand_as(nodes)\n att_weights = basic.masked_softmax(\n logits=self.combiner(nodes), mask=att_mask)\n att_weights_expand = att_weights.unsqueeze(2).expand_as(nodes)\n h = (att_weights_expand * att_mask_expand * nodes).sum(1).unsqueeze(1)\n else:\n assert self.pooling_method is None\n assert h.size(1) == 1 and c.size(1) == 1\n if not return_select_masks:\n return h.squeeze(1), c.squeeze(1)\n else:\n return h.squeeze(1), c.squeeze(1), features, select_masks\n\n\nclass RecursiveTreeLSTMEncoder(TreeLSTMEncoder):\n\n def __init__(self, word_dim, hidden_dim, use_leaf_rnn, pooling_method, bidirectional):\n super(RecursiveTreeLSTMEncoder, self).__init__(\n word_dim, hidden_dim, use_leaf_rnn, pooling_method, bidirectional)\n self.reset_parameters()\n self.pooling_method = pooling_method\n if self.pooling_method == 'attention':\n self.combiner = AttnCombiner(bidirectional, hidden_dim)\n\n def forward(self, inp, length, fixed_masks):\n # print(\"=\"*100)\n # print(inp.shape)\n # print(length)\n # print(fixed_masks)\n max_depth = inp.size(1)\n length_mask = basic.sequence_mask(sequence_length=length, max_length=max_depth)\n features = list()\n\n if self.use_leaf_rnn:\n hs = list()\n cs = list()\n batch_size, max_length, _ = inp.size()\n zero_state = Variable(inp.data.new(batch_size, self.hidden_dim).zero_())\n h_prev = c_prev = zero_state\n for i in range(max_length):\n h, c = self.leaf_rnn_cell(\n input=inp[:, i, :], hx=(h_prev, c_prev))\n hs.append(h)\n cs.append(c)\n h_prev = h\n c_prev = c\n hs = torch.stack(hs, dim=1)\n cs = torch.stack(cs, dim=1)\n\n if self.bidirectional:\n hs_bw = list()\n cs_bw = list()\n h_bw_prev = c_bw_prev = zero_state\n lengths_list = list(length.data)\n input_bw = basic.reverse_padded_sequence(\n inputs=inp, lengths=lengths_list, batch_first=True)\n for i in range(max_length):\n h_bw, c_bw = self.leaf_rnn_cell_bw(\n input=input_bw[:, i, :], hx=(h_bw_prev, c_bw_prev))\n hs_bw.append(h_bw)\n cs_bw.append(c_bw)\n h_bw_prev = h_bw\n c_bw_prev = c_bw\n hs_bw = torch.stack(hs_bw, dim=1)\n cs_bw = torch.stack(cs_bw, dim=1)\n hs_bw = basic.reverse_padded_sequence(\n inputs=hs_bw, lengths=lengths_list, batch_first=True)\n cs_bw = basic.reverse_padded_sequence(\n inputs=cs_bw, lengths=lengths_list, batch_first=True)\n hs = torch.cat([hs, hs_bw], dim=2)\n cs = torch.cat([cs, cs_bw], dim=2)\n state = (hs, cs)\n else:\n state = basic.apply_nd(fn=self.word_linear, input=inp)\n state = state.chunk(chunks=2, dim=2)\n nodes = list()\n if self.pooling_method is not None:\n nodes.append(state[0])\n for i in range(max_depth - 1):\n h, c = state\n left = (h[:, :-1, :], c[:, :-1, :])\n right = (h[:, 1:, :], c[:, 1:, :])\n new_state = self.treelstm_layer(left=left, right=right)\n if i < max_depth - 2:\n # We don't need to greedily select the composition in the\n # last iteration, since it has only one option left.\n new_h, new_c, selected_h = self.compose(\n old_state=state, new_state=new_state, tree_mask=fixed_masks[i])\n new_state = (new_h, new_c)\n features.append(selected_h)\n if self.pooling_method is not None:\n nodes.append(selected_h.unsqueeze(1))\n done_mask = length_mask[:, i + 1]\n state = self.update_state(\n old_state=state, new_state=new_state, done_mask=done_mask)\n if (self.pooling_method is not None) and i >= max_depth - 2:\n done_masks = done_mask.float().unsqueeze(1).unsqueeze(2).expand_as(state[0])\n nodes.append(state[0] * done_masks)\n h, c = state\n if self.pooling_method == 'max':\n nodes = torch.cat(nodes, dim=1)\n h = nodes.max(1)[0].unsqueeze(1)\n elif self.pooling_method == 'mean':\n nodes = torch.cat(nodes, dim=1).sum(1)\n lengths = length * 2 - 1\n lengths = lengths.unsqueeze(1).float().expand_as(nodes)\n h = (nodes / lengths).unsqueeze(1)\n elif self.pooling_method == 'attention':\n nodes = torch.cat(nodes, dim=1)\n att_mask = torch.cat([length_mask, length_mask[:, 1:]], dim=1)\n att_mask_expand = att_mask.float().unsqueeze(2).expand_as(nodes)\n att_weights = basic.masked_softmax(\n logits=self.combiner(nodes), mask=att_mask)\n att_weights_expand = att_weights.unsqueeze(2).expand_as(nodes)\n h = (att_weights_expand * att_mask_expand * nodes).sum(1).unsqueeze(1)\n else:\n assert self.pooling_method is None\n assert h.size(1) == 1 and c.size(1) == 1\n return h.squeeze(1), c.squeeze(1)\n\n @staticmethod\n def compose(old_state, new_state, tree_mask):\n new_h, new_c = new_state\n old_h, old_c = old_state\n old_h_left, old_h_right = old_h[:, :-1, :], old_h[:, 1:, :]\n old_c_left, old_c_right = old_c[:, :-1, :], old_c[:, 1:, :]\n select_mask_expand = tree_mask.unsqueeze(2).expand_as(new_h)\n select_mask_cumsum = tree_mask.cumsum(1)\n left_mask = 1 - select_mask_cumsum\n left_mask_expand = left_mask.unsqueeze(2).expand_as(old_h_left)\n right_mask_leftmost_col = Variable(\n select_mask_cumsum.data.new(new_h.size(0), 1).zero_())\n right_mask = torch.cat(\n [right_mask_leftmost_col, select_mask_cumsum[:, :-1]], dim=1)\n right_mask_expand = right_mask.unsqueeze(2).expand_as(old_h_right)\n new_h = (select_mask_expand * new_h\n + left_mask_expand * old_h_left\n + right_mask_expand * old_h_right)\n new_c = (select_mask_expand * new_c\n + left_mask_expand * old_c_left\n + right_mask_expand * old_c_right)\n selected_h = (select_mask_expand * new_h).sum(1)\n return new_h, new_c, selected_h\n\n def __call__(self, *args, **kwargs):\n return self.forward(*args, **kwargs)\n\n"},{"alpha_fraction":0.4251185953617096,"alphanum_fraction":0.43528348207473755,"avg_line_length":54.32500076293945,"blob_id":"fbf4a95b9c0aaf7efccd2bf0baa0488eda07f127","content_id":"9a4d1000893e8d1854049533adc8a19fa9ef98b1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4427,"license_type":"no_license","max_line_length":172,"num_lines":80,"path":"/validate.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"from tqdm import tqdm\nimport torch\nimport torch.nn.functional as F\nfrom evaluation_utils import *\nfrom termcolor import colored\nfrom utils import *\nimport numpy as np\npreds_stats = predictions_analysis()\ndef validate(model, encode_model, args, epoch, dataset,local_rank):\n device = args.device\n logger = get_logger(args)\n model.eval()\n with tqdm(desc='Validatinging', total=len(dataset)) as pbar:\n acc = Accuracies()\n document_result = {}\n for i, (data, target, paths) in enumerate(dataset):\n if True:\n if i == args.stop_after:\n break\n pbar.update()\n with torch.no_grad():\n encoded_input = encode_model(data)\n output = model(encoded_input)\n output_seg = output.data.cpu().numpy().argmax(axis=1)\n output_softmax = F.softmax(output, 1)\n current_idx = 0\n output_np = output_softmax.data.cpu().numpy()\n for k, t in enumerate(target):\n path = paths[k]\n document_sentence_count = len(t)\n to_idx = int(current_idx + document_sentence_count)\n tmp_output = output_np[current_idx: to_idx, :].tolist()\n if \"_|_\" not in path: # whole document\n document_result[path] = [[tmp_output,t]]\n else: # filtered document\n path,num = path.split(\"_|_\")\n if path not in document_result:\n document_result[path] = [[num,[tmp_output,t]]]\n else:\n document_result[path].append([num,[tmp_output,t]])\n current_idx = to_idx\n targets_var = torch.cat(target, 0).to(device).clone().detach().requires_grad_(False)\n target_seg = targets_var.data.cpu().numpy()\n preds_stats.add(output_seg, target_seg)\n # concat:\n for k in document_result:\n if type(document_result[k][0][0]) == str:\n splited_dic_item = document_result[k]\n complete_dic_item = []\n sorted_splited_dic_item = sorted(splited_dic_item, key=lambda x: int(x[0]))\n for item in sorted_splited_dic_item:\n if len(complete_dic_item) == 0:\n complete_dic_item = [item[1][0], [item[1][1]]]\n else:\n complete_dic_item[0].extend(item[1][0])\n complete_dic_item[1].append(item[1][1])\n # complete_dic_item[1] =\n document_result[k] = complete_dic_item\n document_result[k][1] = torch.cat(document_result[k][1], 0)\n else:\n document_result[k] = [document_result[k][0][0],document_result[k][0][1]]\n acc_input = [[],[],[]] # [all_output_softmax, all_target, all_paths, args.crf]\n for k in document_result:\n acc_input[0].extend(document_result[k][0])\n acc_input[1].append(document_result[k][1])\n acc_input[2].append(k)\n acc_input[0] = np.array(acc_input[0])\n acc.update(acc_input[0], acc_input[1], acc_input[2])\n epoch_pk, epoch_windiff, epoch_b, epoch_s, threshold = acc.calc_accuracy()\n\n rank_logger_info(logger, local_rank, colored('Validating Epoch: {}, accuracy: {:.4}, Pk: {:.4}, Windiff: {:.4}, B: {:.4}, S: {:.4} ,F1: {:.4} . '.format(epoch + 1,\n preds_stats.get_accuracy(),\n epoch_pk,\n epoch_windiff,\n epoch_b,\n epoch_s,\n preds_stats.get_f1()),\"red\"))\n preds_stats.reset()\n\n return epoch_pk, threshold\n\n"},{"alpha_fraction":0.5519116520881653,"alphanum_fraction":0.557858943939209,"avg_line_length":37.20779037475586,"blob_id":"645759a2699d2aae62b9b08716346cce2b51d817","content_id":"8539b2f4f4a7463d7e2563920115b031a6a95a92","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5885,"license_type":"no_license","max_line_length":103,"num_lines":154,"path":"/models/Treelstm/data.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import jsonlines\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset\n\nclass Vocab(object):\n\n def __init__(self, vocab_dict, add_pad, add_unk):\n self._vocab_dict = vocab_dict.copy()\n self._reverse_vocab_dict = dict()\n if add_pad:\n self.pad_word = '<pad>'\n self.pad_id = len(self._vocab_dict)\n self._vocab_dict[self.pad_word] = self.pad_id\n if add_unk:\n self.unk_word = '<unk>'\n self.unk_id = len(self._vocab_dict)\n self._vocab_dict[self.unk_word] = self.unk_id\n for w, i in self._vocab_dict.items():\n self._reverse_vocab_dict[i] = w\n\n @classmethod\n def from_file(cls, path, add_pad=True, add_unk=True, max_size=None):\n vocab_dict = dict()\n with open(path, 'r', encoding='utf-8') as f:\n for i, line in enumerate(f):\n if max_size and i >= max_size:\n break\n word = line.strip().split()[0]\n vocab_dict[word] = len(vocab_dict)\n vocab_dict[word] = len(vocab_dict)\n return cls(vocab_dict=vocab_dict, add_pad=add_pad, add_unk=add_unk)\n\n def word_to_id(self, word):\n if hasattr(self, 'unk_id'):\n return self._vocab_dict.get(word, self.unk_id)\n return self._vocab_dict[word]\n\n def id_to_word(self, id_):\n if hasattr(self, 'unk_word'):\n return self._reverse_vocab_dict.get(id_, self.unk_word)\n return self._reverse_vocab_dict[id_]\n\n def has_word(self, word):\n return word in self._vocab_dict\n\n def __len__(self):\n return len(self._vocab_dict)\n\n\nclass SentenceDataset(Dataset):\n\n def __init__(self, word_vocab, data_path, label_vocab, max_length, lower, min_length=2):\n self.word_vocab = word_vocab\n self.label_vocab = label_vocab\n self.lower = lower\n self._max_length = max_length # 64\n self._min_length = min_length # 2\n self._data = []\n failed_to_parse = 0\n with jsonlines.open(data_path, 'r') as reader:\n for obj in reader: # obj:{\"label\":1,\"sentence\":\"...\"}\n try:\n converted = self._convert_obj(obj)\n if converted:\n self._data.append(converted)\n else:\n failed_to_parse += 1\n except ValueError:\n failed_to_parse += 1\n except AttributeError:\n failed_to_parse += 1\n print('Failed to parse {:d} instances'.format(failed_to_parse))\n\n def _convert_obj(self, obj):\n sentence = obj['sentence']\n if self.lower:\n sentence = sentence.lower()\n word_ids = [self.word_vocab.word_to_id(w) for w in sentence.split()]\n length = len(word_ids)\n if length > self._max_length or length < self._min_length:\n return None\n label = self.label_vocab.word_to_id(obj['label'])\n if 'constituency_tree_encoding' in obj:\n mask_ids = self.tree_encoding_to_mask_ids(obj['constituency_tree_encoding'])\n else:\n mask_ids = None\n if mask_ids is not None and len(mask_ids) != length - 1:\n return None\n return word_ids, sentence, length, mask_ids, label\n\n def pad_sentence(self, data):\n max_length = max(len(d) for d in data)\n padded = [d + [self.word_vocab.pad_id] * (max_length - len(d))\n for d in data]\n return padded\n\n @staticmethod\n def pad_mask(data):\n max_length = max(len(d) for d in data)\n padded = [d + [0] * (max_length - len(d)) for d in data]\n return np.array(padded)\n\n @staticmethod\n def make_one_hot_gold_mask(gold_mask_ids):\n masks = list()\n for num_classes in range(gold_mask_ids.shape[1], 1, -1):\n i = gold_mask_ids.shape[1] - num_classes\n indices = torch.LongTensor(gold_mask_ids[:, i])\n mask = SentenceDataset.convert_to_one_hot(indices, num_classes).float()\n masks.append(mask)\n return masks\n\n def __len__(self):\n return len(self._data)\n\n def __getitem__(self, item):\n return self._data[item]\n\n def collate(self, batch):\n words_batch, raw_sentences_batch, length_batch, mask_ids_batch, label_batch = list(zip(*batch))\n sentences = torch.LongTensor(self.pad_sentence(words_batch))\n lengths = torch.LongTensor(length_batch)\n try:\n masks = self.make_one_hot_gold_mask(self.pad_mask(mask_ids_batch))\n except TypeError:\n masks = None\n labels = torch.LongTensor(label_batch)\n return {'sentences': sentences, 'lengths': lengths, 'masks': masks, 'labels': labels,\n 'raw_sentences': raw_sentences_batch}\n\n @staticmethod\n def tree_encoding_to_mask_ids(tree_encoding):\n items = [int(x) for x in tree_encoding.strip().split(',')]\n sentence_length = len(items) // 3 + 1 # (sentence_length - 1) * 3 = len(items)\n curr_sent = [x for x in range(sentence_length)]\n mask_ids = list()\n assert 3 * (sentence_length - 1) == len(items)\n for i in range(sentence_length - 1):\n left_node = items[i * 3]\n right_node = items[i * 3 + 1]\n father_node = items[i * 3 + 2]\n left_index = curr_sent.index(left_node)\n assert curr_sent[left_index + 1] == right_node\n curr_sent = curr_sent[:left_index] + [father_node] + curr_sent[left_index + 2:]\n mask_ids.append(left_index)\n return mask_ids\n\n @staticmethod\n def convert_to_one_hot(indices, num_classes):\n batch_size = indices.shape[0]\n indices = indices.unsqueeze(1)\n one_hot = indices.new(batch_size, num_classes).zero_().scatter_(1, indices, 1)\n return one_hot\n\n"},{"alpha_fraction":0.5321321487426758,"alphanum_fraction":0.5392192006111145,"avg_line_length":48.266273498535156,"blob_id":"22d5312f25bccfbf35c3acbe246e6651bd03c7a2","content_id":"ca81378c16dd116942a3ab70fd97aa13292afa62","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8325,"license_type":"no_license","max_line_length":196,"num_lines":169,"path":"/data/wiki50_loader.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"from pathlib import Path\nfrom torch.utils.data import Dataset\nfrom utils import *\nfrom data.data_utils import *\nimport re\nimport os\nfrom parameters import create_parser\nsegment_seperator = \"========\"\n\ndef get_seperator_foramt(levels = None):\n level_format = '\\d' if levels == None else '['+ str(levels[0]) + '-' + str(levels[1]) + ']'\n seperator_fromat = segment_seperator + ',' + level_format + \",.*?\\.\"\n return seperator_fromat\n\n\ndef clean_section(section):\n cleaned_section = section.strip('\\n')\n return cleaned_section\n\n\ndef get_scections_from_text(txt, high_granularity=True):\n sections_to_keep_pattern = get_seperator_foramt() if high_granularity else get_seperator_foramt((1, 2))\n if not high_granularity:\n # if low granularity required we should flatten segments within segemnt level 2\n pattern_to_ommit = get_seperator_foramt((3, 999))\n txt = re.sub(pattern_to_ommit, \"\", txt)\n #delete empty lines after re.sub()\n sentences = [s for s in txt.strip().split(\"\\n\") if len(s) > 0 and s != \"\\n\"]\n txt = '\\n'.join(sentences).strip('\\n')\n all_sections = re.split(sections_to_keep_pattern, txt)\n non_empty_sections = [s for s in all_sections if len(s) > 0]\n return non_empty_sections\n\n\ndef get_sections(path, high_granularity=True):\n file = open(str(path), \"r\")\n raw_content = file.read()\n file.close()\n clean_txt = raw_content.encode('utf-8').decode('utf-8').strip()\n sections = [clean_section(s) for s in get_scections_from_text(clean_txt, high_granularity)]\n return sections\n\ndef read_wiki50_file(args,filtered_num, all_num, filtered_document_num, all_document_num,\n path, logger, encoder=None, length_filter=20, sent_num_filter=10, remove_preface_segment=True, ignore_list=False, remove_special_tokens=False,\n return_as_sentences=False, high_granularity=False, only_letters=False):\n data = []\n adjs = []\n targets = []\n all_sections = get_sections(path, high_granularity)\n required_sections = all_sections[1:] if remove_preface_segment and len(all_sections) > 0 else all_sections\n required_non_empty_sections = [section for section in required_sections if len(section) > 0 and section != \"\\n\"]\n for section in required_non_empty_sections:\n sentences = section.split('\\n')\n if sentences:\n targets_flag = 0\n for sentence in sentences:\n is_list_sentence = get_list_token() + \".\" == str(sentence)\n if ignore_list and is_list_sentence:\n continue\n if not return_as_sentences:\n sentence_words = extract_sentence_words(sentence, remove_special_tokens=remove_special_tokens)\n if (len(sentence_words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\n continue\n # length_filter\n if len(sentence_words) > length_filter:\n sentence_words = sentence_words[:length_filter]\n filtered_num += 1\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n sentence_words, adj = build_ing_graph(sentence_words, args)\n adjs.append(adj)\n if encoder:\n data.append(encoder.tokenize(sentence_words))\n else:\n data.append(sentence_words)\n targets_flag += 1\n else: # for the annotation. keep sentence as is.\n if (only_letters):\n sentence = re.sub('[^a-zA-Z0-9 ]+', '', sentence)\n data.append(sentence)\n else:\n data.append(sentence)\n if data:\n all_num += targets_flag\n targets.extend([0]*(targets_flag-1))\n if targets_flag != 0:\n targets.append(1) # segment\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(data, adjs, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n return_dict = generate_gcn_return_dict(return_dict,args)\n else:\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n return return_dict, filtered_num, all_num,filtered_document_num, all_document_num\n\ndef get_cache_path(folder):\n cache_file_path = folder / 'paths_cache'\n return cache_file_path\n\ndef cache_filenames(folder):\n files = Path(folder).glob('*')\n files_lst = []\n for file in files:\n files_lst.append(file)\n cache_path = get_cache_path(folder)\n with cache_path.open(\"w+\") as f:\n for file in files_lst:\n f.write(str(file) + u'\\n')\n\nclass Wiki50DataSet(Dataset):\n def __init__(self,\n root = None,\n args=None,\n encoder = None,\n length_filter = 40,\n sent_num_filter = 60,\n local_rank=-1):\n logger = get_logger(args)\n root_path = Path(root)\n cache_path = get_cache_path(root_path)\n if not cache_path.exists():\n cache_filenames(root_path)\n self.textfiles = cache_path.read_text().splitlines()\n else:\n self.textfiles = cache_path.read_text().splitlines()\n if len(self.textfiles) == 0:\n raise RuntimeError('Found 0 images in subfolders of: {}'.format(root))\n self.data = []\n # sentences\n filtered_num = 0\n all_num = 0\n # documents\n filtered_document_num = 0\n all_document_num = 0\n rank_logger_info(logger, local_rank, \"Reading wiki50 data\")\n for textfile in self.textfiles:\n return_dict, filtered_num, all_num, filtered_document_num, all_document_num = read_wiki50_file(args,filtered_num,\n all_num,\n filtered_document_num,\n all_document_num,\n textfile,\n logger = logger,\n encoder=encoder,\n length_filter=length_filter,\n sent_num_filter=sent_num_filter)\n if return_dict == None:\n continue\n elif type(return_dict) == dict:\n self.data.append(return_dict)\n else: # list\n self.data.extend(return_dict)\n # print(\"one wiki50 example:\",self.data[0])\n if all_num != 0:\n rank_logger_info(logger, local_rank, \"all sentences num:%d\\tfiltered num:%d\\tfiltered percent:%f\"%(all_num, filtered_num,filtered_num/all_num))\n if all_document_num != 0:\n rank_logger_info(logger, local_rank, \"all documents num:%d\\tfiltered num:%d\\tfiltered percent:%f\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\n\n def __getitem__(self, index):\n return self.data[index]\n\n def __len__(self):\n return len(self.data)\n\n\nif __name__ == \"__main__\":\n args = create_parser()\n print(args)\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\n choidataset = Wiki50DataSet(args.wiki50_path, args)"},{"alpha_fraction":0.6061512231826782,"alphanum_fraction":0.6125587224960327,"avg_line_length":45.81632614135742,"blob_id":"0ba4577c41462c8530d9f8333bdc5fa27c2304d4","content_id":"287c98416f57e4394066e92e781941fff2aeb70f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2341,"license_type":"no_license","max_line_length":122,"num_lines":49,"path":"/models/sequence_sr.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\r\nimport torch\r\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\r\nfrom utils import unsort\r\ndef zero_state(module, batch_size, device):\r\n # * 2 is for the two directions\r\n return torch.zeros(module.num_layers * 2, batch_size, module.hidden_dim).to(device), \\\r\n torch.zeros(module.num_layers * 2, batch_size, module.hidden_dim).to(device)\r\n\r\nclass s_model(nn.Module):\r\n def __init__(self, encoder_input_dim, args):\r\n super(s_model, self).__init__()\r\n self.input_dim = encoder_input_dim\r\n self.num_layers = args.bilstm_nl\r\n self.hidden_dim = args.bilstm_hd\r\n self.bidirectional = args.bilstm_bd\r\n self.device = args.device\r\n # self.pooling_method = args.pool_method\r\n self.lstm = nn.LSTM(input_size=self.input_dim,\r\n hidden_size=self.hidden_dim,\r\n num_layers=self.num_layers,\r\n dropout=0,\r\n bidirectional=True) # batch_first=True\r\n\r\n # def pad(self, s, max_length):\r\n # pad_lst = [self.encoder.model.pad_token_id] * (max_length - s.size(0))\r\n # s = torch.LongTensor(s.tolist() + pad_lst).to(self.device)\r\n # return s.unsqueeze(1)\r\n\r\n def forward(self,encoded_input):\r\n embeded_big_tensor,sorted_lengths,sort_order,sentences_per_doc=encoded_input\r\n packed_tensor = pack_padded_sequence(embeded_big_tensor, sorted_lengths) # batch_first=True\r\n batch_size = packed_tensor.batch_sizes[0]\r\n s = zero_state(self, batch_size, self.device)\r\n encoded_sentences,_ = self.lstm(packed_tensor,s)\r\n padded_output, lengths = pad_packed_sequence(encoded_sentences) # (batch, max sentence len,256) ,batch_first=True\r\n maxes = torch.zeros(batch_size, padded_output.size(2)).to(self.device)\r\n for i in range(batch_size):\r\n maxes[i, :] = torch.max(padded_output[:lengths[i],i,:], 0)[0]\r\n unsort_order = torch.LongTensor(unsort(sort_order)).to(self.device)\r\n unsorted_sent_repr = maxes.index_select(0, unsort_order)\r\n return unsorted_sent_repr, sentences_per_doc\r\n\r\n\r\n def get_output_dim(self):\r\n if self.bidirectional:\r\n return self.hidden_dim * 2\r\n else:\r\n return self.hidden_dim"},{"alpha_fraction":0.2798086702823639,"alphanum_fraction":0.28185856342315674,"avg_line_length":55.39215850830078,"blob_id":"0261b53a5cbbfcdb5673ca9bbfe9891f039f8497","content_id":"ab9c4b9e3125993fb79832ea513277c6647f29a9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":3593,"license_type":"no_license","max_line_length":132,"num_lines":51,"path":"/doc/code_tree.md","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"# DSG-SEG整体代码结构 \r\n\r\nDSG-SEG 的整体目录结构介绍如下： \r\n```\r\nDSE-SEG\r\n├── doc //介绍文档\r\n├── data //加载数据集\r\n│ ├── all_data_loader.py //汇总\r\n│ ├── choi_loader.py //choi\r\n│ ├── clinical_loader.py //clinical\r\n│ ├── data_utils.py //工具\r\n│ ├── manifesto_loader.py //manifesto\r\n│ ├── wiki_loader.py //wiki\r\n│ ├── wiki50_loader.py //wiki50\r\n│ ├── wikicities_loader.py //wikicities\r\n│ ├── wikielements_loader.py //wikielements\r\n│ └── wikisection_loader.py //wikisection\r\n├── models //模型\r\n│ ├── GGNN //GGNN网络\r\n│ │ ├── init.py //四种参数初始化方式\r\n│ │ └── layer.py //网络\r\n│ ├── Treelstm //Treelstm模型\r\n│ │ ├── basic.py //功能\r\n│ │ ├── data.py //数据\r\n│ │ ├── models.py //模型\r\n│ │ └── utils.py //工具\r\n│ ├── bert_sr.py //Bert句子表示\r\n│ ├── DSG_SEG.py //DSG_SEG句子表示\r\n│ ├── Encode_model.py //编码模型，数据流向:dataset->collate_fn->data_loader->encoder_model->sr_model->seg_model\r\n│ ├── freq_sr.py //词频句子表示\r\n│ ├── latent_sr.py //隐式句子表示\r\n│ ├── seg_model.py //上层Bilstm网络，用于预测分割点\r\n│ ├── sequence_sr.py //序列句子表示\r\n│ ├── texting.py //texting模型，也就是-SENTNODE消融模型\r\n│ └── treelstm_sr.py //树形句子表示\r\n├── encoder //词向量\r\n│ ├── w2v_encoder.py //word2vec\r\n│ ├── randn_encoder.py //随机初始化\r\n│ ├── one_hot_encoder.py //one-hot\r\n│ ├── glove_encoder.py //glove\r\n│ ├── Encoder.py //汇总\r\n│ └── bert_encoder.py //bert\r\n├── validate.py //模型验证\r\n├── utils.py //工具\r\n├── train.py //模型训练\r\n├── test.py //模型测试\r\n├── run.py //主程序\r\n├── parameters.py //命令行参数\r\n├── judgement.py //样例分析\r\n└── evaluation_utils.py //评估工具\r\n```\r\n"},{"alpha_fraction":0.5757400393486023,"alphanum_fraction":0.5876502990722656,"avg_line_length":39.2279052734375,"blob_id":"e9eaf03cb3edbf8d29c5e2351fc7f04062151f5c","content_id":"9888a231e2909e63bb070424003945a1fbca9277","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8652,"license_type":"no_license","max_line_length":193,"num_lines":215,"path":"/evaluation_utils.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"# coding=UTF-8\nfrom enum import Enum\nfrom segeval.window.pk import pk\nfrom segeval.window.windowdiff import window_diff as WD\nfrom segeval.similarity.boundary import boundary_similarity as B\nfrom segeval.similarity.segmentation import segmentation_similarity as S\nimport numpy as np\nimport os\n\n\nclass predictions_analysis(object):\n def __init__(self):\n self.tp = 0\n self.tn = 0\n self.fp = 0\n self.fn = 0\n\n\n def add(self,predicions, targets):\n self.tp += ((predicions == targets) & (1 == predicions)).sum()\n self.tn += ((predicions == targets) & (0 == predicions)).sum()\n self.fp += ((predicions != targets) & (1 == predicions)).sum()\n self.fn += ((predicions != targets) & (0 == predicions)).sum()\n\n def calc_recall(self):\n if self.tp == 0 and self.fn == 0:\n return -1\n\n return np.true_divide(self.tp, self.tp + self.fn)\n\n def calc_precision(self):\n if self.tp == 0 and self.fp == 0:\n return -1\n\n return np.true_divide(self.tp,self.tp + self.fp)\n\n def get_f1(self):\n if (self.tp + self.fp == 0):\n return 0.0\n if (self.tp + self.fn == 0):\n return 0.0\n precision = self.calc_precision()\n recall = self.calc_recall()\n if (not ((precision + recall) == 0)):\n f1 = 2*(precision*recall) / (precision + recall)\n else:\n f1 = 0.0\n\n return f1\n\n def get_accuracy(self):\n total = self.tp + self.tn + self.fp + self.fn\n if (total == 0) :\n return 0.0\n else:\n return np.true_divide(self.tp + self.tn, total)\n\n def reset(self):\n self.tp = 0\n self.tn = 0\n self.fn = 0\n self.fp = 0\n\nclass CalculateEnum(Enum):\n \"\"\"\n Four indicates: pk,windiff,boundary_similarity,segmentation_similarity.\n \"\"\"\n pk = 0\n windiff = 1\n boundary_similarity = 2,\n segmentation_similarity = 3,\n\nclass Accuracies(object):\n def __init__(self):\n self.thresholds = np.arange(0, 1, 0.05)\n self.accuracies = {k: Accuracy() for k in self.thresholds}\n\n def update(self, output_np, targets_np, paths):\n current_idx = 0\n for k, t in enumerate(targets_np):\n path = paths[k]\n document_sentence_count = len(t)\n to_idx = int(current_idx + document_sentence_count)\n for threshold in self.thresholds:\n output = ((output_np[current_idx: to_idx, :])[:, 1] > threshold)\n h = np.append(output, [1])\n tt = np.append(t, [1])\n self.accuracies[threshold].update(h, tt)\n current_idx = to_idx\n\n\n def calc_accuracy(self):\n min_pk = np.inf\n min_threshold = None\n min_epoch_windiff = None\n min_epoch_b = None\n min_epoch_s = None\n for threshold in self.thresholds:\n epoch_pk, epoch_windiff, epoch_b, epoch_s = self.accuracies[threshold].calc_accuracy()\n if epoch_pk < min_pk:\n min_pk = epoch_pk\n min_threshold = threshold\n min_epoch_windiff = epoch_windiff\n min_epoch_b = epoch_b\n min_epoch_s = epoch_s\n return min_pk, -1.0, -1.0, -1.0, min_threshold\n\nclass Accuracy:\n def __init__(self, threshold=0.3):\n self.pk_to_weight = []\n self.windiff_to_weight = []\n self.B_to_weight = []\n self.S_to_weight = []\n self.threshold = threshold\n self.calculator_dict = {\n CalculateEnum.pk: pk,\n CalculateEnum.windiff: WD,\n CalculateEnum.boundary_similarity: B,\n CalculateEnum.segmentation_similarity: S,\n }\n self.weight_dict = {\n CalculateEnum.pk: self.pk_to_weight,\n CalculateEnum.windiff: self.windiff_to_weight,\n CalculateEnum.boundary_similarity: self.B_to_weight,\n CalculateEnum.segmentation_similarity: self.S_to_weight,\n }\n self.type_str_dict = {\n CalculateEnum.pk: \"pk\",\n CalculateEnum.windiff: \"WD\",\n CalculateEnum.boundary_similarity: \"B\",\n CalculateEnum.segmentation_similarity: \"S\",\n }\n self.pk_value = -1\n self.windiff_value = -1\n self.segmentation_similarity_value = -1\n self.boundary_similarity_value = -1\n self.one_result = {}\n # there is a large dictionary: {\"path\":[\"pred\",\"golden\",pk,wd,b,s]}\n # where pred shows like: \"1,2,3|4,5,6,7|8,9,10|\"\n # by the way, the result is recorded in test_result\n self.all_test_result = {}\n\n def get_seg_boundaries(self, classifications, sentences_length=None):\n curr_seg_length = 0\n boundaries = []\n for i, classification in enumerate(classifications):\n is_split_point = bool(classifications[i])\n add_to_current_segment = 1 if sentences_length is None else sentences_length[i]\n curr_seg_length += add_to_current_segment\n if (is_split_point):\n boundaries.append(curr_seg_length)\n curr_seg_length = 0\n return boundaries\n\n def get_str(self, boundaries):\n all_sentence_idx = [str(i) for i in range(sum(boundaries))]\n all_seg_idx = []\n for i,k in enumerate(boundaries):\n if i == 0:\n all_seg_idx.append(k)\n else:\n all_seg_idx.append(k+all_seg_idx[-1]+1) # why + 1? —— because in all_sentence_idx, we have inserted i-1 seg_idxes in the i-1th circle before. if we dont add 1, there is just i-2\n for seg_idx in all_seg_idx:\n all_sentence_idx.insert(seg_idx, \"|\")\n return \" \".join(all_sentence_idx)\n\n def update(self, h, gold, path=None, window_size=-1, sentences_length=None):\n self.one_result = {}\n h_boundaries = self.get_seg_boundaries(h, sentences_length)\n gold_boundaries = self.get_seg_boundaries(gold, sentences_length)\n self._calculate(h_boundaries, gold_boundaries, window_size, calc_type=CalculateEnum.pk,path=path)\n # self._calculate(h_boundaries, gold_boundaries, window_size, calc_type=CalculateEnum.windiff,path=path)\n # self._calculate(h_boundaries, gold_boundaries, window_size, calc_type=CalculateEnum.boundary_similarity,path=path)\n # self._calculate(h_boundaries, gold_boundaries, window_size, calc_type=CalculateEnum.segmentation_similarity,path=path)\n if path != None:\n h_str = self.get_str(h_boundaries)\n gold_str = self.get_str(gold_boundaries)\n self.one_result[\"pred\"] = h_str\n self.one_result[\"golden\"] = gold_str\n self.all_test_result[str(path)] = self.one_result\n\n def _calculate(self, h_boundaries, gold_boundaries, window_size=-1, calc_type=CalculateEnum.pk,path=None):\n type_str = self.type_str_dict[calc_type]\n calculator = self.calculator_dict[calc_type]\n # if (type_str == \"B\" or type_str == \"S\") and (len(h_boundaries) == len(gold_boundaries) == 1):\n # self.one_result[type_str] = None\n # return\n if window_size != -1:\n result_dict = calculator(h_boundaries, gold_boundaries, window_size=window_size, return_parts=True)\n else:\n result_dict = calculator(h_boundaries, gold_boundaries, return_parts=True)\n false_seg_count = result_dict[0]\n total_count = result_dict[1]\n if total_count == 0:\n false_prob = -1\n else:\n false_prob = float(false_seg_count) / float(total_count)\n # if type_str in [\"B\",\"S\"]:\n # self.one_result[type_str] = false_prob\n # else:\n self.one_result[type_str] = false_prob\n self.weight_dict[calc_type].append((false_prob, total_count))\n\n def _get_result(self, calc_type=CalculateEnum.pk):\n result_value = sum([pw[0] * pw[1] for pw in self.weight_dict[calc_type]]) / sum(\n [pw[1] for pw in self.weight_dict[calc_type]]) if len(\n self.weight_dict[calc_type]) > 0 else -1.0\n return result_value\n\n def calc_accuracy(self):\n self.pk_value = self._get_result(calc_type=CalculateEnum.pk)\n # self.windiff_value = self._get_result(calc_type=CalculateEnum.windiff)\n # self.boundary_similarity_value = self._get_result(calc_type=CalculateEnum.boundary_similarity)\n # self.segmentation_similarity_value = self._get_result(calc_type=CalculateEnum.segmentation_similarity)\n return self.pk_value, -1.0,-1.0,-1.0#self.windiff_value, self.boundary_similarity_value, self.segmentation_similarity_value"},{"alpha_fraction":0.5244134664535522,"alphanum_fraction":0.5275840163230896,"avg_line_length":43.11428451538086,"blob_id":"0830c44195a160e45d2e821dd73f2a151d0de785","content_id":"3a1b097a00f6586c8ab34b00ba5767cf9c83642e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1577,"license_type":"no_license","max_line_length":90,"num_lines":35,"path":"/models/texting.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\r\nimport torch\r\nfrom utils import unsort\r\nfrom models.GGNN.layers import *\r\n\r\nclass g_model_text_ing(nn.Module):\r\n def __init__(self,encoder_input_dim,args):\r\n super(g_model_text_ing,self).__init__()\r\n self.input_dim = encoder_input_dim\r\n self.hidden_dim = args.texting_hidden_dim\r\n self.output_dim = args.texting_output_dim\r\n self.device = args.device\r\n self.layers = []\r\n self.graph_layer = GraphLayer(args,\r\n input_dim=self.input_dim,\r\n output_dim=self.hidden_dim,\r\n act=nn.Tanh(),\r\n dropout_p=0.3)\r\n\r\n self.readout_layer = ReadoutLayer(args,\r\n input_dim=self.hidden_dim,\r\n output_dim=self.output_dim,\r\n act=nn.Tanh(),\r\n dropout_p=0.3)\r\n\r\n def forward(self,encoded_input):\r\n feature, adj, mask, sorted_lengths, sort_order, sentences_per_doc = encoded_input\r\n graph_layer_output = self.graph_layer(feature, adj, mask) # bs, ml , dim\r\n texting_output = self.readout_layer(graph_layer_output,mask)\r\n unsort_order = torch.LongTensor(unsort(sort_order)).to(self.device)\r\n unsorted_sent_repr = texting_output.index_select(0, unsort_order)\r\n return unsorted_sent_repr, sentences_per_doc\r\n\r\n def get_output_dim(self):\r\n return self.output_dim"},{"alpha_fraction":0.5897620916366577,"alphanum_fraction":0.5993751287460327,"avg_line_length":44.206520080566406,"blob_id":"f2b36fb05f8a46e2a2aab0f76eb335031eae47c7","content_id":"b23613d3b9e8d8acbfa7a82110dbc54ae422481b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4161,"license_type":"no_license","max_line_length":126,"num_lines":92,"path":"/models/seg_model.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\nfrom utils import *\nfrom models.sequence_sr import s_model,zero_state\nfrom models.treelstm_sr import t_model_treelstm\nfrom models.freq_sr import f_model\nfrom models.latent_sr import l_model\nfrom models.bert_sr import b_model\nfrom models.texting import g_model_text_ing\nfrom models.DSG_SEG import g_model_DSG_SEG\n\nclass seg_model(nn.Module):\n def __init__(self, sr_choose,encoder_input_dim, args):\n super(seg_model, self).__init__()\n self.device = args.device\n self.pool_method = args.pool_method\n self.encoder_input_dim = encoder_input_dim\n self.hidden_dim=args.hidden_dim\n self.num_layers=args.num_layers\n self.sr_choose = sr_choose\n if sr_choose == \"f_model\":\n self.sr_model = f_model(encoder_input_dim,args)\n elif sr_choose == \"l_model\":\n self.sr_model = l_model(args)\n elif sr_choose == \"s_model\":\n self.sr_model = s_model(encoder_input_dim, args)\n elif sr_choose == \"t_model\":\n assert args.tr_choose in ['balanced', 'left', 'right']\n self.sr_model = t_model_treelstm(encoder_input_dim, args)\n elif sr_choose == \"g_model\":\n if args.gr_choose == 'texting':\n self.sr_model = g_model_text_ing(encoder_input_dim,args)\n elif args.gr_choose == 'DSG_SEG':\n self.sr_model = g_model_DSG_SEG(encoder_input_dim, args)\n elif sr_choose == 'b_model':\n self.sr_model = b_model(encoder_input_dim, args)\n\n if args.sr_choose != \"random_baseline\":\n self.sl_input_dim = self.sr_model.get_output_dim()\n else:\n self.sl_input_dim = 300\n self.sentence_lstm = nn.LSTM(input_size=self.sl_input_dim,\n hidden_size=self.hidden_dim,\n num_layers=self.num_layers,\n batch_first=True,\n dropout=0,\n bidirectional=True)\n self.h2s = nn.Linear(self.hidden_dim*2 , 2)\n\n self.criterion = nn.CrossEntropyLoss()\n\n\n def pad_document(self, d, max_document_length):\n d_length = d.size()[0]\n v = d.unsqueeze(0).unsqueeze(0)\n padded = F.pad(v, (0, 0, 0, max_document_length - d_length)) # (1, 1, max_length, 300)\n shape = padded.size()\n return padded.view(shape[2], 1, shape[3]) # (max_length, 1, 300)\n\n def forward(self, encoded_input):\n batch_size = encoded_input[0]\n unsorted_sent_repr, sentences_per_doc = self.sr_model(encoded_input[1:])\n index = 0\n encoded_documents = []\n for sentences_count in sentences_per_doc:\n end_index = index + sentences_count\n encoded_documents.append(unsorted_sent_repr[index: end_index, :])\n index = end_index\n\n doc_sizes = [doc.size()[0] for doc in encoded_documents]\n max_doc_size = np.max(doc_sizes)\n ordered_document_idx = np.argsort(doc_sizes)[::-1]\n ordered_doc_sizes = sorted(doc_sizes)[::-1]\n ordered_documents = [encoded_documents[idx] for idx in ordered_document_idx]\n padded_docs = [self.pad_document(d, max_doc_size) for d in ordered_documents]\n docs_tensor = torch.cat(padded_docs, 1)\n packed_docs = pack_padded_sequence(docs_tensor, ordered_doc_sizes)\n sentence_lstm_output, _ = self.sentence_lstm(packed_docs, zero_state(self, batch_size=batch_size, device=self.device))\n padded_x, _ = pad_packed_sequence(sentence_lstm_output) # (max sentence len, batch, 256)\n\n doc_outputs = []\n for i, doc_len in enumerate(ordered_doc_sizes):\n doc_outputs.append(padded_x[0:doc_len - 1, i, :]) # -1 to remove last prediction ---> wiki_loader.py\n\n unsorted_doc_outputs = [doc_outputs[i] for i in unsort(ordered_document_idx)]\n sentence_outputs = torch.cat(unsorted_doc_outputs, 0)\n\n x = self.h2s(sentence_outputs)\n return x\n\n\n"},{"alpha_fraction":0.730358898639679,"alphanum_fraction":0.7352085113525391,"avg_line_length":42,"blob_id":"e7a9b7ae3a5d80db6d50fd74d44c49813a699749","content_id":"e34d401fab3e43e3922198e1c969685bf2612785","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1031,"license_type":"no_license","max_line_length":126,"num_lines":24,"path":"/models/GGNN/inits.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport math\nfrom torch.nn import init\n# https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear\ndef glorot(in_features,out_features):\n init_range = np.sqrt(6.0/(in_features+out_features))\n initial = nn.Parameter(torch.nn.init.uniform_(tensor=torch.Tensor(in_features,out_features), a=-init_range, b=init_range))\n return initial\n\ndef kaiming(in_features,out_features): # not used\n initial = nn.Parameter(torch.nn.init.kaiming_uniform_(tensor=torch.Tensor(in_features,out_features), a=math.sqrt(5)))\n return initial\n\ndef xavier(in_features,out_features):\n initial = nn.Parameter(torch.nn.init.xavier_uniform_(tensor=torch.Tensor(in_features,out_features), gain=1))\n return initial\n\ndef uniform_bias(weight,out_features):\n fan_in, _ = init._calculate_fan_in_and_fan_out(weight)\n bound = 1 / math.sqrt(fan_in)\n initial = nn.Parameter(torch.nn.init.uniform_(tensor=torch.Tensor(out_features), a=-bound, b=bound))\n return initial"},{"alpha_fraction":0.4488178491592407,"alphanum_fraction":0.45291242003440857,"avg_line_length":53.46762466430664,"blob_id":"af76f2378cd8c61d14b563b18a198dfb8cca5535","content_id":"9653c45f37c638c92968e4bf40b4861fd1513f63","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7571,"license_type":"no_license","max_line_length":196,"num_lines":139,"path":"/data/wikielements_loader.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"from torch.utils.data import Dataset\nfrom utils import *\nimport os\nfrom parameters import create_parser\nfrom data.data_utils import *\n\ndef get_sections(text_path, segment_path):\n text_file = open(str(text_path), \"r\", encoding=\"utf-8\")\n texts = text_file.read().strip().split(\"\\n\")\n text_file.close()\n segment_file = open(str(segment_path), \"r\", encoding=\"utf-8\")\n segments = segment_file.read().strip().split(\"\\n\")\n segment_file.close()\n all_sections = [] # all document\n sections = [] # one document\n one_section = [] # one section\n document_num = int(segments[0].strip().split(\",\")[0])\n seg_title = segments[0].strip().split(\",\")[2]\n for idx, text in enumerate(texts):\n segment = segments[idx]\n n_doc_num,n_sent_num,n_seg_title = segment.strip().split(\",\")\n n_doc_num,n_sent_num = int(n_doc_num),int(n_sent_num)\n if n_seg_title != seg_title and n_doc_num == document_num:\n sections.append(one_section)\n one_section = []\n seg_title = n_seg_title\n elif n_doc_num != document_num:\n sections.append(one_section)\n all_sections.append(sections)\n one_section = []\n sections = []\n document_num = n_doc_num\n seg_title = n_seg_title\n one_section.append(text)\n return all_sections\n\n\n\ndef read_wikielements_file(args,filtered_num, all_num, filtered_document_num, all_document_num,\n text_path, segment_path,encoder=None,length_filter=10, sent_num_filter=10):\n all_sections_of_all_doc = get_sections(text_path, segment_path)\n all_return_dic = []\n for idx,all_sections in enumerate(all_sections_of_all_doc):\n path = text_path + \"/document_\" + str(idx)\n data = []\n adjs = []\n targets = []\n for sentences in all_sections:\n sentences = [s for s in sentences if len(s.split()) > 0]\n if sentences:\n targets_flag = 0\n for sentence in sentences:\n sentence_words = extract_sentence_words(sentence)\n if (len(sentence_words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\n continue\n if len(sentence_words) > length_filter:\n sentence_words = sentence_words[:length_filter]\n filtered_num += 1\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n sentence_words, adj = build_ing_graph(sentence_words, args)\n adjs.append(adj)\n if encoder:\n data.append(encoder.tokenize(sentence_words))\n else:\n data.append(sentence_words)\n targets_flag += 1\n if data:\n all_num += targets_flag\n targets.extend([0]*(targets_flag-1))\n if targets_flag != 0:\n targets.append(1) # segment\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(data, adjs, targets, path,\n all_document_num,\n filtered_document_num,\n sent_num_filter)\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path,\n all_document_num,\n filtered_document_num,\n sent_num_filter)\n return_dict = generate_gcn_return_dict(return_dict, args)\n else:\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path,\n all_document_num,\n filtered_document_num,\n sent_num_filter)\n if return_dict == None:\n continue\n elif type(return_dict) == dict:\n all_return_dic.append(return_dict)\n else: # list\n all_return_dic.extend(return_dict)\n return all_return_dic, filtered_num, all_num, filtered_document_num, all_document_num\n\nclass WikiElementsDataSet(Dataset):\n def __init__(self,\n root = None,\n args=None,\n encoder = None,\n length_filter = 40,\n sent_num_filter = 60,\n local_rank=-1):\n logger = get_logger(args)\n self.text_path = root + \"/wikielements.text\"\n self.segment_path = root + \"/wikielements.segmenttitles\"\n rank_logger_info(logger, local_rank, \"Reading wikielements data\")\n # sentences\n filtered_num = 0\n all_num = 0\n # documents\n filtered_document_num = 0\n all_document_num = 0\n self.data,filtered_num, all_num, filtered_document_num, all_document_num = read_wikielements_file(args,\n filtered_num,\n all_num,\n filtered_document_num,\n all_document_num,\n text_path= self.text_path,\n segment_path=self.segment_path,\n encoder=encoder,\n length_filter=length_filter,\n sent_num_filter=sent_num_filter)\n if all_num != 0:\n rank_logger_info(logger, local_rank, \"all sentences num:%d\\tfiltered num:%d\\tfiltered percent:%f\"%(all_num, filtered_num,filtered_num/all_num))\n if all_document_num != 0:\n rank_logger_info(logger, local_rank, \"all documents num:%d\\tfiltered num:%d\\tfiltered percent:%f\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\n\n def __getitem__(self, index):\n return self.data[index]\n\n def __len__(self):\n return len(self.data)\n\nif __name__ == \"__main__\":\n args = create_parser()\n print(args)\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\n wikielementsdataset = WikiElementsDataSet(args.wikielements_path, args)\n"},{"alpha_fraction":0.5936213731765747,"alphanum_fraction":0.5946502089500427,"avg_line_length":37.880001068115234,"blob_id":"d3d2eb27e6749d3681cd738aa5d3ff1d9458a0a1","content_id":"e4498d161bd05560e37ddd570f7ce54b7e6f439d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2916,"license_type":"no_license","max_line_length":91,"num_lines":75,"path":"/encoder/bert_encoder.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\nimport torch\nfrom transformers import BertTokenizer, BertModel\nfrom utils import *\nclass bert_model(nn.Module):\n def __init__(self, args, encoder_fine_tune):\n super(bert_model, self).__init__()\n self.args = args\n self.local_rank = args.local_rank\n self.encoder_fine_tune = encoder_fine_tune\n self.model_size = args.model_size\n self.cased = args.cased\n if not self.cased:\n do_lower_case = True\n self.model_name = 'bert' + \"-\" + self.model_size + '-uncased'\n else:\n do_lower_case = False\n self.model_name = 'bert' + \"-\" + self.model_size + '-cased'\n\n self.embedding = self.get_bert_embedding()\n if encoder_fine_tune:\n for param in self.parameters():\n param.requires_grad = True\n else:\n for param in self.parameters():\n param.requires_grad = False\n\n self.device = args.device\n self.tokenizer = BertTokenizer.from_pretrained(\n self.model_name, do_lower_case=do_lower_case)\n self.pad_token_id = self.tokenizer.pad_token_id\n self.num_layers = self.embedding.config.num_hidden_layers + 1\n self.hidden_size = self.embedding.config.hidden_size\n self.weighing_params = nn.Parameter(torch.ones(self.num_layers))\n\n def get_bert_embedding(self):\n logger = get_logger(self.args)\n rank_logger_info(logger, self.local_rank, 'Reading bert embedding.')\n embedding = BertModel.from_pretrained(\n self.model_name, output_hidden_states = True)\n return embedding\n\n def tokenize(self, inp):\n token_ids = self.tokenizer.encode(inp, add_special_tokens=True)\n return token_ids\n\n\n def forward(self, inp, just_last_layer=False):\n input_mask = (inp != self.pad_token_id).float().to(self.device)\n if not self.encoder_fine_tune:\n with torch.no_grad():\n output_bert = self.embedding(\n inp, attention_mask=input_mask, output_hidden_states=True) # B x L x E\n last_hidden_state = output_bert.last_hidden_state\n encoded_layers = output_bert.hidden_states\n else:\n output_bert = self.embedding(\n inp, attention_mask=input_mask, output_hidden_states=True)\n last_hidden_state = output_bert.last_hidden_state\n encoded_layers = output_bert.hidden_states\n\n if just_last_layer:\n output = last_hidden_state\n else:\n wtd_encoded_repr = 0\n soft_weight = nn.functional.softmax(self.weighing_params, dim=0)\n for i in range(self.num_layers):\n wtd_encoded_repr += soft_weight[i] * encoded_layers[i]\n output = wtd_encoded_repr\n\n return output\n\n\n def get_word_dim(self):\n return self.hidden_size\n"},{"alpha_fraction":0.3035320043563843,"alphanum_fraction":0.40066224336624146,"avg_line_length":19.069766998291016,"blob_id":"d27c2b3bc6307464322030abda9f6ddebadb24e0","content_id":"3c868751d4af6a5f45abe0f4826a9d8efae5b096","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1290,"license_type":"no_license","max_line_length":42,"num_lines":43,"path":"/doc/file_tree.md","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"# 文件结构\r\n\r\n/data路径下的整体文件结构介绍如下：\r\n\r\n```\r\n├──data \r\n│ ├──glove.6B.300d.txt \r\n│ ├──GoogleNews-vectors-negative300.bin \r\n│ ├──wiki_727 \r\n│ │ ├──test \r\n│ │ ├──dev \r\n│ │ └──train \r\n│ ├──wiki_50 \r\n│ ├──wikisection \r\n│ │ ├──en_disease_validation \r\n│ │ ├──en_disease_train \r\n│ │ ├──en_disease_test \r\n│ │ └──... \r\n│ ├──choi \r\n│ │ ├──4 \r\n│ │ ├──3 \r\n│ │ ├──2 \r\n│ │ └──1 \r\n│ ├──manifesto \r\n│ │ ├──61620_201211.txt \r\n│ │ ├──61620_200811.txt \r\n│ │ ├──61620_200411.txt \r\n│ │ ├──61320_201211.txt \r\n│ │ ├──61320_200811.txt \r\n│ │ └──61320_200411.txt \r\n│ ├──clinical \r\n│ │ ├──000.ref \r\n│ │ ├──001.ref \r\n│ │ └──... \r\n│ ├──wikicities \r\n│ │ ├──wikicities.merged_text \r\n│ │ ├──wikicities.text \r\n│ │ └──... \r\n│ ├──wikielements \r\n│ │ ├──wikielements.text \r\n│ │ ├──wikielements.vocab \r\n│ │ └──... \r\n```\r\n"},{"alpha_fraction":0.6254278421401978,"alphanum_fraction":0.629253089427948,"avg_line_length":59.95092010498047,"blob_id":"def542f901f1acbaf58da4a0e28f651b44f6136a","content_id":"888d448d6efaafe4e23273122b7e3264d7b96a12","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":9934,"license_type":"no_license","max_line_length":211,"num_lines":163,"path":"/run.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch\nfrom torch.utils.data import DataLoader\nimport os\nfrom pathlib2 import Path\nfrom termcolor import colored\nimport re\nfrom train import train\nfrom test import test_all, test_all_random_baseline \nfrom validate import validate\nfrom encoder.Encoder import Encoder\nfrom models.Encode_model import Encode_model\nfrom data.all_data_loader import get_loader\nfrom utils import *\nfrom models.seg_model import seg_model\nfrom parameters import *\nfrom torch.utils.data.distributed import DistributedSampler\ntest_data_type_list = ['wiki','choi','clinical', 'wikielements','wikicities','wiki50', 'manifesto'] # wikisection\n\ndef main(args):\n global test_data_type_list\n logger = get_logger(args)\n args.checkpoint_dir = Path(args.checkpoint_dir)\n # initial encoder\n encoder = None\n if not os.path.exists(args.encoder_path):\n rank_logger_info(logger, args.local_rank, \"Creating Encoder...\")\n encoder = Encoder(args=args, etype=args.etype, encoder_fine_tune=args.encoder_fine_tune, glove_path=args.glove_path, w2v_path=args.w2v_path)\n if args.local_rank in [-1, 0]:\n torch.save(encoder, args.encoder_path)\n else:\n rank_logger_info(logger, args.local_rank, \"Loading Encoder...\")\n encoder = torch.load(args.encoder_path)\n encode_model = Encode_model(encoder, args)\n # initial dataset\n train_dataset, dev_dataset,test_dataset_dict = None,None,{}\n if not os.path.exists(args.dataset_path): # create\n if not args.infer: # train:read all dataset\n rank_logger_info(logger, args.local_rank, \"Creating dataset...\")\n train_dataset = get_loader(args=args, encoder = encoder, data_type=args.train_data_type, split=\"train\")\n dev_dataset = get_loader(args=args, encoder = encoder, data_type=args.valid_data_type, split=\"dev\")\n for test_data_type in test_data_type_list:\n if test_data_type in [\"wikisection\", \"wiki\"]:\n test_dataset_dict[test_data_type] = get_loader(args=args, encoder = encoder, data_type=test_data_type, split=\"test\")\n else:\n test_dataset_dict[test_data_type] = get_loader(args=args, encoder=encoder, data_type=test_data_type)\n saved_pt = {\n \"train_dataset\":train_dataset,\n \"dev_dataset\":dev_dataset,\n \"test_dataset\":None # test_dataset_dict\n }\n if args.local_rank in [-1, 0]:\n torch.save(saved_pt, args.dataset_path)\n else: # infer:just read test dataset and not save\n for test_data_type in test_data_type_list:\n if test_data_type in [\"wikisection\", \"wiki\"]:\n test_dataset_dict[test_data_type] = get_loader(args=args, encoder = encoder, data_type=test_data_type, split=\"test\")\n else:\n test_dataset_dict[test_data_type] = get_loader(args=args, encoder=encoder, data_type=test_data_type)\n else: # load\n rank_logger_info(logger, args.local_rank, \"Loading dataset...\")\n loaded_pt = torch.load(args.dataset_path)\n train_dataset, dev_dataset,test_dataset_dict = loaded_pt[\"train_dataset\"], loaded_pt[\"dev_dataset\"],loaded_pt[\"test_dataset\"]\n # initial dataloader\n rank_logger_info(logger, args.local_rank, \"Creating dataloader...\")\n collate_fn = get_collate_fn(args)\n if not args.infer:\n train_sampler = DistributedSampler(train_dataset) if args.local_rank != - 1 else None\n train_dl = DataLoader(train_dataset, batch_size=args.train_bs, collate_fn=collate_fn, shuffle = True if not train_sampler else False,\n sampler=train_sampler, num_workers=args.num_workers)\n dev_dl = DataLoader(dev_dataset, batch_size=args.dev_bs, collate_fn=collate_fn, shuffle=False,num_workers=args.num_workers)\n test_dl_dict = {}\n for test_data_type in test_data_type_list:\n test_dl_dict[test_data_type] = DataLoader(test_dataset_dict[test_data_type], batch_size=args.test_bs, collate_fn=collate_fn, shuffle = False,\n num_workers=args.num_workers)\n rank_logger_info(logger, args.local_rank, \"Creating model...\")\n model = seg_model(sr_choose=args.sr_choose, encoder_input_dim=encode_model.encoder.get_input_dim(), args=args)\n model.to(args.device)\n encode_model.to(args.device)\n if args.local_rank != -1:\n model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank,find_unused_parameters=True)\n encode_model = torch.nn.parallel.DistributedDataParallel(encode_model, device_ids=[args.local_rank], output_device=args.local_rank,find_unused_parameters=True)\n\n rank_logger_info(logger, args.local_rank, \"Creating optimizer...\")\n params = []\n trainable_params_num = 0\n for name, param in list(encode_model.named_parameters()) + list(model.named_parameters()):\n if param.requires_grad:\n params.append(param)\n trainable_params_num += param.numel()\n rank_logger_info(logger, args.local_rank,f\"name: {name} | param_num: {param.numel()}\")\n rank_logger_info(logger, args.local_rank, f\"trainable_params_num: {trainable_params_num}\")\n optimizer = getattr(torch.optim, \"Adam\")(params, lr=1e-3) #\n rank_logger_info(logger, args.local_rank, model)\n rank_logger_info(logger, args.local_rank, \"Device on:\" + str(args.device))\n # train\n if not args.infer:\n model.train()\n best_val_pk,early_stop_cot,best_epoch,best_threshold = 1.0,0,0,0\n for j in range(args.epochs):\n train(model=model,encode_model=encode_model, args=args, epoch=j, dataset=train_dl, optimizer=optimizer,local_rank= args.local_rank)\n val_pk, threshold = validate(model,encode_model,args, j, dev_dl,local_rank=args.local_rank)\n if val_pk < best_val_pk:\n best_val_pk,early_stop_cot,best_epoch,best_threshold = val_pk,0,j+1,threshold\n rank_logger_info(logger, args.local_rank, colored(\n 'Current best model from epoch {} with p_k {} and threshold {}'.format(j+1, best_val_pk, threshold),'red'))\n rank_logger_info(logger, args.local_rank,f\"saveing best model with p_k {best_val_pk} and threshold {best_threshold}\")\n saved_dict = {\n \"best_model\":model,\n \"best_val_pk\":best_val_pk,\n \"best_epoch\":best_epoch,\n \"best_threshold\":best_threshold\n }\n if args.local_rank in [-1, 0]:\n with (args.checkpoint_dir / 'best_model.t7').open('wb') as f:\n torch.save(saved_dict, f)\n if args.etype == \"bert\": # only bert encoder may be finetuned\n with (args.checkpoint_dir / 'best_encode_model.t7').open('wb') as f:\n torch.save(encode_model, f)\n else:\n early_stop_cot += 1\n rank_logger_info(logger, args.local_rank,\"pk of epoch {} didnt decrease, early_stop_cot is {} now\".format(j+1,early_stop_cot))\n if early_stop_cot == args.early_stop:\n rank_logger_info(logger, args.local_rank, 'early stopping for epoch {}'.format(j+1))\n break\n # test\n rank_logger_info(logger, args.local_rank, \"Loading best model(and encode model)...\")\n best_dict = torch.load(str(args.checkpoint_dir / 'best_model.t7'))\n best_model, best_threshold, best_encode_model = best_dict[\"best_model\"],best_dict[\"best_threshold\"],torch.load(str(args.checkpoint_dir / 'best_encode_model.t7')) if args.etype == \"bert\" else encode_model\n rank_logger_info(logger, args.local_rank, \"Testing...\")\n test_all(best_model, best_encode_model, args, test_dl_dict,threshold = best_threshold, local_rank=args.local_rank)\n else:\n if args.sr_choose == 'random_baseline':\n rank_logger_info(logger, args.local_rank, \"Getting random baseline results...\")\n test_all_random_baseline(args, test_dl_dict, local_rank=args.local_rank)\n exit(1)\n train_checkpoint_dir = Path(re.sub(\"_infer\",\"\",str(args.checkpoint_dir))) if args.train_checkpoint_dir == None else Path(args.train_checkpoint_dir)\n rank_logger_info(logger, args.local_rank, f\"Loading best model(and encode model) from {str(train_checkpoint_dir)}...\")\n if os.path.exists(str(train_checkpoint_dir / 'best_model.t7')):\n try:\n best_dict = torch.load(str(train_checkpoint_dir / 'best_model.t7'))\n best_model, \\\n best_threshold, \\\n best_encode_model = best_dict[\"best_model\"], \\\n best_dict[\"best_threshold\"], \\\n torch.load(str(train_checkpoint_dir / 'best_encode_model.t7')) if args.etype == \"bert\" else encode_model\n except:\n best_model = torch.load(str(train_checkpoint_dir / 'best_model.t7'))\n assert args.best_threshold != None\n best_threshold = float(args.best_threshold)\n best_encode_model = torch.load(str(train_checkpoint_dir / 'best_encode_model.t7')) if args.etype == \"bert\" else encode_model\n rank_logger_info(logger, args.local_rank, \"Testing...\")\n test_all(best_model, best_encode_model, args, test_dl_dict, threshold=best_threshold,local_rank=args.local_rank)\n else:\n rank_logger_info(logger, args.local_rank, \"No existing best model(and encode model)!!!\")\n exit(1)\n\nif __name__ == '__main__':\n args = create_parser()\n set_seed(args.seed)\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\n rank_logger_info(logger, args.local_rank, stringify_flags(args))\n args.device = set_multi_gpu(args)\n main(args)"},{"alpha_fraction":0.5668557286262512,"alphanum_fraction":0.5802268981933594,"avg_line_length":35.80596923828125,"blob_id":"3780ed1e245af43add69e7bc06fb30096acfae11","content_id":"d1cbdab573ce46a4d5358754b90336baa11dc166","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2468,"license_type":"no_license","max_line_length":96,"num_lines":67,"path":"/encoder/one_hot_encoder.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\nimport torch\nfrom utils import *\nfrom collections import OrderedDict\nimport numpy as np\nclass one_hot_model(nn.Module):\n\n def __init__(self, vocab_path, args, encoder_fine_tune):\n super(one_hot_model,self).__init__()\n self.vocab_path = vocab_path\n self.args = args\n self.encoder_fine_tune = encoder_fine_tune\n self.local_rank = args.local_rank\n self.PADDING_TOKEN = \"<PAD>\"\n self.UNK_TOKEN = \"<UNK>\"\n self.embedding, self.word2idx = self.get_one_hot_embedding()\n self.pad_token_id = self.word2idx[self.PADDING_TOKEN]\n self.idx2word = OrderedDict()\n for item in self.word2idx.items():\n self.idx2word[item[1]] = item[0]\n\n self.embedding = nn.Embedding.from_pretrained(self.embedding)\n if encoder_fine_tune:\n for param in self.parameters():\n param.requires_grad = True\n else:\n for param in self.parameters():\n param.requires_grad = False\n\n def get_one_hot_embedding(self):\n logger = get_logger(self.args)\n rank_logger_info(logger, self.local_rank, 'Reading one-hot Vocab.')\n\n one_hot_word2idx = OrderedDict()\n one_hot_word2idx[self.PADDING_TOKEN] = len(one_hot_word2idx)\n one_hot_word2idx[self.UNK_TOKEN] = len(one_hot_word2idx)\n\n with open(self.vocab_path, encoding='utf-8') as f:\n for i, line in enumerate(f):\n if i == 10000:\n break\n word, vec = line.split(' ', 1)\n if word in one_hot_word2idx:\n continue\n else:\n one_hot_word2idx[word] = len(one_hot_word2idx)\n rank_logger_info(logger, self.local_rank, 'Reading one-hot embeddings.')\n embeddings = np.zeros((len(one_hot_word2idx) , len(one_hot_word2idx)), dtype=np.float32)\n for i in range(len(one_hot_word2idx)):\n embeddings[i][i] = 1\n return torch.tensor(embeddings), one_hot_word2idx\n\n def forward(self, inp):\n return self.embedding(inp)\n\n def tokenize(self, inp):\n word_lst = inp.split()\n idx_lst = []\n for word in word_lst:\n if word in self.word2idx:\n idx_lst.append(self.word2idx[word])\n else:\n idx_lst.append(self.word2idx[self.UNK_TOKEN])\n return idx_lst\n\n def get_word_dim(self):\n return len(self.word2idx)\n\n\n"},{"alpha_fraction":0.5827338099479675,"alphanum_fraction":0.5899280309677124,"avg_line_length":35.90909194946289,"blob_id":"00e86427470d90d0c440459da7392fa125c675af","content_id":"a5e3883739f3043204da6de1515966b16fb4019d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":834,"license_type":"no_license","max_line_length":80,"num_lines":22,"path":"/models/freq_sr.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\r\nimport torch\r\nclass f_model(nn.Module):\r\n def __init__(self, encoder_input_dim, args):\r\n super(f_model, self).__init__()\r\n self.args = args\r\n self.output_dim = encoder_input_dim\r\n self.device = self.args.device\r\n\r\n # def pad(self, s, max_length):\r\n # pad_lst = [self.encoder.model.pad_token_id] * (max_length - s.size(0))\r\n # s = torch.LongTensor(s.tolist() + pad_lst).to(self.device)\r\n # return s.unsqueeze(0) # (1, max_len)\r\n\r\n def forward(self, encoded_input):\r\n embeded_big_tensor, sentences_per_doc = encoded_input\r\n sent_repr = torch.sum(embeded_big_tensor, dim=1) # max_length, dim\r\n sent_repr[:, 0] = 0 # pad_idx\r\n return sent_repr, sentences_per_doc\r\n\r\n def get_output_dim(self):\r\n return self.output_dim\r\n"},{"alpha_fraction":0.5619910359382629,"alphanum_fraction":0.5695236325263977,"avg_line_length":51.39130401611328,"blob_id":"383480ef7a664b35abf76998f8dc023fc1a5688c","content_id":"37cddb7499276cc022b5ce253329898ee1648c71","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":9824,"license_type":"no_license","max_line_length":119,"num_lines":184,"path":"/models/Encode_model.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\r\nimport torch\r\nimport numpy as np\r\nfrom models.Treelstm.utils import *\r\nimport torch.nn.functional as F\r\nclass Encode_model(nn.Module):\r\n def __init__(self, encoder, args):\r\n super(Encode_model , self).__init__()\r\n self.encoder = encoder\r\n self.args = args\r\n self.device = self.args.device\r\n self.sr_choose = self.args.sr_choose\r\n self.tr_choose = self.args.tr_choose\r\n self.gr_choose = self.args.gr_choose\r\n if self.sr_choose == \"t_model\":\r\n if args.tr_choose == 'balanced':\r\n self.generate_mask_function = generate_balance_masks\r\n elif args.tr_choose == 'left':\r\n self.generate_mask_function = generate_left_branch_masks\r\n elif args.tr_choose == 'right':\r\n self.generate_mask_function = generate_right_branch_masks\r\n def pad(self, s, max_length):\r\n s_length = s.size()[0]\r\n v = s.unsqueeze(0).unsqueeze(0)\r\n padded = F.pad(v, (0, 0, 0, max_length - s_length)) # (1, 1, max_length, 300)\r\n shape = padded.size()\r\n return padded.view(shape[2], 1, shape[3]) # (max_length, 1, 300)\r\n\r\n def pad_s_model(self, s, max_length):\r\n pad_lst = [self.encoder.model.pad_token_id] * (max_length - s.size(0))\r\n s = torch.LongTensor(s.tolist() + pad_lst).to(self.device)\r\n return s.unsqueeze(1)\r\n\r\n def pad_l_model(self, s, max_length):\r\n pad_lst = [self.encoder.model.pad_token_id] * (max_length - s.size(0))\r\n s = torch.LongTensor(s.tolist() + pad_lst).to(self.device)\r\n return s.unsqueeze(0) # (1, max_len)\r\n\r\n def preprocess_treelstm(self, sentences_lst):\r\n lengths = [sentence.size(0) for sentence in sentences_lst] # (length,)\r\n max_length = max(lengths)\r\n padded_sentences_lst = [sentence.tolist() + [self.encoder.model.pad_token_id] * (max_length - sentence.size(0))\r\n for sentence in sentences_lst]\r\n masks = self.generate_mask_function(lengths)\r\n padded_sentences_tensor = torch.LongTensor(padded_sentences_lst).to(self.device)\r\n lengths = torch.LongTensor(lengths).to(self.device)\r\n return padded_sentences_tensor, lengths, masks\r\n\r\n def preprocess_gcn_adj(self,max_length,adjs):\r\n mask = torch.zeros((len(adjs), max_length, 1)).to(self.device)\r\n for i,adj in enumerate(adjs):\r\n pad = max_length - adj.shape[0]\r\n # print(\"adj\",adj.shape)\r\n # print(\"pad\",pad)\r\n padded_adj = F.pad(adj, (0,pad,0,pad))\r\n mask[i,:adj.shape[0],:] = 1.\r\n adjs[i] = padded_adj.unsqueeze(0) # 1, max_length, max_length\r\n return adjs, mask # double\r\n\r\n def preprocess_gcn_feature(self, max_length,features):\r\n for i,feature in enumerate(features):\r\n # feature: node_size, dim --> padded_feature: max_length, dim\r\n pad = max_length - feature.shape[0]\r\n padded_feature = F.pad(feature, (0,0,0,pad))\r\n features[i] = padded_feature.unsqueeze(0)\r\n return features\r\n\r\n def forward(self, batch):\r\n batch_size = len(batch)\r\n if self.sr_choose == \"s_model\":\r\n sentences_per_doc = []\r\n all_batch_sentences = []\r\n for document in batch:\r\n all_batch_sentences.extend(document)\r\n sentences_per_doc.append(len(document))\r\n\r\n lengths = [s.size()[0] for s in all_batch_sentences]\r\n sort_order = np.argsort(lengths)[::-1]\r\n sorted_sentences = [all_batch_sentences[i] for i in sort_order]\r\n sorted_lengths = [s.size()[0] for s in sorted_sentences]\r\n\r\n max_length = max(lengths)\r\n padded_sentences = [self.pad_s_model(s, max_length) for s in sorted_sentences]\r\n big_tensor = torch.cat(padded_sentences, 1)\r\n embeded_big_tensor = self.encoder(big_tensor)\r\n return batch_size,embeded_big_tensor,sorted_lengths,sort_order,sentences_per_doc\r\n elif self.sr_choose in [\"f_model\",\"l_model\"]:\r\n sentences_per_doc = []\r\n all_batch_sentences = []\r\n for document in batch:\r\n all_batch_sentences.extend(document)\r\n sentences_per_doc.append(len(document))\r\n lengths = [s.size()[0] for s in all_batch_sentences]\r\n max_length = max(lengths)\r\n padded_sentences = [self.pad_l_model(s, max_length) for s in all_batch_sentences]\r\n big_tensor = torch.cat(padded_sentences, 0) # (batch size, max_length)\r\n embeded_big_tensor = self.encoder(big_tensor) # (max_length, batch size, dim)\r\n return batch_size,embeded_big_tensor,sentences_per_doc\r\n elif self.sr_choose == \"t_model\" and self.tr_choose in [\"balanced\",\"left\",\"right\"]:\r\n sentences_per_doc = []\r\n all_batch_sentences = []\r\n for document in batch:\r\n all_batch_sentences.extend(document)\r\n sentences_per_doc.append(len(document))\r\n sentence, lengths, tree_masks = self.preprocess_treelstm(all_batch_sentences)\r\n embeded_sentence = self.encoder(sentence)\r\n return batch_size,embeded_sentence, lengths, tree_masks, sentences_per_doc\r\n elif self.sr_choose == \"b_model\":\r\n sentences_per_doc = []\r\n all_batch_sentences = []\r\n for document in batch:\r\n all_batch_sentences.extend(document)\r\n sentences_per_doc.append(len(document))\r\n lengths = [s.size()[0] for s in all_batch_sentences]\r\n sort_order = np.argsort(lengths)[::-1]\r\n sorted_sentences = [all_batch_sentences[i] for i in sort_order]\r\n sorted_lengths = [s.size()[0] for s in sorted_sentences]\r\n max_length = max(lengths)\r\n padded_sentences = [self.pad_l_model(s, max_length) for s in sorted_sentences]\r\n big_tensor = torch.cat(padded_sentences, 0) # (batch size,max_length)\r\n embeded_big_tensor = self.encoder(big_tensor)\r\n return batch_size,embeded_big_tensor,sorted_lengths,sort_order,sentences_per_doc\r\n elif self.sr_choose == \"g_model\" and self.gr_choose == \"texting\":\r\n # batch: [[[tensor(sent_num1,), tensor(sent_num1,sent_num1)], ...], [doc2], [doc3], []]\r\n sentences_per_doc = []\r\n all_batch_features = []\r\n all_batch_adjs = []\r\n for document in batch:\r\n sentences_per_doc.append(len(document))\r\n for sentence in document:\r\n feature, adj = sentence\r\n all_batch_features.append(feature)\r\n all_batch_adjs.append(adj)\r\n lengths = [s.size()[0] for s in all_batch_features]\r\n sort_order = np.argsort(lengths)[::-1]\r\n sorted_features = [all_batch_features[i] for i in sort_order]\r\n sorted_adjs = [all_batch_adjs[i] for i in sort_order]\r\n sorted_lengths = [s.size()[0] for s in sorted_features]\r\n max_length = max(lengths)\r\n\r\n padded_features = [self.pad_l_model(s, max_length) for s in sorted_features]\r\n features = torch.cat(padded_features, 0) # batch_size, max_length\r\n\r\n padded_adjs, mask = self.preprocess_gcn_adj(max_length,sorted_adjs)\r\n adjs = torch.cat(padded_adjs, 0).to(self.device)\r\n\r\n embeded_features = self.encoder(features)\r\n return batch_size, embeded_features, adjs, mask, sorted_lengths, sort_order, sentences_per_doc\r\n elif self.sr_choose == \"g_model\" and self.gr_choose == \"DSG_SEG\":\r\n # batch: [[sentences, feature, adj], [doc2], [doc3], []]\r\n # sentences: [[feature_idx1, feature_idx2, feature_idx3, ...],[s2],[s3],...]\r\n sentences_per_doc = []\r\n words_per_doc = []\r\n nodes_per_doc = []\r\n all_batch_sentences = []\r\n all_batch_features = []\r\n all_batch_adjs = []\r\n for document in batch:\r\n sentences, feature, adj = document\r\n sentences_per_doc.append(len(sentences))\r\n words_per_doc.append(feature.size(0))\r\n nodes_per_doc.append(len(sentences) + feature.size(0))\r\n all_batch_sentences.append(sentences)\r\n all_batch_features.append(feature)\r\n all_batch_adjs.append(adj)\r\n max_length = max(words_per_doc)\r\n padded_features = [self.pad_l_model(s, max_length) for s in all_batch_features]\r\n features = torch.cat(padded_features, 0) # batch_size, max_word_num\r\n embeded_features = self.encoder(features) # batch_size, max_word_num, dim\r\n doc_feature_lst = []\r\n for i in range(batch_size):\r\n doc_feature = []\r\n feature = embeded_features[i][:words_per_doc[i]] # word_num, dim\r\n for sentence in all_batch_sentences[i]:\r\n doc_feature.append(torch.max(feature[sentence,:], 0)[0].unsqueeze(0))\r\n doc_feature.append(feature)\r\n doc_feature = torch.cat(doc_feature, 0)\r\n doc_feature_lst.append(doc_feature)\r\n max_node_size = max(nodes_per_doc)\r\n padded_doc_features = self.preprocess_gcn_feature(max_node_size, doc_feature_lst)\r\n doc_features = torch.cat(padded_doc_features, 0).to(self.device) # batch_size, max_node_size, dim\r\n padded_adjs, mask = self.preprocess_gcn_adj(max_node_size, all_batch_adjs)\r\n adjs = torch.cat(padded_adjs, 0).to(self.device) # batch_size, max_node_size, max_node_size\r\n return batch_size, doc_features, adjs, mask, sentences_per_doc\r\n"},{"alpha_fraction":0.5492844581604004,"alphanum_fraction":0.5551468729972839,"avg_line_length":33.52381134033203,"blob_id":"781720079af2acfab1f69f3026db52a3366f0aa4","content_id":"0121f67049d6fa266e00e62cfc9ba954703b22fe","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":17399,"license_type":"no_license","max_line_length":141,"num_lines":504,"path":"/data/data_utils.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import scipy.sparse as sp\nimport numpy as np\nfrom math import log\nfrom nltk.tokenize import RegexpTokenizer\nimport nltk\n\n# from wiki_thresholds.py\nmin_section_char_count = 20\nmin_sentence_in_section = 2\nmax_section_formulas_count = 3\nmax_section_code_snipet_count = 2\nmin_valid_section_count = 3 # This includes preface section\nmin_words_in_sentence = 2\nmin_valid_section_percentage = 0.2\nmax_list_in_section_percentage = 0.5\nmax_words_in_sentence_with_backslash_n = 45\n\ndef remove_last_one(data, targets):\n return data, targets[:-1]\n\ndef remove_ing_last_one(data, adjs, targets):\n return data,adjs, targets[:-1]\n\ndef generate_return_dict(data,targets,path, all_document_num,filtered_document_num,sent_num_filter):\n data, targets = remove_last_one(data, targets)\n sent_num = len(data)\n if sent_num <= 2:\n return None, filtered_document_num, all_document_num\n assert len(targets) == len(data) - 1\n\n all_document_num += 1\n path_flag = -1\n if sent_num > sent_num_filter:\n filtered_document_num += 1\n return_dict = []\n start_idx = 0\n while start_idx <= sent_num - 1:\n path_flag += 1\n end_idx = start_idx + sent_num_filter\n if end_idx >= sent_num:\n end_idx = sent_num\n tmp_data = data[start_idx: end_idx] if end_idx == sent_num else data[\n start_idx: end_idx + 1] # include one more\n tmp_targets = targets[start_idx: end_idx]\n tmp_path = path + \"_|_{}\".format(path_flag)\n tmp_return_dict = {\n \"sentences\": tmp_data,\n \"targets\": tmp_targets,\n \"path\": tmp_path\n }\n if len(tmp_return_dict[\"sentences\"]) >= 2: #\n return_dict.append(tmp_return_dict)\n start_idx = end_idx\n else:\n return_dict = {\n \"sentences\": data,\n \"targets\": targets,\n \"path\": path\n }\n return return_dict, filtered_document_num, all_document_num\n\n\n\n\ndef generate_ing_return_dict(data,adjs,targets,path, all_document_num,filtered_document_num,sent_num_filter):\n data, adjs,targets = remove_ing_last_one(data, adjs,targets)\n sent_num = len(data)\n if sent_num <= 2:\n return None, filtered_document_num, all_document_num\n assert len(targets) == len(data) - 1\n assert len(data) == len(adjs)\n all_document_num += 1\n path_flag = -1\n if sent_num > sent_num_filter:\n filtered_document_num += 1\n return_dict = []\n start_idx = 0\n while start_idx <= sent_num - 1:\n path_flag += 1\n end_idx = start_idx + sent_num_filter\n if end_idx >= sent_num:\n end_idx = sent_num\n tmp_data = data[start_idx: end_idx] if end_idx == sent_num else data[\n start_idx: end_idx + 1] # include one more\n tmp_adj = adjs[start_idx: end_idx] if end_idx == sent_num else adjs[\n start_idx: end_idx + 1] # include one more\n tmp_targets = targets[start_idx: end_idx]\n tmp_path = path + \"_|_{}\".format(path_flag)\n tmp_return_dict = {\n \"sentences\": tmp_data,\n \"adjs\": tmp_adj,\n \"targets\": tmp_targets,\n \"path\": tmp_path\n }\n if len(tmp_return_dict[\"sentences\"]) >= 2: #\n return_dict.append(tmp_return_dict)\n start_idx = end_idx\n else:\n return_dict = {\n \"sentences\": data,\n \"adjs\": adjs,\n \"targets\": targets,\n \"path\": path\n }\n return return_dict, filtered_document_num, all_document_num\n\n\n\ndef build_ing_graph(doc_words, args):\n \"\"\"\n https://github.com/CRIPAC-DIG/TextING/blob/master/build_graph.py\n \"\"\"\n window_size = args.gnn_window_size\n doc_len = len(doc_words)\n doc_vocab = []\n for w in doc_words:\n if w not in doc_vocab:\n doc_vocab.append(w)\n doc_nodes = len(doc_vocab)\n doc_word_id_map = {}\n for j in range(doc_nodes):\n doc_word_id_map[doc_vocab[j]] = j\n # sliding windows\n windows = []\n if doc_len <= window_size:\n windows.append(doc_words)\n else:\n for j in range(doc_len - window_size + 1):\n window = doc_words[j: j + window_size]\n windows.append(window)\n num_window = len(windows)\n word_window_freq = {}\n for window in windows:\n appeared = set()\n for i in range(len(window)):\n if window[i] in appeared:\n continue\n if window[i] in word_window_freq:\n word_window_freq[window[i]] += 1\n else:\n word_window_freq[window[i]] = 1\n appeared.add(window[i])\n word_pair_count = {}\n for window in windows:\n for p in range(1, len(window)):\n for q in range(0, p):\n word_p = window[p]\n word_p_id = doc_word_id_map[word_p]\n word_q = window[q]\n word_q_id = doc_word_id_map[word_q]\n if word_p_id == word_q_id:\n continue\n word_pair_key = (word_p_id, word_q_id)\n # word co-occurrences as weights\n if word_pair_key in word_pair_count:\n word_pair_count[word_pair_key] += 1.\n else:\n word_pair_count[word_pair_key] = 1.\n # bi-direction\n word_pair_key = (word_q_id, word_p_id)\n if word_pair_key in word_pair_count:\n word_pair_count[word_pair_key] += 1.\n else:\n word_pair_count[word_pair_key] = 1.\n row = []\n col = []\n weight = []\n feature = []\n # preprocess of TextING, not used\n # for key in word_pair_count:\n # p = key[0]\n # q = key[1]\n # row.append(p) # p\n # col.append(q) # q\n # weight.append(word_pair_count[key] if weighted_graph else 1.)\n # adj = sp.csr_matrix((weight, (row, col)), shape=(doc_nodes, doc_nodes))\n for key in word_pair_count:\n p = key[0]\n q = key[1]\n count = word_pair_count[key]\n word_freq_p = word_window_freq[doc_vocab[p]]\n word_freq_q = word_window_freq[doc_vocab[q]]\n pmi = log((1.0 * count / num_window) /\n (1.0 * word_freq_p * word_freq_q / (num_window * num_window)))\n if pmi <= 0:\n continue\n row.append(p)\n col.append(q)\n weight.append(pmi)\n adj = sp.csr_matrix((weight, (row, col)), shape=(doc_nodes, doc_nodes))\n for k, v in sorted(doc_word_id_map.items(), key=lambda x: x[1]):\n feature.append(k)\n return feature, normalize_adj(adj)\n\ndef normalize_adj(adj):\n \"\"\"Symmetrically normalize adjacency matrix.\"\"\"\n rowsum = np.array(adj.sum(1)) # D\n with np.errstate(divide='ignore'):\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\n d_mat_inv_sqrt = np.diag(d_inv_sqrt)\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt)\n\ndef generate_gcn_return_dict(return_dict,args):\n if return_dict == None:\n return return_dict\n elif type(return_dict) == dict:\n sentences,targets,path = return_dict[\"sentences\"], return_dict[\"targets\"], return_dict[\"path\"]\n sentences, feature, adj = build_gcn_graph(sentences,args)\n output = {\"sentences\":sentences,\n \"feature\":feature,\n \"adj\":adj,\n \"targets\":targets,\n \"path\":path}\n return output\n else: # list\n output = []\n for tmp_return_dict in return_dict:\n output.append(generate_gcn_return_dict(tmp_return_dict,args))\n return output\n\ndef build_gcn_graph(sentences,args):\n window_size = args.gnn_window_size\n # weighted_graph = args.gnn_weighted_graph\n sent_num = len(sentences)\n doc_word_id_map = {}\n doc_vocab = []\n doc_len_lst = []\n for sentence in sentences:\n doc_len_lst.append(len(sentence))\n for word in sentence:\n if word not in doc_vocab:\n doc_vocab.append(word)\n\n doc_nodes = len(doc_vocab)\n\n for j in range(doc_nodes):\n doc_word_id_map[doc_vocab[j]] = j\n\n doc_nodes += sent_num\n new_sentences = []\n for idx, sentence in enumerate(sentences):\n new_sentences.append([doc_word_id_map[w] for w in sentence])\n\n windows = []\n for sentence in sentences:\n length = len(sentence)\n if length <= window_size:\n windows.append(sentence)\n else:\n for j in range(length - window_size + 1):\n window = sentence[j:j+window_size]\n windows.append(window)\n num_window = len(windows)\n word_window_freq = {}\n for window in windows:\n appeared = set()\n for i in range(len(window)):\n if window[i] in appeared:\n continue\n if window[i] in word_window_freq:\n word_window_freq[window[i]] += 1\n else:\n word_window_freq[window[i]] = 1\n appeared.add(window[i])\n\n word_pair_count = {}\n for window in windows:\n for p in range(1, len(window)):\n for q in range(0, p):\n word_p = window[p]\n word_p_id = doc_word_id_map[word_p]\n word_q = window[q]\n word_q_id = doc_word_id_map[word_q]\n if word_p_id == word_q_id:\n continue\n word_pair_key = (word_p_id, word_q_id)\n # word co-occurrences as weights\n if word_pair_key in word_pair_count:\n word_pair_count[word_pair_key] += 1.\n else:\n word_pair_count[word_pair_key] = 1.\n # bi-direction\n word_pair_key = (word_q_id, word_p_id)\n if word_pair_key in word_pair_count:\n word_pair_count[word_pair_key] += 1.\n else:\n word_pair_count[word_pair_key] = 1.\n\n word_doc_list = {}\n word_doc_freq = {}\n for sentence_idx,sentence in enumerate(sentences):\n appeared = set()\n for word in sentence:\n if word in appeared:\n continue\n if word in word_doc_list:\n word_doc_list[word].append(sentence_idx)\n else:\n word_doc_list[word] = [sentence_idx]\n appeared.add(word)\n for word, doc_list in word_doc_list.items():\n word_doc_freq[word] = len(doc_list)\n\n doc_word_freq = {}\n for sentence_idx,sentence in enumerate(sentences):\n for word in sentence:\n word_idx = doc_word_id_map[word]\n doc_word_str = (sentence_idx,word_idx)\n if doc_word_str in doc_word_freq:\n doc_word_freq[doc_word_str] += 1\n else:\n doc_word_freq[doc_word_str] = 1\n row = []\n col = []\n weight = []\n feature = []\n if args.tfidf:\n for key in doc_word_freq:\n freq = doc_word_freq[key]\n sentence_idx = key[0]\n word_idx = key[1]\n row.append(sentence_idx)\n col.append(word_idx + sent_num) # ***\n idf = log(1.0 * doc_len_lst[sentence_idx] / word_doc_freq[doc_vocab[word_idx]])\n tf_idf = freq * idf\n weight.append(tf_idf)\n else: # -TF-IDF\n for key in doc_word_freq:\n sentence_idx = key[0]\n word_idx = key[1]\n row.append(sentence_idx)\n col.append(word_idx + sent_num) # ***\n weight.append(1)\n if args.pmi:\n for key in word_pair_count:\n p = key[0]\n q = key[1]\n count = word_pair_count[key]\n word_freq_p = word_window_freq[doc_vocab[p]]\n word_freq_q = word_window_freq[doc_vocab[q]]\n pmi = log((1.0 * count / num_window) /\n (1.0 * word_freq_p * word_freq_q / (num_window * num_window)))\n if pmi <= 0:\n continue\n row.append(sent_num + p)\n col.append(sent_num + q)\n weight.append(pmi)\n else: # -PMI\n for key in word_pair_count:\n p = key[0]\n q = key[1]\n row.append(sent_num + p)\n col.append(sent_num + q)\n weight.append(1)\n adj = sp.csr_matrix((weight, (row, col)), shape=(doc_nodes, doc_nodes))\n for k, v in sorted(doc_word_id_map.items(), key=lambda x: x[1]):\n feature.append(k)\n adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)\n return new_sentences, feature, sp.csr_matrix(normalize_adj(adj+sp.eye(adj.shape[0])))\n\n\n\nsegment_seperator = \"========\"\n\n\ndef get_segment_seperator(level,name):\n return segment_seperator + \",\" + str(level) + \",\" +name\n\n\n\ndef get_seperator_foramt(levels = None):\n level_format = '\\d' if levels == None else '['+ str(levels[0]) + '-' + str(levels[1]) + ']'\n seperator_fromat = segment_seperator + ',' + level_format + \",.*?\\.\"\n return seperator_fromat\n\n\n\ndef is_seperator_line(line):\n return line.startswith(segment_seperator)\n\ndef get_segment_level(seperator_line):\n return int (seperator_line.split(',')[1])\n\ndef get_segment_name(seperator_line):\n return seperator_line.split(',')[2]\n\ndef get_list_token():\n return \"***LIST***\"\n\n\ndef get_formula_token():\n return \"***formula***\"\n\n\ndef get_codesnipet_token():\n return \"***codice***\"\n\n\ndef get_special_tokens():\n special_tokens = []\n special_tokens.append(get_list_token())\n special_tokens.append(get_formula_token())\n special_tokens.append(get_codesnipet_token())\n return special_tokens\n\nwords_tokenizer = None\nsentence_tokenizer = None\nmissing_stop_words = set(['of', 'a', 'and', 'to'])\ndef get_words_tokenizer():\n global words_tokenizer\n\n if words_tokenizer:\n return words_tokenizer\n\n words_tokenizer = RegexpTokenizer(r'\\w+')\n return words_tokenizer\n\ndef extract_sentence_words(sentence, remove_missing_emb_words = False,remove_special_tokens = False):\n if (remove_special_tokens):\n for token in get_special_tokens():\n # Can't do on sentence words because tokenizer delete '***' of tokens.\n sentence = sentence.replace(token, \"\")\n tokenizer = get_words_tokenizer()\n sentence_words = tokenizer.tokenize(sentence)\n if remove_missing_emb_words:\n sentence_words = [w for w in sentence_words if w not in missing_stop_words]\n return sentence_words\n\n\ndef get_punkt():\n global sentence_tokenizer\n if sentence_tokenizer:\n return sentence_tokenizer\n\n try:\n tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n except exceptions.LookupError:\n nltk.download('punkt')\n tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n\n sentence_tokenizer = tokenizer\n return sentence_tokenizer\n\n\ndef split_sentence_with_list(sentence):\n\n list_pattern = \"\\n\" + get_list_token() + \".\"\n if sentence.endswith( list_pattern ):\n splited_sentence = [str for str in sentence.split(\"\\n\" + get_list_token() + \".\") if\n len(str) > 0]\n splited_sentence.append(get_list_token() + \".\")\n return splited_sentence\n else:\n return [sentence]\n\ndef split_sentece_colon_new_line(sentence):\n\n splited_sentence = sentence.split(\":\\n\")\n if (len(splited_sentence) == 1):\n return splited_sentence\n new_sentences = []\n # -1 . not to add \":\" to last sentence\n for i in range(len(splited_sentence) - 1):\n if (len(splited_sentence[i]) > 0):\n new_sentences.append(splited_sentence[i] + \":\")\n if (len(splited_sentence[-1]) > 0):\n new_sentences.append(splited_sentence[-1])\n return new_sentences\n\ndef split_long_sentences_with_backslash_n(max_words_in_sentence,sentences, doc_id):\n new_sentences = []\n for sentence in sentences:\n sentence_words = extract_sentence_words(sentence)\n if len(sentence_words) > max_words_in_sentence:\n splitted_sentences = sentence.split('\\n')\n new_sentences.extend(splitted_sentences )\n else:\n new_sentences.append(sentence)\n return new_sentences\n\ndef split_sentences(text, doc_id):\n sentences = get_punkt().tokenize(text)\n senteces_list_fix = []\n for sentence in sentences:\n seplited_list_sentence = split_sentence_with_list(sentence)\n senteces_list_fix.extend(seplited_list_sentence)\n\n sentence_colon_fix = []\n for sentence in senteces_list_fix:\n splitted_colon_sentence = split_sentece_colon_new_line(sentence)\n sentence_colon_fix.extend(splitted_colon_sentence)\n\n sentences_without_backslash_n = split_long_sentences_with_backslash_n(max_words_in_sentence_with_backslash_n, sentence_colon_fix, doc_id)\n\n ret_sentences = []\n for sentence in sentences_without_backslash_n:\n ret_sentences.append(sentence.replace('\\n',' '))\n return ret_sentences\n\n\ndef clean_paragraph(paragraph):\n cleaned_paragraph= paragraph.replace(\"'' \", \" \").replace(\" 's\", \"'s\").replace(\"``\", \"\").strip('\\n')\n return cleaned_paragraph"},{"alpha_fraction":0.5622189044952393,"alphanum_fraction":0.572526216506958,"avg_line_length":38.82089614868164,"blob_id":"266628859d7fcac34eb983fa408669ed56e7c712","content_id":"ddaed2da6ddd9e81b8a884c5c7e8a113054e9798","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5336,"license_type":"no_license","max_line_length":132,"num_lines":134,"path":"/models/GGNN/layers.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch\nimport torch.nn as nn\nfrom models.GGNN.inits import *\n\nclass GraphConvolution(nn.Module):\n def __init__( self, input_dim, \\\n output_dim, \\\n act = None, \\\n dropout_p = 0., \\\n bias=True):\n super(GraphConvolution, self).__init__()\n self.input_dim = input_dim\n self.output_dim = output_dim\n self.dropout_p = dropout_p\n self.weight = glorot(self.input_dim, self.output_dim)\n if bias:\n self.b = uniform_bias(self.weight, self.output_dim)\n\n self.act = act\n self.dropout = nn.Dropout(self.dropout_p)\n\n\n def forward(self, x, adj, mask):\n x = self.dropout(x)\n pre_sup = torch.matmul(x, self.weight) +self.b# bs, node_size, input_dim x input_dim, output_dim = bs, node_size, output_dim\n # pre_sup = x.mm(self.weight)\n pre_sup = mask * pre_sup\n # out = adj.mm(pre_sup)\n out = torch.bmm(adj, pre_sup) # bs, node_size, node_size x node_size, output_dim\n out = self.act(out)\n return out\n\nclass gru_unit(nn.Module):\n def __init__(self, output_dim, act, dropout_p):\n super(gru_unit,self).__init__()\n self.output_dim = output_dim\n self.dropout_p = dropout_p\n self.dropout = nn.Dropout(self.dropout_p)\n self.act = act\n self.z0_weight = glorot(self.output_dim, self.output_dim)\n self.z1_weight = glorot(self.output_dim, self.output_dim)\n self.r0_weight = glorot(self.output_dim, self.output_dim)\n self.r1_weight = glorot(self.output_dim, self.output_dim)\n self.h0_weight = glorot(self.output_dim, self.output_dim)\n self.h1_weight = glorot(self.output_dim, self.output_dim)\n self.z0_bias = uniform_bias(self.z0_weight, self.output_dim)\n self.z1_bias = uniform_bias(self.z1_weight, self.output_dim)\n self.r0_bias = uniform_bias(self.r0_weight, self.output_dim)\n self.r1_bias = uniform_bias(self.r1_weight, self.output_dim)\n self.h0_bias = uniform_bias(self.h0_weight, self.output_dim)\n self.h1_bias = uniform_bias(self.h1_weight, self.output_dim)\n\n def forward(self,adj, x, mask):\n # adj: bs,ml,ml | x: bs,ml,dim\n adj = self.dropout(adj)\n a = torch.matmul(adj, x)\n # updata gate\n z0 = torch.matmul(a, self.z0_weight) + self.z0_bias\n z1 = torch.matmul(x, self.z1_weight) + self.z1_bias\n z = torch.sigmoid(z0+z1)\n # reset gate\n r0 = torch.matmul(a, self.r0_weight) + self.r0_bias\n r1 = torch.matmul(x, self.r1_weight) + self.r1_bias\n r = torch.sigmoid(r0+r1)\n # update embeddings\n h0 = torch.matmul(a, self.h0_weight) + self.h0_bias\n h1 = torch.matmul(r*x, self.h1_weight) + self.h1_bias\n h = self.act(mask * (h0 + h1))\n return h*z + x*(1-z)\n\n\nclass GraphLayer(nn.Module):\n \"\"\"Graph layer.\"\"\"\n def __init__(self, args,\n input_dim,\n output_dim,\n act=nn.Tanh(),\n dropout_p = 0.):\n super(GraphLayer, self).__init__()\n self.args = args\n self.input_dim = input_dim\n self.output_dim = output_dim\n self.act = act\n self.dropout_p = dropout_p\n self.dropout = nn.Dropout(self.dropout_p)\n self.gru_step = self.args.texting_gru_step\n self.gru_unit = gru_unit(self.output_dim, self.act, self.dropout_p)\n # self.dropout\n self.encode_weight = glorot(self.input_dim, self.output_dim)\n self.encode_bias = uniform_bias(self.encode_weight, self.output_dim)\n\n def forward(self, feature, adj, mask):\n feature = self.dropout(feature)\n # encode inputs\n encoded_feature = torch.matmul(feature, self.encode_weight) +self.encode_bias\n output = mask * self.act(encoded_feature)\n # convolve\n for _ in range(self.gru_step):\n output = self.gru_unit(adj, output, mask)\n return output\n\nclass ReadoutLayer(nn.Module):\n \"\"\"Graph Readout Layer.\"\"\"\n def __init__(self, args,\n input_dim,\n output_dim,\n act=nn.ReLU(),\n dropout_p=0.):\n super(ReadoutLayer, self).__init__()\n self.args = args\n self.input_dim = input_dim\n self.output_dim = output_dim\n self.act = act\n self.dropout_p = dropout_p\n self.dropout = nn.Dropout(self.dropout_p)\n self.att_weight = glorot(self.input_dim, 1)\n self.emb_weight = glorot(self.input_dim, self.input_dim)\n self.mlp_weight = glorot(self.input_dim, self.output_dim)\n self.att_bias = uniform_bias(self.att_weight,1)\n self.emb_bias = uniform_bias(self.emb_weight,self.input_dim)\n self.mlp_bias = uniform_bias(self.mlp_weight,self.input_dim)\n\n def forward(self,x,mask):\n # soft attention\n att = torch.sigmoid(torch.matmul(x, self.att_weight)+self.att_bias)\n emb = self.act(torch.matmul(x, self.emb_weight)+self.emb_bias)\n # graph summation\n N = torch.sum(mask, dim=1)\n M = (mask - 1) * 1e9\n # classification\n g = mask * att * emb\n g = torch.sum(g, dim=1)/N + torch.max(g+M,dim=1)[0]\n # g = self.dropout(g)\n return g\n"},{"alpha_fraction":0.7063773274421692,"alphanum_fraction":0.7411426305770874,"avg_line_length":33.47328186035156,"blob_id":"88edfffc5b9d952461510c445435373c0841e434","content_id":"1d89768b24c09505de4bb0f60bafce7bd4393a6f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":5470,"license_type":"no_license","max_line_length":259,"num_lines":131,"path":"/README.md","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"# 《基于篇章结构图网络的话题分割》(Topic Segmentation via Discourse Structure Graph Network)\n该仓库包含了《基于篇章结构图网络的话题分割》文章的代码和数据集，代码使用pytorch框架实现。\n我们的工作主要基于[《Text Segmentation as a Supervised Learning Task》](https://arxiv.org/abs/1803.09337) ，它的源代码是[text-segmentation](https://github.com/koomri/text-segmentation)。\n另外，我们的模型灵感主要来自于 [《Every Document Owns Its Structure: Inductive Text Classification via\nGraph Neural Networks》](https://arxiv.org/abs/2004.13826)([TextING的代码](https://github.com/CRIPAC-DIG/TextING)) 以及 [《Graph Convolutional Networks for Text Classification》](https://arxiv.org/abs/1809.05679)([TextGCN的代码](https://github.com/yao8839836/text_gcn))。\n谢谢他们的工作。\n\n# 论文简介\n\n\n我们提出了一个基于篇章结构图网络的话题分割模型——DSG-SEG，如上图所示.具体的，模型把每一个篇章单独构建成图，图中包含了单词、句子节点，以及单词节点之间、单词和句子节点之间的邻接关系；接着，模型把图作为输入，对GGNN网络进行迭代，句子节点之间因此通过共有的邻接单词节点产生了间接的信息交互；最终，模型得到了具有全局语义信息的句子向量表示，并将其送入Bi-LSTM网络进行分割点的预测。\n\n我们对比了六种基准模型:freq-sr\\latent-sr\\tree-sr-left\\TextSeg\\Bert+Bi-LSTM\\Bert+Bi-LSTMnft（除TextSeg外，其它基准模型皆为我们复现）我们的模型在诸多数据集上同时取得了最好的结果指标和时间性能。\n\n# 环境配置\npython3.7\n```bash\nconda create -n textseg python=3.7\nconda activate textseg\n```\npython依赖库\n```bash\npip install -r requirements.txt\n```\n\n# 资源获取 \n假设所有资源保存在/data路径下，完成这部分，你将得到: \n[查看文件结构](https://github.com/beiweixiaoxu/DSG-SEG/blob/master/doc/file_tree.md)\n\nglove: \nwget http://nlp.stanford.edu/data/glove.6B.zip \nunzip glove.6B.zip \nrm glove.6B.(50d|100d|200d).txt \n \nword2vec: \nwget https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz \ngzip -d GoogleNews-vectors-negative300.bin.gz \n解压完毕得到:GoogleNews-vectors-negative300.bin \n \nWIKI_727K: \nwget https://www.dropbox.com/sh/k3jh0fjbyr0gw0a/AADzAd9SDTrBnvs1qLCJY5cza?dl=0&preview=wiki_727K.tar.bz2 \ntar -xjf wiki_727K.tar.bz2 \n\nwiki_test_50: \nwget https://www.dropbox.com/sh/k3jh0fjbyr0gw0a/AADzAd9SDTrBnvs1qLCJY5cza?dl=0&preview=wiki_test_50.tar.bz2 \ntar -xjf wiki_test_50.tar.bz2 \n\nWIKI-SECTION: \nwget https://github.com/sebastianarnold/WikiSection/raw/master/wikisection_dataset_ref.tar.gz \nmkdir wikisection & tar -zxvf wikisection_dataset_ref.tar.gz -C wikisection \n \nCHOI: \n手动下载 https://github.com/koomri/text-segmentation/tree/master/data/choi \nmkdir choi \n放入文件夹 \n \nMANIFESTO: \n手动下载 https://github.com/koomri/text-segmentation/tree/master/data/manifesto \nmkdir manifesto \n放入文件夹 \n \nCLINICAL: \n手动下载 https://github.com/pinkeshbadjatiya/neuralTextSegmentation/tree/master/code/data/clinical \nmkdir clinical \n放入文件夹 \n \nCITIES: \nwget http://groups.csail.mit.edu/rbg/code/mallows/data/wikicities-english.tar.gz \ntar -zxvf wikicities-english.tar.gz \nrm -r wikicities-english/test \nmv wikicities-english/training/* wikicities-english/ \nrm -r wikicities-english/training \nmv wikicities-english wikicities \n\nELEMENTS: \nwget http://groups.csail.mit.edu/rbg/code/mallows/data/wikielements.tar.gz \ntar -zxvf wikielements.tar.gz \n\n# 代码运行 \n[查看代码结构](https://github.com/beiweixiaoxu/DSG-SEG/blob/master/doc/code_tree.md) \n两个修改: \n1. 永久修改parameters.py中，各个资源的路径，如`parser.add_argument(\"--wiki_path\",type=str, default=\"/data/wiki_727\")` \n2. 永久修改parameters.py中，保存dataset和encoder的路径(用于避免重复读取)，如`parser.add_argument(\"--dataset_dir\",type=str, default=\"/data/saved_dataset\")` \n样例: \n```python\npython run.py [--etype {one_hot,glove,w2v,bert}] [--encoder_fine_tune]\n [--sr_choose {f_model,l_model,s_model,t_model,g_model,b_model,random_baseline}]\n [--tr_choose {balanced,left,right}]\n [--gr_choose {texting,DSE_SEG}]\n ...\n```\nDSG_GCN:\n```python\npython run.py --sr_choose=g_model --gr_choose=DSE_SEG\n```\n-SENTNODE:\n```python\npython run.py --sr_choose=g_model --gr_choose=texting\n```\n-PMI:\n```python\npython run.py --sr_choose=g_model --gr_choose=DSG_SEG --pmi\n```\nBert+Bi-LSTMnft:\n```python\npython run.py --etype=bert --sr_choose=b_model --length_filter=64\n```\nTextSeg:\n```python\npython run.py --sr_choose=s_model\n```\nBert+Bi-LSTM:\n```python\npython run.py --etype=bert --sr_choose=b_model --length_filter=40 --sent_num_filter=60 --train_bs=3 --encoder_fine_tune\n```\ntree-sr-left\n```python\npython run.py --sr_choose=t_model --tr_choose=left --length_filter=40 --sent_num_filter=60\n```\nfreq-sr:\n```python\npython run.py --etype=one-hot --sr_choose=f_model\n```\nlatent-sr:\n```python\npython run.py --etype=one-hot --sr_choose=l_model\n```\nrandom-baseline:\n```python\npython run.py --sr_choose=random_baseline --infer --train_bs=1 --dev_bs=1 --test_bs=1 \n```\n"},{"alpha_fraction":0.5210893154144287,"alphanum_fraction":0.5257389545440674,"avg_line_length":48.776859283447266,"blob_id":"09ebb9b5498986768f1a7b8affd8a9cc65aab5be","content_id":"33ff591fe81136d7c90e8d37084a4200565b78b0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6022,"license_type":"no_license","max_line_length":196,"num_lines":121,"path":"/data/clinical_loader.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"from pathlib import Path\nfrom torch.utils.data import Dataset\nfrom data.data_utils import *\nfrom utils import *\nimport os\nfrom parameters import create_parser\n\ndef clean_paragraph(paragraph):\n cleaned_paragraph= paragraph.replace(\"'' \", \" \").replace(\" 's\", \"'s\").replace(\"``\", \"\").strip('\\n')\n return cleaned_paragraph\n\ndef read_clinical_file(args, filtered_num, all_num, filtered_document_num, all_document_num, path, encoder=None,length_filter=10, sent_num_filter=10):\n seperator = '=========='\n with Path(path).open('r') as f:\n raw_text = f.read()\n paragraphs = [clean_paragraph(p) for p in raw_text.strip().split(seperator)\n if len(p) > 5 and p != \"\\n\"]\n targets = []\n adjs = []\n new_text = []\n for paragraph in paragraphs:\n sentences = [s for s in paragraph.split('\\n') if len(s.split()) > 0]\n if sentences:\n sentences_count =0\n # This is the number of sentences in the paragraph and where we need to split.\n for sentence in sentences:\n words = extract_sentence_words(sentence)\n if (len(words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\n continue\n sentences_count +=1\n # length_filter\n if len(words) > length_filter:\n words = words[:length_filter]\n filtered_num += 1\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n words, adj = build_ing_graph(words, args)\n adjs.append(adj)\n if encoder:\n new_text.append(encoder.tokenize(words))\n else:\n new_text.append(words)\n all_num += sentences_count\n targets.extend([0] * (sentences_count - 1))\n if sentences_count != 0:\n targets.append(1) # segment\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(new_text, adjs, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\n return_dict, filtered_document_num, all_document_num = generate_return_dict(new_text, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n return_dict = generate_gcn_return_dict(return_dict,args)\n else:\n return_dict, filtered_document_num, all_document_num = generate_return_dict(new_text, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n return return_dict, filtered_num, all_num,filtered_document_num, all_document_num\n\ndef get_cache_path(folder):\n cache_file_path = folder / 'paths_cache'\n return cache_file_path\n\ndef cache_filenames(folder):\n files = Path(folder).glob('*.ref')\n cache_path = get_cache_path(folder)\n with cache_path.open(\"w+\") as f:\n for file in files:\n f.write(str(file) + u'\\n')\n\nclass ClinicalDataSet(Dataset):\n def __init__(self,\n root = None,\n args=None,\n encoder = None,\n length_filter = 40,\n sent_num_filter = 60,\n local_rank=-1):\n logger = get_logger(args)\n root_path = Path(root)\n cache_path = get_cache_path(root_path)\n if not cache_path.exists():\n cache_filenames(root_path)\n self.textfiles = cache_path.read_text().splitlines()\n else:\n self.textfiles = cache_path.read_text().splitlines()\n if len(self.textfiles) == 0:\n raise RuntimeError('Found 0 images in subfolders of: {}'.format(root))\n self.data = []\n # sentences\n filtered_num = 0\n all_num = 0\n # documents\n filtered_document_num = 0\n all_document_num = 0\n rank_logger_info(logger, local_rank, \"Reading clinical data\")\n for textfile in self.textfiles:\n return_dict, filtered_num, all_num, filtered_document_num, all_document_num = read_clinical_file(args,filtered_num,\n all_num,\n filtered_document_num,\n all_document_num,\n textfile,\n encoder=encoder,\n length_filter=length_filter,\n sent_num_filter=sent_num_filter)\n if return_dict == None:\n continue\n elif type(return_dict) == dict:\n self.data.append(return_dict)\n else: # list\n self.data.extend(return_dict)\n if all_num != 0:\n rank_logger_info(logger, local_rank, \"all sentences num:%d\\tfiltered num:%d\\tfiltered percent:%f\"%(all_num, filtered_num,filtered_num/all_num))\n if all_document_num != 0:\n rank_logger_info(logger, local_rank, \"all documents num:%d\\tfiltered num:%d\\tfiltered percent:%f\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\n\n def __getitem__(self, index):\n return self.data[index]\n\n def __len__(self):\n return len(self.data)\nif __name__ == \"__main__\":\n args = create_parser()\n print(args)\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\n clinicaldataset = ClinicalDataSet(args.clinical_path, args)"},{"alpha_fraction":0.6526409983634949,"alphanum_fraction":0.6589077711105347,"avg_line_length":41.03845977783203,"blob_id":"da584f5fc26a2d82a56a339b5c60e9a26c55a354","content_id":"2efa775a6b30f6a94deed88fdf44be27e1cb5603","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1117,"license_type":"no_license","max_line_length":111,"num_lines":26,"path":"/models/bert_sr.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\r\nimport numpy as np\r\nimport torch.nn.functional as F\r\nimport torch\r\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\r\nfrom utils import unsort\r\n\r\nclass b_model(nn.Module):\r\n def __init__(self, encoder_input_dim, args):\r\n super(b_model, self).__init__()\r\n self.output_dim = encoder_input_dim\r\n self.device = args.device\r\n\r\n\r\n def forward(self,encoded_input):\r\n embeded_big_tensor,sorted_lengths,sort_order,sentences_per_doc = encoded_input\r\n batch_size, out_dim = embeded_big_tensor.shape[0],embeded_big_tensor.shape[2]\r\n pooled_output = embeded_big_tensor[:,0,:].squeeze(1) # torch.zeros(batch_size, out_dim).to(self.device)\r\n # for i in range(batch_size):\r\n # maxes[i, :] = torch.max(embeded_big_tensor[i, :sorted_lengths[i],:], 0)[0]\r\n unsort_order = torch.LongTensor(unsort(sort_order)).to(self.device)\r\n unsorted_sent_repr = pooled_output.index_select(0, unsort_order)\r\n return unsorted_sent_repr, sentences_per_doc\r\n\r\n def get_output_dim(self):\r\n return self.output_dim"},{"alpha_fraction":0.5677999258041382,"alphanum_fraction":0.5904650092124939,"avg_line_length":34.54166793823242,"blob_id":"21a5d0b3f1df35542c63e9d658411afc4d669926","content_id":"90dddf3df362ed57f63d5ba94ee7297da5a7453f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2559,"license_type":"no_license","max_line_length":96,"num_lines":72,"path":"/encoder/w2v_encoder.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\nimport torch\nfrom utils import *\nfrom collections import OrderedDict\nimport numpy as np\nimport gensim\nclass w2v_model(nn.Module):\n\n def __init__(self, vocab_path, args, encoder_fine_tune):\n super(w2v_model,self).__init__()\n self.vocab_path = vocab_path\n self.args = args\n self.encoder_fine_tune = encoder_fine_tune\n self.local_rank = args.local_rank\n self.PADDING_TOKEN = \"<PAD>\"\n self.UNK_TOKEN = \"<UNK>\"\n self.embedding, self.word2idx = self.get_w2v_embedding()\n self.pad_token_id = self.word2idx[self.PADDING_TOKEN]\n self.idx2word = OrderedDict()\n for item in self.word2idx.items():\n self.idx2word[item[1]] = item[0]\n self.embedding = nn.Embedding.from_pretrained(self.embedding)\n if encoder_fine_tune:\n for param in self.parameters():\n param.requires_grad = True\n else:\n for param in self.parameters():\n param.requires_grad = False\n\n def get_w2v_embedding(self):\n size = 300\n logger = get_logger(self.args)\n rank_logger_info(logger, self.local_rank, 'Reading w2v Vocab.')\n word2vec = gensim.models.KeyedVectors.load_word2vec_format(self.vocab_path, binary=True)\n\n w2v_word2idx = OrderedDict()\n w2v_word2idx[self.PADDING_TOKEN] = len(w2v_word2idx)\n # w2v_word2idx[self.UNK_TOKEN] = len(w2v_word2idx)\n\n for w in word2vec.vocab:\n if w in w2v_word2idx:\n continue\n else:\n w2v_word2idx[w] = len(w2v_word2idx)\n\n embeddings = np.zeros((len(w2v_word2idx), size), dtype=np.float32)\n embeddings[0] = np.zeros((1, size))\n # embeddings[1] = np.random.randn(1, size)\n rank_logger_info(logger, self.local_rank, 'Reading w2v Embeddings.')\n for word in word2vec.vocab:\n vec = word2vec[word]\n if word not in w2v_word2idx:\n continue\n word_id = w2v_word2idx[word]\n embeddings[word_id] = vec\n\n return torch.tensor(embeddings), w2v_word2idx\n\n def tokenize(self, inp):\n word_lst = inp.split()\n idx_lst = []\n for word in word_lst:\n if word in self.word2idx:\n idx_lst.append(self.word2idx[word])\n else:\n idx_lst.append(self.word2idx['UNK']) # self.word2idx[self.UNK_TOKEN]\n return idx_lst\n\n def forward(self,inp):\n return self.embedding(inp)\n def get_word_dim(self):\n return 300\n"},{"alpha_fraction":0.6674103140830994,"alphanum_fraction":0.6840569972991943,"avg_line_length":60.98936080932617,"blob_id":"e6cf9b2c879ba0968ee045c3b2cda99dcfc7ef60","content_id":"d2b1d0b8dab584ca60ba358ba786bec48b4a02e1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5827,"license_type":"no_license","max_line_length":157,"num_lines":94,"path":"/parameters.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"from utils import *\nimport os\nfrom argparse import ArgumentParser\ndef create_parser():\n parser = ArgumentParser()\n parser.add_argument('--logger_name', help='logger name', default='seg')\n parser.add_argument('--early_stop', help='early stopping epoch', type = int, default=5)\n parser.add_argument('--train_bs', help='Batch size', type=int, default=8)\n parser.add_argument('--dev_bs', help='Batch size', type=int, default=5)\n parser.add_argument('--test_bs', help='Batch size', type=int, default=5)\n parser.add_argument('--epochs', help='Number of epochs to run', type=int, default=100)\n parser.add_argument('--checkpoint_dir', help='Checkpoint directory', default='checkpoints')\n parser.add_argument('--stop_after', help='Number of batches to stop after', default=None, type=int)\n parser.add_argument('--num_workers', help='How many workers to use for data loading', type=int, default=0)\n parser.add_argument('--high_granularity', help='Use high granularity for wikipedia dataset segmentation', action='store_true')\n parser.add_argument('--wiki_random', action='store_false')\n # saved_dataset&saved_encoder\n parser.add_argument(\"--dataset_dir\",type=str, default=\"/p300/for_git/saved_dataset\")\n parser.add_argument(\"--encoder_dir\", type=str, default=\"/p300/for_git/saved_encoder\")\n # frac&filter\n parser.add_argument(\"--data_frac\", type=float, default=\"1.0\")\n parser.add_argument(\"--length_filter\", type=int, default=40000)\n parser.add_argument(\"--sent_num_filter\", type=int, default=60000)\n # data path\n parser.add_argument(\"--glove_path\", type=str, default=\"/p300/for_git/glove.6B.300d.txt\")\n parser.add_argument(\"--w2v_path\", type=str, default=\"/p300/for_git/GoogleNews-vectors-negative300.bin\")\n parser.add_argument(\"--wiki_path\",type=str, default=\"/p300/for_git/wiki_727\")\n parser.add_argument(\"--choi_path\", type=str, default=\"/p300/for_git/choi\")\n parser.add_argument(\"--manifesto_path\", type=str, default=\"/p300/for_git/manifesto\")\n parser.add_argument(\"--clinical_path\", type=str, default=\"/p300/for_git/clinical\")\n parser.add_argument(\"--wikisection_path\", type=str, default=\"/p300/for_git/wikisection\")\n parser.add_argument(\"--wiki50_path\", type=str, default=\"/p300/for_git/wiki_50\")\n parser.add_argument(\"--wikicities_path\", type=str, default=\"/p300/for_git/wikicities\")\n parser.add_argument(\"--wikielements_path\", type=str, default=\"/p300/for_git/wikielements\")\n parser.add_argument(\"--train_data_type\", type=str, default=\"wiki\")\n # Encoder\n parser.add_argument(\"--etype\", type=str,choices=[\"one-hot\",\"glove\",\"randn\",\"w2v\",\"bert\"],default=\"w2v\")\n parser.add_argument(\"--encoder_fine_tune\", action = \"store_true\") # for bert\n # model\n parser.add_argument(\"--sr_choose\", type=str,choices =[\"f_model\",\"l_model\",\"s_model\",\"t_model\",\"g_model\",\"b_model\",\"random_baseline\"] , default=\"s_model\")\n parser.add_argument('--hidden_dim', type=int, default = 256)\n parser.add_argument('--num_layers', type=int, default = 2)\n # pooling for bilstm & treelstm\n parser.add_argument(\"--pool_method\",type=str,default=\"max\") # max mean attention 'None'\n parser.add_argument('--attention_hd', type=int, default=300)\n # treelstm\n parser.add_argument('--tr_choose', type=str,choices=[\"balanced\",\"left\",\"right\"], default='balanced')\n parser.add_argument(\"--use_leaf_rnn\",action='store_true')\n parser.add_argument(\"--bidirectional\",action='store_true')\n # bilstm\n parser.add_argument('--bilstm_hd', help=\"hidden dim\",type=int, default = 256)\n parser.add_argument('--bilstm_nl', help=\"num layers\",type=int, default = 2)\n parser.add_argument('--bilstm_bd', help=\"bidirectional\", type=bool, default=True)\n # bert\n parser.add_argument('--model_size', help='model size',type=str, default='base')\n parser.add_argument('--cased', help='cased', action='store_false')\n # gnn\n parser.add_argument('--tfidf',action='store_true')\n parser.add_argument('--pmi', action='store_false')\n parser.add_argument('--gr_choose', choices=[\"texting\",\"DSG_SEG\"],type=str, default='texting')\n parser.add_argument('--gnn_window_size', type=int, default=3)\n # texting\n parser.add_argument('--texting_hidden_dim', type=int, default=300)\n parser.add_argument('--texting_output_dim', type=int, default=300)\n parser.add_argument('--texting_gru_step', type=int, default=2)\n # DSG_SEG\n parser.add_argument('--DSG_SEG_output_dim', type=int, default=300)\n # multi-gpu\n parser.add_argument(\"--local_rank\", type=int, default=-1)\n parser.add_argument(\"--seed\", type=int, default=0)\n # test\n parser.add_argument('--infer', help='inference_dir', action = \"store_true\")\n parser.add_argument('--train_checkpoint_dir', help='best model path', default=None)\n parser.add_argument('--best_threshold', help='models trained before were saved in a different way~', default=None)\n args = parser.parse_args()\n\n args.valid_data_type = args.train_data_type\n checkpoint_name = compute_name(args)\n dataset_name = compute_dataset_name(args)\n encoder_name = compute_encoder_name(args)\n if args.train_checkpoint_dir != None:\n args.train_checkpoint_dir = os.path.join(args.checkpoint_dir, args.train_checkpoint_dir)\n args.checkpoint_dir = os.path.join(args.checkpoint_dir, checkpoint_name)\n # dataset\n args.dataset_path = os.path.join(args.dataset_dir, dataset_name + \".pt\")\n # encoder\n if args.encoder_fine_tune:\n args.encoder_path = os.path.join(args.encoder_dir, encoder_name + \".pt\")\n else:\n args.encoder_path = os.path.join(args.encoder_dir, encoder_name + \".pt\")\n os.makedirs(args.checkpoint_dir, exist_ok=True)\n os.makedirs(args.dataset_dir, exist_ok=True)\n os.makedirs(args.encoder_dir, exist_ok=True)\n return args\n"},{"alpha_fraction":0.6245912313461304,"alphanum_fraction":0.6291694045066833,"avg_line_length":38.23684310913086,"blob_id":"537e2398e9117940bd11196df9e120bf484b0833","content_id":"bd122641e2aa395f54be513b773e6bfc60a3f6c1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1529,"license_type":"no_license","max_line_length":102,"num_lines":38,"path":"/models/treelstm_sr.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch\r\nimport torch.nn as nn\r\nfrom models.Treelstm.basic import *\r\nfrom models.Treelstm.utils import *\r\nfrom models.Treelstm.models import *\r\nclass t_model_treelstm(nn.Module):\r\n def __init__(self,encoder_input_dim,args):\r\n super(t_model_treelstm, self).__init__()\r\n self.use_leaf_rnn = args.use_leaf_rnn\r\n self.pooling_method = args.pool_method\r\n assert self.pooling_method in ['None','max','mean','attention']\r\n self.bidirectional = args.bidirectional\r\n self.device = args.device\r\n self.hidden_dim = args.attention_hd\r\n self.word_dim = encoder_input_dim\r\n self.pooling_method = None if self.pooling_method == 'None' else self.pooling_method\r\n self.treelstm = RecursiveTreeLSTMEncoder(\r\n self.word_dim,\r\n self.hidden_dim,\r\n self.use_leaf_rnn,\r\n self.pooling_method,\r\n self.bidirectional\r\n )\r\n self.transpose = torch.nn.Linear(self.get_dim(), 512) # same input dim with baseline:Textseg\r\n\r\n def forward(self,encoded_input):\r\n embeded_sentence, lengths, tree_masks, sentences_per_doc = encoded_input\r\n treelstm_output,_ = self.treelstm(inp=embeded_sentence, length=lengths,fixed_masks=tree_masks)\r\n return self.transpose(treelstm_output), sentences_per_doc\r\n\r\n def get_dim(self):\r\n if self.bidirectional:\r\n return self.word_dim * 2\r\n else:\r\n return self.word_dim\r\n\r\n def get_output_dim(self):\r\n return 512\r\n"},{"alpha_fraction":0.45652174949645996,"alphanum_fraction":0.6739130616188049,"avg_line_length":14.333333015441895,"blob_id":"fea65ce7dd01b9608f6a4847f027ac46b2a11c8b","content_id":"8c3cb9a7a3c63069f66fa57a0be55cf2476673a8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":184,"license_type":"no_license","max_line_length":20,"num_lines":12,"path":"/requirements.txt","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"pathlib2==2.3.5\ntransformers==4.4.2\nnltk==3.5\nscipy==1.6.2\nsegeval==2.0.11\ngensim==3.8.3\nnumpy==1.20.2\ntermcolor==1.1.0\ntorch==1.7.0\ntqdm==4.59.0\njsonlines==2.0.0\nscikit_learn==0.24.1\n"},{"alpha_fraction":0.6007747650146484,"alphanum_fraction":0.6054701209068298,"avg_line_length":38.262672424316406,"blob_id":"5e29cec55c9889358f517b066fb96b479b3290fb","content_id":"43c8a2ee8d9a1d78f67d63a53b200e5c0ccca648","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8519,"license_type":"no_license","max_line_length":139,"num_lines":217,"path":"/utils.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import json\nimport logging\nimport sys\nimport torch\nimport math\nfrom collections import OrderedDict\nimport hashlib\nimport random\nimport numpy as np\nimport torch.distributed as dist\n\nPADDING_TOKEN = \"PAD\"\nUNK_TOKEN = \"UNK\"\n\n\ndef set_multi_gpu(args):\n if args.local_rank != -1:\n dist_backend = 'nccl'\n dist.init_process_group(backend=dist_backend)\n device = args.local_rank if args.local_rank != -1 else (\n torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu'))\n torch.cuda.set_device(args.local_rank)\n return device\n\ndef rank_logger_info(logger, rank, info):\n if rank in [-1, 0]:\n logger.info(info)\n else:\n pass\n\ndef set_seed(seed):\n random.seed(seed)\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n\ndef get_logger(args):\n return logging.getLogger(args.logger_name)\n\ndef compute_name(args):\n imp_opts = [\"seed\",\n \"train_data_type\",\n \"train_bs\",\"dev_bs\",\"test_bs\",\n \"data_frac\",\"length_filter\",\"sent_num_filter\",\n \"etype\",\"encoder_fine_tune\",\"model_size\",\"cased\",\n \"sr_choose\",\"tr_choose\",\"gr_choose\",\n \"hidden_dim\",\"num_layers\",\"pool_method\",\"attention_hd\",\"use_leaf_rnn\",\"bidirectional\",\n \"bilstm_hd\",\"bilstm_nl\",\"bilstm_bd\",\n \"model_size\"]\n opt_dict = OrderedDict()\n # Only include important options in hash computation\n hp_dict = vars(args)\n for key in imp_opts:\n val = hp_dict[key]\n opt_dict[key] = val\n str_repr = str(opt_dict.items())\n hash_idx = hashlib.md5(str_repr.encode(\"utf-8\")).hexdigest()\n pre_name = str(opt_dict[\"seed\"]) + \"_\" + opt_dict[\"etype\"]\n if args.sr_choose == 'g_model':\n pre_name += \"_\"\n pre_name += opt_dict[\"gr_choose\"]\n elif args.sr_choose == 't_model':\n pre_name += \"_\"\n pre_name += opt_dict[\"tr_choose\"]\n else:\n pre_name += \"_\"\n pre_name += opt_dict[\"sr_choose\"]\n if not args.infer:\n return pre_name + \"_\" + str(hash_idx)\n else:\n return pre_name + \"_\" + \"infer\" + \"_\" + str(hash_idx)\n\ndef compute_dataset_name(args):\n imp_opts=[\"train_data_type\",\"data_frac\",\"length_filter\",\"sent_num_filter\",\"etype\"]\n opt_dict = OrderedDict()\n # Only include important options in hash computation\n hp_dict = vars(args)\n for key in imp_opts:\n val = hp_dict[key]\n opt_dict[key] = val\n str_repr = str(opt_dict.items())\n hash_idx = hashlib.md5(str_repr.encode(\"utf-8\")).hexdigest()\n if args.sr_choose == \"g_model\":\n if args.gr_choose == 'DSG_SEG':\n name = \"gcn\" + \"_\" + str(hash_idx)\n elif args.gr_choose == 'texting':\n name = \"ing\" + \"_\" + str(hash_idx)\n else:\n name = str(hash_idx)\n return args.train_data_type + \"_\" + name\n\ndef compute_encoder_name(args):\n name = args.etype\n if args.encoder_fine_tune:\n name += \"_ft\"\n return name\n\ndef stringify_flags(options):\n # Ignore negative boolean flags.\n flags = {k: v for k, v in options.__dict__.items()}\n return json.dumps(flags, indent=4, sort_keys=True)\n\ndef setup_logger(logger_name, filename, delete_old = False):\n logger = logging.getLogger(logger_name)\n logger.setLevel(logging.DEBUG)\n stderr_handler = logging.StreamHandler(sys.stderr)\n file_handler = logging.FileHandler(filename, mode='w') if delete_old else logging.FileHandler(filename)\n file_handler.setLevel(logging.DEBUG)\n stderr_handler.setLevel(logging.INFO)\n formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n stderr_handler.setFormatter(formatter)\n file_handler.setFormatter(formatter)\n logger.addHandler(stderr_handler)\n logger.addHandler(file_handler)\n return logger\n\ndef unsort(sort_order):\n result = [-1] * len(sort_order)\n\n for i, index in enumerate(sort_order):\n result[index] = i\n\n return result\n\ndef get_collate_fn(args):\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n return ing_collate_fn\n elif args.sr_choose == 'g_model' and args.gr_choose == 'DSG_SEG':\n return gcn_collate_fn\n else:\n return collate_fn\n\ndef collate_fn(batch):\n batched_data = []\n batched_targets = []\n paths = []\n window_size = 1\n before_sentence_count = int(math.ceil(float(window_size - 1) / 2))\n after_sentence_count = window_size - before_sentence_count - 1\n for example in batch:\n data, targets, path = example[\"sentences\"], example[\"targets\"], example[\"path\"]\n try:\n max_index = len(data)\n tensored_data = []\n for curr_sentence_index in range(0, len(data)):\n from_index = max([0, curr_sentence_index - before_sentence_count])\n to_index = min([curr_sentence_index + after_sentence_count + 1, max_index])\n sentences_window = [word for sentence in data[from_index:to_index] for word in sentence]\n tensored_data.append(torch.tensor(sentences_window))\n tensored_targets = torch.tensor(targets).long()\n # tensored_targets = tensored_targets[:-1] # removed in wiki_loader.py\n batched_data.append(tensored_data)\n batched_targets.append(tensored_targets)\n paths.append(path)\n except Exception as e:\n logger.info('Exception \"%s\" in file: \"%s\"', e, path)\n logger.debug('Exception!', exc_info=True)\n continue\n return batched_data, batched_targets, paths\n\ndef ing_collate_fn(batch):\n batched_data = []\n batched_targets = []\n paths = []\n window_size = 1\n before_sentence_count = int(math.ceil(float(window_size - 1) / 2))\n after_sentence_count = window_size - before_sentence_count - 1\n # batch: [[[tensor(sent_num1,), tensor(sent_num1,sent_num1)], ...], [doc2], [doc3], []]\n for example in batch:\n data, adjs, targets, path = example[\"sentences\"],example[\"adjs\"],example[\"targets\"], example[\"path\"]\n try:\n max_index = len(data)\n tensored_data = []\n for curr_sentence_index in range(0, len(data)):\n from_index = max([0, curr_sentence_index - before_sentence_count])\n to_index = min([curr_sentence_index + after_sentence_count + 1, max_index])\n sentences_window = [word for sentence in data[from_index:to_index] for word in sentence]\n adj_window = adjs[from_index:to_index][0] # fault window size = 1\n tensored_data.append([torch.tensor(sentences_window), torch.tensor(adj_window).float()])\n tensored_targets = torch.tensor(targets).long()\n batched_data.append(tensored_data)\n batched_targets.append(tensored_targets)\n paths.append(path)\n except Exception as e:\n logger.info('Exception \"%s\" in file: \"%s\"', e, path)\n logger.debug('Exception!', exc_info=True)\n continue\n return batched_data, batched_targets, paths\n\ndef gcn_collate_fn(batch):\n batched_data = [] # [[sentences, feature, adj], [], [], [],]\n batched_targets = []\n paths = []\n window_size = 1\n before_sentence_count = int(math.ceil(float(window_size - 1) / 2))\n after_sentence_count = window_size - before_sentence_count - 1\n for example in batch:\n sentences, feature, adjs, targets, path = example[\"sentences\"],example[\"feature\"],example[\"adj\"],example[\"targets\"],example[\"path\"]\n try:\n max_index = len(sentences)\n sentences_lst = []\n for curr_sentence_index in range(0, len(sentences)):\n from_index = max([0, curr_sentence_index - before_sentence_count])\n to_index = min([curr_sentence_index + after_sentence_count + 1, max_index])\n sentences_window = [word for sentence in sentences[from_index:to_index] for word in sentence]\n sentences_lst.append(sentences_window)\n doc_lst = [sentences_lst, torch.tensor(feature), torch.tensor(adjs.A).float()]\n batched_data.append(doc_lst)\n tensored_targets = torch.tensor(targets).long()\n batched_targets.append(tensored_targets)\n paths.append(path)\n except Exception as e:\n logger.info('Exception \"%s\" in file: \"%s\"', e, path)\n logger.debug('Exception!', exc_info=True)\n continue\n return batched_data, batched_targets, paths"},{"alpha_fraction":0.5531328320503235,"alphanum_fraction":0.5556390881538391,"avg_line_length":34.62385177612305,"blob_id":"815edc63bb2c5cafed38ddb0cb5253c0f8ae2c9e","content_id":"dfbc2c6912e1313b782463cafb76c73363792f17","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3990,"license_type":"no_license","max_line_length":91,"num_lines":109,"path":"/data/all_data_loader.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import os\r\nfrom data.wikielements_loader import WikiElementsDataSet\r\nfrom data.wikicities_loader import WikiCitiesDataSet\r\nfrom data.wiki50_loader import Wiki50DataSet\r\nfrom data.wikisection_loader import WikiSectioniDataSet\r\nfrom data.clinical_loader import ClinicalDataSet\r\nfrom data.manifesto_loader import ManifestoDataSet\r\nfrom data.choi_loader import ChoiDataSet\r\nfrom data.wiki_loader import WikipediaDataSet\r\ndef get_loader(args, encoder, data_type,split=None):\r\n if data_type == \"wikielements\":\r\n assert split == None\r\n dataset = WikiElementsDataSet(\r\n root = args.wikielements_path,\r\n args = args,\r\n encoder = encoder,\r\n length_filter = args.length_filter,\r\n sent_num_filter = args.sent_num_filter,\r\n local_rank = args.local_rank\r\n )\r\n elif data_type == \"wikicities\":\r\n assert split == None\r\n dataset = WikiCitiesDataSet(\r\n root = args.wikicities_path,\r\n args = args,\r\n encoder = encoder,\r\n length_filter = args.length_filter,\r\n sent_num_filter = args.sent_num_filter,\r\n local_rank = args.local_rank\r\n )\r\n elif data_type == \"wiki50\":\r\n assert split == None\r\n dataset = Wiki50DataSet(\r\n root = args.wiki50_path,\r\n args = args,\r\n encoder = encoder,\r\n length_filter = args.length_filter,\r\n sent_num_filter = args.sent_num_filter,\r\n local_rank = args.local_rank\r\n )\r\n elif data_type == \"wikisection\":\r\n assert split in ['train','dev','test']\r\n split = \"validation\" if split == 'dev' else split\r\n dataset = WikiSectioniDataSet(\r\n root = args.wikisection_path,\r\n args = args,\r\n encoder = encoder,\r\n split = split,\r\n length_filter = args.length_filter,\r\n sent_num_filter = args.sent_num_filter,\r\n local_rank = args.local_rank\r\n )\r\n elif data_type == \"clinical\":\r\n assert split == None\r\n dataset = ClinicalDataSet(\r\n root = args.clinical_path,\r\n args = args,\r\n encoder = encoder,\r\n length_filter = args.length_filter,\r\n sent_num_filter = args.sent_num_filter,\r\n local_rank = args.local_rank\r\n )\r\n elif data_type == \"manifesto\":\r\n assert split == None\r\n dataset = ManifestoDataSet(\r\n root = args.manifesto_path,\r\n args = args,\r\n encoder = encoder,\r\n length_filter = args.length_filter,\r\n sent_num_filter = args.sent_num_filter,\r\n local_rank = args.local_rank\r\n )\r\n elif data_type == \"choi\":\r\n assert split in [None, 'train', 'dev', 'test']\r\n dataset = ChoiDataSet(\r\n root = args.choi_path,\r\n args = args,\r\n encoder = encoder,\r\n split=split,\r\n length_filter = args.length_filter,\r\n sent_num_filter = args.sent_num_filter,\r\n local_rank = args.local_rank\r\n )\r\n elif data_type == \"wiki\":\r\n assert split in ['train', 'dev', 'test']\r\n dataset = WikipediaDataSet(\r\n args = args,\r\n root = args.wiki_path,\r\n split = split,\r\n encoder = encoder,\r\n length_filter = args.length_filter,\r\n sent_num_filter = args.sent_num_filter,\r\n data_frac = args.data_frac,\r\n high_granularity=args.high_granularity,\r\n local_rank=args.local_rank\r\n )\r\n else:\r\n print(f\"{data_type} is false data type\")\r\n return None\r\n return dataset\r\n\r\n\r\nif __name__ == \"__main__\":\r\n from parameters import *\r\n args = create_parser()\r\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\r\n data_type = \"choi\"\r\n split = None\r\n dataset = get_loader(args, None, data_type,split=split)"},{"alpha_fraction":0.5830441117286682,"alphanum_fraction":0.5904809236526489,"avg_line_length":42.86666488647461,"blob_id":"34503de3bd8505842828ad4a362fe35da45626b4","content_id":"5cba776fbaa5a8a7ccec54fc766fe81cb3ee026b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2017,"license_type":"no_license","max_line_length":112,"num_lines":45,"path":"/models/latent_sr.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\r\nimport torch\r\nfrom sklearn.decomposition import PCA\r\n\r\nclass l_model(nn.Module):\r\n def __init__(self, args):\r\n super(l_model, self).__init__()\r\n self.args = args\r\n self.device = self.args.device\r\n self.output_dim = 300\r\n self.pca = PCA(n_components = self.output_dim)\r\n\r\n # def pad(self, s, max_length):\r\n # pad_lst = [self.encoder.model.pad_token_id] * (max_length - s.size(0))\r\n # s = torch.LongTensor(s.tolist() + pad_lst).to(self.device)\r\n # return s.unsqueeze(0) # (1, max_len)\r\n\r\n def pca_function(self, sent_repr):\r\n sents_num = sent_repr.size(0) # (y, dim)\r\n min_samples_num = self.output_dim + 1\r\n if sents_num < min_samples_num:\r\n pad_tensor = torch.zeros((min_samples_num - sents_num, sent_repr.size(1))).to(self.device) # (x,dim)\r\n sent_repr = torch.cat([sent_repr, pad_tensor], dim=0)\r\n sent_repr = sent_repr.cpu()\r\n pca_out = self.pca.fit_transform(sent_repr)[:sents_num]\r\n return torch.tensor(pca_out).to(self.device)\r\n\r\n def forward(self, encoded_input):\r\n # sentences_per_doc = []\r\n # all_batch_sentences = []\r\n # for document in batch:\r\n # all_batch_sentences.extend(document)\r\n # sentences_per_doc.append(len(document))\r\n # lengths = [s.size()[0] for s in all_batch_sentences]\r\n # max_length = max(lengths)\r\n # padded_sentences = [self.pad(s, max_length) for s in all_batch_sentences]\r\n # big_tensor = torch.cat(padded_sentences, 0) # (batch size, max_length)\r\n # embeded_big_tensor = self.encoder(big_tensor) # (max_length, batch size, dim)\r\n embeded_big_tensor,sentences_per_doc = encoded_input\r\n sent_repr = torch.sum(embeded_big_tensor, dim=1) # max_length, dim\r\n sent_repr[:, 0] = 0 # max_length, dim\r\n return self.pca_function(sent_repr).float(), sentences_per_doc\r\n\r\n def get_output_dim(self):\r\n return self.output_dim"},{"alpha_fraction":0.5346283912658691,"alphanum_fraction":0.5380067825317383,"avg_line_length":38.89655303955078,"blob_id":"00a5caa4f193c9e5e5c094d1b29b70539ea54395","content_id":"1966ee651850d8629997e2a0b01a871763421a85","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1184,"license_type":"no_license","max_line_length":95,"num_lines":29,"path":"/models/DSG_SEG.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\r\nimport torch\r\nfrom models.GGNN.layers import *\r\n\r\nclass g_model_DSG_SEG(nn.Module):\r\n def __init__(self,encoder_input_dim,args):\r\n super(g_model_DSG_SEG,self).__init__()\r\n self.input_dim = encoder_input_dim\r\n self.output_dim = args.DSG_SEG_output_dim\r\n self.device = args.device\r\n self.layers = []\r\n self.graph_layer = GraphLayer(args,\r\n input_dim=self.input_dim,\r\n output_dim=self.output_dim,\r\n act=nn.Tanh(),\r\n dropout_p=0.3)\r\n\r\n def forward(self,encoded_input):\r\n doc_feature, adj, mask, sentences_per_doc = encoded_input\r\n batch_size = doc_feature.shape[0]\r\n graph_layer_output = self.graph_layer(doc_feature, adj, mask) # bs, max_node_size , dim\r\n sent_repr = []\r\n for i in range(batch_size):\r\n sent_repr.append(graph_layer_output[i, :sentences_per_doc[i], :])\r\n sent_repr = torch.cat(sent_repr,0)\r\n return sent_repr, sentences_per_doc\r\n\r\n def get_output_dim(self):\r\n return self.output_dim"},{"alpha_fraction":0.5215619206428528,"alphanum_fraction":0.5395838022232056,"avg_line_length":42.39047622680664,"blob_id":"4cdd3d72eadc7f88beb21362fd76c5c64bcb0d4d","content_id":"a292d92cc26464d6e335c0b31c284e5de8cb4ff1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4665,"license_type":"no_license","max_line_length":136,"num_lines":105,"path":"/judgement.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import json\r\nfrom pathlib2 import Path\r\nfrom data.wiki_loader import get_sections\r\nimport wiki_utils\r\nimport os\r\nfrom text_manipulation import extract_sentence_words\r\nimport torch\r\nclass wiki_document:\r\n def __init__(self, wiki_path, bert_path, ING_GCN_path):\r\n self.wiki_path = wiki_path\r\n self.bert_path = bert_path\r\n self.ING_GCN_path = ING_GCN_path\r\n self.document_dict = {} # {\"path1\":{\"golden\":[...,\"====\",...,\"====\"],\"DSG_SEG\":[同],\"bert\":[同]}, \"path2\":...}\r\n self.get_golden_result()\r\n self.get_bert_result()\r\n self.get_ING_GCN_result()\r\n\r\n def get_golden_result(self):\r\n self.paths = Path(self.wiki_path).read_text().splitlines()\r\n for path in self.paths:\r\n data, targets = self.read_wiki_file(path)\r\n self.document_dict[path] = {} # {'/p300/wiki/wiki_727/test/58/46/96/5846960':{}}\r\n self.document_dict[path][\"data\"] = data\r\n self.document_dict[path][\"targets\"] = targets\r\n self.document_dict[path][\"document_len\"] = len(data)\r\n self.document_dict[path][\"seg_num\"] = sum(targets)\r\n self.document_dict[path][\"golden\"] = [] # # {'/p300/wiki/wiki_727/test/58/46/96/5846960':{'golden':[]}}\r\n for idx,sentence in enumerate(data):\r\n self.document_dict[path][\"golden\"].append(sentence)\r\n if targets[idx] == 1:\r\n self.document_dict[path][\"golden\"].append(\"=\"*10)\r\n\r\n\r\n def get_bert_result(self):\r\n self.get_result(self.bert_path)\r\n\r\n def get_ING_GCN_result(self):\r\n self.get_result(self.ING_GCN_path)\r\n\r\n def get_result(self, path):\r\n model_name = None\r\n if \"bert\" in path:\r\n model_name = \"bert\"\r\n elif \"DSG_SEG\" in path:\r\n model_name = \"DSG_SEG\"\r\n with open(path, \"rb\") as f:\r\n result_dict = json.load(f)\r\n for path in self.paths:\r\n self.document_dict[path][model_name] = []\r\n pk = float(result_dict[path][\"pk\"]) # ok\r\n self.document_dict[path][\"{}_pk\".format(model_name)] = pk\r\n pred = result_dict[path][\"pred\"] # pred segments\r\n pred_list = pred.split(\" \")\r\n for idx, one_pred in enumerate(pred_list[:-2]):\r\n if one_pred == \"|\":\r\n self.document_dict[path][model_name].append(\"=\"*10)\r\n else:\r\n self.document_dict[path][model_name].append(self.document_dict[path][\"data\"][int(one_pred)])\r\n\r\n\r\n\r\n def read_wiki_file(self, path,\r\n high_granularity=False,\r\n remove_preface_segment=True,\r\n ignore_list=True,\r\n remove_special_tokens=True):\r\n data = []\r\n targets = []\r\n all_sections = get_sections(path, high_granularity)\r\n required_sections = all_sections[1:] if remove_preface_segment and len(all_sections) > 0 else all_sections\r\n required_non_empty_sections = [section for section in required_sections if len(section) > 0 and section != \"\\n\"]\r\n for section in required_non_empty_sections:\r\n sentences = section.split('\\n')\r\n if sentences:\r\n targets_flag = 0\r\n for sentence in sentences:\r\n is_list_sentence = wiki_utils.get_list_token() + \".\" == str(sentence)\r\n if ignore_list and is_list_sentence:\r\n continue\r\n sentence_words = extract_sentence_words(sentence, remove_special_tokens=remove_special_tokens)\r\n if (len(sentence_words) <= 1):\r\n continue\r\n data.append(\" \".join(sentence_words))\r\n targets_flag += 1\r\n if data:\r\n targets.extend([0] * (targets_flag - 1))\r\n if targets_flag != 0:\r\n targets.append(1) # segment\r\n data = data[:-1]\r\n targets = targets[:-1]\r\n return data, targets\r\n\r\n\r\nif __name__ == \"__main__\":\r\n os.makedirs(r\"/root/DSG-SEG/sampleanalysis\",exist_ok=True)\r\n saved_path = r\"/root/textsegment/sampleanalysis/WIKI_DOCUMENT.pkl\"\r\n if os.path.exists(saved_path):\r\n WIKI_DOCUMENT = torch.load(saved_path)\r\n else:\r\n WIKI_DOCUMENT = wiki_document(\r\n wiki_path = r\"/p300/wiki/wiki_727/test/random_paths\",\r\n bert_path= r\"/root/textsegment/checkpoints/bert/0_wiki_bert_b_model_9fa59240ac158d3ae21c0dcf25da8e81/wiki_best_result.json\",\r\n ING_GCN_path = r\"...\",\r\n )\r\n torch.save(WIKI_DOCUMENT, saved_path)\r\n"},{"alpha_fraction":0.5601013898849487,"alphanum_fraction":0.5738595128059387,"avg_line_length":35.84000015258789,"blob_id":"15b7c8942e779394805e6af3f1133b4520fb7521","content_id":"3964776be31bcad1f0bab6f73dea819c18b5152b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2762,"license_type":"no_license","max_line_length":78,"num_lines":75,"path":"/encoder/glove_encoder.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\nimport torch\nfrom utils import *\nfrom collections import OrderedDict\nimport numpy as np\nclass glove_model(nn.Module):\n\n def __init__(self, vocab_path, args, encoder_fine_tune):\n super(glove_model,self).__init__()\n self.vocab_path = vocab_path\n self.args = args\n self.encoder_fine_tune = encoder_fine_tune\n self.local_rank = args.local_rank\n self.PADDING_TOKEN = \"<PAD>\"\n self.UNK_TOKEN = \"<UNK>\"\n self.embedding, self.word2idx = self.get_glove_embedding()\n self.pad_token_id = self.word2idx[self.PADDING_TOKEN]\n self.idx2word = OrderedDict()\n for item in self.word2idx.items():\n self.idx2word[item[1]] = item[0]\n self.embedding = nn.Embedding.from_pretrained(self.embedding)\n if encoder_fine_tune:\n for param in self.parameters():\n param.requires_grad = True\n else:\n for param in self.parameters():\n param.requires_grad = False\n\n def get_glove_embedding(self):\n size = 300\n logger = get_logger(self.args)\n rank_logger_info(logger, self.local_rank, 'Reading glove Vocab.')\n glove_word2idx = OrderedDict()\n glove_word2idx[self.PADDING_TOKEN] = len(glove_word2idx)\n glove_word2idx[self.UNK_TOKEN] = len(glove_word2idx)\n\n with open(self.vocab_path, encoding='utf-8') as f:\n for i, line in enumerate(f):\n word, vec = line.split(' ', 1)\n if word in glove_word2idx:\n continue\n else:\n glove_word2idx[word] = len(glove_word2idx)\n\n embeddings = np.zeros((len(glove_word2idx), size), dtype=np.float32)\n embeddings[0] = np.random.randn(1, size)\n embeddings[1] = np.random.randn(1, size)\n rank_logger_info(logger, self.local_rank, 'Reading glove Embeddings.')\n with open(self.vocab_path, encoding='utf-8') as f:\n for i,line in enumerate(f):\n word, vec = line.strip().split(' ', 1)\n if word not in glove_word2idx:\n continue\n word_id = glove_word2idx[word]\n vec = np.fromstring(vec, dtype=float, sep=' ')\n embeddings[word_id] = vec\n\n return torch.tensor(embeddings), glove_word2idx\n\n def tokenize(self, inp):\n word_lst = inp.split()\n idx_lst = []\n for word in word_lst:\n if word in self.word2idx:\n idx_lst.append(self.word2idx[word])\n else:\n idx_lst.append(self.word2idx[self.UNK_TOKEN])\n return idx_lst\n\n\n def forward(self,inp):\n return self.embedding(inp)\n\n def get_word_dim(self):\n return 300"},{"alpha_fraction":0.4989272952079773,"alphanum_fraction":0.5073897242546082,"avg_line_length":53.893333435058594,"blob_id":"3b9d2ea91963a5a419020cc48996635cd0ae475f","content_id":"b1958ebd6d5be7d3b3f956e3962bab3da6abed2b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8390,"license_type":"no_license","max_line_length":139,"num_lines":150,"path":"/test.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"from tqdm import tqdm\r\nimport torch\r\nimport json\r\nfrom evaluation_utils import *\r\nfrom utils import *\r\nimport torch.nn.functional as F\r\nfrom termcolor import colored\r\nimport random\r\npreds_stats = predictions_analysis()\r\ndef test(model,encode_model, args, dataset,threshold,local_rank):\r\n logger = get_logger(args)\r\n device = args.device\r\n global output_seg, current_idx\r\n model.eval()\r\n with tqdm(desc='Testing', total=len(dataset)) as pbar:\r\n acc = Accuracy() # accuracies class is not needed\r\n document_result = {}\r\n for i, (data, target, paths) in enumerate(dataset):\r\n if True:\r\n if i == args.stop_after:\r\n break\r\n pbar.update()\r\n with torch.no_grad():\r\n encoded_input = encode_model(data)\r\n output = model(encoded_input)\r\n output_seg = output.data.cpu().numpy().argmax(axis=1)\r\n targets_var = torch.cat(target, 0).to(device).clone().detach().requires_grad_(False)\r\n target_seg = targets_var.data.cpu().numpy()\r\n preds_stats.add(output_seg, target_seg)\r\n output_softmax = F.softmax(output, 1)\r\n current_idx = 0\r\n for k, t in enumerate(target):\r\n path = paths[k]\r\n document_sentence_count = len(t)\r\n to_idx = int(current_idx + document_sentence_count)\r\n output = ((output_softmax.data.cpu().numpy()[current_idx: to_idx, :])[:, 1] > threshold).tolist()\r\n if \"_|_\" not in path: # whole document\r\n document_result[path] = [[output,t]] # [[[1,0,1,0],[1,0,1,0]],....]\r\n else: # filtered document\r\n path,num = path.split(\"_|_\")\r\n if path not in document_result:\r\n document_result[path] = [[num,[output,t]]]\r\n else:\r\n document_result[path].append([num,[output,t]])\r\n current_idx = to_idx\r\n # concat\r\n for k in document_result:\r\n if type(document_result[k][0][0]) == str:\r\n splited_dic_item = document_result[k]\r\n complete_dic_item = []\r\n sorted_splited_dic_item = sorted(splited_dic_item, key=lambda x: int(x[0]))\r\n for item in sorted_splited_dic_item:\r\n if len(complete_dic_item) == 0:\r\n complete_dic_item = [item[1][0], [item[1][1]]]\r\n else:\r\n complete_dic_item[0].extend(item[1][0])\r\n complete_dic_item[1].append(item[1][1])\r\n\r\n document_result[k] = complete_dic_item\r\n document_result[k][1] = torch.cat(document_result[k][1], 0)\r\n else:\r\n document_result[k] = [document_result[k][0][0], document_result[k][0][1]] #[[output],[target]]\r\n document_result[k][0] = np.append(document_result[k][0],[1])\r\n document_result[k][1] = np.append(document_result[k][1],[1])\r\n for k in document_result:\r\n acc.update(document_result[k][0], document_result[k][1], k)\r\n epoch_pk, epoch_windiff, epoch_b, epoch_s = acc.calc_accuracy()\r\n rank_logger_info(logger, local_rank, colored('accuracy: {:.4}, Pk: {:.4}, Windiff: {:.4}, B: {:.4}, S: {:.4} ,F1: {:.4} . '.format(\r\n preds_stats.get_accuracy(),\r\n epoch_pk,\r\n epoch_windiff,\r\n epoch_b,\r\n epoch_s,\r\n preds_stats.get_f1()),\"red\"))\r\n preds_stats.reset()\r\n epoch_result = acc.all_test_result\r\n return epoch_pk, epoch_result\r\n\r\ndef test_all(model, encode_model, args, test_dl_dict, threshold, local_rank):\r\n logger = get_logger(args)\r\n pk_result = {}\r\n for test_data_type in test_dl_dict:\r\n test_dl = test_dl_dict[test_data_type]\r\n rank_logger_info(logger, local_rank, f\"Testing on {test_data_type}...\")\r\n test_pk, test_result = test(model,encode_model, args, test_dl, threshold, local_rank)\r\n pk_result[test_data_type] = test_pk\r\n if local_rank in [-1, 0]:\r\n with open(args.checkpoint_dir / f\"{test_data_type}_best_result.json\", \"w\") as f:\r\n json.dump(test_result, f)\r\n flags = {k: v for k, v in pk_result.items()}\r\n rank_logger_info(logger, local_rank, json.dumps(flags, indent=4, sort_keys=True))\r\n\r\n\r\ndef test_all_random_baseline(args, test_dl_dict, local_rank):\r\n logger = get_logger(args)\r\n pk_result = {}\r\n avg_sent_num_per_seg = {} # all_sent_num / all_seg_num\r\n avg_sent_num_per_doc = {} # all_sent_num / all_doc_num\r\n threshold_dict = {}\r\n avg_seg_num_per_doc = {} # all_seg_num / all_doc_num\r\n avg_sentlength_per_sent = {} # all_sent_length / all_sent_num\r\n for test_data_type in test_dl_dict:\r\n all_doc_num = 0\r\n all_sent_num = 0\r\n all_seg_num = 0\r\n all_sent_length = 0\r\n test_dl = test_dl_dict[test_data_type]\r\n rank_logger_info(logger, local_rank, f\"Getting threshold on {test_data_type}...\")\r\n for i, (data, target, paths) in enumerate(test_dl): # batch size = 1\r\n all_doc_num += len(target)\r\n for idx,t in enumerate(target):\r\n all_sent_num += len(t)\r\n all_seg_num += torch.sum(t).item()\r\n for sent in data[idx]:\r\n all_sent_length += len(sent)\r\n avg_sent_num_per_seg[test_data_type] = all_sent_num / all_seg_num\r\n threshold_dict[test_data_type] = 1-1/avg_sent_num_per_seg[test_data_type]\r\n avg_seg_num_per_doc[test_data_type] = all_seg_num / all_doc_num\r\n avg_sent_num_per_doc[test_data_type] = all_sent_num / all_doc_num\r\n avg_sentlength_per_sent[test_data_type] = all_sent_length / all_sent_num\r\n rank_logger_info(logger, local_rank, f\"avg_sent_num_per_seg:\")\r\n rank_logger_info(logger, local_rank, json.dumps(avg_sent_num_per_seg, indent=4, sort_keys=True))\r\n rank_logger_info(logger, local_rank, f\"avg_seg_num_per_doc:\")\r\n rank_logger_info(logger, local_rank, json.dumps(avg_sent_num_per_doc, indent=4, sort_keys=True))\r\n rank_logger_info(logger, local_rank, f\"avg_sent_num_per_doc:\")\r\n rank_logger_info(logger, local_rank, json.dumps(avg_seg_num_per_doc, indent=4, sort_keys=True))\r\n rank_logger_info(logger, local_rank, f\"avg_sentlength_per_sent:\")\r\n rank_logger_info(logger, local_rank, json.dumps(avg_sentlength_per_sent, indent=4, sort_keys=True))\r\n rank_logger_info(logger, local_rank, f\"threshold:\")\r\n rank_logger_info(logger, local_rank, json.dumps(threshold_dict, indent=4, sort_keys=True))\r\n for test_data_type in test_dl_dict:\r\n rank_logger_info(logger, local_rank, f\"Getting random baseline result on {test_data_type}...\")\r\n threshold = threshold_dict[test_data_type]\r\n acc = Accuracy()\r\n test_dl = test_dl_dict[test_data_type]\r\n for i, (data, target, paths) in enumerate(test_dl): # batch size = 1\r\n for idx,t in enumerate(target):\r\n h = []\r\n for j in range(len(t)):\r\n random_seed = random.random()\r\n if random_seed > threshold:\r\n h.append(1)\r\n else:\r\n h.append(0)\r\n gold = np.append(t, [1])\r\n h = np.append(h,[1])\r\n acc.update(h, gold, paths[idx])\r\n epoch_pk, epoch_windiff, epoch_b, epoch_s = acc.calc_accuracy()\r\n pk_result[test_data_type] = epoch_pk\r\n rank_logger_info(logger, local_rank, json.dumps(pk_result, indent=4, sort_keys=True))\r\n\r\n\r\n\r\n"},{"alpha_fraction":0.5651775598526001,"alphanum_fraction":0.571363091468811,"avg_line_length":41.79901885986328,"blob_id":"d975309ca4f1e558b423d4b9e2f40588d600cff9","content_id":"1cc6a18097dc74794ad60f182f78087d88bc8c75","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8730,"license_type":"no_license","max_line_length":206,"num_lines":204,"path":"/data/wiki_loader.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"from torch.utils.data import Dataset\nfrom pathlib2 import Path\nimport re\nimport random\nfrom utils import *\nfrom data.data_utils import *\nfrom tqdm import tqdm\nsection_delimiter = \"========\"\n\ndef get_files(path):\n all_objects = Path(path).glob('**/*')\n files = [str(p) for p in all_objects if p.is_file()]\n return files\n\n\ndef get_cache_path(wiki_folder):\n cache_file_path = wiki_folder / 'paths_cache'\n return cache_file_path\n\n\ndef cache_wiki_filenames(wiki_folder):\n files = Path(wiki_folder).glob('*/*/*/*')\n cache_file_path = get_cache_path(wiki_folder)\n\n with cache_file_path.open('w+') as f:\n for file in files:\n f.write(str(file) + u'\\n')\n #f.write(unicode(file) + u'\\n')\n\n\ndef clean_section(section):\n cleaned_section = section.strip('\\n')\n return cleaned_section\n\n\n\ndef get_scections_from_text(txt, high_granularity=True):\n sections_to_keep_pattern = get_seperator_foramt() if high_granularity else get_seperator_foramt(\n (1, 2))\n if not high_granularity:\n # if low granularity required we should flatten segments within segemnt level 2\n pattern_to_ommit = get_seperator_foramt((3, 999))\n txt = re.sub(pattern_to_ommit, \"\", txt)\n\n #delete empty lines after re.sub()\n sentences = [s for s in txt.strip().split(\"\\n\") if len(s) > 0 and s != \"\\n\"]\n txt = '\\n'.join(sentences).strip('\\n')\n\n\n all_sections = re.split(sections_to_keep_pattern, txt)\n non_empty_sections = [s for s in all_sections if len(s) > 0]\n\n return non_empty_sections\n\n\n\ndef get_sections(path, high_granularity=True):\n file = open(str(path), \"r\")\n raw_content = file.read()\n file.close()\n clean_txt = raw_content.encode('utf-8').decode('utf-8').strip()\n sections = [clean_section(s) for s in get_scections_from_text(clean_txt, high_granularity)]\n return sections\n\ndef read_wiki_file(args,length_filter, filtered_num, all_num,filtered_document_num, all_document_num, sent_num_filter,\n path, logger,encoder = None, remove_preface_segment=True, ignore_list=False, remove_special_tokens=False,\n return_as_sentences=False, high_granularity=True, only_letters=False):\n data = []\n adjs = []\n targets = []\n all_sections = get_sections(path, high_granularity)\n required_sections = all_sections[1:] if remove_preface_segment and len(all_sections) > 0 else all_sections\n required_non_empty_sections = [section for section in required_sections if len(section) > 0 and section != \"\\n\"]\n for section in required_non_empty_sections:\n sentences = section.split('\\n')\n if sentences:\n targets_flag = 0\n for sentence in sentences:\n is_list_sentence = get_list_token() + \".\" == str(sentence)\n if ignore_list and is_list_sentence:\n continue\n if not return_as_sentences:\n sentence_words = extract_sentence_words(sentence, remove_special_tokens=remove_special_tokens)\n if (len(sentence_words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\n continue\n if len(sentence_words) > length_filter:\n sentence_words = sentence_words[:length_filter]\n filtered_num += 1\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n sentence_words, adj = build_ing_graph(sentence_words, args)\n adjs.append(adj)\n if encoder:\n data.append(encoder.tokenize(sentence_words))\n else:\n data.append(sentence_words)\n targets_flag += 1\n else: # for the annotation. keep sentence as is.\n if (only_letters):\n sentence = re.sub('[^a-zA-Z0-9 ]+', '', sentence)\n data.append(sentence)\n else:\n data.append(sentence)\n\n if data:\n all_num += targets_flag\n targets.extend([0]*(targets_flag-1))\n if targets_flag != 0:\n targets.append(1) # segment\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(data, adjs, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n return_dict = generate_gcn_return_dict(return_dict,args)\n else:\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n return return_dict,filtered_num, all_num,filtered_document_num, all_document_num\n\ndef get_random_files(list,split):\n lenth = len(list)\n rand_list = []\n if split == \"train\":\n num =8000\n else:\n num =1000\n i = 0\n jlist = []\n while i<num:\n j = random.randint(0,lenth)\n if j not in jlist:\n file = list[j]\n rand_list.append(file)\n i+=1\n jlist.append(j)\n return rand_list\n\ndef get_random_file_path(wiki_folder):\n random_file_path = wiki_folder / 'random_paths'\n return random_file_path\n\nclass WikipediaDataSet(Dataset):\n def __init__(self,\n args = None,\n root = None,\n split = None,\n encoder = None,\n length_filter = 50,\n sent_num_filter = 30,\n data_frac=1.0,\n high_granularity=False,\n remove_preface_segment=True,\n ignore_list=True,\n remove_special_tokens=True,\n return_as_sentences=False,\n only_letters=False,\n local_rank=-1):\n logger = get_logger(args)\n root = root + \"/\" + split\n root_path = Path(root)\n cache_path = get_cache_path(root_path)\n if not cache_path.exists():\n cache_wiki_filenames(root_path)\n random_path = get_random_file_path(root_path)\n if not random_path.exists():\n self.textfiles = get_random_files(cache_path.read_text().splitlines(), split)\n with random_path.open('w+') as f:\n for file in self.textfiles:\n f.write(str(file) + u'\\n')\n else:\n self.textfiles = random_path.read_text().splitlines()\n if args.wiki_random == False:\n self.textfiles = cache_path.read_text().splitlines()\n if len(self.textfiles) == 0:\n raise RuntimeError('Found 0 images in subfolders of: {}'.format(root))\n if data_frac < 1.0:\n red_num = int(len(self.textfiles) * data_frac)\n self.textfiles = self.textfiles[:red_num]\n self.data = []\n # sentences\n filtered_num = 0\n all_num = 0\n # documents\n filtered_document_num = 0\n all_document_num = 0\n rank_logger_info(logger, local_rank, \"Reading wiki %s data\" % split)\n for textfile in self.textfiles:\n return_dict, filtered_num, all_num, filtered_document_num, all_document_num = read_wiki_file(args, length_filter, filtered_num, all_num,filtered_document_num,all_document_num,\n sent_num_filter,textfile,logger, encoder, remove_preface_segment, ignore_list, remove_special_tokens,\n return_as_sentences, high_granularity, only_letters)\n if return_dict == None:\n continue\n elif type(return_dict) == dict:\n self.data.append(return_dict)\n else: # list\n self.data.extend(return_dict)\n if all_num != 0:\n rank_logger_info(logger, local_rank, \"all sentences num:%d\\tfiltered num:%d\\tfiltered percent:%f\"%(all_num, filtered_num,filtered_num/all_num))\n if all_document_num != 0:\n rank_logger_info(logger, local_rank, \"all documents num:%d\\tfiltered num:%d\\tfiltered percent:%f\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\n\n def __getitem__(self, index):\n return self.data[index]\n\n def __len__(self):\n return len(self.data)"},{"alpha_fraction":0.4859980046749115,"alphanum_fraction":0.4903492033481598,"avg_line_length":47.71195602416992,"blob_id":"d943e19a719996c07be807808342f8814b2a6692","content_id":"3832429c7043db448219add728cd763bde218f53","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8963,"license_type":"no_license","max_line_length":194,"num_lines":184,"path":"/data/wikicities_loader.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"from nltk.tokenize import RegexpTokenizer\nfrom torch.utils.data import Dataset\nfrom utils import *\nfrom data.data_utils import *\nimport os\nfrom parameters import create_parser\nmissing_stop_words = set(['of', 'a', 'and', 'to'])\n\ndef get_list_token():\n return \"***LIST***\"\n\n\ndef get_formula_token():\n return \"***formula***\"\n\n\ndef get_codesnipet_token():\n return \"***codice***\"\n\n\ndef get_special_tokens():\n special_tokens = []\n special_tokens.append(get_list_token())\n special_tokens.append(get_formula_token())\n special_tokens.append(get_codesnipet_token())\n return special_tokens\n\nwords_tokenizer = None\ndef get_words_tokenizer():\n global words_tokenizer\n if words_tokenizer:\n return words_tokenizer\n words_tokenizer = RegexpTokenizer(r'\\w+')\n return words_tokenizer\n\ndef extract_sentence_words(sentence, remove_missing_emb_words = False,remove_special_tokens = False):\n if (remove_special_tokens):\n for token in get_special_tokens():\n # Can't do on sentence words because tokenizer delete '***' of tokens.\n sentence = sentence.replace(token, \"\")\n tokenizer = get_words_tokenizer()\n sentence_words = tokenizer.tokenize(sentence)\n if remove_missing_emb_words:\n sentence_words = [w for w in sentence_words if w not in missing_stop_words]\n return sentence_words\n\ndef get_sections(text_path, segment_path):\n text_file = open(str(text_path), \"r\", encoding=\"utf-8\")\n texts = text_file.read().strip().split(\"\\n\")\n text_file.close()\n segment_file = open(str(segment_path), \"r\", encoding=\"utf-8\")\n segments = segment_file.read().strip().split(\"\\n\")\n segment_file.close()\n all_sections = [] # all document\n sections = [] # one document\n one_section = [] # one section\n document_num = int(segments[0].strip().split(\",\")[0])\n seg_title = segments[0].strip().split(\",\")[2]\n for idx, text in enumerate(texts):\n text = text.split(\"\\x01\")[1]\n segment = segments[idx]\n if len(segment.strip().split(\",\")) == 3:\n n_doc_num,n_sent_num,n_seg_title = segment.strip().split(\",\")\n else:\n n_doc_num,n_sent_num,n_seg_title1,n_seg_title2 = segment.strip().split(\",\")\n n_seg_title = n_seg_title1 + \",\" + n_seg_title2\n n_doc_num,n_sent_num = int(n_doc_num),int(n_sent_num)\n if n_seg_title != seg_title and n_doc_num == document_num:\n sections.append(one_section)\n one_section = []\n seg_title = n_seg_title\n elif n_doc_num != document_num:\n sections.append(one_section)\n all_sections.append(sections)\n one_section = []\n sections = []\n document_num = n_doc_num\n seg_title = n_seg_title\n one_section.append(text)\n all_sections.append(sections)\n return all_sections\n\n\ndef read_wikicities_file(args, filtered_num, all_num, filtered_document_num, all_document_num,\n text_path, segment_path,encoder=None,length_filter=10, sent_num_filter=10):\n all_sections_of_all_doc = get_sections(text_path, segment_path)\n all_return_dic = []\n for idx,all_sections in enumerate(all_sections_of_all_doc):\n path = text_path + \"/document_\" + str(idx)\n data = []\n adjs = []\n targets = []\n for sentences in all_sections:\n if sentences:\n targets_flag = 0\n for sentence in sentences:\n sentence_words = extract_sentence_words(sentence)\n if (len(sentence_words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\n continue\n if len(sentence_words) > length_filter:\n sentence_words = sentence_words[:length_filter]\n filtered_num += 1\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n sentence_words, adj = build_ing_graph(sentence_words, args)\n adjs.append(adj)\n if encoder:\n data.append(encoder.tokenize(sentence_words))\n else:\n data.append(sentence_words)\n targets_flag += 1\n if data:\n all_num += targets_flag\n targets.extend([0]*(targets_flag-1))\n if targets_flag != 0:\n targets.append(1) # segment\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(data, adjs, targets, path,\n all_document_num,\n filtered_document_num,\n sent_num_filter)\n elif args.sr_choose == 'g_model' and args.gr_choose in ['textgcn', 'ING_GCN','S_ING_GCN']:\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path,\n all_document_num,\n filtered_document_num,\n sent_num_filter)\n return_dict = generate_gcn_return_dict(return_dict, args)\n else:\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path,\n all_document_num,\n filtered_document_num,\n sent_num_filter)\n if return_dict == None:\n continue\n elif type(return_dict) == dict:\n all_return_dic.append(return_dict)\n else: # list\n all_return_dic.extend(return_dict)\n return all_return_dic, filtered_num, all_num, filtered_document_num, all_document_num\n\nclass WikiCitiesDataSet(Dataset):\n def __init__(self,\n root = None,\n args = None,\n encoder = None,\n length_filter = 40,\n sent_num_filter = 60,\n local_rank=-1):\n logger = get_logger(args)\n self.text_path = root + \"/wikicities.text\"\n self.segment_path = root + \"/wikicities.segmenttitles\"\n rank_logger_info(logger, local_rank, \"Reading wikicities data\")\n # sentences\n filtered_num = 0\n all_num = 0\n # documents\n filtered_document_num = 0\n all_document_num = 0\n self.data,filtered_num, all_num, filtered_document_num, all_document_num = read_wikicities_file(args,filtered_num,\n all_num,\n filtered_document_num,\n all_document_num,\n text_path= self.text_path,\n segment_path=self.segment_path,\n encoder=encoder,\n length_filter=length_filter,\n sent_num_filter=sent_num_filter)\n # print(\"one wikicities example:\",self.data[0])\n\n if all_num != 0:\n rank_logger_info(logger, local_rank, \"all sentences num:%d\\tfiltered num:%d\\tfiltered percent:%f\"%(all_num, filtered_num,filtered_num/all_num))\n if all_document_num != 0:\n rank_logger_info(logger, local_rank, \"all documents num:%d\\tfiltered num:%d\\tfiltered percent:%f\"%(all_document_num, filtered_document_num, filtered_document_num / all_document_num))\n\n def __getitem__(self, index):\n return self.data[index]\n\n def __len__(self):\n return len(self.data)\n\nif __name__ == \"__main__\":\n args = create_parser()\n print(args)\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\n wikielementsdataset = WikiCitiesDataSet(args.wikicities_path, args)\n"},{"alpha_fraction":0.47889289259910583,"alphanum_fraction":0.4840097725391388,"avg_line_length":49.292396545410156,"blob_id":"33f3f74d48898dda3f06193e380e8702e8beebde","content_id":"56cad373b54e40d3431f98ea98c2c9b907a9ab6f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8599,"license_type":"no_license","max_line_length":196,"num_lines":171,"path":"/data/choi_loader.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"from pathlib import Path\nfrom torch.utils.data import Dataset\nfrom data.data_utils import *\nfrom utils import *\nimport os\nimport random\nfrom parameters import create_parser\n\ndef clean_paragraph(paragraph):\n cleaned_paragraph= paragraph.replace(\"'' \", \" \").replace(\" 's\", \"'s\").replace(\"``\", \"\").strip('\\n')\n return cleaned_paragraph\n\ndef read_choi_file(args,filtered_num, all_num, filtered_document_num, all_document_num,path, encoder=None,length_filter=10, sent_num_filter=10):\n seperator = '=========='\n with Path(path).open('r') as f:\n raw_text = f.read()\n paragraphs = [clean_paragraph(p) for p in raw_text.strip().split(seperator) if len(p) > 5 and p != \"\\n\"]\n targets = []\n new_text = []\n adjs = []\n for paragraph in paragraphs:\n sentences = [s for s in paragraph.split('\\n') if len(s.split()) > 0]\n if sentences:\n sentences_count =0\n # This is the number of sentences in the paragraph and where we need to split.\n for sentence in sentences:\n words = extract_sentence_words(sentence)\n if (len(words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\n continue\n sentences_count +=1\n # length_filter\n if len(words) > length_filter:\n words = words[:length_filter]\n filtered_num += 1\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n sentence_words, adj = build_ing_graph(words, args)\n adjs.append(adj)\n if encoder:\n new_text.append(encoder.tokenize(words))\n else:\n new_text.append(words)\n all_num += sentences_count\n targets.extend([0] * (sentences_count - 1))\n if sentences_count != 0:\n targets.append(1) # segment\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(new_text, adjs, targets, path,\n all_document_num,\n filtered_document_num,\n sent_num_filter)\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\n return_dict, filtered_document_num, all_document_num = generate_return_dict(new_text, targets, path,\n all_document_num,\n filtered_document_num,\n sent_num_filter)\n return_dict = generate_gcn_return_dict(return_dict, args)\n else:\n return_dict, filtered_document_num, all_document_num = generate_return_dict(new_text, targets, path,\n all_document_num,\n filtered_document_num,\n sent_num_filter)\n return return_dict,filtered_num, all_num,filtered_document_num, all_document_num\n\ndef get_cache_path(folder,split):\n\n if split == None:\n cache_file_path = folder / 'paths_cache'\n elif split == 'train':\n cache_file_path = folder / 'train_paths_cache'\n elif split == 'dev':\n cache_file_path = folder / 'dev_paths_cache'\n elif split == 'test':\n cache_file_path = folder / 'test_paths_cache'\n else:\n cache_file_path = None\n return cache_file_path\n\ndef cache_filenames(folder,split):\n files = Path(folder).glob('*/*/*.ref')\n files = list(files) # otherwise:TypeError: object of type 'generator' has no len()\n cache_path = get_cache_path(folder,split)\n if split == None: # write all\n with cache_path.open(\"w+\") as f:\n for file in files:\n f.write(str(file) + u'\\n')\n elif split == 'train': # write train dev and test\n all_document_num = len(files)\n all_idx = [i for i in range(all_document_num)]\n random.shuffle(all_idx)\n train_num = int(all_document_num / 10 * 8)\n valid_num = int(all_document_num / 10 * 1)\n test_num = int(all_document_num / 10 * 1)\n train_cache_path = get_cache_path(folder,\"train\")\n with train_cache_path.open(\"w+\") as f:\n for i in range(train_num):\n train_idx = all_idx[i]\n f.write(str(files[train_idx]) + u'\\n')\n valid_cache_path = get_cache_path(folder,\"dev\")\n with valid_cache_path.open(\"w+\") as f:\n for i in range(train_num, train_num+valid_num):\n valid_idx = all_idx[i]\n f.write(str(files[valid_idx]) + u'\\n')\n test_cache_path = get_cache_path(folder,\"test\")\n with test_cache_path.open(\"w+\") as f:\n for i in range(train_num+valid_num, train_num+valid_num+test_num):\n test_idx = all_idx[i]\n f.write(str(files[test_idx]) + u'\\n')\n\n\nclass ChoiDataSet(Dataset):\n def __init__(self,\n root = None,\n args=None,\n encoder = None,\n split = None, # None, train, valid, test\n length_filter = 40000,\n sent_num_filter = 60000,\n local_rank=-1):\n logger = get_logger(args)\n root_path = Path(root)\n cache_path = get_cache_path(root_path,split)\n if not cache_path.exists():\n cache_filenames(root_path,split)\n self.textfiles = cache_path.read_text().splitlines()\n else:\n self.textfiles = cache_path.read_text().splitlines()\n if len(self.textfiles) == 0:\n raise RuntimeError('Found 0 images in subfolders of: {}'.format(root))\n self.data = []\n # sentences\n filtered_num = 0\n all_num = 0\n # documents\n filtered_document_num = 0\n all_document_num = 0\n if split == None:\n rank_logger_info(logger, local_rank,\"Reading all choi data for test\")\n else:\n rank_logger_info(logger, local_rank, \"Reading choi %s data\" % (split))\n for textfile in self.textfiles:\n return_dict, filtered_num, all_num, filtered_document_num, all_document_num = read_choi_file(args,filtered_num,\n all_num,\n filtered_document_num,\n all_document_num,\n textfile,\n encoder=encoder,\n length_filter=length_filter,\n sent_num_filter=sent_num_filter)\n if return_dict == None:\n continue\n elif type(return_dict) == dict:\n self.data.append(return_dict)\n else: # list\n self.data.extend(return_dict)\n # print(\"one choi example:\",self.data[0])\n if all_num != 0:\n rank_logger_info(logger, local_rank, \"all sentences num:%d\\tfiltered num:%d\\tfiltered percent:%f\"%(all_num, filtered_num,filtered_num/all_num))\n if all_document_num != 0:\n rank_logger_info(logger, local_rank, \"all documents num:%d\\tfiltered num:%d\\tfiltered percent:%f\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\n\n def __getitem__(self, index):\n return self.data[index]\n\n def __len__(self):\n return len(self.data)\n\nif __name__ == \"__main__\":\n args = create_parser()\n print(args)\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\n choidataset = ChoiDataSet(args.choi_path, args)"},{"alpha_fraction":0.5418270230293274,"alphanum_fraction":0.5474724173545837,"avg_line_length":47.71875,"blob_id":"ff20f8e6177c5ccbadbbd70f222a4d5d2adc0be2","content_id":"fb2a769ed27365a4c46f95ebb5ad7e46e6ca350e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7794,"license_type":"no_license","max_line_length":196,"num_lines":160,"path":"/data/wikisection_loader.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import re\nfrom pathlib import Path\nfrom torch.utils.data import Dataset\nfrom utils import *\nfrom data.data_utils import *\nimport os\nfrom parameters import create_parser\n\n\nsegment_seperator = \"==========\"\ndef get_seperator_foramt():\n seperator_fromat = segment_seperator + \".*\\n?\"\n return seperator_fromat\n\ndef get_scections_from_text(txt):\n sections_to_keep_pattern = get_seperator_foramt()\n all_sections = re.split(sections_to_keep_pattern, txt)\n non_empty_sections = [s for s in all_sections if len(s) > 0]\n return non_empty_sections\n\ndef clean_section(section):\n cleaned_section = section.strip('\\n')\n return cleaned_section\n\ndef get_sections(path):\n file = open(str(path), \"r\", encoding='utf-8')\n raw_content = file.read()\n file.close()\n clean_txt = raw_content.encode('utf-8').decode('utf-8').strip()\n sections = [clean_section(s) for s in get_scections_from_text(clean_txt)]\n return sections\n\ndef remove_last_one(data, targets):\n return data[:-1], targets[:-1]\n\ndef read_section_file(args,filtered_num, all_num, filtered_document_num, all_document_num,\n path, encoder=None, length_filter=20, sent_num_filter=10, remove_preface_segment=True,\n ignore_list=False, remove_special_tokens=False,return_as_sentences=False, only_letters=False):\n data = []\n adjs = []\n targets = []\n all_sections = get_sections(path)\n required_sections = all_sections[1:] if remove_preface_segment and len(all_sections) > 0 else all_sections\n required_non_empty_sections = [section for section in required_sections if len(section.split()) > 0 and section != \"\\n\"]\n for section in required_non_empty_sections:\n sentences = section.split('\\n')\n if sentences:\n targets_flag = 0\n for sentence in sentences:\n if not return_as_sentences:\n sentence_words = extract_sentence_words(sentence, remove_special_tokens=remove_special_tokens)\n if (len(sentence_words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\n continue\n # length_filter\n if len(sentence_words) > length_filter:\n sentence_words = sentence_words[:length_filter]\n filtered_num += 1\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n sentence_words, adj = build_ing_graph(sentence_words, args)\n adjs.append(adj)\n if encoder:\n data.append(encoder.tokenize(sentence_words))\n else:\n data.append(sentence_words)\n targets_flag += 1\n else: # for the annotation. keep sentence as is.\n if (only_letters):\n sentence = re.sub('[^a-zA-Z0-9 ]+', '', sentence)\n data.append(sentence)\n else:\n data.append(sentence)\n if data:\n all_num += targets_flag\n targets.extend([0]*(targets_flag-1))\n if targets_flag != 0:\n targets.append(1) # segment\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(data, adjs, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n return_dict = generate_gcn_return_dict(return_dict,args)\n else:\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\n return return_dict,filtered_num, all_num,filtered_document_num, all_document_num\n\ndef get_cache_path(folder):\n cache_file_path = folder / 'paths_cache'\n return cache_file_path\n\ndef cache_filenames(folder):\n files = Path(folder).glob('*.ref')\n cache_path = get_cache_path(folder)\n with cache_path.open(\"w+\") as f:\n for file in files:\n f.write(str(file) + u'\\n')\n\nclass WikiSectioniDataSet(Dataset):\n def __init__(self,\n root = None,\n args=None,\n encoder = None,\n split = 'train',\n length_filter = 40000,\n sent_num_filter = 60000,\n local_rank=-1):\n logger = get_logger(args)\n root_disease = Path(root+\"/en_disease_\"+split)\n root_city = Path(root+\"/en_city_\"+split)\n cache_disease_path = get_cache_path(root_disease)\n cache_city_path = get_cache_path(root_city)\n\n if not cache_disease_path.exists():\n cache_filenames(root_disease)\n cache_filenames(root_city)\n self.textfiles = cache_disease_path.read_text().splitlines() + cache_city_path.read_text().splitlines()\n else:\n self.textfiles = cache_disease_path.read_text().splitlines() + cache_city_path.read_text().splitlines()\n if len(self.textfiles) == 0:\n raise RuntimeError('Found 0 images in subfolders of: {}'.format('lalala'))\n self.data = []\n # sentences\n filtered_num = 0\n all_num = 0\n # documents\n filtered_document_num = 0\n all_document_num = 0\n rank_logger_info(logger, local_rank, \"Reading wikisection %s data\"%(split))\n for textfile in self.textfiles:\n return_dict, filtered_num, all_num, filtered_document_num, all_document_num = read_section_file(args, filtered_num,\n all_num,\n filtered_document_num,\n all_document_num,\n textfile,\n encoder=encoder,\n length_filter=length_filter,\n sent_num_filter=sent_num_filter)\n if return_dict == None:\n continue\n elif type(return_dict) == dict:\n self.data.append(return_dict)\n else: # list\n self.data.extend(return_dict)\n # print(\"one wikisection example:\",self.data[0])\n if all_num != 0:\n rank_logger_info(logger, local_rank, \"all sentences num:%d\\tfiltered num:%d\\tfiltered percent:%f\"%(all_num, filtered_num,filtered_num/all_num))\n if all_document_num != 0:\n rank_logger_info(logger, local_rank, \"all documents num:%d\\tfiltered num:%d\\tfiltered percent:%f\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\n\n def __getitem__(self, index):\n return self.data[index]\n\n def __len__(self):\n return len(self.data)\n\n\nif __name__ == \"__main__\":\n args = create_parser()\n print(args)\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\n choidataset = WikiSectioniDataSet(args.wikisection_path, args, split=\"validation\") # train validation test"},{"alpha_fraction":0.641672670841217,"alphanum_fraction":0.6459985375404358,"avg_line_length":38.657142639160156,"blob_id":"02730c083150dc03ac8c9e344823a6c719ddcc04","content_id":"5b0078b9dde2aed1e97789f290112c2003851a80","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1387,"license_type":"no_license","max_line_length":87,"num_lines":35,"path":"/encoder/Encoder.py","repo_name":"guxuan123456/DSG-SEG","src_encoding":"UTF-8","text":"import torch.nn as nn\nfrom encoder.one_hot_encoder import one_hot_model\nfrom encoder.glove_encoder import glove_model\nfrom encoder.randn_encoder import randn_model\nfrom encoder.w2v_encoder import w2v_model\nfrom encoder.bert_encoder import bert_model\nfrom utils import *\n\nclass Encoder(nn.Module):\n def __init__(self, args, etype, encoder_fine_tune, glove_path=None, w2v_path=None):\n super(Encoder, self).__init__()\n self.args = args\n if etype == \"one-hot\":\n self.model = one_hot_model(glove_path, args, encoder_fine_tune)\n elif etype == 'glove':\n self.model = glove_model(glove_path, args, encoder_fine_tune)\n elif etype == \"randn\":\n self.model = randn_model(glove_path, args, encoder_fine_tune)\n elif etype == 'w2v':\n self.model = w2v_model(w2v_path, args, encoder_fine_tune)\n elif etype == 'bert':\n self.model = bert_model(args, encoder_fine_tune)\n self.etype=etype\n self.pad_token_id = self.model.pad_token_id\n\n def tokenize(self,sentence): # get_subword_indices=False\n if type(sentence) is list:\n sentence = ' '.join(sentence)\n return self.model.tokenize(sentence)\n\n def forward(self, sentence_lst,just_last_layer=True):\n return self.model(sentence_lst)\n\n def get_input_dim(self):\n return self.model.get_word_dim()"}],"string":"[\n {\n \"alpha_fraction\": 0.5315375328063965,\n \"alphanum_fraction\": 0.5412371158599854,\n \"avg_line_length\": 44.102500915527344,\n \"blob_id\": \"ef19713e9a5582f1a244ca2d55e68ac2107fba68\",\n \"content_id\": \"bd5c38a3926b1276e5bd4fc906f46cae6fc7c854\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 18042,\n \"license_type\": \"no_license\",\n \"max_line_length\": 111,\n \"num_lines\": 400,\n \"path\": \"/models/Treelstm/models.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\nfrom torch.nn import init\\n\\nfrom models.Treelstm import basic\\nfrom models.Treelstm.utils import *\\n\\nclass AttnCombiner(nn.Module):\\n def __init__(self, bidirectional, hidden_size, attention_size=128, dropout=0):\\n super(AttnCombiner, self).__init__()\\n self.num_directions = 2 if bidirectional else 1\\n self.ws1 = nn.Linear(hidden_size * self.num_directions, attention_size, bias=False)\\n self.ws2 = nn.Linear(attention_size, 1, bias=False)\\n init.orthogonal(self.ws1.weight.data)\\n init.orthogonal(self.ws2.weight.data)\\n self.tanh = nn.Tanh()\\n self.drop = nn.Dropout(dropout)\\n\\n def forward(self, hiddens):\\n size = hiddens.size() # [bsz, len, in_dim]\\n x_flat = hiddens.contiguous().view(-1, size[2]) # [bsz*len, in_dim]\\n h_bar = self.tanh(self.ws1(self.drop(x_flat))) # [bsz*len, attn_hid]\\n alphas = self.ws2(h_bar).view(size[0], size[1]) # [bsz, len]\\n return alphas\\n\\n def __call__(self, *args, **kwargs):\\n return self.forward(*args, **kwargs)\\n\\n\\nclass BinaryTreeLSTMLayer(nn.Module):\\n\\n def __init__(self, hidden_dim):\\n super(BinaryTreeLSTMLayer, self).__init__()\\n self.hidden_dim = hidden_dim\\n self.comp_linear = nn.Linear(in_features=2 * hidden_dim,\\n out_features=5 * hidden_dim)\\n self.reset_parameters()\\n\\n def reset_parameters(self):\\n init.kaiming_normal(self.comp_linear.weight.data)\\n init.constant(self.comp_linear.bias.data, val=0)\\n\\n def forward(self, left=None, right=None):\\n \\\"\\\"\\\"\\n Args:\\n left: A (h_l, c_l) tuple, where each value has the size\\n (batch_size, max_length, hidden_dim).\\n right: A (h_r, c_r) tuple, where each value has the size\\n (batch_size, max_length, hidden_dim).\\n Returns:\\n h, c: The hidden and cell state of the composed parent,\\n each of which has the size\\n (batch_size, max_length - 1, hidden_dim).\\n \\\"\\\"\\\"\\n\\n hl, cl = left\\n hr, cr = right\\n hlr_cat = torch.cat([hl, hr], dim=2)\\n treelstm_vector = basic.apply_nd(fn=self.comp_linear, input=hlr_cat)\\n i, fl, fr, u, o = treelstm_vector.chunk(chunks=5, dim=2)\\n c = (cl*(fl + 1).sigmoid() + cr*(fr + 1).sigmoid()\\n + u.tanh()*i.sigmoid())\\n h = o.sigmoid() * c.tanh()\\n return h, c\\n\\n def __call__(self, *args, **kwargs):\\n return self.forward(*args, **kwargs)\\n\\n\\nclass TreeLSTMEncoder(nn.Module):\\n\\n def __init__(self, word_dim, hidden_dim, use_leaf_rnn, pooling_method, bidirectional):\\n super(TreeLSTMEncoder, self).__init__()\\n self.word_dim = word_dim\\n self.hidden_dim = hidden_dim\\n self.use_leaf_rnn = use_leaf_rnn\\n self.pooling_method = pooling_method\\n self.bidirectional = bidirectional\\n\\n assert not (self.bidirectional and not self.use_leaf_rnn)\\n\\n if use_leaf_rnn:\\n self.leaf_rnn_cell = nn.LSTMCell(\\n input_size=word_dim, hidden_size=hidden_dim)\\n if bidirectional:\\n self.leaf_rnn_cell_bw = nn.LSTMCell(\\n input_size=word_dim, hidden_size=hidden_dim)\\n else:\\n self.word_linear = nn.Linear(in_features=word_dim,\\n out_features=2 * hidden_dim)\\n self.treelstm_layer = BinaryTreeLSTMLayer(hidden_dim * 2 if self.bidirectional else hidden_dim)\\n\\n def reset_parameters(self):\\n if self.use_leaf_rnn:\\n init.kaiming_normal(self.leaf_rnn_cell.weight_ih.data)\\n init.orthogonal(self.leaf_rnn_cell.weight_hh.data)\\n init.constant(self.leaf_rnn_cell.bias_ih.data, val=0)\\n init.constant(self.leaf_rnn_cell.bias_hh.data, val=0)\\n # Set forget bias to 1\\n self.leaf_rnn_cell.bias_ih.data.chunk(4)[1].fill_(1)\\n if self.bidirectional:\\n init.kaiming_normal(self.leaf_rnn_cell_bw.weight_ih.data)\\n init.orthogonal(self.leaf_rnn_cell_bw.weight_hh.data)\\n init.constant(self.leaf_rnn_cell_bw.bias_ih.data, val=0)\\n init.constant(self.leaf_rnn_cell_bw.bias_hh.data, val=0)\\n # Set forget bias to 1\\n self.leaf_rnn_cell_bw.bias_ih.data.chunk(4)[1].fill_(1)\\n else:\\n init.kaiming_normal(self.word_linear.weight.data)\\n init.constant(self.word_linear.bias.data, val=0)\\n self.treelstm_layer.reset_parameters()\\n\\n @staticmethod\\n def update_state(old_state, new_state, done_mask):\\n old_h, old_c = old_state\\n new_h, new_c = new_state\\n done_mask = done_mask.float().unsqueeze(1).unsqueeze(2).expand_as(new_h)\\n h = done_mask * new_h + (1 - done_mask) * old_h[:, :-1, :]\\n c = done_mask * new_c + (1 - done_mask) * old_c[:, :-1, :]\\n return h, c\\n\\n def forward(self, *inp):\\n pass\\n\\n\\nclass GumbelTreeLSTMEncoder(TreeLSTMEncoder):\\n\\n def __init__(self, word_dim, hidden_dim, use_leaf_rnn, pooling_method, bidirectional, gumbel_temperature):\\n super(GumbelTreeLSTMEncoder, self).__init__(\\n word_dim, hidden_dim, use_leaf_rnn, pooling_method, bidirectional)\\n self.gumbel_temperature = gumbel_temperature\\n self.comp_query = nn.Parameter(torch.FloatTensor(hidden_dim * 2 if self.bidirectional else hidden_dim))\\n if pooling_method == 'attention':\\n self.combiner = AttnCombiner(bidirectional, hidden_dim)\\n self.reset_parameters()\\n\\n def reset_parameters(self):\\n super(GumbelTreeLSTMEncoder, self).reset_parameters()\\n init.normal(self.comp_query.data, mean=0, std=0.01)\\n\\n def select_composition(self, old_state, new_state, mask):\\n new_h, new_c = new_state\\n old_h, old_c = old_state\\n old_h_left, old_h_right = old_h[:, :-1, :], old_h[:, 1:, :]\\n old_c_left, old_c_right = old_c[:, :-1, :], old_c[:, 1:, :]\\n comp_weights = basic.dot_nd(query=self.comp_query, candidates=new_h)\\n if self.training:\\n select_mask = basic.st_gumbel_softmax(\\n logits=comp_weights, temperature=self.gumbel_temperature, mask=mask)\\n else:\\n select_mask = basic.greedy_select(logits=comp_weights, mask=mask)\\n select_mask = select_mask.float()\\n select_mask_expand = select_mask.unsqueeze(2).expand_as(new_h)\\n select_mask_cumsum = select_mask.cumsum(1)\\n left_mask = 1 - select_mask_cumsum\\n left_mask_expand = left_mask.unsqueeze(2).expand_as(old_h_left)\\n right_mask_leftmost_col = Variable(\\n select_mask_cumsum.data.new(new_h.size(0), 1).zero_())\\n right_mask = torch.cat(\\n [right_mask_leftmost_col, select_mask_cumsum[:, :-1]], dim=1)\\n right_mask_expand = right_mask.unsqueeze(2).expand_as(old_h_right)\\n new_h = (select_mask_expand * new_h\\n + left_mask_expand * old_h_left\\n + right_mask_expand * old_h_right)\\n new_c = (select_mask_expand * new_c\\n + left_mask_expand * old_c_left\\n + right_mask_expand * old_c_right)\\n selected_h = (select_mask_expand * new_h).sum(1)\\n return new_h, new_c, select_mask, selected_h\\n\\n def forward(self, inp, length, return_select_masks=False):\\n max_depth = inp.size(1)\\n length_mask = basic.sequence_mask(sequence_length=length, max_length=max_depth)\\n select_masks = list()\\n features = list()\\n\\n if self.use_leaf_rnn:\\n hs = list()\\n cs = list()\\n batch_size, max_length, _ = inp.size()\\n zero_state = Variable(inp.data.new(batch_size, self.hidden_dim)\\n .zero_())\\n h_prev = c_prev = zero_state\\n for i in range(max_length):\\n h, c = self.leaf_rnn_cell(\\n input=inp[:, i, :], hx=(h_prev, c_prev))\\n hs.append(h)\\n cs.append(c)\\n h_prev = h\\n c_prev = c\\n hs = torch.stack(hs, dim=1)\\n cs = torch.stack(cs, dim=1)\\n\\n if self.bidirectional:\\n hs_bw = list()\\n cs_bw = list()\\n h_bw_prev = c_bw_prev = zero_state\\n lengths_list = list(length.data)\\n input_bw = basic.reverse_padded_sequence(\\n inputs=inp, lengths=lengths_list, batch_first=True)\\n for i in range(max_length):\\n h_bw, c_bw = self.leaf_rnn_cell_bw(\\n input=input_bw[:, i, :], hx=(h_bw_prev, c_bw_prev))\\n hs_bw.append(h_bw)\\n cs_bw.append(c_bw)\\n h_bw_prev = h_bw\\n c_bw_prev = c_bw\\n hs_bw = torch.stack(hs_bw, dim=1)\\n cs_bw = torch.stack(cs_bw, dim=1)\\n hs_bw = basic.reverse_padded_sequence(\\n inputs=hs_bw, lengths=lengths_list, batch_first=True)\\n cs_bw = basic.reverse_padded_sequence(\\n inputs=cs_bw, lengths=lengths_list, batch_first=True)\\n hs = torch.cat([hs, hs_bw], dim=2)\\n cs = torch.cat([cs, cs_bw], dim=2)\\n state = (hs, cs)\\n else:\\n state = basic.apply_nd(fn=self.word_linear, input=inp)\\n state = state.chunk(chunks=2, dim=2)\\n nodes = list()\\n if self.pooling_method is not None:\\n nodes.append(state[0])\\n for i in range(max_depth - 1):\\n h, c = state\\n left = (h[:, :-1, :], c[:, :-1, :])\\n right = (h[:, 1:, :], c[:, 1:, :])\\n new_state = self.treelstm_layer(left=left, right=right)\\n if i < max_depth - 2:\\n # We don't need to greedily select the composition in the\\n # last iteration, since it has only one option left.\\n new_h, new_c, select_mask, selected_h = self.select_composition(\\n old_state=state, new_state=new_state,\\n mask=length_mask[:, i+1:])\\n new_state = (new_h, new_c)\\n select_masks.append(select_mask)\\n features.append(selected_h)\\n if self.pooling_method is not None:\\n nodes.append(selected_h.unsqueeze(1))\\n done_mask = length_mask[:, i+1]\\n state = self.update_state(old_state=state, new_state=new_state,\\n done_mask=done_mask)\\n if (self.pooling_method is not None) and i >= max_depth - 2:\\n nodes.append(state[0])\\n h, c = state\\n if self.pooling_method == 'max':\\n nodes = torch.cat(nodes, dim=1)\\n h = nodes.max(1)[0].unsqueeze(1)\\n elif self.pooling_method == 'mean':\\n nodes = torch.cat(nodes, dim=1).sum(1)\\n lengths = length * 2 - 1\\n lengths = lengths.unsqueeze(1).float().expand_as(nodes)\\n h = (nodes / lengths).unsqueeze(1)\\n elif self.pooling_method == 'attention':\\n nodes = torch.cat(nodes, dim=1)\\n att_mask = torch.cat([length_mask, length_mask[:, 1:]], dim=1)\\n att_mask_expand = att_mask.float().unsqueeze(2).expand_as(nodes)\\n att_weights = basic.masked_softmax(\\n logits=self.combiner(nodes), mask=att_mask)\\n att_weights_expand = att_weights.unsqueeze(2).expand_as(nodes)\\n h = (att_weights_expand * att_mask_expand * nodes).sum(1).unsqueeze(1)\\n else:\\n assert self.pooling_method is None\\n assert h.size(1) == 1 and c.size(1) == 1\\n if not return_select_masks:\\n return h.squeeze(1), c.squeeze(1)\\n else:\\n return h.squeeze(1), c.squeeze(1), features, select_masks\\n\\n\\nclass RecursiveTreeLSTMEncoder(TreeLSTMEncoder):\\n\\n def __init__(self, word_dim, hidden_dim, use_leaf_rnn, pooling_method, bidirectional):\\n super(RecursiveTreeLSTMEncoder, self).__init__(\\n word_dim, hidden_dim, use_leaf_rnn, pooling_method, bidirectional)\\n self.reset_parameters()\\n self.pooling_method = pooling_method\\n if self.pooling_method == 'attention':\\n self.combiner = AttnCombiner(bidirectional, hidden_dim)\\n\\n def forward(self, inp, length, fixed_masks):\\n # print(\\\"=\\\"*100)\\n # print(inp.shape)\\n # print(length)\\n # print(fixed_masks)\\n max_depth = inp.size(1)\\n length_mask = basic.sequence_mask(sequence_length=length, max_length=max_depth)\\n features = list()\\n\\n if self.use_leaf_rnn:\\n hs = list()\\n cs = list()\\n batch_size, max_length, _ = inp.size()\\n zero_state = Variable(inp.data.new(batch_size, self.hidden_dim).zero_())\\n h_prev = c_prev = zero_state\\n for i in range(max_length):\\n h, c = self.leaf_rnn_cell(\\n input=inp[:, i, :], hx=(h_prev, c_prev))\\n hs.append(h)\\n cs.append(c)\\n h_prev = h\\n c_prev = c\\n hs = torch.stack(hs, dim=1)\\n cs = torch.stack(cs, dim=1)\\n\\n if self.bidirectional:\\n hs_bw = list()\\n cs_bw = list()\\n h_bw_prev = c_bw_prev = zero_state\\n lengths_list = list(length.data)\\n input_bw = basic.reverse_padded_sequence(\\n inputs=inp, lengths=lengths_list, batch_first=True)\\n for i in range(max_length):\\n h_bw, c_bw = self.leaf_rnn_cell_bw(\\n input=input_bw[:, i, :], hx=(h_bw_prev, c_bw_prev))\\n hs_bw.append(h_bw)\\n cs_bw.append(c_bw)\\n h_bw_prev = h_bw\\n c_bw_prev = c_bw\\n hs_bw = torch.stack(hs_bw, dim=1)\\n cs_bw = torch.stack(cs_bw, dim=1)\\n hs_bw = basic.reverse_padded_sequence(\\n inputs=hs_bw, lengths=lengths_list, batch_first=True)\\n cs_bw = basic.reverse_padded_sequence(\\n inputs=cs_bw, lengths=lengths_list, batch_first=True)\\n hs = torch.cat([hs, hs_bw], dim=2)\\n cs = torch.cat([cs, cs_bw], dim=2)\\n state = (hs, cs)\\n else:\\n state = basic.apply_nd(fn=self.word_linear, input=inp)\\n state = state.chunk(chunks=2, dim=2)\\n nodes = list()\\n if self.pooling_method is not None:\\n nodes.append(state[0])\\n for i in range(max_depth - 1):\\n h, c = state\\n left = (h[:, :-1, :], c[:, :-1, :])\\n right = (h[:, 1:, :], c[:, 1:, :])\\n new_state = self.treelstm_layer(left=left, right=right)\\n if i < max_depth - 2:\\n # We don't need to greedily select the composition in the\\n # last iteration, since it has only one option left.\\n new_h, new_c, selected_h = self.compose(\\n old_state=state, new_state=new_state, tree_mask=fixed_masks[i])\\n new_state = (new_h, new_c)\\n features.append(selected_h)\\n if self.pooling_method is not None:\\n nodes.append(selected_h.unsqueeze(1))\\n done_mask = length_mask[:, i + 1]\\n state = self.update_state(\\n old_state=state, new_state=new_state, done_mask=done_mask)\\n if (self.pooling_method is not None) and i >= max_depth - 2:\\n done_masks = done_mask.float().unsqueeze(1).unsqueeze(2).expand_as(state[0])\\n nodes.append(state[0] * done_masks)\\n h, c = state\\n if self.pooling_method == 'max':\\n nodes = torch.cat(nodes, dim=1)\\n h = nodes.max(1)[0].unsqueeze(1)\\n elif self.pooling_method == 'mean':\\n nodes = torch.cat(nodes, dim=1).sum(1)\\n lengths = length * 2 - 1\\n lengths = lengths.unsqueeze(1).float().expand_as(nodes)\\n h = (nodes / lengths).unsqueeze(1)\\n elif self.pooling_method == 'attention':\\n nodes = torch.cat(nodes, dim=1)\\n att_mask = torch.cat([length_mask, length_mask[:, 1:]], dim=1)\\n att_mask_expand = att_mask.float().unsqueeze(2).expand_as(nodes)\\n att_weights = basic.masked_softmax(\\n logits=self.combiner(nodes), mask=att_mask)\\n att_weights_expand = att_weights.unsqueeze(2).expand_as(nodes)\\n h = (att_weights_expand * att_mask_expand * nodes).sum(1).unsqueeze(1)\\n else:\\n assert self.pooling_method is None\\n assert h.size(1) == 1 and c.size(1) == 1\\n return h.squeeze(1), c.squeeze(1)\\n\\n @staticmethod\\n def compose(old_state, new_state, tree_mask):\\n new_h, new_c = new_state\\n old_h, old_c = old_state\\n old_h_left, old_h_right = old_h[:, :-1, :], old_h[:, 1:, :]\\n old_c_left, old_c_right = old_c[:, :-1, :], old_c[:, 1:, :]\\n select_mask_expand = tree_mask.unsqueeze(2).expand_as(new_h)\\n select_mask_cumsum = tree_mask.cumsum(1)\\n left_mask = 1 - select_mask_cumsum\\n left_mask_expand = left_mask.unsqueeze(2).expand_as(old_h_left)\\n right_mask_leftmost_col = Variable(\\n select_mask_cumsum.data.new(new_h.size(0), 1).zero_())\\n right_mask = torch.cat(\\n [right_mask_leftmost_col, select_mask_cumsum[:, :-1]], dim=1)\\n right_mask_expand = right_mask.unsqueeze(2).expand_as(old_h_right)\\n new_h = (select_mask_expand * new_h\\n + left_mask_expand * old_h_left\\n + right_mask_expand * old_h_right)\\n new_c = (select_mask_expand * new_c\\n + left_mask_expand * old_c_left\\n + right_mask_expand * old_c_right)\\n selected_h = (select_mask_expand * new_h).sum(1)\\n return new_h, new_c, selected_h\\n\\n def __call__(self, *args, **kwargs):\\n return self.forward(*args, **kwargs)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.4251185953617096,\n \"alphanum_fraction\": 0.43528348207473755,\n \"avg_line_length\": 54.32500076293945,\n \"blob_id\": \"fbf4a95b9c0aaf7efccd2bf0baa0488eda07f127\",\n \"content_id\": \"9a4d1000893e8d1854049533adc8a19fa9ef98b1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4427,\n \"license_type\": \"no_license\",\n \"max_line_length\": 172,\n \"num_lines\": 80,\n \"path\": \"/validate.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from tqdm import tqdm\\nimport torch\\nimport torch.nn.functional as F\\nfrom evaluation_utils import *\\nfrom termcolor import colored\\nfrom utils import *\\nimport numpy as np\\npreds_stats = predictions_analysis()\\ndef validate(model, encode_model, args, epoch, dataset,local_rank):\\n device = args.device\\n logger = get_logger(args)\\n model.eval()\\n with tqdm(desc='Validatinging', total=len(dataset)) as pbar:\\n acc = Accuracies()\\n document_result = {}\\n for i, (data, target, paths) in enumerate(dataset):\\n if True:\\n if i == args.stop_after:\\n break\\n pbar.update()\\n with torch.no_grad():\\n encoded_input = encode_model(data)\\n output = model(encoded_input)\\n output_seg = output.data.cpu().numpy().argmax(axis=1)\\n output_softmax = F.softmax(output, 1)\\n current_idx = 0\\n output_np = output_softmax.data.cpu().numpy()\\n for k, t in enumerate(target):\\n path = paths[k]\\n document_sentence_count = len(t)\\n to_idx = int(current_idx + document_sentence_count)\\n tmp_output = output_np[current_idx: to_idx, :].tolist()\\n if \\\"_|_\\\" not in path: # whole document\\n document_result[path] = [[tmp_output,t]]\\n else: # filtered document\\n path,num = path.split(\\\"_|_\\\")\\n if path not in document_result:\\n document_result[path] = [[num,[tmp_output,t]]]\\n else:\\n document_result[path].append([num,[tmp_output,t]])\\n current_idx = to_idx\\n targets_var = torch.cat(target, 0).to(device).clone().detach().requires_grad_(False)\\n target_seg = targets_var.data.cpu().numpy()\\n preds_stats.add(output_seg, target_seg)\\n # concat:\\n for k in document_result:\\n if type(document_result[k][0][0]) == str:\\n splited_dic_item = document_result[k]\\n complete_dic_item = []\\n sorted_splited_dic_item = sorted(splited_dic_item, key=lambda x: int(x[0]))\\n for item in sorted_splited_dic_item:\\n if len(complete_dic_item) == 0:\\n complete_dic_item = [item[1][0], [item[1][1]]]\\n else:\\n complete_dic_item[0].extend(item[1][0])\\n complete_dic_item[1].append(item[1][1])\\n # complete_dic_item[1] =\\n document_result[k] = complete_dic_item\\n document_result[k][1] = torch.cat(document_result[k][1], 0)\\n else:\\n document_result[k] = [document_result[k][0][0],document_result[k][0][1]]\\n acc_input = [[],[],[]] # [all_output_softmax, all_target, all_paths, args.crf]\\n for k in document_result:\\n acc_input[0].extend(document_result[k][0])\\n acc_input[1].append(document_result[k][1])\\n acc_input[2].append(k)\\n acc_input[0] = np.array(acc_input[0])\\n acc.update(acc_input[0], acc_input[1], acc_input[2])\\n epoch_pk, epoch_windiff, epoch_b, epoch_s, threshold = acc.calc_accuracy()\\n\\n rank_logger_info(logger, local_rank, colored('Validating Epoch: {}, accuracy: {:.4}, Pk: {:.4}, Windiff: {:.4}, B: {:.4}, S: {:.4} ,F1: {:.4} . '.format(epoch + 1,\\n preds_stats.get_accuracy(),\\n epoch_pk,\\n epoch_windiff,\\n epoch_b,\\n epoch_s,\\n preds_stats.get_f1()),\\\"red\\\"))\\n preds_stats.reset()\\n\\n return epoch_pk, threshold\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5519116520881653,\n \"alphanum_fraction\": 0.557858943939209,\n \"avg_line_length\": 37.20779037475586,\n \"blob_id\": \"645759a2699d2aae62b9b08716346cce2b51d817\",\n \"content_id\": \"8539b2f4f4a7463d7e2563920115b031a6a95a92\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5885,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 154,\n \"path\": \"/models/Treelstm/data.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import jsonlines\\nimport numpy as np\\nimport torch\\nfrom torch.utils.data import Dataset\\n\\nclass Vocab(object):\\n\\n def __init__(self, vocab_dict, add_pad, add_unk):\\n self._vocab_dict = vocab_dict.copy()\\n self._reverse_vocab_dict = dict()\\n if add_pad:\\n self.pad_word = '<pad>'\\n self.pad_id = len(self._vocab_dict)\\n self._vocab_dict[self.pad_word] = self.pad_id\\n if add_unk:\\n self.unk_word = '<unk>'\\n self.unk_id = len(self._vocab_dict)\\n self._vocab_dict[self.unk_word] = self.unk_id\\n for w, i in self._vocab_dict.items():\\n self._reverse_vocab_dict[i] = w\\n\\n @classmethod\\n def from_file(cls, path, add_pad=True, add_unk=True, max_size=None):\\n vocab_dict = dict()\\n with open(path, 'r', encoding='utf-8') as f:\\n for i, line in enumerate(f):\\n if max_size and i >= max_size:\\n break\\n word = line.strip().split()[0]\\n vocab_dict[word] = len(vocab_dict)\\n vocab_dict[word] = len(vocab_dict)\\n return cls(vocab_dict=vocab_dict, add_pad=add_pad, add_unk=add_unk)\\n\\n def word_to_id(self, word):\\n if hasattr(self, 'unk_id'):\\n return self._vocab_dict.get(word, self.unk_id)\\n return self._vocab_dict[word]\\n\\n def id_to_word(self, id_):\\n if hasattr(self, 'unk_word'):\\n return self._reverse_vocab_dict.get(id_, self.unk_word)\\n return self._reverse_vocab_dict[id_]\\n\\n def has_word(self, word):\\n return word in self._vocab_dict\\n\\n def __len__(self):\\n return len(self._vocab_dict)\\n\\n\\nclass SentenceDataset(Dataset):\\n\\n def __init__(self, word_vocab, data_path, label_vocab, max_length, lower, min_length=2):\\n self.word_vocab = word_vocab\\n self.label_vocab = label_vocab\\n self.lower = lower\\n self._max_length = max_length # 64\\n self._min_length = min_length # 2\\n self._data = []\\n failed_to_parse = 0\\n with jsonlines.open(data_path, 'r') as reader:\\n for obj in reader: # obj:{\\\"label\\\":1,\\\"sentence\\\":\\\"...\\\"}\\n try:\\n converted = self._convert_obj(obj)\\n if converted:\\n self._data.append(converted)\\n else:\\n failed_to_parse += 1\\n except ValueError:\\n failed_to_parse += 1\\n except AttributeError:\\n failed_to_parse += 1\\n print('Failed to parse {:d} instances'.format(failed_to_parse))\\n\\n def _convert_obj(self, obj):\\n sentence = obj['sentence']\\n if self.lower:\\n sentence = sentence.lower()\\n word_ids = [self.word_vocab.word_to_id(w) for w in sentence.split()]\\n length = len(word_ids)\\n if length > self._max_length or length < self._min_length:\\n return None\\n label = self.label_vocab.word_to_id(obj['label'])\\n if 'constituency_tree_encoding' in obj:\\n mask_ids = self.tree_encoding_to_mask_ids(obj['constituency_tree_encoding'])\\n else:\\n mask_ids = None\\n if mask_ids is not None and len(mask_ids) != length - 1:\\n return None\\n return word_ids, sentence, length, mask_ids, label\\n\\n def pad_sentence(self, data):\\n max_length = max(len(d) for d in data)\\n padded = [d + [self.word_vocab.pad_id] * (max_length - len(d))\\n for d in data]\\n return padded\\n\\n @staticmethod\\n def pad_mask(data):\\n max_length = max(len(d) for d in data)\\n padded = [d + [0] * (max_length - len(d)) for d in data]\\n return np.array(padded)\\n\\n @staticmethod\\n def make_one_hot_gold_mask(gold_mask_ids):\\n masks = list()\\n for num_classes in range(gold_mask_ids.shape[1], 1, -1):\\n i = gold_mask_ids.shape[1] - num_classes\\n indices = torch.LongTensor(gold_mask_ids[:, i])\\n mask = SentenceDataset.convert_to_one_hot(indices, num_classes).float()\\n masks.append(mask)\\n return masks\\n\\n def __len__(self):\\n return len(self._data)\\n\\n def __getitem__(self, item):\\n return self._data[item]\\n\\n def collate(self, batch):\\n words_batch, raw_sentences_batch, length_batch, mask_ids_batch, label_batch = list(zip(*batch))\\n sentences = torch.LongTensor(self.pad_sentence(words_batch))\\n lengths = torch.LongTensor(length_batch)\\n try:\\n masks = self.make_one_hot_gold_mask(self.pad_mask(mask_ids_batch))\\n except TypeError:\\n masks = None\\n labels = torch.LongTensor(label_batch)\\n return {'sentences': sentences, 'lengths': lengths, 'masks': masks, 'labels': labels,\\n 'raw_sentences': raw_sentences_batch}\\n\\n @staticmethod\\n def tree_encoding_to_mask_ids(tree_encoding):\\n items = [int(x) for x in tree_encoding.strip().split(',')]\\n sentence_length = len(items) // 3 + 1 # (sentence_length - 1) * 3 = len(items)\\n curr_sent = [x for x in range(sentence_length)]\\n mask_ids = list()\\n assert 3 * (sentence_length - 1) == len(items)\\n for i in range(sentence_length - 1):\\n left_node = items[i * 3]\\n right_node = items[i * 3 + 1]\\n father_node = items[i * 3 + 2]\\n left_index = curr_sent.index(left_node)\\n assert curr_sent[left_index + 1] == right_node\\n curr_sent = curr_sent[:left_index] + [father_node] + curr_sent[left_index + 2:]\\n mask_ids.append(left_index)\\n return mask_ids\\n\\n @staticmethod\\n def convert_to_one_hot(indices, num_classes):\\n batch_size = indices.shape[0]\\n indices = indices.unsqueeze(1)\\n one_hot = indices.new(batch_size, num_classes).zero_().scatter_(1, indices, 1)\\n return one_hot\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5321321487426758,\n \"alphanum_fraction\": 0.5392192006111145,\n \"avg_line_length\": 48.266273498535156,\n \"blob_id\": \"22d5312f25bccfbf35c3acbe246e6651bd03c7a2\",\n \"content_id\": \"ca81378c16dd116942a3ab70fd97aa13292afa62\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8325,\n \"license_type\": \"no_license\",\n \"max_line_length\": 196,\n \"num_lines\": 169,\n \"path\": \"/data/wiki50_loader.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from pathlib import Path\\nfrom torch.utils.data import Dataset\\nfrom utils import *\\nfrom data.data_utils import *\\nimport re\\nimport os\\nfrom parameters import create_parser\\nsegment_seperator = \\\"========\\\"\\n\\ndef get_seperator_foramt(levels = None):\\n level_format = '\\\\d' if levels == None else '['+ str(levels[0]) + '-' + str(levels[1]) + ']'\\n seperator_fromat = segment_seperator + ',' + level_format + \\\",.*?\\\\.\\\"\\n return seperator_fromat\\n\\n\\ndef clean_section(section):\\n cleaned_section = section.strip('\\\\n')\\n return cleaned_section\\n\\n\\ndef get_scections_from_text(txt, high_granularity=True):\\n sections_to_keep_pattern = get_seperator_foramt() if high_granularity else get_seperator_foramt((1, 2))\\n if not high_granularity:\\n # if low granularity required we should flatten segments within segemnt level 2\\n pattern_to_ommit = get_seperator_foramt((3, 999))\\n txt = re.sub(pattern_to_ommit, \\\"\\\", txt)\\n #delete empty lines after re.sub()\\n sentences = [s for s in txt.strip().split(\\\"\\\\n\\\") if len(s) > 0 and s != \\\"\\\\n\\\"]\\n txt = '\\\\n'.join(sentences).strip('\\\\n')\\n all_sections = re.split(sections_to_keep_pattern, txt)\\n non_empty_sections = [s for s in all_sections if len(s) > 0]\\n return non_empty_sections\\n\\n\\ndef get_sections(path, high_granularity=True):\\n file = open(str(path), \\\"r\\\")\\n raw_content = file.read()\\n file.close()\\n clean_txt = raw_content.encode('utf-8').decode('utf-8').strip()\\n sections = [clean_section(s) for s in get_scections_from_text(clean_txt, high_granularity)]\\n return sections\\n\\ndef read_wiki50_file(args,filtered_num, all_num, filtered_document_num, all_document_num,\\n path, logger, encoder=None, length_filter=20, sent_num_filter=10, remove_preface_segment=True, ignore_list=False, remove_special_tokens=False,\\n return_as_sentences=False, high_granularity=False, only_letters=False):\\n data = []\\n adjs = []\\n targets = []\\n all_sections = get_sections(path, high_granularity)\\n required_sections = all_sections[1:] if remove_preface_segment and len(all_sections) > 0 else all_sections\\n required_non_empty_sections = [section for section in required_sections if len(section) > 0 and section != \\\"\\\\n\\\"]\\n for section in required_non_empty_sections:\\n sentences = section.split('\\\\n')\\n if sentences:\\n targets_flag = 0\\n for sentence in sentences:\\n is_list_sentence = get_list_token() + \\\".\\\" == str(sentence)\\n if ignore_list and is_list_sentence:\\n continue\\n if not return_as_sentences:\\n sentence_words = extract_sentence_words(sentence, remove_special_tokens=remove_special_tokens)\\n if (len(sentence_words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\\n continue\\n # length_filter\\n if len(sentence_words) > length_filter:\\n sentence_words = sentence_words[:length_filter]\\n filtered_num += 1\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n sentence_words, adj = build_ing_graph(sentence_words, args)\\n adjs.append(adj)\\n if encoder:\\n data.append(encoder.tokenize(sentence_words))\\n else:\\n data.append(sentence_words)\\n targets_flag += 1\\n else: # for the annotation. keep sentence as is.\\n if (only_letters):\\n sentence = re.sub('[^a-zA-Z0-9 ]+', '', sentence)\\n data.append(sentence)\\n else:\\n data.append(sentence)\\n if data:\\n all_num += targets_flag\\n targets.extend([0]*(targets_flag-1))\\n if targets_flag != 0:\\n targets.append(1) # segment\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(data, adjs, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n return_dict = generate_gcn_return_dict(return_dict,args)\\n else:\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n return return_dict, filtered_num, all_num,filtered_document_num, all_document_num\\n\\ndef get_cache_path(folder):\\n cache_file_path = folder / 'paths_cache'\\n return cache_file_path\\n\\ndef cache_filenames(folder):\\n files = Path(folder).glob('*')\\n files_lst = []\\n for file in files:\\n files_lst.append(file)\\n cache_path = get_cache_path(folder)\\n with cache_path.open(\\\"w+\\\") as f:\\n for file in files_lst:\\n f.write(str(file) + u'\\\\n')\\n\\nclass Wiki50DataSet(Dataset):\\n def __init__(self,\\n root = None,\\n args=None,\\n encoder = None,\\n length_filter = 40,\\n sent_num_filter = 60,\\n local_rank=-1):\\n logger = get_logger(args)\\n root_path = Path(root)\\n cache_path = get_cache_path(root_path)\\n if not cache_path.exists():\\n cache_filenames(root_path)\\n self.textfiles = cache_path.read_text().splitlines()\\n else:\\n self.textfiles = cache_path.read_text().splitlines()\\n if len(self.textfiles) == 0:\\n raise RuntimeError('Found 0 images in subfolders of: {}'.format(root))\\n self.data = []\\n # sentences\\n filtered_num = 0\\n all_num = 0\\n # documents\\n filtered_document_num = 0\\n all_document_num = 0\\n rank_logger_info(logger, local_rank, \\\"Reading wiki50 data\\\")\\n for textfile in self.textfiles:\\n return_dict, filtered_num, all_num, filtered_document_num, all_document_num = read_wiki50_file(args,filtered_num,\\n all_num,\\n filtered_document_num,\\n all_document_num,\\n textfile,\\n logger = logger,\\n encoder=encoder,\\n length_filter=length_filter,\\n sent_num_filter=sent_num_filter)\\n if return_dict == None:\\n continue\\n elif type(return_dict) == dict:\\n self.data.append(return_dict)\\n else: # list\\n self.data.extend(return_dict)\\n # print(\\\"one wiki50 example:\\\",self.data[0])\\n if all_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all sentences num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\"%(all_num, filtered_num,filtered_num/all_num))\\n if all_document_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all documents num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\\n\\n def __getitem__(self, index):\\n return self.data[index]\\n\\n def __len__(self):\\n return len(self.data)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n args = create_parser()\\n print(args)\\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\\n choidataset = Wiki50DataSet(args.wiki50_path, args)\"\n },\n {\n \"alpha_fraction\": 0.6061512231826782,\n \"alphanum_fraction\": 0.6125587224960327,\n \"avg_line_length\": 45.81632614135742,\n \"blob_id\": \"0ba4577c41462c8530d9f8333bdc5fa27c2304d4\",\n \"content_id\": \"287c98416f57e4394066e92e781941fff2aeb70f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2341,\n \"license_type\": \"no_license\",\n \"max_line_length\": 122,\n \"num_lines\": 49,\n \"path\": \"/models/sequence_sr.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\r\\nimport torch\\r\\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\\r\\nfrom utils import unsort\\r\\ndef zero_state(module, batch_size, device):\\r\\n # * 2 is for the two directions\\r\\n return torch.zeros(module.num_layers * 2, batch_size, module.hidden_dim).to(device), \\\\\\r\\n torch.zeros(module.num_layers * 2, batch_size, module.hidden_dim).to(device)\\r\\n\\r\\nclass s_model(nn.Module):\\r\\n def __init__(self, encoder_input_dim, args):\\r\\n super(s_model, self).__init__()\\r\\n self.input_dim = encoder_input_dim\\r\\n self.num_layers = args.bilstm_nl\\r\\n self.hidden_dim = args.bilstm_hd\\r\\n self.bidirectional = args.bilstm_bd\\r\\n self.device = args.device\\r\\n # self.pooling_method = args.pool_method\\r\\n self.lstm = nn.LSTM(input_size=self.input_dim,\\r\\n hidden_size=self.hidden_dim,\\r\\n num_layers=self.num_layers,\\r\\n dropout=0,\\r\\n bidirectional=True) # batch_first=True\\r\\n\\r\\n # def pad(self, s, max_length):\\r\\n # pad_lst = [self.encoder.model.pad_token_id] * (max_length - s.size(0))\\r\\n # s = torch.LongTensor(s.tolist() + pad_lst).to(self.device)\\r\\n # return s.unsqueeze(1)\\r\\n\\r\\n def forward(self,encoded_input):\\r\\n embeded_big_tensor,sorted_lengths,sort_order,sentences_per_doc=encoded_input\\r\\n packed_tensor = pack_padded_sequence(embeded_big_tensor, sorted_lengths) # batch_first=True\\r\\n batch_size = packed_tensor.batch_sizes[0]\\r\\n s = zero_state(self, batch_size, self.device)\\r\\n encoded_sentences,_ = self.lstm(packed_tensor,s)\\r\\n padded_output, lengths = pad_packed_sequence(encoded_sentences) # (batch, max sentence len,256) ,batch_first=True\\r\\n maxes = torch.zeros(batch_size, padded_output.size(2)).to(self.device)\\r\\n for i in range(batch_size):\\r\\n maxes[i, :] = torch.max(padded_output[:lengths[i],i,:], 0)[0]\\r\\n unsort_order = torch.LongTensor(unsort(sort_order)).to(self.device)\\r\\n unsorted_sent_repr = maxes.index_select(0, unsort_order)\\r\\n return unsorted_sent_repr, sentences_per_doc\\r\\n\\r\\n\\r\\n def get_output_dim(self):\\r\\n if self.bidirectional:\\r\\n return self.hidden_dim * 2\\r\\n else:\\r\\n return self.hidden_dim\"\n },\n {\n \"alpha_fraction\": 0.2798086702823639,\n \"alphanum_fraction\": 0.28185856342315674,\n \"avg_line_length\": 55.39215850830078,\n \"blob_id\": \"0261b53a5cbbfcdb5673ca9bbfe9891f039f8497\",\n \"content_id\": \"ab9c4b9e3125993fb79832ea513277c6647f29a9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 3593,\n \"license_type\": \"no_license\",\n \"max_line_length\": 132,\n \"num_lines\": 51,\n \"path\": \"/doc/code_tree.md\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# DSG-SEG整体代码结构 \\r\\n\\r\\nDSG-SEG 的整体目录结构介绍如下： \\r\\n```\\r\\nDSE-SEG\\r\\n├── doc //介绍文档\\r\\n├── data //加载数据集\\r\\n│ ├── all_data_loader.py //汇总\\r\\n│ ├── choi_loader.py //choi\\r\\n│ ├── clinical_loader.py //clinical\\r\\n│ ├── data_utils.py //工具\\r\\n│ ├── manifesto_loader.py //manifesto\\r\\n│ ├── wiki_loader.py //wiki\\r\\n│ ├── wiki50_loader.py //wiki50\\r\\n│ ├── wikicities_loader.py //wikicities\\r\\n│ ├── wikielements_loader.py //wikielements\\r\\n│ └── wikisection_loader.py //wikisection\\r\\n├── models //模型\\r\\n│ ├── GGNN //GGNN网络\\r\\n│ │ ├── init.py //四种参数初始化方式\\r\\n│ │ └── layer.py //网络\\r\\n│ ├── Treelstm //Treelstm模型\\r\\n│ │ ├── basic.py //功能\\r\\n│ │ ├── data.py //数据\\r\\n│ │ ├── models.py //模型\\r\\n│ │ └── utils.py //工具\\r\\n│ ├── bert_sr.py //Bert句子表示\\r\\n│ ├── DSG_SEG.py //DSG_SEG句子表示\\r\\n│ ├── Encode_model.py //编码模型，数据流向:dataset->collate_fn->data_loader->encoder_model->sr_model->seg_model\\r\\n│ ├── freq_sr.py //词频句子表示\\r\\n│ ├── latent_sr.py //隐式句子表示\\r\\n│ ├── seg_model.py //上层Bilstm网络，用于预测分割点\\r\\n│ ├── sequence_sr.py //序列句子表示\\r\\n│ ├── texting.py //texting模型，也就是-SENTNODE消融模型\\r\\n│ └── treelstm_sr.py //树形句子表示\\r\\n├── encoder //词向量\\r\\n│ ├── w2v_encoder.py //word2vec\\r\\n│ ├── randn_encoder.py //随机初始化\\r\\n│ ├── one_hot_encoder.py //one-hot\\r\\n│ ├── glove_encoder.py //glove\\r\\n│ ├── Encoder.py //汇总\\r\\n│ └── bert_encoder.py //bert\\r\\n├── validate.py //模型验证\\r\\n├── utils.py //工具\\r\\n├── train.py //模型训练\\r\\n├── test.py //模型测试\\r\\n├── run.py //主程序\\r\\n├── parameters.py //命令行参数\\r\\n├── judgement.py //样例分析\\r\\n└── evaluation_utils.py //评估工具\\r\\n```\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5757400393486023,\n \"alphanum_fraction\": 0.5876502990722656,\n \"avg_line_length\": 39.2279052734375,\n \"blob_id\": \"e9eaf03cb3edbf8d29c5e2351fc7f04062151f5c\",\n \"content_id\": \"9888a231e2909e63bb070424003945a1fbca9277\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8652,\n \"license_type\": \"no_license\",\n \"max_line_length\": 193,\n \"num_lines\": 215,\n \"path\": \"/evaluation_utils.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# coding=UTF-8\\nfrom enum import Enum\\nfrom segeval.window.pk import pk\\nfrom segeval.window.windowdiff import window_diff as WD\\nfrom segeval.similarity.boundary import boundary_similarity as B\\nfrom segeval.similarity.segmentation import segmentation_similarity as S\\nimport numpy as np\\nimport os\\n\\n\\nclass predictions_analysis(object):\\n def __init__(self):\\n self.tp = 0\\n self.tn = 0\\n self.fp = 0\\n self.fn = 0\\n\\n\\n def add(self,predicions, targets):\\n self.tp += ((predicions == targets) & (1 == predicions)).sum()\\n self.tn += ((predicions == targets) & (0 == predicions)).sum()\\n self.fp += ((predicions != targets) & (1 == predicions)).sum()\\n self.fn += ((predicions != targets) & (0 == predicions)).sum()\\n\\n def calc_recall(self):\\n if self.tp == 0 and self.fn == 0:\\n return -1\\n\\n return np.true_divide(self.tp, self.tp + self.fn)\\n\\n def calc_precision(self):\\n if self.tp == 0 and self.fp == 0:\\n return -1\\n\\n return np.true_divide(self.tp,self.tp + self.fp)\\n\\n def get_f1(self):\\n if (self.tp + self.fp == 0):\\n return 0.0\\n if (self.tp + self.fn == 0):\\n return 0.0\\n precision = self.calc_precision()\\n recall = self.calc_recall()\\n if (not ((precision + recall) == 0)):\\n f1 = 2*(precision*recall) / (precision + recall)\\n else:\\n f1 = 0.0\\n\\n return f1\\n\\n def get_accuracy(self):\\n total = self.tp + self.tn + self.fp + self.fn\\n if (total == 0) :\\n return 0.0\\n else:\\n return np.true_divide(self.tp + self.tn, total)\\n\\n def reset(self):\\n self.tp = 0\\n self.tn = 0\\n self.fn = 0\\n self.fp = 0\\n\\nclass CalculateEnum(Enum):\\n \\\"\\\"\\\"\\n Four indicates: pk,windiff,boundary_similarity,segmentation_similarity.\\n \\\"\\\"\\\"\\n pk = 0\\n windiff = 1\\n boundary_similarity = 2,\\n segmentation_similarity = 3,\\n\\nclass Accuracies(object):\\n def __init__(self):\\n self.thresholds = np.arange(0, 1, 0.05)\\n self.accuracies = {k: Accuracy() for k in self.thresholds}\\n\\n def update(self, output_np, targets_np, paths):\\n current_idx = 0\\n for k, t in enumerate(targets_np):\\n path = paths[k]\\n document_sentence_count = len(t)\\n to_idx = int(current_idx + document_sentence_count)\\n for threshold in self.thresholds:\\n output = ((output_np[current_idx: to_idx, :])[:, 1] > threshold)\\n h = np.append(output, [1])\\n tt = np.append(t, [1])\\n self.accuracies[threshold].update(h, tt)\\n current_idx = to_idx\\n\\n\\n def calc_accuracy(self):\\n min_pk = np.inf\\n min_threshold = None\\n min_epoch_windiff = None\\n min_epoch_b = None\\n min_epoch_s = None\\n for threshold in self.thresholds:\\n epoch_pk, epoch_windiff, epoch_b, epoch_s = self.accuracies[threshold].calc_accuracy()\\n if epoch_pk < min_pk:\\n min_pk = epoch_pk\\n min_threshold = threshold\\n min_epoch_windiff = epoch_windiff\\n min_epoch_b = epoch_b\\n min_epoch_s = epoch_s\\n return min_pk, -1.0, -1.0, -1.0, min_threshold\\n\\nclass Accuracy:\\n def __init__(self, threshold=0.3):\\n self.pk_to_weight = []\\n self.windiff_to_weight = []\\n self.B_to_weight = []\\n self.S_to_weight = []\\n self.threshold = threshold\\n self.calculator_dict = {\\n CalculateEnum.pk: pk,\\n CalculateEnum.windiff: WD,\\n CalculateEnum.boundary_similarity: B,\\n CalculateEnum.segmentation_similarity: S,\\n }\\n self.weight_dict = {\\n CalculateEnum.pk: self.pk_to_weight,\\n CalculateEnum.windiff: self.windiff_to_weight,\\n CalculateEnum.boundary_similarity: self.B_to_weight,\\n CalculateEnum.segmentation_similarity: self.S_to_weight,\\n }\\n self.type_str_dict = {\\n CalculateEnum.pk: \\\"pk\\\",\\n CalculateEnum.windiff: \\\"WD\\\",\\n CalculateEnum.boundary_similarity: \\\"B\\\",\\n CalculateEnum.segmentation_similarity: \\\"S\\\",\\n }\\n self.pk_value = -1\\n self.windiff_value = -1\\n self.segmentation_similarity_value = -1\\n self.boundary_similarity_value = -1\\n self.one_result = {}\\n # there is a large dictionary: {\\\"path\\\":[\\\"pred\\\",\\\"golden\\\",pk,wd,b,s]}\\n # where pred shows like: \\\"1,2,3|4,5,6,7|8,9,10|\\\"\\n # by the way, the result is recorded in test_result\\n self.all_test_result = {}\\n\\n def get_seg_boundaries(self, classifications, sentences_length=None):\\n curr_seg_length = 0\\n boundaries = []\\n for i, classification in enumerate(classifications):\\n is_split_point = bool(classifications[i])\\n add_to_current_segment = 1 if sentences_length is None else sentences_length[i]\\n curr_seg_length += add_to_current_segment\\n if (is_split_point):\\n boundaries.append(curr_seg_length)\\n curr_seg_length = 0\\n return boundaries\\n\\n def get_str(self, boundaries):\\n all_sentence_idx = [str(i) for i in range(sum(boundaries))]\\n all_seg_idx = []\\n for i,k in enumerate(boundaries):\\n if i == 0:\\n all_seg_idx.append(k)\\n else:\\n all_seg_idx.append(k+all_seg_idx[-1]+1) # why + 1? —— because in all_sentence_idx, we have inserted i-1 seg_idxes in the i-1th circle before. if we dont add 1, there is just i-2\\n for seg_idx in all_seg_idx:\\n all_sentence_idx.insert(seg_idx, \\\"|\\\")\\n return \\\" \\\".join(all_sentence_idx)\\n\\n def update(self, h, gold, path=None, window_size=-1, sentences_length=None):\\n self.one_result = {}\\n h_boundaries = self.get_seg_boundaries(h, sentences_length)\\n gold_boundaries = self.get_seg_boundaries(gold, sentences_length)\\n self._calculate(h_boundaries, gold_boundaries, window_size, calc_type=CalculateEnum.pk,path=path)\\n # self._calculate(h_boundaries, gold_boundaries, window_size, calc_type=CalculateEnum.windiff,path=path)\\n # self._calculate(h_boundaries, gold_boundaries, window_size, calc_type=CalculateEnum.boundary_similarity,path=path)\\n # self._calculate(h_boundaries, gold_boundaries, window_size, calc_type=CalculateEnum.segmentation_similarity,path=path)\\n if path != None:\\n h_str = self.get_str(h_boundaries)\\n gold_str = self.get_str(gold_boundaries)\\n self.one_result[\\\"pred\\\"] = h_str\\n self.one_result[\\\"golden\\\"] = gold_str\\n self.all_test_result[str(path)] = self.one_result\\n\\n def _calculate(self, h_boundaries, gold_boundaries, window_size=-1, calc_type=CalculateEnum.pk,path=None):\\n type_str = self.type_str_dict[calc_type]\\n calculator = self.calculator_dict[calc_type]\\n # if (type_str == \\\"B\\\" or type_str == \\\"S\\\") and (len(h_boundaries) == len(gold_boundaries) == 1):\\n # self.one_result[type_str] = None\\n # return\\n if window_size != -1:\\n result_dict = calculator(h_boundaries, gold_boundaries, window_size=window_size, return_parts=True)\\n else:\\n result_dict = calculator(h_boundaries, gold_boundaries, return_parts=True)\\n false_seg_count = result_dict[0]\\n total_count = result_dict[1]\\n if total_count == 0:\\n false_prob = -1\\n else:\\n false_prob = float(false_seg_count) / float(total_count)\\n # if type_str in [\\\"B\\\",\\\"S\\\"]:\\n # self.one_result[type_str] = false_prob\\n # else:\\n self.one_result[type_str] = false_prob\\n self.weight_dict[calc_type].append((false_prob, total_count))\\n\\n def _get_result(self, calc_type=CalculateEnum.pk):\\n result_value = sum([pw[0] * pw[1] for pw in self.weight_dict[calc_type]]) / sum(\\n [pw[1] for pw in self.weight_dict[calc_type]]) if len(\\n self.weight_dict[calc_type]) > 0 else -1.0\\n return result_value\\n\\n def calc_accuracy(self):\\n self.pk_value = self._get_result(calc_type=CalculateEnum.pk)\\n # self.windiff_value = self._get_result(calc_type=CalculateEnum.windiff)\\n # self.boundary_similarity_value = self._get_result(calc_type=CalculateEnum.boundary_similarity)\\n # self.segmentation_similarity_value = self._get_result(calc_type=CalculateEnum.segmentation_similarity)\\n return self.pk_value, -1.0,-1.0,-1.0#self.windiff_value, self.boundary_similarity_value, self.segmentation_similarity_value\"\n },\n {\n \"alpha_fraction\": 0.5244134664535522,\n \"alphanum_fraction\": 0.5275840163230896,\n \"avg_line_length\": 43.11428451538086,\n \"blob_id\": \"0830c44195a160e45d2e821dd73f2a151d0de785\",\n \"content_id\": \"3a1b097a00f6586c8ab34b00ba5767cf9c83642e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1577,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 35,\n \"path\": \"/models/texting.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\r\\nimport torch\\r\\nfrom utils import unsort\\r\\nfrom models.GGNN.layers import *\\r\\n\\r\\nclass g_model_text_ing(nn.Module):\\r\\n def __init__(self,encoder_input_dim,args):\\r\\n super(g_model_text_ing,self).__init__()\\r\\n self.input_dim = encoder_input_dim\\r\\n self.hidden_dim = args.texting_hidden_dim\\r\\n self.output_dim = args.texting_output_dim\\r\\n self.device = args.device\\r\\n self.layers = []\\r\\n self.graph_layer = GraphLayer(args,\\r\\n input_dim=self.input_dim,\\r\\n output_dim=self.hidden_dim,\\r\\n act=nn.Tanh(),\\r\\n dropout_p=0.3)\\r\\n\\r\\n self.readout_layer = ReadoutLayer(args,\\r\\n input_dim=self.hidden_dim,\\r\\n output_dim=self.output_dim,\\r\\n act=nn.Tanh(),\\r\\n dropout_p=0.3)\\r\\n\\r\\n def forward(self,encoded_input):\\r\\n feature, adj, mask, sorted_lengths, sort_order, sentences_per_doc = encoded_input\\r\\n graph_layer_output = self.graph_layer(feature, adj, mask) # bs, ml , dim\\r\\n texting_output = self.readout_layer(graph_layer_output,mask)\\r\\n unsort_order = torch.LongTensor(unsort(sort_order)).to(self.device)\\r\\n unsorted_sent_repr = texting_output.index_select(0, unsort_order)\\r\\n return unsorted_sent_repr, sentences_per_doc\\r\\n\\r\\n def get_output_dim(self):\\r\\n return self.output_dim\"\n },\n {\n \"alpha_fraction\": 0.5897620916366577,\n \"alphanum_fraction\": 0.5993751287460327,\n \"avg_line_length\": 44.206520080566406,\n \"blob_id\": \"f2b36fb05f8a46e2a2aab0f76eb335031eae47c7\",\n \"content_id\": \"b23613d3b9e8d8acbfa7a82110dbc54ae422481b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4161,\n \"license_type\": \"no_license\",\n \"max_line_length\": 126,\n \"num_lines\": 92,\n \"path\": \"/models/seg_model.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\nimport numpy as np\\nimport torch\\nimport torch.nn.functional as F\\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\\nfrom utils import *\\nfrom models.sequence_sr import s_model,zero_state\\nfrom models.treelstm_sr import t_model_treelstm\\nfrom models.freq_sr import f_model\\nfrom models.latent_sr import l_model\\nfrom models.bert_sr import b_model\\nfrom models.texting import g_model_text_ing\\nfrom models.DSG_SEG import g_model_DSG_SEG\\n\\nclass seg_model(nn.Module):\\n def __init__(self, sr_choose,encoder_input_dim, args):\\n super(seg_model, self).__init__()\\n self.device = args.device\\n self.pool_method = args.pool_method\\n self.encoder_input_dim = encoder_input_dim\\n self.hidden_dim=args.hidden_dim\\n self.num_layers=args.num_layers\\n self.sr_choose = sr_choose\\n if sr_choose == \\\"f_model\\\":\\n self.sr_model = f_model(encoder_input_dim,args)\\n elif sr_choose == \\\"l_model\\\":\\n self.sr_model = l_model(args)\\n elif sr_choose == \\\"s_model\\\":\\n self.sr_model = s_model(encoder_input_dim, args)\\n elif sr_choose == \\\"t_model\\\":\\n assert args.tr_choose in ['balanced', 'left', 'right']\\n self.sr_model = t_model_treelstm(encoder_input_dim, args)\\n elif sr_choose == \\\"g_model\\\":\\n if args.gr_choose == 'texting':\\n self.sr_model = g_model_text_ing(encoder_input_dim,args)\\n elif args.gr_choose == 'DSG_SEG':\\n self.sr_model = g_model_DSG_SEG(encoder_input_dim, args)\\n elif sr_choose == 'b_model':\\n self.sr_model = b_model(encoder_input_dim, args)\\n\\n if args.sr_choose != \\\"random_baseline\\\":\\n self.sl_input_dim = self.sr_model.get_output_dim()\\n else:\\n self.sl_input_dim = 300\\n self.sentence_lstm = nn.LSTM(input_size=self.sl_input_dim,\\n hidden_size=self.hidden_dim,\\n num_layers=self.num_layers,\\n batch_first=True,\\n dropout=0,\\n bidirectional=True)\\n self.h2s = nn.Linear(self.hidden_dim*2 , 2)\\n\\n self.criterion = nn.CrossEntropyLoss()\\n\\n\\n def pad_document(self, d, max_document_length):\\n d_length = d.size()[0]\\n v = d.unsqueeze(0).unsqueeze(0)\\n padded = F.pad(v, (0, 0, 0, max_document_length - d_length)) # (1, 1, max_length, 300)\\n shape = padded.size()\\n return padded.view(shape[2], 1, shape[3]) # (max_length, 1, 300)\\n\\n def forward(self, encoded_input):\\n batch_size = encoded_input[0]\\n unsorted_sent_repr, sentences_per_doc = self.sr_model(encoded_input[1:])\\n index = 0\\n encoded_documents = []\\n for sentences_count in sentences_per_doc:\\n end_index = index + sentences_count\\n encoded_documents.append(unsorted_sent_repr[index: end_index, :])\\n index = end_index\\n\\n doc_sizes = [doc.size()[0] for doc in encoded_documents]\\n max_doc_size = np.max(doc_sizes)\\n ordered_document_idx = np.argsort(doc_sizes)[::-1]\\n ordered_doc_sizes = sorted(doc_sizes)[::-1]\\n ordered_documents = [encoded_documents[idx] for idx in ordered_document_idx]\\n padded_docs = [self.pad_document(d, max_doc_size) for d in ordered_documents]\\n docs_tensor = torch.cat(padded_docs, 1)\\n packed_docs = pack_padded_sequence(docs_tensor, ordered_doc_sizes)\\n sentence_lstm_output, _ = self.sentence_lstm(packed_docs, zero_state(self, batch_size=batch_size, device=self.device))\\n padded_x, _ = pad_packed_sequence(sentence_lstm_output) # (max sentence len, batch, 256)\\n\\n doc_outputs = []\\n for i, doc_len in enumerate(ordered_doc_sizes):\\n doc_outputs.append(padded_x[0:doc_len - 1, i, :]) # -1 to remove last prediction ---> wiki_loader.py\\n\\n unsorted_doc_outputs = [doc_outputs[i] for i in unsort(ordered_document_idx)]\\n sentence_outputs = torch.cat(unsorted_doc_outputs, 0)\\n\\n x = self.h2s(sentence_outputs)\\n return x\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.730358898639679,\n \"alphanum_fraction\": 0.7352085113525391,\n \"avg_line_length\": 42,\n \"blob_id\": \"e7a9b7ae3a5d80db6d50fd74d44c49813a699749\",\n \"content_id\": \"e34d401fab3e43e3922198e1c969685bf2612785\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1031,\n \"license_type\": \"no_license\",\n \"max_line_length\": 126,\n \"num_lines\": 24,\n \"path\": \"/models/GGNN/inits.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport torch\\nimport torch.nn as nn\\nimport math\\nfrom torch.nn import init\\n# https://pytorch.org/docs/stable/_modules/torch/nn/modules/linear.html#Linear\\ndef glorot(in_features,out_features):\\n init_range = np.sqrt(6.0/(in_features+out_features))\\n initial = nn.Parameter(torch.nn.init.uniform_(tensor=torch.Tensor(in_features,out_features), a=-init_range, b=init_range))\\n return initial\\n\\ndef kaiming(in_features,out_features): # not used\\n initial = nn.Parameter(torch.nn.init.kaiming_uniform_(tensor=torch.Tensor(in_features,out_features), a=math.sqrt(5)))\\n return initial\\n\\ndef xavier(in_features,out_features):\\n initial = nn.Parameter(torch.nn.init.xavier_uniform_(tensor=torch.Tensor(in_features,out_features), gain=1))\\n return initial\\n\\ndef uniform_bias(weight,out_features):\\n fan_in, _ = init._calculate_fan_in_and_fan_out(weight)\\n bound = 1 / math.sqrt(fan_in)\\n initial = nn.Parameter(torch.nn.init.uniform_(tensor=torch.Tensor(out_features), a=-bound, b=bound))\\n return initial\"\n },\n {\n \"alpha_fraction\": 0.4488178491592407,\n \"alphanum_fraction\": 0.45291242003440857,\n \"avg_line_length\": 53.46762466430664,\n \"blob_id\": \"af76f2378cd8c61d14b563b18a198dfb8cca5535\",\n \"content_id\": \"9653c45f37c638c92968e4bf40b4861fd1513f63\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7571,\n \"license_type\": \"no_license\",\n \"max_line_length\": 196,\n \"num_lines\": 139,\n \"path\": \"/data/wikielements_loader.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from torch.utils.data import Dataset\\nfrom utils import *\\nimport os\\nfrom parameters import create_parser\\nfrom data.data_utils import *\\n\\ndef get_sections(text_path, segment_path):\\n text_file = open(str(text_path), \\\"r\\\", encoding=\\\"utf-8\\\")\\n texts = text_file.read().strip().split(\\\"\\\\n\\\")\\n text_file.close()\\n segment_file = open(str(segment_path), \\\"r\\\", encoding=\\\"utf-8\\\")\\n segments = segment_file.read().strip().split(\\\"\\\\n\\\")\\n segment_file.close()\\n all_sections = [] # all document\\n sections = [] # one document\\n one_section = [] # one section\\n document_num = int(segments[0].strip().split(\\\",\\\")[0])\\n seg_title = segments[0].strip().split(\\\",\\\")[2]\\n for idx, text in enumerate(texts):\\n segment = segments[idx]\\n n_doc_num,n_sent_num,n_seg_title = segment.strip().split(\\\",\\\")\\n n_doc_num,n_sent_num = int(n_doc_num),int(n_sent_num)\\n if n_seg_title != seg_title and n_doc_num == document_num:\\n sections.append(one_section)\\n one_section = []\\n seg_title = n_seg_title\\n elif n_doc_num != document_num:\\n sections.append(one_section)\\n all_sections.append(sections)\\n one_section = []\\n sections = []\\n document_num = n_doc_num\\n seg_title = n_seg_title\\n one_section.append(text)\\n return all_sections\\n\\n\\n\\ndef read_wikielements_file(args,filtered_num, all_num, filtered_document_num, all_document_num,\\n text_path, segment_path,encoder=None,length_filter=10, sent_num_filter=10):\\n all_sections_of_all_doc = get_sections(text_path, segment_path)\\n all_return_dic = []\\n for idx,all_sections in enumerate(all_sections_of_all_doc):\\n path = text_path + \\\"/document_\\\" + str(idx)\\n data = []\\n adjs = []\\n targets = []\\n for sentences in all_sections:\\n sentences = [s for s in sentences if len(s.split()) > 0]\\n if sentences:\\n targets_flag = 0\\n for sentence in sentences:\\n sentence_words = extract_sentence_words(sentence)\\n if (len(sentence_words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\\n continue\\n if len(sentence_words) > length_filter:\\n sentence_words = sentence_words[:length_filter]\\n filtered_num += 1\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n sentence_words, adj = build_ing_graph(sentence_words, args)\\n adjs.append(adj)\\n if encoder:\\n data.append(encoder.tokenize(sentence_words))\\n else:\\n data.append(sentence_words)\\n targets_flag += 1\\n if data:\\n all_num += targets_flag\\n targets.extend([0]*(targets_flag-1))\\n if targets_flag != 0:\\n targets.append(1) # segment\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(data, adjs, targets, path,\\n all_document_num,\\n filtered_document_num,\\n sent_num_filter)\\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path,\\n all_document_num,\\n filtered_document_num,\\n sent_num_filter)\\n return_dict = generate_gcn_return_dict(return_dict, args)\\n else:\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path,\\n all_document_num,\\n filtered_document_num,\\n sent_num_filter)\\n if return_dict == None:\\n continue\\n elif type(return_dict) == dict:\\n all_return_dic.append(return_dict)\\n else: # list\\n all_return_dic.extend(return_dict)\\n return all_return_dic, filtered_num, all_num, filtered_document_num, all_document_num\\n\\nclass WikiElementsDataSet(Dataset):\\n def __init__(self,\\n root = None,\\n args=None,\\n encoder = None,\\n length_filter = 40,\\n sent_num_filter = 60,\\n local_rank=-1):\\n logger = get_logger(args)\\n self.text_path = root + \\\"/wikielements.text\\\"\\n self.segment_path = root + \\\"/wikielements.segmenttitles\\\"\\n rank_logger_info(logger, local_rank, \\\"Reading wikielements data\\\")\\n # sentences\\n filtered_num = 0\\n all_num = 0\\n # documents\\n filtered_document_num = 0\\n all_document_num = 0\\n self.data,filtered_num, all_num, filtered_document_num, all_document_num = read_wikielements_file(args,\\n filtered_num,\\n all_num,\\n filtered_document_num,\\n all_document_num,\\n text_path= self.text_path,\\n segment_path=self.segment_path,\\n encoder=encoder,\\n length_filter=length_filter,\\n sent_num_filter=sent_num_filter)\\n if all_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all sentences num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\"%(all_num, filtered_num,filtered_num/all_num))\\n if all_document_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all documents num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\\n\\n def __getitem__(self, index):\\n return self.data[index]\\n\\n def __len__(self):\\n return len(self.data)\\n\\nif __name__ == \\\"__main__\\\":\\n args = create_parser()\\n print(args)\\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\\n wikielementsdataset = WikiElementsDataSet(args.wikielements_path, args)\\n\"\n },\n {\n \"alpha_fraction\": 0.5936213731765747,\n \"alphanum_fraction\": 0.5946502089500427,\n \"avg_line_length\": 37.880001068115234,\n \"blob_id\": \"d3d2eb27e6749d3681cd738aa5d3ff1d9458a0a1\",\n \"content_id\": \"e4498d161bd05560e37ddd570f7ce54b7e6f439d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2916,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 75,\n \"path\": \"/encoder/bert_encoder.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\nimport torch\\nfrom transformers import BertTokenizer, BertModel\\nfrom utils import *\\nclass bert_model(nn.Module):\\n def __init__(self, args, encoder_fine_tune):\\n super(bert_model, self).__init__()\\n self.args = args\\n self.local_rank = args.local_rank\\n self.encoder_fine_tune = encoder_fine_tune\\n self.model_size = args.model_size\\n self.cased = args.cased\\n if not self.cased:\\n do_lower_case = True\\n self.model_name = 'bert' + \\\"-\\\" + self.model_size + '-uncased'\\n else:\\n do_lower_case = False\\n self.model_name = 'bert' + \\\"-\\\" + self.model_size + '-cased'\\n\\n self.embedding = self.get_bert_embedding()\\n if encoder_fine_tune:\\n for param in self.parameters():\\n param.requires_grad = True\\n else:\\n for param in self.parameters():\\n param.requires_grad = False\\n\\n self.device = args.device\\n self.tokenizer = BertTokenizer.from_pretrained(\\n self.model_name, do_lower_case=do_lower_case)\\n self.pad_token_id = self.tokenizer.pad_token_id\\n self.num_layers = self.embedding.config.num_hidden_layers + 1\\n self.hidden_size = self.embedding.config.hidden_size\\n self.weighing_params = nn.Parameter(torch.ones(self.num_layers))\\n\\n def get_bert_embedding(self):\\n logger = get_logger(self.args)\\n rank_logger_info(logger, self.local_rank, 'Reading bert embedding.')\\n embedding = BertModel.from_pretrained(\\n self.model_name, output_hidden_states = True)\\n return embedding\\n\\n def tokenize(self, inp):\\n token_ids = self.tokenizer.encode(inp, add_special_tokens=True)\\n return token_ids\\n\\n\\n def forward(self, inp, just_last_layer=False):\\n input_mask = (inp != self.pad_token_id).float().to(self.device)\\n if not self.encoder_fine_tune:\\n with torch.no_grad():\\n output_bert = self.embedding(\\n inp, attention_mask=input_mask, output_hidden_states=True) # B x L x E\\n last_hidden_state = output_bert.last_hidden_state\\n encoded_layers = output_bert.hidden_states\\n else:\\n output_bert = self.embedding(\\n inp, attention_mask=input_mask, output_hidden_states=True)\\n last_hidden_state = output_bert.last_hidden_state\\n encoded_layers = output_bert.hidden_states\\n\\n if just_last_layer:\\n output = last_hidden_state\\n else:\\n wtd_encoded_repr = 0\\n soft_weight = nn.functional.softmax(self.weighing_params, dim=0)\\n for i in range(self.num_layers):\\n wtd_encoded_repr += soft_weight[i] * encoded_layers[i]\\n output = wtd_encoded_repr\\n\\n return output\\n\\n\\n def get_word_dim(self):\\n return self.hidden_size\\n\"\n },\n {\n \"alpha_fraction\": 0.3035320043563843,\n \"alphanum_fraction\": 0.40066224336624146,\n \"avg_line_length\": 19.069766998291016,\n \"blob_id\": \"d27c2b3bc6307464322030abda9f6ddebadb24e0\",\n \"content_id\": \"3c868751d4af6a5f45abe0f4826a9d8efae5b096\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1290,\n \"license_type\": \"no_license\",\n \"max_line_length\": 42,\n \"num_lines\": 43,\n \"path\": \"/doc/file_tree.md\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 文件结构\\r\\n\\r\\n/data路径下的整体文件结构介绍如下：\\r\\n\\r\\n```\\r\\n├──data \\r\\n│ ├──glove.6B.300d.txt \\r\\n│ ├──GoogleNews-vectors-negative300.bin \\r\\n│ ├──wiki_727 \\r\\n│ │ ├──test \\r\\n│ │ ├──dev \\r\\n│ │ └──train \\r\\n│ ├──wiki_50 \\r\\n│ ├──wikisection \\r\\n│ │ ├──en_disease_validation \\r\\n│ │ ├──en_disease_train \\r\\n│ │ ├──en_disease_test \\r\\n│ │ └──... \\r\\n│ ├──choi \\r\\n│ │ ├──4 \\r\\n│ │ ├──3 \\r\\n│ │ ├──2 \\r\\n│ │ └──1 \\r\\n│ ├──manifesto \\r\\n│ │ ├──61620_201211.txt \\r\\n│ │ ├──61620_200811.txt \\r\\n│ │ ├──61620_200411.txt \\r\\n│ │ ├──61320_201211.txt \\r\\n│ │ ├──61320_200811.txt \\r\\n│ │ └──61320_200411.txt \\r\\n│ ├──clinical \\r\\n│ │ ├──000.ref \\r\\n│ │ ├──001.ref \\r\\n│ │ └──... \\r\\n│ ├──wikicities \\r\\n│ │ ├──wikicities.merged_text \\r\\n│ │ ├──wikicities.text \\r\\n│ │ └──... \\r\\n│ ├──wikielements \\r\\n│ │ ├──wikielements.text \\r\\n│ │ ├──wikielements.vocab \\r\\n│ │ └──... \\r\\n```\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6254278421401978,\n \"alphanum_fraction\": 0.629253089427948,\n \"avg_line_length\": 59.95092010498047,\n \"blob_id\": \"def542f901f1acbaf58da4a0e28f651b44f6136a\",\n \"content_id\": \"888d448d6efaafe4e23273122b7e3264d7b96a12\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 9934,\n \"license_type\": \"no_license\",\n \"max_line_length\": 211,\n \"num_lines\": 163,\n \"path\": \"/run.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch\\nfrom torch.utils.data import DataLoader\\nimport os\\nfrom pathlib2 import Path\\nfrom termcolor import colored\\nimport re\\nfrom train import train\\nfrom test import test_all, test_all_random_baseline \\nfrom validate import validate\\nfrom encoder.Encoder import Encoder\\nfrom models.Encode_model import Encode_model\\nfrom data.all_data_loader import get_loader\\nfrom utils import *\\nfrom models.seg_model import seg_model\\nfrom parameters import *\\nfrom torch.utils.data.distributed import DistributedSampler\\ntest_data_type_list = ['wiki','choi','clinical', 'wikielements','wikicities','wiki50', 'manifesto'] # wikisection\\n\\ndef main(args):\\n global test_data_type_list\\n logger = get_logger(args)\\n args.checkpoint_dir = Path(args.checkpoint_dir)\\n # initial encoder\\n encoder = None\\n if not os.path.exists(args.encoder_path):\\n rank_logger_info(logger, args.local_rank, \\\"Creating Encoder...\\\")\\n encoder = Encoder(args=args, etype=args.etype, encoder_fine_tune=args.encoder_fine_tune, glove_path=args.glove_path, w2v_path=args.w2v_path)\\n if args.local_rank in [-1, 0]:\\n torch.save(encoder, args.encoder_path)\\n else:\\n rank_logger_info(logger, args.local_rank, \\\"Loading Encoder...\\\")\\n encoder = torch.load(args.encoder_path)\\n encode_model = Encode_model(encoder, args)\\n # initial dataset\\n train_dataset, dev_dataset,test_dataset_dict = None,None,{}\\n if not os.path.exists(args.dataset_path): # create\\n if not args.infer: # train:read all dataset\\n rank_logger_info(logger, args.local_rank, \\\"Creating dataset...\\\")\\n train_dataset = get_loader(args=args, encoder = encoder, data_type=args.train_data_type, split=\\\"train\\\")\\n dev_dataset = get_loader(args=args, encoder = encoder, data_type=args.valid_data_type, split=\\\"dev\\\")\\n for test_data_type in test_data_type_list:\\n if test_data_type in [\\\"wikisection\\\", \\\"wiki\\\"]:\\n test_dataset_dict[test_data_type] = get_loader(args=args, encoder = encoder, data_type=test_data_type, split=\\\"test\\\")\\n else:\\n test_dataset_dict[test_data_type] = get_loader(args=args, encoder=encoder, data_type=test_data_type)\\n saved_pt = {\\n \\\"train_dataset\\\":train_dataset,\\n \\\"dev_dataset\\\":dev_dataset,\\n \\\"test_dataset\\\":None # test_dataset_dict\\n }\\n if args.local_rank in [-1, 0]:\\n torch.save(saved_pt, args.dataset_path)\\n else: # infer:just read test dataset and not save\\n for test_data_type in test_data_type_list:\\n if test_data_type in [\\\"wikisection\\\", \\\"wiki\\\"]:\\n test_dataset_dict[test_data_type] = get_loader(args=args, encoder = encoder, data_type=test_data_type, split=\\\"test\\\")\\n else:\\n test_dataset_dict[test_data_type] = get_loader(args=args, encoder=encoder, data_type=test_data_type)\\n else: # load\\n rank_logger_info(logger, args.local_rank, \\\"Loading dataset...\\\")\\n loaded_pt = torch.load(args.dataset_path)\\n train_dataset, dev_dataset,test_dataset_dict = loaded_pt[\\\"train_dataset\\\"], loaded_pt[\\\"dev_dataset\\\"],loaded_pt[\\\"test_dataset\\\"]\\n # initial dataloader\\n rank_logger_info(logger, args.local_rank, \\\"Creating dataloader...\\\")\\n collate_fn = get_collate_fn(args)\\n if not args.infer:\\n train_sampler = DistributedSampler(train_dataset) if args.local_rank != - 1 else None\\n train_dl = DataLoader(train_dataset, batch_size=args.train_bs, collate_fn=collate_fn, shuffle = True if not train_sampler else False,\\n sampler=train_sampler, num_workers=args.num_workers)\\n dev_dl = DataLoader(dev_dataset, batch_size=args.dev_bs, collate_fn=collate_fn, shuffle=False,num_workers=args.num_workers)\\n test_dl_dict = {}\\n for test_data_type in test_data_type_list:\\n test_dl_dict[test_data_type] = DataLoader(test_dataset_dict[test_data_type], batch_size=args.test_bs, collate_fn=collate_fn, shuffle = False,\\n num_workers=args.num_workers)\\n rank_logger_info(logger, args.local_rank, \\\"Creating model...\\\")\\n model = seg_model(sr_choose=args.sr_choose, encoder_input_dim=encode_model.encoder.get_input_dim(), args=args)\\n model.to(args.device)\\n encode_model.to(args.device)\\n if args.local_rank != -1:\\n model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank,find_unused_parameters=True)\\n encode_model = torch.nn.parallel.DistributedDataParallel(encode_model, device_ids=[args.local_rank], output_device=args.local_rank,find_unused_parameters=True)\\n\\n rank_logger_info(logger, args.local_rank, \\\"Creating optimizer...\\\")\\n params = []\\n trainable_params_num = 0\\n for name, param in list(encode_model.named_parameters()) + list(model.named_parameters()):\\n if param.requires_grad:\\n params.append(param)\\n trainable_params_num += param.numel()\\n rank_logger_info(logger, args.local_rank,f\\\"name: {name} | param_num: {param.numel()}\\\")\\n rank_logger_info(logger, args.local_rank, f\\\"trainable_params_num: {trainable_params_num}\\\")\\n optimizer = getattr(torch.optim, \\\"Adam\\\")(params, lr=1e-3) #\\n rank_logger_info(logger, args.local_rank, model)\\n rank_logger_info(logger, args.local_rank, \\\"Device on:\\\" + str(args.device))\\n # train\\n if not args.infer:\\n model.train()\\n best_val_pk,early_stop_cot,best_epoch,best_threshold = 1.0,0,0,0\\n for j in range(args.epochs):\\n train(model=model,encode_model=encode_model, args=args, epoch=j, dataset=train_dl, optimizer=optimizer,local_rank= args.local_rank)\\n val_pk, threshold = validate(model,encode_model,args, j, dev_dl,local_rank=args.local_rank)\\n if val_pk < best_val_pk:\\n best_val_pk,early_stop_cot,best_epoch,best_threshold = val_pk,0,j+1,threshold\\n rank_logger_info(logger, args.local_rank, colored(\\n 'Current best model from epoch {} with p_k {} and threshold {}'.format(j+1, best_val_pk, threshold),'red'))\\n rank_logger_info(logger, args.local_rank,f\\\"saveing best model with p_k {best_val_pk} and threshold {best_threshold}\\\")\\n saved_dict = {\\n \\\"best_model\\\":model,\\n \\\"best_val_pk\\\":best_val_pk,\\n \\\"best_epoch\\\":best_epoch,\\n \\\"best_threshold\\\":best_threshold\\n }\\n if args.local_rank in [-1, 0]:\\n with (args.checkpoint_dir / 'best_model.t7').open('wb') as f:\\n torch.save(saved_dict, f)\\n if args.etype == \\\"bert\\\": # only bert encoder may be finetuned\\n with (args.checkpoint_dir / 'best_encode_model.t7').open('wb') as f:\\n torch.save(encode_model, f)\\n else:\\n early_stop_cot += 1\\n rank_logger_info(logger, args.local_rank,\\\"pk of epoch {} didnt decrease, early_stop_cot is {} now\\\".format(j+1,early_stop_cot))\\n if early_stop_cot == args.early_stop:\\n rank_logger_info(logger, args.local_rank, 'early stopping for epoch {}'.format(j+1))\\n break\\n # test\\n rank_logger_info(logger, args.local_rank, \\\"Loading best model(and encode model)...\\\")\\n best_dict = torch.load(str(args.checkpoint_dir / 'best_model.t7'))\\n best_model, best_threshold, best_encode_model = best_dict[\\\"best_model\\\"],best_dict[\\\"best_threshold\\\"],torch.load(str(args.checkpoint_dir / 'best_encode_model.t7')) if args.etype == \\\"bert\\\" else encode_model\\n rank_logger_info(logger, args.local_rank, \\\"Testing...\\\")\\n test_all(best_model, best_encode_model, args, test_dl_dict,threshold = best_threshold, local_rank=args.local_rank)\\n else:\\n if args.sr_choose == 'random_baseline':\\n rank_logger_info(logger, args.local_rank, \\\"Getting random baseline results...\\\")\\n test_all_random_baseline(args, test_dl_dict, local_rank=args.local_rank)\\n exit(1)\\n train_checkpoint_dir = Path(re.sub(\\\"_infer\\\",\\\"\\\",str(args.checkpoint_dir))) if args.train_checkpoint_dir == None else Path(args.train_checkpoint_dir)\\n rank_logger_info(logger, args.local_rank, f\\\"Loading best model(and encode model) from {str(train_checkpoint_dir)}...\\\")\\n if os.path.exists(str(train_checkpoint_dir / 'best_model.t7')):\\n try:\\n best_dict = torch.load(str(train_checkpoint_dir / 'best_model.t7'))\\n best_model, \\\\\\n best_threshold, \\\\\\n best_encode_model = best_dict[\\\"best_model\\\"], \\\\\\n best_dict[\\\"best_threshold\\\"], \\\\\\n torch.load(str(train_checkpoint_dir / 'best_encode_model.t7')) if args.etype == \\\"bert\\\" else encode_model\\n except:\\n best_model = torch.load(str(train_checkpoint_dir / 'best_model.t7'))\\n assert args.best_threshold != None\\n best_threshold = float(args.best_threshold)\\n best_encode_model = torch.load(str(train_checkpoint_dir / 'best_encode_model.t7')) if args.etype == \\\"bert\\\" else encode_model\\n rank_logger_info(logger, args.local_rank, \\\"Testing...\\\")\\n test_all(best_model, best_encode_model, args, test_dl_dict, threshold=best_threshold,local_rank=args.local_rank)\\n else:\\n rank_logger_info(logger, args.local_rank, \\\"No existing best model(and encode model)!!!\\\")\\n exit(1)\\n\\nif __name__ == '__main__':\\n args = create_parser()\\n set_seed(args.seed)\\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\\n rank_logger_info(logger, args.local_rank, stringify_flags(args))\\n args.device = set_multi_gpu(args)\\n main(args)\"\n },\n {\n \"alpha_fraction\": 0.5668557286262512,\n \"alphanum_fraction\": 0.5802268981933594,\n \"avg_line_length\": 35.80596923828125,\n \"blob_id\": \"3780ed1e245af43add69e7bc06fb30096acfae11\",\n \"content_id\": \"d1cbdab573ce46a4d5358754b90336baa11dc166\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2468,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 67,\n \"path\": \"/encoder/one_hot_encoder.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\nimport torch\\nfrom utils import *\\nfrom collections import OrderedDict\\nimport numpy as np\\nclass one_hot_model(nn.Module):\\n\\n def __init__(self, vocab_path, args, encoder_fine_tune):\\n super(one_hot_model,self).__init__()\\n self.vocab_path = vocab_path\\n self.args = args\\n self.encoder_fine_tune = encoder_fine_tune\\n self.local_rank = args.local_rank\\n self.PADDING_TOKEN = \\\"<PAD>\\\"\\n self.UNK_TOKEN = \\\"<UNK>\\\"\\n self.embedding, self.word2idx = self.get_one_hot_embedding()\\n self.pad_token_id = self.word2idx[self.PADDING_TOKEN]\\n self.idx2word = OrderedDict()\\n for item in self.word2idx.items():\\n self.idx2word[item[1]] = item[0]\\n\\n self.embedding = nn.Embedding.from_pretrained(self.embedding)\\n if encoder_fine_tune:\\n for param in self.parameters():\\n param.requires_grad = True\\n else:\\n for param in self.parameters():\\n param.requires_grad = False\\n\\n def get_one_hot_embedding(self):\\n logger = get_logger(self.args)\\n rank_logger_info(logger, self.local_rank, 'Reading one-hot Vocab.')\\n\\n one_hot_word2idx = OrderedDict()\\n one_hot_word2idx[self.PADDING_TOKEN] = len(one_hot_word2idx)\\n one_hot_word2idx[self.UNK_TOKEN] = len(one_hot_word2idx)\\n\\n with open(self.vocab_path, encoding='utf-8') as f:\\n for i, line in enumerate(f):\\n if i == 10000:\\n break\\n word, vec = line.split(' ', 1)\\n if word in one_hot_word2idx:\\n continue\\n else:\\n one_hot_word2idx[word] = len(one_hot_word2idx)\\n rank_logger_info(logger, self.local_rank, 'Reading one-hot embeddings.')\\n embeddings = np.zeros((len(one_hot_word2idx) , len(one_hot_word2idx)), dtype=np.float32)\\n for i in range(len(one_hot_word2idx)):\\n embeddings[i][i] = 1\\n return torch.tensor(embeddings), one_hot_word2idx\\n\\n def forward(self, inp):\\n return self.embedding(inp)\\n\\n def tokenize(self, inp):\\n word_lst = inp.split()\\n idx_lst = []\\n for word in word_lst:\\n if word in self.word2idx:\\n idx_lst.append(self.word2idx[word])\\n else:\\n idx_lst.append(self.word2idx[self.UNK_TOKEN])\\n return idx_lst\\n\\n def get_word_dim(self):\\n return len(self.word2idx)\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5827338099479675,\n \"alphanum_fraction\": 0.5899280309677124,\n \"avg_line_length\": 35.90909194946289,\n \"blob_id\": \"00e86427470d90d0c440459da7392fa125c675af\",\n \"content_id\": \"a5e3883739f3043204da6de1515966b16fb4019d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 834,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 22,\n \"path\": \"/models/freq_sr.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\r\\nimport torch\\r\\nclass f_model(nn.Module):\\r\\n def __init__(self, encoder_input_dim, args):\\r\\n super(f_model, self).__init__()\\r\\n self.args = args\\r\\n self.output_dim = encoder_input_dim\\r\\n self.device = self.args.device\\r\\n\\r\\n # def pad(self, s, max_length):\\r\\n # pad_lst = [self.encoder.model.pad_token_id] * (max_length - s.size(0))\\r\\n # s = torch.LongTensor(s.tolist() + pad_lst).to(self.device)\\r\\n # return s.unsqueeze(0) # (1, max_len)\\r\\n\\r\\n def forward(self, encoded_input):\\r\\n embeded_big_tensor, sentences_per_doc = encoded_input\\r\\n sent_repr = torch.sum(embeded_big_tensor, dim=1) # max_length, dim\\r\\n sent_repr[:, 0] = 0 # pad_idx\\r\\n return sent_repr, sentences_per_doc\\r\\n\\r\\n def get_output_dim(self):\\r\\n return self.output_dim\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5619910359382629,\n \"alphanum_fraction\": 0.5695236325263977,\n \"avg_line_length\": 51.39130401611328,\n \"blob_id\": \"383480ef7a664b35abf76998f8dc023fc1a5688c\",\n \"content_id\": \"37cddb7499276cc022b5ce253329898ee1648c71\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 9824,\n \"license_type\": \"no_license\",\n \"max_line_length\": 119,\n \"num_lines\": 184,\n \"path\": \"/models/Encode_model.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\r\\nimport torch\\r\\nimport numpy as np\\r\\nfrom models.Treelstm.utils import *\\r\\nimport torch.nn.functional as F\\r\\nclass Encode_model(nn.Module):\\r\\n def __init__(self, encoder, args):\\r\\n super(Encode_model , self).__init__()\\r\\n self.encoder = encoder\\r\\n self.args = args\\r\\n self.device = self.args.device\\r\\n self.sr_choose = self.args.sr_choose\\r\\n self.tr_choose = self.args.tr_choose\\r\\n self.gr_choose = self.args.gr_choose\\r\\n if self.sr_choose == \\\"t_model\\\":\\r\\n if args.tr_choose == 'balanced':\\r\\n self.generate_mask_function = generate_balance_masks\\r\\n elif args.tr_choose == 'left':\\r\\n self.generate_mask_function = generate_left_branch_masks\\r\\n elif args.tr_choose == 'right':\\r\\n self.generate_mask_function = generate_right_branch_masks\\r\\n def pad(self, s, max_length):\\r\\n s_length = s.size()[0]\\r\\n v = s.unsqueeze(0).unsqueeze(0)\\r\\n padded = F.pad(v, (0, 0, 0, max_length - s_length)) # (1, 1, max_length, 300)\\r\\n shape = padded.size()\\r\\n return padded.view(shape[2], 1, shape[3]) # (max_length, 1, 300)\\r\\n\\r\\n def pad_s_model(self, s, max_length):\\r\\n pad_lst = [self.encoder.model.pad_token_id] * (max_length - s.size(0))\\r\\n s = torch.LongTensor(s.tolist() + pad_lst).to(self.device)\\r\\n return s.unsqueeze(1)\\r\\n\\r\\n def pad_l_model(self, s, max_length):\\r\\n pad_lst = [self.encoder.model.pad_token_id] * (max_length - s.size(0))\\r\\n s = torch.LongTensor(s.tolist() + pad_lst).to(self.device)\\r\\n return s.unsqueeze(0) # (1, max_len)\\r\\n\\r\\n def preprocess_treelstm(self, sentences_lst):\\r\\n lengths = [sentence.size(0) for sentence in sentences_lst] # (length,)\\r\\n max_length = max(lengths)\\r\\n padded_sentences_lst = [sentence.tolist() + [self.encoder.model.pad_token_id] * (max_length - sentence.size(0))\\r\\n for sentence in sentences_lst]\\r\\n masks = self.generate_mask_function(lengths)\\r\\n padded_sentences_tensor = torch.LongTensor(padded_sentences_lst).to(self.device)\\r\\n lengths = torch.LongTensor(lengths).to(self.device)\\r\\n return padded_sentences_tensor, lengths, masks\\r\\n\\r\\n def preprocess_gcn_adj(self,max_length,adjs):\\r\\n mask = torch.zeros((len(adjs), max_length, 1)).to(self.device)\\r\\n for i,adj in enumerate(adjs):\\r\\n pad = max_length - adj.shape[0]\\r\\n # print(\\\"adj\\\",adj.shape)\\r\\n # print(\\\"pad\\\",pad)\\r\\n padded_adj = F.pad(adj, (0,pad,0,pad))\\r\\n mask[i,:adj.shape[0],:] = 1.\\r\\n adjs[i] = padded_adj.unsqueeze(0) # 1, max_length, max_length\\r\\n return adjs, mask # double\\r\\n\\r\\n def preprocess_gcn_feature(self, max_length,features):\\r\\n for i,feature in enumerate(features):\\r\\n # feature: node_size, dim --> padded_feature: max_length, dim\\r\\n pad = max_length - feature.shape[0]\\r\\n padded_feature = F.pad(feature, (0,0,0,pad))\\r\\n features[i] = padded_feature.unsqueeze(0)\\r\\n return features\\r\\n\\r\\n def forward(self, batch):\\r\\n batch_size = len(batch)\\r\\n if self.sr_choose == \\\"s_model\\\":\\r\\n sentences_per_doc = []\\r\\n all_batch_sentences = []\\r\\n for document in batch:\\r\\n all_batch_sentences.extend(document)\\r\\n sentences_per_doc.append(len(document))\\r\\n\\r\\n lengths = [s.size()[0] for s in all_batch_sentences]\\r\\n sort_order = np.argsort(lengths)[::-1]\\r\\n sorted_sentences = [all_batch_sentences[i] for i in sort_order]\\r\\n sorted_lengths = [s.size()[0] for s in sorted_sentences]\\r\\n\\r\\n max_length = max(lengths)\\r\\n padded_sentences = [self.pad_s_model(s, max_length) for s in sorted_sentences]\\r\\n big_tensor = torch.cat(padded_sentences, 1)\\r\\n embeded_big_tensor = self.encoder(big_tensor)\\r\\n return batch_size,embeded_big_tensor,sorted_lengths,sort_order,sentences_per_doc\\r\\n elif self.sr_choose in [\\\"f_model\\\",\\\"l_model\\\"]:\\r\\n sentences_per_doc = []\\r\\n all_batch_sentences = []\\r\\n for document in batch:\\r\\n all_batch_sentences.extend(document)\\r\\n sentences_per_doc.append(len(document))\\r\\n lengths = [s.size()[0] for s in all_batch_sentences]\\r\\n max_length = max(lengths)\\r\\n padded_sentences = [self.pad_l_model(s, max_length) for s in all_batch_sentences]\\r\\n big_tensor = torch.cat(padded_sentences, 0) # (batch size, max_length)\\r\\n embeded_big_tensor = self.encoder(big_tensor) # (max_length, batch size, dim)\\r\\n return batch_size,embeded_big_tensor,sentences_per_doc\\r\\n elif self.sr_choose == \\\"t_model\\\" and self.tr_choose in [\\\"balanced\\\",\\\"left\\\",\\\"right\\\"]:\\r\\n sentences_per_doc = []\\r\\n all_batch_sentences = []\\r\\n for document in batch:\\r\\n all_batch_sentences.extend(document)\\r\\n sentences_per_doc.append(len(document))\\r\\n sentence, lengths, tree_masks = self.preprocess_treelstm(all_batch_sentences)\\r\\n embeded_sentence = self.encoder(sentence)\\r\\n return batch_size,embeded_sentence, lengths, tree_masks, sentences_per_doc\\r\\n elif self.sr_choose == \\\"b_model\\\":\\r\\n sentences_per_doc = []\\r\\n all_batch_sentences = []\\r\\n for document in batch:\\r\\n all_batch_sentences.extend(document)\\r\\n sentences_per_doc.append(len(document))\\r\\n lengths = [s.size()[0] for s in all_batch_sentences]\\r\\n sort_order = np.argsort(lengths)[::-1]\\r\\n sorted_sentences = [all_batch_sentences[i] for i in sort_order]\\r\\n sorted_lengths = [s.size()[0] for s in sorted_sentences]\\r\\n max_length = max(lengths)\\r\\n padded_sentences = [self.pad_l_model(s, max_length) for s in sorted_sentences]\\r\\n big_tensor = torch.cat(padded_sentences, 0) # (batch size,max_length)\\r\\n embeded_big_tensor = self.encoder(big_tensor)\\r\\n return batch_size,embeded_big_tensor,sorted_lengths,sort_order,sentences_per_doc\\r\\n elif self.sr_choose == \\\"g_model\\\" and self.gr_choose == \\\"texting\\\":\\r\\n # batch: [[[tensor(sent_num1,), tensor(sent_num1,sent_num1)], ...], [doc2], [doc3], []]\\r\\n sentences_per_doc = []\\r\\n all_batch_features = []\\r\\n all_batch_adjs = []\\r\\n for document in batch:\\r\\n sentences_per_doc.append(len(document))\\r\\n for sentence in document:\\r\\n feature, adj = sentence\\r\\n all_batch_features.append(feature)\\r\\n all_batch_adjs.append(adj)\\r\\n lengths = [s.size()[0] for s in all_batch_features]\\r\\n sort_order = np.argsort(lengths)[::-1]\\r\\n sorted_features = [all_batch_features[i] for i in sort_order]\\r\\n sorted_adjs = [all_batch_adjs[i] for i in sort_order]\\r\\n sorted_lengths = [s.size()[0] for s in sorted_features]\\r\\n max_length = max(lengths)\\r\\n\\r\\n padded_features = [self.pad_l_model(s, max_length) for s in sorted_features]\\r\\n features = torch.cat(padded_features, 0) # batch_size, max_length\\r\\n\\r\\n padded_adjs, mask = self.preprocess_gcn_adj(max_length,sorted_adjs)\\r\\n adjs = torch.cat(padded_adjs, 0).to(self.device)\\r\\n\\r\\n embeded_features = self.encoder(features)\\r\\n return batch_size, embeded_features, adjs, mask, sorted_lengths, sort_order, sentences_per_doc\\r\\n elif self.sr_choose == \\\"g_model\\\" and self.gr_choose == \\\"DSG_SEG\\\":\\r\\n # batch: [[sentences, feature, adj], [doc2], [doc3], []]\\r\\n # sentences: [[feature_idx1, feature_idx2, feature_idx3, ...],[s2],[s3],...]\\r\\n sentences_per_doc = []\\r\\n words_per_doc = []\\r\\n nodes_per_doc = []\\r\\n all_batch_sentences = []\\r\\n all_batch_features = []\\r\\n all_batch_adjs = []\\r\\n for document in batch:\\r\\n sentences, feature, adj = document\\r\\n sentences_per_doc.append(len(sentences))\\r\\n words_per_doc.append(feature.size(0))\\r\\n nodes_per_doc.append(len(sentences) + feature.size(0))\\r\\n all_batch_sentences.append(sentences)\\r\\n all_batch_features.append(feature)\\r\\n all_batch_adjs.append(adj)\\r\\n max_length = max(words_per_doc)\\r\\n padded_features = [self.pad_l_model(s, max_length) for s in all_batch_features]\\r\\n features = torch.cat(padded_features, 0) # batch_size, max_word_num\\r\\n embeded_features = self.encoder(features) # batch_size, max_word_num, dim\\r\\n doc_feature_lst = []\\r\\n for i in range(batch_size):\\r\\n doc_feature = []\\r\\n feature = embeded_features[i][:words_per_doc[i]] # word_num, dim\\r\\n for sentence in all_batch_sentences[i]:\\r\\n doc_feature.append(torch.max(feature[sentence,:], 0)[0].unsqueeze(0))\\r\\n doc_feature.append(feature)\\r\\n doc_feature = torch.cat(doc_feature, 0)\\r\\n doc_feature_lst.append(doc_feature)\\r\\n max_node_size = max(nodes_per_doc)\\r\\n padded_doc_features = self.preprocess_gcn_feature(max_node_size, doc_feature_lst)\\r\\n doc_features = torch.cat(padded_doc_features, 0).to(self.device) # batch_size, max_node_size, dim\\r\\n padded_adjs, mask = self.preprocess_gcn_adj(max_node_size, all_batch_adjs)\\r\\n adjs = torch.cat(padded_adjs, 0).to(self.device) # batch_size, max_node_size, max_node_size\\r\\n return batch_size, doc_features, adjs, mask, sentences_per_doc\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5492844581604004,\n \"alphanum_fraction\": 0.5551468729972839,\n \"avg_line_length\": 33.52381134033203,\n \"blob_id\": \"781720079af2acfab1f69f3026db52a3366f0aa4\",\n \"content_id\": \"0121f67049d6fa266e00e62cfc9ba954703b22fe\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 17399,\n \"license_type\": \"no_license\",\n \"max_line_length\": 141,\n \"num_lines\": 504,\n \"path\": \"/data/data_utils.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import scipy.sparse as sp\\nimport numpy as np\\nfrom math import log\\nfrom nltk.tokenize import RegexpTokenizer\\nimport nltk\\n\\n# from wiki_thresholds.py\\nmin_section_char_count = 20\\nmin_sentence_in_section = 2\\nmax_section_formulas_count = 3\\nmax_section_code_snipet_count = 2\\nmin_valid_section_count = 3 # This includes preface section\\nmin_words_in_sentence = 2\\nmin_valid_section_percentage = 0.2\\nmax_list_in_section_percentage = 0.5\\nmax_words_in_sentence_with_backslash_n = 45\\n\\ndef remove_last_one(data, targets):\\n return data, targets[:-1]\\n\\ndef remove_ing_last_one(data, adjs, targets):\\n return data,adjs, targets[:-1]\\n\\ndef generate_return_dict(data,targets,path, all_document_num,filtered_document_num,sent_num_filter):\\n data, targets = remove_last_one(data, targets)\\n sent_num = len(data)\\n if sent_num <= 2:\\n return None, filtered_document_num, all_document_num\\n assert len(targets) == len(data) - 1\\n\\n all_document_num += 1\\n path_flag = -1\\n if sent_num > sent_num_filter:\\n filtered_document_num += 1\\n return_dict = []\\n start_idx = 0\\n while start_idx <= sent_num - 1:\\n path_flag += 1\\n end_idx = start_idx + sent_num_filter\\n if end_idx >= sent_num:\\n end_idx = sent_num\\n tmp_data = data[start_idx: end_idx] if end_idx == sent_num else data[\\n start_idx: end_idx + 1] # include one more\\n tmp_targets = targets[start_idx: end_idx]\\n tmp_path = path + \\\"_|_{}\\\".format(path_flag)\\n tmp_return_dict = {\\n \\\"sentences\\\": tmp_data,\\n \\\"targets\\\": tmp_targets,\\n \\\"path\\\": tmp_path\\n }\\n if len(tmp_return_dict[\\\"sentences\\\"]) >= 2: #\\n return_dict.append(tmp_return_dict)\\n start_idx = end_idx\\n else:\\n return_dict = {\\n \\\"sentences\\\": data,\\n \\\"targets\\\": targets,\\n \\\"path\\\": path\\n }\\n return return_dict, filtered_document_num, all_document_num\\n\\n\\n\\n\\ndef generate_ing_return_dict(data,adjs,targets,path, all_document_num,filtered_document_num,sent_num_filter):\\n data, adjs,targets = remove_ing_last_one(data, adjs,targets)\\n sent_num = len(data)\\n if sent_num <= 2:\\n return None, filtered_document_num, all_document_num\\n assert len(targets) == len(data) - 1\\n assert len(data) == len(adjs)\\n all_document_num += 1\\n path_flag = -1\\n if sent_num > sent_num_filter:\\n filtered_document_num += 1\\n return_dict = []\\n start_idx = 0\\n while start_idx <= sent_num - 1:\\n path_flag += 1\\n end_idx = start_idx + sent_num_filter\\n if end_idx >= sent_num:\\n end_idx = sent_num\\n tmp_data = data[start_idx: end_idx] if end_idx == sent_num else data[\\n start_idx: end_idx + 1] # include one more\\n tmp_adj = adjs[start_idx: end_idx] if end_idx == sent_num else adjs[\\n start_idx: end_idx + 1] # include one more\\n tmp_targets = targets[start_idx: end_idx]\\n tmp_path = path + \\\"_|_{}\\\".format(path_flag)\\n tmp_return_dict = {\\n \\\"sentences\\\": tmp_data,\\n \\\"adjs\\\": tmp_adj,\\n \\\"targets\\\": tmp_targets,\\n \\\"path\\\": tmp_path\\n }\\n if len(tmp_return_dict[\\\"sentences\\\"]) >= 2: #\\n return_dict.append(tmp_return_dict)\\n start_idx = end_idx\\n else:\\n return_dict = {\\n \\\"sentences\\\": data,\\n \\\"adjs\\\": adjs,\\n \\\"targets\\\": targets,\\n \\\"path\\\": path\\n }\\n return return_dict, filtered_document_num, all_document_num\\n\\n\\n\\ndef build_ing_graph(doc_words, args):\\n \\\"\\\"\\\"\\n https://github.com/CRIPAC-DIG/TextING/blob/master/build_graph.py\\n \\\"\\\"\\\"\\n window_size = args.gnn_window_size\\n doc_len = len(doc_words)\\n doc_vocab = []\\n for w in doc_words:\\n if w not in doc_vocab:\\n doc_vocab.append(w)\\n doc_nodes = len(doc_vocab)\\n doc_word_id_map = {}\\n for j in range(doc_nodes):\\n doc_word_id_map[doc_vocab[j]] = j\\n # sliding windows\\n windows = []\\n if doc_len <= window_size:\\n windows.append(doc_words)\\n else:\\n for j in range(doc_len - window_size + 1):\\n window = doc_words[j: j + window_size]\\n windows.append(window)\\n num_window = len(windows)\\n word_window_freq = {}\\n for window in windows:\\n appeared = set()\\n for i in range(len(window)):\\n if window[i] in appeared:\\n continue\\n if window[i] in word_window_freq:\\n word_window_freq[window[i]] += 1\\n else:\\n word_window_freq[window[i]] = 1\\n appeared.add(window[i])\\n word_pair_count = {}\\n for window in windows:\\n for p in range(1, len(window)):\\n for q in range(0, p):\\n word_p = window[p]\\n word_p_id = doc_word_id_map[word_p]\\n word_q = window[q]\\n word_q_id = doc_word_id_map[word_q]\\n if word_p_id == word_q_id:\\n continue\\n word_pair_key = (word_p_id, word_q_id)\\n # word co-occurrences as weights\\n if word_pair_key in word_pair_count:\\n word_pair_count[word_pair_key] += 1.\\n else:\\n word_pair_count[word_pair_key] = 1.\\n # bi-direction\\n word_pair_key = (word_q_id, word_p_id)\\n if word_pair_key in word_pair_count:\\n word_pair_count[word_pair_key] += 1.\\n else:\\n word_pair_count[word_pair_key] = 1.\\n row = []\\n col = []\\n weight = []\\n feature = []\\n # preprocess of TextING, not used\\n # for key in word_pair_count:\\n # p = key[0]\\n # q = key[1]\\n # row.append(p) # p\\n # col.append(q) # q\\n # weight.append(word_pair_count[key] if weighted_graph else 1.)\\n # adj = sp.csr_matrix((weight, (row, col)), shape=(doc_nodes, doc_nodes))\\n for key in word_pair_count:\\n p = key[0]\\n q = key[1]\\n count = word_pair_count[key]\\n word_freq_p = word_window_freq[doc_vocab[p]]\\n word_freq_q = word_window_freq[doc_vocab[q]]\\n pmi = log((1.0 * count / num_window) /\\n (1.0 * word_freq_p * word_freq_q / (num_window * num_window)))\\n if pmi <= 0:\\n continue\\n row.append(p)\\n col.append(q)\\n weight.append(pmi)\\n adj = sp.csr_matrix((weight, (row, col)), shape=(doc_nodes, doc_nodes))\\n for k, v in sorted(doc_word_id_map.items(), key=lambda x: x[1]):\\n feature.append(k)\\n return feature, normalize_adj(adj)\\n\\ndef normalize_adj(adj):\\n \\\"\\\"\\\"Symmetrically normalize adjacency matrix.\\\"\\\"\\\"\\n rowsum = np.array(adj.sum(1)) # D\\n with np.errstate(divide='ignore'):\\n d_inv_sqrt = np.power(rowsum, -0.5).flatten()\\n d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.\\n d_mat_inv_sqrt = np.diag(d_inv_sqrt)\\n return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt)\\n\\ndef generate_gcn_return_dict(return_dict,args):\\n if return_dict == None:\\n return return_dict\\n elif type(return_dict) == dict:\\n sentences,targets,path = return_dict[\\\"sentences\\\"], return_dict[\\\"targets\\\"], return_dict[\\\"path\\\"]\\n sentences, feature, adj = build_gcn_graph(sentences,args)\\n output = {\\\"sentences\\\":sentences,\\n \\\"feature\\\":feature,\\n \\\"adj\\\":adj,\\n \\\"targets\\\":targets,\\n \\\"path\\\":path}\\n return output\\n else: # list\\n output = []\\n for tmp_return_dict in return_dict:\\n output.append(generate_gcn_return_dict(tmp_return_dict,args))\\n return output\\n\\ndef build_gcn_graph(sentences,args):\\n window_size = args.gnn_window_size\\n # weighted_graph = args.gnn_weighted_graph\\n sent_num = len(sentences)\\n doc_word_id_map = {}\\n doc_vocab = []\\n doc_len_lst = []\\n for sentence in sentences:\\n doc_len_lst.append(len(sentence))\\n for word in sentence:\\n if word not in doc_vocab:\\n doc_vocab.append(word)\\n\\n doc_nodes = len(doc_vocab)\\n\\n for j in range(doc_nodes):\\n doc_word_id_map[doc_vocab[j]] = j\\n\\n doc_nodes += sent_num\\n new_sentences = []\\n for idx, sentence in enumerate(sentences):\\n new_sentences.append([doc_word_id_map[w] for w in sentence])\\n\\n windows = []\\n for sentence in sentences:\\n length = len(sentence)\\n if length <= window_size:\\n windows.append(sentence)\\n else:\\n for j in range(length - window_size + 1):\\n window = sentence[j:j+window_size]\\n windows.append(window)\\n num_window = len(windows)\\n word_window_freq = {}\\n for window in windows:\\n appeared = set()\\n for i in range(len(window)):\\n if window[i] in appeared:\\n continue\\n if window[i] in word_window_freq:\\n word_window_freq[window[i]] += 1\\n else:\\n word_window_freq[window[i]] = 1\\n appeared.add(window[i])\\n\\n word_pair_count = {}\\n for window in windows:\\n for p in range(1, len(window)):\\n for q in range(0, p):\\n word_p = window[p]\\n word_p_id = doc_word_id_map[word_p]\\n word_q = window[q]\\n word_q_id = doc_word_id_map[word_q]\\n if word_p_id == word_q_id:\\n continue\\n word_pair_key = (word_p_id, word_q_id)\\n # word co-occurrences as weights\\n if word_pair_key in word_pair_count:\\n word_pair_count[word_pair_key] += 1.\\n else:\\n word_pair_count[word_pair_key] = 1.\\n # bi-direction\\n word_pair_key = (word_q_id, word_p_id)\\n if word_pair_key in word_pair_count:\\n word_pair_count[word_pair_key] += 1.\\n else:\\n word_pair_count[word_pair_key] = 1.\\n\\n word_doc_list = {}\\n word_doc_freq = {}\\n for sentence_idx,sentence in enumerate(sentences):\\n appeared = set()\\n for word in sentence:\\n if word in appeared:\\n continue\\n if word in word_doc_list:\\n word_doc_list[word].append(sentence_idx)\\n else:\\n word_doc_list[word] = [sentence_idx]\\n appeared.add(word)\\n for word, doc_list in word_doc_list.items():\\n word_doc_freq[word] = len(doc_list)\\n\\n doc_word_freq = {}\\n for sentence_idx,sentence in enumerate(sentences):\\n for word in sentence:\\n word_idx = doc_word_id_map[word]\\n doc_word_str = (sentence_idx,word_idx)\\n if doc_word_str in doc_word_freq:\\n doc_word_freq[doc_word_str] += 1\\n else:\\n doc_word_freq[doc_word_str] = 1\\n row = []\\n col = []\\n weight = []\\n feature = []\\n if args.tfidf:\\n for key in doc_word_freq:\\n freq = doc_word_freq[key]\\n sentence_idx = key[0]\\n word_idx = key[1]\\n row.append(sentence_idx)\\n col.append(word_idx + sent_num) # ***\\n idf = log(1.0 * doc_len_lst[sentence_idx] / word_doc_freq[doc_vocab[word_idx]])\\n tf_idf = freq * idf\\n weight.append(tf_idf)\\n else: # -TF-IDF\\n for key in doc_word_freq:\\n sentence_idx = key[0]\\n word_idx = key[1]\\n row.append(sentence_idx)\\n col.append(word_idx + sent_num) # ***\\n weight.append(1)\\n if args.pmi:\\n for key in word_pair_count:\\n p = key[0]\\n q = key[1]\\n count = word_pair_count[key]\\n word_freq_p = word_window_freq[doc_vocab[p]]\\n word_freq_q = word_window_freq[doc_vocab[q]]\\n pmi = log((1.0 * count / num_window) /\\n (1.0 * word_freq_p * word_freq_q / (num_window * num_window)))\\n if pmi <= 0:\\n continue\\n row.append(sent_num + p)\\n col.append(sent_num + q)\\n weight.append(pmi)\\n else: # -PMI\\n for key in word_pair_count:\\n p = key[0]\\n q = key[1]\\n row.append(sent_num + p)\\n col.append(sent_num + q)\\n weight.append(1)\\n adj = sp.csr_matrix((weight, (row, col)), shape=(doc_nodes, doc_nodes))\\n for k, v in sorted(doc_word_id_map.items(), key=lambda x: x[1]):\\n feature.append(k)\\n adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)\\n return new_sentences, feature, sp.csr_matrix(normalize_adj(adj+sp.eye(adj.shape[0])))\\n\\n\\n\\nsegment_seperator = \\\"========\\\"\\n\\n\\ndef get_segment_seperator(level,name):\\n return segment_seperator + \\\",\\\" + str(level) + \\\",\\\" +name\\n\\n\\n\\ndef get_seperator_foramt(levels = None):\\n level_format = '\\\\d' if levels == None else '['+ str(levels[0]) + '-' + str(levels[1]) + ']'\\n seperator_fromat = segment_seperator + ',' + level_format + \\\",.*?\\\\.\\\"\\n return seperator_fromat\\n\\n\\n\\ndef is_seperator_line(line):\\n return line.startswith(segment_seperator)\\n\\ndef get_segment_level(seperator_line):\\n return int (seperator_line.split(',')[1])\\n\\ndef get_segment_name(seperator_line):\\n return seperator_line.split(',')[2]\\n\\ndef get_list_token():\\n return \\\"***LIST***\\\"\\n\\n\\ndef get_formula_token():\\n return \\\"***formula***\\\"\\n\\n\\ndef get_codesnipet_token():\\n return \\\"***codice***\\\"\\n\\n\\ndef get_special_tokens():\\n special_tokens = []\\n special_tokens.append(get_list_token())\\n special_tokens.append(get_formula_token())\\n special_tokens.append(get_codesnipet_token())\\n return special_tokens\\n\\nwords_tokenizer = None\\nsentence_tokenizer = None\\nmissing_stop_words = set(['of', 'a', 'and', 'to'])\\ndef get_words_tokenizer():\\n global words_tokenizer\\n\\n if words_tokenizer:\\n return words_tokenizer\\n\\n words_tokenizer = RegexpTokenizer(r'\\\\w+')\\n return words_tokenizer\\n\\ndef extract_sentence_words(sentence, remove_missing_emb_words = False,remove_special_tokens = False):\\n if (remove_special_tokens):\\n for token in get_special_tokens():\\n # Can't do on sentence words because tokenizer delete '***' of tokens.\\n sentence = sentence.replace(token, \\\"\\\")\\n tokenizer = get_words_tokenizer()\\n sentence_words = tokenizer.tokenize(sentence)\\n if remove_missing_emb_words:\\n sentence_words = [w for w in sentence_words if w not in missing_stop_words]\\n return sentence_words\\n\\n\\ndef get_punkt():\\n global sentence_tokenizer\\n if sentence_tokenizer:\\n return sentence_tokenizer\\n\\n try:\\n tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\\n except exceptions.LookupError:\\n nltk.download('punkt')\\n tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\\n\\n sentence_tokenizer = tokenizer\\n return sentence_tokenizer\\n\\n\\ndef split_sentence_with_list(sentence):\\n\\n list_pattern = \\\"\\\\n\\\" + get_list_token() + \\\".\\\"\\n if sentence.endswith( list_pattern ):\\n splited_sentence = [str for str in sentence.split(\\\"\\\\n\\\" + get_list_token() + \\\".\\\") if\\n len(str) > 0]\\n splited_sentence.append(get_list_token() + \\\".\\\")\\n return splited_sentence\\n else:\\n return [sentence]\\n\\ndef split_sentece_colon_new_line(sentence):\\n\\n splited_sentence = sentence.split(\\\":\\\\n\\\")\\n if (len(splited_sentence) == 1):\\n return splited_sentence\\n new_sentences = []\\n # -1 . not to add \\\":\\\" to last sentence\\n for i in range(len(splited_sentence) - 1):\\n if (len(splited_sentence[i]) > 0):\\n new_sentences.append(splited_sentence[i] + \\\":\\\")\\n if (len(splited_sentence[-1]) > 0):\\n new_sentences.append(splited_sentence[-1])\\n return new_sentences\\n\\ndef split_long_sentences_with_backslash_n(max_words_in_sentence,sentences, doc_id):\\n new_sentences = []\\n for sentence in sentences:\\n sentence_words = extract_sentence_words(sentence)\\n if len(sentence_words) > max_words_in_sentence:\\n splitted_sentences = sentence.split('\\\\n')\\n new_sentences.extend(splitted_sentences )\\n else:\\n new_sentences.append(sentence)\\n return new_sentences\\n\\ndef split_sentences(text, doc_id):\\n sentences = get_punkt().tokenize(text)\\n senteces_list_fix = []\\n for sentence in sentences:\\n seplited_list_sentence = split_sentence_with_list(sentence)\\n senteces_list_fix.extend(seplited_list_sentence)\\n\\n sentence_colon_fix = []\\n for sentence in senteces_list_fix:\\n splitted_colon_sentence = split_sentece_colon_new_line(sentence)\\n sentence_colon_fix.extend(splitted_colon_sentence)\\n\\n sentences_without_backslash_n = split_long_sentences_with_backslash_n(max_words_in_sentence_with_backslash_n, sentence_colon_fix, doc_id)\\n\\n ret_sentences = []\\n for sentence in sentences_without_backslash_n:\\n ret_sentences.append(sentence.replace('\\\\n',' '))\\n return ret_sentences\\n\\n\\ndef clean_paragraph(paragraph):\\n cleaned_paragraph= paragraph.replace(\\\"'' \\\", \\\" \\\").replace(\\\" 's\\\", \\\"'s\\\").replace(\\\"``\\\", \\\"\\\").strip('\\\\n')\\n return cleaned_paragraph\"\n },\n {\n \"alpha_fraction\": 0.5622189044952393,\n \"alphanum_fraction\": 0.572526216506958,\n \"avg_line_length\": 38.82089614868164,\n \"blob_id\": \"266628859d7fcac34eb983fa408669ed56e7c712\",\n \"content_id\": \"ddaed2da6ddd9e81b8a884c5c7e8a113054e9798\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5336,\n \"license_type\": \"no_license\",\n \"max_line_length\": 132,\n \"num_lines\": 134,\n \"path\": \"/models/GGNN/layers.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch\\nimport torch.nn as nn\\nfrom models.GGNN.inits import *\\n\\nclass GraphConvolution(nn.Module):\\n def __init__( self, input_dim, \\\\\\n output_dim, \\\\\\n act = None, \\\\\\n dropout_p = 0., \\\\\\n bias=True):\\n super(GraphConvolution, self).__init__()\\n self.input_dim = input_dim\\n self.output_dim = output_dim\\n self.dropout_p = dropout_p\\n self.weight = glorot(self.input_dim, self.output_dim)\\n if bias:\\n self.b = uniform_bias(self.weight, self.output_dim)\\n\\n self.act = act\\n self.dropout = nn.Dropout(self.dropout_p)\\n\\n\\n def forward(self, x, adj, mask):\\n x = self.dropout(x)\\n pre_sup = torch.matmul(x, self.weight) +self.b# bs, node_size, input_dim x input_dim, output_dim = bs, node_size, output_dim\\n # pre_sup = x.mm(self.weight)\\n pre_sup = mask * pre_sup\\n # out = adj.mm(pre_sup)\\n out = torch.bmm(adj, pre_sup) # bs, node_size, node_size x node_size, output_dim\\n out = self.act(out)\\n return out\\n\\nclass gru_unit(nn.Module):\\n def __init__(self, output_dim, act, dropout_p):\\n super(gru_unit,self).__init__()\\n self.output_dim = output_dim\\n self.dropout_p = dropout_p\\n self.dropout = nn.Dropout(self.dropout_p)\\n self.act = act\\n self.z0_weight = glorot(self.output_dim, self.output_dim)\\n self.z1_weight = glorot(self.output_dim, self.output_dim)\\n self.r0_weight = glorot(self.output_dim, self.output_dim)\\n self.r1_weight = glorot(self.output_dim, self.output_dim)\\n self.h0_weight = glorot(self.output_dim, self.output_dim)\\n self.h1_weight = glorot(self.output_dim, self.output_dim)\\n self.z0_bias = uniform_bias(self.z0_weight, self.output_dim)\\n self.z1_bias = uniform_bias(self.z1_weight, self.output_dim)\\n self.r0_bias = uniform_bias(self.r0_weight, self.output_dim)\\n self.r1_bias = uniform_bias(self.r1_weight, self.output_dim)\\n self.h0_bias = uniform_bias(self.h0_weight, self.output_dim)\\n self.h1_bias = uniform_bias(self.h1_weight, self.output_dim)\\n\\n def forward(self,adj, x, mask):\\n # adj: bs,ml,ml | x: bs,ml,dim\\n adj = self.dropout(adj)\\n a = torch.matmul(adj, x)\\n # updata gate\\n z0 = torch.matmul(a, self.z0_weight) + self.z0_bias\\n z1 = torch.matmul(x, self.z1_weight) + self.z1_bias\\n z = torch.sigmoid(z0+z1)\\n # reset gate\\n r0 = torch.matmul(a, self.r0_weight) + self.r0_bias\\n r1 = torch.matmul(x, self.r1_weight) + self.r1_bias\\n r = torch.sigmoid(r0+r1)\\n # update embeddings\\n h0 = torch.matmul(a, self.h0_weight) + self.h0_bias\\n h1 = torch.matmul(r*x, self.h1_weight) + self.h1_bias\\n h = self.act(mask * (h0 + h1))\\n return h*z + x*(1-z)\\n\\n\\nclass GraphLayer(nn.Module):\\n \\\"\\\"\\\"Graph layer.\\\"\\\"\\\"\\n def __init__(self, args,\\n input_dim,\\n output_dim,\\n act=nn.Tanh(),\\n dropout_p = 0.):\\n super(GraphLayer, self).__init__()\\n self.args = args\\n self.input_dim = input_dim\\n self.output_dim = output_dim\\n self.act = act\\n self.dropout_p = dropout_p\\n self.dropout = nn.Dropout(self.dropout_p)\\n self.gru_step = self.args.texting_gru_step\\n self.gru_unit = gru_unit(self.output_dim, self.act, self.dropout_p)\\n # self.dropout\\n self.encode_weight = glorot(self.input_dim, self.output_dim)\\n self.encode_bias = uniform_bias(self.encode_weight, self.output_dim)\\n\\n def forward(self, feature, adj, mask):\\n feature = self.dropout(feature)\\n # encode inputs\\n encoded_feature = torch.matmul(feature, self.encode_weight) +self.encode_bias\\n output = mask * self.act(encoded_feature)\\n # convolve\\n for _ in range(self.gru_step):\\n output = self.gru_unit(adj, output, mask)\\n return output\\n\\nclass ReadoutLayer(nn.Module):\\n \\\"\\\"\\\"Graph Readout Layer.\\\"\\\"\\\"\\n def __init__(self, args,\\n input_dim,\\n output_dim,\\n act=nn.ReLU(),\\n dropout_p=0.):\\n super(ReadoutLayer, self).__init__()\\n self.args = args\\n self.input_dim = input_dim\\n self.output_dim = output_dim\\n self.act = act\\n self.dropout_p = dropout_p\\n self.dropout = nn.Dropout(self.dropout_p)\\n self.att_weight = glorot(self.input_dim, 1)\\n self.emb_weight = glorot(self.input_dim, self.input_dim)\\n self.mlp_weight = glorot(self.input_dim, self.output_dim)\\n self.att_bias = uniform_bias(self.att_weight,1)\\n self.emb_bias = uniform_bias(self.emb_weight,self.input_dim)\\n self.mlp_bias = uniform_bias(self.mlp_weight,self.input_dim)\\n\\n def forward(self,x,mask):\\n # soft attention\\n att = torch.sigmoid(torch.matmul(x, self.att_weight)+self.att_bias)\\n emb = self.act(torch.matmul(x, self.emb_weight)+self.emb_bias)\\n # graph summation\\n N = torch.sum(mask, dim=1)\\n M = (mask - 1) * 1e9\\n # classification\\n g = mask * att * emb\\n g = torch.sum(g, dim=1)/N + torch.max(g+M,dim=1)[0]\\n # g = self.dropout(g)\\n return g\\n\"\n },\n {\n \"alpha_fraction\": 0.7063773274421692,\n \"alphanum_fraction\": 0.7411426305770874,\n \"avg_line_length\": 33.47328186035156,\n \"blob_id\": \"88edfffc5b9d952461510c445435373c0841e434\",\n \"content_id\": \"1d89768b24c09505de4bb0f60bafce7bd4393a6f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 5470,\n \"license_type\": \"no_license\",\n \"max_line_length\": 259,\n \"num_lines\": 131,\n \"path\": \"/README.md\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# 《基于篇章结构图网络的话题分割》(Topic Segmentation via Discourse Structure Graph Network)\\n该仓库包含了《基于篇章结构图网络的话题分割》文章的代码和数据集，代码使用pytorch框架实现。\\n我们的工作主要基于[《Text Segmentation as a Supervised Learning Task》](https://arxiv.org/abs/1803.09337) ，它的源代码是[text-segmentation](https://github.com/koomri/text-segmentation)。\\n另外，我们的模型灵感主要来自于 [《Every Document Owns Its Structure: Inductive Text Classification via\\nGraph Neural Networks》](https://arxiv.org/abs/2004.13826)([TextING的代码](https://github.com/CRIPAC-DIG/TextING)) 以及 [《Graph Convolutional Networks for Text Classification》](https://arxiv.org/abs/1809.05679)([TextGCN的代码](https://github.com/yao8839836/text_gcn))。\\n谢谢他们的工作。\\n\\n# 论文简介\\n\\n\\n我们提出了一个基于篇章结构图网络的话题分割模型——DSG-SEG，如上图所示.具体的，模型把每一个篇章单独构建成图，图中包含了单词、句子节点，以及单词节点之间、单词和句子节点之间的邻接关系；接着，模型把图作为输入，对GGNN网络进行迭代，句子节点之间因此通过共有的邻接单词节点产生了间接的信息交互；最终，模型得到了具有全局语义信息的句子向量表示，并将其送入Bi-LSTM网络进行分割点的预测。\\n\\n我们对比了六种基准模型:freq-sr\\\\latent-sr\\\\tree-sr-left\\\\TextSeg\\\\Bert+Bi-LSTM\\\\Bert+Bi-LSTMnft（除TextSeg外，其它基准模型皆为我们复现）我们的模型在诸多数据集上同时取得了最好的结果指标和时间性能。\\n\\n# 环境配置\\npython3.7\\n```bash\\nconda create -n textseg python=3.7\\nconda activate textseg\\n```\\npython依赖库\\n```bash\\npip install -r requirements.txt\\n```\\n\\n# 资源获取 \\n假设所有资源保存在/data路径下，完成这部分，你将得到: \\n[查看文件结构](https://github.com/beiweixiaoxu/DSG-SEG/blob/master/doc/file_tree.md)\\n\\nglove: \\nwget http://nlp.stanford.edu/data/glove.6B.zip \\nunzip glove.6B.zip \\nrm glove.6B.(50d|100d|200d).txt \\n \\nword2vec: \\nwget https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz \\ngzip -d GoogleNews-vectors-negative300.bin.gz \\n解压完毕得到:GoogleNews-vectors-negative300.bin \\n \\nWIKI_727K: \\nwget https://www.dropbox.com/sh/k3jh0fjbyr0gw0a/AADzAd9SDTrBnvs1qLCJY5cza?dl=0&preview=wiki_727K.tar.bz2 \\ntar -xjf wiki_727K.tar.bz2 \\n\\nwiki_test_50: \\nwget https://www.dropbox.com/sh/k3jh0fjbyr0gw0a/AADzAd9SDTrBnvs1qLCJY5cza?dl=0&preview=wiki_test_50.tar.bz2 \\ntar -xjf wiki_test_50.tar.bz2 \\n\\nWIKI-SECTION: \\nwget https://github.com/sebastianarnold/WikiSection/raw/master/wikisection_dataset_ref.tar.gz \\nmkdir wikisection & tar -zxvf wikisection_dataset_ref.tar.gz -C wikisection \\n \\nCHOI: \\n手动下载 https://github.com/koomri/text-segmentation/tree/master/data/choi \\nmkdir choi \\n放入文件夹 \\n \\nMANIFESTO: \\n手动下载 https://github.com/koomri/text-segmentation/tree/master/data/manifesto \\nmkdir manifesto \\n放入文件夹 \\n \\nCLINICAL: \\n手动下载 https://github.com/pinkeshbadjatiya/neuralTextSegmentation/tree/master/code/data/clinical \\nmkdir clinical \\n放入文件夹 \\n \\nCITIES: \\nwget http://groups.csail.mit.edu/rbg/code/mallows/data/wikicities-english.tar.gz \\ntar -zxvf wikicities-english.tar.gz \\nrm -r wikicities-english/test \\nmv wikicities-english/training/* wikicities-english/ \\nrm -r wikicities-english/training \\nmv wikicities-english wikicities \\n\\nELEMENTS: \\nwget http://groups.csail.mit.edu/rbg/code/mallows/data/wikielements.tar.gz \\ntar -zxvf wikielements.tar.gz \\n\\n# 代码运行 \\n[查看代码结构](https://github.com/beiweixiaoxu/DSG-SEG/blob/master/doc/code_tree.md) \\n两个修改: \\n1. 永久修改parameters.py中，各个资源的路径，如`parser.add_argument(\\\"--wiki_path\\\",type=str, default=\\\"/data/wiki_727\\\")` \\n2. 永久修改parameters.py中，保存dataset和encoder的路径(用于避免重复读取)，如`parser.add_argument(\\\"--dataset_dir\\\",type=str, default=\\\"/data/saved_dataset\\\")` \\n样例: \\n```python\\npython run.py [--etype {one_hot,glove,w2v,bert}] [--encoder_fine_tune]\\n [--sr_choose {f_model,l_model,s_model,t_model,g_model,b_model,random_baseline}]\\n [--tr_choose {balanced,left,right}]\\n [--gr_choose {texting,DSE_SEG}]\\n ...\\n```\\nDSG_GCN:\\n```python\\npython run.py --sr_choose=g_model --gr_choose=DSE_SEG\\n```\\n-SENTNODE:\\n```python\\npython run.py --sr_choose=g_model --gr_choose=texting\\n```\\n-PMI:\\n```python\\npython run.py --sr_choose=g_model --gr_choose=DSG_SEG --pmi\\n```\\nBert+Bi-LSTMnft:\\n```python\\npython run.py --etype=bert --sr_choose=b_model --length_filter=64\\n```\\nTextSeg:\\n```python\\npython run.py --sr_choose=s_model\\n```\\nBert+Bi-LSTM:\\n```python\\npython run.py --etype=bert --sr_choose=b_model --length_filter=40 --sent_num_filter=60 --train_bs=3 --encoder_fine_tune\\n```\\ntree-sr-left\\n```python\\npython run.py --sr_choose=t_model --tr_choose=left --length_filter=40 --sent_num_filter=60\\n```\\nfreq-sr:\\n```python\\npython run.py --etype=one-hot --sr_choose=f_model\\n```\\nlatent-sr:\\n```python\\npython run.py --etype=one-hot --sr_choose=l_model\\n```\\nrandom-baseline:\\n```python\\npython run.py --sr_choose=random_baseline --infer --train_bs=1 --dev_bs=1 --test_bs=1 \\n```\\n\"\n },\n {\n \"alpha_fraction\": 0.5210893154144287,\n \"alphanum_fraction\": 0.5257389545440674,\n \"avg_line_length\": 48.776859283447266,\n \"blob_id\": \"09ebb9b5498986768f1a7b8affd8a9cc65aab5be\",\n \"content_id\": \"33ff591fe81136d7c90e8d37084a4200565b78b0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6022,\n \"license_type\": \"no_license\",\n \"max_line_length\": 196,\n \"num_lines\": 121,\n \"path\": \"/data/clinical_loader.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from pathlib import Path\\nfrom torch.utils.data import Dataset\\nfrom data.data_utils import *\\nfrom utils import *\\nimport os\\nfrom parameters import create_parser\\n\\ndef clean_paragraph(paragraph):\\n cleaned_paragraph= paragraph.replace(\\\"'' \\\", \\\" \\\").replace(\\\" 's\\\", \\\"'s\\\").replace(\\\"``\\\", \\\"\\\").strip('\\\\n')\\n return cleaned_paragraph\\n\\ndef read_clinical_file(args, filtered_num, all_num, filtered_document_num, all_document_num, path, encoder=None,length_filter=10, sent_num_filter=10):\\n seperator = '=========='\\n with Path(path).open('r') as f:\\n raw_text = f.read()\\n paragraphs = [clean_paragraph(p) for p in raw_text.strip().split(seperator)\\n if len(p) > 5 and p != \\\"\\\\n\\\"]\\n targets = []\\n adjs = []\\n new_text = []\\n for paragraph in paragraphs:\\n sentences = [s for s in paragraph.split('\\\\n') if len(s.split()) > 0]\\n if sentences:\\n sentences_count =0\\n # This is the number of sentences in the paragraph and where we need to split.\\n for sentence in sentences:\\n words = extract_sentence_words(sentence)\\n if (len(words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\\n continue\\n sentences_count +=1\\n # length_filter\\n if len(words) > length_filter:\\n words = words[:length_filter]\\n filtered_num += 1\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n words, adj = build_ing_graph(words, args)\\n adjs.append(adj)\\n if encoder:\\n new_text.append(encoder.tokenize(words))\\n else:\\n new_text.append(words)\\n all_num += sentences_count\\n targets.extend([0] * (sentences_count - 1))\\n if sentences_count != 0:\\n targets.append(1) # segment\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(new_text, adjs, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(new_text, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n return_dict = generate_gcn_return_dict(return_dict,args)\\n else:\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(new_text, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n return return_dict, filtered_num, all_num,filtered_document_num, all_document_num\\n\\ndef get_cache_path(folder):\\n cache_file_path = folder / 'paths_cache'\\n return cache_file_path\\n\\ndef cache_filenames(folder):\\n files = Path(folder).glob('*.ref')\\n cache_path = get_cache_path(folder)\\n with cache_path.open(\\\"w+\\\") as f:\\n for file in files:\\n f.write(str(file) + u'\\\\n')\\n\\nclass ClinicalDataSet(Dataset):\\n def __init__(self,\\n root = None,\\n args=None,\\n encoder = None,\\n length_filter = 40,\\n sent_num_filter = 60,\\n local_rank=-1):\\n logger = get_logger(args)\\n root_path = Path(root)\\n cache_path = get_cache_path(root_path)\\n if not cache_path.exists():\\n cache_filenames(root_path)\\n self.textfiles = cache_path.read_text().splitlines()\\n else:\\n self.textfiles = cache_path.read_text().splitlines()\\n if len(self.textfiles) == 0:\\n raise RuntimeError('Found 0 images in subfolders of: {}'.format(root))\\n self.data = []\\n # sentences\\n filtered_num = 0\\n all_num = 0\\n # documents\\n filtered_document_num = 0\\n all_document_num = 0\\n rank_logger_info(logger, local_rank, \\\"Reading clinical data\\\")\\n for textfile in self.textfiles:\\n return_dict, filtered_num, all_num, filtered_document_num, all_document_num = read_clinical_file(args,filtered_num,\\n all_num,\\n filtered_document_num,\\n all_document_num,\\n textfile,\\n encoder=encoder,\\n length_filter=length_filter,\\n sent_num_filter=sent_num_filter)\\n if return_dict == None:\\n continue\\n elif type(return_dict) == dict:\\n self.data.append(return_dict)\\n else: # list\\n self.data.extend(return_dict)\\n if all_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all sentences num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\"%(all_num, filtered_num,filtered_num/all_num))\\n if all_document_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all documents num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\\n\\n def __getitem__(self, index):\\n return self.data[index]\\n\\n def __len__(self):\\n return len(self.data)\\nif __name__ == \\\"__main__\\\":\\n args = create_parser()\\n print(args)\\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\\n clinicaldataset = ClinicalDataSet(args.clinical_path, args)\"\n },\n {\n \"alpha_fraction\": 0.6526409983634949,\n \"alphanum_fraction\": 0.6589077711105347,\n \"avg_line_length\": 41.03845977783203,\n \"blob_id\": \"da584f5fc26a2d82a56a339b5c60e9a26c55a354\",\n \"content_id\": \"2efa775a6b30f6a94deed88fdf44be27e1cb5603\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1117,\n \"license_type\": \"no_license\",\n \"max_line_length\": 111,\n \"num_lines\": 26,\n \"path\": \"/models/bert_sr.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\r\\nimport numpy as np\\r\\nimport torch.nn.functional as F\\r\\nimport torch\\r\\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\\r\\nfrom utils import unsort\\r\\n\\r\\nclass b_model(nn.Module):\\r\\n def __init__(self, encoder_input_dim, args):\\r\\n super(b_model, self).__init__()\\r\\n self.output_dim = encoder_input_dim\\r\\n self.device = args.device\\r\\n\\r\\n\\r\\n def forward(self,encoded_input):\\r\\n embeded_big_tensor,sorted_lengths,sort_order,sentences_per_doc = encoded_input\\r\\n batch_size, out_dim = embeded_big_tensor.shape[0],embeded_big_tensor.shape[2]\\r\\n pooled_output = embeded_big_tensor[:,0,:].squeeze(1) # torch.zeros(batch_size, out_dim).to(self.device)\\r\\n # for i in range(batch_size):\\r\\n # maxes[i, :] = torch.max(embeded_big_tensor[i, :sorted_lengths[i],:], 0)[0]\\r\\n unsort_order = torch.LongTensor(unsort(sort_order)).to(self.device)\\r\\n unsorted_sent_repr = pooled_output.index_select(0, unsort_order)\\r\\n return unsorted_sent_repr, sentences_per_doc\\r\\n\\r\\n def get_output_dim(self):\\r\\n return self.output_dim\"\n },\n {\n \"alpha_fraction\": 0.5677999258041382,\n \"alphanum_fraction\": 0.5904650092124939,\n \"avg_line_length\": 34.54166793823242,\n \"blob_id\": \"21a5d0b3f1df35542c63e9d658411afc4d669926\",\n \"content_id\": \"90dddf3df362ed57f63d5ba94ee7297da5a7453f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2559,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 72,\n \"path\": \"/encoder/w2v_encoder.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\nimport torch\\nfrom utils import *\\nfrom collections import OrderedDict\\nimport numpy as np\\nimport gensim\\nclass w2v_model(nn.Module):\\n\\n def __init__(self, vocab_path, args, encoder_fine_tune):\\n super(w2v_model,self).__init__()\\n self.vocab_path = vocab_path\\n self.args = args\\n self.encoder_fine_tune = encoder_fine_tune\\n self.local_rank = args.local_rank\\n self.PADDING_TOKEN = \\\"<PAD>\\\"\\n self.UNK_TOKEN = \\\"<UNK>\\\"\\n self.embedding, self.word2idx = self.get_w2v_embedding()\\n self.pad_token_id = self.word2idx[self.PADDING_TOKEN]\\n self.idx2word = OrderedDict()\\n for item in self.word2idx.items():\\n self.idx2word[item[1]] = item[0]\\n self.embedding = nn.Embedding.from_pretrained(self.embedding)\\n if encoder_fine_tune:\\n for param in self.parameters():\\n param.requires_grad = True\\n else:\\n for param in self.parameters():\\n param.requires_grad = False\\n\\n def get_w2v_embedding(self):\\n size = 300\\n logger = get_logger(self.args)\\n rank_logger_info(logger, self.local_rank, 'Reading w2v Vocab.')\\n word2vec = gensim.models.KeyedVectors.load_word2vec_format(self.vocab_path, binary=True)\\n\\n w2v_word2idx = OrderedDict()\\n w2v_word2idx[self.PADDING_TOKEN] = len(w2v_word2idx)\\n # w2v_word2idx[self.UNK_TOKEN] = len(w2v_word2idx)\\n\\n for w in word2vec.vocab:\\n if w in w2v_word2idx:\\n continue\\n else:\\n w2v_word2idx[w] = len(w2v_word2idx)\\n\\n embeddings = np.zeros((len(w2v_word2idx), size), dtype=np.float32)\\n embeddings[0] = np.zeros((1, size))\\n # embeddings[1] = np.random.randn(1, size)\\n rank_logger_info(logger, self.local_rank, 'Reading w2v Embeddings.')\\n for word in word2vec.vocab:\\n vec = word2vec[word]\\n if word not in w2v_word2idx:\\n continue\\n word_id = w2v_word2idx[word]\\n embeddings[word_id] = vec\\n\\n return torch.tensor(embeddings), w2v_word2idx\\n\\n def tokenize(self, inp):\\n word_lst = inp.split()\\n idx_lst = []\\n for word in word_lst:\\n if word in self.word2idx:\\n idx_lst.append(self.word2idx[word])\\n else:\\n idx_lst.append(self.word2idx['UNK']) # self.word2idx[self.UNK_TOKEN]\\n return idx_lst\\n\\n def forward(self,inp):\\n return self.embedding(inp)\\n def get_word_dim(self):\\n return 300\\n\"\n },\n {\n \"alpha_fraction\": 0.6674103140830994,\n \"alphanum_fraction\": 0.6840569972991943,\n \"avg_line_length\": 60.98936080932617,\n \"blob_id\": \"e6cf9b2c879ba0968ee045c3b2cda99dcfc7ef60\",\n \"content_id\": \"d2b1d0b8dab584ca60ba358ba786bec48b4a02e1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5827,\n \"license_type\": \"no_license\",\n \"max_line_length\": 157,\n \"num_lines\": 94,\n \"path\": \"/parameters.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from utils import *\\nimport os\\nfrom argparse import ArgumentParser\\ndef create_parser():\\n parser = ArgumentParser()\\n parser.add_argument('--logger_name', help='logger name', default='seg')\\n parser.add_argument('--early_stop', help='early stopping epoch', type = int, default=5)\\n parser.add_argument('--train_bs', help='Batch size', type=int, default=8)\\n parser.add_argument('--dev_bs', help='Batch size', type=int, default=5)\\n parser.add_argument('--test_bs', help='Batch size', type=int, default=5)\\n parser.add_argument('--epochs', help='Number of epochs to run', type=int, default=100)\\n parser.add_argument('--checkpoint_dir', help='Checkpoint directory', default='checkpoints')\\n parser.add_argument('--stop_after', help='Number of batches to stop after', default=None, type=int)\\n parser.add_argument('--num_workers', help='How many workers to use for data loading', type=int, default=0)\\n parser.add_argument('--high_granularity', help='Use high granularity for wikipedia dataset segmentation', action='store_true')\\n parser.add_argument('--wiki_random', action='store_false')\\n # saved_dataset&saved_encoder\\n parser.add_argument(\\\"--dataset_dir\\\",type=str, default=\\\"/p300/for_git/saved_dataset\\\")\\n parser.add_argument(\\\"--encoder_dir\\\", type=str, default=\\\"/p300/for_git/saved_encoder\\\")\\n # frac&filter\\n parser.add_argument(\\\"--data_frac\\\", type=float, default=\\\"1.0\\\")\\n parser.add_argument(\\\"--length_filter\\\", type=int, default=40000)\\n parser.add_argument(\\\"--sent_num_filter\\\", type=int, default=60000)\\n # data path\\n parser.add_argument(\\\"--glove_path\\\", type=str, default=\\\"/p300/for_git/glove.6B.300d.txt\\\")\\n parser.add_argument(\\\"--w2v_path\\\", type=str, default=\\\"/p300/for_git/GoogleNews-vectors-negative300.bin\\\")\\n parser.add_argument(\\\"--wiki_path\\\",type=str, default=\\\"/p300/for_git/wiki_727\\\")\\n parser.add_argument(\\\"--choi_path\\\", type=str, default=\\\"/p300/for_git/choi\\\")\\n parser.add_argument(\\\"--manifesto_path\\\", type=str, default=\\\"/p300/for_git/manifesto\\\")\\n parser.add_argument(\\\"--clinical_path\\\", type=str, default=\\\"/p300/for_git/clinical\\\")\\n parser.add_argument(\\\"--wikisection_path\\\", type=str, default=\\\"/p300/for_git/wikisection\\\")\\n parser.add_argument(\\\"--wiki50_path\\\", type=str, default=\\\"/p300/for_git/wiki_50\\\")\\n parser.add_argument(\\\"--wikicities_path\\\", type=str, default=\\\"/p300/for_git/wikicities\\\")\\n parser.add_argument(\\\"--wikielements_path\\\", type=str, default=\\\"/p300/for_git/wikielements\\\")\\n parser.add_argument(\\\"--train_data_type\\\", type=str, default=\\\"wiki\\\")\\n # Encoder\\n parser.add_argument(\\\"--etype\\\", type=str,choices=[\\\"one-hot\\\",\\\"glove\\\",\\\"randn\\\",\\\"w2v\\\",\\\"bert\\\"],default=\\\"w2v\\\")\\n parser.add_argument(\\\"--encoder_fine_tune\\\", action = \\\"store_true\\\") # for bert\\n # model\\n parser.add_argument(\\\"--sr_choose\\\", type=str,choices =[\\\"f_model\\\",\\\"l_model\\\",\\\"s_model\\\",\\\"t_model\\\",\\\"g_model\\\",\\\"b_model\\\",\\\"random_baseline\\\"] , default=\\\"s_model\\\")\\n parser.add_argument('--hidden_dim', type=int, default = 256)\\n parser.add_argument('--num_layers', type=int, default = 2)\\n # pooling for bilstm & treelstm\\n parser.add_argument(\\\"--pool_method\\\",type=str,default=\\\"max\\\") # max mean attention 'None'\\n parser.add_argument('--attention_hd', type=int, default=300)\\n # treelstm\\n parser.add_argument('--tr_choose', type=str,choices=[\\\"balanced\\\",\\\"left\\\",\\\"right\\\"], default='balanced')\\n parser.add_argument(\\\"--use_leaf_rnn\\\",action='store_true')\\n parser.add_argument(\\\"--bidirectional\\\",action='store_true')\\n # bilstm\\n parser.add_argument('--bilstm_hd', help=\\\"hidden dim\\\",type=int, default = 256)\\n parser.add_argument('--bilstm_nl', help=\\\"num layers\\\",type=int, default = 2)\\n parser.add_argument('--bilstm_bd', help=\\\"bidirectional\\\", type=bool, default=True)\\n # bert\\n parser.add_argument('--model_size', help='model size',type=str, default='base')\\n parser.add_argument('--cased', help='cased', action='store_false')\\n # gnn\\n parser.add_argument('--tfidf',action='store_true')\\n parser.add_argument('--pmi', action='store_false')\\n parser.add_argument('--gr_choose', choices=[\\\"texting\\\",\\\"DSG_SEG\\\"],type=str, default='texting')\\n parser.add_argument('--gnn_window_size', type=int, default=3)\\n # texting\\n parser.add_argument('--texting_hidden_dim', type=int, default=300)\\n parser.add_argument('--texting_output_dim', type=int, default=300)\\n parser.add_argument('--texting_gru_step', type=int, default=2)\\n # DSG_SEG\\n parser.add_argument('--DSG_SEG_output_dim', type=int, default=300)\\n # multi-gpu\\n parser.add_argument(\\\"--local_rank\\\", type=int, default=-1)\\n parser.add_argument(\\\"--seed\\\", type=int, default=0)\\n # test\\n parser.add_argument('--infer', help='inference_dir', action = \\\"store_true\\\")\\n parser.add_argument('--train_checkpoint_dir', help='best model path', default=None)\\n parser.add_argument('--best_threshold', help='models trained before were saved in a different way~', default=None)\\n args = parser.parse_args()\\n\\n args.valid_data_type = args.train_data_type\\n checkpoint_name = compute_name(args)\\n dataset_name = compute_dataset_name(args)\\n encoder_name = compute_encoder_name(args)\\n if args.train_checkpoint_dir != None:\\n args.train_checkpoint_dir = os.path.join(args.checkpoint_dir, args.train_checkpoint_dir)\\n args.checkpoint_dir = os.path.join(args.checkpoint_dir, checkpoint_name)\\n # dataset\\n args.dataset_path = os.path.join(args.dataset_dir, dataset_name + \\\".pt\\\")\\n # encoder\\n if args.encoder_fine_tune:\\n args.encoder_path = os.path.join(args.encoder_dir, encoder_name + \\\".pt\\\")\\n else:\\n args.encoder_path = os.path.join(args.encoder_dir, encoder_name + \\\".pt\\\")\\n os.makedirs(args.checkpoint_dir, exist_ok=True)\\n os.makedirs(args.dataset_dir, exist_ok=True)\\n os.makedirs(args.encoder_dir, exist_ok=True)\\n return args\\n\"\n },\n {\n \"alpha_fraction\": 0.6245912313461304,\n \"alphanum_fraction\": 0.6291694045066833,\n \"avg_line_length\": 38.23684310913086,\n \"blob_id\": \"537e2398e9117940bd11196df9e120bf484b0833\",\n \"content_id\": \"bd122641e2aa395f54be513b773e6bfc60a3f6c1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1529,\n \"license_type\": \"no_license\",\n \"max_line_length\": 102,\n \"num_lines\": 38,\n \"path\": \"/models/treelstm_sr.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch\\r\\nimport torch.nn as nn\\r\\nfrom models.Treelstm.basic import *\\r\\nfrom models.Treelstm.utils import *\\r\\nfrom models.Treelstm.models import *\\r\\nclass t_model_treelstm(nn.Module):\\r\\n def __init__(self,encoder_input_dim,args):\\r\\n super(t_model_treelstm, self).__init__()\\r\\n self.use_leaf_rnn = args.use_leaf_rnn\\r\\n self.pooling_method = args.pool_method\\r\\n assert self.pooling_method in ['None','max','mean','attention']\\r\\n self.bidirectional = args.bidirectional\\r\\n self.device = args.device\\r\\n self.hidden_dim = args.attention_hd\\r\\n self.word_dim = encoder_input_dim\\r\\n self.pooling_method = None if self.pooling_method == 'None' else self.pooling_method\\r\\n self.treelstm = RecursiveTreeLSTMEncoder(\\r\\n self.word_dim,\\r\\n self.hidden_dim,\\r\\n self.use_leaf_rnn,\\r\\n self.pooling_method,\\r\\n self.bidirectional\\r\\n )\\r\\n self.transpose = torch.nn.Linear(self.get_dim(), 512) # same input dim with baseline:Textseg\\r\\n\\r\\n def forward(self,encoded_input):\\r\\n embeded_sentence, lengths, tree_masks, sentences_per_doc = encoded_input\\r\\n treelstm_output,_ = self.treelstm(inp=embeded_sentence, length=lengths,fixed_masks=tree_masks)\\r\\n return self.transpose(treelstm_output), sentences_per_doc\\r\\n\\r\\n def get_dim(self):\\r\\n if self.bidirectional:\\r\\n return self.word_dim * 2\\r\\n else:\\r\\n return self.word_dim\\r\\n\\r\\n def get_output_dim(self):\\r\\n return 512\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.45652174949645996,\n \"alphanum_fraction\": 0.6739130616188049,\n \"avg_line_length\": 14.333333015441895,\n \"blob_id\": \"fea65ce7dd01b9608f6a4847f027ac46b2a11c8b\",\n \"content_id\": \"8c3cb9a7a3c63069f66fa57a0be55cf2476673a8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 184,\n \"license_type\": \"no_license\",\n \"max_line_length\": 20,\n \"num_lines\": 12,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"pathlib2==2.3.5\\ntransformers==4.4.2\\nnltk==3.5\\nscipy==1.6.2\\nsegeval==2.0.11\\ngensim==3.8.3\\nnumpy==1.20.2\\ntermcolor==1.1.0\\ntorch==1.7.0\\ntqdm==4.59.0\\njsonlines==2.0.0\\nscikit_learn==0.24.1\\n\"\n },\n {\n \"alpha_fraction\": 0.6007747650146484,\n \"alphanum_fraction\": 0.6054701209068298,\n \"avg_line_length\": 38.262672424316406,\n \"blob_id\": \"5e29cec55c9889358f517b066fb96b479b3290fb\",\n \"content_id\": \"43c8a2ee8d9a1d78f67d63a53b200e5c0ccca648\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8519,\n \"license_type\": \"no_license\",\n \"max_line_length\": 139,\n \"num_lines\": 217,\n \"path\": \"/utils.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import json\\nimport logging\\nimport sys\\nimport torch\\nimport math\\nfrom collections import OrderedDict\\nimport hashlib\\nimport random\\nimport numpy as np\\nimport torch.distributed as dist\\n\\nPADDING_TOKEN = \\\"PAD\\\"\\nUNK_TOKEN = \\\"UNK\\\"\\n\\n\\ndef set_multi_gpu(args):\\n if args.local_rank != -1:\\n dist_backend = 'nccl'\\n dist.init_process_group(backend=dist_backend)\\n device = args.local_rank if args.local_rank != -1 else (\\n torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu'))\\n torch.cuda.set_device(args.local_rank)\\n return device\\n\\ndef rank_logger_info(logger, rank, info):\\n if rank in [-1, 0]:\\n logger.info(info)\\n else:\\n pass\\n\\ndef set_seed(seed):\\n random.seed(seed)\\n random.seed(seed)\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n torch.cuda.manual_seed(seed)\\n\\ndef get_logger(args):\\n return logging.getLogger(args.logger_name)\\n\\ndef compute_name(args):\\n imp_opts = [\\\"seed\\\",\\n \\\"train_data_type\\\",\\n \\\"train_bs\\\",\\\"dev_bs\\\",\\\"test_bs\\\",\\n \\\"data_frac\\\",\\\"length_filter\\\",\\\"sent_num_filter\\\",\\n \\\"etype\\\",\\\"encoder_fine_tune\\\",\\\"model_size\\\",\\\"cased\\\",\\n \\\"sr_choose\\\",\\\"tr_choose\\\",\\\"gr_choose\\\",\\n \\\"hidden_dim\\\",\\\"num_layers\\\",\\\"pool_method\\\",\\\"attention_hd\\\",\\\"use_leaf_rnn\\\",\\\"bidirectional\\\",\\n \\\"bilstm_hd\\\",\\\"bilstm_nl\\\",\\\"bilstm_bd\\\",\\n \\\"model_size\\\"]\\n opt_dict = OrderedDict()\\n # Only include important options in hash computation\\n hp_dict = vars(args)\\n for key in imp_opts:\\n val = hp_dict[key]\\n opt_dict[key] = val\\n str_repr = str(opt_dict.items())\\n hash_idx = hashlib.md5(str_repr.encode(\\\"utf-8\\\")).hexdigest()\\n pre_name = str(opt_dict[\\\"seed\\\"]) + \\\"_\\\" + opt_dict[\\\"etype\\\"]\\n if args.sr_choose == 'g_model':\\n pre_name += \\\"_\\\"\\n pre_name += opt_dict[\\\"gr_choose\\\"]\\n elif args.sr_choose == 't_model':\\n pre_name += \\\"_\\\"\\n pre_name += opt_dict[\\\"tr_choose\\\"]\\n else:\\n pre_name += \\\"_\\\"\\n pre_name += opt_dict[\\\"sr_choose\\\"]\\n if not args.infer:\\n return pre_name + \\\"_\\\" + str(hash_idx)\\n else:\\n return pre_name + \\\"_\\\" + \\\"infer\\\" + \\\"_\\\" + str(hash_idx)\\n\\ndef compute_dataset_name(args):\\n imp_opts=[\\\"train_data_type\\\",\\\"data_frac\\\",\\\"length_filter\\\",\\\"sent_num_filter\\\",\\\"etype\\\"]\\n opt_dict = OrderedDict()\\n # Only include important options in hash computation\\n hp_dict = vars(args)\\n for key in imp_opts:\\n val = hp_dict[key]\\n opt_dict[key] = val\\n str_repr = str(opt_dict.items())\\n hash_idx = hashlib.md5(str_repr.encode(\\\"utf-8\\\")).hexdigest()\\n if args.sr_choose == \\\"g_model\\\":\\n if args.gr_choose == 'DSG_SEG':\\n name = \\\"gcn\\\" + \\\"_\\\" + str(hash_idx)\\n elif args.gr_choose == 'texting':\\n name = \\\"ing\\\" + \\\"_\\\" + str(hash_idx)\\n else:\\n name = str(hash_idx)\\n return args.train_data_type + \\\"_\\\" + name\\n\\ndef compute_encoder_name(args):\\n name = args.etype\\n if args.encoder_fine_tune:\\n name += \\\"_ft\\\"\\n return name\\n\\ndef stringify_flags(options):\\n # Ignore negative boolean flags.\\n flags = {k: v for k, v in options.__dict__.items()}\\n return json.dumps(flags, indent=4, sort_keys=True)\\n\\ndef setup_logger(logger_name, filename, delete_old = False):\\n logger = logging.getLogger(logger_name)\\n logger.setLevel(logging.DEBUG)\\n stderr_handler = logging.StreamHandler(sys.stderr)\\n file_handler = logging.FileHandler(filename, mode='w') if delete_old else logging.FileHandler(filename)\\n file_handler.setLevel(logging.DEBUG)\\n stderr_handler.setLevel(logging.INFO)\\n formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\\n stderr_handler.setFormatter(formatter)\\n file_handler.setFormatter(formatter)\\n logger.addHandler(stderr_handler)\\n logger.addHandler(file_handler)\\n return logger\\n\\ndef unsort(sort_order):\\n result = [-1] * len(sort_order)\\n\\n for i, index in enumerate(sort_order):\\n result[index] = i\\n\\n return result\\n\\ndef get_collate_fn(args):\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n return ing_collate_fn\\n elif args.sr_choose == 'g_model' and args.gr_choose == 'DSG_SEG':\\n return gcn_collate_fn\\n else:\\n return collate_fn\\n\\ndef collate_fn(batch):\\n batched_data = []\\n batched_targets = []\\n paths = []\\n window_size = 1\\n before_sentence_count = int(math.ceil(float(window_size - 1) / 2))\\n after_sentence_count = window_size - before_sentence_count - 1\\n for example in batch:\\n data, targets, path = example[\\\"sentences\\\"], example[\\\"targets\\\"], example[\\\"path\\\"]\\n try:\\n max_index = len(data)\\n tensored_data = []\\n for curr_sentence_index in range(0, len(data)):\\n from_index = max([0, curr_sentence_index - before_sentence_count])\\n to_index = min([curr_sentence_index + after_sentence_count + 1, max_index])\\n sentences_window = [word for sentence in data[from_index:to_index] for word in sentence]\\n tensored_data.append(torch.tensor(sentences_window))\\n tensored_targets = torch.tensor(targets).long()\\n # tensored_targets = tensored_targets[:-1] # removed in wiki_loader.py\\n batched_data.append(tensored_data)\\n batched_targets.append(tensored_targets)\\n paths.append(path)\\n except Exception as e:\\n logger.info('Exception \\\"%s\\\" in file: \\\"%s\\\"', e, path)\\n logger.debug('Exception!', exc_info=True)\\n continue\\n return batched_data, batched_targets, paths\\n\\ndef ing_collate_fn(batch):\\n batched_data = []\\n batched_targets = []\\n paths = []\\n window_size = 1\\n before_sentence_count = int(math.ceil(float(window_size - 1) / 2))\\n after_sentence_count = window_size - before_sentence_count - 1\\n # batch: [[[tensor(sent_num1,), tensor(sent_num1,sent_num1)], ...], [doc2], [doc3], []]\\n for example in batch:\\n data, adjs, targets, path = example[\\\"sentences\\\"],example[\\\"adjs\\\"],example[\\\"targets\\\"], example[\\\"path\\\"]\\n try:\\n max_index = len(data)\\n tensored_data = []\\n for curr_sentence_index in range(0, len(data)):\\n from_index = max([0, curr_sentence_index - before_sentence_count])\\n to_index = min([curr_sentence_index + after_sentence_count + 1, max_index])\\n sentences_window = [word for sentence in data[from_index:to_index] for word in sentence]\\n adj_window = adjs[from_index:to_index][0] # fault window size = 1\\n tensored_data.append([torch.tensor(sentences_window), torch.tensor(adj_window).float()])\\n tensored_targets = torch.tensor(targets).long()\\n batched_data.append(tensored_data)\\n batched_targets.append(tensored_targets)\\n paths.append(path)\\n except Exception as e:\\n logger.info('Exception \\\"%s\\\" in file: \\\"%s\\\"', e, path)\\n logger.debug('Exception!', exc_info=True)\\n continue\\n return batched_data, batched_targets, paths\\n\\ndef gcn_collate_fn(batch):\\n batched_data = [] # [[sentences, feature, adj], [], [], [],]\\n batched_targets = []\\n paths = []\\n window_size = 1\\n before_sentence_count = int(math.ceil(float(window_size - 1) / 2))\\n after_sentence_count = window_size - before_sentence_count - 1\\n for example in batch:\\n sentences, feature, adjs, targets, path = example[\\\"sentences\\\"],example[\\\"feature\\\"],example[\\\"adj\\\"],example[\\\"targets\\\"],example[\\\"path\\\"]\\n try:\\n max_index = len(sentences)\\n sentences_lst = []\\n for curr_sentence_index in range(0, len(sentences)):\\n from_index = max([0, curr_sentence_index - before_sentence_count])\\n to_index = min([curr_sentence_index + after_sentence_count + 1, max_index])\\n sentences_window = [word for sentence in sentences[from_index:to_index] for word in sentence]\\n sentences_lst.append(sentences_window)\\n doc_lst = [sentences_lst, torch.tensor(feature), torch.tensor(adjs.A).float()]\\n batched_data.append(doc_lst)\\n tensored_targets = torch.tensor(targets).long()\\n batched_targets.append(tensored_targets)\\n paths.append(path)\\n except Exception as e:\\n logger.info('Exception \\\"%s\\\" in file: \\\"%s\\\"', e, path)\\n logger.debug('Exception!', exc_info=True)\\n continue\\n return batched_data, batched_targets, paths\"\n },\n {\n \"alpha_fraction\": 0.5531328320503235,\n \"alphanum_fraction\": 0.5556390881538391,\n \"avg_line_length\": 34.62385177612305,\n \"blob_id\": \"815edc63bb2c5cafed38ddb0cb5253c0f8ae2c9e\",\n \"content_id\": \"dfbc2c6912e1313b782463cafb76c73363792f17\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3990,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 109,\n \"path\": \"/data/all_data_loader.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\r\\nfrom data.wikielements_loader import WikiElementsDataSet\\r\\nfrom data.wikicities_loader import WikiCitiesDataSet\\r\\nfrom data.wiki50_loader import Wiki50DataSet\\r\\nfrom data.wikisection_loader import WikiSectioniDataSet\\r\\nfrom data.clinical_loader import ClinicalDataSet\\r\\nfrom data.manifesto_loader import ManifestoDataSet\\r\\nfrom data.choi_loader import ChoiDataSet\\r\\nfrom data.wiki_loader import WikipediaDataSet\\r\\ndef get_loader(args, encoder, data_type,split=None):\\r\\n if data_type == \\\"wikielements\\\":\\r\\n assert split == None\\r\\n dataset = WikiElementsDataSet(\\r\\n root = args.wikielements_path,\\r\\n args = args,\\r\\n encoder = encoder,\\r\\n length_filter = args.length_filter,\\r\\n sent_num_filter = args.sent_num_filter,\\r\\n local_rank = args.local_rank\\r\\n )\\r\\n elif data_type == \\\"wikicities\\\":\\r\\n assert split == None\\r\\n dataset = WikiCitiesDataSet(\\r\\n root = args.wikicities_path,\\r\\n args = args,\\r\\n encoder = encoder,\\r\\n length_filter = args.length_filter,\\r\\n sent_num_filter = args.sent_num_filter,\\r\\n local_rank = args.local_rank\\r\\n )\\r\\n elif data_type == \\\"wiki50\\\":\\r\\n assert split == None\\r\\n dataset = Wiki50DataSet(\\r\\n root = args.wiki50_path,\\r\\n args = args,\\r\\n encoder = encoder,\\r\\n length_filter = args.length_filter,\\r\\n sent_num_filter = args.sent_num_filter,\\r\\n local_rank = args.local_rank\\r\\n )\\r\\n elif data_type == \\\"wikisection\\\":\\r\\n assert split in ['train','dev','test']\\r\\n split = \\\"validation\\\" if split == 'dev' else split\\r\\n dataset = WikiSectioniDataSet(\\r\\n root = args.wikisection_path,\\r\\n args = args,\\r\\n encoder = encoder,\\r\\n split = split,\\r\\n length_filter = args.length_filter,\\r\\n sent_num_filter = args.sent_num_filter,\\r\\n local_rank = args.local_rank\\r\\n )\\r\\n elif data_type == \\\"clinical\\\":\\r\\n assert split == None\\r\\n dataset = ClinicalDataSet(\\r\\n root = args.clinical_path,\\r\\n args = args,\\r\\n encoder = encoder,\\r\\n length_filter = args.length_filter,\\r\\n sent_num_filter = args.sent_num_filter,\\r\\n local_rank = args.local_rank\\r\\n )\\r\\n elif data_type == \\\"manifesto\\\":\\r\\n assert split == None\\r\\n dataset = ManifestoDataSet(\\r\\n root = args.manifesto_path,\\r\\n args = args,\\r\\n encoder = encoder,\\r\\n length_filter = args.length_filter,\\r\\n sent_num_filter = args.sent_num_filter,\\r\\n local_rank = args.local_rank\\r\\n )\\r\\n elif data_type == \\\"choi\\\":\\r\\n assert split in [None, 'train', 'dev', 'test']\\r\\n dataset = ChoiDataSet(\\r\\n root = args.choi_path,\\r\\n args = args,\\r\\n encoder = encoder,\\r\\n split=split,\\r\\n length_filter = args.length_filter,\\r\\n sent_num_filter = args.sent_num_filter,\\r\\n local_rank = args.local_rank\\r\\n )\\r\\n elif data_type == \\\"wiki\\\":\\r\\n assert split in ['train', 'dev', 'test']\\r\\n dataset = WikipediaDataSet(\\r\\n args = args,\\r\\n root = args.wiki_path,\\r\\n split = split,\\r\\n encoder = encoder,\\r\\n length_filter = args.length_filter,\\r\\n sent_num_filter = args.sent_num_filter,\\r\\n data_frac = args.data_frac,\\r\\n high_granularity=args.high_granularity,\\r\\n local_rank=args.local_rank\\r\\n )\\r\\n else:\\r\\n print(f\\\"{data_type} is false data type\\\")\\r\\n return None\\r\\n return dataset\\r\\n\\r\\n\\r\\nif __name__ == \\\"__main__\\\":\\r\\n from parameters import *\\r\\n args = create_parser()\\r\\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\\r\\n data_type = \\\"choi\\\"\\r\\n split = None\\r\\n dataset = get_loader(args, None, data_type,split=split)\"\n },\n {\n \"alpha_fraction\": 0.5830441117286682,\n \"alphanum_fraction\": 0.5904809236526489,\n \"avg_line_length\": 42.86666488647461,\n \"blob_id\": \"34503de3bd8505842828ad4a362fe35da45626b4\",\n \"content_id\": \"5cba776fbaa5a8a7ccec54fc766fe81cb3ee026b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2017,\n \"license_type\": \"no_license\",\n \"max_line_length\": 112,\n \"num_lines\": 45,\n \"path\": \"/models/latent_sr.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\r\\nimport torch\\r\\nfrom sklearn.decomposition import PCA\\r\\n\\r\\nclass l_model(nn.Module):\\r\\n def __init__(self, args):\\r\\n super(l_model, self).__init__()\\r\\n self.args = args\\r\\n self.device = self.args.device\\r\\n self.output_dim = 300\\r\\n self.pca = PCA(n_components = self.output_dim)\\r\\n\\r\\n # def pad(self, s, max_length):\\r\\n # pad_lst = [self.encoder.model.pad_token_id] * (max_length - s.size(0))\\r\\n # s = torch.LongTensor(s.tolist() + pad_lst).to(self.device)\\r\\n # return s.unsqueeze(0) # (1, max_len)\\r\\n\\r\\n def pca_function(self, sent_repr):\\r\\n sents_num = sent_repr.size(0) # (y, dim)\\r\\n min_samples_num = self.output_dim + 1\\r\\n if sents_num < min_samples_num:\\r\\n pad_tensor = torch.zeros((min_samples_num - sents_num, sent_repr.size(1))).to(self.device) # (x,dim)\\r\\n sent_repr = torch.cat([sent_repr, pad_tensor], dim=0)\\r\\n sent_repr = sent_repr.cpu()\\r\\n pca_out = self.pca.fit_transform(sent_repr)[:sents_num]\\r\\n return torch.tensor(pca_out).to(self.device)\\r\\n\\r\\n def forward(self, encoded_input):\\r\\n # sentences_per_doc = []\\r\\n # all_batch_sentences = []\\r\\n # for document in batch:\\r\\n # all_batch_sentences.extend(document)\\r\\n # sentences_per_doc.append(len(document))\\r\\n # lengths = [s.size()[0] for s in all_batch_sentences]\\r\\n # max_length = max(lengths)\\r\\n # padded_sentences = [self.pad(s, max_length) for s in all_batch_sentences]\\r\\n # big_tensor = torch.cat(padded_sentences, 0) # (batch size, max_length)\\r\\n # embeded_big_tensor = self.encoder(big_tensor) # (max_length, batch size, dim)\\r\\n embeded_big_tensor,sentences_per_doc = encoded_input\\r\\n sent_repr = torch.sum(embeded_big_tensor, dim=1) # max_length, dim\\r\\n sent_repr[:, 0] = 0 # max_length, dim\\r\\n return self.pca_function(sent_repr).float(), sentences_per_doc\\r\\n\\r\\n def get_output_dim(self):\\r\\n return self.output_dim\"\n },\n {\n \"alpha_fraction\": 0.5346283912658691,\n \"alphanum_fraction\": 0.5380067825317383,\n \"avg_line_length\": 38.89655303955078,\n \"blob_id\": \"00a5caa4f193c9e5e5c094d1b29b70539ea54395\",\n \"content_id\": \"1966ee651850d8629997e2a0b01a871763421a85\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1184,\n \"license_type\": \"no_license\",\n \"max_line_length\": 95,\n \"num_lines\": 29,\n \"path\": \"/models/DSG_SEG.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\r\\nimport torch\\r\\nfrom models.GGNN.layers import *\\r\\n\\r\\nclass g_model_DSG_SEG(nn.Module):\\r\\n def __init__(self,encoder_input_dim,args):\\r\\n super(g_model_DSG_SEG,self).__init__()\\r\\n self.input_dim = encoder_input_dim\\r\\n self.output_dim = args.DSG_SEG_output_dim\\r\\n self.device = args.device\\r\\n self.layers = []\\r\\n self.graph_layer = GraphLayer(args,\\r\\n input_dim=self.input_dim,\\r\\n output_dim=self.output_dim,\\r\\n act=nn.Tanh(),\\r\\n dropout_p=0.3)\\r\\n\\r\\n def forward(self,encoded_input):\\r\\n doc_feature, adj, mask, sentences_per_doc = encoded_input\\r\\n batch_size = doc_feature.shape[0]\\r\\n graph_layer_output = self.graph_layer(doc_feature, adj, mask) # bs, max_node_size , dim\\r\\n sent_repr = []\\r\\n for i in range(batch_size):\\r\\n sent_repr.append(graph_layer_output[i, :sentences_per_doc[i], :])\\r\\n sent_repr = torch.cat(sent_repr,0)\\r\\n return sent_repr, sentences_per_doc\\r\\n\\r\\n def get_output_dim(self):\\r\\n return self.output_dim\"\n },\n {\n \"alpha_fraction\": 0.5215619206428528,\n \"alphanum_fraction\": 0.5395838022232056,\n \"avg_line_length\": 42.39047622680664,\n \"blob_id\": \"4cdd3d72eadc7f88beb21362fd76c5c64bcb0d4d\",\n \"content_id\": \"a292d92cc26464d6e335c0b31c284e5de8cb4ff1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4665,\n \"license_type\": \"no_license\",\n \"max_line_length\": 136,\n \"num_lines\": 105,\n \"path\": \"/judgement.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import json\\r\\nfrom pathlib2 import Path\\r\\nfrom data.wiki_loader import get_sections\\r\\nimport wiki_utils\\r\\nimport os\\r\\nfrom text_manipulation import extract_sentence_words\\r\\nimport torch\\r\\nclass wiki_document:\\r\\n def __init__(self, wiki_path, bert_path, ING_GCN_path):\\r\\n self.wiki_path = wiki_path\\r\\n self.bert_path = bert_path\\r\\n self.ING_GCN_path = ING_GCN_path\\r\\n self.document_dict = {} # {\\\"path1\\\":{\\\"golden\\\":[...,\\\"====\\\",...,\\\"====\\\"],\\\"DSG_SEG\\\":[同],\\\"bert\\\":[同]}, \\\"path2\\\":...}\\r\\n self.get_golden_result()\\r\\n self.get_bert_result()\\r\\n self.get_ING_GCN_result()\\r\\n\\r\\n def get_golden_result(self):\\r\\n self.paths = Path(self.wiki_path).read_text().splitlines()\\r\\n for path in self.paths:\\r\\n data, targets = self.read_wiki_file(path)\\r\\n self.document_dict[path] = {} # {'/p300/wiki/wiki_727/test/58/46/96/5846960':{}}\\r\\n self.document_dict[path][\\\"data\\\"] = data\\r\\n self.document_dict[path][\\\"targets\\\"] = targets\\r\\n self.document_dict[path][\\\"document_len\\\"] = len(data)\\r\\n self.document_dict[path][\\\"seg_num\\\"] = sum(targets)\\r\\n self.document_dict[path][\\\"golden\\\"] = [] # # {'/p300/wiki/wiki_727/test/58/46/96/5846960':{'golden':[]}}\\r\\n for idx,sentence in enumerate(data):\\r\\n self.document_dict[path][\\\"golden\\\"].append(sentence)\\r\\n if targets[idx] == 1:\\r\\n self.document_dict[path][\\\"golden\\\"].append(\\\"=\\\"*10)\\r\\n\\r\\n\\r\\n def get_bert_result(self):\\r\\n self.get_result(self.bert_path)\\r\\n\\r\\n def get_ING_GCN_result(self):\\r\\n self.get_result(self.ING_GCN_path)\\r\\n\\r\\n def get_result(self, path):\\r\\n model_name = None\\r\\n if \\\"bert\\\" in path:\\r\\n model_name = \\\"bert\\\"\\r\\n elif \\\"DSG_SEG\\\" in path:\\r\\n model_name = \\\"DSG_SEG\\\"\\r\\n with open(path, \\\"rb\\\") as f:\\r\\n result_dict = json.load(f)\\r\\n for path in self.paths:\\r\\n self.document_dict[path][model_name] = []\\r\\n pk = float(result_dict[path][\\\"pk\\\"]) # ok\\r\\n self.document_dict[path][\\\"{}_pk\\\".format(model_name)] = pk\\r\\n pred = result_dict[path][\\\"pred\\\"] # pred segments\\r\\n pred_list = pred.split(\\\" \\\")\\r\\n for idx, one_pred in enumerate(pred_list[:-2]):\\r\\n if one_pred == \\\"|\\\":\\r\\n self.document_dict[path][model_name].append(\\\"=\\\"*10)\\r\\n else:\\r\\n self.document_dict[path][model_name].append(self.document_dict[path][\\\"data\\\"][int(one_pred)])\\r\\n\\r\\n\\r\\n\\r\\n def read_wiki_file(self, path,\\r\\n high_granularity=False,\\r\\n remove_preface_segment=True,\\r\\n ignore_list=True,\\r\\n remove_special_tokens=True):\\r\\n data = []\\r\\n targets = []\\r\\n all_sections = get_sections(path, high_granularity)\\r\\n required_sections = all_sections[1:] if remove_preface_segment and len(all_sections) > 0 else all_sections\\r\\n required_non_empty_sections = [section for section in required_sections if len(section) > 0 and section != \\\"\\\\n\\\"]\\r\\n for section in required_non_empty_sections:\\r\\n sentences = section.split('\\\\n')\\r\\n if sentences:\\r\\n targets_flag = 0\\r\\n for sentence in sentences:\\r\\n is_list_sentence = wiki_utils.get_list_token() + \\\".\\\" == str(sentence)\\r\\n if ignore_list and is_list_sentence:\\r\\n continue\\r\\n sentence_words = extract_sentence_words(sentence, remove_special_tokens=remove_special_tokens)\\r\\n if (len(sentence_words) <= 1):\\r\\n continue\\r\\n data.append(\\\" \\\".join(sentence_words))\\r\\n targets_flag += 1\\r\\n if data:\\r\\n targets.extend([0] * (targets_flag - 1))\\r\\n if targets_flag != 0:\\r\\n targets.append(1) # segment\\r\\n data = data[:-1]\\r\\n targets = targets[:-1]\\r\\n return data, targets\\r\\n\\r\\n\\r\\nif __name__ == \\\"__main__\\\":\\r\\n os.makedirs(r\\\"/root/DSG-SEG/sampleanalysis\\\",exist_ok=True)\\r\\n saved_path = r\\\"/root/textsegment/sampleanalysis/WIKI_DOCUMENT.pkl\\\"\\r\\n if os.path.exists(saved_path):\\r\\n WIKI_DOCUMENT = torch.load(saved_path)\\r\\n else:\\r\\n WIKI_DOCUMENT = wiki_document(\\r\\n wiki_path = r\\\"/p300/wiki/wiki_727/test/random_paths\\\",\\r\\n bert_path= r\\\"/root/textsegment/checkpoints/bert/0_wiki_bert_b_model_9fa59240ac158d3ae21c0dcf25da8e81/wiki_best_result.json\\\",\\r\\n ING_GCN_path = r\\\"...\\\",\\r\\n )\\r\\n torch.save(WIKI_DOCUMENT, saved_path)\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5601013898849487,\n \"alphanum_fraction\": 0.5738595128059387,\n \"avg_line_length\": 35.84000015258789,\n \"blob_id\": \"15b7c8942e779394805e6af3f1133b4520fb7521\",\n \"content_id\": \"3964776be31bcad1f0bab6f73dea819c18b5152b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2762,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 75,\n \"path\": \"/encoder/glove_encoder.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\nimport torch\\nfrom utils import *\\nfrom collections import OrderedDict\\nimport numpy as np\\nclass glove_model(nn.Module):\\n\\n def __init__(self, vocab_path, args, encoder_fine_tune):\\n super(glove_model,self).__init__()\\n self.vocab_path = vocab_path\\n self.args = args\\n self.encoder_fine_tune = encoder_fine_tune\\n self.local_rank = args.local_rank\\n self.PADDING_TOKEN = \\\"<PAD>\\\"\\n self.UNK_TOKEN = \\\"<UNK>\\\"\\n self.embedding, self.word2idx = self.get_glove_embedding()\\n self.pad_token_id = self.word2idx[self.PADDING_TOKEN]\\n self.idx2word = OrderedDict()\\n for item in self.word2idx.items():\\n self.idx2word[item[1]] = item[0]\\n self.embedding = nn.Embedding.from_pretrained(self.embedding)\\n if encoder_fine_tune:\\n for param in self.parameters():\\n param.requires_grad = True\\n else:\\n for param in self.parameters():\\n param.requires_grad = False\\n\\n def get_glove_embedding(self):\\n size = 300\\n logger = get_logger(self.args)\\n rank_logger_info(logger, self.local_rank, 'Reading glove Vocab.')\\n glove_word2idx = OrderedDict()\\n glove_word2idx[self.PADDING_TOKEN] = len(glove_word2idx)\\n glove_word2idx[self.UNK_TOKEN] = len(glove_word2idx)\\n\\n with open(self.vocab_path, encoding='utf-8') as f:\\n for i, line in enumerate(f):\\n word, vec = line.split(' ', 1)\\n if word in glove_word2idx:\\n continue\\n else:\\n glove_word2idx[word] = len(glove_word2idx)\\n\\n embeddings = np.zeros((len(glove_word2idx), size), dtype=np.float32)\\n embeddings[0] = np.random.randn(1, size)\\n embeddings[1] = np.random.randn(1, size)\\n rank_logger_info(logger, self.local_rank, 'Reading glove Embeddings.')\\n with open(self.vocab_path, encoding='utf-8') as f:\\n for i,line in enumerate(f):\\n word, vec = line.strip().split(' ', 1)\\n if word not in glove_word2idx:\\n continue\\n word_id = glove_word2idx[word]\\n vec = np.fromstring(vec, dtype=float, sep=' ')\\n embeddings[word_id] = vec\\n\\n return torch.tensor(embeddings), glove_word2idx\\n\\n def tokenize(self, inp):\\n word_lst = inp.split()\\n idx_lst = []\\n for word in word_lst:\\n if word in self.word2idx:\\n idx_lst.append(self.word2idx[word])\\n else:\\n idx_lst.append(self.word2idx[self.UNK_TOKEN])\\n return idx_lst\\n\\n\\n def forward(self,inp):\\n return self.embedding(inp)\\n\\n def get_word_dim(self):\\n return 300\"\n },\n {\n \"alpha_fraction\": 0.4989272952079773,\n \"alphanum_fraction\": 0.5073897242546082,\n \"avg_line_length\": 53.893333435058594,\n \"blob_id\": \"3b9d2ea91963a5a419020cc48996635cd0ae475f\",\n \"content_id\": \"b1958ebd6d5be7d3b3f956e3962bab3da6abed2b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8390,\n \"license_type\": \"no_license\",\n \"max_line_length\": 139,\n \"num_lines\": 150,\n \"path\": \"/test.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from tqdm import tqdm\\r\\nimport torch\\r\\nimport json\\r\\nfrom evaluation_utils import *\\r\\nfrom utils import *\\r\\nimport torch.nn.functional as F\\r\\nfrom termcolor import colored\\r\\nimport random\\r\\npreds_stats = predictions_analysis()\\r\\ndef test(model,encode_model, args, dataset,threshold,local_rank):\\r\\n logger = get_logger(args)\\r\\n device = args.device\\r\\n global output_seg, current_idx\\r\\n model.eval()\\r\\n with tqdm(desc='Testing', total=len(dataset)) as pbar:\\r\\n acc = Accuracy() # accuracies class is not needed\\r\\n document_result = {}\\r\\n for i, (data, target, paths) in enumerate(dataset):\\r\\n if True:\\r\\n if i == args.stop_after:\\r\\n break\\r\\n pbar.update()\\r\\n with torch.no_grad():\\r\\n encoded_input = encode_model(data)\\r\\n output = model(encoded_input)\\r\\n output_seg = output.data.cpu().numpy().argmax(axis=1)\\r\\n targets_var = torch.cat(target, 0).to(device).clone().detach().requires_grad_(False)\\r\\n target_seg = targets_var.data.cpu().numpy()\\r\\n preds_stats.add(output_seg, target_seg)\\r\\n output_softmax = F.softmax(output, 1)\\r\\n current_idx = 0\\r\\n for k, t in enumerate(target):\\r\\n path = paths[k]\\r\\n document_sentence_count = len(t)\\r\\n to_idx = int(current_idx + document_sentence_count)\\r\\n output = ((output_softmax.data.cpu().numpy()[current_idx: to_idx, :])[:, 1] > threshold).tolist()\\r\\n if \\\"_|_\\\" not in path: # whole document\\r\\n document_result[path] = [[output,t]] # [[[1,0,1,0],[1,0,1,0]],....]\\r\\n else: # filtered document\\r\\n path,num = path.split(\\\"_|_\\\")\\r\\n if path not in document_result:\\r\\n document_result[path] = [[num,[output,t]]]\\r\\n else:\\r\\n document_result[path].append([num,[output,t]])\\r\\n current_idx = to_idx\\r\\n # concat\\r\\n for k in document_result:\\r\\n if type(document_result[k][0][0]) == str:\\r\\n splited_dic_item = document_result[k]\\r\\n complete_dic_item = []\\r\\n sorted_splited_dic_item = sorted(splited_dic_item, key=lambda x: int(x[0]))\\r\\n for item in sorted_splited_dic_item:\\r\\n if len(complete_dic_item) == 0:\\r\\n complete_dic_item = [item[1][0], [item[1][1]]]\\r\\n else:\\r\\n complete_dic_item[0].extend(item[1][0])\\r\\n complete_dic_item[1].append(item[1][1])\\r\\n\\r\\n document_result[k] = complete_dic_item\\r\\n document_result[k][1] = torch.cat(document_result[k][1], 0)\\r\\n else:\\r\\n document_result[k] = [document_result[k][0][0], document_result[k][0][1]] #[[output],[target]]\\r\\n document_result[k][0] = np.append(document_result[k][0],[1])\\r\\n document_result[k][1] = np.append(document_result[k][1],[1])\\r\\n for k in document_result:\\r\\n acc.update(document_result[k][0], document_result[k][1], k)\\r\\n epoch_pk, epoch_windiff, epoch_b, epoch_s = acc.calc_accuracy()\\r\\n rank_logger_info(logger, local_rank, colored('accuracy: {:.4}, Pk: {:.4}, Windiff: {:.4}, B: {:.4}, S: {:.4} ,F1: {:.4} . '.format(\\r\\n preds_stats.get_accuracy(),\\r\\n epoch_pk,\\r\\n epoch_windiff,\\r\\n epoch_b,\\r\\n epoch_s,\\r\\n preds_stats.get_f1()),\\\"red\\\"))\\r\\n preds_stats.reset()\\r\\n epoch_result = acc.all_test_result\\r\\n return epoch_pk, epoch_result\\r\\n\\r\\ndef test_all(model, encode_model, args, test_dl_dict, threshold, local_rank):\\r\\n logger = get_logger(args)\\r\\n pk_result = {}\\r\\n for test_data_type in test_dl_dict:\\r\\n test_dl = test_dl_dict[test_data_type]\\r\\n rank_logger_info(logger, local_rank, f\\\"Testing on {test_data_type}...\\\")\\r\\n test_pk, test_result = test(model,encode_model, args, test_dl, threshold, local_rank)\\r\\n pk_result[test_data_type] = test_pk\\r\\n if local_rank in [-1, 0]:\\r\\n with open(args.checkpoint_dir / f\\\"{test_data_type}_best_result.json\\\", \\\"w\\\") as f:\\r\\n json.dump(test_result, f)\\r\\n flags = {k: v for k, v in pk_result.items()}\\r\\n rank_logger_info(logger, local_rank, json.dumps(flags, indent=4, sort_keys=True))\\r\\n\\r\\n\\r\\ndef test_all_random_baseline(args, test_dl_dict, local_rank):\\r\\n logger = get_logger(args)\\r\\n pk_result = {}\\r\\n avg_sent_num_per_seg = {} # all_sent_num / all_seg_num\\r\\n avg_sent_num_per_doc = {} # all_sent_num / all_doc_num\\r\\n threshold_dict = {}\\r\\n avg_seg_num_per_doc = {} # all_seg_num / all_doc_num\\r\\n avg_sentlength_per_sent = {} # all_sent_length / all_sent_num\\r\\n for test_data_type in test_dl_dict:\\r\\n all_doc_num = 0\\r\\n all_sent_num = 0\\r\\n all_seg_num = 0\\r\\n all_sent_length = 0\\r\\n test_dl = test_dl_dict[test_data_type]\\r\\n rank_logger_info(logger, local_rank, f\\\"Getting threshold on {test_data_type}...\\\")\\r\\n for i, (data, target, paths) in enumerate(test_dl): # batch size = 1\\r\\n all_doc_num += len(target)\\r\\n for idx,t in enumerate(target):\\r\\n all_sent_num += len(t)\\r\\n all_seg_num += torch.sum(t).item()\\r\\n for sent in data[idx]:\\r\\n all_sent_length += len(sent)\\r\\n avg_sent_num_per_seg[test_data_type] = all_sent_num / all_seg_num\\r\\n threshold_dict[test_data_type] = 1-1/avg_sent_num_per_seg[test_data_type]\\r\\n avg_seg_num_per_doc[test_data_type] = all_seg_num / all_doc_num\\r\\n avg_sent_num_per_doc[test_data_type] = all_sent_num / all_doc_num\\r\\n avg_sentlength_per_sent[test_data_type] = all_sent_length / all_sent_num\\r\\n rank_logger_info(logger, local_rank, f\\\"avg_sent_num_per_seg:\\\")\\r\\n rank_logger_info(logger, local_rank, json.dumps(avg_sent_num_per_seg, indent=4, sort_keys=True))\\r\\n rank_logger_info(logger, local_rank, f\\\"avg_seg_num_per_doc:\\\")\\r\\n rank_logger_info(logger, local_rank, json.dumps(avg_sent_num_per_doc, indent=4, sort_keys=True))\\r\\n rank_logger_info(logger, local_rank, f\\\"avg_sent_num_per_doc:\\\")\\r\\n rank_logger_info(logger, local_rank, json.dumps(avg_seg_num_per_doc, indent=4, sort_keys=True))\\r\\n rank_logger_info(logger, local_rank, f\\\"avg_sentlength_per_sent:\\\")\\r\\n rank_logger_info(logger, local_rank, json.dumps(avg_sentlength_per_sent, indent=4, sort_keys=True))\\r\\n rank_logger_info(logger, local_rank, f\\\"threshold:\\\")\\r\\n rank_logger_info(logger, local_rank, json.dumps(threshold_dict, indent=4, sort_keys=True))\\r\\n for test_data_type in test_dl_dict:\\r\\n rank_logger_info(logger, local_rank, f\\\"Getting random baseline result on {test_data_type}...\\\")\\r\\n threshold = threshold_dict[test_data_type]\\r\\n acc = Accuracy()\\r\\n test_dl = test_dl_dict[test_data_type]\\r\\n for i, (data, target, paths) in enumerate(test_dl): # batch size = 1\\r\\n for idx,t in enumerate(target):\\r\\n h = []\\r\\n for j in range(len(t)):\\r\\n random_seed = random.random()\\r\\n if random_seed > threshold:\\r\\n h.append(1)\\r\\n else:\\r\\n h.append(0)\\r\\n gold = np.append(t, [1])\\r\\n h = np.append(h,[1])\\r\\n acc.update(h, gold, paths[idx])\\r\\n epoch_pk, epoch_windiff, epoch_b, epoch_s = acc.calc_accuracy()\\r\\n pk_result[test_data_type] = epoch_pk\\r\\n rank_logger_info(logger, local_rank, json.dumps(pk_result, indent=4, sort_keys=True))\\r\\n\\r\\n\\r\\n\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5651775598526001,\n \"alphanum_fraction\": 0.571363091468811,\n \"avg_line_length\": 41.79901885986328,\n \"blob_id\": \"d975309ca4f1e558b423d4b9e2f40588d600cff9\",\n \"content_id\": \"1cc6a18097dc74794ad60f182f78087d88bc8c75\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8730,\n \"license_type\": \"no_license\",\n \"max_line_length\": 206,\n \"num_lines\": 204,\n \"path\": \"/data/wiki_loader.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from torch.utils.data import Dataset\\nfrom pathlib2 import Path\\nimport re\\nimport random\\nfrom utils import *\\nfrom data.data_utils import *\\nfrom tqdm import tqdm\\nsection_delimiter = \\\"========\\\"\\n\\ndef get_files(path):\\n all_objects = Path(path).glob('**/*')\\n files = [str(p) for p in all_objects if p.is_file()]\\n return files\\n\\n\\ndef get_cache_path(wiki_folder):\\n cache_file_path = wiki_folder / 'paths_cache'\\n return cache_file_path\\n\\n\\ndef cache_wiki_filenames(wiki_folder):\\n files = Path(wiki_folder).glob('*/*/*/*')\\n cache_file_path = get_cache_path(wiki_folder)\\n\\n with cache_file_path.open('w+') as f:\\n for file in files:\\n f.write(str(file) + u'\\\\n')\\n #f.write(unicode(file) + u'\\\\n')\\n\\n\\ndef clean_section(section):\\n cleaned_section = section.strip('\\\\n')\\n return cleaned_section\\n\\n\\n\\ndef get_scections_from_text(txt, high_granularity=True):\\n sections_to_keep_pattern = get_seperator_foramt() if high_granularity else get_seperator_foramt(\\n (1, 2))\\n if not high_granularity:\\n # if low granularity required we should flatten segments within segemnt level 2\\n pattern_to_ommit = get_seperator_foramt((3, 999))\\n txt = re.sub(pattern_to_ommit, \\\"\\\", txt)\\n\\n #delete empty lines after re.sub()\\n sentences = [s for s in txt.strip().split(\\\"\\\\n\\\") if len(s) > 0 and s != \\\"\\\\n\\\"]\\n txt = '\\\\n'.join(sentences).strip('\\\\n')\\n\\n\\n all_sections = re.split(sections_to_keep_pattern, txt)\\n non_empty_sections = [s for s in all_sections if len(s) > 0]\\n\\n return non_empty_sections\\n\\n\\n\\ndef get_sections(path, high_granularity=True):\\n file = open(str(path), \\\"r\\\")\\n raw_content = file.read()\\n file.close()\\n clean_txt = raw_content.encode('utf-8').decode('utf-8').strip()\\n sections = [clean_section(s) for s in get_scections_from_text(clean_txt, high_granularity)]\\n return sections\\n\\ndef read_wiki_file(args,length_filter, filtered_num, all_num,filtered_document_num, all_document_num, sent_num_filter,\\n path, logger,encoder = None, remove_preface_segment=True, ignore_list=False, remove_special_tokens=False,\\n return_as_sentences=False, high_granularity=True, only_letters=False):\\n data = []\\n adjs = []\\n targets = []\\n all_sections = get_sections(path, high_granularity)\\n required_sections = all_sections[1:] if remove_preface_segment and len(all_sections) > 0 else all_sections\\n required_non_empty_sections = [section for section in required_sections if len(section) > 0 and section != \\\"\\\\n\\\"]\\n for section in required_non_empty_sections:\\n sentences = section.split('\\\\n')\\n if sentences:\\n targets_flag = 0\\n for sentence in sentences:\\n is_list_sentence = get_list_token() + \\\".\\\" == str(sentence)\\n if ignore_list and is_list_sentence:\\n continue\\n if not return_as_sentences:\\n sentence_words = extract_sentence_words(sentence, remove_special_tokens=remove_special_tokens)\\n if (len(sentence_words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\\n continue\\n if len(sentence_words) > length_filter:\\n sentence_words = sentence_words[:length_filter]\\n filtered_num += 1\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n sentence_words, adj = build_ing_graph(sentence_words, args)\\n adjs.append(adj)\\n if encoder:\\n data.append(encoder.tokenize(sentence_words))\\n else:\\n data.append(sentence_words)\\n targets_flag += 1\\n else: # for the annotation. keep sentence as is.\\n if (only_letters):\\n sentence = re.sub('[^a-zA-Z0-9 ]+', '', sentence)\\n data.append(sentence)\\n else:\\n data.append(sentence)\\n\\n if data:\\n all_num += targets_flag\\n targets.extend([0]*(targets_flag-1))\\n if targets_flag != 0:\\n targets.append(1) # segment\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(data, adjs, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n return_dict = generate_gcn_return_dict(return_dict,args)\\n else:\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n return return_dict,filtered_num, all_num,filtered_document_num, all_document_num\\n\\ndef get_random_files(list,split):\\n lenth = len(list)\\n rand_list = []\\n if split == \\\"train\\\":\\n num =8000\\n else:\\n num =1000\\n i = 0\\n jlist = []\\n while i<num:\\n j = random.randint(0,lenth)\\n if j not in jlist:\\n file = list[j]\\n rand_list.append(file)\\n i+=1\\n jlist.append(j)\\n return rand_list\\n\\ndef get_random_file_path(wiki_folder):\\n random_file_path = wiki_folder / 'random_paths'\\n return random_file_path\\n\\nclass WikipediaDataSet(Dataset):\\n def __init__(self,\\n args = None,\\n root = None,\\n split = None,\\n encoder = None,\\n length_filter = 50,\\n sent_num_filter = 30,\\n data_frac=1.0,\\n high_granularity=False,\\n remove_preface_segment=True,\\n ignore_list=True,\\n remove_special_tokens=True,\\n return_as_sentences=False,\\n only_letters=False,\\n local_rank=-1):\\n logger = get_logger(args)\\n root = root + \\\"/\\\" + split\\n root_path = Path(root)\\n cache_path = get_cache_path(root_path)\\n if not cache_path.exists():\\n cache_wiki_filenames(root_path)\\n random_path = get_random_file_path(root_path)\\n if not random_path.exists():\\n self.textfiles = get_random_files(cache_path.read_text().splitlines(), split)\\n with random_path.open('w+') as f:\\n for file in self.textfiles:\\n f.write(str(file) + u'\\\\n')\\n else:\\n self.textfiles = random_path.read_text().splitlines()\\n if args.wiki_random == False:\\n self.textfiles = cache_path.read_text().splitlines()\\n if len(self.textfiles) == 0:\\n raise RuntimeError('Found 0 images in subfolders of: {}'.format(root))\\n if data_frac < 1.0:\\n red_num = int(len(self.textfiles) * data_frac)\\n self.textfiles = self.textfiles[:red_num]\\n self.data = []\\n # sentences\\n filtered_num = 0\\n all_num = 0\\n # documents\\n filtered_document_num = 0\\n all_document_num = 0\\n rank_logger_info(logger, local_rank, \\\"Reading wiki %s data\\\" % split)\\n for textfile in self.textfiles:\\n return_dict, filtered_num, all_num, filtered_document_num, all_document_num = read_wiki_file(args, length_filter, filtered_num, all_num,filtered_document_num,all_document_num,\\n sent_num_filter,textfile,logger, encoder, remove_preface_segment, ignore_list, remove_special_tokens,\\n return_as_sentences, high_granularity, only_letters)\\n if return_dict == None:\\n continue\\n elif type(return_dict) == dict:\\n self.data.append(return_dict)\\n else: # list\\n self.data.extend(return_dict)\\n if all_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all sentences num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\"%(all_num, filtered_num,filtered_num/all_num))\\n if all_document_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all documents num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\\n\\n def __getitem__(self, index):\\n return self.data[index]\\n\\n def __len__(self):\\n return len(self.data)\"\n },\n {\n \"alpha_fraction\": 0.4859980046749115,\n \"alphanum_fraction\": 0.4903492033481598,\n \"avg_line_length\": 47.71195602416992,\n \"blob_id\": \"d943e19a719996c07be807808342f8814b2a6692\",\n \"content_id\": \"3832429c7043db448219add728cd763bde218f53\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8963,\n \"license_type\": \"no_license\",\n \"max_line_length\": 194,\n \"num_lines\": 184,\n \"path\": \"/data/wikicities_loader.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from nltk.tokenize import RegexpTokenizer\\nfrom torch.utils.data import Dataset\\nfrom utils import *\\nfrom data.data_utils import *\\nimport os\\nfrom parameters import create_parser\\nmissing_stop_words = set(['of', 'a', 'and', 'to'])\\n\\ndef get_list_token():\\n return \\\"***LIST***\\\"\\n\\n\\ndef get_formula_token():\\n return \\\"***formula***\\\"\\n\\n\\ndef get_codesnipet_token():\\n return \\\"***codice***\\\"\\n\\n\\ndef get_special_tokens():\\n special_tokens = []\\n special_tokens.append(get_list_token())\\n special_tokens.append(get_formula_token())\\n special_tokens.append(get_codesnipet_token())\\n return special_tokens\\n\\nwords_tokenizer = None\\ndef get_words_tokenizer():\\n global words_tokenizer\\n if words_tokenizer:\\n return words_tokenizer\\n words_tokenizer = RegexpTokenizer(r'\\\\w+')\\n return words_tokenizer\\n\\ndef extract_sentence_words(sentence, remove_missing_emb_words = False,remove_special_tokens = False):\\n if (remove_special_tokens):\\n for token in get_special_tokens():\\n # Can't do on sentence words because tokenizer delete '***' of tokens.\\n sentence = sentence.replace(token, \\\"\\\")\\n tokenizer = get_words_tokenizer()\\n sentence_words = tokenizer.tokenize(sentence)\\n if remove_missing_emb_words:\\n sentence_words = [w for w in sentence_words if w not in missing_stop_words]\\n return sentence_words\\n\\ndef get_sections(text_path, segment_path):\\n text_file = open(str(text_path), \\\"r\\\", encoding=\\\"utf-8\\\")\\n texts = text_file.read().strip().split(\\\"\\\\n\\\")\\n text_file.close()\\n segment_file = open(str(segment_path), \\\"r\\\", encoding=\\\"utf-8\\\")\\n segments = segment_file.read().strip().split(\\\"\\\\n\\\")\\n segment_file.close()\\n all_sections = [] # all document\\n sections = [] # one document\\n one_section = [] # one section\\n document_num = int(segments[0].strip().split(\\\",\\\")[0])\\n seg_title = segments[0].strip().split(\\\",\\\")[2]\\n for idx, text in enumerate(texts):\\n text = text.split(\\\"\\\\x01\\\")[1]\\n segment = segments[idx]\\n if len(segment.strip().split(\\\",\\\")) == 3:\\n n_doc_num,n_sent_num,n_seg_title = segment.strip().split(\\\",\\\")\\n else:\\n n_doc_num,n_sent_num,n_seg_title1,n_seg_title2 = segment.strip().split(\\\",\\\")\\n n_seg_title = n_seg_title1 + \\\",\\\" + n_seg_title2\\n n_doc_num,n_sent_num = int(n_doc_num),int(n_sent_num)\\n if n_seg_title != seg_title and n_doc_num == document_num:\\n sections.append(one_section)\\n one_section = []\\n seg_title = n_seg_title\\n elif n_doc_num != document_num:\\n sections.append(one_section)\\n all_sections.append(sections)\\n one_section = []\\n sections = []\\n document_num = n_doc_num\\n seg_title = n_seg_title\\n one_section.append(text)\\n all_sections.append(sections)\\n return all_sections\\n\\n\\ndef read_wikicities_file(args, filtered_num, all_num, filtered_document_num, all_document_num,\\n text_path, segment_path,encoder=None,length_filter=10, sent_num_filter=10):\\n all_sections_of_all_doc = get_sections(text_path, segment_path)\\n all_return_dic = []\\n for idx,all_sections in enumerate(all_sections_of_all_doc):\\n path = text_path + \\\"/document_\\\" + str(idx)\\n data = []\\n adjs = []\\n targets = []\\n for sentences in all_sections:\\n if sentences:\\n targets_flag = 0\\n for sentence in sentences:\\n sentence_words = extract_sentence_words(sentence)\\n if (len(sentence_words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\\n continue\\n if len(sentence_words) > length_filter:\\n sentence_words = sentence_words[:length_filter]\\n filtered_num += 1\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n sentence_words, adj = build_ing_graph(sentence_words, args)\\n adjs.append(adj)\\n if encoder:\\n data.append(encoder.tokenize(sentence_words))\\n else:\\n data.append(sentence_words)\\n targets_flag += 1\\n if data:\\n all_num += targets_flag\\n targets.extend([0]*(targets_flag-1))\\n if targets_flag != 0:\\n targets.append(1) # segment\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(data, adjs, targets, path,\\n all_document_num,\\n filtered_document_num,\\n sent_num_filter)\\n elif args.sr_choose == 'g_model' and args.gr_choose in ['textgcn', 'ING_GCN','S_ING_GCN']:\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path,\\n all_document_num,\\n filtered_document_num,\\n sent_num_filter)\\n return_dict = generate_gcn_return_dict(return_dict, args)\\n else:\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path,\\n all_document_num,\\n filtered_document_num,\\n sent_num_filter)\\n if return_dict == None:\\n continue\\n elif type(return_dict) == dict:\\n all_return_dic.append(return_dict)\\n else: # list\\n all_return_dic.extend(return_dict)\\n return all_return_dic, filtered_num, all_num, filtered_document_num, all_document_num\\n\\nclass WikiCitiesDataSet(Dataset):\\n def __init__(self,\\n root = None,\\n args = None,\\n encoder = None,\\n length_filter = 40,\\n sent_num_filter = 60,\\n local_rank=-1):\\n logger = get_logger(args)\\n self.text_path = root + \\\"/wikicities.text\\\"\\n self.segment_path = root + \\\"/wikicities.segmenttitles\\\"\\n rank_logger_info(logger, local_rank, \\\"Reading wikicities data\\\")\\n # sentences\\n filtered_num = 0\\n all_num = 0\\n # documents\\n filtered_document_num = 0\\n all_document_num = 0\\n self.data,filtered_num, all_num, filtered_document_num, all_document_num = read_wikicities_file(args,filtered_num,\\n all_num,\\n filtered_document_num,\\n all_document_num,\\n text_path= self.text_path,\\n segment_path=self.segment_path,\\n encoder=encoder,\\n length_filter=length_filter,\\n sent_num_filter=sent_num_filter)\\n # print(\\\"one wikicities example:\\\",self.data[0])\\n\\n if all_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all sentences num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\"%(all_num, filtered_num,filtered_num/all_num))\\n if all_document_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all documents num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\"%(all_document_num, filtered_document_num, filtered_document_num / all_document_num))\\n\\n def __getitem__(self, index):\\n return self.data[index]\\n\\n def __len__(self):\\n return len(self.data)\\n\\nif __name__ == \\\"__main__\\\":\\n args = create_parser()\\n print(args)\\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\\n wikielementsdataset = WikiCitiesDataSet(args.wikicities_path, args)\\n\"\n },\n {\n \"alpha_fraction\": 0.47889289259910583,\n \"alphanum_fraction\": 0.4840097725391388,\n \"avg_line_length\": 49.292396545410156,\n \"blob_id\": \"33f3f74d48898dda3f06193e380e8702e8beebde\",\n \"content_id\": \"56cad373b54e40d3431f98ea98c2c9b907a9ab6f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8599,\n \"license_type\": \"no_license\",\n \"max_line_length\": 196,\n \"num_lines\": 171,\n \"path\": \"/data/choi_loader.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from pathlib import Path\\nfrom torch.utils.data import Dataset\\nfrom data.data_utils import *\\nfrom utils import *\\nimport os\\nimport random\\nfrom parameters import create_parser\\n\\ndef clean_paragraph(paragraph):\\n cleaned_paragraph= paragraph.replace(\\\"'' \\\", \\\" \\\").replace(\\\" 's\\\", \\\"'s\\\").replace(\\\"``\\\", \\\"\\\").strip('\\\\n')\\n return cleaned_paragraph\\n\\ndef read_choi_file(args,filtered_num, all_num, filtered_document_num, all_document_num,path, encoder=None,length_filter=10, sent_num_filter=10):\\n seperator = '=========='\\n with Path(path).open('r') as f:\\n raw_text = f.read()\\n paragraphs = [clean_paragraph(p) for p in raw_text.strip().split(seperator) if len(p) > 5 and p != \\\"\\\\n\\\"]\\n targets = []\\n new_text = []\\n adjs = []\\n for paragraph in paragraphs:\\n sentences = [s for s in paragraph.split('\\\\n') if len(s.split()) > 0]\\n if sentences:\\n sentences_count =0\\n # This is the number of sentences in the paragraph and where we need to split.\\n for sentence in sentences:\\n words = extract_sentence_words(sentence)\\n if (len(words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\\n continue\\n sentences_count +=1\\n # length_filter\\n if len(words) > length_filter:\\n words = words[:length_filter]\\n filtered_num += 1\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n sentence_words, adj = build_ing_graph(words, args)\\n adjs.append(adj)\\n if encoder:\\n new_text.append(encoder.tokenize(words))\\n else:\\n new_text.append(words)\\n all_num += sentences_count\\n targets.extend([0] * (sentences_count - 1))\\n if sentences_count != 0:\\n targets.append(1) # segment\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(new_text, adjs, targets, path,\\n all_document_num,\\n filtered_document_num,\\n sent_num_filter)\\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(new_text, targets, path,\\n all_document_num,\\n filtered_document_num,\\n sent_num_filter)\\n return_dict = generate_gcn_return_dict(return_dict, args)\\n else:\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(new_text, targets, path,\\n all_document_num,\\n filtered_document_num,\\n sent_num_filter)\\n return return_dict,filtered_num, all_num,filtered_document_num, all_document_num\\n\\ndef get_cache_path(folder,split):\\n\\n if split == None:\\n cache_file_path = folder / 'paths_cache'\\n elif split == 'train':\\n cache_file_path = folder / 'train_paths_cache'\\n elif split == 'dev':\\n cache_file_path = folder / 'dev_paths_cache'\\n elif split == 'test':\\n cache_file_path = folder / 'test_paths_cache'\\n else:\\n cache_file_path = None\\n return cache_file_path\\n\\ndef cache_filenames(folder,split):\\n files = Path(folder).glob('*/*/*.ref')\\n files = list(files) # otherwise:TypeError: object of type 'generator' has no len()\\n cache_path = get_cache_path(folder,split)\\n if split == None: # write all\\n with cache_path.open(\\\"w+\\\") as f:\\n for file in files:\\n f.write(str(file) + u'\\\\n')\\n elif split == 'train': # write train dev and test\\n all_document_num = len(files)\\n all_idx = [i for i in range(all_document_num)]\\n random.shuffle(all_idx)\\n train_num = int(all_document_num / 10 * 8)\\n valid_num = int(all_document_num / 10 * 1)\\n test_num = int(all_document_num / 10 * 1)\\n train_cache_path = get_cache_path(folder,\\\"train\\\")\\n with train_cache_path.open(\\\"w+\\\") as f:\\n for i in range(train_num):\\n train_idx = all_idx[i]\\n f.write(str(files[train_idx]) + u'\\\\n')\\n valid_cache_path = get_cache_path(folder,\\\"dev\\\")\\n with valid_cache_path.open(\\\"w+\\\") as f:\\n for i in range(train_num, train_num+valid_num):\\n valid_idx = all_idx[i]\\n f.write(str(files[valid_idx]) + u'\\\\n')\\n test_cache_path = get_cache_path(folder,\\\"test\\\")\\n with test_cache_path.open(\\\"w+\\\") as f:\\n for i in range(train_num+valid_num, train_num+valid_num+test_num):\\n test_idx = all_idx[i]\\n f.write(str(files[test_idx]) + u'\\\\n')\\n\\n\\nclass ChoiDataSet(Dataset):\\n def __init__(self,\\n root = None,\\n args=None,\\n encoder = None,\\n split = None, # None, train, valid, test\\n length_filter = 40000,\\n sent_num_filter = 60000,\\n local_rank=-1):\\n logger = get_logger(args)\\n root_path = Path(root)\\n cache_path = get_cache_path(root_path,split)\\n if not cache_path.exists():\\n cache_filenames(root_path,split)\\n self.textfiles = cache_path.read_text().splitlines()\\n else:\\n self.textfiles = cache_path.read_text().splitlines()\\n if len(self.textfiles) == 0:\\n raise RuntimeError('Found 0 images in subfolders of: {}'.format(root))\\n self.data = []\\n # sentences\\n filtered_num = 0\\n all_num = 0\\n # documents\\n filtered_document_num = 0\\n all_document_num = 0\\n if split == None:\\n rank_logger_info(logger, local_rank,\\\"Reading all choi data for test\\\")\\n else:\\n rank_logger_info(logger, local_rank, \\\"Reading choi %s data\\\" % (split))\\n for textfile in self.textfiles:\\n return_dict, filtered_num, all_num, filtered_document_num, all_document_num = read_choi_file(args,filtered_num,\\n all_num,\\n filtered_document_num,\\n all_document_num,\\n textfile,\\n encoder=encoder,\\n length_filter=length_filter,\\n sent_num_filter=sent_num_filter)\\n if return_dict == None:\\n continue\\n elif type(return_dict) == dict:\\n self.data.append(return_dict)\\n else: # list\\n self.data.extend(return_dict)\\n # print(\\\"one choi example:\\\",self.data[0])\\n if all_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all sentences num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\"%(all_num, filtered_num,filtered_num/all_num))\\n if all_document_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all documents num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\\n\\n def __getitem__(self, index):\\n return self.data[index]\\n\\n def __len__(self):\\n return len(self.data)\\n\\nif __name__ == \\\"__main__\\\":\\n args = create_parser()\\n print(args)\\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\\n choidataset = ChoiDataSet(args.choi_path, args)\"\n },\n {\n \"alpha_fraction\": 0.5418270230293274,\n \"alphanum_fraction\": 0.5474724173545837,\n \"avg_line_length\": 47.71875,\n \"blob_id\": \"ff20f8e6177c5ccbadbbd70f222a4d5d2adc0be2\",\n \"content_id\": \"fb2a769ed27365a4c46f95ebb5ad7e46e6ca350e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7794,\n \"license_type\": \"no_license\",\n \"max_line_length\": 196,\n \"num_lines\": 160,\n \"path\": \"/data/wikisection_loader.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import re\\nfrom pathlib import Path\\nfrom torch.utils.data import Dataset\\nfrom utils import *\\nfrom data.data_utils import *\\nimport os\\nfrom parameters import create_parser\\n\\n\\nsegment_seperator = \\\"==========\\\"\\ndef get_seperator_foramt():\\n seperator_fromat = segment_seperator + \\\".*\\\\n?\\\"\\n return seperator_fromat\\n\\ndef get_scections_from_text(txt):\\n sections_to_keep_pattern = get_seperator_foramt()\\n all_sections = re.split(sections_to_keep_pattern, txt)\\n non_empty_sections = [s for s in all_sections if len(s) > 0]\\n return non_empty_sections\\n\\ndef clean_section(section):\\n cleaned_section = section.strip('\\\\n')\\n return cleaned_section\\n\\ndef get_sections(path):\\n file = open(str(path), \\\"r\\\", encoding='utf-8')\\n raw_content = file.read()\\n file.close()\\n clean_txt = raw_content.encode('utf-8').decode('utf-8').strip()\\n sections = [clean_section(s) for s in get_scections_from_text(clean_txt)]\\n return sections\\n\\ndef remove_last_one(data, targets):\\n return data[:-1], targets[:-1]\\n\\ndef read_section_file(args,filtered_num, all_num, filtered_document_num, all_document_num,\\n path, encoder=None, length_filter=20, sent_num_filter=10, remove_preface_segment=True,\\n ignore_list=False, remove_special_tokens=False,return_as_sentences=False, only_letters=False):\\n data = []\\n adjs = []\\n targets = []\\n all_sections = get_sections(path)\\n required_sections = all_sections[1:] if remove_preface_segment and len(all_sections) > 0 else all_sections\\n required_non_empty_sections = [section for section in required_sections if len(section.split()) > 0 and section != \\\"\\\\n\\\"]\\n for section in required_non_empty_sections:\\n sentences = section.split('\\\\n')\\n if sentences:\\n targets_flag = 0\\n for sentence in sentences:\\n if not return_as_sentences:\\n sentence_words = extract_sentence_words(sentence, remove_special_tokens=remove_special_tokens)\\n if (len(sentence_words) <= 1): # remove sentences whose length <= 1 otherwise treelstm cant run\\n continue\\n # length_filter\\n if len(sentence_words) > length_filter:\\n sentence_words = sentence_words[:length_filter]\\n filtered_num += 1\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n sentence_words, adj = build_ing_graph(sentence_words, args)\\n adjs.append(adj)\\n if encoder:\\n data.append(encoder.tokenize(sentence_words))\\n else:\\n data.append(sentence_words)\\n targets_flag += 1\\n else: # for the annotation. keep sentence as is.\\n if (only_letters):\\n sentence = re.sub('[^a-zA-Z0-9 ]+', '', sentence)\\n data.append(sentence)\\n else:\\n data.append(sentence)\\n if data:\\n all_num += targets_flag\\n targets.extend([0]*(targets_flag-1))\\n if targets_flag != 0:\\n targets.append(1) # segment\\n if args.sr_choose == 'g_model' and args.gr_choose == 'texting':\\n return_dict, filtered_document_num, all_document_num = generate_ing_return_dict(data, adjs, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n elif args.sr_choose == 'g_model' and args.gr_choose in 'DSG_SEG':\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n return_dict = generate_gcn_return_dict(return_dict,args)\\n else:\\n return_dict, filtered_document_num, all_document_num = generate_return_dict(data, targets, path, all_document_num,filtered_document_num,sent_num_filter)\\n return return_dict,filtered_num, all_num,filtered_document_num, all_document_num\\n\\ndef get_cache_path(folder):\\n cache_file_path = folder / 'paths_cache'\\n return cache_file_path\\n\\ndef cache_filenames(folder):\\n files = Path(folder).glob('*.ref')\\n cache_path = get_cache_path(folder)\\n with cache_path.open(\\\"w+\\\") as f:\\n for file in files:\\n f.write(str(file) + u'\\\\n')\\n\\nclass WikiSectioniDataSet(Dataset):\\n def __init__(self,\\n root = None,\\n args=None,\\n encoder = None,\\n split = 'train',\\n length_filter = 40000,\\n sent_num_filter = 60000,\\n local_rank=-1):\\n logger = get_logger(args)\\n root_disease = Path(root+\\\"/en_disease_\\\"+split)\\n root_city = Path(root+\\\"/en_city_\\\"+split)\\n cache_disease_path = get_cache_path(root_disease)\\n cache_city_path = get_cache_path(root_city)\\n\\n if not cache_disease_path.exists():\\n cache_filenames(root_disease)\\n cache_filenames(root_city)\\n self.textfiles = cache_disease_path.read_text().splitlines() + cache_city_path.read_text().splitlines()\\n else:\\n self.textfiles = cache_disease_path.read_text().splitlines() + cache_city_path.read_text().splitlines()\\n if len(self.textfiles) == 0:\\n raise RuntimeError('Found 0 images in subfolders of: {}'.format('lalala'))\\n self.data = []\\n # sentences\\n filtered_num = 0\\n all_num = 0\\n # documents\\n filtered_document_num = 0\\n all_document_num = 0\\n rank_logger_info(logger, local_rank, \\\"Reading wikisection %s data\\\"%(split))\\n for textfile in self.textfiles:\\n return_dict, filtered_num, all_num, filtered_document_num, all_document_num = read_section_file(args, filtered_num,\\n all_num,\\n filtered_document_num,\\n all_document_num,\\n textfile,\\n encoder=encoder,\\n length_filter=length_filter,\\n sent_num_filter=sent_num_filter)\\n if return_dict == None:\\n continue\\n elif type(return_dict) == dict:\\n self.data.append(return_dict)\\n else: # list\\n self.data.extend(return_dict)\\n # print(\\\"one wikisection example:\\\",self.data[0])\\n if all_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all sentences num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\"%(all_num, filtered_num,filtered_num/all_num))\\n if all_document_num != 0:\\n rank_logger_info(logger, local_rank, \\\"all documents num:%d\\\\tfiltered num:%d\\\\tfiltered percent:%f\\\" % (all_document_num, filtered_document_num, filtered_document_num / all_document_num))\\n\\n def __getitem__(self, index):\\n return self.data[index]\\n\\n def __len__(self):\\n return len(self.data)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n args = create_parser()\\n print(args)\\n logger = setup_logger(args.logger_name, os.path.join(args.checkpoint_dir, 'train.log'))\\n choidataset = WikiSectioniDataSet(args.wikisection_path, args, split=\\\"validation\\\") # train validation test\"\n },\n {\n \"alpha_fraction\": 0.641672670841217,\n \"alphanum_fraction\": 0.6459985375404358,\n \"avg_line_length\": 38.657142639160156,\n \"blob_id\": \"02730c083150dc03ac8c9e344823a6c719ddcc04\",\n \"content_id\": \"5b0078b9dde2aed1e97789f290112c2003851a80\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1387,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 35,\n \"path\": \"/encoder/Encoder.py\",\n \"repo_name\": \"guxuan123456/DSG-SEG\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import torch.nn as nn\\nfrom encoder.one_hot_encoder import one_hot_model\\nfrom encoder.glove_encoder import glove_model\\nfrom encoder.randn_encoder import randn_model\\nfrom encoder.w2v_encoder import w2v_model\\nfrom encoder.bert_encoder import bert_model\\nfrom utils import *\\n\\nclass Encoder(nn.Module):\\n def __init__(self, args, etype, encoder_fine_tune, glove_path=None, w2v_path=None):\\n super(Encoder, self).__init__()\\n self.args = args\\n if etype == \\\"one-hot\\\":\\n self.model = one_hot_model(glove_path, args, encoder_fine_tune)\\n elif etype == 'glove':\\n self.model = glove_model(glove_path, args, encoder_fine_tune)\\n elif etype == \\\"randn\\\":\\n self.model = randn_model(glove_path, args, encoder_fine_tune)\\n elif etype == 'w2v':\\n self.model = w2v_model(w2v_path, args, encoder_fine_tune)\\n elif etype == 'bert':\\n self.model = bert_model(args, encoder_fine_tune)\\n self.etype=etype\\n self.pad_token_id = self.model.pad_token_id\\n\\n def tokenize(self,sentence): # get_subword_indices=False\\n if type(sentence) is list:\\n sentence = ' '.join(sentence)\\n return self.model.tokenize(sentence)\\n\\n def forward(self, sentence_lst,just_last_layer=True):\\n return self.model(sentence_lst)\\n\\n def get_input_dim(self):\\n return self.model.get_word_dim()\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":38,"string":"38"}}},{"rowIdx":2979157,"cells":{"repo_name":{"kind":"string","value":"etiennechabert/kolibree_backend_test"},"repo_url":{"kind":"string","value":"https://github.com/etiennechabert/kolibree_backend_test"},"snapshot_id":{"kind":"string","value":"3befeb1ac1bb50ffd03964ec60758c217987466d"},"revision_id":{"kind":"string","value":"6c8fcd503420ff25718beb20bf94804c138c27d5"},"directory_id":{"kind":"string","value":"9a8a2caf73060d1b4ae1bf2b54ba03f189a0ed56"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-16T21:50:36.164180","string":"2021-01-16T21:50:36.164180"},"revision_date":{"kind":"timestamp","value":"2015-10-15T13:06:54","string":"2015-10-15T13:06:54"},"committer_date":{"kind":"timestamp","value":"2015-10-15T13:06:54","string":"2015-10-15T13:06:54"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.597836434841156,"alphanum_fraction":0.6096086502075195,"avg_line_length":35.98823547363281,"blob_id":"84455ee1c804905ed2796628f635c65df8938b05","content_id":"9b604ca0fd8b48bac402a1f6e48ff467eae4e0db","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3143,"license_type":"no_license","max_line_length":110,"num_lines":85,"path":"/api/models/user.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"from django.contrib.auth.hashers import make_password, check_password\nfrom django.core.exceptions import ValidationError, ObjectDoesNotExist\nfrom django.db import models, IntegrityError\nfrom django.db.models.signals import pre_save\nfrom django.dispatch import receiver\nfrom rest_framework import serializers\nfrom rest_framework.status import HTTP_400_BAD_REQUEST, HTTP_200_OK, HTTP_409_CONFLICT, HTTP_401_UNAUTHORIZED\n\nclass User(models.Model):\n first_name = models.CharField(max_length=100, blank=False, null=False)\n last_name = models.CharField(max_length=100, blank=False, null=False)\n date_of_birth = models.DateField()\n email = models.EmailField(unique=True, blank=False)\n password = models.CharField(max_length=100, blank=False, null=False)\n\n def handle_create(self, data):\n for k, v in data.items():\n setattr(self, k, v)\n try:\n self.clean_fields()\n self.save()\n except ValidationError as e:\n return {\n 'code' : HTTP_400_BAD_REQUEST,\n 'data' : e.message_dict.items()\n }\n except IntegrityError as e:\n return {\n 'code' : HTTP_409_CONFLICT,\n 'data' : {'error' : e.args[1]}\n }\n return {\n 'code' : HTTP_200_OK,\n 'data' : 'new user created'\n }\n\n # This helper method create a new token in case of the previous one expired (or does not exist)\n def get_valid_token(self):\n element = self.usertoken_set.all().order_by('-creation_dateTime').first()\n if element == None or element.is_valid() == False:\n return self.usertoken_set.create()\n return element\n\n# Helper class, it's helped me to handle the Auth logic, Django probably provide mecanism of auth we could use\n# I just preferred to develop mine to practise a bit\nclass UserAuth():\n def auth_process(self, params):\n try:\n user = User.objects.get(email=params['email'])\n if check_password(params['password'], user.password):\n return self.auth_process_success(user)\n return self.auth_process_fail()\n except ObjectDoesNotExist:\n return self.auth_process_fail()\n\n def auth_process_success(self, user):\n token = user.get_valid_token()\n return {\n 'data' : {\n 'text' : 'valid authentication',\n 'token' : token.token.__str__()\n },\n 'code' : HTTP_200_OK\n }\n\n def auth_process_fail(self):\n return {\n 'data' : {\n 'text' : 'Bad email/password',\n },\n 'code' : HTTP_401_UNAUTHORIZED\n }\n\n# Serializer for User rendering\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n date_of_birth = serializers.DateField()\n\n class Meta:\n model = User\n fields = ('first_name', 'last_name', 'email', 'date_of_birth')\n\n@receiver(pre_save, sender=User)\ndef userPassword_encryption(sender, instance, *args, **kwargs):\n if instance.id == None:\n instance.password = make_password(instance.password)"},{"alpha_fraction":0.6285714507102966,"alphanum_fraction":0.6285714507102966,"avg_line_length":19.5,"blob_id":"59d3046edf6f53218d3a577248570277268d8027","content_id":"37a927cebbd1032c3a34cc43224e6774da185918","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":245,"license_type":"no_license","max_line_length":43,"num_lines":12,"path":"/api/urls.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"from django.conf.urls import include, url\nfrom api.views import user\n\nuser_api_urls = [\n url(r'^$', user.users),\n url(r'^auth$', user.auth),\n url(r'^retrieve', user.retrieve)\n]\n\napi_urls = [\n url(r'^users/', include(user_api_urls))\n]"},{"alpha_fraction":0.5690608024597168,"alphanum_fraction":0.6114180684089661,"avg_line_length":23.68181800842285,"blob_id":"b4a42e0467e445605f7acd1a69f13f25028a555a","content_id":"d84a5522102071c8960a88cd9f748323ca4e2b0d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":543,"license_type":"no_license","max_line_length":106,"num_lines":22,"path":"/api/migrations/0005_user_date_of_birth.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport datetime\nfrom django.utils.timezone import utc\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('api', '0004_user_usertoken'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='user',\n name='date_of_birth',\n field=models.DateField(default=datetime.datetime(2015, 9, 29, 19, 33, 6, 150950, tzinfo=utc)),\n preserve_default=False,\n ),\n ]\n"},{"alpha_fraction":0.6419667601585388,"alphanum_fraction":0.6585872769355774,"avg_line_length":42.75757598876953,"blob_id":"04075d5ba7db3e4d28ee8b3b2681499ea87e8d9f","content_id":"da0821ee492642d572d9121c41493e1ae6ecb6ad","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1444,"license_type":"no_license","max_line_length":149,"num_lines":33,"path":"/api/tests/models/test_user_model.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"from unittest import TestCase\nfrom django.core.exceptions import ValidationError\nfrom django.db import IntegrityError\nfrom api.models.user import User\n\nclass userTestCase(TestCase):\n def setUp(self):\n self.user = User()\n\n def test_user_model_fail(self):\n with self.assertRaises(ValidationError): # Fully empty model\n self.user.clean_fields()\n\n self.user.first_name = 'Etienne'\n with self.assertRaises(ValidationError): # Just a first name\n self.user.clean_fields()\n\n self.user.last_name = 'Chabert'\n self.user.email = 'fake_mail'\n self.user.password = 'password'\n with self.assertRaises(ValidationError): # Invalid email\n self.user.clean_fields()\n\n def test_user_model_success(self):\n User.objects.all().delete();\n user = User.objects.create(first_name='Etienne', last_name='Chabert', email='etienne.chabert@gmail.com', password='pass', date_of_birth='1990-11-18')\n self.assertEqual(user.full_clean(), None)\n\n def test_user_model_duplicate(self):\n User.objects.all().delete();\n with self.assertRaises(IntegrityError):\n user = User.objects.create(first_name='Etienne', last_name='Chabert', email='etienne.chabert@gmail.com', password='pass', date_of_birth='1990-11-18')\n user = User.objects.create(first_name='Etienne', last_name='Chabert', email='etienne.chabert@gmail.com', password='pass', date_of_birth='1990-11-18')\n"},{"alpha_fraction":0.6827957034111023,"alphanum_fraction":0.6827957034111023,"avg_line_length":22.25,"blob_id":"7b40750ead353950375cfcee4bc686aec917bb37","content_id":"e659b071249431b8e17e9c76bbe82047333d1762","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":186,"license_type":"no_license","max_line_length":41,"num_lines":8,"path":"/kolibree_backend_test/urls.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"from django.conf.urls import include, url\nfrom api.views import index\nfrom api.urls import api_urls\n\nurlpatterns = [\n url(r'^$', index.index),\n url(r'^api/', include(api_urls)),\n]\n"},{"alpha_fraction":0.5741350650787354,"alphanum_fraction":0.5919824242591858,"avg_line_length":35.069305419921875,"blob_id":"bcfeacb308aed0fd6e12335d3c493892ede653b6","content_id":"a59f40fdb7be331d825c55080059ec7e537f3574","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3642,"license_type":"no_license","max_line_length":138,"num_lines":101,"path":"/api/tests/views/test_user_api.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"from datetime import timedelta\nimport uuid\nfrom rest_framework.test import APIRequestFactory\nfrom rest_framework.test import APITestCase\nfrom api.models import User\n\nfactory = APIRequestFactory()\nrequest = factory.get('/users')\n\nclass TestUserApiCreate(APITestCase):\n def test_users_api_usage(self):\n response = self.client.get('/api/users/', {}, format='json')\n self.assertEqual(response.status_code, 200)\n\n def test_create_user_good(self):\n data = {\n 'first_name' : 'Etienne',\n 'last_name' : 'Chabert',\n 'email' : 'etienne.chabert@gmail.com',\n 'password' : 'password',\n 'date_of_birth' : '1990-11-18'\n }\n response = self.client.post('/api/users/', data, format='json')\n self.assertEqual(response.status_code, 200) # good test\n\n response = self.client.post('/api/users/', data, format='json')\n self.assertEqual(response.status_code, 409) # duplicate user test\n\n def test_create_user_false(self):\n data = {\n 'first_name' : 'Etienne',\n 'last_name' : 'Chabert',\n 'email' : 'etienne.chabert@gmail.com',\n 'password' : 'password',\n 'date_of_birth' : '18-11-1990'\n }\n response = self.client.post('/api/users/', data, format='json')\n self.assertEqual(response.status_code, 400) # failing test\n\nclass TestUserApiAuth(APITestCase):\n def setUp(self):\n self.user = User(first_name='Etienne',last_name='Chabert',email='etienne.chabert@gmail.com',password='password',date_of_birth='1990-11-18').save()\n\n def test_user_valid_authentication(self):\n data = {\n 'email' : 'etienne.chabert@gmail.com',\n 'password' : 'password'\n }\n response = self.client.post('/api/users/auth', data)\n self.assertEqual(response.status_code, 200)\n self.assertEqual('valid authentication' in response.content, True)\n self.assertEqual('token' in response.content, True)\n\n def test_user_bad_password(self):\n data = {\n 'email' : 'etienne.chabert@gmail.com',\n 'password' : 'pass'\n }\n response = self.client.post('/api/users/auth', data)\n self.assertEqual(response.status_code, 401)\n self.assertEqual('Bad email/password' in response.content, True)\n\n def test_user_bad_email(self):\n data = {\n 'email' : 'etinne.chabert@gmail.com',\n 'password' : 'password'\n }\n response = self.client.post('/api/users/auth', data)\n self.assertEqual(response.status_code, 401)\n self.assertEqual('Bad email/password' in response.content, True)\n\nclass TestUserApiRetrieve(APITestCase):\n def setUp(self):\n self.user = User(first_name='Etienne',last_name='Chabert',email='etienne.chabert@gmail.com',password='password',date_of_birth='1990-11-18')\n self.user.save()\n\n def test_user_valid_token(self):\n token = self.user.get_valid_token()\n data = {\n 'token' : token.token.__str__()\n }\n response = self.client.post('/api/users/retrieve',data)\n self.assertEqual(response.status_code, 200)\n\n def test_user_expired_token(self):\n token = self.user.get_valid_token()\n token.creation_dateTime -= timedelta(days=42)\n token.save()\n\n data = {\n 'token' : token.token.__str__()\n }\n response = self.client.post('/api/users/retrieve',data)\n self.assertEqual(response.status_code, 400)\n\n def test_user_unknown_token(self):\n data = {\n 'token' : uuid.uuid4()\n }\n response = self.client.post('/api/users/retrieve',data)\n self.assertEqual(response.status_code, 400)"},{"alpha_fraction":0.48988351225852966,"alphanum_fraction":0.4935622215270996,"avg_line_length":32.97916793823242,"blob_id":"a99449f7a6e20c7f4f74f27a7f6bd378ecf6355a","content_id":"295ff6cae5fa3eae21d2f9e22a7b97cc4a56bdb2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1631,"license_type":"no_license","max_line_length":99,"num_lines":48,"path":"/api/views/user.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"from rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework.status import HTTP_200_OK\nfrom api.models.user import User, UserSerializer, UserAuth\nfrom api.models.user_token import get_user_for_token\n\ndef users_usage():\n return {\n 'methods' : {\n 'api/users' : {\n 'methods' : {\n 'GET' : 'Access to this Usage',\n 'POST' : 'Create the new user'\n }\n },\n 'api/users/auth' : {\n 'methods' : {\n 'POST' : 'Try to authenticate (get a token)'\n }\n },\n 'api/users/retrieve' : {\n 'methods' : {\n 'POST' : 'Access user\\'s data according to the token (require a token)'\n }\n },\n\n }\n }\n\n@api_view(['GET', 'POST'])\ndef users(request):\n if request.method == 'GET':\n return Response(users_usage(), HTTP_200_OK)\n elif request.method == 'POST':\n response = User().handle_create(request.data)\n return Response(response['data'], response['code'])\n\n@api_view(['POST'])\ndef auth(request):\n data = request.data\n response = UserAuth().auth_process(data)\n return Response(response['data'], response['code'])\n\n@api_view(['POST'])\ndef retrieve(request):\n data = request.data\n response = get_user_for_token(data)\n return Response(response['data'], response['code'])\n"},{"alpha_fraction":0.7549019455909729,"alphanum_fraction":0.7549019455909729,"avg_line_length":24.5,"blob_id":"ca6494c3bc025f64fd5fef4a159d83cc6785ec7d","content_id":"cbb70a6e41389ed8fa628e86fc32d0f4b1fd55be","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":102,"license_type":"no_license","max_line_length":43,"num_lines":4,"path":"/api/models/__init__.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"__author__ = 'chaber_e'\n\nfrom api.models.user import User\nfrom api.models.user_token import UserToken\n"},{"alpha_fraction":0.6645161509513855,"alphanum_fraction":0.6693548560142517,"avg_line_length":36.60606002807617,"blob_id":"d0cfcca51196ffb37f3f9934a91cb1c33785c8db","content_id":"99590a6c92704d1911e030fb712078063a4c48e4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1240,"license_type":"no_license","max_line_length":79,"num_lines":33,"path":"/api/tests/models/test_user_token_model.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"from unittest import TestCase\nfrom datetime import timedelta\nfrom api.models.user import User\nfrom api.models.user_token import UserToken\n\nclass userTokenTestCase(TestCase):\n def setUp(self):\n self.user = User.objects.get(first_name='Etienne', last_name='Chabert')\n self.user.usertoken_set.all().delete()\n\n def test_unique_token(self):\n self.user.usertoken_set.create()\n self.user.usertoken_set.create()\n self.assertEqual(self.user.usertoken_set.count(), 2)\n\n def test_get_valid_token(self):\n self.user.get_valid_token()\n self.user.get_valid_token()\n self.assertEqual(self.user.usertoken_set.count(), 1)\n\n def test_valid_token(self):\n token = self.user.usertoken_set.create()\n self.assertEqual(token.is_valid(), True)\n\n def test_exipired_token(self):\n token = self.user.usertoken_set.create()\n token.creation_dateTime = token.creation_dateTime - timedelta(\n days=2*UserToken.TOKEN_VALIDITY_IN_DAYS,\n hours=2*UserToken.TOKEN_VALIDITY_IN_HOURS,\n minutes=2*UserToken.TOKEN_VALIDITY_IN_MINUTES,\n seconds=2*UserToken.TOKEN_VALIDITY_IN_SECONDS\n )\n self.assertEqual(token.is_valid(), False)"},{"alpha_fraction":0.7337192296981812,"alphanum_fraction":0.7428846955299377,"avg_line_length":78.73076629638672,"blob_id":"3c053ad0db5f2095937035c6a3806805b5f78db3","content_id":"ec64526c04e63f231cc8055f2fe1ecd90b3d318f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2073,"license_type":"no_license","max_line_length":450,"num_lines":26,"path":"/README.md","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"# Details about this Repository\nThis is the first application I made with Django (A Python Framwork). I made it in about 10 hours in september 2015. The goal was to design a simple API (Register / Authenticate / Get user's details)\nI made it for a Job interview. On the 2nd part (-> Additionnal details about authentication) I am explaning the logic of my implementation and answering this question : 'How to avoid entering a password for the user ?'.\n\n# API Actions\n* API Root page (api/users [GET])\n * A short usage about possible actions of the API\n\n* Register users (api/users [POST])\n * Example of a usable json (just copy past it)\n{ \"first_name\" : \"Etienne\", \"last_name\" : \"Chabert\", \"date_of_birth\" : \"1990-11-18\", \"password\" : \"mon_pass\", \"email\" : \"mail@mail.com }\n\n* Auth user (api/users/auth [POST])\n * Exemple of a usable json (just copy past it)\n{ \"email\" : \"mail@mail.com\", \"password\" : \"mon_pass\" }\n\n* Retrieve user (api/users/retrieve [POST])\n * Simply copy past the response of the previous action, this is your token (1H validity)\n\n# Additionnal details about authentication\n\nTo handle the expiration of the token : The token is a separate model, with a 1-N relation with the user (1 User - N Tokens). When the user authenticate if we are not able to found an active token (mean still valid) we create a new one. On the creation we attribute a datetime to the token. By this way we know when the token expirate (1 hour latter).\n\nThe advantage of using a separate table for the token is to keep traces of all the authentication of the user (we may imagine tracking IP, bad authentication and so on).\n\nA 'sexy' by heavy authentication system to avoid entering password : During the registration step of a User, the server generate a private / public key. The public key is shared with the User, we could then imagine fully encrypted exchange between server and client. No need to ask a password to the client, all requests will be impossible to read without the appropriate key. The password is only required to get / generate a new key (if necessary).\n"},{"alpha_fraction":0.5416666865348816,"alphanum_fraction":0.5416666865348816,"avg_line_length":23,"blob_id":"0f3f1216f938f35288544673fc4a0299a4da8589","content_id":"20b9d7b299636ebc14daefd7c29c3e7c696aa7cb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":24,"license_type":"no_license","max_line_length":23,"num_lines":1,"path":"/api/tests/views/__init__.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"__author__ = 'chaber_e'\n"},{"alpha_fraction":0.6551724076271057,"alphanum_fraction":0.6613430380821228,"avg_line_length":33.4375,"blob_id":"61201890b6e95fa67b79571caaca3fc02f5a8057","content_id":"1f2aa1bde28b72a30b320fcfea1d647b98fa8abe","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2755,"license_type":"no_license","max_line_length":86,"num_lines":80,"path":"/api/models/user_token.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"import uuid\nfrom django.utils import timezone\nfrom datetime import datetime, timedelta\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.db import models\nfrom django.db.models import CASCADE\nfrom django.db.models.signals import pre_save\nfrom django.dispatch import receiver\nfrom api.models.user import UserSerializer\nfrom rest_framework import serializers\nfrom rest_framework.status import HTTP_400_BAD_REQUEST, HTTP_200_OK\n\nclass UserToken(models.Model):\n TOKEN_VALIDITY_IN_DAYS = 0\n TOKEN_VALIDITY_IN_HOURS = 1\n TOKEN_VALIDITY_IN_MINUTES = 0\n TOKEN_VALIDITY_IN_SECONDS = 0\n\n user = models.ForeignKey('User', on_delete=CASCADE)\n token = models.UUIDField(primary_key=True)\n creation_dateTime = models.DateTimeField()\n\n # I overload the setup method to be able to assign a datetime on the first save\n def save(self, *args, **kwargs):\n if not self.creation_dateTime:\n self.creation_dateTime = timezone.now()\n return super(UserToken, self).save(*args, **kwargs)\n\n def is_valid(self):\n validity_delta = timedelta(\n days=self.TOKEN_VALIDITY_IN_DAYS,\n hours=self.TOKEN_VALIDITY_IN_HOURS,\n minutes=self.TOKEN_VALIDITY_IN_MINUTES,\n seconds=self.TOKEN_VALIDITY_IN_SECONDS\n )\n now = datetime.now(self.creation_dateTime.tzinfo)\n if self.creation_dateTime + validity_delta > now:\n return True\n return False\n\n# Serialize class for userToken\nclass UserTokenSerializer(serializers.HyperlinkedModelSerializer):\n creation_dateTime = serializers.DateTimeField()\n\n class Meta:\n model = UserToken\n fields = ('token', 'creation_dateTime')\n\n# The assignation of the token is handle by this callback\n# We check the uniqueness of the randomized token by catching a DoesNotExist exception\n@receiver(pre_save, sender=UserToken)\ndef userToken_attribution(sender, instance, *args, **kwargs):\n while instance.token == None:\n new_token = uuid.uuid4()\n try:\n UserToken.objects.get(token=new_token)\n except ObjectDoesNotExist:\n instance.token = new_token\n\n# This helper method is used from the view\n# Probably a bad idea to return HTML_RESPONSE code here,\n# But I prefer to have short views\ndef get_user_for_token(data):\n try:\n token = UserToken.objects.get(token=data['token'])\n if token.is_valid():\n return {\n 'data' : {\n 'user' : UserSerializer(token.user).data\n },\n 'code' : HTTP_200_OK\n }\n except ObjectDoesNotExist as e:\n pass\n return {\n 'data' : {\n 'user' : None,\n },\n 'code' : HTTP_400_BAD_REQUEST\n }\n"},{"alpha_fraction":0.671875,"alphanum_fraction":0.6812499761581421,"avg_line_length":28.090909957885742,"blob_id":"278afe21038d84163b36bb09fcabc6a1b85adc72","content_id":"ab3424ab725f2457db2218299c5b5f7b2e3d1cdb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":320,"license_type":"no_license","max_line_length":61,"num_lines":11,"path":"/api/tests/views/test_user_views.py","repo_name":"etiennechabert/kolibree_backend_test","src_encoding":"UTF-8","text":"import unittest\nfrom django.test import Client\n\nclass TestUserViews(unittest.TestCase):\n def setUp(self):\n self.client = Client()\n\n def test_index(self):\n response = self.client.get('')\n self.assertEqual(response.status_code, 200)\n self.assertEqual('Welcome' in response.content, True)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.597836434841156,\n \"alphanum_fraction\": 0.6096086502075195,\n \"avg_line_length\": 35.98823547363281,\n \"blob_id\": \"84455ee1c804905ed2796628f635c65df8938b05\",\n \"content_id\": \"9b604ca0fd8b48bac402a1f6e48ff467eae4e0db\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3143,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 85,\n \"path\": \"/api/models/user.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib.auth.hashers import make_password, check_password\\nfrom django.core.exceptions import ValidationError, ObjectDoesNotExist\\nfrom django.db import models, IntegrityError\\nfrom django.db.models.signals import pre_save\\nfrom django.dispatch import receiver\\nfrom rest_framework import serializers\\nfrom rest_framework.status import HTTP_400_BAD_REQUEST, HTTP_200_OK, HTTP_409_CONFLICT, HTTP_401_UNAUTHORIZED\\n\\nclass User(models.Model):\\n first_name = models.CharField(max_length=100, blank=False, null=False)\\n last_name = models.CharField(max_length=100, blank=False, null=False)\\n date_of_birth = models.DateField()\\n email = models.EmailField(unique=True, blank=False)\\n password = models.CharField(max_length=100, blank=False, null=False)\\n\\n def handle_create(self, data):\\n for k, v in data.items():\\n setattr(self, k, v)\\n try:\\n self.clean_fields()\\n self.save()\\n except ValidationError as e:\\n return {\\n 'code' : HTTP_400_BAD_REQUEST,\\n 'data' : e.message_dict.items()\\n }\\n except IntegrityError as e:\\n return {\\n 'code' : HTTP_409_CONFLICT,\\n 'data' : {'error' : e.args[1]}\\n }\\n return {\\n 'code' : HTTP_200_OK,\\n 'data' : 'new user created'\\n }\\n\\n # This helper method create a new token in case of the previous one expired (or does not exist)\\n def get_valid_token(self):\\n element = self.usertoken_set.all().order_by('-creation_dateTime').first()\\n if element == None or element.is_valid() == False:\\n return self.usertoken_set.create()\\n return element\\n\\n# Helper class, it's helped me to handle the Auth logic, Django probably provide mecanism of auth we could use\\n# I just preferred to develop mine to practise a bit\\nclass UserAuth():\\n def auth_process(self, params):\\n try:\\n user = User.objects.get(email=params['email'])\\n if check_password(params['password'], user.password):\\n return self.auth_process_success(user)\\n return self.auth_process_fail()\\n except ObjectDoesNotExist:\\n return self.auth_process_fail()\\n\\n def auth_process_success(self, user):\\n token = user.get_valid_token()\\n return {\\n 'data' : {\\n 'text' : 'valid authentication',\\n 'token' : token.token.__str__()\\n },\\n 'code' : HTTP_200_OK\\n }\\n\\n def auth_process_fail(self):\\n return {\\n 'data' : {\\n 'text' : 'Bad email/password',\\n },\\n 'code' : HTTP_401_UNAUTHORIZED\\n }\\n\\n# Serializer for User rendering\\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\\n date_of_birth = serializers.DateField()\\n\\n class Meta:\\n model = User\\n fields = ('first_name', 'last_name', 'email', 'date_of_birth')\\n\\n@receiver(pre_save, sender=User)\\ndef userPassword_encryption(sender, instance, *args, **kwargs):\\n if instance.id == None:\\n instance.password = make_password(instance.password)\"\n },\n {\n \"alpha_fraction\": 0.6285714507102966,\n \"alphanum_fraction\": 0.6285714507102966,\n \"avg_line_length\": 19.5,\n \"blob_id\": \"59d3046edf6f53218d3a577248570277268d8027\",\n \"content_id\": \"37a927cebbd1032c3a34cc43224e6774da185918\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 245,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 12,\n \"path\": \"/api/urls.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.conf.urls import include, url\\nfrom api.views import user\\n\\nuser_api_urls = [\\n url(r'^$', user.users),\\n url(r'^auth$', user.auth),\\n url(r'^retrieve', user.retrieve)\\n]\\n\\napi_urls = [\\n url(r'^users/', include(user_api_urls))\\n]\"\n },\n {\n \"alpha_fraction\": 0.5690608024597168,\n \"alphanum_fraction\": 0.6114180684089661,\n \"avg_line_length\": 23.68181800842285,\n \"blob_id\": \"b4a42e0467e445605f7acd1a69f13f25028a555a\",\n \"content_id\": \"d84a5522102071c8960a88cd9f748323ca4e2b0d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 543,\n \"license_type\": \"no_license\",\n \"max_line_length\": 106,\n \"num_lines\": 22,\n \"path\": \"/api/migrations/0005_user_date_of_birth.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom __future__ import unicode_literals\\n\\nfrom django.db import models, migrations\\nimport datetime\\nfrom django.utils.timezone import utc\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('api', '0004_user_usertoken'),\\n ]\\n\\n operations = [\\n migrations.AddField(\\n model_name='user',\\n name='date_of_birth',\\n field=models.DateField(default=datetime.datetime(2015, 9, 29, 19, 33, 6, 150950, tzinfo=utc)),\\n preserve_default=False,\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6419667601585388,\n \"alphanum_fraction\": 0.6585872769355774,\n \"avg_line_length\": 42.75757598876953,\n \"blob_id\": \"04075d5ba7db3e4d28ee8b3b2681499ea87e8d9f\",\n \"content_id\": \"da0821ee492642d572d9121c41493e1ae6ecb6ad\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1444,\n \"license_type\": \"no_license\",\n \"max_line_length\": 149,\n \"num_lines\": 33,\n \"path\": \"/api/tests/models/test_user_model.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from unittest import TestCase\\nfrom django.core.exceptions import ValidationError\\nfrom django.db import IntegrityError\\nfrom api.models.user import User\\n\\nclass userTestCase(TestCase):\\n def setUp(self):\\n self.user = User()\\n\\n def test_user_model_fail(self):\\n with self.assertRaises(ValidationError): # Fully empty model\\n self.user.clean_fields()\\n\\n self.user.first_name = 'Etienne'\\n with self.assertRaises(ValidationError): # Just a first name\\n self.user.clean_fields()\\n\\n self.user.last_name = 'Chabert'\\n self.user.email = 'fake_mail'\\n self.user.password = 'password'\\n with self.assertRaises(ValidationError): # Invalid email\\n self.user.clean_fields()\\n\\n def test_user_model_success(self):\\n User.objects.all().delete();\\n user = User.objects.create(first_name='Etienne', last_name='Chabert', email='etienne.chabert@gmail.com', password='pass', date_of_birth='1990-11-18')\\n self.assertEqual(user.full_clean(), None)\\n\\n def test_user_model_duplicate(self):\\n User.objects.all().delete();\\n with self.assertRaises(IntegrityError):\\n user = User.objects.create(first_name='Etienne', last_name='Chabert', email='etienne.chabert@gmail.com', password='pass', date_of_birth='1990-11-18')\\n user = User.objects.create(first_name='Etienne', last_name='Chabert', email='etienne.chabert@gmail.com', password='pass', date_of_birth='1990-11-18')\\n\"\n },\n {\n \"alpha_fraction\": 0.6827957034111023,\n \"alphanum_fraction\": 0.6827957034111023,\n \"avg_line_length\": 22.25,\n \"blob_id\": \"7b40750ead353950375cfcee4bc686aec917bb37\",\n \"content_id\": \"e659b071249431b8e17e9c76bbe82047333d1762\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 186,\n \"license_type\": \"no_license\",\n \"max_line_length\": 41,\n \"num_lines\": 8,\n \"path\": \"/kolibree_backend_test/urls.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.conf.urls import include, url\\nfrom api.views import index\\nfrom api.urls import api_urls\\n\\nurlpatterns = [\\n url(r'^$', index.index),\\n url(r'^api/', include(api_urls)),\\n]\\n\"\n },\n {\n \"alpha_fraction\": 0.5741350650787354,\n \"alphanum_fraction\": 0.5919824242591858,\n \"avg_line_length\": 35.069305419921875,\n \"blob_id\": \"bcfeacb308aed0fd6e12335d3c493892ede653b6\",\n \"content_id\": \"a59f40fdb7be331d825c55080059ec7e537f3574\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3642,\n \"license_type\": \"no_license\",\n \"max_line_length\": 138,\n \"num_lines\": 101,\n \"path\": \"/api/tests/views/test_user_api.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import timedelta\\nimport uuid\\nfrom rest_framework.test import APIRequestFactory\\nfrom rest_framework.test import APITestCase\\nfrom api.models import User\\n\\nfactory = APIRequestFactory()\\nrequest = factory.get('/users')\\n\\nclass TestUserApiCreate(APITestCase):\\n def test_users_api_usage(self):\\n response = self.client.get('/api/users/', {}, format='json')\\n self.assertEqual(response.status_code, 200)\\n\\n def test_create_user_good(self):\\n data = {\\n 'first_name' : 'Etienne',\\n 'last_name' : 'Chabert',\\n 'email' : 'etienne.chabert@gmail.com',\\n 'password' : 'password',\\n 'date_of_birth' : '1990-11-18'\\n }\\n response = self.client.post('/api/users/', data, format='json')\\n self.assertEqual(response.status_code, 200) # good test\\n\\n response = self.client.post('/api/users/', data, format='json')\\n self.assertEqual(response.status_code, 409) # duplicate user test\\n\\n def test_create_user_false(self):\\n data = {\\n 'first_name' : 'Etienne',\\n 'last_name' : 'Chabert',\\n 'email' : 'etienne.chabert@gmail.com',\\n 'password' : 'password',\\n 'date_of_birth' : '18-11-1990'\\n }\\n response = self.client.post('/api/users/', data, format='json')\\n self.assertEqual(response.status_code, 400) # failing test\\n\\nclass TestUserApiAuth(APITestCase):\\n def setUp(self):\\n self.user = User(first_name='Etienne',last_name='Chabert',email='etienne.chabert@gmail.com',password='password',date_of_birth='1990-11-18').save()\\n\\n def test_user_valid_authentication(self):\\n data = {\\n 'email' : 'etienne.chabert@gmail.com',\\n 'password' : 'password'\\n }\\n response = self.client.post('/api/users/auth', data)\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual('valid authentication' in response.content, True)\\n self.assertEqual('token' in response.content, True)\\n\\n def test_user_bad_password(self):\\n data = {\\n 'email' : 'etienne.chabert@gmail.com',\\n 'password' : 'pass'\\n }\\n response = self.client.post('/api/users/auth', data)\\n self.assertEqual(response.status_code, 401)\\n self.assertEqual('Bad email/password' in response.content, True)\\n\\n def test_user_bad_email(self):\\n data = {\\n 'email' : 'etinne.chabert@gmail.com',\\n 'password' : 'password'\\n }\\n response = self.client.post('/api/users/auth', data)\\n self.assertEqual(response.status_code, 401)\\n self.assertEqual('Bad email/password' in response.content, True)\\n\\nclass TestUserApiRetrieve(APITestCase):\\n def setUp(self):\\n self.user = User(first_name='Etienne',last_name='Chabert',email='etienne.chabert@gmail.com',password='password',date_of_birth='1990-11-18')\\n self.user.save()\\n\\n def test_user_valid_token(self):\\n token = self.user.get_valid_token()\\n data = {\\n 'token' : token.token.__str__()\\n }\\n response = self.client.post('/api/users/retrieve',data)\\n self.assertEqual(response.status_code, 200)\\n\\n def test_user_expired_token(self):\\n token = self.user.get_valid_token()\\n token.creation_dateTime -= timedelta(days=42)\\n token.save()\\n\\n data = {\\n 'token' : token.token.__str__()\\n }\\n response = self.client.post('/api/users/retrieve',data)\\n self.assertEqual(response.status_code, 400)\\n\\n def test_user_unknown_token(self):\\n data = {\\n 'token' : uuid.uuid4()\\n }\\n response = self.client.post('/api/users/retrieve',data)\\n self.assertEqual(response.status_code, 400)\"\n },\n {\n \"alpha_fraction\": 0.48988351225852966,\n \"alphanum_fraction\": 0.4935622215270996,\n \"avg_line_length\": 32.97916793823242,\n \"blob_id\": \"a99449f7a6e20c7f4f74f27a7f6bd378ecf6355a\",\n \"content_id\": \"295ff6cae5fa3eae21d2f9e22a7b97cc4a56bdb2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1631,\n \"license_type\": \"no_license\",\n \"max_line_length\": 99,\n \"num_lines\": 48,\n \"path\": \"/api/views/user.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from rest_framework.decorators import api_view\\nfrom rest_framework.response import Response\\nfrom rest_framework.status import HTTP_200_OK\\nfrom api.models.user import User, UserSerializer, UserAuth\\nfrom api.models.user_token import get_user_for_token\\n\\ndef users_usage():\\n return {\\n 'methods' : {\\n 'api/users' : {\\n 'methods' : {\\n 'GET' : 'Access to this Usage',\\n 'POST' : 'Create the new user'\\n }\\n },\\n 'api/users/auth' : {\\n 'methods' : {\\n 'POST' : 'Try to authenticate (get a token)'\\n }\\n },\\n 'api/users/retrieve' : {\\n 'methods' : {\\n 'POST' : 'Access user\\\\'s data according to the token (require a token)'\\n }\\n },\\n\\n }\\n }\\n\\n@api_view(['GET', 'POST'])\\ndef users(request):\\n if request.method == 'GET':\\n return Response(users_usage(), HTTP_200_OK)\\n elif request.method == 'POST':\\n response = User().handle_create(request.data)\\n return Response(response['data'], response['code'])\\n\\n@api_view(['POST'])\\ndef auth(request):\\n data = request.data\\n response = UserAuth().auth_process(data)\\n return Response(response['data'], response['code'])\\n\\n@api_view(['POST'])\\ndef retrieve(request):\\n data = request.data\\n response = get_user_for_token(data)\\n return Response(response['data'], response['code'])\\n\"\n },\n {\n \"alpha_fraction\": 0.7549019455909729,\n \"alphanum_fraction\": 0.7549019455909729,\n \"avg_line_length\": 24.5,\n \"blob_id\": \"ca6494c3bc025f64fd5fef4a159d83cc6785ec7d\",\n \"content_id\": \"cbb70a6e41389ed8fa628e86fc32d0f4b1fd55be\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 102,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 4,\n \"path\": \"/api/models/__init__.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"__author__ = 'chaber_e'\\n\\nfrom api.models.user import User\\nfrom api.models.user_token import UserToken\\n\"\n },\n {\n \"alpha_fraction\": 0.6645161509513855,\n \"alphanum_fraction\": 0.6693548560142517,\n \"avg_line_length\": 36.60606002807617,\n \"blob_id\": \"d0cfcca51196ffb37f3f9934a91cb1c33785c8db\",\n \"content_id\": \"99590a6c92704d1911e030fb712078063a4c48e4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1240,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 33,\n \"path\": \"/api/tests/models/test_user_token_model.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from unittest import TestCase\\nfrom datetime import timedelta\\nfrom api.models.user import User\\nfrom api.models.user_token import UserToken\\n\\nclass userTokenTestCase(TestCase):\\n def setUp(self):\\n self.user = User.objects.get(first_name='Etienne', last_name='Chabert')\\n self.user.usertoken_set.all().delete()\\n\\n def test_unique_token(self):\\n self.user.usertoken_set.create()\\n self.user.usertoken_set.create()\\n self.assertEqual(self.user.usertoken_set.count(), 2)\\n\\n def test_get_valid_token(self):\\n self.user.get_valid_token()\\n self.user.get_valid_token()\\n self.assertEqual(self.user.usertoken_set.count(), 1)\\n\\n def test_valid_token(self):\\n token = self.user.usertoken_set.create()\\n self.assertEqual(token.is_valid(), True)\\n\\n def test_exipired_token(self):\\n token = self.user.usertoken_set.create()\\n token.creation_dateTime = token.creation_dateTime - timedelta(\\n days=2*UserToken.TOKEN_VALIDITY_IN_DAYS,\\n hours=2*UserToken.TOKEN_VALIDITY_IN_HOURS,\\n minutes=2*UserToken.TOKEN_VALIDITY_IN_MINUTES,\\n seconds=2*UserToken.TOKEN_VALIDITY_IN_SECONDS\\n )\\n self.assertEqual(token.is_valid(), False)\"\n },\n {\n \"alpha_fraction\": 0.7337192296981812,\n \"alphanum_fraction\": 0.7428846955299377,\n \"avg_line_length\": 78.73076629638672,\n \"blob_id\": \"3c053ad0db5f2095937035c6a3806805b5f78db3\",\n \"content_id\": \"ec64526c04e63f231cc8055f2fe1ecd90b3d318f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2073,\n \"license_type\": \"no_license\",\n \"max_line_length\": 450,\n \"num_lines\": 26,\n \"path\": \"/README.md\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Details about this Repository\\nThis is the first application I made with Django (A Python Framwork). I made it in about 10 hours in september 2015. The goal was to design a simple API (Register / Authenticate / Get user's details)\\nI made it for a Job interview. On the 2nd part (-> Additionnal details about authentication) I am explaning the logic of my implementation and answering this question : 'How to avoid entering a password for the user ?'.\\n\\n# API Actions\\n* API Root page (api/users [GET])\\n * A short usage about possible actions of the API\\n\\n* Register users (api/users [POST])\\n * Example of a usable json (just copy past it)\\n{ \\\"first_name\\\" : \\\"Etienne\\\", \\\"last_name\\\" : \\\"Chabert\\\", \\\"date_of_birth\\\" : \\\"1990-11-18\\\", \\\"password\\\" : \\\"mon_pass\\\", \\\"email\\\" : \\\"mail@mail.com }\\n\\n* Auth user (api/users/auth [POST])\\n * Exemple of a usable json (just copy past it)\\n{ \\\"email\\\" : \\\"mail@mail.com\\\", \\\"password\\\" : \\\"mon_pass\\\" }\\n\\n* Retrieve user (api/users/retrieve [POST])\\n * Simply copy past the response of the previous action, this is your token (1H validity)\\n\\n# Additionnal details about authentication\\n\\nTo handle the expiration of the token : The token is a separate model, with a 1-N relation with the user (1 User - N Tokens). When the user authenticate if we are not able to found an active token (mean still valid) we create a new one. On the creation we attribute a datetime to the token. By this way we know when the token expirate (1 hour latter).\\n\\nThe advantage of using a separate table for the token is to keep traces of all the authentication of the user (we may imagine tracking IP, bad authentication and so on).\\n\\nA 'sexy' by heavy authentication system to avoid entering password : During the registration step of a User, the server generate a private / public key. The public key is shared with the User, we could then imagine fully encrypted exchange between server and client. No need to ask a password to the client, all requests will be impossible to read without the appropriate key. The password is only required to get / generate a new key (if necessary).\\n\"\n },\n {\n \"alpha_fraction\": 0.5416666865348816,\n \"alphanum_fraction\": 0.5416666865348816,\n \"avg_line_length\": 23,\n \"blob_id\": \"0f3f1216f938f35288544673fc4a0299a4da8589\",\n \"content_id\": \"20b9d7b299636ebc14daefd7c29c3e7c696aa7cb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 24,\n \"license_type\": \"no_license\",\n \"max_line_length\": 23,\n \"num_lines\": 1,\n \"path\": \"/api/tests/views/__init__.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"__author__ = 'chaber_e'\\n\"\n },\n {\n \"alpha_fraction\": 0.6551724076271057,\n \"alphanum_fraction\": 0.6613430380821228,\n \"avg_line_length\": 33.4375,\n \"blob_id\": \"61201890b6e95fa67b79571caaca3fc02f5a8057\",\n \"content_id\": \"1f2aa1bde28b72a30b320fcfea1d647b98fa8abe\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2755,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 80,\n \"path\": \"/api/models/user_token.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import uuid\\nfrom django.utils import timezone\\nfrom datetime import datetime, timedelta\\nfrom django.core.exceptions import ObjectDoesNotExist\\nfrom django.db import models\\nfrom django.db.models import CASCADE\\nfrom django.db.models.signals import pre_save\\nfrom django.dispatch import receiver\\nfrom api.models.user import UserSerializer\\nfrom rest_framework import serializers\\nfrom rest_framework.status import HTTP_400_BAD_REQUEST, HTTP_200_OK\\n\\nclass UserToken(models.Model):\\n TOKEN_VALIDITY_IN_DAYS = 0\\n TOKEN_VALIDITY_IN_HOURS = 1\\n TOKEN_VALIDITY_IN_MINUTES = 0\\n TOKEN_VALIDITY_IN_SECONDS = 0\\n\\n user = models.ForeignKey('User', on_delete=CASCADE)\\n token = models.UUIDField(primary_key=True)\\n creation_dateTime = models.DateTimeField()\\n\\n # I overload the setup method to be able to assign a datetime on the first save\\n def save(self, *args, **kwargs):\\n if not self.creation_dateTime:\\n self.creation_dateTime = timezone.now()\\n return super(UserToken, self).save(*args, **kwargs)\\n\\n def is_valid(self):\\n validity_delta = timedelta(\\n days=self.TOKEN_VALIDITY_IN_DAYS,\\n hours=self.TOKEN_VALIDITY_IN_HOURS,\\n minutes=self.TOKEN_VALIDITY_IN_MINUTES,\\n seconds=self.TOKEN_VALIDITY_IN_SECONDS\\n )\\n now = datetime.now(self.creation_dateTime.tzinfo)\\n if self.creation_dateTime + validity_delta > now:\\n return True\\n return False\\n\\n# Serialize class for userToken\\nclass UserTokenSerializer(serializers.HyperlinkedModelSerializer):\\n creation_dateTime = serializers.DateTimeField()\\n\\n class Meta:\\n model = UserToken\\n fields = ('token', 'creation_dateTime')\\n\\n# The assignation of the token is handle by this callback\\n# We check the uniqueness of the randomized token by catching a DoesNotExist exception\\n@receiver(pre_save, sender=UserToken)\\ndef userToken_attribution(sender, instance, *args, **kwargs):\\n while instance.token == None:\\n new_token = uuid.uuid4()\\n try:\\n UserToken.objects.get(token=new_token)\\n except ObjectDoesNotExist:\\n instance.token = new_token\\n\\n# This helper method is used from the view\\n# Probably a bad idea to return HTML_RESPONSE code here,\\n# But I prefer to have short views\\ndef get_user_for_token(data):\\n try:\\n token = UserToken.objects.get(token=data['token'])\\n if token.is_valid():\\n return {\\n 'data' : {\\n 'user' : UserSerializer(token.user).data\\n },\\n 'code' : HTTP_200_OK\\n }\\n except ObjectDoesNotExist as e:\\n pass\\n return {\\n 'data' : {\\n 'user' : None,\\n },\\n 'code' : HTTP_400_BAD_REQUEST\\n }\\n\"\n },\n {\n \"alpha_fraction\": 0.671875,\n \"alphanum_fraction\": 0.6812499761581421,\n \"avg_line_length\": 28.090909957885742,\n \"blob_id\": \"278afe21038d84163b36bb09fcabc6a1b85adc72\",\n \"content_id\": \"ab3424ab725f2457db2218299c5b5f7b2e3d1cdb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 320,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 11,\n \"path\": \"/api/tests/views/test_user_views.py\",\n \"repo_name\": \"etiennechabert/kolibree_backend_test\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import unittest\\nfrom django.test import Client\\n\\nclass TestUserViews(unittest.TestCase):\\n def setUp(self):\\n self.client = Client()\\n\\n def test_index(self):\\n response = self.client.get('')\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual('Welcome' in response.content, True)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":13,"string":"13"}}},{"rowIdx":2979158,"cells":{"repo_name":{"kind":"string","value":"D45H7/KunjungAN"},"repo_url":{"kind":"string","value":"https://github.com/D45H7/KunjungAN"},"snapshot_id":{"kind":"string","value":"7a095d4faff0e8a22eb56a705fc510d4e96001d2"},"revision_id":{"kind":"string","value":"e656963764687bb878d3c139db508914a5d957e2"},"directory_id":{"kind":"string","value":"cca095cd18155402b33045811adeb40fb9500b5c"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-02-05T04:53:01.192161","string":"2023-02-05T04:53:01.192161"},"revision_date":{"kind":"timestamp","value":"2020-12-29T23:06:24","string":"2020-12-29T23:06:24"},"committer_date":{"kind":"timestamp","value":"2020-12-29T23:06:24","string":"2020-12-29T23:06:24"},"github_id":{"kind":"number","value":325404369,"string":"325,404,369"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7687861323356628,"alphanum_fraction":0.7716763019561768,"avg_line_length":37.44444274902344,"blob_id":"9b828c2e5865c3cdb0c3281e3c27fc676c88aa29","content_id":"412b11058101e8e612b5d03e8382d0fb4d9f935b","detected_licenses":["BSL-1.0"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":346,"license_type":"permissive","max_line_length":166,"num_lines":9,"path":"/README.md","repo_name":"D45H7/KunjungAN","src_encoding":"UTF-8","text":"# KunjungAN\nAuto visitor's bot for add traffic in your websites. Warn if your ip is used for this. Tool is not use the proxy, be aware your IP blocked for blogspot or web hoster.\n\n# Usage\nInstall python3 first: apt-get install python\n<br>Then, install modules: pip install requests\n<br>And then run it:\n<br>cd KunjungAN\n<br>python autovisitor.py\n"},{"alpha_fraction":0.5975854992866516,"alphanum_fraction":0.6629778742790222,"avg_line_length":26.63888931274414,"blob_id":"77ae1fbb9c7b29da2804c23dd01744eace6dd62c","content_id":"d59e09c5e1a881b1c2f7f109c027d09d20857daa","detected_licenses":["BSL-1.0"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":995,"license_type":"permissive","max_line_length":122,"num_lines":36,"path":"/auto-visitor.py","repo_name":"D45H7/KunjungAN","src_encoding":"UTF-8","text":"#!/usr/bin/python39\n#EasyMade by EtcAug10\n\nimport os\n\ntry:\n\timport requests,urllib\nexcept:\n\tos.system(\"pip install requests && pip install urllib\")\n\tquit(\"Silahkan jalankan ulang skrip\")\n\nbanner = \"\"\"\n/)) ((\\\\\n/)) Auto Visit bot ((\\\\\n/)) Made by EtcAug10 ((\\\\\n/)) ©2020 D45H7 ((\\\\\n\"\"\"\n\ndef visit(t,u):\n\th = {'user-agent':u}\n\treq = requests.post(t,headers=h)\n\tif req.status_code == 200:\n\t\tprint(\"\\033[00;0mSedang mengunjungi \\033[32;1m\",t,\"\\033[00;0m dengan \\033[31;1m\",u)\n\telse:\n\t\tprint(\"\\033[35;1mGagal, \\033[00;0mterdapat error \\033[31;1m\",req.status_code)\n\nprint(\"\\033[00;0m\",banner)\nprint(\"\\nAuto Visitor bot requests for bind in system\")\nurl = input(\"Masukkan url target (http): \")\nuseragents = urllib.request.urlopen(\"https://raw.githubusercontent.com/cvandeplas/pystemon/master/user-agents.txt\").read()\nuseragents = useragents.decode(\"utf-8\").splitlines()\nfor ua in useragents:\n\tvisit(url,ua)\n\tcontinue\n\nquit(\"Hadeh.. Capek gan.. Istirahat dulu.. :D\")"}],"string":"[\n {\n \"alpha_fraction\": 0.7687861323356628,\n \"alphanum_fraction\": 0.7716763019561768,\n \"avg_line_length\": 37.44444274902344,\n \"blob_id\": \"9b828c2e5865c3cdb0c3281e3c27fc676c88aa29\",\n \"content_id\": \"412b11058101e8e612b5d03e8382d0fb4d9f935b\",\n \"detected_licenses\": [\n \"BSL-1.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 346,\n \"license_type\": \"permissive\",\n \"max_line_length\": 166,\n \"num_lines\": 9,\n \"path\": \"/README.md\",\n \"repo_name\": \"D45H7/KunjungAN\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# KunjungAN\\nAuto visitor's bot for add traffic in your websites. Warn if your ip is used for this. Tool is not use the proxy, be aware your IP blocked for blogspot or web hoster.\\n\\n# Usage\\nInstall python3 first: apt-get install python\\n<br>Then, install modules: pip install requests\\n<br>And then run it:\\n<br>cd KunjungAN\\n<br>python autovisitor.py\\n\"\n },\n {\n \"alpha_fraction\": 0.5975854992866516,\n \"alphanum_fraction\": 0.6629778742790222,\n \"avg_line_length\": 26.63888931274414,\n \"blob_id\": \"77ae1fbb9c7b29da2804c23dd01744eace6dd62c\",\n \"content_id\": \"d59e09c5e1a881b1c2f7f109c027d09d20857daa\",\n \"detected_licenses\": [\n \"BSL-1.0\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 995,\n \"license_type\": \"permissive\",\n \"max_line_length\": 122,\n \"num_lines\": 36,\n \"path\": \"/auto-visitor.py\",\n \"repo_name\": \"D45H7/KunjungAN\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python39\\n#EasyMade by EtcAug10\\n\\nimport os\\n\\ntry:\\n\\timport requests,urllib\\nexcept:\\n\\tos.system(\\\"pip install requests && pip install urllib\\\")\\n\\tquit(\\\"Silahkan jalankan ulang skrip\\\")\\n\\nbanner = \\\"\\\"\\\"\\n/)) ((\\\\\\\\\\n/)) Auto Visit bot ((\\\\\\\\\\n/)) Made by EtcAug10 ((\\\\\\\\\\n/)) ©2020 D45H7 ((\\\\\\\\\\n\\\"\\\"\\\"\\n\\ndef visit(t,u):\\n\\th = {'user-agent':u}\\n\\treq = requests.post(t,headers=h)\\n\\tif req.status_code == 200:\\n\\t\\tprint(\\\"\\\\033[00;0mSedang mengunjungi \\\\033[32;1m\\\",t,\\\"\\\\033[00;0m dengan \\\\033[31;1m\\\",u)\\n\\telse:\\n\\t\\tprint(\\\"\\\\033[35;1mGagal, \\\\033[00;0mterdapat error \\\\033[31;1m\\\",req.status_code)\\n\\nprint(\\\"\\\\033[00;0m\\\",banner)\\nprint(\\\"\\\\nAuto Visitor bot requests for bind in system\\\")\\nurl = input(\\\"Masukkan url target (http): \\\")\\nuseragents = urllib.request.urlopen(\\\"https://raw.githubusercontent.com/cvandeplas/pystemon/master/user-agents.txt\\\").read()\\nuseragents = useragents.decode(\\\"utf-8\\\").splitlines()\\nfor ua in useragents:\\n\\tvisit(url,ua)\\n\\tcontinue\\n\\nquit(\\\"Hadeh.. Capek gan.. Istirahat dulu.. :D\\\")\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979159,"cells":{"repo_name":{"kind":"string","value":"es3a10/fuente_web"},"repo_url":{"kind":"string","value":"https://github.com/es3a10/fuente_web"},"snapshot_id":{"kind":"string","value":"d12f8c5c9442dd240997e2b2e3ccf44d50e7608f"},"revision_id":{"kind":"string","value":"69fc338dbad104207e3df39fe6351a4c452a2cfc"},"directory_id":{"kind":"string","value":"a48c7de2585c5bcfb644c528e0203bd7d54b5e84"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-15T15:41:52.247773","string":"2021-01-15T15:41:52.247773"},"revision_date":{"kind":"timestamp","value":"2016-11-15T18:40:10","string":"2016-11-15T18:40:10"},"committer_date":{"kind":"timestamp","value":"2016-11-15T18:40:10","string":"2016-11-15T18:40:10"},"github_id":{"kind":"number","value":39762240,"string":"39,762,240"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6745068430900574,"alphanum_fraction":0.6820940971374512,"avg_line_length":25.897958755493164,"blob_id":"3d791adcceb4810f7ecc47be794dd24f8fdd240f","content_id":"f1608f79b5fc1d0bfca51324addfc547fd841201","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1320,"license_type":"no_license","max_line_length":77,"num_lines":49,"path":"/pelicanconf.py","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\n\n\nAUTHOR = u'Carlos Rodr\\xedguez'\nSITENAME = u'Bit\\xe1cora Personal'\nSITEURL = ''\n\nPATH = 'content'\nTHEME = \"notmyidea\"\n#THEME = \"simple\"\n\nTIMEZONE = 'Europe/Paris'\nDEFAULT_LANG = u'es'\nSTATIC_PATHS = [\"images\" ]\nLOAD_CONTENT_CACHE = False\n# The minimum number of articles allowed on the last page. Use this when you\n# don’t want to have a last page with very few articles.\nDEFAULT_ORPHANS = 0\n# The maximum number of articles to include on a page, not including orphans.\nDEFAULT_PAGINATION = 3\n#Activate pagination.\nWITH_PAGINATION = True\n\n\n# Feed generation is usually not desired when developing\nFEED_ALL_ATOM = None\nCATEGORY_FEED_ATOM = None\nTRANSLATION_FEED_ATOM = None\nAUTHOR_FEED_ATOM = None\nAUTHOR_FEED_RSS = None\n\n# Blogroll\nLINKS = (('Pelican', 'http://getpelican.com/'),\n ('Python.org', 'http://python.org/'),\n ('Jinja2', 'http://jinja.pocoo.org/'),\n ('You can modify those links in your config file', '#'),)\n\n# Social widget\n#SOCIAL = (('You can add links in your config file', '#'),\n# ('Another social link', '#'),)\nSOCIAL = (('twitter', 'https://twitter.com/es3a10'),)\n\n\nDEFAULT_PAGINATION = 10\n\n# Uncomment following line if you want document-relative URLs when developing\n#RELATIVE_URLS = True\n"},{"alpha_fraction":0.731249988079071,"alphanum_fraction":0.7383928298950195,"avg_line_length":85.17948913574219,"blob_id":"b3361fae89f4a940c708cfe6cbc391d423ecfa13","content_id":"466da8b8777cbaf4edc5e4107636326ebbba7f8c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":3400,"license_type":"no_license","max_line_length":508,"num_lines":39,"path":"/content/art006.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"¿Qué vamos a hacer en el makespace?\n#################################################\n\n:date: 2015-10-06\n:tags: Maker\n:category: Maker\n:authors: Carlos Rodríguez\n:summary: Inicio de proyecto Makespace Cortes\n\nComo suele ser costumbre, realizar todo a última hora no es una buena práctica y sin embargo la mayoría de las veces siempre termino así, dejando todo para el final. \n\nEl curso ya está empezado y de alguna manera tenemos que empezar el proyecto. A la espera de lo que mis compañeros puedan aportar al proyecto, y teniendo en cuenta que llevo un grupo de PMAR (programa de mejora del aprendizaje) la idea que llevo hacer es la siguiente:\n\n - Elaborar un **mobiliario móvil** que, hasta que tengamos asignado un espacio físico real, permita dividir una estancia en dos ambientes a través de separadores móviles elaborados con palets. También con Palets podremos crear algún tipo de bancada que dé al espacio un toque \"maker\".\n \n - Fomentar el pensamiento computacional como forma de afrontar y resolver problemas o retos y que tiene similitudes con el algebra como forma de abstraer y resolver matemáticamente problemas de la vida real. Para ello vamos a hacer el curso de **sctratch** que tenemos en la página **código 21**: http://aprendecodigo21.educacion.navarra.es/\n \n - Introducción a la electrónica mediante proyectos con Arduino. El año pasado, en el proyecto de la impresora 3D descubrimos que para el modelado 3D existe una herramienta online muy sencilla e intuitiva: **Tinkercad** (https://www.tinkercad.com/). Autodesk, actual propietaria de Tinkercad, está detrás de un montón de aplicaciones online educativas y relacionadas con el mundo maker. La aplicación **123d Circuits** permite implementar proyectos con Arduino de forma virtual (https://123d.circuits.io/)\n \n - Aprovechando el Huerto Escolar, trabajaremos los hábitos de salud estudiando las virtudes de los productos de la huerta y ayudaremos mediante la **impresión 3D** a la elaboración de letreros y soportes para el huerto \n \nPor lo tanto he identificado las siguientes necesidades de material (para comprar o reciclar):\n \n - **Palets**:\n - Pintura\n - Herramientas --> Para quitar láminas, para lijar, para pintar, para desmontar, Ruedas, Escuadras, Alfombra verde, martillos, ... \n - Material de seguridad, gafas y guantes \n - Madera para letreros \n - **Router** --> Queremos llevar internet al *makespace*\n - **Multimetro** --> Ley de Ohm llevada a la práctica, a la vez de que permite asegurarnos de que todo funciona\n - **Arduinos**: \n\t - kit \n\t - servos y \n\t - sensores\n - **Robot BQ**: Al menos un robot para un montaje sencillo. Por ejemplo: http://diwo.bq.com/evolution-cc-by-sa-2/ y su programación para evitar obstáculos que viene aquí: http://diwo.bq.com/printbot-evolution-obstaculos/\n - **Raspberry Pi** y tarjetas SD: Para montar ordenadores o proyectos dedicados. Otra forma de obtener ordenadores sería mediante el reciclaje. Pidiendo su donación a empresas de la zona (que igual también pueden donar palets)\n - **Lcd para impresora 3D**: Echamos en falta que la impresora 3D pueda imprimir sin tener un ordenador dedicado\n - **Micro proyector**: La difusión del conocimiento es la clave para que la comunidad *maker* crezca\n - **¿Máquina de Coser?**: Todo *maker* debería saber coser -yo no sé, :-( -"},{"alpha_fraction":0.6792452931404114,"alphanum_fraction":0.7082728743553162,"avg_line_length":28.95652198791504,"blob_id":"385def254f2229b851d020df0708cf2517e8f96c","content_id":"0ed4d3b9310893e0527d70dae3f8532d747f877c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":698,"license_type":"no_license","max_line_length":135,"num_lines":23,"path":"/content/art004.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"Mapas de Google para imprimir en 3D\n####################################\n\n:date: 2015-09-05\n:tags: 3D\n:category: 3D\n:authors: Carlos Rodríguez\n:summary: Imprimir en 3D mapas\n\nPara poder imprimir en 3D mapas de Google Maps, he ecnontrado una aplicación\nen *Github* de **ThatcherC** llamada **Terrain2STL**.\n\nEl proyecto está aquí: https://github.com/ThatcherC/Terrain2STL\n\nY la aplicación web aquí: http://jthatch.com/terrain2stl/\n\nCuando lo haya probado, comentaré mis impresiones.\n\nDe momento aquí va una imagen del mapa 3D del Moncayo y las Bardenas Reales (Aragón, Castilla-León y Navarra) vistos desde **FreeCad**:\n \n.. image:: ./img/moncayo_freecad.png\n\t:width:\t400 px\n\t:alt:\t'moncayo_freecad'\n"},{"alpha_fraction":0.6909090876579285,"alphanum_fraction":0.717090904712677,"avg_line_length":46.41379165649414,"blob_id":"1f7ae177d29d8f22320111172fc75892fdd33bd9","content_id":"1cab3614d39dbda1978047482d7f65fac9d13f74","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":1396,"license_type":"no_license","max_line_length":240,"num_lines":29,"path":"/content/art011.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"Robot controlado por Aplicación app inventor\n##########################################################\n\n:date: 2016-11-14\n:tags: Robots, Maker\n:category: Maker\n:authors: Carlos Rodríguez\n:summary: Robot controlado por Aplicación app inventor\n\n#. Hola Mundo para probar el módulo HC05\n \n He empezado siguiendo este enlace: \n \n `Tutorial de arduino con bluetooth por kio4 <http://kio4.com/appinventor/9bluetootharduino.htm>`_\n \n **IMPORTANTE:** Un problema encontrado es que cuando quiero subir el programa arduino si tengo conectados los pines tx y rx al módulo bt falla, por eso cualquier modificación al programa arduino hay que hacerlo con el módulo quitado.\n \n En mi caso el módulo se llama HC051 y funciona a 57600 baudios, ya que en su día lo configuré así para que funcionará una extensión de Scratch en Python para programar Arduino desde Scratch con `S2A <https://github.com/MrYsLab/s2a_fm>`_\n \n Hice une módulo bt shield, éste va a 9600 baudios y se llama HC-05. Por último otro para zowi, que se llama zowi y va a 19200.\n\n\n \n\n La aplicación del móvil permite conectarse por Bluetooth al arduino y encender o apagar el led 13 pulsando los botones correspondientes\n \n#. Del led a los motores\n \n Bueno, una vez probado el módulo Bluetooth estamos en disposición de programar el robot y la aplicación para su manejo desde el móvil ;-)\n"},{"alpha_fraction":0.6942098736763,"alphanum_fraction":0.7092882990837097,"avg_line_length":50.4375,"blob_id":"b14f3d02d94bee5468b93ad0e260a33cc99640e2","content_id":"0522cdde85f8efb686e5df5957a8b206bd02f623","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":1664,"license_type":"no_license","max_line_length":213,"num_lines":32,"path":"/content/art010.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"Recetas de cacharreo con la Raspberry Pi\n##########################################################\n\n:date: 2016-11-04\n:tags: Raspberry\n:category: Raspberry\n:authors: Carlos Rodríguez\n:summary: Recetas de cacharreo con la Raspberry Pi\n\n#. Configurar la raspberry como router\n\n Como en casa el router que tengo no me da buena cobertura por todos los sitios, he decidido poner un repetidor en mitad de casa cuando necesite una mayor cobertura.\n \n He empezado por ponerlo como punto de acceso y para ello he seguido el tutorial de este enlace: \n \n `Raspberry Pi como Punto de Acceso <http://www.redeszone.net/raspberry-pi/manual-para-configurar-raspberry-pi-como-un-router-wi-fi/>`_\n \n \n De todas las tarjetas wifi usb que tengo por casa, la única que admite modo AP en una Dlink DWL-G22 con chipset Ralink RT2571W.\n \n Respecto a los pasos que pone que hay que seguir, he hecho todo igual salvo en la configuración */etc/network/interfaces*, que en lugar de poner *allow hotplug wlan0*, he tenido que poner *allow-hotplug wlan0*\n \n#. Configurar sonic-pi en ubuntu\n\n He tenido problema con el jack, la solución la he encontrado en:\n \n `ConfigurarSonicPi <https://www.miskatonic.org/2015/01/17/setting-up-sonic-pi-on-ubuntu/>`_\n \n También hay que tener en cuenta que no arrancará sonic-pi a no ser que tengamos iniciado el jack.\n Para ello tenemos que arrancarlo desde el terminal ejecutando:*qjackctl* y dándole a *iniciar*.\n Si no nos funcionara, nos vamos a *setup* y seleccionamos interfaz: *hw:PCH,0*\n Si al cerrar todo no tenemos acceso a los dispositivos de audio, desde la terminal: *pulseaudio*\n \n \n "},{"alpha_fraction":0.7365967631340027,"alphanum_fraction":0.746503472328186,"avg_line_length":38.906978607177734,"blob_id":"bfc4b5b76f718d7392f852122a1d42d6ff832147","content_id":"5b483474246ece2724f17369996b10219c108277","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":1731,"license_type":"no_license","max_line_length":108,"num_lines":43,"path":"/content/art002.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"¿Cómo está hecho el blog?\n#########################\n\n:date: 2015-07-28\n:tags: \n:category: Pelican\n:authors: Carlos Rodríguez\n:summary: Github y Pelican para crear el blog\n\nEste blog está hecho con **Pelican** y está alojado en **Github Pages**. La\nprincipal razón es la de utilizar Software Libre, y documentar en \nRestructuredtext (.rst). Aparte de las ventajas de usar software libre,\nel poder documentar el blog offline me ha animado a aprender Pelican y \nusarlo.\n\nHe creado un repositorio *fuente_web* que contiene los artículos en formato .rst\n\nEl caso es que el contenido en .rst hay que transformarlo a .html y\nademás hay que alojarlo en el repositorio de *GitHub* que hace de servidor web.\nPues bien, además de crear el repositorio *es3a10.github.io* hay que pasar\na las fuentes unos script de python que manden el contenido. \nEn un terminal y desde la carpeta donde tengamos el repositorio local *fuente_web* \ntendremos que ejecutar los siguientes comandos: ::\n\n\t$ pelican content -o output -s pelicanconf.py\n\t$ ghp-import output\n\t$ git push git@github.com:es3a10/es3a10.github.io.git gh-pages:master\n\n**NOTA:** En el caso de que en lugar de ser la página personal sea una página de proyecto,\npor ejemplo, el proyecto matemáticas, los comandos serían: ::\n\n\t$ pelican ./fuente_web/content -o ./fuente_web/output -s ./fuente_web/pelicanconf.py\n\t$ ghp-import ./fuente_web/output\n\t$ git push origin gh-pages\n \n\n\nOtra cosa que hay que hacer, si no se ha hecho, crear una clave pública-privada para\nla conexión ssh al servidor *GitHub*. La clave RSA hay que meterla en los *settings*\nde nuestra cuenta *GitHub*.\n\n\n**NOTA:** Si ponemos fotos en una carpeta aparte, tendremos que copiar esa carpeta en la carpeta *./output*.\n"},{"alpha_fraction":0.7807488441467285,"alphanum_fraction":0.7843294143676758,"avg_line_length":59.90951156616211,"blob_id":"e57aaad2d86094acaf019c1bb62e781cb356c27c","content_id":"9277dcc198f4928ae67d00a680f11a07c0d4dd3d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":26625,"license_type":"no_license","max_line_length":193,"num_lines":431,"path":"/content/art001.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"Presentación y Proyecto\n#######################\n\n:date: 2015-07-27\n:tags: \n:category: General\n:authors: Carlos Rodríguez\n:summary: Presentación del blog\n\nEste año vamos a intentar realizar un espacio Maker en el instituto IESO Bardenas Reales. La idea se presentó como proyecto de innovación y ha sido aceptado. Veremos qué somos capaces de hacer.\n\nOs dejo con la descripción del proyecto:\n\n\n\n**Imagina, crea, difunde: \"Makespace\" Bardenas** \n\nMayo, 2015\n\n*\"If you have an apple and I have an apple and we exchange apples then you and I will still each have one apple. \nBut if you have an idea and I have an idea and we exchange these ideas, then each of us will have two ideas.\" - George Bernard Shaw*\n\n\n#. Título del proyecto\n\tImagina, crea, difunde: \"Makespace\" Bardenas\n\n#. Palabras clave\n\tCreatividad, Tecnología, Conocimiento Libre\n\n#. Nombre del centro o centros donde se desarrollará el proyecto.\n\tIESO Bardenas Reales\n\n#. Nombre y apellidos del coordinador o coordinadora.\n\tEl autor del documento es Carlos Rodríguez Jaso.\n\n#. Breve descripción del proyecto\n\tSe pretende crear un espacio informal de fabricación digital con algunas herramientas que\n\tla tecnología actual nos proporciona: Arduino, Scratch, Raspberry Pi e Impresión 3D (entre otras)\n\n#. Índice\n\t1. Título del proyecto\n\t2. Palabras clave\n\t3. Nombre del centro o centros donde se desarrollará el proyecto\n\t4. Nombre y apellidos del coordinador o coordinadora\n\t5. Breve descripción del proyecto\n\t6. Índice\n\t7. Justificación \n\t8. Objetivos\n\t9. Contenidos y actividades\n\t10. Metodología\n\t11. Competencias clave y materias o asignaturas relacionadas\n\t12. Equipo docente implicado\n\t13. Alumnado\n\t14. Participación de la Comunidad Educativa\n\t15. Recursos humanos y materiales y alianzas con otras instituciones\n\t16. TIC que se van a incorporar\n\t17. Fases y temporalización\n\t18. Formas de evaluación\n\t19. Formación.\n\t20. Presupuesto.\n\n#. Justificación\n\tComo resultado de la creación de una impresora 3d en el centro, se ha descubierto\n\tun tipo de aprendizaje informal que constituye la base de este proyecto y que si bien\n\tya se ha trabajado, se pretende explorar en mayor profundidad. Si la construcción de la impresora\n\t3d surgió como una evolución al trabajo realizado hace dos años en el campo de la robótica, \n\tla propuesta actual surge como evolución del de la impresora.\n\t\n\tLa democratización de los medios de producción digitales, y últimamente\n\tla extensión de este fenómeno al mundo físico a través de impresoras 3d y máquinas de fabricación digital,\n\tha hecho que hayan proliferado centros espontáneos donde se juntan entusiastas de la fabricación \"tecnológica\" DIY -Do it yourself- :\n\tHablamos de los Fab Labs, Makespaces u otras denominaciones que vienen a ser\n\tparecidas. \n\t\n\tEn este tipo de centros se reúnen personas que ven en el \"hacer cosas\" (makers en inglés)\n\tuna forma de aprender\n\ty de emplear su tiempo de ocio. Este tipo de reuniones suelen ser informales, y\n\taparte de fomentar los encuentros sociales, pretenden compartir y difundir al resto de integrantes\n\tlos proyectos realizados o que se encuentran en proceso. \n\tAparte de esas ganas por crear cosas y difundirlas, aunque no\n\tsea requisito indispensable, estos \"inventores\" del s.xxi se caracterizan \n\tpor la pasión por la tecnología y por promover el conocimiento libre como un bien público y colectivo.\n\t \n\tLa organización de estos centros se basa en la meritocracia, y por tanto\n\tcada uno es \"libre\" de entrar y salir cuando quiera, y de aportar o recibir lo que le interese.\n\tAunque el perfil de \"maker\" es eminentemente tecnológico, ya que ésta es la que ha propiciado la\n\tdemocratización de los medios de autoproducción, la \"creación\" de objetos no se limita a objetos\n\ttecnológicos. Se considera \"maker\" a todo el que construye cosas por sus propios medios, \n\tsean muebles, vestidos, alimentos, etc.\n\t\n\t(Referencias: http://makespacemadrid.org/que-es-el-makespace/ , http://es.wikipedia.org/wiki/Fab_lab, \n\thttps://www.youtube.com/watch?v=N2apWHiX02c&feature=youtu.be)\n\t\n\tDe acuerdo con todo lo anterior se propone construir un espacio en el centro donde se puedan\n\tcrear cosas fomentando, además de las relaciones sociales, aspectos como\n\tla creatividad y proactividad del alumnado, el conocimiento libre, el trabajo colectivo,\n\tel consumo responsable, etc. Pero sobre todo, transmitirlos al resto de la comunidad educativa como una\n\tforma de aprendizaje significativo e informal y de conciencia social.\n\t\n\t#. Principales ideas sobre las que subyace el proyecto\n\t\n\t\t- La creatividad es una forma natural de aprendizaje. Si observamos a los niños veremos que de\n\t\t manera natural crean sus juguetes, sus disfraces, les encanta desmontar las cosas para\n\t\t volver a montarlas. Desde este proyecto se pretende recuperar o potenciar esta \n\t\t actividad también en los adolescentes.\n\t\t \n\t\t- Mediante la \n\t\t construcción o manipulación de objetos, normalmente obtenemos un aprendizaje más significativo y duradero\n\t\t que si la adquisición del conocimiento se hace por otros medios.\n\t\n\t\t- El conocimiento libre se basa en compartir el conocimiento, en difundirlo.\n\t\t Por una parte el propio proceso de documentar, explicar y compartir lo que se ha hecho\n\t\t constituye una excelente forma de aprender y reforzar contenidos, pero por otra, \n\t\t el hacerlo público permite que \n\t\t otras personas puedan aprender e incluso mejorar ese conocimiento aportado. En definitiva,\n\t\t en la construcción de un conocimiento colectivo donde la suma total es mayor que\n\t\t la suma individual si no se pusieran al servicio del resto.\n\t\n\t\t- Nuestra sociedad se caracteriza por el alto nivel tecnológico. Por tanto es\n\t\t conveniente que nuestro alumnado conozca y domine la tecnología, que sepa cómo está\n\t\t hecha.\n\t\n\t\t- La creatividad como revulsivo a la crisis. Los países occidentales no pueden competir\n\t\t con la producción desde países emergentes. Los costes de producción siempre serán menores\n\t\t y por lo tanto estamos condenados a que en un mercado globalizado como el actual, la\n\t\t producción acabará realizándose en aquellos países con salarios más bajos. Es por ello que\n\t\t una alternativa puede estar en dirigir los esfuerzos hacia la innovación y desarrollo de nuevas\n\t\t ideas que se puedan materializar en nuevos modelos de negocio. Fomentando la creatividad ayudamos\n\t\t a desarrollar\n\t\t la proactividad y el emprendimiento personal del alumnado. Por tanto lo estamos preparando para ese, más que probable,\n\t\t requerimiento del sistema productivo.\n\t\t\n\t\t- Productor-Consumidor frente a Consumidor. La tecnología nos permite acceder a la\n\t\t \"producción\" y/o fabricación de objetos. Durante épocas anteriores la sociedad\n\t\t se ha caracterizado por el elevado consumo. La disminución de la capacidad adquisitiva \n\t\t de nuestra sociedad en crisis, el medio ambiente, e incluso la ética y respeto hacia esa parte \n\t\t del mundo sin recursos, son solo algunas de las razones por las que hay tomar conciencia de que el \n\t\t consumo ha de ser responsable. En ese sentido, el reciclaje y la autofabricación pueden servir de ayuda.\n\t\n\t#. Líneas de actuación\n\t\n\t\tPara elaborar el espacio maker se pretende trabajar desde diferentes ámbitos pero que\n\t\ten realidad están interrelacionados:\n\t\t\n\t\t- Espacio maker: Dotar al espacio de ordenadores, proyector, equipos de soldadura, equipos de comunicación, ...\n\t\t\n\t\t- Programación: Introducir al alumnado en el mundo de la programación a través de Scratch (https://scratch.mit.edu/about/)\n\t\t\n\t\t- Robótica: Participar el First Lego League (http://www.firstlegoleague.es/) y replicar el trabajo con Printbots (robots imprimibles de bajo coste)\n\t\t\n\t\t- Impresión 3D: Mejorar la impresora 3D y profundizar en el modelado y diseño en 3D\n\t\t\n\t\t- Hardware libre: Realizar diferentes proyectos de iniciación con Arduino (http://www.arduino.cc/) y Raspberry Pi (https://www.raspberrypi.org/help/what-is-a-raspberry-pi/)\n\n#. Objetivos\n\tPodemos enumerar los siguientes objetivos a largo plazo:\n\t\n\t\t- Dotar al centro de un espacio \"maker\"\n\t\t- Promocionar en el centro la creatividad como forma de aprendizaje informal\n\t\t- Desarrollar en el alumnado la proactividad, la autonomía y la responsabilidad en la gestión de un espacio propio \n\t\t- Promocionar en el centro el uso de la tecnología y el conocimiento libre como herramientas para desarrollar la creatividad\n\t\t- Fomentar, mediante el espacio maker, las relaciones sociales, la difusión de experiencias creativas y la consecución de objetivos colectivos\n\t\t- Abrir el espacio \"maker\" al exterior mediante la celebración de eventos como Scratch Day, Arduino Day o una específica de tecnología 3D\n\t\t- Evolucionar la Impresora 3D y los conocimientos en modelado 3D que ya se hicieron en el centro\n\t\t- Prestar servicio de impresión 3D a todo aquel que lo requiera con fines educativos\n\t\t- Ampliar los conocimientos de programación Scratch y extenderlos al mundo físico mediante el uso de Arduino\n\t\t- Desarrollar desafíos de robótica tanto de robots comerciales Lego como robots \"imprimibles\" con Arduino\n\t\t- Extender el concepto de creatividad a otros ámbitos y áreas: Por ejemplo el huerto, muebles DIY , \n\t\t cocina creativa, canal de youtube con noticias del centro, papiroflexia, \n\t\t creaciones literarias, plásticas, musicales, ..\n\t\t- Fomentar hábitos de consumo responsable a través de la autofabricación y el reciclaje\n\t\t- Acercar al alumnado experiencias creativas relacionadas con diferentes familias profesionales \n\t\t como pueda ser diseño, electrónica, mecanizado o informática, \n\t\t de manera que puedan servir de orientación de cara a estudios posteriores (bien de formación\n\t\t profesional o de grados)\n\t\t\n#. Contenidos y Actividades\n \tPor la naturaleza del proyecto, el listado de contenidos depende de las propuestas de los propios componentes\n\tdel proyecto, y por tanto la lista que aparece a continuación podría ser orientativa.\n \n \tContenidos curriculares básicos:\n \t- Mecánica y física: Conceptos como velocidades lineales, angulares, engranajes, ...\n \t- Ley de Ohm y Corriente eléctrica\n \t- Componentes electrónicos básicos: Resistencias, Tipos de Motores, ...\n \t- Arquitectura y redes de Ordenadores\n \t- Fundamentos de Programación a través de Scratch (si finalmente aparece en el currículo de Secundaria, como en Primaria)\n \tContenidos específicos del Espacio Maker:\n\t - Montaje de equipos con Raspberry Pi \n\t - Reciclaje de equipos\n \t- Router o Punto de Acceso: Configuración de un Punto de Acceso a Internet\n \t- Protocolo de seguridad en el uso del Espacio Maker\n \t- Mantenimiento de un portal o blog, en el que se documenten los trabajos realizados\n\t\t- Desarrollo de talleres abiertos al público: Scratch Day, Arduino Day y Taller 3D\n \tContenidos específicos de la Impresora 3D:\n\t\t- Servicio de Impresión Remoto con Octoprint (http://octoprint.org/)\n\t\t- Pantalla lcd para impresión desde tarjeta SD\n\t\t- Modelado de Objetos 3D\n \tContenidos de Robótica:\n\t\t- Desafío de la First Lego League\n\t\t- Desarrollo de un Printbot que emule el desafío FLF\n \tContenidos de Programación con Scratch:\n\t\t- Animaciones\n\t\t- Videojuego\n\t\t- Problemas geométricos\n\t\t- Simulaciones virtuales de Robots\n \tContenidos de Hardware Libre:\n\t\t- Iniciación a Arduino con el programa online 123d Circuits (http://123d.circuits.io/)\n\t\t- Control desde Arduino de los principales componentes electrónicos. \n\t\t- Instalación de sistema operativo en Raspberry Pi\n\t\t- Acceso Remoto a Raspberry Pi\n\t\t- Instalación en Raspberry Pi del Software Octoprint para controlar la Impresora 3D \n\t\n\tEn todo caso, las actividades vendrán determinados por documentación libre que puedas ser aprovechada y siguiendo\n\tla metodología del proyecto.\n\tAlgunos ejemplos de entornos de documentación sobre los que se podría trabajar serían:\n\t\n\t\t- Portal Código21 del Gobierno de Navarra sobre tecnologías creativas: http://codigo21.educacion.navarra.es/alumnado/recursos/\n\t\t- Escuela Maker de bq: http://diwo.bq.com/\n\t\t- Revista libre sobre el ecosistema Raspberry Pi: https://www.raspberrypi.org/magpi/\n\t\t- Portal de la comunidad de Arduino: http://www.arduino.cc/#\n\t\t\n\n#. Metodología\n\tLa metodología principal a aplicar aparece resumida en el título del proyecto: Imagina, crea y difunde. Y está\n\tinspirada, entre otras, en la que promueve Mitch Resnick, responsable del proyecto Scratch y que se resume en \n\tla siguiente sentencia de pseudocódigo: \n\t*forever(imagine, program, share);*\n\t\n\t**Imagina:**\n\tAunque\n\ten los contenidos ya aparece qué se quiere hacer, esto no cierra la puerta a que en cualquier momento, se\n\tacepten propuestas o ideas diferentes a realizar. De hecho, es importante que aquellos que quieran participar\n\ten el proyecto se involucren en la toma de decisiones. A su vez, dentro de cada línea de trabajo, las actividades\n\tserán lo más abiertas posibles para que sean lo propios \"makers\" los que decidan qué hacer -Por ejemplo, si\n\testamos trabajando con Scratch, las actividades propuestas serán del tipo: Invéntate una historia que ...-\n\t\n\t**Crea:**\n\tSe trata de aprender haciendo, que los participantes se sientan responsables de los productos finales. La experiencia\n\tde haber montado un Impresora 3D nos ha demostrado que cuando uno crea algo, aparte de aprender, durante ese\n\tproceso en muchas ocasiones aparecen nuevas ideas para desarrollar, entrando en una especie de espiral creativa y por\n\ttanto de aprendizaje que parece no tener fin -Por ejemplo, durante la construcción de la impresora, vimos que podíamos modificarla para\n\tconvertirla en un plotter. Eso nos hizo plantearnos dibujos geométricos, que a su vez nos hicieron investigar \n\tlas coordenadas polares. Pero para pasarlas a cartesianas, nos hizo también introducir conocimientos de trigonometría-\n\t\n\t**Difunde:**\n\tEsta es la idea principal del conocimiento libre: se trata de compartir el conocimiento adquirido. Básicamente por tres razones:\n\t\n\t\t- Al documentar y explicar lo construido, reforzamos los conocimientos adquiridos\n\t\t- Permitimos que otras personas aprendan de lo que hemos hecho\n\t\t- Nos permite aprender del trabajo de los demás y mejorarlo\n\t\t\n\tEste punto es el que más suele costar al alumnado, sin embargo constituye la clave del éxito del proyecto por lo que se pretende darle especial relevancia. \t\n\tEn este apartado de difusión, se pretende además, abrir las puertas del centro con una\n\tserie de actividades abiertas al público: Padres, niños, otros centros, etc.\n\t\n\tA nivel organizativo, la característica principal estará en la flexibilidad. \n\tBasándonos en la experiencia de la construcción de la Impresora 3d, donde\n\ttodos los viernes en el segundo recreo se hacía una reunión informal para\n\tcomentar lo que se había hecho durante esa semana, se propone seguir con \n\tesa dinámica. Todos lo viernes en el segundo recreo se quedará para compartir\n\texperiencias.\n\tAdemás, como los protagonistas deben ser los alumnos, se va a intentar que \n\tpuedan ir a este espacio a trabajar durante los segundos recreos de manera\n\tautónoma y responsable, por lo que deberán organizarse ellos y velar por el buen funcionamiento \n\tdel espacio. \n\tConviene resaltar que este tipo de metodología y organización permite\n\tatender bastante bien a la diversidad, puesto que se cede autonomía\n\tal alumnado para determinar qué se quiere crear y en qué quiere participar.\n\tEn todo proceso creativo aparecerán tareas o actividades de diferente dificultad\n\tpermitiendo a cada alumno elegir en que contribuye al éxito colectivo. En los últimos años\n\themos observado una gran heterogeneidad en el alumnado, encontrando o muy buenos o simplemente\n\t\"menos\" buenos y este tipo de propuestas pueden ser positivas.\n\n\tCualquier otro aspecto quedaría pendiente de definir, ya que se desconocen los\n\tgrupos, horarios y profesores con los que se va a contar. Pero es indudable, que\n\tsiempre se va a poder adaptar tiempos y programaciones para poder contribuir al\n\tproyecto -Un ejemplo: emplear alguna hora de tutoría para diseñar y crear carteles\n\tpara promocionar el Scratch Day-\n\n\tDesde el Departamento de Educación del Gobierno de Navarra aparece una clara intención\n\tde potenciar aspectos como la programación y la robótica, tal como aparece en el portal\n\tCódigo21 (http://codigo21.educacion.navarra.es/). Algunos de los contenidos que aparecen en este proyecto \n\tsí que aparecen\n\tconcretados en el currículo de Primaria, sin embargo, a fecha de elaboración de este \n\tdocumento, en Secundaria no se sabe dónde y si se concretarán. \n\tPor tanto es posible que para el próximo curso haya algo más definido para asignaturas\n\tcomo matemáticas o tecnología, y en ese caso se podrían modificar las programaciones para \n\tadaptarlas al proyecto.\n\n\tEn cualquier caso, bajo mi punto de vista, no todo el alumnado estará lo \n\tsuficientemente motivado\n\tpara desarrollar estas capacidades, por lo que la creación de un espacio específico de uso voluntario dentro\n\tdel centro permitirá potenciar la creatividad de aquellos alumnos que sientan inquietud\n\tpor el mundo \"maker\" sin necesidad de obligar al resto.\n\t \n#. Competencias clave y materias o asignaturas relacionadas con el proyecto\n\tEl escenario planteado supone un excelente marco para desarrollar las competencias claves. \n\tLa adquisición de las mismas irá ligada al mayor o menor éxito en el desarrollo del proyecto:\n\n\t- **Comunicación lingüística:**\n\t Esta competencia se desarrollará durante todo el proyecto: al documentarse para\n\t ver qué se hace, al tomar decisiones y sobre todo al difundir los resultados. Se intentará, además, \n\t que algunos resultados se produzcan en otras lenguas para su mayor difusión en \n\t Internet (euskera, inglés o francés).\n\t- **Competencia matemática y competencias básicas en ciencia y tecnología.**\n\t La mayoría de los contenidos propuestos requieren desarrollar la inteligencia\n\t lógico-matemática y el razonamiento formal. Además se desarrollan conceptos\n\t científicos fundamentalmente de mecánica y electrónica.\n \t- **Competencia digital:**\n\t El proyecto consiste en la gestión de un espacio de fabricación digital, por lo\n\t que todas las \"creaciones\" tendrán su componente digital. En ese sentido, conviene\n\t destacar que algunas de las propuestas suponen la digitalización del mundo físico:\n\t impresión de objetos reales mediante el modelado digital de objetos 3D, la interacción\n\t con el mundo físico mediante la programación de Arduino mediante sensores y\n\t actuadores, etc. \t \n\t- **Aprender a aprender:**\n\t Si algo tiene este tipo de aprendizaje informal es la necesidad de obtener la\n\t la documentación de lo que se quiere hacer de fuentes externas, por lo que\n\t se está, en realidad, transmitiendo una forma de aprender por uno mismo.\n\t- **Competencias sociales y cívicas:**\n\t El espacio \"maker\" en realidad es un punto de encuentro para trabajar y compartir\n\t experiencias, por lo que continuamente se desarrollarán las habilidades sociales.\n\t Además la responsabilidad de la gestión del espacio recaerá sobre todo el colectivo\n\t participante.\n\t- **Sentido de iniciativa y espíritu emprendedor:**\n\t La creatividad fomenta la generación de ideas y la proactividad, cualidades imprescindibles\n\t para la iniciativa y emprendimiento. Por no extender el alcance del proyecto,\n\t no se desarrollarán estas competencias desde el punto de vista de la gestión empresarial.\n\t- **Conciencia y expresiones culturales:**\n\t La creatividad, de nuevo, permite la expresión más artística del alumnado. Hay que resaltar que los\n\t verdaderos \"makespaces\" y \"fablabs\" son también centros de creación de arte digital.\n\n#. Equipo docente implicado\n\tPor las características de los contenidos el perfil de profesorado que más se ajusta es el de tecnología, y los\n\tprofesores de matemáticas que realizaron el curso de robótica educativa hace un par de años. \n\tSin embargo, dada la naturaleza flexible\n\tdel proyecto, también podrá participar aquel profesorado con inquietudes y propuestas creativas.\n\n#. Alumnado\n\tSe propondrá la participación voluntaria a todo el alumnado del centro, que tendrá libertad para \n\tacudir al espacio maker durante los segundos recreos.\n\tEn el caso de que algún profesor implicado pueda realizar alguna adaptación de la programación en alguno de \n\tsus grupos, dicho alumnado también participará en los trabajos que correspondan. En este sentido, por ejemplo, \n\tpara la difusión y documentación \n\tdel proyecto se puede contar con el profesorado del Área de Lenguas y de Educación Plástica y Visual.\n\n#. Participación de la Comunidad Educativa\n\tDurante las tres jornadas de apertura del espacio, con sus talleres correspondientes, el resto de la comunidad\n\teducativa -padres, alumnos y profesores de otros centros, antiguos profesores, etc.- y no educativa \n\t-la entrada será libre- podrá participar junto al alumnado del centro.\n\n#. Recursos humanos y materiales y alianzas con otras instituciones\n\tMuchas personas creativas (y sobre todo \"hacedoras DIY\") se caracterizan por saber adaptarse a los\n\trecursos que tiene y reciclar en la medida de lo posible. Este tipo de actitud, como hemos dicho, se intentará transmitir dentro del espacio \"maker\".\n\tA pesar de ello, el acceso a una serie de recursos facilitaría su implantación.\n\tPodemos enumerar los recursos por línea de actuación:\n\t\n\t\t- Espacio Maker: Se necesitarían tres ordenadores de bajo coste para aprender primero, y poder trabajar \n\t\t después. Montarlos a partir de placas Raspberry Pi puede ser una buena alternativa. \n\t\t Las Raspberry solo son placas de procesamiento, para montar los ordenadores completos se necesitan además\n\t\t periféricos: Monitores,\n\t\t teclados, etc. Una opción interesante sería reciclar componentes de empresas y entidades de la zona.\n\t\t La impresora 3D ya está en el centro, por lo que pasaría a ubicarse en este espacio.\n\t\t Se intentaría dotar al espacio de un punto de acceso, y que el alumnado \n\t\t configure y gestione el punto de acceso. Potenciar el uso de sus propios dispositivos en este entorno \n\t\t puede ser una experiencia piloto para acercar el concepto BYOD -Bring your own device- al centro.\n\t\t Puesto que el apartado de explicar y difundir lo realizado tiene especial relevancia, no\n\t\t estaría de más dotar al espacio de un proyector y un equipo de sonido inalámbrico.\n\t\t- Tecnología 3D: La impresora 3D ya está operativa, pero puede evolucionarse y mejorar.\n\t\t Entre estas mejoras estaría añadir una pantalla lcd, para imprimir sin ordenador, y\n\t\t también montar un servidor de impresión remoto para poder mandar a imprimir desde cualquier\n\t\t ordenador.\n\t\t El hardware para montar el servidor de impresión remoto se haría sobre uno de los ordenadores del punto anterior.\n\t\t También sería necesario adquirir bobinas de filamento para imprimir.\n\t\t- Robótica: La electrónica de los robots se puede hacer con placas Arduino. Sin embargo, para simplificar \n\t\t los cableados posteriores sería más conveniente utilizar unas placas adaptadas que comercializa, por ejemplo, la empresa bq. \n\t\t El resto de componentes electrónicos (servomotores, resistencias, ...) también se pordrían solicitar a bq.\n\t\t- Hardware Libre: Tanto los Arduinos como las Raspberry Pi serían los mismo que los utilizados en los puntos anteriores\n\n#. TIC que se van a incorporar\n\tTeniendo en cuenta que la impresora 3D ya se tiene y que se han probado herramientas de modelado 3D\n\t(FreeCad y TinkerCad), básicamente se incorporarán como \n\tnuevas las siguientes TIC:\n\t\n\t\t- Hardware: Electrónica Arduino y Ordenadores educativos Raspberry PI. Robots imprimibles\n\t\t- Software: Scratch, Variaciones de Scratch para programar Arduinos. Sistemas operativos para Raspberry PI\n\n#. Fases y temporalización\n\tSe propone el siguiente calendario orientativo:\n\t\n\t- Espacio Maker\n\t\t- Montaje de equipos e instalación de sistemas operativos: De Octubre a Diciembre\n\t\t- Montaje del punto de acceso: De Octubre a Noviembre\n\t- Programación\n\t\t- Introducción a la programación con Scratch: de Octubre a Diciembre\n\t\t- Programación con Scratch y Arduino: de Enero a Junio\n\t- Robótica \n\t\t- Desafío First Lego League: De Octubre a Mayo\n\t\t- Robots imprimibles: De Marzo a Junio\n\t- Tecnología 3D\n\t\t- Introducción al Modelado de Objetos en 3D: De Octubre a Diciembre\n\t\t- Modelado avanzado de Objetos en 3D: De Diciembre a Junio\n\t\t- Instalación del servidor de impresión remoto Octoprint y pantalla LCD: Enero\n\t- Hardware Libre\n\t\t- Proyectos sencillos con Arduino y Raspberry Pi: De Enero a Marzo\n\t\t- Proyectos de robótica con Arduino: De Marzo a Junio \n\t\t\n\t\n#. Formas de evaluación\n\tPara evaluar las actividades se propone valorar cada proyecto o trabajo con una encuesta a los integrantes. \n\tSe podría hacer, además, a través de Twitter como forma adicional de difusión del mismo de forma que, por ejemplo, si\n\tvaloran positivamente el proyecto hacen un retuit y si no lo valoran lo marcan como favorito.\n\tPara evaluar el trabajo, los profesores participantes podrán incluir criterios de calificación en sus \n\tprogramaciones. En el caso del autor de este documento, valorará el trabajo de aquellos alumnos que\n\tpertenezcan a alguno de sus grupos dentro del apartado de actitud que normalmente incluye en sus programaciones.\n\n#. Formación\n\tLa formación queda contemplada en la metodología del proyecto: Autoformación a partir de fuentes externas.\n\tDebido al esfuerzo que implica, la creación de un grupo de trabajo en centro facilitaría la formación\n\tdel profesorado que participe en el proyecto. De esta forma se adquiriría al menos la base tecnológica \n\tnecesaria para asesorar al alumnado.\n\t\n#. Presupuesto.\n\tArduinos, Raspberry Pis, Monitores y teclados, Punto de acceso, Bobinas de filamento para la impresora,\n\tProyector y equipo de sonido para el espacio \"maker\", Consumibles, ... son algunos de los materiales o\n\tproductos que habría que comprar. Establecer un presupuesto exacto resulta complicado porque ni los propios\n\tcontenidos del proyecto está concretados al 100%. Sin embargo, la mayoría de los componentes son\n\tde bajo coste, por lo que con una cantidad de unos 1000€ creemos que es posible montar un espacio que\n\tsiente las bases de una verdadero \"espacio maker\". Posiblemente, el primero hasta la fecha en un centro educativo\n\tnavarro, \n\ty avalado por el trabajo previo del montaje y desarrollo de nuestra impresora 3D. \n"},{"alpha_fraction":0.6628289222717285,"alphanum_fraction":0.6891447305679321,"avg_line_length":29.450000762939453,"blob_id":"983266043aedff6e7b0a457a379c93b4d5af6e7b","content_id":"e0ce94babc30543ad63faf2619e0236253cc5feb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":617,"license_type":"no_license","max_line_length":170,"num_lines":20,"path":"/content/art005.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"Insertar código latex en nuestros artículos .rst\n#################################################\n\n:date: 2015-09-29\n:tags: latex\n:category: Pelican\n:authors: Carlos Rodríguez\n:summary: Insertar código *latex* en un Pelican\n\nExiste la posibilidad de insertar código latex:\n\n\t:math:`\\frac{3}{2}\\cdot\\sqrt{12}`\n\n```\nEn realidad el código latex sería :math:`\\frac{3}{2}\\cdot\\sqrt{12}`.\n```\n\nEl renderizado que *Pelican* hace inicialmente del código latex no está muy logrado (cuando lo pasa a html). Si queremos mejores resultados podemos utilizar algún plugin:\n\n\thttps://github.com/barrysteyn/pelican_plugin-render_math"},{"alpha_fraction":0.7419354915618896,"alphanum_fraction":0.7903226017951965,"avg_line_length":40,"blob_id":"35a57ebcbcdffb961d58bfd63635fa3aa93a5c23","content_id":"0b72e19f971395a07f8b03840a1494dc953a270e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":124,"license_type":"no_license","max_line_length":60,"num_lines":3,"path":"/script_subir.sh","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"pelican content -o output -s pelicanconf.py\nghp-import output\ngit push git@github.com:es3a10/es3a10.github.io.git gh-pages:master\n\n"},{"alpha_fraction":0.7745392322540283,"alphanum_fraction":0.7835404872894287,"avg_line_length":79.44827270507812,"blob_id":"501e55ed2da103e78e142ef29a8feb2363c2c51c","content_id":"fa9acc27d073b654951b7f17289b7f4a66316859","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":4727,"license_type":"no_license","max_line_length":604,"num_lines":58,"path":"/content/art007.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"Artículo para el diario de Navarra\n#################################################\n\n:date: 2015-10-07\n:tags: Maker\n:category: Maker\n:authors: Carlos Rodríguez\n:summary: Artículo Espacio Maker para el Diario de Navarra\n\n Este año vamos a intentar realizar un espacio Maker en el instituto IESO Bardenas Reales. La idea se presentó como proyecto de innovación y ha sido aceptada. Veremos qué somos capaces de hacer. \n\nQué es un espacio Maker y cómo surge la iniciativa:\n\nCortes, curso 2014-2015: El centro se propone como reto construir una Impresora 3D de bajo coste. Algo que parecía ciencia-ficción era posible de hacer gracias a la información disponible en Internet: proyecto *REPRAP* (http://www.reprap.org/).\nLa idea principal del proyecto *REPRAP* era la construcción de impresoras auto-replicantes, es decir, que están construidas con piezas que hayan sido impresas por otras impresoras.\n\nAlgunas fotos del año pasado:\n\n.. figure:: ./img/probando.JPG\n\t:align: right\n\t:width:\t400 px\n\t:alt:\t'Probando la Impresora'\n\t\n\t(Probando la impresora)\n\t\t \n.. figure:: ./img/impresora_completa.jpg\n\t:width:\t400 px\n\t:alt:\t'Impresora completa'\n\t\n\t(La impresora completa)\n\t\n.. figure:: ./img/construyendo.JPG\n\t:width:\t400 px\n\t:alt:\t'Construyendo sólidos'\n\t\n\t(Construyendo sólidos platónicos)\n\t\nPero al buscar información en Internet de estas impresoras 3D, vimos que existía detrás una comunidad extremadamente activa cuya principal motivación era la construcción de cosas para aprender. Se trata de una versión moderna de los antiguos aficionados al bricolaje e inventores caseros, pero basados en la tecnología y con la ventaja de utilizar Internet para difundir todo el conocimiento, y por tanto de hacerlo accesible. En EEUU, a este tipo de personas se les llamó makers y pronto aparecieron locales donde socializar, construir y compartir ideas: Fablab o Makespace son los nombres más comunes. \n\nPedagógicamente, el movimiento maker ha aportado a la educación el fomento de la creatividad e innovación. También ha contribuido al desarrollo de un nuevo paradigma educativo de siglas STEAM (acrónimo de Cience, Technology, Engineering, Arts y Mathematics), y que promulga el desarrollo de dichas disciplinas desde una perspectiva transversal y conjunta.\n\nLa importancia que se le está dando a este movimiento ha hecho que a hasta el presidente de los EEUU, Barak Obama, esté promoviendo el uso de impresoras 3D en los centros educativos o que se organice desde la Casa Blanca la Semana Maker (https://www.whitehouse.gov/nation-of-makers)\n\nCon las ideas anteriores, desde el centro pretendemos construir un espacio de creación digital y no digital en el que se puedan desarrollar diferentes proyectos. Algunos de los retos planteados para este año son:\n\n - Elaborar un **mobiliario móvil** que, hasta que tengamos asignado un espacio físico real, permita dividir una estancia en dos ambientes a través de separadores móviles elaborados con palets. También con Palets podremos crear algún tipo de bancada que dé al espacio un toque \"maker\".\n \n - Fomentar el pensamiento computacional como forma de afrontar y resolver problemas o retos y que tiene similitudes con el algebra como forma de abstraer y resolver matemáticamente problemas de la vida real. Para ello vamos a hacer el curso de **sctratch** que tenemos en la página **código 21**: http://aprendecodigo21.educacion.navarra.es/\n \n - Introducción a la electrónica mediante proyectos con Arduino. El año pasado, en el proyecto de la impresora 3D descubrimos que para el modelado 3D existe una herramienta online muy sencilla e intuitiva: **Tinkercad** (https://www.tinkercad.com/). Autodesk, actual propietaria de Tinkercad, está detrás de un montón de aplicaciones online educativas y relacionadas con el mundo maker. La aplicación **123d Circuits** permite implementar proyectos con Arduino de forma virtual (https://123d.circuits.io/)\n \n - Aprovechando el Huerto Escolar, trabajar los hábitos de salud estudiando las virtudes de los productos de la huerta y ayudar mediante la **impresión 3D** a la elaboración de letreros y soportes decorativos \n\nPedagñiogicamente, el movimiento maker aporta a la educación el fomento creatividad e innovación y ha aparecido un nuevo paradigma educativotransversal de siglas STEM\n\nLa importancia que se le está dando a este movimiento ha hecho que a hasta el presidente de los EEUU Obama esté promoviendo el uso de impresoras 3D en los centros educativos o que se haya celebrado una feria en la misma CasaBlanca y la maker faire\n\nPara saber más sobre el movimiento maker, se puede consultar, por ejemplo, la web del Makespace Madrid (http://makespacemadrid.org)\n"},{"alpha_fraction":0.6788104176521301,"alphanum_fraction":0.7211896181106567,"avg_line_length":50.69230651855469,"blob_id":"dea5e94041eca118ae8a34cd0d04a8cd288def51","content_id":"6bea6b06b80ffaf09312987e944577f63485b19e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":1358,"license_type":"no_license","max_line_length":277,"num_lines":26,"path":"/content/art009.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"Parámetros para el cálculo de pasos para un escornabot\n##########################################################\n\n:date: 2016-10-28\n:tags: Varios\n:category: Maker\n:authors: Carlos Rodríguez\n:summary: Cálculo de pasos para un escornabot\n\nA la hora de calibrar un robot de dos ruedas siempre surge el tema de qué orden hay que darle al robot para que recorra cierta distancia o gire cierto número de grados. \n\nPara el recorrido recto influye el diámetro de la rueda que estemos usando, y en el giro la distancia entre las ruedas, que al final será el diámetro de la circunferencia que realice el robot mientras gira.\n\nVamos a poner el ejemplo del escornabot (modelo placidus) que es el que he montado. Según datos teóricos, el número de pasos es de **64** por **64** de la reductora, por lo que da un total de 4096 pasos por vuelta (para el modelo de motor que tengo recomiendan poner **4076**).\n\nEl diámetro de la rueda es de 72mm y la distancia entre ruedas es de 100mm.\n\n.. image:: ./img/escornabot.jpg\n :width: 400 px\n :alt: 'handout'\n\n\nCon simples reglas de tres y que pi*diametro es una vuelta podemos determinar los pasos que tiene que dar nuestro robot:\n\nPasos hacia para desplazarse: Si 4076 es pix72, entonces 200 será x Lo que da que x=3603 pasos\nPasos para girar 90 grados: Si 4076 es pix72, entonces (pix100)/4 Luego x=1415 \n"},{"alpha_fraction":0.4285714328289032,"alphanum_fraction":0.5357142686843872,"avg_line_length":15.142857551574707,"blob_id":"ae21c4807abda0a8a6259957362a0e3e672950d3","content_id":"445ee30aa2d89fa6c4f2579e6181ba5701dfced5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":115,"license_type":"no_license","max_line_length":33,"num_lines":7,"path":"/planificacion_curso.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"Fechas:\n\n\t3 sesiones de dos y media horas:\n \tDe 14:30 a 17:00\n 1ª Sesi\n 2ª Sesi\n 3ª Sesi"},{"alpha_fraction":0.651442289352417,"alphanum_fraction":0.6778846383094788,"avg_line_length":28.714284896850586,"blob_id":"986f35cfb86aca38709e8e656cb2f6706df8243c","content_id":"4c9d3da7f2fb13048a87a404aba5156768e4971f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":417,"license_type":"no_license","max_line_length":172,"num_lines":14,"path":"/content/art003.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"Handouts en documentos Lyx\n################################\n\n:date: 2015-08-21\n:tags: \n:category: Lyx\n:authors: Carlos Rodríguez\n:summary: Handouts en documentos Beamer con Lyx\n\nPara poner compilar los documentos sin tener en cuenta las pausas que pongamos a nuestra presentación, tenemos que indicar la clase *handouts* a nuestro documento *beamer*:\n\n.. image:: ./img/handout_lyx.png\n :width: 400 px\n :alt: 'handout'\n"},{"alpha_fraction":0.6930817365646362,"alphanum_fraction":0.7069182395935059,"avg_line_length":45.64706039428711,"blob_id":"6c03831e6cdc9a35698c4279626ee9e2dcc85e77","content_id":"83a06de5a7c0a5159ef933473b915c43b78325c9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"reStructuredText","length_bytes":802,"license_type":"no_license","max_line_length":113,"num_lines":17,"path":"/content/art008.rst","repo_name":"es3a10/fuente_web","src_encoding":"UTF-8","text":"Programas para modelar en 3D\n#################################################\n\n:date: 2015-12-22\n:tags: Varios\n:category: Maker\n:authors: Carlos Rodríguez\n:summary: Programas para modelar en 3D\n\nAquí van una colección de programas para modelar en 3D\n\n\t#. FreeCAD: Posiblemente el más completo y profesional (http://www.freecadweb.org)\n\t#. Blender: No lo he probado pero parece el más artístico (https://www.einsteinsworkshop.com/outreach/blockscad)\n\t#. OpenScad: Modelado paramétrico (http://www.openscad.org/)\n\t#. BlocksCad: Interfaz limitada de OpenScad, con la ventaja de poder trabajar online\n\t#. TinkerCad: Sencillo y online, ideal para llevarlo al aula -como el anterior- (https://www.tinkercad.com/)\n\t#. OnShapes: Otro online, parece más profesional. No lo he probado (https://www.onshape.com/)\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6745068430900574,\n \"alphanum_fraction\": 0.6820940971374512,\n \"avg_line_length\": 25.897958755493164,\n \"blob_id\": \"3d791adcceb4810f7ecc47be794dd24f8fdd240f\",\n \"content_id\": \"f1608f79b5fc1d0bfca51324addfc547fd841201\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1320,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 49,\n \"path\": \"/pelicanconf.py\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# -*- coding: utf-8 -*- #\\nfrom __future__ import unicode_literals\\n\\n\\nAUTHOR = u'Carlos Rodr\\\\xedguez'\\nSITENAME = u'Bit\\\\xe1cora Personal'\\nSITEURL = ''\\n\\nPATH = 'content'\\nTHEME = \\\"notmyidea\\\"\\n#THEME = \\\"simple\\\"\\n\\nTIMEZONE = 'Europe/Paris'\\nDEFAULT_LANG = u'es'\\nSTATIC_PATHS = [\\\"images\\\" ]\\nLOAD_CONTENT_CACHE = False\\n# The minimum number of articles allowed on the last page. Use this when you\\n# don’t want to have a last page with very few articles.\\nDEFAULT_ORPHANS = 0\\n# The maximum number of articles to include on a page, not including orphans.\\nDEFAULT_PAGINATION = 3\\n#Activate pagination.\\nWITH_PAGINATION = True\\n\\n\\n# Feed generation is usually not desired when developing\\nFEED_ALL_ATOM = None\\nCATEGORY_FEED_ATOM = None\\nTRANSLATION_FEED_ATOM = None\\nAUTHOR_FEED_ATOM = None\\nAUTHOR_FEED_RSS = None\\n\\n# Blogroll\\nLINKS = (('Pelican', 'http://getpelican.com/'),\\n ('Python.org', 'http://python.org/'),\\n ('Jinja2', 'http://jinja.pocoo.org/'),\\n ('You can modify those links in your config file', '#'),)\\n\\n# Social widget\\n#SOCIAL = (('You can add links in your config file', '#'),\\n# ('Another social link', '#'),)\\nSOCIAL = (('twitter', 'https://twitter.com/es3a10'),)\\n\\n\\nDEFAULT_PAGINATION = 10\\n\\n# Uncomment following line if you want document-relative URLs when developing\\n#RELATIVE_URLS = True\\n\"\n },\n {\n \"alpha_fraction\": 0.731249988079071,\n \"alphanum_fraction\": 0.7383928298950195,\n \"avg_line_length\": 85.17948913574219,\n \"blob_id\": \"b3361fae89f4a940c708cfe6cbc391d423ecfa13\",\n \"content_id\": \"466da8b8777cbaf4edc5e4107636326ebbba7f8c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 3400,\n \"license_type\": \"no_license\",\n \"max_line_length\": 508,\n \"num_lines\": 39,\n \"path\": \"/content/art006.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"¿Qué vamos a hacer en el makespace?\\n#################################################\\n\\n:date: 2015-10-06\\n:tags: Maker\\n:category: Maker\\n:authors: Carlos Rodríguez\\n:summary: Inicio de proyecto Makespace Cortes\\n\\nComo suele ser costumbre, realizar todo a última hora no es una buena práctica y sin embargo la mayoría de las veces siempre termino así, dejando todo para el final. \\n\\nEl curso ya está empezado y de alguna manera tenemos que empezar el proyecto. A la espera de lo que mis compañeros puedan aportar al proyecto, y teniendo en cuenta que llevo un grupo de PMAR (programa de mejora del aprendizaje) la idea que llevo hacer es la siguiente:\\n\\n - Elaborar un **mobiliario móvil** que, hasta que tengamos asignado un espacio físico real, permita dividir una estancia en dos ambientes a través de separadores móviles elaborados con palets. También con Palets podremos crear algún tipo de bancada que dé al espacio un toque \\\"maker\\\".\\n \\n - Fomentar el pensamiento computacional como forma de afrontar y resolver problemas o retos y que tiene similitudes con el algebra como forma de abstraer y resolver matemáticamente problemas de la vida real. Para ello vamos a hacer el curso de **sctratch** que tenemos en la página **código 21**: http://aprendecodigo21.educacion.navarra.es/\\n \\n - Introducción a la electrónica mediante proyectos con Arduino. El año pasado, en el proyecto de la impresora 3D descubrimos que para el modelado 3D existe una herramienta online muy sencilla e intuitiva: **Tinkercad** (https://www.tinkercad.com/). Autodesk, actual propietaria de Tinkercad, está detrás de un montón de aplicaciones online educativas y relacionadas con el mundo maker. La aplicación **123d Circuits** permite implementar proyectos con Arduino de forma virtual (https://123d.circuits.io/)\\n \\n - Aprovechando el Huerto Escolar, trabajaremos los hábitos de salud estudiando las virtudes de los productos de la huerta y ayudaremos mediante la **impresión 3D** a la elaboración de letreros y soportes para el huerto \\n \\nPor lo tanto he identificado las siguientes necesidades de material (para comprar o reciclar):\\n \\n - **Palets**:\\n - Pintura\\n - Herramientas --> Para quitar láminas, para lijar, para pintar, para desmontar, Ruedas, Escuadras, Alfombra verde, martillos, ... \\n - Material de seguridad, gafas y guantes \\n - Madera para letreros \\n - **Router** --> Queremos llevar internet al *makespace*\\n - **Multimetro** --> Ley de Ohm llevada a la práctica, a la vez de que permite asegurarnos de que todo funciona\\n - **Arduinos**: \\n\\t - kit \\n\\t - servos y \\n\\t - sensores\\n - **Robot BQ**: Al menos un robot para un montaje sencillo. Por ejemplo: http://diwo.bq.com/evolution-cc-by-sa-2/ y su programación para evitar obstáculos que viene aquí: http://diwo.bq.com/printbot-evolution-obstaculos/\\n - **Raspberry Pi** y tarjetas SD: Para montar ordenadores o proyectos dedicados. Otra forma de obtener ordenadores sería mediante el reciclaje. Pidiendo su donación a empresas de la zona (que igual también pueden donar palets)\\n - **Lcd para impresora 3D**: Echamos en falta que la impresora 3D pueda imprimir sin tener un ordenador dedicado\\n - **Micro proyector**: La difusión del conocimiento es la clave para que la comunidad *maker* crezca\\n - **¿Máquina de Coser?**: Todo *maker* debería saber coser -yo no sé, :-( -\"\n },\n {\n \"alpha_fraction\": 0.6792452931404114,\n \"alphanum_fraction\": 0.7082728743553162,\n \"avg_line_length\": 28.95652198791504,\n \"blob_id\": \"385def254f2229b851d020df0708cf2517e8f96c\",\n \"content_id\": \"0ed4d3b9310893e0527d70dae3f8532d747f877c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 698,\n \"license_type\": \"no_license\",\n \"max_line_length\": 135,\n \"num_lines\": 23,\n \"path\": \"/content/art004.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Mapas de Google para imprimir en 3D\\n####################################\\n\\n:date: 2015-09-05\\n:tags: 3D\\n:category: 3D\\n:authors: Carlos Rodríguez\\n:summary: Imprimir en 3D mapas\\n\\nPara poder imprimir en 3D mapas de Google Maps, he ecnontrado una aplicación\\nen *Github* de **ThatcherC** llamada **Terrain2STL**.\\n\\nEl proyecto está aquí: https://github.com/ThatcherC/Terrain2STL\\n\\nY la aplicación web aquí: http://jthatch.com/terrain2stl/\\n\\nCuando lo haya probado, comentaré mis impresiones.\\n\\nDe momento aquí va una imagen del mapa 3D del Moncayo y las Bardenas Reales (Aragón, Castilla-León y Navarra) vistos desde **FreeCad**:\\n \\n.. image:: ./img/moncayo_freecad.png\\n\\t:width:\\t400 px\\n\\t:alt:\\t'moncayo_freecad'\\n\"\n },\n {\n \"alpha_fraction\": 0.6909090876579285,\n \"alphanum_fraction\": 0.717090904712677,\n \"avg_line_length\": 46.41379165649414,\n \"blob_id\": \"1f7ae177d29d8f22320111172fc75892fdd33bd9\",\n \"content_id\": \"1cab3614d39dbda1978047482d7f65fac9d13f74\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 1396,\n \"license_type\": \"no_license\",\n \"max_line_length\": 240,\n \"num_lines\": 29,\n \"path\": \"/content/art011.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Robot controlado por Aplicación app inventor\\n##########################################################\\n\\n:date: 2016-11-14\\n:tags: Robots, Maker\\n:category: Maker\\n:authors: Carlos Rodríguez\\n:summary: Robot controlado por Aplicación app inventor\\n\\n#. Hola Mundo para probar el módulo HC05\\n \\n He empezado siguiendo este enlace: \\n \\n `Tutorial de arduino con bluetooth por kio4 <http://kio4.com/appinventor/9bluetootharduino.htm>`_\\n \\n **IMPORTANTE:** Un problema encontrado es que cuando quiero subir el programa arduino si tengo conectados los pines tx y rx al módulo bt falla, por eso cualquier modificación al programa arduino hay que hacerlo con el módulo quitado.\\n \\n En mi caso el módulo se llama HC051 y funciona a 57600 baudios, ya que en su día lo configuré así para que funcionará una extensión de Scratch en Python para programar Arduino desde Scratch con `S2A <https://github.com/MrYsLab/s2a_fm>`_\\n \\n Hice une módulo bt shield, éste va a 9600 baudios y se llama HC-05. Por último otro para zowi, que se llama zowi y va a 19200.\\n\\n\\n \\n\\n La aplicación del móvil permite conectarse por Bluetooth al arduino y encender o apagar el led 13 pulsando los botones correspondientes\\n \\n#. Del led a los motores\\n \\n Bueno, una vez probado el módulo Bluetooth estamos en disposición de programar el robot y la aplicación para su manejo desde el móvil ;-)\\n\"\n },\n {\n \"alpha_fraction\": 0.6942098736763,\n \"alphanum_fraction\": 0.7092882990837097,\n \"avg_line_length\": 50.4375,\n \"blob_id\": \"b14f3d02d94bee5468b93ad0e260a33cc99640e2\",\n \"content_id\": \"0522cdde85f8efb686e5df5957a8b206bd02f623\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 1664,\n \"license_type\": \"no_license\",\n \"max_line_length\": 213,\n \"num_lines\": 32,\n \"path\": \"/content/art010.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Recetas de cacharreo con la Raspberry Pi\\n##########################################################\\n\\n:date: 2016-11-04\\n:tags: Raspberry\\n:category: Raspberry\\n:authors: Carlos Rodríguez\\n:summary: Recetas de cacharreo con la Raspberry Pi\\n\\n#. Configurar la raspberry como router\\n\\n Como en casa el router que tengo no me da buena cobertura por todos los sitios, he decidido poner un repetidor en mitad de casa cuando necesite una mayor cobertura.\\n \\n He empezado por ponerlo como punto de acceso y para ello he seguido el tutorial de este enlace: \\n \\n `Raspberry Pi como Punto de Acceso <http://www.redeszone.net/raspberry-pi/manual-para-configurar-raspberry-pi-como-un-router-wi-fi/>`_\\n \\n \\n De todas las tarjetas wifi usb que tengo por casa, la única que admite modo AP en una Dlink DWL-G22 con chipset Ralink RT2571W.\\n \\n Respecto a los pasos que pone que hay que seguir, he hecho todo igual salvo en la configuración */etc/network/interfaces*, que en lugar de poner *allow hotplug wlan0*, he tenido que poner *allow-hotplug wlan0*\\n \\n#. Configurar sonic-pi en ubuntu\\n\\n He tenido problema con el jack, la solución la he encontrado en:\\n \\n `ConfigurarSonicPi <https://www.miskatonic.org/2015/01/17/setting-up-sonic-pi-on-ubuntu/>`_\\n \\n También hay que tener en cuenta que no arrancará sonic-pi a no ser que tengamos iniciado el jack.\\n Para ello tenemos que arrancarlo desde el terminal ejecutando:*qjackctl* y dándole a *iniciar*.\\n Si no nos funcionara, nos vamos a *setup* y seleccionamos interfaz: *hw:PCH,0*\\n Si al cerrar todo no tenemos acceso a los dispositivos de audio, desde la terminal: *pulseaudio*\\n \\n \\n \"\n },\n {\n \"alpha_fraction\": 0.7365967631340027,\n \"alphanum_fraction\": 0.746503472328186,\n \"avg_line_length\": 38.906978607177734,\n \"blob_id\": \"bfc4b5b76f718d7392f852122a1d42d6ff832147\",\n \"content_id\": \"5b483474246ece2724f17369996b10219c108277\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 1731,\n \"license_type\": \"no_license\",\n \"max_line_length\": 108,\n \"num_lines\": 43,\n \"path\": \"/content/art002.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"¿Cómo está hecho el blog?\\n#########################\\n\\n:date: 2015-07-28\\n:tags: \\n:category: Pelican\\n:authors: Carlos Rodríguez\\n:summary: Github y Pelican para crear el blog\\n\\nEste blog está hecho con **Pelican** y está alojado en **Github Pages**. La\\nprincipal razón es la de utilizar Software Libre, y documentar en \\nRestructuredtext (.rst). Aparte de las ventajas de usar software libre,\\nel poder documentar el blog offline me ha animado a aprender Pelican y \\nusarlo.\\n\\nHe creado un repositorio *fuente_web* que contiene los artículos en formato .rst\\n\\nEl caso es que el contenido en .rst hay que transformarlo a .html y\\nademás hay que alojarlo en el repositorio de *GitHub* que hace de servidor web.\\nPues bien, además de crear el repositorio *es3a10.github.io* hay que pasar\\na las fuentes unos script de python que manden el contenido. \\nEn un terminal y desde la carpeta donde tengamos el repositorio local *fuente_web* \\ntendremos que ejecutar los siguientes comandos: ::\\n\\n\\t$ pelican content -o output -s pelicanconf.py\\n\\t$ ghp-import output\\n\\t$ git push git@github.com:es3a10/es3a10.github.io.git gh-pages:master\\n\\n**NOTA:** En el caso de que en lugar de ser la página personal sea una página de proyecto,\\npor ejemplo, el proyecto matemáticas, los comandos serían: ::\\n\\n\\t$ pelican ./fuente_web/content -o ./fuente_web/output -s ./fuente_web/pelicanconf.py\\n\\t$ ghp-import ./fuente_web/output\\n\\t$ git push origin gh-pages\\n \\n\\n\\nOtra cosa que hay que hacer, si no se ha hecho, crear una clave pública-privada para\\nla conexión ssh al servidor *GitHub*. La clave RSA hay que meterla en los *settings*\\nde nuestra cuenta *GitHub*.\\n\\n\\n**NOTA:** Si ponemos fotos en una carpeta aparte, tendremos que copiar esa carpeta en la carpeta *./output*.\\n\"\n },\n {\n \"alpha_fraction\": 0.7807488441467285,\n \"alphanum_fraction\": 0.7843294143676758,\n \"avg_line_length\": 59.90951156616211,\n \"blob_id\": \"e57aaad2d86094acaf019c1bb62e781cb356c27c\",\n \"content_id\": \"9277dcc198f4928ae67d00a680f11a07c0d4dd3d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 26625,\n \"license_type\": \"no_license\",\n \"max_line_length\": 193,\n \"num_lines\": 431,\n \"path\": \"/content/art001.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Presentación y Proyecto\\n#######################\\n\\n:date: 2015-07-27\\n:tags: \\n:category: General\\n:authors: Carlos Rodríguez\\n:summary: Presentación del blog\\n\\nEste año vamos a intentar realizar un espacio Maker en el instituto IESO Bardenas Reales. La idea se presentó como proyecto de innovación y ha sido aceptado. Veremos qué somos capaces de hacer.\\n\\nOs dejo con la descripción del proyecto:\\n\\n\\n\\n**Imagina, crea, difunde: \\\"Makespace\\\" Bardenas** \\n\\nMayo, 2015\\n\\n*\\\"If you have an apple and I have an apple and we exchange apples then you and I will still each have one apple. \\nBut if you have an idea and I have an idea and we exchange these ideas, then each of us will have two ideas.\\\" - George Bernard Shaw*\\n\\n\\n#. Título del proyecto\\n\\tImagina, crea, difunde: \\\"Makespace\\\" Bardenas\\n\\n#. Palabras clave\\n\\tCreatividad, Tecnología, Conocimiento Libre\\n\\n#. Nombre del centro o centros donde se desarrollará el proyecto.\\n\\tIESO Bardenas Reales\\n\\n#. Nombre y apellidos del coordinador o coordinadora.\\n\\tEl autor del documento es Carlos Rodríguez Jaso.\\n\\n#. Breve descripción del proyecto\\n\\tSe pretende crear un espacio informal de fabricación digital con algunas herramientas que\\n\\tla tecnología actual nos proporciona: Arduino, Scratch, Raspberry Pi e Impresión 3D (entre otras)\\n\\n#. Índice\\n\\t1. Título del proyecto\\n\\t2. Palabras clave\\n\\t3. Nombre del centro o centros donde se desarrollará el proyecto\\n\\t4. Nombre y apellidos del coordinador o coordinadora\\n\\t5. Breve descripción del proyecto\\n\\t6. Índice\\n\\t7. Justificación \\n\\t8. Objetivos\\n\\t9. Contenidos y actividades\\n\\t10. Metodología\\n\\t11. Competencias clave y materias o asignaturas relacionadas\\n\\t12. Equipo docente implicado\\n\\t13. Alumnado\\n\\t14. Participación de la Comunidad Educativa\\n\\t15. Recursos humanos y materiales y alianzas con otras instituciones\\n\\t16. TIC que se van a incorporar\\n\\t17. Fases y temporalización\\n\\t18. Formas de evaluación\\n\\t19. Formación.\\n\\t20. Presupuesto.\\n\\n#. Justificación\\n\\tComo resultado de la creación de una impresora 3d en el centro, se ha descubierto\\n\\tun tipo de aprendizaje informal que constituye la base de este proyecto y que si bien\\n\\tya se ha trabajado, se pretende explorar en mayor profundidad. Si la construcción de la impresora\\n\\t3d surgió como una evolución al trabajo realizado hace dos años en el campo de la robótica, \\n\\tla propuesta actual surge como evolución del de la impresora.\\n\\t\\n\\tLa democratización de los medios de producción digitales, y últimamente\\n\\tla extensión de este fenómeno al mundo físico a través de impresoras 3d y máquinas de fabricación digital,\\n\\tha hecho que hayan proliferado centros espontáneos donde se juntan entusiastas de la fabricación \\\"tecnológica\\\" DIY -Do it yourself- :\\n\\tHablamos de los Fab Labs, Makespaces u otras denominaciones que vienen a ser\\n\\tparecidas. \\n\\t\\n\\tEn este tipo de centros se reúnen personas que ven en el \\\"hacer cosas\\\" (makers en inglés)\\n\\tuna forma de aprender\\n\\ty de emplear su tiempo de ocio. Este tipo de reuniones suelen ser informales, y\\n\\taparte de fomentar los encuentros sociales, pretenden compartir y difundir al resto de integrantes\\n\\tlos proyectos realizados o que se encuentran en proceso. \\n\\tAparte de esas ganas por crear cosas y difundirlas, aunque no\\n\\tsea requisito indispensable, estos \\\"inventores\\\" del s.xxi se caracterizan \\n\\tpor la pasión por la tecnología y por promover el conocimiento libre como un bien público y colectivo.\\n\\t \\n\\tLa organización de estos centros se basa en la meritocracia, y por tanto\\n\\tcada uno es \\\"libre\\\" de entrar y salir cuando quiera, y de aportar o recibir lo que le interese.\\n\\tAunque el perfil de \\\"maker\\\" es eminentemente tecnológico, ya que ésta es la que ha propiciado la\\n\\tdemocratización de los medios de autoproducción, la \\\"creación\\\" de objetos no se limita a objetos\\n\\ttecnológicos. Se considera \\\"maker\\\" a todo el que construye cosas por sus propios medios, \\n\\tsean muebles, vestidos, alimentos, etc.\\n\\t\\n\\t(Referencias: http://makespacemadrid.org/que-es-el-makespace/ , http://es.wikipedia.org/wiki/Fab_lab, \\n\\thttps://www.youtube.com/watch?v=N2apWHiX02c&feature=youtu.be)\\n\\t\\n\\tDe acuerdo con todo lo anterior se propone construir un espacio en el centro donde se puedan\\n\\tcrear cosas fomentando, además de las relaciones sociales, aspectos como\\n\\tla creatividad y proactividad del alumnado, el conocimiento libre, el trabajo colectivo,\\n\\tel consumo responsable, etc. Pero sobre todo, transmitirlos al resto de la comunidad educativa como una\\n\\tforma de aprendizaje significativo e informal y de conciencia social.\\n\\t\\n\\t#. Principales ideas sobre las que subyace el proyecto\\n\\t\\n\\t\\t- La creatividad es una forma natural de aprendizaje. Si observamos a los niños veremos que de\\n\\t\\t manera natural crean sus juguetes, sus disfraces, les encanta desmontar las cosas para\\n\\t\\t volver a montarlas. Desde este proyecto se pretende recuperar o potenciar esta \\n\\t\\t actividad también en los adolescentes.\\n\\t\\t \\n\\t\\t- Mediante la \\n\\t\\t construcción o manipulación de objetos, normalmente obtenemos un aprendizaje más significativo y duradero\\n\\t\\t que si la adquisición del conocimiento se hace por otros medios.\\n\\t\\n\\t\\t- El conocimiento libre se basa en compartir el conocimiento, en difundirlo.\\n\\t\\t Por una parte el propio proceso de documentar, explicar y compartir lo que se ha hecho\\n\\t\\t constituye una excelente forma de aprender y reforzar contenidos, pero por otra, \\n\\t\\t el hacerlo público permite que \\n\\t\\t otras personas puedan aprender e incluso mejorar ese conocimiento aportado. En definitiva,\\n\\t\\t en la construcción de un conocimiento colectivo donde la suma total es mayor que\\n\\t\\t la suma individual si no se pusieran al servicio del resto.\\n\\t\\n\\t\\t- Nuestra sociedad se caracteriza por el alto nivel tecnológico. Por tanto es\\n\\t\\t conveniente que nuestro alumnado conozca y domine la tecnología, que sepa cómo está\\n\\t\\t hecha.\\n\\t\\n\\t\\t- La creatividad como revulsivo a la crisis. Los países occidentales no pueden competir\\n\\t\\t con la producción desde países emergentes. Los costes de producción siempre serán menores\\n\\t\\t y por lo tanto estamos condenados a que en un mercado globalizado como el actual, la\\n\\t\\t producción acabará realizándose en aquellos países con salarios más bajos. Es por ello que\\n\\t\\t una alternativa puede estar en dirigir los esfuerzos hacia la innovación y desarrollo de nuevas\\n\\t\\t ideas que se puedan materializar en nuevos modelos de negocio. Fomentando la creatividad ayudamos\\n\\t\\t a desarrollar\\n\\t\\t la proactividad y el emprendimiento personal del alumnado. Por tanto lo estamos preparando para ese, más que probable,\\n\\t\\t requerimiento del sistema productivo.\\n\\t\\t\\n\\t\\t- Productor-Consumidor frente a Consumidor. La tecnología nos permite acceder a la\\n\\t\\t \\\"producción\\\" y/o fabricación de objetos. Durante épocas anteriores la sociedad\\n\\t\\t se ha caracterizado por el elevado consumo. La disminución de la capacidad adquisitiva \\n\\t\\t de nuestra sociedad en crisis, el medio ambiente, e incluso la ética y respeto hacia esa parte \\n\\t\\t del mundo sin recursos, son solo algunas de las razones por las que hay tomar conciencia de que el \\n\\t\\t consumo ha de ser responsable. En ese sentido, el reciclaje y la autofabricación pueden servir de ayuda.\\n\\t\\n\\t#. Líneas de actuación\\n\\t\\n\\t\\tPara elaborar el espacio maker se pretende trabajar desde diferentes ámbitos pero que\\n\\t\\ten realidad están interrelacionados:\\n\\t\\t\\n\\t\\t- Espacio maker: Dotar al espacio de ordenadores, proyector, equipos de soldadura, equipos de comunicación, ...\\n\\t\\t\\n\\t\\t- Programación: Introducir al alumnado en el mundo de la programación a través de Scratch (https://scratch.mit.edu/about/)\\n\\t\\t\\n\\t\\t- Robótica: Participar el First Lego League (http://www.firstlegoleague.es/) y replicar el trabajo con Printbots (robots imprimibles de bajo coste)\\n\\t\\t\\n\\t\\t- Impresión 3D: Mejorar la impresora 3D y profundizar en el modelado y diseño en 3D\\n\\t\\t\\n\\t\\t- Hardware libre: Realizar diferentes proyectos de iniciación con Arduino (http://www.arduino.cc/) y Raspberry Pi (https://www.raspberrypi.org/help/what-is-a-raspberry-pi/)\\n\\n#. Objetivos\\n\\tPodemos enumerar los siguientes objetivos a largo plazo:\\n\\t\\n\\t\\t- Dotar al centro de un espacio \\\"maker\\\"\\n\\t\\t- Promocionar en el centro la creatividad como forma de aprendizaje informal\\n\\t\\t- Desarrollar en el alumnado la proactividad, la autonomía y la responsabilidad en la gestión de un espacio propio \\n\\t\\t- Promocionar en el centro el uso de la tecnología y el conocimiento libre como herramientas para desarrollar la creatividad\\n\\t\\t- Fomentar, mediante el espacio maker, las relaciones sociales, la difusión de experiencias creativas y la consecución de objetivos colectivos\\n\\t\\t- Abrir el espacio \\\"maker\\\" al exterior mediante la celebración de eventos como Scratch Day, Arduino Day o una específica de tecnología 3D\\n\\t\\t- Evolucionar la Impresora 3D y los conocimientos en modelado 3D que ya se hicieron en el centro\\n\\t\\t- Prestar servicio de impresión 3D a todo aquel que lo requiera con fines educativos\\n\\t\\t- Ampliar los conocimientos de programación Scratch y extenderlos al mundo físico mediante el uso de Arduino\\n\\t\\t- Desarrollar desafíos de robótica tanto de robots comerciales Lego como robots \\\"imprimibles\\\" con Arduino\\n\\t\\t- Extender el concepto de creatividad a otros ámbitos y áreas: Por ejemplo el huerto, muebles DIY , \\n\\t\\t cocina creativa, canal de youtube con noticias del centro, papiroflexia, \\n\\t\\t creaciones literarias, plásticas, musicales, ..\\n\\t\\t- Fomentar hábitos de consumo responsable a través de la autofabricación y el reciclaje\\n\\t\\t- Acercar al alumnado experiencias creativas relacionadas con diferentes familias profesionales \\n\\t\\t como pueda ser diseño, electrónica, mecanizado o informática, \\n\\t\\t de manera que puedan servir de orientación de cara a estudios posteriores (bien de formación\\n\\t\\t profesional o de grados)\\n\\t\\t\\n#. Contenidos y Actividades\\n \\tPor la naturaleza del proyecto, el listado de contenidos depende de las propuestas de los propios componentes\\n\\tdel proyecto, y por tanto la lista que aparece a continuación podría ser orientativa.\\n \\n \\tContenidos curriculares básicos:\\n \\t- Mecánica y física: Conceptos como velocidades lineales, angulares, engranajes, ...\\n \\t- Ley de Ohm y Corriente eléctrica\\n \\t- Componentes electrónicos básicos: Resistencias, Tipos de Motores, ...\\n \\t- Arquitectura y redes de Ordenadores\\n \\t- Fundamentos de Programación a través de Scratch (si finalmente aparece en el currículo de Secundaria, como en Primaria)\\n \\tContenidos específicos del Espacio Maker:\\n\\t - Montaje de equipos con Raspberry Pi \\n\\t - Reciclaje de equipos\\n \\t- Router o Punto de Acceso: Configuración de un Punto de Acceso a Internet\\n \\t- Protocolo de seguridad en el uso del Espacio Maker\\n \\t- Mantenimiento de un portal o blog, en el que se documenten los trabajos realizados\\n\\t\\t- Desarrollo de talleres abiertos al público: Scratch Day, Arduino Day y Taller 3D\\n \\tContenidos específicos de la Impresora 3D:\\n\\t\\t- Servicio de Impresión Remoto con Octoprint (http://octoprint.org/)\\n\\t\\t- Pantalla lcd para impresión desde tarjeta SD\\n\\t\\t- Modelado de Objetos 3D\\n \\tContenidos de Robótica:\\n\\t\\t- Desafío de la First Lego League\\n\\t\\t- Desarrollo de un Printbot que emule el desafío FLF\\n \\tContenidos de Programación con Scratch:\\n\\t\\t- Animaciones\\n\\t\\t- Videojuego\\n\\t\\t- Problemas geométricos\\n\\t\\t- Simulaciones virtuales de Robots\\n \\tContenidos de Hardware Libre:\\n\\t\\t- Iniciación a Arduino con el programa online 123d Circuits (http://123d.circuits.io/)\\n\\t\\t- Control desde Arduino de los principales componentes electrónicos. \\n\\t\\t- Instalación de sistema operativo en Raspberry Pi\\n\\t\\t- Acceso Remoto a Raspberry Pi\\n\\t\\t- Instalación en Raspberry Pi del Software Octoprint para controlar la Impresora 3D \\n\\t\\n\\tEn todo caso, las actividades vendrán determinados por documentación libre que puedas ser aprovechada y siguiendo\\n\\tla metodología del proyecto.\\n\\tAlgunos ejemplos de entornos de documentación sobre los que se podría trabajar serían:\\n\\t\\n\\t\\t- Portal Código21 del Gobierno de Navarra sobre tecnologías creativas: http://codigo21.educacion.navarra.es/alumnado/recursos/\\n\\t\\t- Escuela Maker de bq: http://diwo.bq.com/\\n\\t\\t- Revista libre sobre el ecosistema Raspberry Pi: https://www.raspberrypi.org/magpi/\\n\\t\\t- Portal de la comunidad de Arduino: http://www.arduino.cc/#\\n\\t\\t\\n\\n#. Metodología\\n\\tLa metodología principal a aplicar aparece resumida en el título del proyecto: Imagina, crea y difunde. Y está\\n\\tinspirada, entre otras, en la que promueve Mitch Resnick, responsable del proyecto Scratch y que se resume en \\n\\tla siguiente sentencia de pseudocódigo: \\n\\t*forever(imagine, program, share);*\\n\\t\\n\\t**Imagina:**\\n\\tAunque\\n\\ten los contenidos ya aparece qué se quiere hacer, esto no cierra la puerta a que en cualquier momento, se\\n\\tacepten propuestas o ideas diferentes a realizar. De hecho, es importante que aquellos que quieran participar\\n\\ten el proyecto se involucren en la toma de decisiones. A su vez, dentro de cada línea de trabajo, las actividades\\n\\tserán lo más abiertas posibles para que sean lo propios \\\"makers\\\" los que decidan qué hacer -Por ejemplo, si\\n\\testamos trabajando con Scratch, las actividades propuestas serán del tipo: Invéntate una historia que ...-\\n\\t\\n\\t**Crea:**\\n\\tSe trata de aprender haciendo, que los participantes se sientan responsables de los productos finales. La experiencia\\n\\tde haber montado un Impresora 3D nos ha demostrado que cuando uno crea algo, aparte de aprender, durante ese\\n\\tproceso en muchas ocasiones aparecen nuevas ideas para desarrollar, entrando en una especie de espiral creativa y por\\n\\ttanto de aprendizaje que parece no tener fin -Por ejemplo, durante la construcción de la impresora, vimos que podíamos modificarla para\\n\\tconvertirla en un plotter. Eso nos hizo plantearnos dibujos geométricos, que a su vez nos hicieron investigar \\n\\tlas coordenadas polares. Pero para pasarlas a cartesianas, nos hizo también introducir conocimientos de trigonometría-\\n\\t\\n\\t**Difunde:**\\n\\tEsta es la idea principal del conocimiento libre: se trata de compartir el conocimiento adquirido. Básicamente por tres razones:\\n\\t\\n\\t\\t- Al documentar y explicar lo construido, reforzamos los conocimientos adquiridos\\n\\t\\t- Permitimos que otras personas aprendan de lo que hemos hecho\\n\\t\\t- Nos permite aprender del trabajo de los demás y mejorarlo\\n\\t\\t\\n\\tEste punto es el que más suele costar al alumnado, sin embargo constituye la clave del éxito del proyecto por lo que se pretende darle especial relevancia. \\t\\n\\tEn este apartado de difusión, se pretende además, abrir las puertas del centro con una\\n\\tserie de actividades abiertas al público: Padres, niños, otros centros, etc.\\n\\t\\n\\tA nivel organizativo, la característica principal estará en la flexibilidad. \\n\\tBasándonos en la experiencia de la construcción de la Impresora 3d, donde\\n\\ttodos los viernes en el segundo recreo se hacía una reunión informal para\\n\\tcomentar lo que se había hecho durante esa semana, se propone seguir con \\n\\tesa dinámica. Todos lo viernes en el segundo recreo se quedará para compartir\\n\\texperiencias.\\n\\tAdemás, como los protagonistas deben ser los alumnos, se va a intentar que \\n\\tpuedan ir a este espacio a trabajar durante los segundos recreos de manera\\n\\tautónoma y responsable, por lo que deberán organizarse ellos y velar por el buen funcionamiento \\n\\tdel espacio. \\n\\tConviene resaltar que este tipo de metodología y organización permite\\n\\tatender bastante bien a la diversidad, puesto que se cede autonomía\\n\\tal alumnado para determinar qué se quiere crear y en qué quiere participar.\\n\\tEn todo proceso creativo aparecerán tareas o actividades de diferente dificultad\\n\\tpermitiendo a cada alumno elegir en que contribuye al éxito colectivo. En los últimos años\\n\\themos observado una gran heterogeneidad en el alumnado, encontrando o muy buenos o simplemente\\n\\t\\\"menos\\\" buenos y este tipo de propuestas pueden ser positivas.\\n\\n\\tCualquier otro aspecto quedaría pendiente de definir, ya que se desconocen los\\n\\tgrupos, horarios y profesores con los que se va a contar. Pero es indudable, que\\n\\tsiempre se va a poder adaptar tiempos y programaciones para poder contribuir al\\n\\tproyecto -Un ejemplo: emplear alguna hora de tutoría para diseñar y crear carteles\\n\\tpara promocionar el Scratch Day-\\n\\n\\tDesde el Departamento de Educación del Gobierno de Navarra aparece una clara intención\\n\\tde potenciar aspectos como la programación y la robótica, tal como aparece en el portal\\n\\tCódigo21 (http://codigo21.educacion.navarra.es/). Algunos de los contenidos que aparecen en este proyecto \\n\\tsí que aparecen\\n\\tconcretados en el currículo de Primaria, sin embargo, a fecha de elaboración de este \\n\\tdocumento, en Secundaria no se sabe dónde y si se concretarán. \\n\\tPor tanto es posible que para el próximo curso haya algo más definido para asignaturas\\n\\tcomo matemáticas o tecnología, y en ese caso se podrían modificar las programaciones para \\n\\tadaptarlas al proyecto.\\n\\n\\tEn cualquier caso, bajo mi punto de vista, no todo el alumnado estará lo \\n\\tsuficientemente motivado\\n\\tpara desarrollar estas capacidades, por lo que la creación de un espacio específico de uso voluntario dentro\\n\\tdel centro permitirá potenciar la creatividad de aquellos alumnos que sientan inquietud\\n\\tpor el mundo \\\"maker\\\" sin necesidad de obligar al resto.\\n\\t \\n#. Competencias clave y materias o asignaturas relacionadas con el proyecto\\n\\tEl escenario planteado supone un excelente marco para desarrollar las competencias claves. \\n\\tLa adquisición de las mismas irá ligada al mayor o menor éxito en el desarrollo del proyecto:\\n\\n\\t- **Comunicación lingüística:**\\n\\t Esta competencia se desarrollará durante todo el proyecto: al documentarse para\\n\\t ver qué se hace, al tomar decisiones y sobre todo al difundir los resultados. Se intentará, además, \\n\\t que algunos resultados se produzcan en otras lenguas para su mayor difusión en \\n\\t Internet (euskera, inglés o francés).\\n\\t- **Competencia matemática y competencias básicas en ciencia y tecnología.**\\n\\t La mayoría de los contenidos propuestos requieren desarrollar la inteligencia\\n\\t lógico-matemática y el razonamiento formal. Además se desarrollan conceptos\\n\\t científicos fundamentalmente de mecánica y electrónica.\\n \\t- **Competencia digital:**\\n\\t El proyecto consiste en la gestión de un espacio de fabricación digital, por lo\\n\\t que todas las \\\"creaciones\\\" tendrán su componente digital. En ese sentido, conviene\\n\\t destacar que algunas de las propuestas suponen la digitalización del mundo físico:\\n\\t impresión de objetos reales mediante el modelado digital de objetos 3D, la interacción\\n\\t con el mundo físico mediante la programación de Arduino mediante sensores y\\n\\t actuadores, etc. \\t \\n\\t- **Aprender a aprender:**\\n\\t Si algo tiene este tipo de aprendizaje informal es la necesidad de obtener la\\n\\t la documentación de lo que se quiere hacer de fuentes externas, por lo que\\n\\t se está, en realidad, transmitiendo una forma de aprender por uno mismo.\\n\\t- **Competencias sociales y cívicas:**\\n\\t El espacio \\\"maker\\\" en realidad es un punto de encuentro para trabajar y compartir\\n\\t experiencias, por lo que continuamente se desarrollarán las habilidades sociales.\\n\\t Además la responsabilidad de la gestión del espacio recaerá sobre todo el colectivo\\n\\t participante.\\n\\t- **Sentido de iniciativa y espíritu emprendedor:**\\n\\t La creatividad fomenta la generación de ideas y la proactividad, cualidades imprescindibles\\n\\t para la iniciativa y emprendimiento. Por no extender el alcance del proyecto,\\n\\t no se desarrollarán estas competencias desde el punto de vista de la gestión empresarial.\\n\\t- **Conciencia y expresiones culturales:**\\n\\t La creatividad, de nuevo, permite la expresión más artística del alumnado. Hay que resaltar que los\\n\\t verdaderos \\\"makespaces\\\" y \\\"fablabs\\\" son también centros de creación de arte digital.\\n\\n#. Equipo docente implicado\\n\\tPor las características de los contenidos el perfil de profesorado que más se ajusta es el de tecnología, y los\\n\\tprofesores de matemáticas que realizaron el curso de robótica educativa hace un par de años. \\n\\tSin embargo, dada la naturaleza flexible\\n\\tdel proyecto, también podrá participar aquel profesorado con inquietudes y propuestas creativas.\\n\\n#. Alumnado\\n\\tSe propondrá la participación voluntaria a todo el alumnado del centro, que tendrá libertad para \\n\\tacudir al espacio maker durante los segundos recreos.\\n\\tEn el caso de que algún profesor implicado pueda realizar alguna adaptación de la programación en alguno de \\n\\tsus grupos, dicho alumnado también participará en los trabajos que correspondan. En este sentido, por ejemplo, \\n\\tpara la difusión y documentación \\n\\tdel proyecto se puede contar con el profesorado del Área de Lenguas y de Educación Plástica y Visual.\\n\\n#. Participación de la Comunidad Educativa\\n\\tDurante las tres jornadas de apertura del espacio, con sus talleres correspondientes, el resto de la comunidad\\n\\teducativa -padres, alumnos y profesores de otros centros, antiguos profesores, etc.- y no educativa \\n\\t-la entrada será libre- podrá participar junto al alumnado del centro.\\n\\n#. Recursos humanos y materiales y alianzas con otras instituciones\\n\\tMuchas personas creativas (y sobre todo \\\"hacedoras DIY\\\") se caracterizan por saber adaptarse a los\\n\\trecursos que tiene y reciclar en la medida de lo posible. Este tipo de actitud, como hemos dicho, se intentará transmitir dentro del espacio \\\"maker\\\".\\n\\tA pesar de ello, el acceso a una serie de recursos facilitaría su implantación.\\n\\tPodemos enumerar los recursos por línea de actuación:\\n\\t\\n\\t\\t- Espacio Maker: Se necesitarían tres ordenadores de bajo coste para aprender primero, y poder trabajar \\n\\t\\t después. Montarlos a partir de placas Raspberry Pi puede ser una buena alternativa. \\n\\t\\t Las Raspberry solo son placas de procesamiento, para montar los ordenadores completos se necesitan además\\n\\t\\t periféricos: Monitores,\\n\\t\\t teclados, etc. Una opción interesante sería reciclar componentes de empresas y entidades de la zona.\\n\\t\\t La impresora 3D ya está en el centro, por lo que pasaría a ubicarse en este espacio.\\n\\t\\t Se intentaría dotar al espacio de un punto de acceso, y que el alumnado \\n\\t\\t configure y gestione el punto de acceso. Potenciar el uso de sus propios dispositivos en este entorno \\n\\t\\t puede ser una experiencia piloto para acercar el concepto BYOD -Bring your own device- al centro.\\n\\t\\t Puesto que el apartado de explicar y difundir lo realizado tiene especial relevancia, no\\n\\t\\t estaría de más dotar al espacio de un proyector y un equipo de sonido inalámbrico.\\n\\t\\t- Tecnología 3D: La impresora 3D ya está operativa, pero puede evolucionarse y mejorar.\\n\\t\\t Entre estas mejoras estaría añadir una pantalla lcd, para imprimir sin ordenador, y\\n\\t\\t también montar un servidor de impresión remoto para poder mandar a imprimir desde cualquier\\n\\t\\t ordenador.\\n\\t\\t El hardware para montar el servidor de impresión remoto se haría sobre uno de los ordenadores del punto anterior.\\n\\t\\t También sería necesario adquirir bobinas de filamento para imprimir.\\n\\t\\t- Robótica: La electrónica de los robots se puede hacer con placas Arduino. Sin embargo, para simplificar \\n\\t\\t los cableados posteriores sería más conveniente utilizar unas placas adaptadas que comercializa, por ejemplo, la empresa bq. \\n\\t\\t El resto de componentes electrónicos (servomotores, resistencias, ...) también se pordrían solicitar a bq.\\n\\t\\t- Hardware Libre: Tanto los Arduinos como las Raspberry Pi serían los mismo que los utilizados en los puntos anteriores\\n\\n#. TIC que se van a incorporar\\n\\tTeniendo en cuenta que la impresora 3D ya se tiene y que se han probado herramientas de modelado 3D\\n\\t(FreeCad y TinkerCad), básicamente se incorporarán como \\n\\tnuevas las siguientes TIC:\\n\\t\\n\\t\\t- Hardware: Electrónica Arduino y Ordenadores educativos Raspberry PI. Robots imprimibles\\n\\t\\t- Software: Scratch, Variaciones de Scratch para programar Arduinos. Sistemas operativos para Raspberry PI\\n\\n#. Fases y temporalización\\n\\tSe propone el siguiente calendario orientativo:\\n\\t\\n\\t- Espacio Maker\\n\\t\\t- Montaje de equipos e instalación de sistemas operativos: De Octubre a Diciembre\\n\\t\\t- Montaje del punto de acceso: De Octubre a Noviembre\\n\\t- Programación\\n\\t\\t- Introducción a la programación con Scratch: de Octubre a Diciembre\\n\\t\\t- Programación con Scratch y Arduino: de Enero a Junio\\n\\t- Robótica \\n\\t\\t- Desafío First Lego League: De Octubre a Mayo\\n\\t\\t- Robots imprimibles: De Marzo a Junio\\n\\t- Tecnología 3D\\n\\t\\t- Introducción al Modelado de Objetos en 3D: De Octubre a Diciembre\\n\\t\\t- Modelado avanzado de Objetos en 3D: De Diciembre a Junio\\n\\t\\t- Instalación del servidor de impresión remoto Octoprint y pantalla LCD: Enero\\n\\t- Hardware Libre\\n\\t\\t- Proyectos sencillos con Arduino y Raspberry Pi: De Enero a Marzo\\n\\t\\t- Proyectos de robótica con Arduino: De Marzo a Junio \\n\\t\\t\\n\\t\\n#. Formas de evaluación\\n\\tPara evaluar las actividades se propone valorar cada proyecto o trabajo con una encuesta a los integrantes. \\n\\tSe podría hacer, además, a través de Twitter como forma adicional de difusión del mismo de forma que, por ejemplo, si\\n\\tvaloran positivamente el proyecto hacen un retuit y si no lo valoran lo marcan como favorito.\\n\\tPara evaluar el trabajo, los profesores participantes podrán incluir criterios de calificación en sus \\n\\tprogramaciones. En el caso del autor de este documento, valorará el trabajo de aquellos alumnos que\\n\\tpertenezcan a alguno de sus grupos dentro del apartado de actitud que normalmente incluye en sus programaciones.\\n\\n#. Formación\\n\\tLa formación queda contemplada en la metodología del proyecto: Autoformación a partir de fuentes externas.\\n\\tDebido al esfuerzo que implica, la creación de un grupo de trabajo en centro facilitaría la formación\\n\\tdel profesorado que participe en el proyecto. De esta forma se adquiriría al menos la base tecnológica \\n\\tnecesaria para asesorar al alumnado.\\n\\t\\n#. Presupuesto.\\n\\tArduinos, Raspberry Pis, Monitores y teclados, Punto de acceso, Bobinas de filamento para la impresora,\\n\\tProyector y equipo de sonido para el espacio \\\"maker\\\", Consumibles, ... son algunos de los materiales o\\n\\tproductos que habría que comprar. Establecer un presupuesto exacto resulta complicado porque ni los propios\\n\\tcontenidos del proyecto está concretados al 100%. Sin embargo, la mayoría de los componentes son\\n\\tde bajo coste, por lo que con una cantidad de unos 1000€ creemos que es posible montar un espacio que\\n\\tsiente las bases de una verdadero \\\"espacio maker\\\". Posiblemente, el primero hasta la fecha en un centro educativo\\n\\tnavarro, \\n\\ty avalado por el trabajo previo del montaje y desarrollo de nuestra impresora 3D. \\n\"\n },\n {\n \"alpha_fraction\": 0.6628289222717285,\n \"alphanum_fraction\": 0.6891447305679321,\n \"avg_line_length\": 29.450000762939453,\n \"blob_id\": \"983266043aedff6e7b0a457a379c93b4d5af6e7b\",\n \"content_id\": \"e0ce94babc30543ad63faf2619e0236253cc5feb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 617,\n \"license_type\": \"no_license\",\n \"max_line_length\": 170,\n \"num_lines\": 20,\n \"path\": \"/content/art005.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Insertar código latex en nuestros artículos .rst\\n#################################################\\n\\n:date: 2015-09-29\\n:tags: latex\\n:category: Pelican\\n:authors: Carlos Rodríguez\\n:summary: Insertar código *latex* en un Pelican\\n\\nExiste la posibilidad de insertar código latex:\\n\\n\\t:math:`\\\\frac{3}{2}\\\\cdot\\\\sqrt{12}`\\n\\n```\\nEn realidad el código latex sería :math:`\\\\frac{3}{2}\\\\cdot\\\\sqrt{12}`.\\n```\\n\\nEl renderizado que *Pelican* hace inicialmente del código latex no está muy logrado (cuando lo pasa a html). Si queremos mejores resultados podemos utilizar algún plugin:\\n\\n\\thttps://github.com/barrysteyn/pelican_plugin-render_math\"\n },\n {\n \"alpha_fraction\": 0.7419354915618896,\n \"alphanum_fraction\": 0.7903226017951965,\n \"avg_line_length\": 40,\n \"blob_id\": \"35a57ebcbcdffb961d58bfd63635fa3aa93a5c23\",\n \"content_id\": \"0b72e19f971395a07f8b03840a1494dc953a270e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 124,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 3,\n \"path\": \"/script_subir.sh\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"pelican content -o output -s pelicanconf.py\\nghp-import output\\ngit push git@github.com:es3a10/es3a10.github.io.git gh-pages:master\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7745392322540283,\n \"alphanum_fraction\": 0.7835404872894287,\n \"avg_line_length\": 79.44827270507812,\n \"blob_id\": \"501e55ed2da103e78e142ef29a8feb2363c2c51c\",\n \"content_id\": \"fa9acc27d073b654951b7f17289b7f4a66316859\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 4727,\n \"license_type\": \"no_license\",\n \"max_line_length\": 604,\n \"num_lines\": 58,\n \"path\": \"/content/art007.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Artículo para el diario de Navarra\\n#################################################\\n\\n:date: 2015-10-07\\n:tags: Maker\\n:category: Maker\\n:authors: Carlos Rodríguez\\n:summary: Artículo Espacio Maker para el Diario de Navarra\\n\\n Este año vamos a intentar realizar un espacio Maker en el instituto IESO Bardenas Reales. La idea se presentó como proyecto de innovación y ha sido aceptada. Veremos qué somos capaces de hacer. \\n\\nQué es un espacio Maker y cómo surge la iniciativa:\\n\\nCortes, curso 2014-2015: El centro se propone como reto construir una Impresora 3D de bajo coste. Algo que parecía ciencia-ficción era posible de hacer gracias a la información disponible en Internet: proyecto *REPRAP* (http://www.reprap.org/).\\nLa idea principal del proyecto *REPRAP* era la construcción de impresoras auto-replicantes, es decir, que están construidas con piezas que hayan sido impresas por otras impresoras.\\n\\nAlgunas fotos del año pasado:\\n\\n.. figure:: ./img/probando.JPG\\n\\t:align: right\\n\\t:width:\\t400 px\\n\\t:alt:\\t'Probando la Impresora'\\n\\t\\n\\t(Probando la impresora)\\n\\t\\t \\n.. figure:: ./img/impresora_completa.jpg\\n\\t:width:\\t400 px\\n\\t:alt:\\t'Impresora completa'\\n\\t\\n\\t(La impresora completa)\\n\\t\\n.. figure:: ./img/construyendo.JPG\\n\\t:width:\\t400 px\\n\\t:alt:\\t'Construyendo sólidos'\\n\\t\\n\\t(Construyendo sólidos platónicos)\\n\\t\\nPero al buscar información en Internet de estas impresoras 3D, vimos que existía detrás una comunidad extremadamente activa cuya principal motivación era la construcción de cosas para aprender. Se trata de una versión moderna de los antiguos aficionados al bricolaje e inventores caseros, pero basados en la tecnología y con la ventaja de utilizar Internet para difundir todo el conocimiento, y por tanto de hacerlo accesible. En EEUU, a este tipo de personas se les llamó makers y pronto aparecieron locales donde socializar, construir y compartir ideas: Fablab o Makespace son los nombres más comunes. \\n\\nPedagógicamente, el movimiento maker ha aportado a la educación el fomento de la creatividad e innovación. También ha contribuido al desarrollo de un nuevo paradigma educativo de siglas STEAM (acrónimo de Cience, Technology, Engineering, Arts y Mathematics), y que promulga el desarrollo de dichas disciplinas desde una perspectiva transversal y conjunta.\\n\\nLa importancia que se le está dando a este movimiento ha hecho que a hasta el presidente de los EEUU, Barak Obama, esté promoviendo el uso de impresoras 3D en los centros educativos o que se organice desde la Casa Blanca la Semana Maker (https://www.whitehouse.gov/nation-of-makers)\\n\\nCon las ideas anteriores, desde el centro pretendemos construir un espacio de creación digital y no digital en el que se puedan desarrollar diferentes proyectos. Algunos de los retos planteados para este año son:\\n\\n - Elaborar un **mobiliario móvil** que, hasta que tengamos asignado un espacio físico real, permita dividir una estancia en dos ambientes a través de separadores móviles elaborados con palets. También con Palets podremos crear algún tipo de bancada que dé al espacio un toque \\\"maker\\\".\\n \\n - Fomentar el pensamiento computacional como forma de afrontar y resolver problemas o retos y que tiene similitudes con el algebra como forma de abstraer y resolver matemáticamente problemas de la vida real. Para ello vamos a hacer el curso de **sctratch** que tenemos en la página **código 21**: http://aprendecodigo21.educacion.navarra.es/\\n \\n - Introducción a la electrónica mediante proyectos con Arduino. El año pasado, en el proyecto de la impresora 3D descubrimos que para el modelado 3D existe una herramienta online muy sencilla e intuitiva: **Tinkercad** (https://www.tinkercad.com/). Autodesk, actual propietaria de Tinkercad, está detrás de un montón de aplicaciones online educativas y relacionadas con el mundo maker. La aplicación **123d Circuits** permite implementar proyectos con Arduino de forma virtual (https://123d.circuits.io/)\\n \\n - Aprovechando el Huerto Escolar, trabajar los hábitos de salud estudiando las virtudes de los productos de la huerta y ayudar mediante la **impresión 3D** a la elaboración de letreros y soportes decorativos \\n\\nPedagñiogicamente, el movimiento maker aporta a la educación el fomento creatividad e innovación y ha aparecido un nuevo paradigma educativotransversal de siglas STEM\\n\\nLa importancia que se le está dando a este movimiento ha hecho que a hasta el presidente de los EEUU Obama esté promoviendo el uso de impresoras 3D en los centros educativos o que se haya celebrado una feria en la misma CasaBlanca y la maker faire\\n\\nPara saber más sobre el movimiento maker, se puede consultar, por ejemplo, la web del Makespace Madrid (http://makespacemadrid.org)\\n\"\n },\n {\n \"alpha_fraction\": 0.6788104176521301,\n \"alphanum_fraction\": 0.7211896181106567,\n \"avg_line_length\": 50.69230651855469,\n \"blob_id\": \"dea5e94041eca118ae8a34cd0d04a8cd288def51\",\n \"content_id\": \"6bea6b06b80ffaf09312987e944577f63485b19e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 1358,\n \"license_type\": \"no_license\",\n \"max_line_length\": 277,\n \"num_lines\": 26,\n \"path\": \"/content/art009.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Parámetros para el cálculo de pasos para un escornabot\\n##########################################################\\n\\n:date: 2016-10-28\\n:tags: Varios\\n:category: Maker\\n:authors: Carlos Rodríguez\\n:summary: Cálculo de pasos para un escornabot\\n\\nA la hora de calibrar un robot de dos ruedas siempre surge el tema de qué orden hay que darle al robot para que recorra cierta distancia o gire cierto número de grados. \\n\\nPara el recorrido recto influye el diámetro de la rueda que estemos usando, y en el giro la distancia entre las ruedas, que al final será el diámetro de la circunferencia que realice el robot mientras gira.\\n\\nVamos a poner el ejemplo del escornabot (modelo placidus) que es el que he montado. Según datos teóricos, el número de pasos es de **64** por **64** de la reductora, por lo que da un total de 4096 pasos por vuelta (para el modelo de motor que tengo recomiendan poner **4076**).\\n\\nEl diámetro de la rueda es de 72mm y la distancia entre ruedas es de 100mm.\\n\\n.. image:: ./img/escornabot.jpg\\n :width: 400 px\\n :alt: 'handout'\\n\\n\\nCon simples reglas de tres y que pi*diametro es una vuelta podemos determinar los pasos que tiene que dar nuestro robot:\\n\\nPasos hacia para desplazarse: Si 4076 es pix72, entonces 200 será x Lo que da que x=3603 pasos\\nPasos para girar 90 grados: Si 4076 es pix72, entonces (pix100)/4 Luego x=1415 \\n\"\n },\n {\n \"alpha_fraction\": 0.4285714328289032,\n \"alphanum_fraction\": 0.5357142686843872,\n \"avg_line_length\": 15.142857551574707,\n \"blob_id\": \"ae21c4807abda0a8a6259957362a0e3e672950d3\",\n \"content_id\": \"445ee30aa2d89fa6c4f2579e6181ba5701dfced5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 115,\n \"license_type\": \"no_license\",\n \"max_line_length\": 33,\n \"num_lines\": 7,\n \"path\": \"/planificacion_curso.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Fechas:\\n\\n\\t3 sesiones de dos y media horas:\\n \\tDe 14:30 a 17:00\\n 1ª Sesi\\n 2ª Sesi\\n 3ª Sesi\"\n },\n {\n \"alpha_fraction\": 0.651442289352417,\n \"alphanum_fraction\": 0.6778846383094788,\n \"avg_line_length\": 28.714284896850586,\n \"blob_id\": \"986f35cfb86aca38709e8e656cb2f6706df8243c\",\n \"content_id\": \"4c9d3da7f2fb13048a87a404aba5156768e4971f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 417,\n \"license_type\": \"no_license\",\n \"max_line_length\": 172,\n \"num_lines\": 14,\n \"path\": \"/content/art003.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Handouts en documentos Lyx\\n################################\\n\\n:date: 2015-08-21\\n:tags: \\n:category: Lyx\\n:authors: Carlos Rodríguez\\n:summary: Handouts en documentos Beamer con Lyx\\n\\nPara poner compilar los documentos sin tener en cuenta las pausas que pongamos a nuestra presentación, tenemos que indicar la clase *handouts* a nuestro documento *beamer*:\\n\\n.. image:: ./img/handout_lyx.png\\n :width: 400 px\\n :alt: 'handout'\\n\"\n },\n {\n \"alpha_fraction\": 0.6930817365646362,\n \"alphanum_fraction\": 0.7069182395935059,\n \"avg_line_length\": 45.64706039428711,\n \"blob_id\": \"6c03831e6cdc9a35698c4279626ee9e2dcc85e77\",\n \"content_id\": \"83a06de5a7c0a5159ef933473b915c43b78325c9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"reStructuredText\",\n \"length_bytes\": 802,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 17,\n \"path\": \"/content/art008.rst\",\n \"repo_name\": \"es3a10/fuente_web\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Programas para modelar en 3D\\n#################################################\\n\\n:date: 2015-12-22\\n:tags: Varios\\n:category: Maker\\n:authors: Carlos Rodríguez\\n:summary: Programas para modelar en 3D\\n\\nAquí van una colección de programas para modelar en 3D\\n\\n\\t#. FreeCAD: Posiblemente el más completo y profesional (http://www.freecadweb.org)\\n\\t#. Blender: No lo he probado pero parece el más artístico (https://www.einsteinsworkshop.com/outreach/blockscad)\\n\\t#. OpenScad: Modelado paramétrico (http://www.openscad.org/)\\n\\t#. BlocksCad: Interfaz limitada de OpenScad, con la ventaja de poder trabajar online\\n\\t#. TinkerCad: Sencillo y online, ideal para llevarlo al aula -como el anterior- (https://www.tinkercad.com/)\\n\\t#. OnShapes: Otro online, parece más profesional. No lo he probado (https://www.onshape.com/)\\n\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":14,"string":"14"}}},{"rowIdx":2979160,"cells":{"repo_name":{"kind":"string","value":"justinlundy/illumos-live"},"repo_url":{"kind":"string","value":"https://github.com/justinlundy/illumos-live"},"snapshot_id":{"kind":"string","value":"ee6761d8170353ecd5ebb73538ebf7012c2c873f"},"revision_id":{"kind":"string","value":"0edca8475368b6450fe94516828e6c5bd3e946d6"},"directory_id":{"kind":"string","value":"1352c2d85f47719c1b1c49edd450bf98a8e024ec"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2016-09-25T02:13:31.291755","string":"2016-09-25T02:13:31.291755"},"revision_date":{"kind":"timestamp","value":"2011-02-23T17:59:48","string":"2011-02-23T17:59:48"},"committer_date":{"kind":"timestamp","value":"2011-02-23T17:59:54","string":"2011-02-23T17:59:54"},"github_id":{"kind":"number","value":1830750,"string":"1,830,750"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7181389331817627,"alphanum_fraction":0.7262306213378906,"avg_line_length":29.89583396911621,"blob_id":"0bb882d2499d0c52fe682b1ec52e0094023b85d6","content_id":"3f204854bf891b8d97b047c88edea3357af82bd3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1483,"license_type":"no_license","max_line_length":160,"num_lines":48,"path":"/tools/build_illumos","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"#!/usr/bin/env bash\n#\n# CDDL HEADER START\n#\n# The contents of this file are subject to the terms of the\n# Common Development and Distribution License, Version 1.0 only\n# (the \"License\"). You may not use this file except in compliance\n# with the License.\n#\n# You can obtain a copy of the license at COPYING\n# See the License for the specific language governing permissions\n# and limitations under the License.\n#\n# When distributing Covered Code, include this CDDL HEADER in each\n# file and include the License file at COPYING.\n# If applicable, add the following below this CDDL HEADER, with the\n# fields enclosed by brackets \"[]\" replaced with your own identifying\n# information: Portions Copyright [yyyy] [name of copyright owner]\n#\n# CDDL HEADER END\n#\n# Copyright (c) 2010,2011 Joyent Inc.\n#\n\n# Setup for building stuff!\nunset CC\nunset CXX\nROOT=`pwd`\nexport PATH=/opt/onbld/bin:/sbin:/usr/sbin:/usr/bin:/usr/ccs/bin:/opt/SUNWspro/bin:/opt/local/bin\n\nBUILDSTAMP=`TZ=UTC date \"+%Y%m%dT%H%M%SZ\"`; export BUILDSTAMP\nmkdir -p ${ROOT}/proto\necho ${BUILDSTAMP} > ${ROOT}/proto/buildstamp\n\necho \"==> Building Illumos (${BUILDSTAMP})\"\n\nset -o pipefail\nset -e\n\ncd ${ROOT}/projects/illumos\n[[ -d \"log\" ]] || mkdir -p log\ncp usr/src/tools/scripts/nightly.sh ./nightly\nchmod u+x ./nightly\nksh93 ./usr/src/tools/scripts/bldenv.sh -d illumos.sh 'cd $CODEMGR_WS/usr/src && export PATH=\"$PATH:/opt/local/bin\" && dmake setup && cd tools && dmake install'\ntime ./nightly illumos.sh\nres=$?\n\nexit $res\n"},{"alpha_fraction":0.5361509919166565,"alphanum_fraction":0.5829346179962158,"avg_line_length":38.599998474121094,"blob_id":"3bd72952cc5409edf7d084a4a4e925fb00d99348","content_id":"c24da8a6b937cf80ca3e4d4680c807e5b6f380de","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":3762,"license_type":"no_license","max_line_length":69,"num_lines":95,"path":"/tools/builder/users.c","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"/*\n* CDDL HEADER START\n*\n* The contents of this file are subject to the terms of the\n* Common Development and Distribution License, Version 1.0 only\n* (the \"License\"). You may not use this file except in compliance\n* with the License.\n*\n* You can obtain a copy of the license at COPYING\n* See the License for the specific language governing permissions\n* and limitations under the License.\n*\n* When distributing Covered Code, include this CDDL HEADER in each\n* file and include the License file at COPYING.\n* If applicable, add the following below this CDDL HEADER, with the\n* fields enclosed by brackets \"[]\" replaced with your own identifying\n* information: Portions Copyright [yyyy] [name of copyright owner]\n*\n* CDDL HEADER END\n*\n* Copyright (c) 2010,2011 Joyent Inc.\n*\n*/\n\n#include <stdio.h>\n#include <string.h>\n\nint gid_from_name(const char *group)\n{\n int gid = -1;\n if (strcmp(\"root\", group) == 0) gid = 0;\n else if (strcmp(\"other\", group) == 0) gid = 1;\n else if (strcmp(\"bin\", group) == 0) gid = 2;\n else if (strcmp(\"sys\", group) == 0) gid = 3;\n else if (strcmp(\"adm\", group) == 0) gid = 4;\n else if (strcmp(\"uucp\", group) == 0) gid = 5;\n else if (strcmp(\"mail\", group) == 0) gid = 6;\n else if (strcmp(\"tty\", group) == 0) gid = 7;\n else if (strcmp(\"lp\", group) == 0) gid = 8;\n else if (strcmp(\"nuucp\", group) == 0) gid = 9;\n else if (strcmp(\"staff\", group) == 0) gid = 10;\n else if (strcmp(\"daemon\", group) == 0) gid = 12;\n else if (strcmp(\"sysadmin\", group) == 0) gid = 14;\n else if (strcmp(\"games\", group) == 0) gid = 20;\n else if (strcmp(\"smmsp\", group) == 0) gid = 25;\n else if (strcmp(\"gdm\", group) == 0) gid = 50;\n else if (strcmp(\"upnp\", group) == 0) gid = 52;\n else if (strcmp(\"xvm\", group) == 0) gid = 60;\n else if (strcmp(\"mysql\", group) == 0) gid = 70;\n else if (strcmp(\"openldap\", group) == 0) gid = 75;\n else if (strcmp(\"webservd\", group) == 0) gid = 80;\n else if (strcmp(\"postgres\", group) == 0) gid = 90;\n else if (strcmp(\"slocate\", group) == 0) gid = 95;\n else if (strcmp(\"unknown\", group) == 0) gid = 96;\n else if (strcmp(\"nobody\", group) == 0) gid = 60001;\n else if (strcmp(\"noaccess\", group) == 0) gid = 60002;\n else if (strcmp(\"nogroup\", group) == 0) gid = 65534;\n else if (strcmp(\"netadm\", group) == 0) gid = 65;\n\n return(gid);\n};\n\nint uid_from_name(const char *user)\n{\n int uid = -1;\n\n if (strcmp(\"root\", user) == 0) uid = 0;\n else if (strcmp(\"daemon\", user) == 0) uid = 1;\n else if (strcmp(\"bin\", user) == 0) uid = 2;\n else if (strcmp(\"sys\", user) == 0) uid = 3;\n else if (strcmp(\"adm\", user) == 0) uid = 4;\n else if (strcmp(\"lp\", user) == 0) uid = 71;\n else if (strcmp(\"uucp\", user) == 0) uid = 5;\n else if (strcmp(\"nuucp\", user) == 0) uid = 9;\n else if (strcmp(\"dladm\", user) == 0) uid = 15;\n else if (strcmp(\"netadm\", user) == 0) uid = 16;\n else if (strcmp(\"netcfg\", user) == 0) uid = 17;\n else if (strcmp(\"smmsp\", user) == 0) uid = 25;\n else if (strcmp(\"listen\", user) == 0) uid = 37;\n else if (strcmp(\"gdm\", user) == 0) uid = 50;\n else if (strcmp(\"zfssnap\", user) == 0) uid = 51;\n else if (strcmp(\"upnp\", user) == 0) uid = 52;\n else if (strcmp(\"xvm\", user) == 0) uid = 60;\n else if (strcmp(\"mysql\", user) == 0) uid = 70;\n else if (strcmp(\"openldap\", user) == 0) uid = 75;\n else if (strcmp(\"webservd\", user) == 0) uid = 80;\n else if (strcmp(\"postgres\", user) == 0) uid = 90;\n else if (strcmp(\"svctag\", user) == 0) uid = 95;\n else if (strcmp(\"unknown\", user) == 0) uid = 96;\n else if (strcmp(\"nobody\", user) == 0) uid = 60001;\n else if (strcmp(\"noaccess\", user) == 0) uid = 60002;\n else if (strcmp(\"nobody4\", user) == 0) uid = 65534;\n\n return(uid);\n};\n"},{"alpha_fraction":0.6773374080657959,"alphanum_fraction":0.6920731663703918,"avg_line_length":31.26229476928711,"blob_id":"681ff289f1772fc429f1830253a387180277ae67","content_id":"49d085cd805b8eee04466601070b6edfbb1f0a3c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1968,"license_type":"no_license","max_line_length":80,"num_lines":61,"path":"/tools/customize","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"#!/bin/bash\n#\n# CDDL HEADER START\n#\n# The contents of this file are subject to the terms of the\n# Common Development and Distribution License, Version 1.0 only\n# (the \"License\"). You may not use this file except in compliance\n# with the License.\n#\n# You can obtain a copy of the license at COPYING\n# See the License for the specific language governing permissions\n# and limitations under the License.\n#\n# When distributing Covered Code, include this CDDL HEADER in each\n# file and include the License file at COPYING.\n# If applicable, add the following below this CDDL HEADER, with the\n# fields enclosed by brackets \"[]\" replaced with your own identifying\n# information: Portions Copyright [yyyy] [name of copyright owner]\n#\n# CDDL HEADER END\n#\n# Copyright (c) 2010,2011 Joyent Inc.\n#\n\nBUILDSTAMP=$2\nif [ -z \"${BUILDSTAMP}\" ]; then\n BUILDSTAMP=`TZ=UTC date \"+%Y%m%dT%H%M%SZ\"`\nfi\n\nrdmnt=$1\nif [ -z \"${rdmnt}\" ]; then\n echo \"Usage: $0 <rdmnt directory> [buildstamp]\"\n exit 1\nfi\n\necho \"Customizing ${rdmnt}\"\npfexec cp /dev/null ${rdmnt}/etc/mnttab\npfexec cp /dev/null ${rdmnt}/var/log/syslog\npfexec cp /dev/null ${rdmnt}/var/adm/wtmpx\n\n# This rebuilds the man index but needs to work around the fact that\n# isaexec binaries don't work properly in a chroot.\n(cd ${rdmnt}/usr/bin && pfexec mv sh sh.$$ && ln -s bash sh)\npfexec mount -F lofs /dev ${rdmnt}/dev\n# If we're in a GZ there will be a /devices we need to\nif [[ -d /devices ]]; then\n pfexec mount -F lofs /devices ${rdmnt}/devices\nfi\npfexec chroot ${rdmnt} /usr/bin/catman\nif [[ -d /devices ]]; then\n pfexec umount ${rdmnt}/devices\nfi\npfexec umount ${rdmnt}/dev\n(cd ${rdmnt}/usr/bin && pfexec rm sh && pfexec mv sh.$$ sh)\n\npfexec chown root:root ${rdmnt}\necho \"${BUILDSTAMP}\" | pfexec tee ${rdmnt}/etc/joyent_buildstamp >/dev/null 2>&1\npfexec gsed -i -e \"s/ [0-9]\\{8\\}T[0-9]\\{6\\}Z/ ${BUILDSTAMP}/\" ${rdmnt}/etc/motd\npfexec gsed -i -e \"s/ [0-9]\\{8\\}T[0-9]\\{6\\}Z/ ${BUILDSTAMP}/\" ${rdmnt}/etc/issue\n\nexit 0\n"},{"alpha_fraction":0.7166276574134827,"alphanum_fraction":0.727556586265564,"avg_line_length":23.634614944458008,"blob_id":"061c926993c0aacf4d68824adb6917c19a2d37e3","content_id":"a5db6bd24ccc803f2c9f5a454472c9356050dfc8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Makefile","length_bytes":1281,"license_type":"no_license","max_line_length":69,"num_lines":52,"path":"/src/Makefile","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"#\n# CDDL HEADER START\n#\n# The contents of this file are subject to the terms of the\n# Common Development and Distribution License, Version 1.0 only\n# (the \"License\"). You may not use this file except in compliance\n# with the License.\n#\n# You can obtain a copy of the license at COPYING\n# See the License for the specific language governing permissions\n# and limitations under the License.\n#\n# When distributing Covered Code, include this CDDL HEADER in each\n# file and include the License file at COPYING.\n# If applicable, add the following below this CDDL HEADER, with the\n# fields enclosed by brackets \"[]\" replaced with your own identifying\n# information: Portions Copyright [yyyy] [name of copyright owner]\n#\n# CDDL HEADER END\n#\n# Copyright (c) 2010,2011 Joyent Inc.\n#\n\nCC=gcc\nCFLAGS=-Wall\nTARGETS=bootparams disklist removable_disk disk_size\nDESTDIR=../proto\n\nall: $(TARGETS) sysinfo\n\ninstall: $(TARGETS) sysinfo\n\tmkdir -p $(DESTDIR)/usr/bin\n\tpfexec cp -p $(TARGETS) sysinfo $(DESTDIR)/usr/bin\n\nbootparams: bootparams.c\n\t$(CC) $(CFLAGS) -o $@ $^ -ldevinfo\n\nremovable_disk: removable_disk.c\n\t$(CC) $(CFLAGS) -o $@ $^\n\ndisk_size: disk_size.c\n\t$(CC) $(CFLAGS) -o $@ $^\n\ndisklist:\n\tcp disklist.sh disklist\n\tchmod 0755 disklist\n\nsysinfo:\n\ttouch sysinfo\n\nclean:\n\t@rm -f $(TARGETS)\n"},{"alpha_fraction":0.6718003153800964,"alphanum_fraction":0.6829527616500854,"avg_line_length":23.454545974731445,"blob_id":"d256e461c121afa51772133b4579bb9f226a2aa6","content_id":"d65ce5ba6082f578f585b9dc58d07838b0067b79","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":1883,"license_type":"no_license","max_line_length":76,"num_lines":77,"path":"/src/disk_size.c","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"/*\n* CDDL HEADER START\n*\n* The contents of this file are subject to the terms of the\n* Common Development and Distribution License, Version 1.0 only\n* (the \"License\"). You may not use this file except in compliance\n* with the License.\n*\n* You can obtain a copy of the license at COPYING\n* See the License for the specific language governing permissions\n* and limitations under the License.\n*\n* When distributing Covered Code, include this CDDL HEADER in each\n* file and include the License file at COPYING.\n* If applicable, add the following below this CDDL HEADER, with the\n* fields enclosed by brackets \"[]\" replaced with your own identifying\n* information: Portions Copyright [yyyy] [name of copyright owner]\n*\n* CDDL HEADER END\n*\n* Copyright (c) 2010,2011 Joyent Inc.\n*\n*/\n\n\n/* \n * This tool takes the character device node for a disk and prints the size\n * in *bytes* for that disk. On any error, a message explaining the problem\n * is printed to STDERR and the exit code is non-zero.\n *\n */\n\n\n#include <stdio.h>\n#include <stdlib.h>\n#include <unistd.h>\n#include <fcntl.h>\n#include <sys/stat.h>\n#include <sys/dkio.h>\n#include <sys/vtoc.h>\n\nvoid\nusage(char* argv0) {\n\tprintf(\"Usage: %s [char dev]\\n\", argv0);\n\texit(1);\n}\n\nint\nmain(int argc, char* argv[]) {\n\tchar* devpath = argv[1];\n int devnode;\n\tint ret;\n unsigned long long bytes;\n struct dk_minfo mediainfo;\n\t\n\tif (argc != 2) {\n\t\tfprintf(stderr, \"FATAL: Device argument required\\n\");\n\t\tusage(argv[0]);\n\t}\n\t\n if ((devnode = open(devpath, O_RDONLY)) < 0 ) {\n\t\tfprintf(stderr, \"FATAL: Could not open %s\\n\", devpath);\n\t\tusage(argv[0]);\n }\n \n\tret = ioctl(devnode, DKIOCGMEDIAINFO, &mediainfo);\n\tclose(devnode);\n\n\tif (ret < 0) {\n fprintf(stderr, \"FATAL: DKIOCGMEDIAINFO failed\\n\");\n exit(1);\n\t}\n\n bytes = (mediainfo.dki_capacity * mediainfo.dki_lbsize);\n printf(\"%llu\\n\", bytes);\n\texit(0);\n}\n"},{"alpha_fraction":0.625806450843811,"alphanum_fraction":0.6380645036697388,"avg_line_length":21.14285659790039,"blob_id":"a8daf37f5bde9728784c7bef258bfa5cc857edd3","content_id":"7bbbaef3c6915eac5b3e9af7b29dfe0a03ac14ff","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":1550,"license_type":"no_license","max_line_length":75,"num_lines":70,"path":"/tools/cryptpass.c","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"/*\n* CDDL HEADER START\n*\n* The contents of this file are subject to the terms of the\n* Common Development and Distribution License, Version 1.0 only\n* (the \"License\"). You may not use this file except in compliance\n* with the License.\n*\n* You can obtain a copy of the license at COPYING\n* See the License for the specific language governing permissions\n* and limitations under the License.\n*\n* When distributing Covered Code, include this CDDL HEADER in each\n* file and include the License file at COPYING.\n* If applicable, add the following below this CDDL HEADER, with the\n* fields enclosed by brackets \"[]\" replaced with your own identifying\n* information: Portions Copyright [yyyy] [name of copyright owner]\n*\n* CDDL HEADER END\n*\n* Copyright (c) 2010,2011 Joyent Inc.\n*\n*/\n\n/*\n * Summary:\n *\n * Takes plain-text password as cmdline arg and outputs a crypt() version.\n *\n */\n\n#include <crypt.h>\n#include <errno.h>\n#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n\nvoid usage()\n{\n printf(\"Usage: cryptpass <password>\\n\");\n exit(1);\n}\n\nint main(int argc, char *argv[])\n{\n char *salt, *enc;\n\n if ((argc != 2) || (strlen(argv[1]) < 1)) {\n usage();\n }\n\n errno=0;\n salt=crypt_gensalt(NULL, NULL);\n if (salt == NULL) {\n printf(\"FATAL: crypt_gensalt() errno=%d\\n\", errno);\n exit(1);\n }\n\n enc = crypt(argv[1], salt);\n if (enc == NULL) {\n printf(\"FATAL: crypt() errno=%d\\n\", errno);\n free(salt);\n exit(1);\n }\n\n printf(\"%s\\n\", enc);\n\n free(salt);\n exit(0);\n}\n"},{"alpha_fraction":0.7239915132522583,"alphanum_fraction":0.7324841022491455,"avg_line_length":33.254547119140625,"blob_id":"6b444dbe0efc6dfd461c12c318398cbe97027ce9","content_id":"f8faaf87c8f7e0a052f30edf21b778e4dfe0d9ec","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1884,"license_type":"no_license","max_line_length":103,"num_lines":55,"path":"/tools/smf_import","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"#!/bin/bash\n#\n# CDDL HEADER START\n#\n# The contents of this file are subject to the terms of the\n# Common Development and Distribution License, Version 1.0 only\n# (the \"License\"). You may not use this file except in compliance\n# with the License.\n#\n# You can obtain a copy of the license at COPYING\n# See the License for the specific language governing permissions\n# and limitations under the License.\n#\n# When distributing Covered Code, include this CDDL HEADER in each\n# file and include the License file at COPYING.\n# If applicable, add the following below this CDDL HEADER, with the\n# fields enclosed by brackets \"[]\" replaced with your own identifying\n# information: Portions Copyright [yyyy] [name of copyright owner]\n#\n# CDDL HEADER END\n#\n# Copyright (c) 2010,2011 Joyent Inc.\n#\n\nset -e\n\nrdmnt=$1\nif [ -z \"${rdmnt}\" ] || [ ! -d \"${rdmnt}\" ]; then\n echo \"FATAL: Invalid ramdisk root '${rdmnt}'\"\n echo \"Usage: $0 <rdmnt>\"\n exit 1\nfi\n\n# TODO check that we're root\n\nREPO_DB=${rdmnt}/etc/svc/repository.db\nexport SVCCFG_CHECKHASH=1\nexport PKG_INSTALL_ROOT=${rdmnt}\nexport SVCCFG_DTD=${rdmnt}/usr/share/lib/xml/dtd/service_bundle.dtd.1\nexport SVCCFG_REPOSITORY=${REPO_DB}\nexport SVCCFG_CONFIGD_PATH=/lib/svc/bin/svc.configd\n\nSVCCFG=\"/usr/sbin/svccfg\"\n${SVCCFG} -v repository ${REPO_DB}\n${SVCCFG} -v add smf/manifest\n${SVCCFG} -v import ${rdmnt}/lib/svc/manifest\n${SVCCFG} -s \"system/boot-config:default\" setprop config/fastreboot_default=false\n${SVCCFG} -s \"system/auditd:default\" setprop general/enabled=true\n${SVCCFG} -s \"system/coreadm:default\" setprop config_params/global_enabled=true\n${SVCCFG} -s \"system/coreadm:default\" setprop config_params/global_pattern='/zones/%z/cores/core.%f.%p'\n# ${SVCCFG} -s \"system/manifest-import\" setprop start/exec=:true\n${SVCCFG} -v apply ${rdmnt}/etc/svc/profile/generic.xml\n${SVCCFG} -v apply ${rdmnt}/etc/svc/profile/platform.xml\n\nexit 0\n"},{"alpha_fraction":0.6865267157554626,"alphanum_fraction":0.6987136006355286,"avg_line_length":28.540000915527344,"blob_id":"e0f7b4de9ca62773755dbdb68272b65a2e9160c7","content_id":"772f592b4f16ccd22d635e6cf8bd09c74844c6c1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1477,"license_type":"no_license","max_line_length":78,"num_lines":50,"path":"/tools/sorter","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"#!/usr/bin/python\n#\n# Takes a 'manifest' style file and outputs a sorted version, warning about\n# any duplicates.\n#\n# CDDL HEADER START\n#\n# The contents of this file are subject to the terms of the\n# Common Development and Distribution License, Version 1.0 only\n# (the \"License\"). You may not use this file except in compliance\n# with the License.\n#\n# You can obtain a copy of the license at COPYING\n# See the License for the specific language governing permissions\n# and limitations under the License.\n#\n# When distributing Covered Code, include this CDDL HEADER in each\n# file and include the License file at COPYING.\n# If applicable, add the following below this CDDL HEADER, with the\n# fields enclosed by brackets \"[]\" replaced with your own identifying\n# information: Portions Copyright [yyyy] [name of copyright owner]\n#\n# CDDL HEADER END\n#\n# Copyright (c) 2010,2011 Joyent Inc.\n#\n\n\nimport os\nimport sys\nfiles = dict()\nfilename = 'manifest'\n\n# if filename is passed as first argument, use that file instead\nif len(sys.argv) >= 2 and os.path.isfile(sys.argv[1]):\n filename = sys.argv[1]\n\nfor line in open(filename).readlines():\n line=line.lstrip()\n if len(line) == 0 or line[0] == '#':\n next\n fields = line.rstrip().split(' ')\n if fields[1] in files:\n print >> sys.stderr, \"WARNING: duplicate entry for '%s'\" % (fields[1])\n files[fields[1]] = line.rstrip('\\n')\n\nk = files.keys()\nks = k.sort()\nfor key in k:\n print files[key].rstrip(' ')\n"},{"alpha_fraction":0.6104234457015991,"alphanum_fraction":0.6188924908638,"avg_line_length":26.410715103149414,"blob_id":"ead72966417445cf75d9fd901f32cf4f01044511","content_id":"7437d2e74f6be604089043ec4dfaa7327b5dfd32","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1535,"license_type":"no_license","max_line_length":88,"num_lines":56,"path":"/tools/get_status","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"#!/bin/bash\n#\n# CDDL HEADER START\n#\n# The contents of this file are subject to the terms of the\n# Common Development and Distribution License, Version 1.0 only\n# (the \"License\"). You may not use this file except in compliance\n# with the License.\n#\n# You can obtain a copy of the license at COPYING\n# See the License for the specific language governing permissions\n# and limitations under the License.\n#\n# When distributing Covered Code, include this CDDL HEADER in each\n# file and include the License file at COPYING.\n# If applicable, add the following below this CDDL HEADER, with the\n# fields enclosed by brackets \"[]\" replaced with your own identifying\n# information: Portions Copyright [yyyy] [name of copyright owner]\n#\n# CDDL HEADER END\n#\n# Copyright (c) 2010,2011 Joyent Inc.\n#\n\nif [[ -z ${ROOT} ]]; then\n ROOT=$(pwd)\nfi\n\nfunction get_status()\n{\n dir=$1\n\n if [[ -z ${dir} ]]; then\n echo \"WARNING: skilling empty \\$dir\"\n return 0\n fi\n \n oldpwd=$(pwd)\n cd ${dir}\n branch=$(git branch | grep \"^* \" | cut -d ' ' -f2)\n rev=$(git rev-parse HEAD)\n\n if [[ -n ${branch} ]] && [[ -n ${rev} ]]; then\n echo \"\\\"branch\\\": \\\"${branch}\\\", \\\"rev\\\": \\\"${rev}\\\"\"\n else\n echo \"\\\"branch\\\": \\\"unknown\\\"\"\n fi\n\n cd ${oldpwd}\n}\n\necho \"[\"\necho \" { \\\"repo\\\": \\\"illumos-live\\\", $(get_status ${ROOT}) }\"\necho \" , { \\\"repo\\\": \\\"illumos-extra\\\", $(get_status ${ROOT}/projects/illumos-extra) }\"\necho \" , { \\\"repo\\\": \\\"illumos-joyent\\\", $(get_status ${ROOT}/projects/illumos) }\"\necho \"]\"\n"},{"alpha_fraction":0.6538222432136536,"alphanum_fraction":0.6699813604354858,"avg_line_length":21.34722137451172,"blob_id":"ad6f2fc5f15f070ef9eb7642299f86414f5a8564","content_id":"87a9925a42f631a2e618818453a8742f3c784d5c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C","length_bytes":1609,"license_type":"no_license","max_line_length":69,"num_lines":72,"path":"/src/removable_disk.c","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"/*\n* CDDL HEADER START\n*\n* The contents of this file are subject to the terms of the\n* Common Development and Distribution License, Version 1.0 only\n* (the \"License\"). You may not use this file except in compliance\n* with the License.\n*\n* You can obtain a copy of the license at COPYING\n* See the License for the specific language governing permissions\n* and limitations under the License.\n*\n* When distributing Covered Code, include this CDDL HEADER in each\n* file and include the License file at COPYING.\n* If applicable, add the following below this CDDL HEADER, with the\n* fields enclosed by brackets \"[]\" replaced with your own identifying\n* information: Portions Copyright [yyyy] [name of copyright owner]\n*\n* CDDL HEADER END\n*\n* Copyright (c) 2010,2011 Joyent Inc.\n*\n*/\n\n#include <stdio.h>\n#include <unistd.h>\n#include <fcntl.h>\n#include <sys/stat.h>\n#include <sys/dkio.h>\n#include <sys/vtoc.h>\n\nvoid\nusage(char* argv0) {\n\tprintf(\"Usage: %s [char dev]\\n\", argv0);\n}\n\nint\nmain(int argc, char* argv[]) {\n\tchar* devpath = argv[1];\n int devnode;\n\tstruct stat buf;\n\tint ret;\n\tint removable;\n\t\n\tif(argc != 2) {\n\t\tusage(argv[0]);\n\t\treturn -1;\n\t}\n\t\n if ((devnode = open(devpath, O_RDONLY)) < 0 ) {\n\t\tprintf(\"Could not open %s\\n\", devpath);\n\t\tusage(argv[0]);\n\t\treturn -1;\n }\n \n\tif (fstat(devnode, &buf) == -1 || !S_ISCHR(buf.st_mode)) {\n\t\tprintf(\"%s: not a character device\\n\", devpath);\n\t\tusage(argv[0]);\n\t\tclose(devnode);\n\t\treturn -1;\n\t}\n\t\n\tret = ioctl(devnode, DKIOCREMOVABLE, &removable);\n\n\tif ((ret >= 0) && (removable != 0)) {\n\t\tclose(devnode);\n\t\treturn 0;\n\t}\n\t\n\tclose(devnode);\n\treturn 1;\n}\n"},{"alpha_fraction":0.6844961047172546,"alphanum_fraction":0.6949612498283386,"avg_line_length":34.342464447021484,"blob_id":"df34de755ee69ee9604e568b735c65d14d30ede1","content_id":"abf69c71cb732959e0a61f8047ebd916121671e2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Makefile","length_bytes":2580,"license_type":"no_license","max_line_length":150,"num_lines":73,"path":"/Makefile","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"#\n#\n# CDDL HEADER START\n#\n# The contents of this file are subject to the terms of the\n# Common Development and Distribution License, Version 1.0 only\n# (the \"License\"). You may not use this file except in compliance\n# with the License.\n#\n# You can obtain a copy of the license at COPYING\n# See the License for the specific language governing permissions\n# and limitations under the License.\n#\n# When distributing Covered Code, include this CDDL HEADER in each\n# file and include the License file at COPYING.\n# If applicable, add the following below this CDDL HEADER, with the\n# fields enclosed by brackets \"[]\" replaced with your own identifying\n# information: Portions Copyright [yyyy] [name of copyright owner]\n#\n# CDDL HEADER END\n#\n# Copyright (c) 2010,2011 Joyent Inc.\n#\n\nROOT=$(PWD)\nPROTO=$(ROOT)/proto\nPATH=/opt/local/bin:/opt/local/sbin:/opt/local/gcc34/bin:/usr/xpg4/bin:/usr/bin:/usr/sbin:/usr/sfw/bin:/usr/openwin/bin:/opt/SUNWspro/bin:/usr/ccs/bin\nLOCAL_SUBDIRS=\"\"\n\nworld: 0-illumos-stamp 0-extra-stamp 0-livesrc-stamp 0-local-stamp 0-tools-stamp\n\nlive: world\n\tmkdir -p ${ROOT}/log\n\t(cd $(ROOT) && pfexec ./tools/build_live $(ROOT)/manifest $(ROOT)/output $(ROOT)/overlay $(ROOT)/proto $(ROOT)/projects/opensolaris-man /)\n\nupdate:\n\t@(git pull --rebase)\n\t@(cd projects/illumos; git pull --rebase || hg pull -u)\n\t@(cd projects/illumos-extra; git pull --rebase)\n\t[ ! -d projects/local ] || for dir in $(LOCAL_SUBDIRS); do (cd projects/local/$${dir} && git pull --rebase); done\n\n0-local-stamp:\n\t[ ! -d projects/local ] || for dir in $(LOCAL_SUBDIRS); do (cd projects/local/$${dir} && gmake && gmake DESTDIR=$(PROTO) install); done\n\n0-illumos-stamp:\n\t(cd $(ROOT) && ./tools/build_illumos)\n\ttouch 0-illumos-stamp\n\n0-extra-stamp:\n\t(cd $(ROOT)/projects/illumos-extra && gmake DESTDIR=$(PROTO) && gmake DESTDIR=$(PROTO) install)\n\ttouch 0-extra-stamp\n\n0-livesrc-stamp: src/bootparams.c\n\t(cd $(ROOT)/src && gmake DESTDIR=$(PROTO) && gmake DESTDIR=$(PROTO) install)\n\n0-tools-stamp: tools/builder/builder tools/pwgen tools/cryptpass\n\ntools/builder/builder:\n\t(cd $(ROOT)/tools/builder && gmake builder)\n\ntools/pwgen:\n\t(cd ${ROOT}/tools/pwgen-* && ./configure && make && cp pwgen ${ROOT}/tools)\n\ntools/cryptpass: tools/cryptpass.c\n\t(cd ${ROOT}/tools && gcc -Wall -W -O2 -o cryptpass cryptpass.c)\n\nclean:\n\t(cd $(ROOT)/src && gmake clean)\n\t(cd $(ROOT)/projects/illumos-extra && gmake clean)\n\t[ ! -d projects/local ] || for dir in $(LOCAL_SUBDIRS); do (cd projects/local/$${dir} && gmake clean); done\n\t(cd $(ROOT) && rm -rf $(PROTO))\n\t(cd $(ROOT) && mkdir -p $(PROTO))\n\trm -f 0-*-stamp\n"},{"alpha_fraction":0.5076692700386047,"alphanum_fraction":0.5185185074806213,"avg_line_length":31.192771911621094,"blob_id":"fae753882b5ebb5770119489f64907ea0c2b30d7","content_id":"11170008f65b0f1139b138c7ab6afb3c68c71dd2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":2673,"license_type":"no_license","max_line_length":114,"num_lines":83,"path":"/tools/scanner","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"#!/bin/bash\n#\n# CDDL HEADER START\n#\n# The contents of this file are subject to the terms of the\n# Common Development and Distribution License, Version 1.0 only\n# (the \"License\"). You may not use this file except in compliance\n# with the License.\n#\n# You can obtain a copy of the license at COPYING\n# See the License for the specific language governing permissions\n# and limitations under the License.\n#\n# When distributing Covered Code, include this CDDL HEADER in each\n# file and include the License file at COPYING.\n# If applicable, add the following below this CDDL HEADER, with the\n# fields enclosed by brackets \"[]\" replaced with your own identifying\n# information: Portions Copyright [yyyy] [name of copyright owner]\n#\n# CDDL HEADER END\n#\n# Copyright (c) 2010,2011 Joyent Inc.\n#\n\nROOT=$1\nDIR=$2\ncommand=\"pfexec find $DIR -ls\"\n\nif [ ! -z ${ROOT} ] && [ \"${ROOT}\" == \"-\" ]; then\n ROOT=\"/\"\n DIR=\"-\"\n command=\"cat -\"\nfi\n\nif [ -z \"${ROOT}\" ] || [ -z \"${DIR}\" ]; then\n echo \"\"\n echo \"Use this program to create manifest entries for use with the live image creation\"\n echo \"script. eg. '$0 / etc' will generate a manifest list that could be used for /etc\"\n echo \"\"\n echo \"Usage: $0 <ROOT> <DIR>\"\n exit 1\nfi\n\ncd ${ROOT}\nsh -c \"${command}\" | \\\n{ \n while read line; do \n # Squeeze multiple spaces/tabs into one space\n line=`echo \"${line}\" | sed -e \"s/ / /\" | tr -s ' '`\n\n # Remove leading/trailing spaces\n line=`echo \"${line}\" | sed -e \"s/ +$//\" | sed -e \"s/^ +//\"`\n\n echo \"${line}\" | { read l_inode l_size l_hmode l_links l_user l_group l_size l_month l_day l_year l_path ;\n l_md5sum=\n l_type=`echo \"${l_hmode}\" | cut -c1`\n if [ \"${l_type}\" != \"l\" ] && [ \"${l_type}\" != \"c\" ] && [ \"${l_type}\" != \"b\" ]; then\n l_mode=`pfexec stat --printf \"%04a\" ${l_path}`\n else\n l_type=\"s\"\n l_path=`echo \"${l_path}\" | sed -e \"s/ -> /=/\"`\n l_mode= #\"0777\"\n l_user= #\"root\"\n l_group= #\"root\"\n fi\n\n if [ \"${l_type}\" == \"-\" ]; then\n l_type=\"f\"\n fi\n\n if [ \"${l_type}\" == \"f\" ] && [ ! -z \"${INCLUDE_MD5SUM}\" ]; then\n # include md5sum for files if requested.\n l_md5sum=`md5sum ${l_path} | cut -d' ' -f1`\n fi\n\n # ignore char and block devices for now\n if [ \"${l_type}\" != \"c\" ] && [ \"${l_type}\" != \"b\" ]; then\n echo \"${l_type} ${l_path} ${l_mode} ${l_user} ${l_group} ${l_md5sum}\" \\\n | sed -e \"s/ +$//\"\n fi\n }\n done\n}\n\n"},{"alpha_fraction":0.5745062828063965,"alphanum_fraction":0.5870736241340637,"avg_line_length":23.940298080444336,"blob_id":"a1cbfe6794b10858282e94f9d2e652fb087b57dd","content_id":"0d4518b0d0e29d2daba59c7f133531bc44a8c752","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1671,"license_type":"no_license","max_line_length":86,"num_lines":67,"path":"/src/disklist.sh","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"#!/bin/sh\n#\n#\n# CDDL HEADER START\n#\n# The contents of this file are subject to the terms of the\n# Common Development and Distribution License, Version 1.0 only\n# (the \"License\"). You may not use this file except in compliance\n# with the License.\n#\n# You can obtain a copy of the license at COPYING\n# See the License for the specific language governing permissions\n# and limitations under the License.\n#\n# When distributing Covered Code, include this CDDL HEADER in each\n# file and include the License file at COPYING.\n# If applicable, add the following below this CDDL HEADER, with the\n# fields enclosed by brackets \"[]\" replaced with your own identifying\n# information: Portions Copyright [yyyy] [name of copyright owner]\n#\n# CDDL HEADER END\n#\n# Copyright (c) 2010,2011 Joyent Inc.\n#\n\nREMDISKS=\"\"\nNONREMDISKS=\"\"\n\nALLDISKS=`/usr/bin/ls /dev/dsk | /usr/bin/awk \"/s2$/ {print}\" | /usr/bin/sed \"s/s2//\"`\n\nfor disk in $ALLDISKS; \ndo\n pfexec removable_disk /dev/rdsk/${disk}p0 2>&1 >> /dev/null\n case \"$?\" in\n 0)\n REMDISKS=$REMDISKS\" \"$disk;\n ;;\n 1)\n NONREMDISKS=$NONREMDISKS\" \"$disk;\n ;;\n esac;\ndone\n\nwhile getopts 'anrs' OPTION\ndo\n case $OPTION in\n a)\n echo $REMDISKS\" \"$NONREMDISKS\n ;;\n n)\n echo $NONREMDISKS\n ;;\n r)\n echo $REMDISKS\n ;;\n s)\n for disk in $NONREMDISKS; do\n size=`pfexec disk_size /dev/rdsk/${disk}p0`\n echo \"${disk}=${size}\"\n done\n ;;\n ?)\n printf \"Usage: %s: [-anr]\\n\" $(basename $0) >&2\n exit 2\n ;;\n esac\ndone\n"},{"alpha_fraction":0.5182694792747498,"alphanum_fraction":0.5337520837783813,"avg_line_length":27.18703269958496,"blob_id":"6f956529731aed16e489aab4a0ebf38b28f1002e","content_id":"4f49c74c516b4884582c81b89b26f2efa839249b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":11303,"license_type":"no_license","max_line_length":156,"num_lines":401,"path":"/src/sysinfo","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"#!/bin/bash\n#\n# CDDL HEADER START\n#\n# The contents of this file are subject to the terms of the\n# Common Development and Distribution License, Version 1.0 only\n# (the \"License\"). You may not use this file except in compliance\n# with the License.\n#\n# You can obtain a copy of the license at COPYING\n# See the License for the specific language governing permissions\n# and limitations under the License.\n#\n# When distributing Covered Code, include this CDDL HEADER in each\n# file and include the License file at COPYING.\n# If applicable, add the following below this CDDL HEADER, with the\n# fields enclosed by brackets \"[]\" replaced with your own identifying\n# information: Portions Copyright [yyyy] [name of copyright owner]\n#\n# CDDL HEADER END\n#\n# Copyright (c) 2010,2011 Joyent Inc.\n#\n# Output some info representing the system. Default: JSON\n#\n# Important: Don't use anything here that you don't include in the live image!\n#\n\nPARSABLE=\nif [[ $1 == \"-p\" ]]; then\n PARSABLE=\"true\"\nfi\n\nif [[ $(zonename) != \"global\" ]]; then\n echo \"This program is for use in the global zone only.\"\n exit 1\nfi\n\nconfigfile=\"$(svcprop -p 'joyentfs/usb_copy_path' svc:/system/filesystem/smartdc:default 2>/dev/null)/config\"\nif [[ ! -f ${configfile} ]]; then\n configfile=\"/mnt/$(svcprop -p 'joyentfs/usb_mountpoint' svc:/system/filesystem/smartdc:default 2>/dev/null)/config\"\nfi\n\n# helper to set global \"normalized\" to the expanded version of MAC ($1)\nfunction normalize_mac()\n{\n mac=$1\n if [ -z \"${mac}\" ]; then\n echo \"FATAL: unable to normalize empty mac!\"\n exit 1\n fi\n\n normalized=`echo ${mac} | awk -F':' '{ print $1, $2, $3, $4, $5, $6 }' | { read o1 o2 o3 o4 o5 o6 junk\n printf \"%02x:%02x:%02x:%02x:%02x:%02x\" 0x${o1} 0x${o2} 0x${o3} 0x${o4} 0x${o5} 0x${o6}\n }`\n\n # ensure results are 'normal'\n echo \"${normalized}\" | grep \"[a-fA-F0-9]\\{2\\}\\:[a-fA-F0-9]\\{2\\}\\:[a-fA-F0-9]\\{2\\}\\:[a-fA-F0-9]\\{2\\}\\:[a-fA-F0-9]\\{2\\}\\:[a-fA-F0-9]\\{2\\}\" >/dev/null 2>&1\n if [ \"$?\" != \"0\" ]; then\n echo \"FATAL: failed to normalize MAC '${mac}'\"\n exit 1\n fi\n}\n\nfunction get_smbios_system_info()\n{\n # This puts the variables we're pulling out into the local environment\n eval $(smbios -t SMB_TYPE_SYSTEM \\\n | egrep \"Manufacturer: |Product: |Serial Number: |UUID: \" \\\n | sed -e 's/^ *//' \\\n | sed -e 's/: /=\"/' \\\n | sed -e 's/ *$/\"/' \\\n | sed -e 's/Serial Number/Serial_Number/')\n\n #echo \"${UUID}\"\n #echo \"${Product}\"\n #echo \"${Serial_Number}\"\n #echo \"${Manufacturer}\"\n}\n\nfunction get_memory_mib()\n{\n # Get (misnamed in prtconf) memory size in Mebibytes\n Memory_in_MiB=`prtconf \\\n | grep \"Memory size: [0-9]* Megabytes\" \\\n | cut -d' ' -f3`\n\n #echo \"${Memory_in_MiB}\"\n}\n\nfunction get_cpu_info()\n{\n CPU_Version=`smbios -t SMB_TYPE_PROCESSOR | grep -v \"Version: 0000000000\" \\\n | grep \"Version: \" \\\n | head -n1 \\\n | tr -s ' ' \\\n | sed -e 's/^ *Version: //' \\\n | sed -e 's/ *$//'`\n\n CPU_Count=`smbios -t SMB_TYPE_PROCESSOR | grep -v \"Version: 0000000000\" \\\n | grep \"Version: \" \\\n | wc -l \\\n | tr -d ' '`\n\n CPU_Cores=`psrinfo \\\n | grep \"^[0-9]\" \\\n | wc -l \\\n | tr -d ' '`\n\n #echo \"${CPU_Version}\"\n #echo \"${CPU_Count}\"\n #echo \"${CPU_Cores}\"\n}\n\nfunction get_live_image_buildstamp()\n{\n # Add joyent_buildstamp to SYSTEM_INFO\n Live_Image=$(cat /etc/joyent_buildstamp 2>/dev/null)\n\n #echo \"${Live_Image}\"\n}\n\nfunction get_hostname()\n{\n Hostname=$(hostname)\n}\n\nfunction get_disks()\n{\n ORIGIFS=$IFS\n\n # set $IFS to end-of-line\n IFS=`echo -en \"\\n\\b\"`\n\n count=1\n for line in $(/usr/bin/disklist -s 2>/dev/null); do\n Disks[${count}]=${line}\n ((count++))\n done\n\n # set $IFS back\n IFS=$ORIGIFS\n\n #for entry in \"${Disks[@]}\"\n #do\n # fields=(${entry//=/ })\n # disk=${fields[0]}\n # size=${fields[1]}\n # echo \"disk: ${disk} size: ${size}\"\n #done\n}\n\nfunction get_nic_mappings()\n{\n admin_nic=`/usr/bin/bootparams 2>/dev/null | grep \"admin_nic\" | cut -d '=' -f2-`\n if [[ -z ${admin_nic} ]] && [[ -f ${configfile} ]]; then\n admin_nic=`grep \"^admin_nic=\" ${configfile} | cut -f2 -d'='`\n fi\n if [[ -n ${admin_nic} ]]; then\n normalize_mac ${admin_nic}\n ADMIN_NIC=${normalized}\n fi\n external_nic=`/usr/bin/bootparams 2>/dev/null | grep \"external_nic\" | cut -d '=' -f2-`\n if [[ -z ${external_nic} ]] && [[ -f ${configfile} ]]; then\n external_nic=`grep \"^external_nic=\" ${configfile} | cut -f2 -d'='`\n fi\n if [[ -n ${external_nic} ]]; then\n normalize_mac ${external_nic}\n EXTERNAL_NIC=${normalized}\n fi\n internal_nic=`/usr/bin/bootparams 2>/dev/null | grep \"internal_nic\" | cut -d '=' -f2-`\n if [[ -z ${internal_nic} ]] && [[ -f ${configfile} ]]; then\n internal_nic=`grep \"^internal_nic=\" ${configfile} | cut -f2 -d'='`\n fi\n if [[ -n ${internal_nic} ]]; then\n normalize_mac ${internal_nic}\n INTERNAL_NIC=${normalized}\n fi\n\n #echo \"${ADMIN_NIC}\"\n #echo \"${EXTERNAL_NIC}\"\n #echo \"${INTERNAL_NIC}\"\n}\n\n\nfunction get_network_interfaces()\n{\n count=1\n # ignore 'LINK SLOT ADDRESS INUSE CLIENT' line\n for line in $(pfexec /sbin/dladm show-phys -m | grep -v \"^LINK \" | awk '{ print $1,$3 }' | tr ' ' '='); do\n nicnames=\n fields=(${line//=/ })\n iface=${fields[0]}\n normalize_mac ${fields[1]}\n mac=${normalized}\n ip4addr=$(ifconfig ${iface} 2>/dev/null | grep \"inet \" | awk '{ print $2 }')\n\n if [[ -n \"${ADMIN_NIC}\" ]] && [[ \"${ADMIN_NIC}\" == \"${mac}\" ]]; then\n nicnames=\"admin\"\n NIC_admin=${iface}\n fi\n if [[ -n \"${EXTERNAL_NIC}\" ]] && [[ \"${EXTERNAL_NIC}\" == \"${mac}\" ]]; then\n [[ -n ${nicnames} ]] && nicnames=\"${nicnames},\"\n nicnames=\"${nicnames}external\"\n NIC_external=${iface}\n fi\n if [[ -n \"${INTERNAL_NIC}\" ]] && [[ \"${INTERNAL_NIC}\" == \"${mac}\" ]]; then\n [[ -n ${nicnames} ]] && nicnames=\"${nicnames},\"\n nicnames=\"${nicnames}internal\"\n NIC_internal=${iface}\n fi\n\n NetworkInterfaces[${count}]=${iface}\n eval \"Network_Interface_${iface}_MAC_Address=${mac}\"\n eval \"Network_Interface_${iface}_IPv4_Address=${ip4addr}\"\n if [[ -n ${nicnames} ]]; then\n eval \"Network_Interface_${iface}_NIC_Names=${nicnames}\"\n fi\n ((count++))\n done\n\n #echo \"${NIC_admin}\"\n #echo \"${NIC_external}\"\n #echo \"${NIC_internal}\"\n #for iface in \"${NetworkInterfaces[@]}\"\n #do\n # eval \"mac=\\${Network_Interface_${iface}_MAC_Address}\"\n # eval \"ipv4=\\${Network_Interface_${iface}_IPv4_Address}\"\n # eval \"nicnames=\\${Network_Interface_${iface}_NIC_Names}\"\n # echo \"mac: ${mac} ---- ${ipv4} ---- ${nicnames}\"\n #done\n}\n\nfunction get_bootparams()\n{\n count=1\n for line in $(/usr/bin/bootparams); do\n fields=(${line//=/ })\n key=$(echo ${fields[0]} | sed -e \"s/\\-/\\_/g\")\n if ! (echo \"${key}\" | /usr/xpg4/bin/grep \\\n -e \"^tty\" \\\n -e \"^atapi\" \\\n -e \"^ata_dma\" \\\n -e \"^keyboard_\" \\\n -e \"^bios_boot_device\" \\\n -e \"^lba_access_\" \\\n -e \"^boot_ncpus\" \\\n -e \"^boot_file\" \\\n -e \"^whoami\" \\\n -e \"^mfg_name\" \\\n -e \"^impl_arch_name\" \\\n -e \"_max_ncpus$\" \\\n >/dev/null); then\n Bootparams[${count}]=${line}\n ((count++))\n fi\n done\n}\n\nfunction output_parsable()\n{\n cat <<END\nLive_Image='${Live_Image}'\nManufacturer='${Manufacturer}'\nProduct='${Product}'\nSerial_Number='${Serial_Number}'\nUUID='${UUID}'\nHostname='${Hostname}'\nCPU_Type='${CPU_Version}'\nCPU_Physical_Cores=${CPU_Count}\nCPU_Total_Cores=${CPU_Cores}\nMiB_of_Memory=${Memory_in_MiB}\nEND\n\n for entry in \"${Disks[@]}\"; do\n fields=(${entry//=/ })\n disk=${fields[0]}\n size=${fields[1]}\n gb_size=$((${size} / 1000000000))\n echo \"Disk_${disk}_size_in_GB=${gb_size}\"\n done\n\n [[ -n ${NIC_admin} ]] && echo \"NIC_admin='${NIC_admin}'\"\n [[ -n ${NIC_external} ]] && echo \"NIC_external='${NIC_external}'\"\n [[ -n ${NIC_internal} ]] && echo \"NIC_internal='${NIC_internal}'\"\n\n for iface in \"${NetworkInterfaces[@]}\"; do\n mac_var=\"Network_Interface_${iface}_MAC_Address\"\n ipv4_var=\"Network_Interface_${iface}_IPv4_Address\"\n nicnames_var=\"Network_Interface_${iface}_NIC_Names\"\n\n eval \"mac=\\${${mac_var}}\"\n eval \"ipv4=\\${${ipv4_var}}\"\n eval \"nicnames=\\${${nicnames_var}}\"\n\n echo \"${mac_var}='${mac}'\"\n echo \"${ipv4_var}='${ipv4}'\"\n echo \"${nicnames_var}='${nicnames}'\"\n done\n\n for entry in \"${Bootparams[@]}\"; do\n fields=(${entry//=/ })\n key=$(echo ${fields[0]} | sed -e \"s/\\-/\\_/g\")\n val=${fields[1]}\n echo \"Bootparam_${key}='${val}'\"\n done\n}\n\nfunction output_json()\n{\n cat <<END\n{\n \"Live Image\": \"${Live_Image}\",\n \"Manufacturer\": \"${Manufacturer}\",\n \"Product\": \"${Product}\",\n \"Serial Number\": \"${Serial_Number}\",\n \"UUID\": \"${UUID}\",\n \"Hostname\": \"${Hostname}\",\n \"CPU Type\": \"${CPU_Version}\",\n \"CPU Physical Cores\": ${CPU_Count},\n \"CPU Total Cores\": ${CPU_Cores},\n \"MiB of Memory\": \"${Memory_in_MiB}\",\n \"Disks\": {\nEND\n\n printed=0\n for entry in \"${Disks[@]}\"; do\n fields=(${entry//=/ })\n disk=${fields[0]}\n size=${fields[1]}\n gb_size=$((${size} / 1000000000))\n ((printed++))\n trailing_comma=\",\"\n [[ ${printed} -eq ${#Disks[*]} ]] && trailing_comma=''\n echo \" \\\"${disk}\\\": {\\\"Size in GB\\\": ${gb_size}}${trailing_comma}\"\n done\n\n cat <<END\n },\n \"Boot Parameters\": {\nEND\n printed=0\n for entry in \"${Bootparams[@]}\"; do\n fields=(${entry//=/ })\n key=$(echo ${fields[0]} | sed -e \"s/\\-/\\_/g\")\n val=${fields[1]}\n ((printed++))\n trailing_comma=\",\"\n [[ ${printed} -eq ${#Bootparams[*]} ]] && trailing_comma=''\n echo \" \\\"${key}\\\": \\\"$val\\\"${trailing_comma}\"\n done\n cat <<END\n },\n \"Network Interfaces\": {\nEND\n\n printed=0\n for iface in \"${NetworkInterfaces[@]}\"; do\n mac_var=\"Network_Interface_${iface}_MAC_Address\"\n ipv4_var=\"Network_Interface_${iface}_IPv4_Address\"\n nicnames_var=\"Network_Interface_${iface}_NIC_Names\"\n nic_names_fmt=\n\n eval \"mac=\\${${mac_var}}\"\n eval \"ipv4=\\${${ipv4_var}}\"\n eval \"nicnames=\\${${nicnames_var}}\"\n\n nic_names_array=$(echo \"${nicnames}\" | sed -e \"s/,/\\\", \\\"/g\")\n if [[ -n ${nic_names_array} ]]; then\n nic_names_fmt=\"\\\"${nic_names_array}\\\"\"\n fi\n\n ((printed++))\n trailing_comma=\",\"\n [[ ${printed} -eq ${#NetworkInterfaces[*]} ]] && trailing_comma=''\n echo \" \\\"${iface}\\\": {\\\"MAC Address\\\": \\\"${mac}\\\", \\\"ip4addr\\\": \\\"${ipv4}\\\", \\\"NIC Names\\\": [${nic_names_fmt}]}${trailing_comma}\"\n done\n\n cat <<END\n }\n}\nEND\n}\n\nget_smbios_system_info\nget_memory_mib\nget_cpu_info\nget_live_image_buildstamp\nget_disks\nget_hostname\nget_nic_mappings\nget_network_interfaces\nget_bootparams\n\nif [[ ${PARSABLE} == \"true\" ]]; then\n output_parsable\nelse\n output_json\nfi\n\nexit 0\n"},{"alpha_fraction":0.6375187039375305,"alphanum_fraction":0.6532137393951416,"avg_line_length":23.77777862548828,"blob_id":"030f6f7bd330ab44a968f10865f2d18f4d993a2f","content_id":"5adc0433a513b6654cf1fcc6218ad9babba96ffc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1338,"license_type":"no_license","max_line_length":141,"num_lines":54,"path":"/tools/builder/build_users_c.sh","repo_name":"justinlundy/illumos-live","src_encoding":"UTF-8","text":"#!/usr/bin/bash\n#\n# CDDL HEADER START\n#\n# The contents of this file are subject to the terms of the\n# Common Development and Distribution License, Version 1.0 only\n# (the \"License\"). You may not use this file except in compliance\n# with the License.\n#\n# You can obtain a copy of the license at COPYING\n# See the License for the specific language governing permissions\n# and limitations under the License.\n#\n# When distributing Covered Code, include this CDDL HEADER in each\n# file and include the License file at COPYING.\n# If applicable, add the following below this CDDL HEADER, with the\n# fields enclosed by brackets \"[]\" replaced with your own identifying\n# information: Portions Copyright [yyyy] [name of copyright owner]\n#\n# CDDL HEADER END\n#\n# Copyright (c) 2010,2011 Joyent Inc.\n#\n\nROOT=$1\n\ncat <<EOF\n#include <stdio.h>\n#include <string.h>\n\nint gid_from_name(const char *group)\n{\n int gid = -1;\nEOF\ncat ${ROOT}/etc/group | awk -F':' 'NR>1{ printf \"else \" };{ print \"if (strcmp(\\\"\" $1 \"\\\", group) == 0) gid = \" $3 \";\" }' | sed -e \"s/^/ /\"\ncat <<EOF\n\n return(gid);\n};\n\nEOF\n\ncat <<EOF\nint uid_from_name(const char *user)\n{\n int uid = -1;\n\nEOF\ncat ${ROOT}/etc/passwd | awk -F':' 'NR>1{ printf \"else \" };{ print \"if (strcmp(\\\"\" $1 \"\\\", user) == 0) uid = \" $3 \";\" }' | sed -e \"s/^/ /\"\ncat <<EOF\n\n return(uid);\n};\nEOF\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7181389331817627,\n \"alphanum_fraction\": 0.7262306213378906,\n \"avg_line_length\": 29.89583396911621,\n \"blob_id\": \"0bb882d2499d0c52fe682b1ec52e0094023b85d6\",\n \"content_id\": \"3f204854bf891b8d97b047c88edea3357af82bd3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1483,\n \"license_type\": \"no_license\",\n \"max_line_length\": 160,\n \"num_lines\": 48,\n \"path\": \"/tools/build_illumos\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env bash\\n#\\n# CDDL HEADER START\\n#\\n# The contents of this file are subject to the terms of the\\n# Common Development and Distribution License, Version 1.0 only\\n# (the \\\"License\\\"). You may not use this file except in compliance\\n# with the License.\\n#\\n# You can obtain a copy of the license at COPYING\\n# See the License for the specific language governing permissions\\n# and limitations under the License.\\n#\\n# When distributing Covered Code, include this CDDL HEADER in each\\n# file and include the License file at COPYING.\\n# If applicable, add the following below this CDDL HEADER, with the\\n# fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n# information: Portions Copyright [yyyy] [name of copyright owner]\\n#\\n# CDDL HEADER END\\n#\\n# Copyright (c) 2010,2011 Joyent Inc.\\n#\\n\\n# Setup for building stuff!\\nunset CC\\nunset CXX\\nROOT=`pwd`\\nexport PATH=/opt/onbld/bin:/sbin:/usr/sbin:/usr/bin:/usr/ccs/bin:/opt/SUNWspro/bin:/opt/local/bin\\n\\nBUILDSTAMP=`TZ=UTC date \\\"+%Y%m%dT%H%M%SZ\\\"`; export BUILDSTAMP\\nmkdir -p ${ROOT}/proto\\necho ${BUILDSTAMP} > ${ROOT}/proto/buildstamp\\n\\necho \\\"==> Building Illumos (${BUILDSTAMP})\\\"\\n\\nset -o pipefail\\nset -e\\n\\ncd ${ROOT}/projects/illumos\\n[[ -d \\\"log\\\" ]] || mkdir -p log\\ncp usr/src/tools/scripts/nightly.sh ./nightly\\nchmod u+x ./nightly\\nksh93 ./usr/src/tools/scripts/bldenv.sh -d illumos.sh 'cd $CODEMGR_WS/usr/src && export PATH=\\\"$PATH:/opt/local/bin\\\" && dmake setup && cd tools && dmake install'\\ntime ./nightly illumos.sh\\nres=$?\\n\\nexit $res\\n\"\n },\n {\n \"alpha_fraction\": 0.5361509919166565,\n \"alphanum_fraction\": 0.5829346179962158,\n \"avg_line_length\": 38.599998474121094,\n \"blob_id\": \"3bd72952cc5409edf7d084a4a4e925fb00d99348\",\n \"content_id\": \"c24da8a6b937cf80ca3e4d4680c807e5b6f380de\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 3762,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 95,\n \"path\": \"/tools/builder/users.c\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/*\\n* CDDL HEADER START\\n*\\n* The contents of this file are subject to the terms of the\\n* Common Development and Distribution License, Version 1.0 only\\n* (the \\\"License\\\"). You may not use this file except in compliance\\n* with the License.\\n*\\n* You can obtain a copy of the license at COPYING\\n* See the License for the specific language governing permissions\\n* and limitations under the License.\\n*\\n* When distributing Covered Code, include this CDDL HEADER in each\\n* file and include the License file at COPYING.\\n* If applicable, add the following below this CDDL HEADER, with the\\n* fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n* information: Portions Copyright [yyyy] [name of copyright owner]\\n*\\n* CDDL HEADER END\\n*\\n* Copyright (c) 2010,2011 Joyent Inc.\\n*\\n*/\\n\\n#include <stdio.h>\\n#include <string.h>\\n\\nint gid_from_name(const char *group)\\n{\\n int gid = -1;\\n if (strcmp(\\\"root\\\", group) == 0) gid = 0;\\n else if (strcmp(\\\"other\\\", group) == 0) gid = 1;\\n else if (strcmp(\\\"bin\\\", group) == 0) gid = 2;\\n else if (strcmp(\\\"sys\\\", group) == 0) gid = 3;\\n else if (strcmp(\\\"adm\\\", group) == 0) gid = 4;\\n else if (strcmp(\\\"uucp\\\", group) == 0) gid = 5;\\n else if (strcmp(\\\"mail\\\", group) == 0) gid = 6;\\n else if (strcmp(\\\"tty\\\", group) == 0) gid = 7;\\n else if (strcmp(\\\"lp\\\", group) == 0) gid = 8;\\n else if (strcmp(\\\"nuucp\\\", group) == 0) gid = 9;\\n else if (strcmp(\\\"staff\\\", group) == 0) gid = 10;\\n else if (strcmp(\\\"daemon\\\", group) == 0) gid = 12;\\n else if (strcmp(\\\"sysadmin\\\", group) == 0) gid = 14;\\n else if (strcmp(\\\"games\\\", group) == 0) gid = 20;\\n else if (strcmp(\\\"smmsp\\\", group) == 0) gid = 25;\\n else if (strcmp(\\\"gdm\\\", group) == 0) gid = 50;\\n else if (strcmp(\\\"upnp\\\", group) == 0) gid = 52;\\n else if (strcmp(\\\"xvm\\\", group) == 0) gid = 60;\\n else if (strcmp(\\\"mysql\\\", group) == 0) gid = 70;\\n else if (strcmp(\\\"openldap\\\", group) == 0) gid = 75;\\n else if (strcmp(\\\"webservd\\\", group) == 0) gid = 80;\\n else if (strcmp(\\\"postgres\\\", group) == 0) gid = 90;\\n else if (strcmp(\\\"slocate\\\", group) == 0) gid = 95;\\n else if (strcmp(\\\"unknown\\\", group) == 0) gid = 96;\\n else if (strcmp(\\\"nobody\\\", group) == 0) gid = 60001;\\n else if (strcmp(\\\"noaccess\\\", group) == 0) gid = 60002;\\n else if (strcmp(\\\"nogroup\\\", group) == 0) gid = 65534;\\n else if (strcmp(\\\"netadm\\\", group) == 0) gid = 65;\\n\\n return(gid);\\n};\\n\\nint uid_from_name(const char *user)\\n{\\n int uid = -1;\\n\\n if (strcmp(\\\"root\\\", user) == 0) uid = 0;\\n else if (strcmp(\\\"daemon\\\", user) == 0) uid = 1;\\n else if (strcmp(\\\"bin\\\", user) == 0) uid = 2;\\n else if (strcmp(\\\"sys\\\", user) == 0) uid = 3;\\n else if (strcmp(\\\"adm\\\", user) == 0) uid = 4;\\n else if (strcmp(\\\"lp\\\", user) == 0) uid = 71;\\n else if (strcmp(\\\"uucp\\\", user) == 0) uid = 5;\\n else if (strcmp(\\\"nuucp\\\", user) == 0) uid = 9;\\n else if (strcmp(\\\"dladm\\\", user) == 0) uid = 15;\\n else if (strcmp(\\\"netadm\\\", user) == 0) uid = 16;\\n else if (strcmp(\\\"netcfg\\\", user) == 0) uid = 17;\\n else if (strcmp(\\\"smmsp\\\", user) == 0) uid = 25;\\n else if (strcmp(\\\"listen\\\", user) == 0) uid = 37;\\n else if (strcmp(\\\"gdm\\\", user) == 0) uid = 50;\\n else if (strcmp(\\\"zfssnap\\\", user) == 0) uid = 51;\\n else if (strcmp(\\\"upnp\\\", user) == 0) uid = 52;\\n else if (strcmp(\\\"xvm\\\", user) == 0) uid = 60;\\n else if (strcmp(\\\"mysql\\\", user) == 0) uid = 70;\\n else if (strcmp(\\\"openldap\\\", user) == 0) uid = 75;\\n else if (strcmp(\\\"webservd\\\", user) == 0) uid = 80;\\n else if (strcmp(\\\"postgres\\\", user) == 0) uid = 90;\\n else if (strcmp(\\\"svctag\\\", user) == 0) uid = 95;\\n else if (strcmp(\\\"unknown\\\", user) == 0) uid = 96;\\n else if (strcmp(\\\"nobody\\\", user) == 0) uid = 60001;\\n else if (strcmp(\\\"noaccess\\\", user) == 0) uid = 60002;\\n else if (strcmp(\\\"nobody4\\\", user) == 0) uid = 65534;\\n\\n return(uid);\\n};\\n\"\n },\n {\n \"alpha_fraction\": 0.6773374080657959,\n \"alphanum_fraction\": 0.6920731663703918,\n \"avg_line_length\": 31.26229476928711,\n \"blob_id\": \"681ff289f1772fc429f1830253a387180277ae67\",\n \"content_id\": \"49d085cd805b8eee04466601070b6edfbb1f0a3c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1968,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 61,\n \"path\": \"/tools/customize\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n#\\n# CDDL HEADER START\\n#\\n# The contents of this file are subject to the terms of the\\n# Common Development and Distribution License, Version 1.0 only\\n# (the \\\"License\\\"). You may not use this file except in compliance\\n# with the License.\\n#\\n# You can obtain a copy of the license at COPYING\\n# See the License for the specific language governing permissions\\n# and limitations under the License.\\n#\\n# When distributing Covered Code, include this CDDL HEADER in each\\n# file and include the License file at COPYING.\\n# If applicable, add the following below this CDDL HEADER, with the\\n# fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n# information: Portions Copyright [yyyy] [name of copyright owner]\\n#\\n# CDDL HEADER END\\n#\\n# Copyright (c) 2010,2011 Joyent Inc.\\n#\\n\\nBUILDSTAMP=$2\\nif [ -z \\\"${BUILDSTAMP}\\\" ]; then\\n BUILDSTAMP=`TZ=UTC date \\\"+%Y%m%dT%H%M%SZ\\\"`\\nfi\\n\\nrdmnt=$1\\nif [ -z \\\"${rdmnt}\\\" ]; then\\n echo \\\"Usage: $0 <rdmnt directory> [buildstamp]\\\"\\n exit 1\\nfi\\n\\necho \\\"Customizing ${rdmnt}\\\"\\npfexec cp /dev/null ${rdmnt}/etc/mnttab\\npfexec cp /dev/null ${rdmnt}/var/log/syslog\\npfexec cp /dev/null ${rdmnt}/var/adm/wtmpx\\n\\n# This rebuilds the man index but needs to work around the fact that\\n# isaexec binaries don't work properly in a chroot.\\n(cd ${rdmnt}/usr/bin && pfexec mv sh sh.$$ && ln -s bash sh)\\npfexec mount -F lofs /dev ${rdmnt}/dev\\n# If we're in a GZ there will be a /devices we need to\\nif [[ -d /devices ]]; then\\n pfexec mount -F lofs /devices ${rdmnt}/devices\\nfi\\npfexec chroot ${rdmnt} /usr/bin/catman\\nif [[ -d /devices ]]; then\\n pfexec umount ${rdmnt}/devices\\nfi\\npfexec umount ${rdmnt}/dev\\n(cd ${rdmnt}/usr/bin && pfexec rm sh && pfexec mv sh.$$ sh)\\n\\npfexec chown root:root ${rdmnt}\\necho \\\"${BUILDSTAMP}\\\" | pfexec tee ${rdmnt}/etc/joyent_buildstamp >/dev/null 2>&1\\npfexec gsed -i -e \\\"s/ [0-9]\\\\{8\\\\}T[0-9]\\\\{6\\\\}Z/ ${BUILDSTAMP}/\\\" ${rdmnt}/etc/motd\\npfexec gsed -i -e \\\"s/ [0-9]\\\\{8\\\\}T[0-9]\\\\{6\\\\}Z/ ${BUILDSTAMP}/\\\" ${rdmnt}/etc/issue\\n\\nexit 0\\n\"\n },\n {\n \"alpha_fraction\": 0.7166276574134827,\n \"alphanum_fraction\": 0.727556586265564,\n \"avg_line_length\": 23.634614944458008,\n \"blob_id\": \"061c926993c0aacf4d68824adb6917c19a2d37e3\",\n \"content_id\": \"a5db6bd24ccc803f2c9f5a454472c9356050dfc8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Makefile\",\n \"length_bytes\": 1281,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 52,\n \"path\": \"/src/Makefile\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#\\n# CDDL HEADER START\\n#\\n# The contents of this file are subject to the terms of the\\n# Common Development and Distribution License, Version 1.0 only\\n# (the \\\"License\\\"). You may not use this file except in compliance\\n# with the License.\\n#\\n# You can obtain a copy of the license at COPYING\\n# See the License for the specific language governing permissions\\n# and limitations under the License.\\n#\\n# When distributing Covered Code, include this CDDL HEADER in each\\n# file and include the License file at COPYING.\\n# If applicable, add the following below this CDDL HEADER, with the\\n# fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n# information: Portions Copyright [yyyy] [name of copyright owner]\\n#\\n# CDDL HEADER END\\n#\\n# Copyright (c) 2010,2011 Joyent Inc.\\n#\\n\\nCC=gcc\\nCFLAGS=-Wall\\nTARGETS=bootparams disklist removable_disk disk_size\\nDESTDIR=../proto\\n\\nall: $(TARGETS) sysinfo\\n\\ninstall: $(TARGETS) sysinfo\\n\\tmkdir -p $(DESTDIR)/usr/bin\\n\\tpfexec cp -p $(TARGETS) sysinfo $(DESTDIR)/usr/bin\\n\\nbootparams: bootparams.c\\n\\t$(CC) $(CFLAGS) -o $@ $^ -ldevinfo\\n\\nremovable_disk: removable_disk.c\\n\\t$(CC) $(CFLAGS) -o $@ $^\\n\\ndisk_size: disk_size.c\\n\\t$(CC) $(CFLAGS) -o $@ $^\\n\\ndisklist:\\n\\tcp disklist.sh disklist\\n\\tchmod 0755 disklist\\n\\nsysinfo:\\n\\ttouch sysinfo\\n\\nclean:\\n\\t@rm -f $(TARGETS)\\n\"\n },\n {\n \"alpha_fraction\": 0.6718003153800964,\n \"alphanum_fraction\": 0.6829527616500854,\n \"avg_line_length\": 23.454545974731445,\n \"blob_id\": \"d256e461c121afa51772133b4579bb9f226a2aa6\",\n \"content_id\": \"d65ce5ba6082f578f585b9dc58d07838b0067b79\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 1883,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 77,\n \"path\": \"/src/disk_size.c\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/*\\n* CDDL HEADER START\\n*\\n* The contents of this file are subject to the terms of the\\n* Common Development and Distribution License, Version 1.0 only\\n* (the \\\"License\\\"). You may not use this file except in compliance\\n* with the License.\\n*\\n* You can obtain a copy of the license at COPYING\\n* See the License for the specific language governing permissions\\n* and limitations under the License.\\n*\\n* When distributing Covered Code, include this CDDL HEADER in each\\n* file and include the License file at COPYING.\\n* If applicable, add the following below this CDDL HEADER, with the\\n* fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n* information: Portions Copyright [yyyy] [name of copyright owner]\\n*\\n* CDDL HEADER END\\n*\\n* Copyright (c) 2010,2011 Joyent Inc.\\n*\\n*/\\n\\n\\n/* \\n * This tool takes the character device node for a disk and prints the size\\n * in *bytes* for that disk. On any error, a message explaining the problem\\n * is printed to STDERR and the exit code is non-zero.\\n *\\n */\\n\\n\\n#include <stdio.h>\\n#include <stdlib.h>\\n#include <unistd.h>\\n#include <fcntl.h>\\n#include <sys/stat.h>\\n#include <sys/dkio.h>\\n#include <sys/vtoc.h>\\n\\nvoid\\nusage(char* argv0) {\\n\\tprintf(\\\"Usage: %s [char dev]\\\\n\\\", argv0);\\n\\texit(1);\\n}\\n\\nint\\nmain(int argc, char* argv[]) {\\n\\tchar* devpath = argv[1];\\n int devnode;\\n\\tint ret;\\n unsigned long long bytes;\\n struct dk_minfo mediainfo;\\n\\t\\n\\tif (argc != 2) {\\n\\t\\tfprintf(stderr, \\\"FATAL: Device argument required\\\\n\\\");\\n\\t\\tusage(argv[0]);\\n\\t}\\n\\t\\n if ((devnode = open(devpath, O_RDONLY)) < 0 ) {\\n\\t\\tfprintf(stderr, \\\"FATAL: Could not open %s\\\\n\\\", devpath);\\n\\t\\tusage(argv[0]);\\n }\\n \\n\\tret = ioctl(devnode, DKIOCGMEDIAINFO, &mediainfo);\\n\\tclose(devnode);\\n\\n\\tif (ret < 0) {\\n fprintf(stderr, \\\"FATAL: DKIOCGMEDIAINFO failed\\\\n\\\");\\n exit(1);\\n\\t}\\n\\n bytes = (mediainfo.dki_capacity * mediainfo.dki_lbsize);\\n printf(\\\"%llu\\\\n\\\", bytes);\\n\\texit(0);\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.625806450843811,\n \"alphanum_fraction\": 0.6380645036697388,\n \"avg_line_length\": 21.14285659790039,\n \"blob_id\": \"a8daf37f5bde9728784c7bef258bfa5cc857edd3\",\n \"content_id\": \"7bbbaef3c6915eac5b3e9af7b29dfe0a03ac14ff\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 1550,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 70,\n \"path\": \"/tools/cryptpass.c\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/*\\n* CDDL HEADER START\\n*\\n* The contents of this file are subject to the terms of the\\n* Common Development and Distribution License, Version 1.0 only\\n* (the \\\"License\\\"). You may not use this file except in compliance\\n* with the License.\\n*\\n* You can obtain a copy of the license at COPYING\\n* See the License for the specific language governing permissions\\n* and limitations under the License.\\n*\\n* When distributing Covered Code, include this CDDL HEADER in each\\n* file and include the License file at COPYING.\\n* If applicable, add the following below this CDDL HEADER, with the\\n* fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n* information: Portions Copyright [yyyy] [name of copyright owner]\\n*\\n* CDDL HEADER END\\n*\\n* Copyright (c) 2010,2011 Joyent Inc.\\n*\\n*/\\n\\n/*\\n * Summary:\\n *\\n * Takes plain-text password as cmdline arg and outputs a crypt() version.\\n *\\n */\\n\\n#include <crypt.h>\\n#include <errno.h>\\n#include <stdio.h>\\n#include <stdlib.h>\\n#include <string.h>\\n\\nvoid usage()\\n{\\n printf(\\\"Usage: cryptpass <password>\\\\n\\\");\\n exit(1);\\n}\\n\\nint main(int argc, char *argv[])\\n{\\n char *salt, *enc;\\n\\n if ((argc != 2) || (strlen(argv[1]) < 1)) {\\n usage();\\n }\\n\\n errno=0;\\n salt=crypt_gensalt(NULL, NULL);\\n if (salt == NULL) {\\n printf(\\\"FATAL: crypt_gensalt() errno=%d\\\\n\\\", errno);\\n exit(1);\\n }\\n\\n enc = crypt(argv[1], salt);\\n if (enc == NULL) {\\n printf(\\\"FATAL: crypt() errno=%d\\\\n\\\", errno);\\n free(salt);\\n exit(1);\\n }\\n\\n printf(\\\"%s\\\\n\\\", enc);\\n\\n free(salt);\\n exit(0);\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.7239915132522583,\n \"alphanum_fraction\": 0.7324841022491455,\n \"avg_line_length\": 33.254547119140625,\n \"blob_id\": \"6b444dbe0efc6dfd461c12c318398cbe97027ce9\",\n \"content_id\": \"f8faaf87c8f7e0a052f30edf21b778e4dfe0d9ec\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1884,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 55,\n \"path\": \"/tools/smf_import\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n#\\n# CDDL HEADER START\\n#\\n# The contents of this file are subject to the terms of the\\n# Common Development and Distribution License, Version 1.0 only\\n# (the \\\"License\\\"). You may not use this file except in compliance\\n# with the License.\\n#\\n# You can obtain a copy of the license at COPYING\\n# See the License for the specific language governing permissions\\n# and limitations under the License.\\n#\\n# When distributing Covered Code, include this CDDL HEADER in each\\n# file and include the License file at COPYING.\\n# If applicable, add the following below this CDDL HEADER, with the\\n# fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n# information: Portions Copyright [yyyy] [name of copyright owner]\\n#\\n# CDDL HEADER END\\n#\\n# Copyright (c) 2010,2011 Joyent Inc.\\n#\\n\\nset -e\\n\\nrdmnt=$1\\nif [ -z \\\"${rdmnt}\\\" ] || [ ! -d \\\"${rdmnt}\\\" ]; then\\n echo \\\"FATAL: Invalid ramdisk root '${rdmnt}'\\\"\\n echo \\\"Usage: $0 <rdmnt>\\\"\\n exit 1\\nfi\\n\\n# TODO check that we're root\\n\\nREPO_DB=${rdmnt}/etc/svc/repository.db\\nexport SVCCFG_CHECKHASH=1\\nexport PKG_INSTALL_ROOT=${rdmnt}\\nexport SVCCFG_DTD=${rdmnt}/usr/share/lib/xml/dtd/service_bundle.dtd.1\\nexport SVCCFG_REPOSITORY=${REPO_DB}\\nexport SVCCFG_CONFIGD_PATH=/lib/svc/bin/svc.configd\\n\\nSVCCFG=\\\"/usr/sbin/svccfg\\\"\\n${SVCCFG} -v repository ${REPO_DB}\\n${SVCCFG} -v add smf/manifest\\n${SVCCFG} -v import ${rdmnt}/lib/svc/manifest\\n${SVCCFG} -s \\\"system/boot-config:default\\\" setprop config/fastreboot_default=false\\n${SVCCFG} -s \\\"system/auditd:default\\\" setprop general/enabled=true\\n${SVCCFG} -s \\\"system/coreadm:default\\\" setprop config_params/global_enabled=true\\n${SVCCFG} -s \\\"system/coreadm:default\\\" setprop config_params/global_pattern='/zones/%z/cores/core.%f.%p'\\n# ${SVCCFG} -s \\\"system/manifest-import\\\" setprop start/exec=:true\\n${SVCCFG} -v apply ${rdmnt}/etc/svc/profile/generic.xml\\n${SVCCFG} -v apply ${rdmnt}/etc/svc/profile/platform.xml\\n\\nexit 0\\n\"\n },\n {\n \"alpha_fraction\": 0.6865267157554626,\n \"alphanum_fraction\": 0.6987136006355286,\n \"avg_line_length\": 28.540000915527344,\n \"blob_id\": \"e0f7b4de9ca62773755dbdb68272b65a2e9160c7\",\n \"content_id\": \"772f592b4f16ccd22d635e6cf8bd09c74844c6c1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1477,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 50,\n \"path\": \"/tools/sorter\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n#\\n# Takes a 'manifest' style file and outputs a sorted version, warning about\\n# any duplicates.\\n#\\n# CDDL HEADER START\\n#\\n# The contents of this file are subject to the terms of the\\n# Common Development and Distribution License, Version 1.0 only\\n# (the \\\"License\\\"). You may not use this file except in compliance\\n# with the License.\\n#\\n# You can obtain a copy of the license at COPYING\\n# See the License for the specific language governing permissions\\n# and limitations under the License.\\n#\\n# When distributing Covered Code, include this CDDL HEADER in each\\n# file and include the License file at COPYING.\\n# If applicable, add the following below this CDDL HEADER, with the\\n# fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n# information: Portions Copyright [yyyy] [name of copyright owner]\\n#\\n# CDDL HEADER END\\n#\\n# Copyright (c) 2010,2011 Joyent Inc.\\n#\\n\\n\\nimport os\\nimport sys\\nfiles = dict()\\nfilename = 'manifest'\\n\\n# if filename is passed as first argument, use that file instead\\nif len(sys.argv) >= 2 and os.path.isfile(sys.argv[1]):\\n filename = sys.argv[1]\\n\\nfor line in open(filename).readlines():\\n line=line.lstrip()\\n if len(line) == 0 or line[0] == '#':\\n next\\n fields = line.rstrip().split(' ')\\n if fields[1] in files:\\n print >> sys.stderr, \\\"WARNING: duplicate entry for '%s'\\\" % (fields[1])\\n files[fields[1]] = line.rstrip('\\\\n')\\n\\nk = files.keys()\\nks = k.sort()\\nfor key in k:\\n print files[key].rstrip(' ')\\n\"\n },\n {\n \"alpha_fraction\": 0.6104234457015991,\n \"alphanum_fraction\": 0.6188924908638,\n \"avg_line_length\": 26.410715103149414,\n \"blob_id\": \"ead72966417445cf75d9fd901f32cf4f01044511\",\n \"content_id\": \"7437d2e74f6be604089043ec4dfaa7327b5dfd32\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1535,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 56,\n \"path\": \"/tools/get_status\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n#\\n# CDDL HEADER START\\n#\\n# The contents of this file are subject to the terms of the\\n# Common Development and Distribution License, Version 1.0 only\\n# (the \\\"License\\\"). You may not use this file except in compliance\\n# with the License.\\n#\\n# You can obtain a copy of the license at COPYING\\n# See the License for the specific language governing permissions\\n# and limitations under the License.\\n#\\n# When distributing Covered Code, include this CDDL HEADER in each\\n# file and include the License file at COPYING.\\n# If applicable, add the following below this CDDL HEADER, with the\\n# fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n# information: Portions Copyright [yyyy] [name of copyright owner]\\n#\\n# CDDL HEADER END\\n#\\n# Copyright (c) 2010,2011 Joyent Inc.\\n#\\n\\nif [[ -z ${ROOT} ]]; then\\n ROOT=$(pwd)\\nfi\\n\\nfunction get_status()\\n{\\n dir=$1\\n\\n if [[ -z ${dir} ]]; then\\n echo \\\"WARNING: skilling empty \\\\$dir\\\"\\n return 0\\n fi\\n \\n oldpwd=$(pwd)\\n cd ${dir}\\n branch=$(git branch | grep \\\"^* \\\" | cut -d ' ' -f2)\\n rev=$(git rev-parse HEAD)\\n\\n if [[ -n ${branch} ]] && [[ -n ${rev} ]]; then\\n echo \\\"\\\\\\\"branch\\\\\\\": \\\\\\\"${branch}\\\\\\\", \\\\\\\"rev\\\\\\\": \\\\\\\"${rev}\\\\\\\"\\\"\\n else\\n echo \\\"\\\\\\\"branch\\\\\\\": \\\\\\\"unknown\\\\\\\"\\\"\\n fi\\n\\n cd ${oldpwd}\\n}\\n\\necho \\\"[\\\"\\necho \\\" { \\\\\\\"repo\\\\\\\": \\\\\\\"illumos-live\\\\\\\", $(get_status ${ROOT}) }\\\"\\necho \\\" , { \\\\\\\"repo\\\\\\\": \\\\\\\"illumos-extra\\\\\\\", $(get_status ${ROOT}/projects/illumos-extra) }\\\"\\necho \\\" , { \\\\\\\"repo\\\\\\\": \\\\\\\"illumos-joyent\\\\\\\", $(get_status ${ROOT}/projects/illumos) }\\\"\\necho \\\"]\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.6538222432136536,\n \"alphanum_fraction\": 0.6699813604354858,\n \"avg_line_length\": 21.34722137451172,\n \"blob_id\": \"ad6f2fc5f15f070ef9eb7642299f86414f5a8564\",\n \"content_id\": \"87a9925a42f631a2e618818453a8742f3c784d5c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C\",\n \"length_bytes\": 1609,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 72,\n \"path\": \"/src/removable_disk.c\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"/*\\n* CDDL HEADER START\\n*\\n* The contents of this file are subject to the terms of the\\n* Common Development and Distribution License, Version 1.0 only\\n* (the \\\"License\\\"). You may not use this file except in compliance\\n* with the License.\\n*\\n* You can obtain a copy of the license at COPYING\\n* See the License for the specific language governing permissions\\n* and limitations under the License.\\n*\\n* When distributing Covered Code, include this CDDL HEADER in each\\n* file and include the License file at COPYING.\\n* If applicable, add the following below this CDDL HEADER, with the\\n* fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n* information: Portions Copyright [yyyy] [name of copyright owner]\\n*\\n* CDDL HEADER END\\n*\\n* Copyright (c) 2010,2011 Joyent Inc.\\n*\\n*/\\n\\n#include <stdio.h>\\n#include <unistd.h>\\n#include <fcntl.h>\\n#include <sys/stat.h>\\n#include <sys/dkio.h>\\n#include <sys/vtoc.h>\\n\\nvoid\\nusage(char* argv0) {\\n\\tprintf(\\\"Usage: %s [char dev]\\\\n\\\", argv0);\\n}\\n\\nint\\nmain(int argc, char* argv[]) {\\n\\tchar* devpath = argv[1];\\n int devnode;\\n\\tstruct stat buf;\\n\\tint ret;\\n\\tint removable;\\n\\t\\n\\tif(argc != 2) {\\n\\t\\tusage(argv[0]);\\n\\t\\treturn -1;\\n\\t}\\n\\t\\n if ((devnode = open(devpath, O_RDONLY)) < 0 ) {\\n\\t\\tprintf(\\\"Could not open %s\\\\n\\\", devpath);\\n\\t\\tusage(argv[0]);\\n\\t\\treturn -1;\\n }\\n \\n\\tif (fstat(devnode, &buf) == -1 || !S_ISCHR(buf.st_mode)) {\\n\\t\\tprintf(\\\"%s: not a character device\\\\n\\\", devpath);\\n\\t\\tusage(argv[0]);\\n\\t\\tclose(devnode);\\n\\t\\treturn -1;\\n\\t}\\n\\t\\n\\tret = ioctl(devnode, DKIOCREMOVABLE, &removable);\\n\\n\\tif ((ret >= 0) && (removable != 0)) {\\n\\t\\tclose(devnode);\\n\\t\\treturn 0;\\n\\t}\\n\\t\\n\\tclose(devnode);\\n\\treturn 1;\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6844961047172546,\n \"alphanum_fraction\": 0.6949612498283386,\n \"avg_line_length\": 34.342464447021484,\n \"blob_id\": \"df34de755ee69ee9604e568b735c65d14d30ede1\",\n \"content_id\": \"abf69c71cb732959e0a61f8047ebd916121671e2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Makefile\",\n \"length_bytes\": 2580,\n \"license_type\": \"no_license\",\n \"max_line_length\": 150,\n \"num_lines\": 73,\n \"path\": \"/Makefile\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#\\n#\\n# CDDL HEADER START\\n#\\n# The contents of this file are subject to the terms of the\\n# Common Development and Distribution License, Version 1.0 only\\n# (the \\\"License\\\"). You may not use this file except in compliance\\n# with the License.\\n#\\n# You can obtain a copy of the license at COPYING\\n# See the License for the specific language governing permissions\\n# and limitations under the License.\\n#\\n# When distributing Covered Code, include this CDDL HEADER in each\\n# file and include the License file at COPYING.\\n# If applicable, add the following below this CDDL HEADER, with the\\n# fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n# information: Portions Copyright [yyyy] [name of copyright owner]\\n#\\n# CDDL HEADER END\\n#\\n# Copyright (c) 2010,2011 Joyent Inc.\\n#\\n\\nROOT=$(PWD)\\nPROTO=$(ROOT)/proto\\nPATH=/opt/local/bin:/opt/local/sbin:/opt/local/gcc34/bin:/usr/xpg4/bin:/usr/bin:/usr/sbin:/usr/sfw/bin:/usr/openwin/bin:/opt/SUNWspro/bin:/usr/ccs/bin\\nLOCAL_SUBDIRS=\\\"\\\"\\n\\nworld: 0-illumos-stamp 0-extra-stamp 0-livesrc-stamp 0-local-stamp 0-tools-stamp\\n\\nlive: world\\n\\tmkdir -p ${ROOT}/log\\n\\t(cd $(ROOT) && pfexec ./tools/build_live $(ROOT)/manifest $(ROOT)/output $(ROOT)/overlay $(ROOT)/proto $(ROOT)/projects/opensolaris-man /)\\n\\nupdate:\\n\\t@(git pull --rebase)\\n\\t@(cd projects/illumos; git pull --rebase || hg pull -u)\\n\\t@(cd projects/illumos-extra; git pull --rebase)\\n\\t[ ! -d projects/local ] || for dir in $(LOCAL_SUBDIRS); do (cd projects/local/$${dir} && git pull --rebase); done\\n\\n0-local-stamp:\\n\\t[ ! -d projects/local ] || for dir in $(LOCAL_SUBDIRS); do (cd projects/local/$${dir} && gmake && gmake DESTDIR=$(PROTO) install); done\\n\\n0-illumos-stamp:\\n\\t(cd $(ROOT) && ./tools/build_illumos)\\n\\ttouch 0-illumos-stamp\\n\\n0-extra-stamp:\\n\\t(cd $(ROOT)/projects/illumos-extra && gmake DESTDIR=$(PROTO) && gmake DESTDIR=$(PROTO) install)\\n\\ttouch 0-extra-stamp\\n\\n0-livesrc-stamp: src/bootparams.c\\n\\t(cd $(ROOT)/src && gmake DESTDIR=$(PROTO) && gmake DESTDIR=$(PROTO) install)\\n\\n0-tools-stamp: tools/builder/builder tools/pwgen tools/cryptpass\\n\\ntools/builder/builder:\\n\\t(cd $(ROOT)/tools/builder && gmake builder)\\n\\ntools/pwgen:\\n\\t(cd ${ROOT}/tools/pwgen-* && ./configure && make && cp pwgen ${ROOT}/tools)\\n\\ntools/cryptpass: tools/cryptpass.c\\n\\t(cd ${ROOT}/tools && gcc -Wall -W -O2 -o cryptpass cryptpass.c)\\n\\nclean:\\n\\t(cd $(ROOT)/src && gmake clean)\\n\\t(cd $(ROOT)/projects/illumos-extra && gmake clean)\\n\\t[ ! -d projects/local ] || for dir in $(LOCAL_SUBDIRS); do (cd projects/local/$${dir} && gmake clean); done\\n\\t(cd $(ROOT) && rm -rf $(PROTO))\\n\\t(cd $(ROOT) && mkdir -p $(PROTO))\\n\\trm -f 0-*-stamp\\n\"\n },\n {\n \"alpha_fraction\": 0.5076692700386047,\n \"alphanum_fraction\": 0.5185185074806213,\n \"avg_line_length\": 31.192771911621094,\n \"blob_id\": \"fae753882b5ebb5770119489f64907ea0c2b30d7\",\n \"content_id\": \"11170008f65b0f1139b138c7ab6afb3c68c71dd2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 2673,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 83,\n \"path\": \"/tools/scanner\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n#\\n# CDDL HEADER START\\n#\\n# The contents of this file are subject to the terms of the\\n# Common Development and Distribution License, Version 1.0 only\\n# (the \\\"License\\\"). You may not use this file except in compliance\\n# with the License.\\n#\\n# You can obtain a copy of the license at COPYING\\n# See the License for the specific language governing permissions\\n# and limitations under the License.\\n#\\n# When distributing Covered Code, include this CDDL HEADER in each\\n# file and include the License file at COPYING.\\n# If applicable, add the following below this CDDL HEADER, with the\\n# fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n# information: Portions Copyright [yyyy] [name of copyright owner]\\n#\\n# CDDL HEADER END\\n#\\n# Copyright (c) 2010,2011 Joyent Inc.\\n#\\n\\nROOT=$1\\nDIR=$2\\ncommand=\\\"pfexec find $DIR -ls\\\"\\n\\nif [ ! -z ${ROOT} ] && [ \\\"${ROOT}\\\" == \\\"-\\\" ]; then\\n ROOT=\\\"/\\\"\\n DIR=\\\"-\\\"\\n command=\\\"cat -\\\"\\nfi\\n\\nif [ -z \\\"${ROOT}\\\" ] || [ -z \\\"${DIR}\\\" ]; then\\n echo \\\"\\\"\\n echo \\\"Use this program to create manifest entries for use with the live image creation\\\"\\n echo \\\"script. eg. '$0 / etc' will generate a manifest list that could be used for /etc\\\"\\n echo \\\"\\\"\\n echo \\\"Usage: $0 <ROOT> <DIR>\\\"\\n exit 1\\nfi\\n\\ncd ${ROOT}\\nsh -c \\\"${command}\\\" | \\\\\\n{ \\n while read line; do \\n # Squeeze multiple spaces/tabs into one space\\n line=`echo \\\"${line}\\\" | sed -e \\\"s/ / /\\\" | tr -s ' '`\\n\\n # Remove leading/trailing spaces\\n line=`echo \\\"${line}\\\" | sed -e \\\"s/ +$//\\\" | sed -e \\\"s/^ +//\\\"`\\n\\n echo \\\"${line}\\\" | { read l_inode l_size l_hmode l_links l_user l_group l_size l_month l_day l_year l_path ;\\n l_md5sum=\\n l_type=`echo \\\"${l_hmode}\\\" | cut -c1`\\n if [ \\\"${l_type}\\\" != \\\"l\\\" ] && [ \\\"${l_type}\\\" != \\\"c\\\" ] && [ \\\"${l_type}\\\" != \\\"b\\\" ]; then\\n l_mode=`pfexec stat --printf \\\"%04a\\\" ${l_path}`\\n else\\n l_type=\\\"s\\\"\\n l_path=`echo \\\"${l_path}\\\" | sed -e \\\"s/ -> /=/\\\"`\\n l_mode= #\\\"0777\\\"\\n l_user= #\\\"root\\\"\\n l_group= #\\\"root\\\"\\n fi\\n\\n if [ \\\"${l_type}\\\" == \\\"-\\\" ]; then\\n l_type=\\\"f\\\"\\n fi\\n\\n if [ \\\"${l_type}\\\" == \\\"f\\\" ] && [ ! -z \\\"${INCLUDE_MD5SUM}\\\" ]; then\\n # include md5sum for files if requested.\\n l_md5sum=`md5sum ${l_path} | cut -d' ' -f1`\\n fi\\n\\n # ignore char and block devices for now\\n if [ \\\"${l_type}\\\" != \\\"c\\\" ] && [ \\\"${l_type}\\\" != \\\"b\\\" ]; then\\n echo \\\"${l_type} ${l_path} ${l_mode} ${l_user} ${l_group} ${l_md5sum}\\\" \\\\\\n | sed -e \\\"s/ +$//\\\"\\n fi\\n }\\n done\\n}\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5745062828063965,\n \"alphanum_fraction\": 0.5870736241340637,\n \"avg_line_length\": 23.940298080444336,\n \"blob_id\": \"a1cbfe6794b10858282e94f9d2e652fb087b57dd\",\n \"content_id\": \"0d4518b0d0e29d2daba59c7f133531bc44a8c752\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1671,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 67,\n \"path\": \"/src/disklist.sh\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/sh\\n#\\n#\\n# CDDL HEADER START\\n#\\n# The contents of this file are subject to the terms of the\\n# Common Development and Distribution License, Version 1.0 only\\n# (the \\\"License\\\"). You may not use this file except in compliance\\n# with the License.\\n#\\n# You can obtain a copy of the license at COPYING\\n# See the License for the specific language governing permissions\\n# and limitations under the License.\\n#\\n# When distributing Covered Code, include this CDDL HEADER in each\\n# file and include the License file at COPYING.\\n# If applicable, add the following below this CDDL HEADER, with the\\n# fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n# information: Portions Copyright [yyyy] [name of copyright owner]\\n#\\n# CDDL HEADER END\\n#\\n# Copyright (c) 2010,2011 Joyent Inc.\\n#\\n\\nREMDISKS=\\\"\\\"\\nNONREMDISKS=\\\"\\\"\\n\\nALLDISKS=`/usr/bin/ls /dev/dsk | /usr/bin/awk \\\"/s2$/ {print}\\\" | /usr/bin/sed \\\"s/s2//\\\"`\\n\\nfor disk in $ALLDISKS; \\ndo\\n pfexec removable_disk /dev/rdsk/${disk}p0 2>&1 >> /dev/null\\n case \\\"$?\\\" in\\n 0)\\n REMDISKS=$REMDISKS\\\" \\\"$disk;\\n ;;\\n 1)\\n NONREMDISKS=$NONREMDISKS\\\" \\\"$disk;\\n ;;\\n esac;\\ndone\\n\\nwhile getopts 'anrs' OPTION\\ndo\\n case $OPTION in\\n a)\\n echo $REMDISKS\\\" \\\"$NONREMDISKS\\n ;;\\n n)\\n echo $NONREMDISKS\\n ;;\\n r)\\n echo $REMDISKS\\n ;;\\n s)\\n for disk in $NONREMDISKS; do\\n size=`pfexec disk_size /dev/rdsk/${disk}p0`\\n echo \\\"${disk}=${size}\\\"\\n done\\n ;;\\n ?)\\n printf \\\"Usage: %s: [-anr]\\\\n\\\" $(basename $0) >&2\\n exit 2\\n ;;\\n esac\\ndone\\n\"\n },\n {\n \"alpha_fraction\": 0.5182694792747498,\n \"alphanum_fraction\": 0.5337520837783813,\n \"avg_line_length\": 27.18703269958496,\n \"blob_id\": \"6f956529731aed16e489aab4a0ebf38b28f1002e\",\n \"content_id\": \"4f49c74c516b4884582c81b89b26f2efa839249b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 11303,\n \"license_type\": \"no_license\",\n \"max_line_length\": 156,\n \"num_lines\": 401,\n \"path\": \"/src/sysinfo\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n#\\n# CDDL HEADER START\\n#\\n# The contents of this file are subject to the terms of the\\n# Common Development and Distribution License, Version 1.0 only\\n# (the \\\"License\\\"). You may not use this file except in compliance\\n# with the License.\\n#\\n# You can obtain a copy of the license at COPYING\\n# See the License for the specific language governing permissions\\n# and limitations under the License.\\n#\\n# When distributing Covered Code, include this CDDL HEADER in each\\n# file and include the License file at COPYING.\\n# If applicable, add the following below this CDDL HEADER, with the\\n# fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n# information: Portions Copyright [yyyy] [name of copyright owner]\\n#\\n# CDDL HEADER END\\n#\\n# Copyright (c) 2010,2011 Joyent Inc.\\n#\\n# Output some info representing the system. Default: JSON\\n#\\n# Important: Don't use anything here that you don't include in the live image!\\n#\\n\\nPARSABLE=\\nif [[ $1 == \\\"-p\\\" ]]; then\\n PARSABLE=\\\"true\\\"\\nfi\\n\\nif [[ $(zonename) != \\\"global\\\" ]]; then\\n echo \\\"This program is for use in the global zone only.\\\"\\n exit 1\\nfi\\n\\nconfigfile=\\\"$(svcprop -p 'joyentfs/usb_copy_path' svc:/system/filesystem/smartdc:default 2>/dev/null)/config\\\"\\nif [[ ! -f ${configfile} ]]; then\\n configfile=\\\"/mnt/$(svcprop -p 'joyentfs/usb_mountpoint' svc:/system/filesystem/smartdc:default 2>/dev/null)/config\\\"\\nfi\\n\\n# helper to set global \\\"normalized\\\" to the expanded version of MAC ($1)\\nfunction normalize_mac()\\n{\\n mac=$1\\n if [ -z \\\"${mac}\\\" ]; then\\n echo \\\"FATAL: unable to normalize empty mac!\\\"\\n exit 1\\n fi\\n\\n normalized=`echo ${mac} | awk -F':' '{ print $1, $2, $3, $4, $5, $6 }' | { read o1 o2 o3 o4 o5 o6 junk\\n printf \\\"%02x:%02x:%02x:%02x:%02x:%02x\\\" 0x${o1} 0x${o2} 0x${o3} 0x${o4} 0x${o5} 0x${o6}\\n }`\\n\\n # ensure results are 'normal'\\n echo \\\"${normalized}\\\" | grep \\\"[a-fA-F0-9]\\\\{2\\\\}\\\\:[a-fA-F0-9]\\\\{2\\\\}\\\\:[a-fA-F0-9]\\\\{2\\\\}\\\\:[a-fA-F0-9]\\\\{2\\\\}\\\\:[a-fA-F0-9]\\\\{2\\\\}\\\\:[a-fA-F0-9]\\\\{2\\\\}\\\" >/dev/null 2>&1\\n if [ \\\"$?\\\" != \\\"0\\\" ]; then\\n echo \\\"FATAL: failed to normalize MAC '${mac}'\\\"\\n exit 1\\n fi\\n}\\n\\nfunction get_smbios_system_info()\\n{\\n # This puts the variables we're pulling out into the local environment\\n eval $(smbios -t SMB_TYPE_SYSTEM \\\\\\n | egrep \\\"Manufacturer: |Product: |Serial Number: |UUID: \\\" \\\\\\n | sed -e 's/^ *//' \\\\\\n | sed -e 's/: /=\\\"/' \\\\\\n | sed -e 's/ *$/\\\"/' \\\\\\n | sed -e 's/Serial Number/Serial_Number/')\\n\\n #echo \\\"${UUID}\\\"\\n #echo \\\"${Product}\\\"\\n #echo \\\"${Serial_Number}\\\"\\n #echo \\\"${Manufacturer}\\\"\\n}\\n\\nfunction get_memory_mib()\\n{\\n # Get (misnamed in prtconf) memory size in Mebibytes\\n Memory_in_MiB=`prtconf \\\\\\n | grep \\\"Memory size: [0-9]* Megabytes\\\" \\\\\\n | cut -d' ' -f3`\\n\\n #echo \\\"${Memory_in_MiB}\\\"\\n}\\n\\nfunction get_cpu_info()\\n{\\n CPU_Version=`smbios -t SMB_TYPE_PROCESSOR | grep -v \\\"Version: 0000000000\\\" \\\\\\n | grep \\\"Version: \\\" \\\\\\n | head -n1 \\\\\\n | tr -s ' ' \\\\\\n | sed -e 's/^ *Version: //' \\\\\\n | sed -e 's/ *$//'`\\n\\n CPU_Count=`smbios -t SMB_TYPE_PROCESSOR | grep -v \\\"Version: 0000000000\\\" \\\\\\n | grep \\\"Version: \\\" \\\\\\n | wc -l \\\\\\n | tr -d ' '`\\n\\n CPU_Cores=`psrinfo \\\\\\n | grep \\\"^[0-9]\\\" \\\\\\n | wc -l \\\\\\n | tr -d ' '`\\n\\n #echo \\\"${CPU_Version}\\\"\\n #echo \\\"${CPU_Count}\\\"\\n #echo \\\"${CPU_Cores}\\\"\\n}\\n\\nfunction get_live_image_buildstamp()\\n{\\n # Add joyent_buildstamp to SYSTEM_INFO\\n Live_Image=$(cat /etc/joyent_buildstamp 2>/dev/null)\\n\\n #echo \\\"${Live_Image}\\\"\\n}\\n\\nfunction get_hostname()\\n{\\n Hostname=$(hostname)\\n}\\n\\nfunction get_disks()\\n{\\n ORIGIFS=$IFS\\n\\n # set $IFS to end-of-line\\n IFS=`echo -en \\\"\\\\n\\\\b\\\"`\\n\\n count=1\\n for line in $(/usr/bin/disklist -s 2>/dev/null); do\\n Disks[${count}]=${line}\\n ((count++))\\n done\\n\\n # set $IFS back\\n IFS=$ORIGIFS\\n\\n #for entry in \\\"${Disks[@]}\\\"\\n #do\\n # fields=(${entry//=/ })\\n # disk=${fields[0]}\\n # size=${fields[1]}\\n # echo \\\"disk: ${disk} size: ${size}\\\"\\n #done\\n}\\n\\nfunction get_nic_mappings()\\n{\\n admin_nic=`/usr/bin/bootparams 2>/dev/null | grep \\\"admin_nic\\\" | cut -d '=' -f2-`\\n if [[ -z ${admin_nic} ]] && [[ -f ${configfile} ]]; then\\n admin_nic=`grep \\\"^admin_nic=\\\" ${configfile} | cut -f2 -d'='`\\n fi\\n if [[ -n ${admin_nic} ]]; then\\n normalize_mac ${admin_nic}\\n ADMIN_NIC=${normalized}\\n fi\\n external_nic=`/usr/bin/bootparams 2>/dev/null | grep \\\"external_nic\\\" | cut -d '=' -f2-`\\n if [[ -z ${external_nic} ]] && [[ -f ${configfile} ]]; then\\n external_nic=`grep \\\"^external_nic=\\\" ${configfile} | cut -f2 -d'='`\\n fi\\n if [[ -n ${external_nic} ]]; then\\n normalize_mac ${external_nic}\\n EXTERNAL_NIC=${normalized}\\n fi\\n internal_nic=`/usr/bin/bootparams 2>/dev/null | grep \\\"internal_nic\\\" | cut -d '=' -f2-`\\n if [[ -z ${internal_nic} ]] && [[ -f ${configfile} ]]; then\\n internal_nic=`grep \\\"^internal_nic=\\\" ${configfile} | cut -f2 -d'='`\\n fi\\n if [[ -n ${internal_nic} ]]; then\\n normalize_mac ${internal_nic}\\n INTERNAL_NIC=${normalized}\\n fi\\n\\n #echo \\\"${ADMIN_NIC}\\\"\\n #echo \\\"${EXTERNAL_NIC}\\\"\\n #echo \\\"${INTERNAL_NIC}\\\"\\n}\\n\\n\\nfunction get_network_interfaces()\\n{\\n count=1\\n # ignore 'LINK SLOT ADDRESS INUSE CLIENT' line\\n for line in $(pfexec /sbin/dladm show-phys -m | grep -v \\\"^LINK \\\" | awk '{ print $1,$3 }' | tr ' ' '='); do\\n nicnames=\\n fields=(${line//=/ })\\n iface=${fields[0]}\\n normalize_mac ${fields[1]}\\n mac=${normalized}\\n ip4addr=$(ifconfig ${iface} 2>/dev/null | grep \\\"inet \\\" | awk '{ print $2 }')\\n\\n if [[ -n \\\"${ADMIN_NIC}\\\" ]] && [[ \\\"${ADMIN_NIC}\\\" == \\\"${mac}\\\" ]]; then\\n nicnames=\\\"admin\\\"\\n NIC_admin=${iface}\\n fi\\n if [[ -n \\\"${EXTERNAL_NIC}\\\" ]] && [[ \\\"${EXTERNAL_NIC}\\\" == \\\"${mac}\\\" ]]; then\\n [[ -n ${nicnames} ]] && nicnames=\\\"${nicnames},\\\"\\n nicnames=\\\"${nicnames}external\\\"\\n NIC_external=${iface}\\n fi\\n if [[ -n \\\"${INTERNAL_NIC}\\\" ]] && [[ \\\"${INTERNAL_NIC}\\\" == \\\"${mac}\\\" ]]; then\\n [[ -n ${nicnames} ]] && nicnames=\\\"${nicnames},\\\"\\n nicnames=\\\"${nicnames}internal\\\"\\n NIC_internal=${iface}\\n fi\\n\\n NetworkInterfaces[${count}]=${iface}\\n eval \\\"Network_Interface_${iface}_MAC_Address=${mac}\\\"\\n eval \\\"Network_Interface_${iface}_IPv4_Address=${ip4addr}\\\"\\n if [[ -n ${nicnames} ]]; then\\n eval \\\"Network_Interface_${iface}_NIC_Names=${nicnames}\\\"\\n fi\\n ((count++))\\n done\\n\\n #echo \\\"${NIC_admin}\\\"\\n #echo \\\"${NIC_external}\\\"\\n #echo \\\"${NIC_internal}\\\"\\n #for iface in \\\"${NetworkInterfaces[@]}\\\"\\n #do\\n # eval \\\"mac=\\\\${Network_Interface_${iface}_MAC_Address}\\\"\\n # eval \\\"ipv4=\\\\${Network_Interface_${iface}_IPv4_Address}\\\"\\n # eval \\\"nicnames=\\\\${Network_Interface_${iface}_NIC_Names}\\\"\\n # echo \\\"mac: ${mac} ---- ${ipv4} ---- ${nicnames}\\\"\\n #done\\n}\\n\\nfunction get_bootparams()\\n{\\n count=1\\n for line in $(/usr/bin/bootparams); do\\n fields=(${line//=/ })\\n key=$(echo ${fields[0]} | sed -e \\\"s/\\\\-/\\\\_/g\\\")\\n if ! (echo \\\"${key}\\\" | /usr/xpg4/bin/grep \\\\\\n -e \\\"^tty\\\" \\\\\\n -e \\\"^atapi\\\" \\\\\\n -e \\\"^ata_dma\\\" \\\\\\n -e \\\"^keyboard_\\\" \\\\\\n -e \\\"^bios_boot_device\\\" \\\\\\n -e \\\"^lba_access_\\\" \\\\\\n -e \\\"^boot_ncpus\\\" \\\\\\n -e \\\"^boot_file\\\" \\\\\\n -e \\\"^whoami\\\" \\\\\\n -e \\\"^mfg_name\\\" \\\\\\n -e \\\"^impl_arch_name\\\" \\\\\\n -e \\\"_max_ncpus$\\\" \\\\\\n >/dev/null); then\\n Bootparams[${count}]=${line}\\n ((count++))\\n fi\\n done\\n}\\n\\nfunction output_parsable()\\n{\\n cat <<END\\nLive_Image='${Live_Image}'\\nManufacturer='${Manufacturer}'\\nProduct='${Product}'\\nSerial_Number='${Serial_Number}'\\nUUID='${UUID}'\\nHostname='${Hostname}'\\nCPU_Type='${CPU_Version}'\\nCPU_Physical_Cores=${CPU_Count}\\nCPU_Total_Cores=${CPU_Cores}\\nMiB_of_Memory=${Memory_in_MiB}\\nEND\\n\\n for entry in \\\"${Disks[@]}\\\"; do\\n fields=(${entry//=/ })\\n disk=${fields[0]}\\n size=${fields[1]}\\n gb_size=$((${size} / 1000000000))\\n echo \\\"Disk_${disk}_size_in_GB=${gb_size}\\\"\\n done\\n\\n [[ -n ${NIC_admin} ]] && echo \\\"NIC_admin='${NIC_admin}'\\\"\\n [[ -n ${NIC_external} ]] && echo \\\"NIC_external='${NIC_external}'\\\"\\n [[ -n ${NIC_internal} ]] && echo \\\"NIC_internal='${NIC_internal}'\\\"\\n\\n for iface in \\\"${NetworkInterfaces[@]}\\\"; do\\n mac_var=\\\"Network_Interface_${iface}_MAC_Address\\\"\\n ipv4_var=\\\"Network_Interface_${iface}_IPv4_Address\\\"\\n nicnames_var=\\\"Network_Interface_${iface}_NIC_Names\\\"\\n\\n eval \\\"mac=\\\\${${mac_var}}\\\"\\n eval \\\"ipv4=\\\\${${ipv4_var}}\\\"\\n eval \\\"nicnames=\\\\${${nicnames_var}}\\\"\\n\\n echo \\\"${mac_var}='${mac}'\\\"\\n echo \\\"${ipv4_var}='${ipv4}'\\\"\\n echo \\\"${nicnames_var}='${nicnames}'\\\"\\n done\\n\\n for entry in \\\"${Bootparams[@]}\\\"; do\\n fields=(${entry//=/ })\\n key=$(echo ${fields[0]} | sed -e \\\"s/\\\\-/\\\\_/g\\\")\\n val=${fields[1]}\\n echo \\\"Bootparam_${key}='${val}'\\\"\\n done\\n}\\n\\nfunction output_json()\\n{\\n cat <<END\\n{\\n \\\"Live Image\\\": \\\"${Live_Image}\\\",\\n \\\"Manufacturer\\\": \\\"${Manufacturer}\\\",\\n \\\"Product\\\": \\\"${Product}\\\",\\n \\\"Serial Number\\\": \\\"${Serial_Number}\\\",\\n \\\"UUID\\\": \\\"${UUID}\\\",\\n \\\"Hostname\\\": \\\"${Hostname}\\\",\\n \\\"CPU Type\\\": \\\"${CPU_Version}\\\",\\n \\\"CPU Physical Cores\\\": ${CPU_Count},\\n \\\"CPU Total Cores\\\": ${CPU_Cores},\\n \\\"MiB of Memory\\\": \\\"${Memory_in_MiB}\\\",\\n \\\"Disks\\\": {\\nEND\\n\\n printed=0\\n for entry in \\\"${Disks[@]}\\\"; do\\n fields=(${entry//=/ })\\n disk=${fields[0]}\\n size=${fields[1]}\\n gb_size=$((${size} / 1000000000))\\n ((printed++))\\n trailing_comma=\\\",\\\"\\n [[ ${printed} -eq ${#Disks[*]} ]] && trailing_comma=''\\n echo \\\" \\\\\\\"${disk}\\\\\\\": {\\\\\\\"Size in GB\\\\\\\": ${gb_size}}${trailing_comma}\\\"\\n done\\n\\n cat <<END\\n },\\n \\\"Boot Parameters\\\": {\\nEND\\n printed=0\\n for entry in \\\"${Bootparams[@]}\\\"; do\\n fields=(${entry//=/ })\\n key=$(echo ${fields[0]} | sed -e \\\"s/\\\\-/\\\\_/g\\\")\\n val=${fields[1]}\\n ((printed++))\\n trailing_comma=\\\",\\\"\\n [[ ${printed} -eq ${#Bootparams[*]} ]] && trailing_comma=''\\n echo \\\" \\\\\\\"${key}\\\\\\\": \\\\\\\"$val\\\\\\\"${trailing_comma}\\\"\\n done\\n cat <<END\\n },\\n \\\"Network Interfaces\\\": {\\nEND\\n\\n printed=0\\n for iface in \\\"${NetworkInterfaces[@]}\\\"; do\\n mac_var=\\\"Network_Interface_${iface}_MAC_Address\\\"\\n ipv4_var=\\\"Network_Interface_${iface}_IPv4_Address\\\"\\n nicnames_var=\\\"Network_Interface_${iface}_NIC_Names\\\"\\n nic_names_fmt=\\n\\n eval \\\"mac=\\\\${${mac_var}}\\\"\\n eval \\\"ipv4=\\\\${${ipv4_var}}\\\"\\n eval \\\"nicnames=\\\\${${nicnames_var}}\\\"\\n\\n nic_names_array=$(echo \\\"${nicnames}\\\" | sed -e \\\"s/,/\\\\\\\", \\\\\\\"/g\\\")\\n if [[ -n ${nic_names_array} ]]; then\\n nic_names_fmt=\\\"\\\\\\\"${nic_names_array}\\\\\\\"\\\"\\n fi\\n\\n ((printed++))\\n trailing_comma=\\\",\\\"\\n [[ ${printed} -eq ${#NetworkInterfaces[*]} ]] && trailing_comma=''\\n echo \\\" \\\\\\\"${iface}\\\\\\\": {\\\\\\\"MAC Address\\\\\\\": \\\\\\\"${mac}\\\\\\\", \\\\\\\"ip4addr\\\\\\\": \\\\\\\"${ipv4}\\\\\\\", \\\\\\\"NIC Names\\\\\\\": [${nic_names_fmt}]}${trailing_comma}\\\"\\n done\\n\\n cat <<END\\n }\\n}\\nEND\\n}\\n\\nget_smbios_system_info\\nget_memory_mib\\nget_cpu_info\\nget_live_image_buildstamp\\nget_disks\\nget_hostname\\nget_nic_mappings\\nget_network_interfaces\\nget_bootparams\\n\\nif [[ ${PARSABLE} == \\\"true\\\" ]]; then\\n output_parsable\\nelse\\n output_json\\nfi\\n\\nexit 0\\n\"\n },\n {\n \"alpha_fraction\": 0.6375187039375305,\n \"alphanum_fraction\": 0.6532137393951416,\n \"avg_line_length\": 23.77777862548828,\n \"blob_id\": \"030f6f7bd330ab44a968f10865f2d18f4d993a2f\",\n \"content_id\": \"5adc0433a513b6654cf1fcc6218ad9babba96ffc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1338,\n \"license_type\": \"no_license\",\n \"max_line_length\": 141,\n \"num_lines\": 54,\n \"path\": \"/tools/builder/build_users_c.sh\",\n \"repo_name\": \"justinlundy/illumos-live\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/bash\\n#\\n# CDDL HEADER START\\n#\\n# The contents of this file are subject to the terms of the\\n# Common Development and Distribution License, Version 1.0 only\\n# (the \\\"License\\\"). You may not use this file except in compliance\\n# with the License.\\n#\\n# You can obtain a copy of the license at COPYING\\n# See the License for the specific language governing permissions\\n# and limitations under the License.\\n#\\n# When distributing Covered Code, include this CDDL HEADER in each\\n# file and include the License file at COPYING.\\n# If applicable, add the following below this CDDL HEADER, with the\\n# fields enclosed by brackets \\\"[]\\\" replaced with your own identifying\\n# information: Portions Copyright [yyyy] [name of copyright owner]\\n#\\n# CDDL HEADER END\\n#\\n# Copyright (c) 2010,2011 Joyent Inc.\\n#\\n\\nROOT=$1\\n\\ncat <<EOF\\n#include <stdio.h>\\n#include <string.h>\\n\\nint gid_from_name(const char *group)\\n{\\n int gid = -1;\\nEOF\\ncat ${ROOT}/etc/group | awk -F':' 'NR>1{ printf \\\"else \\\" };{ print \\\"if (strcmp(\\\\\\\"\\\" $1 \\\"\\\\\\\", group) == 0) gid = \\\" $3 \\\";\\\" }' | sed -e \\\"s/^/ /\\\"\\ncat <<EOF\\n\\n return(gid);\\n};\\n\\nEOF\\n\\ncat <<EOF\\nint uid_from_name(const char *user)\\n{\\n int uid = -1;\\n\\nEOF\\ncat ${ROOT}/etc/passwd | awk -F':' 'NR>1{ printf \\\"else \\\" };{ print \\\"if (strcmp(\\\\\\\"\\\" $1 \\\"\\\\\\\", user) == 0) uid = \\\" $3 \\\";\\\" }' | sed -e \\\"s/^/ /\\\"\\ncat <<EOF\\n\\n return(uid);\\n};\\nEOF\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":15,"string":"15"}}},{"rowIdx":2979161,"cells":{"repo_name":{"kind":"string","value":"Laischon/P7M-Extract"},"repo_url":{"kind":"string","value":"https://github.com/Laischon/P7M-Extract"},"snapshot_id":{"kind":"string","value":"474385efcf756b3d2e88e5a864de5f2468210faf"},"revision_id":{"kind":"string","value":"dcaeba35e53c064f8c7c5c1203b02adcf29fd237"},"directory_id":{"kind":"string","value":"c51ee0cfc3775bc2976e28a1e9664aa6003a254a"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-08-01T03:29:07.661083","string":"2020-08-01T03:29:07.661083"},"revision_date":{"kind":"timestamp","value":"2019-09-25T13:06:02","string":"2019-09-25T13:06:02"},"committer_date":{"kind":"timestamp","value":"2019-09-25T13:06:02","string":"2019-09-25T13:06:02"},"github_id":{"kind":"number","value":210845649,"string":"210,845,649"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6899110078811646,"alphanum_fraction":0.7077151536941528,"avg_line_length":29.636363983154297,"blob_id":"062d1c3124f3d8bf77ca3ec7cec5977d834d85de","content_id":"4a768629e69220d508563baa5f289f48d67f3f7e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":674,"license_type":"no_license","max_line_length":115,"num_lines":22,"path":"/p7m_extractor.py","repo_name":"Laischon/P7M-Extract","src_encoding":"UTF-8","text":"import sys\nfrom OpenSSL import crypto\nfrom OpenSSL._util import (\n\tffi as _ffi,\n\tlib as _lib,\n)\n\nwith open(sys.argv[1], 'rb') as f:\n\tp7data = f.read()\np7 = crypto.load_pkcs7_data(crypto.FILETYPE_ASN1, p7data)\n\nbio_out =crypto._new_mem_buf()\nres = _lib.PKCS7_verify(p7._pkcs7, _ffi.NULL, _ffi.NULL, _ffi.NULL, bio_out, _lib.PKCS7_NOVERIFY|_lib.PKCS7_NOSIGS)\nif res == 1:\n\tdatabytes = crypto._bio_to_string(bio_out)\n\tprint(databytes)\nelse:\n\terrno = _lib.ERR_get_error()\n\terrstrlib = _ffi.string(_lib.ERR_lib_error_string(errno))\n\terrstrfunc = _ffi.string(_lib.ERR_func_error_string(errno))\n\terrstrreason = _ffi.string(_lib.ERR_reason_error_string(errno))\n\tprint(errstrreason)\n"},{"alpha_fraction":0.707602322101593,"alphanum_fraction":0.719298243522644,"avg_line_length":29.176469802856445,"blob_id":"b44abade6e680157174b169520ef893e98795d97","content_id":"f22aed1a224fb1a653174edb0c77609f2e2d9413","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":513,"license_type":"no_license","max_line_length":131,"num_lines":17,"path":"/README.md","repo_name":"Laischon/P7M-Extract","src_encoding":"UTF-8","text":"# P7M-Extract\n<b>[ENG] </b><br>\nPython script to extract plain content from a p7m signed file ; <br>\nUsed to extract plain E-Invoice in xml format from a signed .p7m file ; <br> \n\n<b>[ITA]</b><br>\nScript Python utile per estrarre il contenuto di un file firmato in formato .p7m ; <br>\nNel caso specifico e' stato utilizzato per estrarre il contenuto XML delle fatture elettroniche italiane in formato .xml.p7m ; <br>\n\n\n<b>How to use:</b>\n\npython p7m_extractor.py [file]\n\n<b>Output:</b>\n\nThe content of the [file]\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6899110078811646,\n \"alphanum_fraction\": 0.7077151536941528,\n \"avg_line_length\": 29.636363983154297,\n \"blob_id\": \"062d1c3124f3d8bf77ca3ec7cec5977d834d85de\",\n \"content_id\": \"4a768629e69220d508563baa5f289f48d67f3f7e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 674,\n \"license_type\": \"no_license\",\n \"max_line_length\": 115,\n \"num_lines\": 22,\n \"path\": \"/p7m_extractor.py\",\n \"repo_name\": \"Laischon/P7M-Extract\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nfrom OpenSSL import crypto\\nfrom OpenSSL._util import (\\n\\tffi as _ffi,\\n\\tlib as _lib,\\n)\\n\\nwith open(sys.argv[1], 'rb') as f:\\n\\tp7data = f.read()\\np7 = crypto.load_pkcs7_data(crypto.FILETYPE_ASN1, p7data)\\n\\nbio_out =crypto._new_mem_buf()\\nres = _lib.PKCS7_verify(p7._pkcs7, _ffi.NULL, _ffi.NULL, _ffi.NULL, bio_out, _lib.PKCS7_NOVERIFY|_lib.PKCS7_NOSIGS)\\nif res == 1:\\n\\tdatabytes = crypto._bio_to_string(bio_out)\\n\\tprint(databytes)\\nelse:\\n\\terrno = _lib.ERR_get_error()\\n\\terrstrlib = _ffi.string(_lib.ERR_lib_error_string(errno))\\n\\terrstrfunc = _ffi.string(_lib.ERR_func_error_string(errno))\\n\\terrstrreason = _ffi.string(_lib.ERR_reason_error_string(errno))\\n\\tprint(errstrreason)\\n\"\n },\n {\n \"alpha_fraction\": 0.707602322101593,\n \"alphanum_fraction\": 0.719298243522644,\n \"avg_line_length\": 29.176469802856445,\n \"blob_id\": \"b44abade6e680157174b169520ef893e98795d97\",\n \"content_id\": \"f22aed1a224fb1a653174edb0c77609f2e2d9413\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 513,\n \"license_type\": \"no_license\",\n \"max_line_length\": 131,\n \"num_lines\": 17,\n \"path\": \"/README.md\",\n \"repo_name\": \"Laischon/P7M-Extract\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# P7M-Extract\\n<b>[ENG] </b><br>\\nPython script to extract plain content from a p7m signed file ; <br>\\nUsed to extract plain E-Invoice in xml format from a signed .p7m file ; <br> \\n\\n<b>[ITA]</b><br>\\nScript Python utile per estrarre il contenuto di un file firmato in formato .p7m ; <br>\\nNel caso specifico e' stato utilizzato per estrarre il contenuto XML delle fatture elettroniche italiane in formato .xml.p7m ; <br>\\n\\n\\n<b>How to use:</b>\\n\\npython p7m_extractor.py [file]\\n\\n<b>Output:</b>\\n\\nThe content of the [file]\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979162,"cells":{"repo_name":{"kind":"string","value":"faircloth-lab/alfred_zotquery"},"repo_url":{"kind":"string","value":"https://github.com/faircloth-lab/alfred_zotquery"},"snapshot_id":{"kind":"string","value":"4635649607402bc3786e93ef7a6d27fd7e2e431b"},"revision_id":{"kind":"string","value":"964d9c7575b8ff57e4c3d2a0c485aa6d76e88fb7"},"directory_id":{"kind":"string","value":"52dfef197b882e58bb088558a80e3d3178653db1"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-18T12:17:16.919171","string":"2021-01-18T12:17:16.919171"},"revision_date":{"kind":"timestamp","value":"2014-06-07T20:09:52","string":"2014-06-07T20:09:52"},"committer_date":{"kind":"timestamp","value":"2014-06-07T20:09:52","string":"2014-06-07T20:09:52"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6044718623161316,"alphanum_fraction":0.624518096446991,"avg_line_length":23.018518447875977,"blob_id":"9bda4ef9834a4fde69bc0a3461dc5a676240b3c3","content_id":"bb884f684fd508bfccb3a78f8ede2a6725d66777","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1297,"license_type":"no_license","max_line_length":69,"num_lines":54,"path":"/test_space.py","repo_name":"faircloth-lab/alfred_zotquery","src_encoding":"UTF-8","text":"#!/usr/bin/python\n# encoding: utf-8\nfrom __future__ import unicode_literals\n\nimport sys\nimport json\nimport os.path\nimport subprocess\nfrom StringIO import StringIO\n\nfrom deps import xmltodict\nimport zotquery as z\n\nos.environ['__CF_USER_TEXT_ENCODING'] = \"0x1F5:0x8000100:0x8000100\"\n\ndef to_unicode(obj, encoding='utf-8'):\n \"\"\"Detects if object is a string and if so converts to unicode\"\"\"\n if isinstance(obj, basestring):\n if not isinstance(obj, unicode):\n obj = unicode(obj, encoding)\n return obj\n\ndef get_clipboard(): \n \"\"\"Get objects of clipboard\"\"\"\n scpt = \"\"\"return the clipboard\"\"\"\n data = subprocess.call(['osascript', '-e', scpt])\n data = to_unicode(data)\n return data\n\ndef capture_output(zot_filter):\n \"\"\"Capture `sys` output in var\"\"\"\n old_stdout = sys.stdout\n sys.stdout = mystdout = StringIO()\n zot_filter.filter()\n sys.stdout = old_stdout\n res_dict = xmltodict.parse(mystdout.getvalue())\n return res_dict\n\ndef main():\n \"\"\"main\"\"\"\n proc = subprocess.Popen(['pbpaste'], stdout=subprocess.PIPE)\n proc.wait()\n data = proc.stdout.read()\n\n for i in ['utf-8', 'cp1252']:\n try:\n print data.decode(i)\n print i\n except:\n pass\n\n \nif __name__ == '__main__':\n main()\n"},{"alpha_fraction":0.6472602486610413,"alphanum_fraction":0.6506849527359009,"avg_line_length":16.696969985961914,"blob_id":"aa0a4e0a53b4f7d23d98fd5fd29b23142627df5a","content_id":"e7045e6a6536f8c58eb5e8a460c9665faa55bc3f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":584,"license_type":"no_license","max_line_length":60,"num_lines":33,"path":"/temp/test_space.py","repo_name":"faircloth-lab/alfred_zotquery","src_encoding":"UTF-8","text":"#!/usr/bin/python\n# encoding: utf-8\nfrom __future__ import unicode_literals\n\nimport sys\nimport os.path\nfrom workflow import Workflow\n\nBASE = os.path.expanduser(\n '~/Library/Application Support/Alfred 2/Workflow Data/')\nBUNDLER = \"alfred.bundler-aries/assets/python\"\nPATH = os.path.join(BASE, BUNDLER)\n\n\n\n\n\n\n\n\ndef main(wf_obj):\n \"\"\"main\"\"\"\n WF_DIR = os.path.dirname(os.path.realpath(__file__))\n PLIST = os.path.join(WF_DIR, 'info.plist')\n print os.path.exists(PLIST)\n\n\n \n\n \nif __name__ == '__main__':\n WF = Workflow(libraries=[PATH])\n sys.exit(WF.run(main))\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6044718623161316,\n \"alphanum_fraction\": 0.624518096446991,\n \"avg_line_length\": 23.018518447875977,\n \"blob_id\": \"9bda4ef9834a4fde69bc0a3461dc5a676240b3c3\",\n \"content_id\": \"bb884f684fd508bfccb3a78f8ede2a6725d66777\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1297,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 54,\n \"path\": \"/test_space.py\",\n \"repo_name\": \"faircloth-lab/alfred_zotquery\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n# encoding: utf-8\\nfrom __future__ import unicode_literals\\n\\nimport sys\\nimport json\\nimport os.path\\nimport subprocess\\nfrom StringIO import StringIO\\n\\nfrom deps import xmltodict\\nimport zotquery as z\\n\\nos.environ['__CF_USER_TEXT_ENCODING'] = \\\"0x1F5:0x8000100:0x8000100\\\"\\n\\ndef to_unicode(obj, encoding='utf-8'):\\n \\\"\\\"\\\"Detects if object is a string and if so converts to unicode\\\"\\\"\\\"\\n if isinstance(obj, basestring):\\n if not isinstance(obj, unicode):\\n obj = unicode(obj, encoding)\\n return obj\\n\\ndef get_clipboard(): \\n \\\"\\\"\\\"Get objects of clipboard\\\"\\\"\\\"\\n scpt = \\\"\\\"\\\"return the clipboard\\\"\\\"\\\"\\n data = subprocess.call(['osascript', '-e', scpt])\\n data = to_unicode(data)\\n return data\\n\\ndef capture_output(zot_filter):\\n \\\"\\\"\\\"Capture `sys` output in var\\\"\\\"\\\"\\n old_stdout = sys.stdout\\n sys.stdout = mystdout = StringIO()\\n zot_filter.filter()\\n sys.stdout = old_stdout\\n res_dict = xmltodict.parse(mystdout.getvalue())\\n return res_dict\\n\\ndef main():\\n \\\"\\\"\\\"main\\\"\\\"\\\"\\n proc = subprocess.Popen(['pbpaste'], stdout=subprocess.PIPE)\\n proc.wait()\\n data = proc.stdout.read()\\n\\n for i in ['utf-8', 'cp1252']:\\n try:\\n print data.decode(i)\\n print i\\n except:\\n pass\\n\\n \\nif __name__ == '__main__':\\n main()\\n\"\n },\n {\n \"alpha_fraction\": 0.6472602486610413,\n \"alphanum_fraction\": 0.6506849527359009,\n \"avg_line_length\": 16.696969985961914,\n \"blob_id\": \"aa0a4e0a53b4f7d23d98fd5fd29b23142627df5a\",\n \"content_id\": \"e7045e6a6536f8c58eb5e8a460c9665faa55bc3f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 584,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 33,\n \"path\": \"/temp/test_space.py\",\n \"repo_name\": \"faircloth-lab/alfred_zotquery\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n# encoding: utf-8\\nfrom __future__ import unicode_literals\\n\\nimport sys\\nimport os.path\\nfrom workflow import Workflow\\n\\nBASE = os.path.expanduser(\\n '~/Library/Application Support/Alfred 2/Workflow Data/')\\nBUNDLER = \\\"alfred.bundler-aries/assets/python\\\"\\nPATH = os.path.join(BASE, BUNDLER)\\n\\n\\n\\n\\n\\n\\n\\n\\ndef main(wf_obj):\\n \\\"\\\"\\\"main\\\"\\\"\\\"\\n WF_DIR = os.path.dirname(os.path.realpath(__file__))\\n PLIST = os.path.join(WF_DIR, 'info.plist')\\n print os.path.exists(PLIST)\\n\\n\\n \\n\\n \\nif __name__ == '__main__':\\n WF = Workflow(libraries=[PATH])\\n sys.exit(WF.run(main))\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979163,"cells":{"repo_name":{"kind":"string","value":"sophiiasun/ViBot-Submission"},"repo_url":{"kind":"string","value":"https://github.com/sophiiasun/ViBot-Submission"},"snapshot_id":{"kind":"string","value":"aa21226004863b1ad72a983f1c51963ec2504252"},"revision_id":{"kind":"string","value":"684ab538a7b5497775a49f28b5db5b71eb0a4435"},"directory_id":{"kind":"string","value":"5d1ab08e8f91d9fd9dab009d66d44dc92b53dfaf"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-05-10T17:39:50.327640","string":"2023-05-10T17:39:50.327640"},"revision_date":{"kind":"timestamp","value":"2021-05-30T14:18:59","string":"2021-05-30T14:18:59"},"committer_date":{"kind":"timestamp","value":"2021-05-30T14:18:59","string":"2021-05-30T14:18:59"},"github_id":{"kind":"number","value":361263173,"string":"361,263,173"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6203632354736328,"alphanum_fraction":0.6346725225448608,"avg_line_length":50.91714096069336,"blob_id":"0427293da3ce3643696d5bd7dddc3cc54d1c8513","content_id":"ab35b585eed02b4516c5175421da056f9eb00c92","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":18182,"license_type":"no_license","max_line_length":459,"num_lines":350,"path":"/ViBot.py","repo_name":"sophiiasun/ViBot-Submission","src_encoding":"UTF-8","text":"import discord\nfrom discord.utils import get\nfrom discord.ext import commands\nimport nltk\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\nfrom nltk import tokenize\nnltk.download('punkt')\nnltk.download('wordnet')\nimport random\nimport pyodbc\nfrom decouple import config\n\nclient = commands.Bot(command_prefix = 'v!', help_command=None)\nTOKEN = config('TOKEN')\n\ngloomyList = [\n [\"https://www.youtube.com/watch?v=w5_X-QFwmoo\", \"Epiphany\", \"BTS\"],\n [\"https://www.youtube.com/watch?v=AG-erEMhumc\", \"you broke me first\", \"Tate McRae\"],\n [\"https://www.youtube.com/watch?v=EtLtZoCwquw\", \"I hate u I love u\", \"gnash, Olivia O'Brien\"],\n [\"https://www.youtube.com/watch?v=RqcjBLMaWCg\", \"2U\", \"David Guetta, Justin Bieber\"],\n [\"https://www.youtube.com/watch?v=2tJjplMngFE\", \"princess don’t cry\", \"Carys\"],\n [\"https://www.youtube.com/watch?v=4vzJA0nWF1g\", \"lost boy\", \"Ruth. B\"],\n [\"https://www.youtube.com/watch?v=h9hdorPqukU\", \"if you\", \"BIGBANG\"],\n [\"https://www.youtube.com/watch?v=tscMSXk_jaQ\", \"if we have each other\", \"Alec Benjamin\"],\n [\"https://www.youtube.com/watch?v=pBET-22qits\", \"six feet apart\", \"Alec Benjamin\"],\n [\"https://www.youtube.com/watch?v=Xc695u4RFeI\", \"dangerously\", \"Charlie Puth\"],\n [\"https://www.youtube.com/watch?v=v-FVihIlU2g\", \"as long as you love me\", \"Backstreet Boys\"],\n [\"https://www.youtube.com/watch?v=BwcFtiIPogo\", \"how miserable do I have to be before you’re happy\", \"Beowulf\"],\n [\"https://www.youtube.com/watch?v=L7mfjvdnPno\", \"falling\", \"Trevor Daniel\"],\n [\"https://www.youtube.com/watch?v=AddwSHpQuSM\", \"superficial love\", \"Ruth B.\"],\n [\"https://www.youtube.com/watch?v=VjSmpJDhF0U\", \"complicated\", \"Olivia O'Brien\"],\n [\"https://www.youtube.com/watch?v=ZzUbjigZTpM\", \"selfish\", \"Madison Beer\"],\n [\"https://www.youtube.com/watch?v=0TSxB_Tb2F0\", \"sweater weather\", \"The Neighbourhood Covered by Max and Alyston Stoner\"],\n [\"https://www.youtube.com/watch?v=dMEF8Olrvyc\", \"superhero\", \"Hard\"],\n [\"https://www.youtube.com/watch?v=0XRx5Jwjp4k\", \"wait wait wait\", \"Kun\"],\n [\"https://www.youtube.com/watch?v=jhVc-phLHJA\", \"one day\", \"Tate McRae\"]\n]\n\nsadList = [\n [\"https://www.youtube.com/watch?v=iWZmdoY1aTE\", \"happier\", \"Ed Sheeran \"],\n [\"https://www.youtube.com/watch?v=KRaWnd3LJfs\", \"payphone\", \"Marron 5\"],\n [\"https://www.youtube.com/watch?v=7C2z4GqqS5E\", \"fake love\", \"BTS\"],\n [\"https://www.youtube.com/watch?v=ogalmk6K0zs\", \"cry for me\", \"TWICE\"],\n [\"https://www.youtube.com/watch?v=-RJSbO8UZVY\", \"young blood\", \"5 Seconds of Summer\"],\n [\"https://www.youtube.com/watch?v=NFNQEPcgsmA\", \"better\", \"Zayn\"],\n [\"https://www.youtube.com/watch?v=LdH7aFjDzjI\", \"I’m a mess\", \"Bebe Rexha\"],\n [\"https://www.youtube.com/watch?v=eNd4tt9raeg\", \"dusk til dawn\", \"Zayn, Sia\"],\n [\"https://www.youtube.com/watch?v=C_3d6GntKbk\", \"pillow talk\", \"Jeff Kaele\"],\n [\"https://www.youtube.com/watch?v=sZSf5dpZdlI\", \"centuries\", \"Fall Out Boy\"],\n [\"https://www.youtube.com/watch?v=fXw0jcYbqdo\", \"talking to the moon\", \"Bruno Mars\"],\n [\"https://www.youtube.com/watch?v=7JJfJgyHYwU\", \"me and my broken heart\", \"Rixton\"],\n [\"https://www.youtube.com/watch?v=GDTD24KsdGc\", \"love like you\", \"Steven Universe\"],\n [\"https://www.youtube.com/watch?v=4h26oYuE2h0\", \"trampoline\", \"Shaed and Zayn\"],\n [\"https://www.youtube.com/watch?v=HiXZGbLRQrY\", \"running back to you\", \"Martin Jensen, Alle Farben, Nico Santos\"],\n [\"https://www.youtube.com/watch?v=izIyhdEHSPo\", \"love lies\", \"Khalid, Normani\"],\n [\"https://www.youtube.com/watch?v=109HJ6_1rfg\", \"Tyler Durden\", \"Madison Beer\"],\n [\"https://www.youtube.com/watch?v=BwBeHrCCJ6M\", \"boys like you\", \"Anna Clendening\"],\n [\"https://www.youtube.com/watch?v=PXGycbkbtW0\", \"it’s you\", \"Alie Gatie\"],\n [\"https://www.youtube.com/watch?v=3mWbRB3Y4R8\", \"don’t you worry child\", \"Swedish House Mafia, John Martin\"],\n [\"https://www.youtube.com/watch?v=Qzc_aX8c8g4\", \"dancing with your ghost\", \"Sasha Sloan\"],\n]\n\nneutralList = [\n [\"https://www.youtube.com/watch?v=W8a4sUabCUo\", \"dandelions\", \"Ruth. B\"],\n [\"https://www.youtube.com/watch?v=-grLLLTza6k\", \"wolves\", \"Selena Gomez\"],\n [\"https://www.youtube.com/watch?v=ycy30LIbq4w\", \"lost in Japan\", \"Shawn Mendes\"],\n [\"https://www.youtube.com/watch?v=SlPhMPnQ58k\", \"memories\", \"Maroon 5\"],\n [\"https://www.youtube.com/watch?v=ulNswX3If6U\", \"back to you\", \"Selena Gomez\"],\n [\"https://www.youtube.com/watch?v=j60ClcNYWu4\", \"zero\", \"Imagine Dragons\"],\n [\"https://www.youtube.com/watch?v=8qZgRHgguXk\", \"willow\", \"Taylor Swift\"],\n [\"https://www.youtube.com/watch?v=g9aNdMLYUrk\", \"champagne problems\", \"Taylor Swift\"],\n [\"https://www.youtube.com/watch?v=GTWqwSNQCcg\", \"city of stars\", \"La La Land Original Motion Picture Soundtrack\"],\n [\"https://www.youtube.com/watch?v=tk36ovCMsU8\", \"silence\", \"Marshmello\"],\n [\"https://www.youtube.com/watch?v=av5JD1dfj_c\", \"dancing with a stranger\", \"Sam Smith, Normani\"],\n [\"https://www.youtube.com/watch?v=Y2E71oe0aSM\", \"10000 hours\", \"Dan + Shay, Justin Bieber\"],\n [\"https://www.youtube.com/watch?v=0yi5tiv9FwM\", \"life goes on\", \"BTS\"],\n [\"https://www.youtube.com/watch?v=Rdlc4b5NL5g\", \"peace and love\", \"Steven Universe\"],\n [\"https://www.youtube.com/watch?v=pTOC_q0NLTk\", \"ophelia\", \"The Lumineers\"],\n [\"https://www.youtube.com/watch?v=FlSxBCHul1A\", \"all the things she said\", \"Seraphine\"],\n [\"https://www.youtube.com/watch?v=Rnu83PtOQfc\", \"made me this way\", \"Seraphine\"],\n [\"https://www.youtube.com/watch?v=Yp4yF5hb89I\", \"come thru\", \"Jeremy Zucker\"],\n [\"https://www.youtube.com/watch?v=eY_eqNHguwg\", \"fools\", \"Madison Beer\"],\n [\"https://www.youtube.com/watch?v=cMg8KaMdDYo\", \"fly away\", \"TheFatRat\"],\n [\"https://www.youtube.com/watch?v=Uh3cbLgVXP0\", \"summer wind\", \"Various Artists\"],\n [\"https://www.youtube.com/watch?v=pk7ESz6vtyA&t=5s\", \"winter bear\", \"V\"],\n [\"https://www.youtube.com/watch?v=CKZvWhCqx1s\", \"on the ground\", \"Rose\"]\n]\n\nhappyList = [\n [\"https://www.youtube.com/watch?v=qDRORgoZxZU\", \"Me Too\", \"Meghan Trainor\"],\n [\"https://www.youtube.com/watch?v=BQ0mxQXmLsk\", \"Havana\", \"Camila Cabello ft. Young Thug\"],\n [\"https://www.youtube.com/watch?v=PKWHYLtmAGA\", \"Salt\", \"Ava Max\"],\n [\"https://www.youtube.com/watch?v=bQCg73ox1Rc\", \"Childhood Dreams\", \"Cover by Seraphine\"],\n [\"https://www.youtube.com/watch?v=jiXP-FRrSOg\", \"Everybody Wants You\", \"Johnny Orlando\"],\n [\"https://www.youtube.com/watch?v=Il-an3K9pjg\", \"2002\", \"Anna-Marie\"],\n [\"https://www.youtube.com/watch?v=2z5un3c8hbo\", \"I Love You 3000 II\", \"Stephanie Poetri ft. Jackson Wang\"],\n [\"https://www.youtube.com/watch?v=Y7ix6RITXM0\", \"Maps\", \"Maroon 5\"],\n [\"https://www.youtube.com/watch?v=an3R3XQ4sgk\", \"North Star\", \"Tyler Shaw\"],\n [\"https://www.youtube.com/watch?v=6RLLOEzdxsM\", \"All Falls Down\", \"Alan Walker\"],\n [\"https://www.youtube.com/watch?v=u-qYMl9T9wQ\", \"Liar Liar\", \"Cris Cab\"],\n [\"https://www.youtube.com/watch?v=2NbC8kGGNus\", \"Counting Stars\", \"OneRepublic\"],\n [\"https://www.youtube.com/watch?v=0yBnIUX0QAE\", \"Dancing in the Moonlight\", \"Toploader\"],\n [\"https://www.youtube.com/watch?v=Y2V6yjjPbX0\", \"Handclap\", \"Fitz and the Trantrums\"],\n [\"https://www.youtube.com/watch?v=VPRjCeoBqrI\", \"A Sky Full of Stars\", \"Coldplay\"],\n [\"https://www.youtube.com/watch?v=kOkQ4T5WO9E\", \"This Is What You Came For\", \"Calvin Harris ft. Rihanna\"],\n [\"https://www.youtube.com/watch?v=CvBfHwUxHIk\", \"Umbrella\", \"Rihanna ft. JAY-Z\"],\n [\"https://www.youtube.com/watch?v=KQetemT1sWc\", \"Here Comes The Sun\", \"The Beatles\"],\n [\"https://www.youtube.com/watch?v=Km3wOjaRyE4\", \"We Can’t Stop\", \"Miley Cyrus\"],\n [\"https://www.youtube.com/watch?v=FDWTP4_5eps\", \"Yellow Hearts\", \"Ant Saunders\"]\n]\n\necstaticList = [\n [\"https://www.youtube.com/watch?v=MBdVXkSdhwU\", \"DNA\", \"BTS\"],\n [\"https://www.youtube.com/watch?v=kTlv5_Bs8aw\", \"Mic Drop (Steve Aoki Remix)\", \"BTS\"],\n [\"https://www.youtube.com/watch?v=gdZLi9oWNZg\", \"Dynamite\", \"BTS\"],\n [\"https://www.youtube.com/watch?v=wS7anrSXKrY\", \"Blue Flame\", \"ASTRO\"],\n [\"https://www.youtube.com/watch?v=Zz7YZtpz2Ek\", \"Knock\", \"ASTRO\"],\n [\"https://www.youtube.com/watch?v=3VTkBuxU4yk\", \"MORE\", \"K/DA\"],\n [\"https://www.youtube.com/watch?v=OPf0YbXqDm0\", \"Uptown Funk\", \"Mark Ronson ft. Bruno Mars\"],\n [\"https://www.youtube.com/watch?v=PHn5Q7hCjxw\", \"Immortals\", \"Fall Out Boys\"],\n [\"https://www.youtube.com/watch?v=7-x3uD5z1bQ\", \"Watermelon Sugar\", \"Harry Styles\"],\n [\"https://www.youtube.com/watch?v=ZbZSe6N_BXs\", \"Happy\", \"Pharrell Williams\"],\n [\"https://www.youtube.com/watch?v=TzFRVk2ektI\", \"Blueberry Eyes\", \"MAX ft. SUGA of BTS\"],\n [\"https://www.youtube.com/watch?v=KhTeiaCezwM\", \"HIP\", \"MAMAMOO\"],\n [\"https://www.youtube.com/watch?v=YswhUHH6Ufc\", \"Bang Bang\", \"Jessie J, Ariana Grande, Nicki Minaj\"],\n [\"https://www.youtube.com/watch?v=glKgDqZ1ABU\", \"Miss Alissa\", \"Eagles Of Death Metal\"],\n [\"https://www.youtube.com/watch?v=pmanD_s7G3U\", \"Gurenge\", \"LiSA\"],\n [\"https://www.youtube.com/watch?v=DwRndYRTuyw\", \"Dionysius\", \"BTS\"]\n]\n\n@client.event\nasync def on_ready():\n general_channel = client.get_channel(835324404841709589)\n mbed = discord.Embed(\n title = 'ViBot is Now Online!',\n description = 'See `v!help` for a list of commands. Enjoy!',\n color = discord.Color.green()\n )\n await general_channel.send(embed = mbed)\n\ndef getScore(txt):\n analyzer = SentimentIntensityAnalyzer()\n return analyzer.polarity_scores(txt)[\"compound\"]\n\ndef getEmotion(score):\n if score > 0.6:\n return 'ecstatic'\n elif score > 0.2:\n return 'happy'\n elif score < -0.6:\n return 'gloomy'\n elif score < -0.2:\n return 'sad'\n else:\n return 'neutral'\n \ndef storeScore(name, score):\n driverstr = config('driverstr')\n with pyodbc.connect(driverstr) as conn:\n with conn.cursor() as cursor:\n cnt = cursor.execute(\"select count(*) as cnt from vibeScores where username = '\" + str(name) + \"'\").fetchone().cnt\n if cnt > 0: # user exists, update\n cursor.execute(\"update vibeScores set recent = '\" + str(score) + \"' where username = '\" + str(name) + \"'\")\n cursor.execute(\"update vibeScores set average = (average*vibeCount + '\" + str(score) + \"')/(vibecount+1) where username = '\" + str(name) + \"';\")\n cursor.execute(\"update vibeScores set vibeCount = vibeCount + 1 where username = '\" + str(name) + \"'\")\n else:\n cursor.execute(\"insert into vibeScores (username, recent, average, vibeCount) Values ('\" + str(name) + \"', '\" + str(score) + \"', '\" + str(score) + \"', '1');\")\n cursor.commit()\n\ndef getLink(emotion):\n if(emotion=='ecstatic'):\n idx = random.randint(0, len(ecstaticList)-1)\n return ecstaticList[idx]\n elif(emotion=='happy'):\n idx = random.randint(0, len(happyList)-1)\n return happyList[idx]\n elif(emotion=='neutral'):\n idx = random.randint(0, len(neutralList)-1)\n return neutralList[idx]\n elif(emotion=='sad'):\n idx = random.randint(0, len(sadList)-1)\n return sadList[idx]\n else:\n idx = random.randint(0, len(gloomyList)-1)\n return gloomyList[idx]\n\ndef getEmoji(emotion):\n if(emotion=='ecstatic'):\n return ':smiling_face_with_3_hearts :'\n elif(emotion=='happy'):\n return ':slight_smile:'\n elif(emotion=='neutral'):\n return ':neutral_face:'\n elif(emotion=='sad'):\n return ':slight_frown:'\n else:\n return ':cry:'\n\ndef getStoredScore(name):\n driverstr = config('driverstr')\n with pyodbc.connect(driverstr) as conn:\n with conn.cursor() as cursor:\n cnt = cursor.execute(\"select count(*) as cnt from vibeScores where username = '\" + str(name) + \"'\").fetchone().cnt\n if cnt > 0:\n return cursor.execute(\"select recent from vibeScores where username = '\" + str(name) + \"'\").fetchone().recent\n else:\n return -2\n cursor.commit()\n\ndef getStoredAvg(name):\n driverstr = config('driverstr')\n with pyodbc.connect(driverstr) as conn:\n with conn.cursor() as cursor:\n cnt = cursor.execute(\"select count(*) as cnt from vibeScores where username = '\" + str(name) + \"'\").fetchone().cnt\n if cnt > 0:\n return cursor.execute(\"select average from vibeScores where username = '\" + str(name) + \"'\").fetchone().average\n else:\n return -2\n cursor.commit()\n\ndef getStoredCount(name):\n driverstr = config('driverstr')\n with pyodbc.connect(driverstr) as conn:\n with conn.cursor() as cursor:\n return cursor.execute(\"select count(*) as cnt from vibeScores where username = '\" + str(name) + \"'\").fetchone().cnt\n\ndef getCompare(score, average):\n if score > average:\n return 'better'\n elif score == average:\n return 'the same'\n else:\n return 'worse'\n\n@client.command()\nasync def mood(ctx, *, message):\n name = ctx.author\n score = getScore(message)\n emotion = getEmotion(score)\n link = getLink(emotion)\n emoji = getEmoji(emotion)\n avg = getStoredAvg(name)\n compare = getCompare(score, avg)\n storeScore(name, score)\n\n mbed = discord.Embed(\n title = 'Calculating mood...',\n description = name.mention + ', check out your emotions!',\n color = discord.Color.blue()\n )\n mbed.add_field(name='You are currently ' + emotion + ' ' + emoji, value='From a scale of -1 to 1, your vibeScore is ' + str(round(score, 4)) + '. You are feeling ' + compare + ' than usual!', inline=False) # ADD DESCRIPTION ?? and insert youtube link or smt (figure link embed)\n mbed.add_field(name='Listen to this Song ~ ', value=link[1] + ' by ' + link[2], inline=False)\n await ctx.send(embed = mbed)\n await ctx.send(link[0])\n\n@client.command()\nasync def dm(ctx):\n name = ctx.author\n mbed1 = discord.Embed(\n title = 'Hey ' + str(name).split('#')[0] + '!',\n description = 'Let\\'s chat! Feel free to use ViBot commands to interact. For more info, type `p!help`.',\n color = discord.Color.blue()\n )\n await name.send(embed=mbed1)\n mbed2 = discord.Embed(\n title = \"Direct Message Sent!\",\n description = \"Hey \" + name.mention + \", check your DMs! :)\",\n color = discord.Color.blue()\n )\n await ctx.send(embed=mbed2)\n\n\n@client.command()\nasync def repeat(ctx):\n name = ctx.author\n cnt = getStoredCount(name)\n if cnt > 0: # user exists, update\n score = getStoredScore(name)\n avg = getStoredAvg(name)\n emotion = getEmotion(score)\n emoji = getEmoji(emotion)\n link = getLink(emotion)\n compare = getCompare(score, avg)\n\n mbed = discord.Embed(\n title = 'Retrieving mood...',\n description = name.mention + ', check out your emotions!',\n color = discord.Color.blue()\n )\n mbed.add_field(name='You are currently ' + emotion + ' ' + emoji, value='From a scale of -1 to 1, your vibeScore is ' + str(round(score, 4)) + '. You are feeling ' + compare + ' than usual!', inline=False)\n mbed.add_field(name='Listen to this Song ~ ', value=link[1] + ' by ' + link[2], inline=False)\n await ctx.send(embed=mbed)\n await ctx.send(link[0])\n else:\n mbed = discord.Embed(\n title = 'You are Currently Moodless :face_with_raised_eyebrow:',\n description = 'We don\\'t have your recent moods saved! Calculate your mood using `v!mood` first before asking for another song.',\n color = discord.Color.red()\n )\n await ctx.send(embed=mbed)\n\n@client.command()\nasync def clear(ctx):\n name = ctx.author\n driverstr = config('driverstr')\n with pyodbc.connect(driverstr) as conn:\n with conn.cursor() as cursor:\n cnt = cursor.execute(\"delete from vibeScores where username = '\" + str(name) + \"'\")\n cursor.commit()\n\n mbed = discord.Embed(\n title = 'Clearing Data',\n description = 'Your average mood has been reset! Once you have checked your mood ten times, your average will be displayed.',\n color = discord.Color.blue()\n )\n await ctx.send(embed = mbed)\n\n@client.command()\nasync def help(ctx):\n mbed = discord.Embed(\n title = 'Welcome to our help page! Here is a list of all ViBot commands:',\n color = 16776960\n )\n mbed.add_field(name='About Page: `v!about`', value='The About Page will introduce you to the purpose of ViBot and tell you a little about the creators!', inline=False)\n mbed.add_field(name='Clearing Data: `v!clear`', value='Use this command to clear your previous mood scores and averages! Start fresh for the new you.', inline=False)\n mbed.add_field(name='Direct Messaging: `v!dm`', value='If you want some private conversation between just you and ViBot, ask for a DM :eyes:')\n mbed.add_field(name='Help Page: `v!help`', value = 'The Help Page will show you a list of commands and what they can be used for... Try experimenting with them!', inline=False)\n mbed.add_field(name='Mood Detector: `v!mood <message>`', value='Tell us about your day! ViBot will calculate your current emotions and recommend you a sick song to vibe to!', inline=False)\n mbed.add_field(name='Get Another Song: `v!repeat`', value='If you enjoyed the last song ViBot recommended, request for another song without changing your mood!', inline=False)\n await ctx.send(embed = mbed)\n\n@client.command()\nasync def about(ctx):\n mbed = discord.Embed(\n title = 'About:',\n description = \"This bot reads in message with the `v!mood` command followed by a message. After detecting the mood of the message, the bot then sends a corresponding song that matches the mood and produces an emotion score ranging from -1 (sad) to 1 (happy). Your previous emotion scored will be stores and the average is calculated to compare with your current emotion. For information on which commands ViBot supports, visit `v!help`. Have fun! :)\",\n color = 16776960\n )\n await ctx.send(embed = mbed)\n\n@client.event\nasync def on_command_error(ctx, error):\n mbed = discord.Embed(\n title = 'Error: Command does not Exist!',\n description = 'Visit `v!about` to learn about ViBot, or see `v!help` for a list of commands.',\n color = discord.Color.red()\n )\n await ctx.send(embed = mbed)\n\nclient.run(TOKEN)"},{"alpha_fraction":0.7258522510528564,"alphanum_fraction":0.7571022510528564,"avg_line_length":36.05263137817383,"blob_id":"4c3d1380d56d67772a2f8e955c1c76b3ea60527e","content_id":"fff9306b6a0f79c9755e5465b5ae3a0a046fed85","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":704,"license_type":"no_license","max_line_length":220,"num_lines":19,"path":"/README.md","repo_name":"sophiiasun/ViBot-Submission","src_encoding":"UTF-8","text":"# ViBot-Submission\n\nViBot is a Discord Bot who can suggest songs based off of the emotion detected from a user's message, scoring it on a rating from -1 to 1. Then, based off of your message category, it will recommend a corresponding song!\n\nCommands : \n\nPrefix : Use v! to call commands on the bot \n\nv!about : Learn about what the bot does!\n\nv!mood : detect the mood of the msg (bot replies with score of the emotion)\n\nv!help : reach the help page for the bot \n\nv!repeat : recommend a song based off of mood \n\nv!clear : clear the logged data of recent moods (average of all previous moods)\n\nLink to add bot : https://discord.com/api/oauth2/authorize?client_id=835299556010885130&permissions=0&scope=bot\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6203632354736328,\n \"alphanum_fraction\": 0.6346725225448608,\n \"avg_line_length\": 50.91714096069336,\n \"blob_id\": \"0427293da3ce3643696d5bd7dddc3cc54d1c8513\",\n \"content_id\": \"ab35b585eed02b4516c5175421da056f9eb00c92\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 18182,\n \"license_type\": \"no_license\",\n \"max_line_length\": 459,\n \"num_lines\": 350,\n \"path\": \"/ViBot.py\",\n \"repo_name\": \"sophiiasun/ViBot-Submission\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import discord\\nfrom discord.utils import get\\nfrom discord.ext import commands\\nimport nltk\\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\\nfrom nltk import tokenize\\nnltk.download('punkt')\\nnltk.download('wordnet')\\nimport random\\nimport pyodbc\\nfrom decouple import config\\n\\nclient = commands.Bot(command_prefix = 'v!', help_command=None)\\nTOKEN = config('TOKEN')\\n\\ngloomyList = [\\n [\\\"https://www.youtube.com/watch?v=w5_X-QFwmoo\\\", \\\"Epiphany\\\", \\\"BTS\\\"],\\n [\\\"https://www.youtube.com/watch?v=AG-erEMhumc\\\", \\\"you broke me first\\\", \\\"Tate McRae\\\"],\\n [\\\"https://www.youtube.com/watch?v=EtLtZoCwquw\\\", \\\"I hate u I love u\\\", \\\"gnash, Olivia O'Brien\\\"],\\n [\\\"https://www.youtube.com/watch?v=RqcjBLMaWCg\\\", \\\"2U\\\", \\\"David Guetta, Justin Bieber\\\"],\\n [\\\"https://www.youtube.com/watch?v=2tJjplMngFE\\\", \\\"princess don’t cry\\\", \\\"Carys\\\"],\\n [\\\"https://www.youtube.com/watch?v=4vzJA0nWF1g\\\", \\\"lost boy\\\", \\\"Ruth. B\\\"],\\n [\\\"https://www.youtube.com/watch?v=h9hdorPqukU\\\", \\\"if you\\\", \\\"BIGBANG\\\"],\\n [\\\"https://www.youtube.com/watch?v=tscMSXk_jaQ\\\", \\\"if we have each other\\\", \\\"Alec Benjamin\\\"],\\n [\\\"https://www.youtube.com/watch?v=pBET-22qits\\\", \\\"six feet apart\\\", \\\"Alec Benjamin\\\"],\\n [\\\"https://www.youtube.com/watch?v=Xc695u4RFeI\\\", \\\"dangerously\\\", \\\"Charlie Puth\\\"],\\n [\\\"https://www.youtube.com/watch?v=v-FVihIlU2g\\\", \\\"as long as you love me\\\", \\\"Backstreet Boys\\\"],\\n [\\\"https://www.youtube.com/watch?v=BwcFtiIPogo\\\", \\\"how miserable do I have to be before you’re happy\\\", \\\"Beowulf\\\"],\\n [\\\"https://www.youtube.com/watch?v=L7mfjvdnPno\\\", \\\"falling\\\", \\\"Trevor Daniel\\\"],\\n [\\\"https://www.youtube.com/watch?v=AddwSHpQuSM\\\", \\\"superficial love\\\", \\\"Ruth B.\\\"],\\n [\\\"https://www.youtube.com/watch?v=VjSmpJDhF0U\\\", \\\"complicated\\\", \\\"Olivia O'Brien\\\"],\\n [\\\"https://www.youtube.com/watch?v=ZzUbjigZTpM\\\", \\\"selfish\\\", \\\"Madison Beer\\\"],\\n [\\\"https://www.youtube.com/watch?v=0TSxB_Tb2F0\\\", \\\"sweater weather\\\", \\\"The Neighbourhood Covered by Max and Alyston Stoner\\\"],\\n [\\\"https://www.youtube.com/watch?v=dMEF8Olrvyc\\\", \\\"superhero\\\", \\\"Hard\\\"],\\n [\\\"https://www.youtube.com/watch?v=0XRx5Jwjp4k\\\", \\\"wait wait wait\\\", \\\"Kun\\\"],\\n [\\\"https://www.youtube.com/watch?v=jhVc-phLHJA\\\", \\\"one day\\\", \\\"Tate McRae\\\"]\\n]\\n\\nsadList = [\\n [\\\"https://www.youtube.com/watch?v=iWZmdoY1aTE\\\", \\\"happier\\\", \\\"Ed Sheeran \\\"],\\n [\\\"https://www.youtube.com/watch?v=KRaWnd3LJfs\\\", \\\"payphone\\\", \\\"Marron 5\\\"],\\n [\\\"https://www.youtube.com/watch?v=7C2z4GqqS5E\\\", \\\"fake love\\\", \\\"BTS\\\"],\\n [\\\"https://www.youtube.com/watch?v=ogalmk6K0zs\\\", \\\"cry for me\\\", \\\"TWICE\\\"],\\n [\\\"https://www.youtube.com/watch?v=-RJSbO8UZVY\\\", \\\"young blood\\\", \\\"5 Seconds of Summer\\\"],\\n [\\\"https://www.youtube.com/watch?v=NFNQEPcgsmA\\\", \\\"better\\\", \\\"Zayn\\\"],\\n [\\\"https://www.youtube.com/watch?v=LdH7aFjDzjI\\\", \\\"I’m a mess\\\", \\\"Bebe Rexha\\\"],\\n [\\\"https://www.youtube.com/watch?v=eNd4tt9raeg\\\", \\\"dusk til dawn\\\", \\\"Zayn, Sia\\\"],\\n [\\\"https://www.youtube.com/watch?v=C_3d6GntKbk\\\", \\\"pillow talk\\\", \\\"Jeff Kaele\\\"],\\n [\\\"https://www.youtube.com/watch?v=sZSf5dpZdlI\\\", \\\"centuries\\\", \\\"Fall Out Boy\\\"],\\n [\\\"https://www.youtube.com/watch?v=fXw0jcYbqdo\\\", \\\"talking to the moon\\\", \\\"Bruno Mars\\\"],\\n [\\\"https://www.youtube.com/watch?v=7JJfJgyHYwU\\\", \\\"me and my broken heart\\\", \\\"Rixton\\\"],\\n [\\\"https://www.youtube.com/watch?v=GDTD24KsdGc\\\", \\\"love like you\\\", \\\"Steven Universe\\\"],\\n [\\\"https://www.youtube.com/watch?v=4h26oYuE2h0\\\", \\\"trampoline\\\", \\\"Shaed and Zayn\\\"],\\n [\\\"https://www.youtube.com/watch?v=HiXZGbLRQrY\\\", \\\"running back to you\\\", \\\"Martin Jensen, Alle Farben, Nico Santos\\\"],\\n [\\\"https://www.youtube.com/watch?v=izIyhdEHSPo\\\", \\\"love lies\\\", \\\"Khalid, Normani\\\"],\\n [\\\"https://www.youtube.com/watch?v=109HJ6_1rfg\\\", \\\"Tyler Durden\\\", \\\"Madison Beer\\\"],\\n [\\\"https://www.youtube.com/watch?v=BwBeHrCCJ6M\\\", \\\"boys like you\\\", \\\"Anna Clendening\\\"],\\n [\\\"https://www.youtube.com/watch?v=PXGycbkbtW0\\\", \\\"it’s you\\\", \\\"Alie Gatie\\\"],\\n [\\\"https://www.youtube.com/watch?v=3mWbRB3Y4R8\\\", \\\"don’t you worry child\\\", \\\"Swedish House Mafia, John Martin\\\"],\\n [\\\"https://www.youtube.com/watch?v=Qzc_aX8c8g4\\\", \\\"dancing with your ghost\\\", \\\"Sasha Sloan\\\"],\\n]\\n\\nneutralList = [\\n [\\\"https://www.youtube.com/watch?v=W8a4sUabCUo\\\", \\\"dandelions\\\", \\\"Ruth. B\\\"],\\n [\\\"https://www.youtube.com/watch?v=-grLLLTza6k\\\", \\\"wolves\\\", \\\"Selena Gomez\\\"],\\n [\\\"https://www.youtube.com/watch?v=ycy30LIbq4w\\\", \\\"lost in Japan\\\", \\\"Shawn Mendes\\\"],\\n [\\\"https://www.youtube.com/watch?v=SlPhMPnQ58k\\\", \\\"memories\\\", \\\"Maroon 5\\\"],\\n [\\\"https://www.youtube.com/watch?v=ulNswX3If6U\\\", \\\"back to you\\\", \\\"Selena Gomez\\\"],\\n [\\\"https://www.youtube.com/watch?v=j60ClcNYWu4\\\", \\\"zero\\\", \\\"Imagine Dragons\\\"],\\n [\\\"https://www.youtube.com/watch?v=8qZgRHgguXk\\\", \\\"willow\\\", \\\"Taylor Swift\\\"],\\n [\\\"https://www.youtube.com/watch?v=g9aNdMLYUrk\\\", \\\"champagne problems\\\", \\\"Taylor Swift\\\"],\\n [\\\"https://www.youtube.com/watch?v=GTWqwSNQCcg\\\", \\\"city of stars\\\", \\\"La La Land Original Motion Picture Soundtrack\\\"],\\n [\\\"https://www.youtube.com/watch?v=tk36ovCMsU8\\\", \\\"silence\\\", \\\"Marshmello\\\"],\\n [\\\"https://www.youtube.com/watch?v=av5JD1dfj_c\\\", \\\"dancing with a stranger\\\", \\\"Sam Smith, Normani\\\"],\\n [\\\"https://www.youtube.com/watch?v=Y2E71oe0aSM\\\", \\\"10000 hours\\\", \\\"Dan + Shay, Justin Bieber\\\"],\\n [\\\"https://www.youtube.com/watch?v=0yi5tiv9FwM\\\", \\\"life goes on\\\", \\\"BTS\\\"],\\n [\\\"https://www.youtube.com/watch?v=Rdlc4b5NL5g\\\", \\\"peace and love\\\", \\\"Steven Universe\\\"],\\n [\\\"https://www.youtube.com/watch?v=pTOC_q0NLTk\\\", \\\"ophelia\\\", \\\"The Lumineers\\\"],\\n [\\\"https://www.youtube.com/watch?v=FlSxBCHul1A\\\", \\\"all the things she said\\\", \\\"Seraphine\\\"],\\n [\\\"https://www.youtube.com/watch?v=Rnu83PtOQfc\\\", \\\"made me this way\\\", \\\"Seraphine\\\"],\\n [\\\"https://www.youtube.com/watch?v=Yp4yF5hb89I\\\", \\\"come thru\\\", \\\"Jeremy Zucker\\\"],\\n [\\\"https://www.youtube.com/watch?v=eY_eqNHguwg\\\", \\\"fools\\\", \\\"Madison Beer\\\"],\\n [\\\"https://www.youtube.com/watch?v=cMg8KaMdDYo\\\", \\\"fly away\\\", \\\"TheFatRat\\\"],\\n [\\\"https://www.youtube.com/watch?v=Uh3cbLgVXP0\\\", \\\"summer wind\\\", \\\"Various Artists\\\"],\\n [\\\"https://www.youtube.com/watch?v=pk7ESz6vtyA&t=5s\\\", \\\"winter bear\\\", \\\"V\\\"],\\n [\\\"https://www.youtube.com/watch?v=CKZvWhCqx1s\\\", \\\"on the ground\\\", \\\"Rose\\\"]\\n]\\n\\nhappyList = [\\n [\\\"https://www.youtube.com/watch?v=qDRORgoZxZU\\\", \\\"Me Too\\\", \\\"Meghan Trainor\\\"],\\n [\\\"https://www.youtube.com/watch?v=BQ0mxQXmLsk\\\", \\\"Havana\\\", \\\"Camila Cabello ft. Young Thug\\\"],\\n [\\\"https://www.youtube.com/watch?v=PKWHYLtmAGA\\\", \\\"Salt\\\", \\\"Ava Max\\\"],\\n [\\\"https://www.youtube.com/watch?v=bQCg73ox1Rc\\\", \\\"Childhood Dreams\\\", \\\"Cover by Seraphine\\\"],\\n [\\\"https://www.youtube.com/watch?v=jiXP-FRrSOg\\\", \\\"Everybody Wants You\\\", \\\"Johnny Orlando\\\"],\\n [\\\"https://www.youtube.com/watch?v=Il-an3K9pjg\\\", \\\"2002\\\", \\\"Anna-Marie\\\"],\\n [\\\"https://www.youtube.com/watch?v=2z5un3c8hbo\\\", \\\"I Love You 3000 II\\\", \\\"Stephanie Poetri ft. Jackson Wang\\\"],\\n [\\\"https://www.youtube.com/watch?v=Y7ix6RITXM0\\\", \\\"Maps\\\", \\\"Maroon 5\\\"],\\n [\\\"https://www.youtube.com/watch?v=an3R3XQ4sgk\\\", \\\"North Star\\\", \\\"Tyler Shaw\\\"],\\n [\\\"https://www.youtube.com/watch?v=6RLLOEzdxsM\\\", \\\"All Falls Down\\\", \\\"Alan Walker\\\"],\\n [\\\"https://www.youtube.com/watch?v=u-qYMl9T9wQ\\\", \\\"Liar Liar\\\", \\\"Cris Cab\\\"],\\n [\\\"https://www.youtube.com/watch?v=2NbC8kGGNus\\\", \\\"Counting Stars\\\", \\\"OneRepublic\\\"],\\n [\\\"https://www.youtube.com/watch?v=0yBnIUX0QAE\\\", \\\"Dancing in the Moonlight\\\", \\\"Toploader\\\"],\\n [\\\"https://www.youtube.com/watch?v=Y2V6yjjPbX0\\\", \\\"Handclap\\\", \\\"Fitz and the Trantrums\\\"],\\n [\\\"https://www.youtube.com/watch?v=VPRjCeoBqrI\\\", \\\"A Sky Full of Stars\\\", \\\"Coldplay\\\"],\\n [\\\"https://www.youtube.com/watch?v=kOkQ4T5WO9E\\\", \\\"This Is What You Came For\\\", \\\"Calvin Harris ft. Rihanna\\\"],\\n [\\\"https://www.youtube.com/watch?v=CvBfHwUxHIk\\\", \\\"Umbrella\\\", \\\"Rihanna ft. JAY-Z\\\"],\\n [\\\"https://www.youtube.com/watch?v=KQetemT1sWc\\\", \\\"Here Comes The Sun\\\", \\\"The Beatles\\\"],\\n [\\\"https://www.youtube.com/watch?v=Km3wOjaRyE4\\\", \\\"We Can’t Stop\\\", \\\"Miley Cyrus\\\"],\\n [\\\"https://www.youtube.com/watch?v=FDWTP4_5eps\\\", \\\"Yellow Hearts\\\", \\\"Ant Saunders\\\"]\\n]\\n\\necstaticList = [\\n [\\\"https://www.youtube.com/watch?v=MBdVXkSdhwU\\\", \\\"DNA\\\", \\\"BTS\\\"],\\n [\\\"https://www.youtube.com/watch?v=kTlv5_Bs8aw\\\", \\\"Mic Drop (Steve Aoki Remix)\\\", \\\"BTS\\\"],\\n [\\\"https://www.youtube.com/watch?v=gdZLi9oWNZg\\\", \\\"Dynamite\\\", \\\"BTS\\\"],\\n [\\\"https://www.youtube.com/watch?v=wS7anrSXKrY\\\", \\\"Blue Flame\\\", \\\"ASTRO\\\"],\\n [\\\"https://www.youtube.com/watch?v=Zz7YZtpz2Ek\\\", \\\"Knock\\\", \\\"ASTRO\\\"],\\n [\\\"https://www.youtube.com/watch?v=3VTkBuxU4yk\\\", \\\"MORE\\\", \\\"K/DA\\\"],\\n [\\\"https://www.youtube.com/watch?v=OPf0YbXqDm0\\\", \\\"Uptown Funk\\\", \\\"Mark Ronson ft. Bruno Mars\\\"],\\n [\\\"https://www.youtube.com/watch?v=PHn5Q7hCjxw\\\", \\\"Immortals\\\", \\\"Fall Out Boys\\\"],\\n [\\\"https://www.youtube.com/watch?v=7-x3uD5z1bQ\\\", \\\"Watermelon Sugar\\\", \\\"Harry Styles\\\"],\\n [\\\"https://www.youtube.com/watch?v=ZbZSe6N_BXs\\\", \\\"Happy\\\", \\\"Pharrell Williams\\\"],\\n [\\\"https://www.youtube.com/watch?v=TzFRVk2ektI\\\", \\\"Blueberry Eyes\\\", \\\"MAX ft. SUGA of BTS\\\"],\\n [\\\"https://www.youtube.com/watch?v=KhTeiaCezwM\\\", \\\"HIP\\\", \\\"MAMAMOO\\\"],\\n [\\\"https://www.youtube.com/watch?v=YswhUHH6Ufc\\\", \\\"Bang Bang\\\", \\\"Jessie J, Ariana Grande, Nicki Minaj\\\"],\\n [\\\"https://www.youtube.com/watch?v=glKgDqZ1ABU\\\", \\\"Miss Alissa\\\", \\\"Eagles Of Death Metal\\\"],\\n [\\\"https://www.youtube.com/watch?v=pmanD_s7G3U\\\", \\\"Gurenge\\\", \\\"LiSA\\\"],\\n [\\\"https://www.youtube.com/watch?v=DwRndYRTuyw\\\", \\\"Dionysius\\\", \\\"BTS\\\"]\\n]\\n\\n@client.event\\nasync def on_ready():\\n general_channel = client.get_channel(835324404841709589)\\n mbed = discord.Embed(\\n title = 'ViBot is Now Online!',\\n description = 'See `v!help` for a list of commands. Enjoy!',\\n color = discord.Color.green()\\n )\\n await general_channel.send(embed = mbed)\\n\\ndef getScore(txt):\\n analyzer = SentimentIntensityAnalyzer()\\n return analyzer.polarity_scores(txt)[\\\"compound\\\"]\\n\\ndef getEmotion(score):\\n if score > 0.6:\\n return 'ecstatic'\\n elif score > 0.2:\\n return 'happy'\\n elif score < -0.6:\\n return 'gloomy'\\n elif score < -0.2:\\n return 'sad'\\n else:\\n return 'neutral'\\n \\ndef storeScore(name, score):\\n driverstr = config('driverstr')\\n with pyodbc.connect(driverstr) as conn:\\n with conn.cursor() as cursor:\\n cnt = cursor.execute(\\\"select count(*) as cnt from vibeScores where username = '\\\" + str(name) + \\\"'\\\").fetchone().cnt\\n if cnt > 0: # user exists, update\\n cursor.execute(\\\"update vibeScores set recent = '\\\" + str(score) + \\\"' where username = '\\\" + str(name) + \\\"'\\\")\\n cursor.execute(\\\"update vibeScores set average = (average*vibeCount + '\\\" + str(score) + \\\"')/(vibecount+1) where username = '\\\" + str(name) + \\\"';\\\")\\n cursor.execute(\\\"update vibeScores set vibeCount = vibeCount + 1 where username = '\\\" + str(name) + \\\"'\\\")\\n else:\\n cursor.execute(\\\"insert into vibeScores (username, recent, average, vibeCount) Values ('\\\" + str(name) + \\\"', '\\\" + str(score) + \\\"', '\\\" + str(score) + \\\"', '1');\\\")\\n cursor.commit()\\n\\ndef getLink(emotion):\\n if(emotion=='ecstatic'):\\n idx = random.randint(0, len(ecstaticList)-1)\\n return ecstaticList[idx]\\n elif(emotion=='happy'):\\n idx = random.randint(0, len(happyList)-1)\\n return happyList[idx]\\n elif(emotion=='neutral'):\\n idx = random.randint(0, len(neutralList)-1)\\n return neutralList[idx]\\n elif(emotion=='sad'):\\n idx = random.randint(0, len(sadList)-1)\\n return sadList[idx]\\n else:\\n idx = random.randint(0, len(gloomyList)-1)\\n return gloomyList[idx]\\n\\ndef getEmoji(emotion):\\n if(emotion=='ecstatic'):\\n return ':smiling_face_with_3_hearts :'\\n elif(emotion=='happy'):\\n return ':slight_smile:'\\n elif(emotion=='neutral'):\\n return ':neutral_face:'\\n elif(emotion=='sad'):\\n return ':slight_frown:'\\n else:\\n return ':cry:'\\n\\ndef getStoredScore(name):\\n driverstr = config('driverstr')\\n with pyodbc.connect(driverstr) as conn:\\n with conn.cursor() as cursor:\\n cnt = cursor.execute(\\\"select count(*) as cnt from vibeScores where username = '\\\" + str(name) + \\\"'\\\").fetchone().cnt\\n if cnt > 0:\\n return cursor.execute(\\\"select recent from vibeScores where username = '\\\" + str(name) + \\\"'\\\").fetchone().recent\\n else:\\n return -2\\n cursor.commit()\\n\\ndef getStoredAvg(name):\\n driverstr = config('driverstr')\\n with pyodbc.connect(driverstr) as conn:\\n with conn.cursor() as cursor:\\n cnt = cursor.execute(\\\"select count(*) as cnt from vibeScores where username = '\\\" + str(name) + \\\"'\\\").fetchone().cnt\\n if cnt > 0:\\n return cursor.execute(\\\"select average from vibeScores where username = '\\\" + str(name) + \\\"'\\\").fetchone().average\\n else:\\n return -2\\n cursor.commit()\\n\\ndef getStoredCount(name):\\n driverstr = config('driverstr')\\n with pyodbc.connect(driverstr) as conn:\\n with conn.cursor() as cursor:\\n return cursor.execute(\\\"select count(*) as cnt from vibeScores where username = '\\\" + str(name) + \\\"'\\\").fetchone().cnt\\n\\ndef getCompare(score, average):\\n if score > average:\\n return 'better'\\n elif score == average:\\n return 'the same'\\n else:\\n return 'worse'\\n\\n@client.command()\\nasync def mood(ctx, *, message):\\n name = ctx.author\\n score = getScore(message)\\n emotion = getEmotion(score)\\n link = getLink(emotion)\\n emoji = getEmoji(emotion)\\n avg = getStoredAvg(name)\\n compare = getCompare(score, avg)\\n storeScore(name, score)\\n\\n mbed = discord.Embed(\\n title = 'Calculating mood...',\\n description = name.mention + ', check out your emotions!',\\n color = discord.Color.blue()\\n )\\n mbed.add_field(name='You are currently ' + emotion + ' ' + emoji, value='From a scale of -1 to 1, your vibeScore is ' + str(round(score, 4)) + '. You are feeling ' + compare + ' than usual!', inline=False) # ADD DESCRIPTION ?? and insert youtube link or smt (figure link embed)\\n mbed.add_field(name='Listen to this Song ~ ', value=link[1] + ' by ' + link[2], inline=False)\\n await ctx.send(embed = mbed)\\n await ctx.send(link[0])\\n\\n@client.command()\\nasync def dm(ctx):\\n name = ctx.author\\n mbed1 = discord.Embed(\\n title = 'Hey ' + str(name).split('#')[0] + '!',\\n description = 'Let\\\\'s chat! Feel free to use ViBot commands to interact. For more info, type `p!help`.',\\n color = discord.Color.blue()\\n )\\n await name.send(embed=mbed1)\\n mbed2 = discord.Embed(\\n title = \\\"Direct Message Sent!\\\",\\n description = \\\"Hey \\\" + name.mention + \\\", check your DMs! :)\\\",\\n color = discord.Color.blue()\\n )\\n await ctx.send(embed=mbed2)\\n\\n\\n@client.command()\\nasync def repeat(ctx):\\n name = ctx.author\\n cnt = getStoredCount(name)\\n if cnt > 0: # user exists, update\\n score = getStoredScore(name)\\n avg = getStoredAvg(name)\\n emotion = getEmotion(score)\\n emoji = getEmoji(emotion)\\n link = getLink(emotion)\\n compare = getCompare(score, avg)\\n\\n mbed = discord.Embed(\\n title = 'Retrieving mood...',\\n description = name.mention + ', check out your emotions!',\\n color = discord.Color.blue()\\n )\\n mbed.add_field(name='You are currently ' + emotion + ' ' + emoji, value='From a scale of -1 to 1, your vibeScore is ' + str(round(score, 4)) + '. You are feeling ' + compare + ' than usual!', inline=False)\\n mbed.add_field(name='Listen to this Song ~ ', value=link[1] + ' by ' + link[2], inline=False)\\n await ctx.send(embed=mbed)\\n await ctx.send(link[0])\\n else:\\n mbed = discord.Embed(\\n title = 'You are Currently Moodless :face_with_raised_eyebrow:',\\n description = 'We don\\\\'t have your recent moods saved! Calculate your mood using `v!mood` first before asking for another song.',\\n color = discord.Color.red()\\n )\\n await ctx.send(embed=mbed)\\n\\n@client.command()\\nasync def clear(ctx):\\n name = ctx.author\\n driverstr = config('driverstr')\\n with pyodbc.connect(driverstr) as conn:\\n with conn.cursor() as cursor:\\n cnt = cursor.execute(\\\"delete from vibeScores where username = '\\\" + str(name) + \\\"'\\\")\\n cursor.commit()\\n\\n mbed = discord.Embed(\\n title = 'Clearing Data',\\n description = 'Your average mood has been reset! Once you have checked your mood ten times, your average will be displayed.',\\n color = discord.Color.blue()\\n )\\n await ctx.send(embed = mbed)\\n\\n@client.command()\\nasync def help(ctx):\\n mbed = discord.Embed(\\n title = 'Welcome to our help page! Here is a list of all ViBot commands:',\\n color = 16776960\\n )\\n mbed.add_field(name='About Page: `v!about`', value='The About Page will introduce you to the purpose of ViBot and tell you a little about the creators!', inline=False)\\n mbed.add_field(name='Clearing Data: `v!clear`', value='Use this command to clear your previous mood scores and averages! Start fresh for the new you.', inline=False)\\n mbed.add_field(name='Direct Messaging: `v!dm`', value='If you want some private conversation between just you and ViBot, ask for a DM :eyes:')\\n mbed.add_field(name='Help Page: `v!help`', value = 'The Help Page will show you a list of commands and what they can be used for... Try experimenting with them!', inline=False)\\n mbed.add_field(name='Mood Detector: `v!mood <message>`', value='Tell us about your day! ViBot will calculate your current emotions and recommend you a sick song to vibe to!', inline=False)\\n mbed.add_field(name='Get Another Song: `v!repeat`', value='If you enjoyed the last song ViBot recommended, request for another song without changing your mood!', inline=False)\\n await ctx.send(embed = mbed)\\n\\n@client.command()\\nasync def about(ctx):\\n mbed = discord.Embed(\\n title = 'About:',\\n description = \\\"This bot reads in message with the `v!mood` command followed by a message. After detecting the mood of the message, the bot then sends a corresponding song that matches the mood and produces an emotion score ranging from -1 (sad) to 1 (happy). Your previous emotion scored will be stores and the average is calculated to compare with your current emotion. For information on which commands ViBot supports, visit `v!help`. Have fun! :)\\\",\\n color = 16776960\\n )\\n await ctx.send(embed = mbed)\\n\\n@client.event\\nasync def on_command_error(ctx, error):\\n mbed = discord.Embed(\\n title = 'Error: Command does not Exist!',\\n description = 'Visit `v!about` to learn about ViBot, or see `v!help` for a list of commands.',\\n color = discord.Color.red()\\n )\\n await ctx.send(embed = mbed)\\n\\nclient.run(TOKEN)\"\n },\n {\n \"alpha_fraction\": 0.7258522510528564,\n \"alphanum_fraction\": 0.7571022510528564,\n \"avg_line_length\": 36.05263137817383,\n \"blob_id\": \"4c3d1380d56d67772a2f8e955c1c76b3ea60527e\",\n \"content_id\": \"fff9306b6a0f79c9755e5465b5ae3a0a046fed85\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 704,\n \"license_type\": \"no_license\",\n \"max_line_length\": 220,\n \"num_lines\": 19,\n \"path\": \"/README.md\",\n \"repo_name\": \"sophiiasun/ViBot-Submission\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# ViBot-Submission\\n\\nViBot is a Discord Bot who can suggest songs based off of the emotion detected from a user's message, scoring it on a rating from -1 to 1. Then, based off of your message category, it will recommend a corresponding song!\\n\\nCommands : \\n\\nPrefix : Use v! to call commands on the bot \\n\\nv!about : Learn about what the bot does!\\n\\nv!mood : detect the mood of the msg (bot replies with score of the emotion)\\n\\nv!help : reach the help page for the bot \\n\\nv!repeat : recommend a song based off of mood \\n\\nv!clear : clear the logged data of recent moods (average of all previous moods)\\n\\nLink to add bot : https://discord.com/api/oauth2/authorize?client_id=835299556010885130&permissions=0&scope=bot\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979164,"cells":{"repo_name":{"kind":"string","value":"LittleBlackSubmarine/rgrv_lv4"},"repo_url":{"kind":"string","value":"https://github.com/LittleBlackSubmarine/rgrv_lv4"},"snapshot_id":{"kind":"string","value":"567754c07f1339971721a246c0477f931fc53258"},"revision_id":{"kind":"string","value":"ce6ae7e136ec734ca40a8d3a6f81fd23a1f35ffd"},"directory_id":{"kind":"string","value":"df1b21a56b1a20603d8c10f2576b70c5b9266f31"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-11-25T14:18:49.959782","string":"2020-11-25T14:18:49.959782"},"revision_date":{"kind":"timestamp","value":"2019-12-17T22:14:38","string":"2019-12-17T22:14:38"},"committer_date":{"kind":"timestamp","value":"2019-12-17T22:14:38","string":"2019-12-17T22:14:38"},"github_id":{"kind":"number","value":228709475,"string":"228,709,475"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6620330214500427,"alphanum_fraction":0.7098175287246704,"avg_line_length":40.011905670166016,"blob_id":"2781b4b42e39498f121ad2dcc0d8afac59fe09ff","content_id":"c17ca24d02187988a9935dc2b427000d75b9eb89","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3453,"license_type":"no_license","max_line_length":218,"num_lines":84,"path":"/2DObjectDetection_SiftFeatures.py","repo_name":"LittleBlackSubmarine/rgrv_lv4","src_encoding":"UTF-8","text":"import cv2 as cv\nimport numpy as np\n\n\n\nscene1 = cv.imread(\"ImageT0.jpg\")\nscene2 = cv.imread(\"ImageT2.jpg\")\n\ncv.imshow(\"Scene\", scene2)\nroi = cv.selectROI('Object of interest selection', scene1, False)\n\nu0 = roi[0]\nv0 = roi[1]\nobject_width = roi[2]\nobject_height = roi[3]\n\nobject_image = scene1[v0:v0+object_height, u0:u0+object_width]\n\ncv.destroyAllWindows()\n\nsift = cv.xfeatures2d.SIFT_create()\n\nobject_keypoints, object_descriptors = sift.detectAndCompute(object_image, None)\nprint(object_keypoints)\nscene_keypoints, scene_descriptors = sift.detectAndCompute(scene2, None)\n\nobject_keypoints_drawed = object_image.copy()\nscene_keypoints_drawed = scene2.copy()\n\ncv.drawKeypoints(object_image, object_keypoints, object_keypoints_drawed)\ncv.drawKeypoints(scene2, scene_keypoints, scene_keypoints_drawed)\n\nobject_kp_drawed_resized = cv.copyMakeBorder(object_keypoints_drawed, scene2.shape[0]-object_keypoints_drawed.shape[0], 0, scene2.shape[1]-object_keypoints_drawed.shape[1], 0, cv.BORDER_CONSTANT, value=[255, 255, 255])\nkeypoints_image = np.hstack((object_kp_drawed_resized, scene_keypoints_drawed))\n\ncv.imshow(\"keypoints\", keypoints_image)\ncv.waitKey()\ncv.destroyAllWindows()\n\ndescriptor_matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_FLANNBASED)\nknn_matches = descriptor_matcher.knnMatch(object_descriptors, scene_descriptors, 2)\n\nmatches = []\n\nfor x, y in knn_matches:\n if x.distance < 0.75 * y.distance:\n matches.append(x)\n\n\n\n\nmatches_img = np.empty((max(object_image.shape[0], scene2.shape[0]), object_image.shape[1]+scene2.shape[1], 3), dtype=np.uint8)\ncv.drawMatches(object_image, object_keypoints, scene2, scene_keypoints, matches, matches_img, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)\ncv.imshow(\"good matches\", matches_img)\ncv.waitKey()\ncv.destroyAllWindows()\n\nobject_ = np.empty((len(matches), 2), np.float32)\nscene = np.empty((len(matches), 2), np.float32)\n\nfor t in range(len(matches)):\n object_[t, 0] = object_keypoints[matches[t].queryIdx].pt[0]\n object_[t, 1] = object_keypoints[matches[t].queryIdx].pt[1]\n scene[t, 0] = scene_keypoints[matches[t].trainIdx].pt[0]\n scene[t, 1] = scene_keypoints[matches[t].trainIdx].pt[1]\n\nhomography, r = cv.findHomography(object_, scene, cv.RANSAC)\n\nobject_corners = np.zeros((4, 1, 2))\nobject_corners[1, 0, 0] = object_image.shape[1]\nobject_corners[2, 0, 0] = object_image.shape[1]\nobject_corners[2, 0, 1] = object_image.shape[0]\nobject_corners[3, 0, 1] = object_image.shape[0]\n\nscene_corners = cv.perspectiveTransform(object_corners, homography)\n\ncv.line(matches_img, (int(scene_corners[0, 0, 0] + object_image.shape[1]), int(scene_corners[0, 0, 1])), (int(scene_corners[1, 0, 0] + object_image.shape[1]), int(scene_corners[1, 0, 1])), (0, 0, 255), 4)\ncv.line(matches_img, (int(scene_corners[1, 0, 0] + object_image.shape[1]), int(scene_corners[1, 0, 1])), (int(scene_corners[2, 0, 0] + object_image.shape[1]), int(scene_corners[2, 0, 1])), (0, 0, 255), 4)\ncv.line(matches_img, (int(scene_corners[2, 0, 0] + object_image.shape[1]), int(scene_corners[2, 0, 1])), (int(scene_corners[3, 0, 0] + object_image.shape[1]), int(scene_corners[3, 0, 1])), (0, 0, 255), 4)\ncv.line(matches_img, (int(scene_corners[3, 0, 0] + object_image.shape[1]), int(scene_corners[3, 0, 1])), (int(scene_corners[0, 0, 0] + object_image.shape[1]), int(scene_corners[0, 0, 1])), (0, 0, 255), 4)\n\ncv.imshow(\"Detetected object\", matches_img)\ncv.waitKey()\ncv.destroyAllWindows()\n\n\n\n\n\n\n\n\n"},{"alpha_fraction":0.7792207598686218,"alphanum_fraction":0.8051947951316833,"avg_line_length":18.25,"blob_id":"bf6abfee4a19e7f55417faa704426efd7e5a4513","content_id":"6f4616499e965d5699bc48bc0df7c54c67182ec9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":77,"license_type":"no_license","max_line_length":54,"num_lines":4,"path":"/README.md","repo_name":"LittleBlackSubmarine/rgrv_lv4","src_encoding":"UTF-8","text":"# rgrv_lv4\nRGiRV_lv4\n\nObject detection using SIFT features and RANSAC method\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6620330214500427,\n \"alphanum_fraction\": 0.7098175287246704,\n \"avg_line_length\": 40.011905670166016,\n \"blob_id\": \"2781b4b42e39498f121ad2dcc0d8afac59fe09ff\",\n \"content_id\": \"c17ca24d02187988a9935dc2b427000d75b9eb89\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3453,\n \"license_type\": \"no_license\",\n \"max_line_length\": 218,\n \"num_lines\": 84,\n \"path\": \"/2DObjectDetection_SiftFeatures.py\",\n \"repo_name\": \"LittleBlackSubmarine/rgrv_lv4\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import cv2 as cv\\nimport numpy as np\\n\\n\\n\\nscene1 = cv.imread(\\\"ImageT0.jpg\\\")\\nscene2 = cv.imread(\\\"ImageT2.jpg\\\")\\n\\ncv.imshow(\\\"Scene\\\", scene2)\\nroi = cv.selectROI('Object of interest selection', scene1, False)\\n\\nu0 = roi[0]\\nv0 = roi[1]\\nobject_width = roi[2]\\nobject_height = roi[3]\\n\\nobject_image = scene1[v0:v0+object_height, u0:u0+object_width]\\n\\ncv.destroyAllWindows()\\n\\nsift = cv.xfeatures2d.SIFT_create()\\n\\nobject_keypoints, object_descriptors = sift.detectAndCompute(object_image, None)\\nprint(object_keypoints)\\nscene_keypoints, scene_descriptors = sift.detectAndCompute(scene2, None)\\n\\nobject_keypoints_drawed = object_image.copy()\\nscene_keypoints_drawed = scene2.copy()\\n\\ncv.drawKeypoints(object_image, object_keypoints, object_keypoints_drawed)\\ncv.drawKeypoints(scene2, scene_keypoints, scene_keypoints_drawed)\\n\\nobject_kp_drawed_resized = cv.copyMakeBorder(object_keypoints_drawed, scene2.shape[0]-object_keypoints_drawed.shape[0], 0, scene2.shape[1]-object_keypoints_drawed.shape[1], 0, cv.BORDER_CONSTANT, value=[255, 255, 255])\\nkeypoints_image = np.hstack((object_kp_drawed_resized, scene_keypoints_drawed))\\n\\ncv.imshow(\\\"keypoints\\\", keypoints_image)\\ncv.waitKey()\\ncv.destroyAllWindows()\\n\\ndescriptor_matcher = cv.DescriptorMatcher_create(cv.DescriptorMatcher_FLANNBASED)\\nknn_matches = descriptor_matcher.knnMatch(object_descriptors, scene_descriptors, 2)\\n\\nmatches = []\\n\\nfor x, y in knn_matches:\\n if x.distance < 0.75 * y.distance:\\n matches.append(x)\\n\\n\\n\\n\\nmatches_img = np.empty((max(object_image.shape[0], scene2.shape[0]), object_image.shape[1]+scene2.shape[1], 3), dtype=np.uint8)\\ncv.drawMatches(object_image, object_keypoints, scene2, scene_keypoints, matches, matches_img, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)\\ncv.imshow(\\\"good matches\\\", matches_img)\\ncv.waitKey()\\ncv.destroyAllWindows()\\n\\nobject_ = np.empty((len(matches), 2), np.float32)\\nscene = np.empty((len(matches), 2), np.float32)\\n\\nfor t in range(len(matches)):\\n object_[t, 0] = object_keypoints[matches[t].queryIdx].pt[0]\\n object_[t, 1] = object_keypoints[matches[t].queryIdx].pt[1]\\n scene[t, 0] = scene_keypoints[matches[t].trainIdx].pt[0]\\n scene[t, 1] = scene_keypoints[matches[t].trainIdx].pt[1]\\n\\nhomography, r = cv.findHomography(object_, scene, cv.RANSAC)\\n\\nobject_corners = np.zeros((4, 1, 2))\\nobject_corners[1, 0, 0] = object_image.shape[1]\\nobject_corners[2, 0, 0] = object_image.shape[1]\\nobject_corners[2, 0, 1] = object_image.shape[0]\\nobject_corners[3, 0, 1] = object_image.shape[0]\\n\\nscene_corners = cv.perspectiveTransform(object_corners, homography)\\n\\ncv.line(matches_img, (int(scene_corners[0, 0, 0] + object_image.shape[1]), int(scene_corners[0, 0, 1])), (int(scene_corners[1, 0, 0] + object_image.shape[1]), int(scene_corners[1, 0, 1])), (0, 0, 255), 4)\\ncv.line(matches_img, (int(scene_corners[1, 0, 0] + object_image.shape[1]), int(scene_corners[1, 0, 1])), (int(scene_corners[2, 0, 0] + object_image.shape[1]), int(scene_corners[2, 0, 1])), (0, 0, 255), 4)\\ncv.line(matches_img, (int(scene_corners[2, 0, 0] + object_image.shape[1]), int(scene_corners[2, 0, 1])), (int(scene_corners[3, 0, 0] + object_image.shape[1]), int(scene_corners[3, 0, 1])), (0, 0, 255), 4)\\ncv.line(matches_img, (int(scene_corners[3, 0, 0] + object_image.shape[1]), int(scene_corners[3, 0, 1])), (int(scene_corners[0, 0, 0] + object_image.shape[1]), int(scene_corners[0, 0, 1])), (0, 0, 255), 4)\\n\\ncv.imshow(\\\"Detetected object\\\", matches_img)\\ncv.waitKey()\\ncv.destroyAllWindows()\\n\\n\\n\\n\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7792207598686218,\n \"alphanum_fraction\": 0.8051947951316833,\n \"avg_line_length\": 18.25,\n \"blob_id\": \"bf6abfee4a19e7f55417faa704426efd7e5a4513\",\n \"content_id\": \"6f4616499e965d5699bc48bc0df7c54c67182ec9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 77,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 4,\n \"path\": \"/README.md\",\n \"repo_name\": \"LittleBlackSubmarine/rgrv_lv4\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# rgrv_lv4\\nRGiRV_lv4\\n\\nObject detection using SIFT features and RANSAC method\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979165,"cells":{"repo_name":{"kind":"string","value":"Rajatjaiswa/Rajat-jaiswal"},"repo_url":{"kind":"string","value":"https://github.com/Rajatjaiswa/Rajat-jaiswal"},"snapshot_id":{"kind":"string","value":"90a4cf765a8ee48308c940ffe9d4498732292dfb"},"revision_id":{"kind":"string","value":"5ebdea11a3ddadf6c8349400043667122a8bd4d5"},"directory_id":{"kind":"string","value":"6b130e0a67851b12a06123c8df000ebba99cd0dd"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-08-13T15:37:10.858407","string":"2023-08-13T15:37:10.858407"},"revision_date":{"kind":"timestamp","value":"2021-10-11T08:43:57","string":"2021-10-11T08:43:57"},"committer_date":{"kind":"timestamp","value":"2021-10-11T08:43:57","string":"2021-10-11T08:43:57"},"github_id":{"kind":"number","value":415843346,"string":"415,843,346"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5843416452407837,"alphanum_fraction":0.6103202700614929,"avg_line_length":33.54430389404297,"blob_id":"4c90551316085d4a7b0ca3bcc9677a0cfd93b0ec","content_id":"5be443f0487d40ba39515c543b5079599cab9f93","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2810,"license_type":"no_license","max_line_length":117,"num_lines":79,"path":"/chatbot rajat.py","repo_name":"Rajatjaiswa/Rajat-jaiswal","src_encoding":"UTF-8","text":"from tkinter import*\r\nfrom tkinter import ttk\r\n\r\nroot=Tk()\r\nroot.title(\"chatbot\")\r\nroot.geometry(\"700x650+300+10\")\r\n\r\ndef clear():\r\n text.delete('1.0',END)\r\n entry.set('')\r\n\r\ndef send():\r\n send=\"\\t\\t\\t\"+\"You \"+entry.get()\r\n text.insert(END,'\\n'+send)\r\n if(entry.get()==''):\r\n msg='please enter some input'\r\n label_2.config(text=msg,fg='red')\r\n else:\r\n msg=''\r\n label_2.config(text=msg,fg='red')\r\n if(entry.get()=='hello'):\r\n text.insert(END,'\\n\\n'+'Bot:Hi')\r\n elif(entry.get()=='hi'):\r\n text.insert(END,'\\n\\n'+'Bot:Hello')\r\n elif(entry.get()=='how are you?'):\r\n text.insert(END,'\\n\\n'+'Bot:i m good would you like to have tea or cofee')\r\n elif(entry.get()=='cofee'):\r\n text.insert(END,'\\n\\n'+'Bot: ok sir pls wait... ')\r\n elif(entry.get()=='tea'):\r\n text.insert(END,'\\n\\n'+'Bot: ok sir pls wait... ')\r\n elif(entry.get()=='who created you?'):\r\n text.insert(END,'\\n\\n'+'Bot:Rajat_j created me')\r\n elif(entry.get()=='who are you?'):\r\n text.insert(END,'\\n\\n'+'Bot:i am chat bot.')\r\n elif(entry.get()=='where can i use you?'):\r\n text.insert(END,'\\n\\n'+'Bot:in any vender or website or application')\r\n elif(entry.get()=='why do you do?'):\r\n text.insert(END,'\\n\\n'+'Bot:i can give you detail information')\r\n elif(entry.get()=='thank you'):\r\n text.insert(END,'\\n\\n'+'Bot:you most welcome')\r\n elif(entry.get()=='bye'):\r\n text.insert(END,'\\n\\n'+'Bot:bye')\r\n else:\r\n text.insert(END,'\\n\\n'+'Bot:sorry, i didnot recognised you') \r\n\r\nmain_frame=Frame(root,bd=4,bg=\"powder blue\",width=430)\r\nmain_frame.pack()\r\n\r\nlabel=Label(main_frame,bd=3,width=430,text=\"chat with me\",bg='lightgreen',fg='red',font=('arial',30,'bold'))\r\nlabel.pack()\r\n\r\nscroll_y=ttk.Scrollbar(main_frame,orient=VERTICAL)\r\ntext=Text(main_frame,width=65,height=20,bd=20,relief=RAISED,font=('arial',14),yscrollcommand=scroll_y.set)\r\nscroll_y.pack(side=RIGHT,fill=Y)\r\ntext.pack()\r\n\r\nbtn_frame=Frame(root,bd=4,bg=\"white\",width=430)\r\nbtn_frame.pack()\r\nlabel_1=Label(btn_frame,text=\"Type something\",bg='white',fg='green',font=('arial',15,'bold'))\r\nlabel_1.grid(row=0,column=0,padx=5,sticky=W)\r\n\r\nentry=StringVar()\r\nentry1=Entry(btn_frame,width=80,font=('arial',15,'bold'),bd=5,textvariable=entry)\r\nentry1.grid(row=0,column=1,ipady=6)\r\n\r\nbtn=Button(btn_frame,text='send>>',font=('arial',15,'bold'),width=8,bg='green',bd=6,command=send)\r\nbtn.grid(row=1,column=1)\r\n\r\nbtn_clear=Button(btn_frame,text='clear data',font=('arial',15,'bold'),width=8,bg='red',bd=6,fg='white',command=clear)\r\nbtn_clear.grid(row=1,column=0,sticky=W)\r\n\r\nmsg=''\r\nlabel_2=Label(btn_frame,text=msg,bg='white',fg='green',font=('arial',15,'bold'))\r\nlabel_2.grid(row=1,column=1,padx=5,sticky=W)\r\n\r\n\r\n\r\n\r\nroot.mainloop()\r\n\r\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5843416452407837,\n \"alphanum_fraction\": 0.6103202700614929,\n \"avg_line_length\": 33.54430389404297,\n \"blob_id\": \"4c90551316085d4a7b0ca3bcc9677a0cfd93b0ec\",\n \"content_id\": \"5be443f0487d40ba39515c543b5079599cab9f93\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2810,\n \"license_type\": \"no_license\",\n \"max_line_length\": 117,\n \"num_lines\": 79,\n \"path\": \"/chatbot rajat.py\",\n \"repo_name\": \"Rajatjaiswa/Rajat-jaiswal\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from tkinter import*\\r\\nfrom tkinter import ttk\\r\\n\\r\\nroot=Tk()\\r\\nroot.title(\\\"chatbot\\\")\\r\\nroot.geometry(\\\"700x650+300+10\\\")\\r\\n\\r\\ndef clear():\\r\\n text.delete('1.0',END)\\r\\n entry.set('')\\r\\n\\r\\ndef send():\\r\\n send=\\\"\\\\t\\\\t\\\\t\\\"+\\\"You \\\"+entry.get()\\r\\n text.insert(END,'\\\\n'+send)\\r\\n if(entry.get()==''):\\r\\n msg='please enter some input'\\r\\n label_2.config(text=msg,fg='red')\\r\\n else:\\r\\n msg=''\\r\\n label_2.config(text=msg,fg='red')\\r\\n if(entry.get()=='hello'):\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot:Hi')\\r\\n elif(entry.get()=='hi'):\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot:Hello')\\r\\n elif(entry.get()=='how are you?'):\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot:i m good would you like to have tea or cofee')\\r\\n elif(entry.get()=='cofee'):\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot: ok sir pls wait... ')\\r\\n elif(entry.get()=='tea'):\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot: ok sir pls wait... ')\\r\\n elif(entry.get()=='who created you?'):\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot:Rajat_j created me')\\r\\n elif(entry.get()=='who are you?'):\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot:i am chat bot.')\\r\\n elif(entry.get()=='where can i use you?'):\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot:in any vender or website or application')\\r\\n elif(entry.get()=='why do you do?'):\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot:i can give you detail information')\\r\\n elif(entry.get()=='thank you'):\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot:you most welcome')\\r\\n elif(entry.get()=='bye'):\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot:bye')\\r\\n else:\\r\\n text.insert(END,'\\\\n\\\\n'+'Bot:sorry, i didnot recognised you') \\r\\n\\r\\nmain_frame=Frame(root,bd=4,bg=\\\"powder blue\\\",width=430)\\r\\nmain_frame.pack()\\r\\n\\r\\nlabel=Label(main_frame,bd=3,width=430,text=\\\"chat with me\\\",bg='lightgreen',fg='red',font=('arial',30,'bold'))\\r\\nlabel.pack()\\r\\n\\r\\nscroll_y=ttk.Scrollbar(main_frame,orient=VERTICAL)\\r\\ntext=Text(main_frame,width=65,height=20,bd=20,relief=RAISED,font=('arial',14),yscrollcommand=scroll_y.set)\\r\\nscroll_y.pack(side=RIGHT,fill=Y)\\r\\ntext.pack()\\r\\n\\r\\nbtn_frame=Frame(root,bd=4,bg=\\\"white\\\",width=430)\\r\\nbtn_frame.pack()\\r\\nlabel_1=Label(btn_frame,text=\\\"Type something\\\",bg='white',fg='green',font=('arial',15,'bold'))\\r\\nlabel_1.grid(row=0,column=0,padx=5,sticky=W)\\r\\n\\r\\nentry=StringVar()\\r\\nentry1=Entry(btn_frame,width=80,font=('arial',15,'bold'),bd=5,textvariable=entry)\\r\\nentry1.grid(row=0,column=1,ipady=6)\\r\\n\\r\\nbtn=Button(btn_frame,text='send>>',font=('arial',15,'bold'),width=8,bg='green',bd=6,command=send)\\r\\nbtn.grid(row=1,column=1)\\r\\n\\r\\nbtn_clear=Button(btn_frame,text='clear data',font=('arial',15,'bold'),width=8,bg='red',bd=6,fg='white',command=clear)\\r\\nbtn_clear.grid(row=1,column=0,sticky=W)\\r\\n\\r\\nmsg=''\\r\\nlabel_2=Label(btn_frame,text=msg,bg='white',fg='green',font=('arial',15,'bold'))\\r\\nlabel_2.grid(row=1,column=1,padx=5,sticky=W)\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nroot.mainloop()\\r\\n\\r\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979166,"cells":{"repo_name":{"kind":"string","value":"johnlinp/falcon-raml"},"repo_url":{"kind":"string","value":"https://github.com/johnlinp/falcon-raml"},"snapshot_id":{"kind":"string","value":"796ffc022f4103c4e2509c971fa9911382ce25cf"},"revision_id":{"kind":"string","value":"ef15f83dfa0e295de58d0d83bd4ff35c36f769db"},"directory_id":{"kind":"string","value":"7f4ff1025bfa3c8253e61f500d5b17e4ed0c500e"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-12-10T00:09:41.317869","string":"2022-12-10T00:09:41.317869"},"revision_date":{"kind":"timestamp","value":"2022-12-08T22:24:29","string":"2022-12-08T22:24:29"},"committer_date":{"kind":"timestamp","value":"2022-12-08T22:24:29","string":"2022-12-08T22:24:29"},"github_id":{"kind":"number","value":77845528,"string":"77,845,528"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"string","value":"BSD-3-Clause"},"gha_created_at":{"kind":"timestamp","value":"2017-01-02T16:11:54","string":"2017-01-02T16:11:54"},"gha_updated_at":{"kind":"timestamp","value":"2022-10-28T19:22:20","string":"2022-10-28T19:22:20"},"gha_pushed_at":{"kind":"timestamp","value":"2022-11-04T02:49:55","string":"2022-11-04T02:49:55"},"gha_language":{"kind":"string","value":"Python"},"files":{"kind":"list like","value":[{"alpha_fraction":0.709864616394043,"alphanum_fraction":0.709864616394043,"avg_line_length":21.478260040283203,"blob_id":"6e2d97f33a07961d01cfc69ad475df9cc427cbe8","content_id":"a53ba4b649a879fb30081c9ce07a07d350d68140","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":517,"license_type":"permissive","max_line_length":65,"num_lines":23,"path":"/test/tests/formbody/app.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport falconraml\n\n\nclass Dummy(object):\n def on_post(self, request, response):\n pass\n\n\napi = falcon.API(middleware=[\n falconraml.JsonTranslator(),\n falconraml.ParameterChecker('test/tests/formbody/spec.raml'),\n])\n\n\napi.req_options.auto_parse_form_urlencoded = True\n\n\napi.add_route('/required/string', Dummy())\napi.add_route('/required/number', Dummy())\napi.add_route('/required/integer', Dummy())\napi.add_route('/required/boolean', Dummy())\napi.add_route('/nonrequired/mixed', Dummy())\n"},{"alpha_fraction":0.7009063363075256,"alphanum_fraction":0.7009063363075256,"avg_line_length":18.47058868408203,"blob_id":"00a5c57f8874eaba36e60b2f152d883495aae349","content_id":"6e0c3ce3a835b72f9e893528d5b03921cb658097","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":331,"license_type":"permissive","max_line_length":65,"num_lines":17,"path":"/test/tests/jsonbody/app.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport falconraml\n\n\nclass Dummy(object):\n def on_post(self, request, response):\n pass\n\n\napi = falcon.API(middleware=[\n falconraml.JsonTranslator(),\n falconraml.ParameterChecker('test/tests/jsonbody/spec.raml'),\n])\n\n\napi.add_route('/simple/string', Dummy())\napi.add_route('/malformed/schema', Dummy())\n"},{"alpha_fraction":0.5724621415138245,"alphanum_fraction":0.5823686718940735,"avg_line_length":35.080322265625,"blob_id":"487d32a4c40aa188b564749b58b5a1b1ae639c35","content_id":"d50ac90f4d0cb341170d02b2bea5b06c1158ac16","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8984,"license_type":"permissive","max_line_length":76,"num_lines":249,"path":"/test/tests/queryparams/test.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport falcon.testing\nfrom tests.queryparams import app\n\n\nclass TestQueryParamRequiredString(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/string'\n self.query_param_name = 'required_string'\n self.query_param_values = [\n 'hello',\n r'!@#$%^&*(){}',\n ]\n\n def test_success(self):\n for value in self.query_param_values:\n response = self.simulate_get(self.path, params={\n self.query_param_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_query_params(self):\n response = self.simulate_get(self.path)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.query_param_name)\n )\n\n def test_wrong_param(self):\n response = self.simulate_get(self.path, params={\n 'wrong_param': self.query_param_values[0],\n })\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.query_param_name)\n )\n\n def test_with_empty_string(self):\n response = self.simulate_get(self.path, params={\n self.query_param_name: '',\n })\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.query_param_name)\n )\n\n def test_with_number(self):\n response = self.simulate_get(self.path, params={\n self.query_param_name: '42',\n })\n # number strings are strings too\n self.assertEqual(response.status, falcon.HTTP_200)\n\n\nclass TestQueryParamsRequiredNumber(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/number'\n self.query_param_name = 'required_number'\n self.query_param_values = [\n '55.66',\n '42',\n '0',\n '-55.66',\n ]\n\n def test_success(self):\n for value in self.query_param_values:\n response = self.simulate_get(self.path, params={\n self.query_param_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_query_params(self):\n response = self.simulate_get(self.path)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.query_param_name)\n )\n\n def test_not_numbers(self):\n wrong_values = [\n 'hello',\n 'a123',\n '123a',\n ]\n\n for value in wrong_values:\n response = self.simulate_get(self.path, params={\n self.query_param_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is invalid. '\n 'Should be a number.'.format(self.query_param_name)\n )\n\n\nclass TestQueryParamsRequiredInteger(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/integer'\n self.query_param_name = 'required_integer'\n self.query_param_values = [\n '42',\n '0',\n '-42',\n ]\n\n def test_success(self):\n for value in self.query_param_values:\n response = self.simulate_get(self.path, params={\n self.query_param_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_query_params(self):\n response = self.simulate_get(self.path)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.query_param_name)\n )\n\n def test_not_integers(self):\n wrong_values = [\n 'hello',\n '55.66',\n ]\n\n for value in wrong_values:\n response = self.simulate_get(self.path, params={\n self.query_param_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is invalid. '\n 'Should be an integer.'.format(self.query_param_name)\n )\n\n\nclass TestQueryParamsRequiredBoolean(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/boolean'\n self.query_param_name = 'required_boolean'\n self.query_param_values = [\n 'true',\n 'false',\n ]\n\n def test_success(self):\n for value in self.query_param_values:\n response = self.simulate_get(self.path, params={\n self.query_param_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_query_params(self):\n response = self.simulate_get(self.path)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.query_param_name)\n )\n\n def test_not_booleans(self):\n wrong_values = [\n 'hello',\n 'TRUE',\n 'FALSE',\n 'yes',\n 'no',\n ]\n\n for value in wrong_values:\n response = self.simulate_get(self.path, params={\n self.query_param_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is invalid. '\n 'Should be \"true\" or \"false\".'.format(self.query_param_name)\n )\n\n\nclass TestQueryParamsNonRequiredMixed(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/nonrequired/mixed'\n self.string_query_param_name = 'non_required_string'\n self.number_query_param_name = 'non_required_number'\n self.integer_query_param_name = 'non_required_integer'\n self.boolean_query_param_name = 'non_required_boolean'\n self.string_query_param_value = 'hello'\n self.number_query_param_value = '55.66'\n self.integer_query_param_value = '42'\n self.boolean_query_param_value = 'true'\n\n def test_no_params(self):\n response = self.simulate_get(self.path, params={})\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_all_params(self):\n response = self.simulate_get(self.path, params={\n self.string_query_param_name: self.string_query_param_value,\n self.number_query_param_name: self.number_query_param_value,\n self.integer_query_param_name: self.integer_query_param_value,\n self.boolean_query_param_name: self.boolean_query_param_value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_only_string(self):\n response = self.simulate_get(self.path, params={\n self.string_query_param_name: self.string_query_param_value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_wrong_number(self):\n response = self.simulate_get(self.path, params={\n self.string_query_param_name: self.string_query_param_value,\n self.number_query_param_name: 'hey',\n self.integer_query_param_name: self.integer_query_param_value,\n self.boolean_query_param_name: self.boolean_query_param_value,\n })\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is invalid. '\n 'Should be a number.'.format(self.number_query_param_name)\n )\n"},{"alpha_fraction":0.7049808502197266,"alphanum_fraction":0.7049808502197266,"avg_line_length":16.399999618530273,"blob_id":"b5f25baeb4ef605c565b5b768e869a36b4f7b77f","content_id":"09e1e8dab8cfbf9822f39d3b9b8b271c28028bf6","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":261,"license_type":"permissive","max_line_length":73,"num_lines":15,"path":"/test/tests/nojsontranslator/app.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport falconraml\n\n\nclass Dummy(object):\n def on_post(self, request, response):\n pass\n\n\napi = falcon.API(middleware=[\n falconraml.ParameterChecker('test/tests/nojsontranslator/spec.raml'),\n])\n\n\napi.add_route('/basic/string', Dummy())\n"},{"alpha_fraction":0.6959707140922546,"alphanum_fraction":0.6959707140922546,"avg_line_length":17.200000762939453,"blob_id":"20bcc355d3548b6a5feb937c9ae7bb5516c73dbf","content_id":"54216fa114c89151ec845466ba0665a764ce433e","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":273,"license_type":"permissive","max_line_length":70,"num_lines":15,"path":"/test/tests/templatedpath/app.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport falconraml\n\n\nclass Dummy(object):\n def on_get(self, request, response, some_id):\n pass\n\n\napi = falcon.API(middleware=[\n falconraml.ParameterChecker('test/tests/templatedpath/spec.raml'),\n])\n\n\napi.add_route('/templated/{some_id}', Dummy())\n"},{"alpha_fraction":0.5663115978240967,"alphanum_fraction":0.5767294764518738,"avg_line_length":34.01229476928711,"blob_id":"89ade40747b82ac60786bfb162123cd501f8e0e4","content_id":"bafaccf5e363d4e8296b24540530dee13cf790d4","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8543,"license_type":"permissive","max_line_length":76,"num_lines":244,"path":"/test/tests/headers/test.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport falcon.testing\nfrom tests.headers import app\n\n\nclass TestHeadersRequiredString(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/string'\n self.header_name = 'X-Required-String'\n self.header_values = [\n 'hello',\n r'!@#$%^&*(){}',\n ]\n\n def test_success(self):\n for value in self.header_values:\n response = self.simulate_get(self.path, headers={\n self.header_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_header(self):\n response = self.simulate_get(self.path)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing header value')\n self.assertEqual(\n response.json['description'],\n 'The {} header is required.'.format(self.header_name)\n )\n\n def test_wrong_header(self):\n response = self.simulate_get(self.path, headers={\n 'X-Wrong-Header': self.header_values[0],\n })\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing header value')\n self.assertEqual(\n response.json['description'],\n 'The {} header is required.'.format(self.header_name)\n )\n\n def test_with_empty_string(self):\n response = self.simulate_get(self.path, headers={\n self.header_name: '',\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_with_number(self):\n response = self.simulate_get(self.path, headers={\n self.header_name: '42',\n })\n # number strings are strings too\n self.assertEqual(response.status, falcon.HTTP_200)\n\n\nclass TestHeadersRequiredNumber(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/number'\n self.header_name = 'X-Required-Number'\n self.header_values = [\n '55.66',\n '42',\n '0',\n '-55.66',\n ]\n\n def test_success(self):\n for value in self.header_values:\n response = self.simulate_get(self.path, headers={\n self.header_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_header(self):\n response = self.simulate_get(self.path)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing header value')\n self.assertEqual(\n response.json['description'],\n 'The {} header is required.'.format(self.header_name)\n )\n\n def test_not_numbers(self):\n wrong_values = [\n 'hello',\n 'a123',\n '123a',\n ]\n\n for value in wrong_values:\n response = self.simulate_get(self.path, headers={\n self.header_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid header value')\n self.assertEqual(\n response.json['description'],\n 'The value provided for the {} header is invalid. '\n 'Should be a number.'.format(self.header_name)\n )\n\n\nclass TestHeadersRequiredInteger(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/integer'\n self.header_name = 'X-Required-Integer'\n self.header_values = [\n '42',\n '0',\n '-42',\n ]\n\n def test_success(self):\n for value in self.header_values:\n response = self.simulate_get(self.path, headers={\n self.header_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_header(self):\n response = self.simulate_get(self.path)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing header value')\n self.assertEqual(\n response.json['description'],\n 'The {} header is required.'.format(self.header_name)\n )\n\n def test_not_integers(self):\n wrong_values = [\n 'hello',\n '55.66',\n ]\n\n for value in wrong_values:\n response = self.simulate_get(self.path, headers={\n self.header_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid header value')\n self.assertEqual(\n response.json['description'],\n 'The value provided for the {} header is invalid. '\n 'Should be an integer.'.format(self.header_name)\n )\n\n\nclass TestHeadersRequiredBoolean(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/boolean'\n self.header_name = 'X-Required-Boolean'\n self.header_values = [\n 'true',\n 'false',\n ]\n\n def test_success(self):\n for value in self.header_values:\n response = self.simulate_get(self.path, headers={\n self.header_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_header(self):\n response = self.simulate_get(self.path)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing header value')\n self.assertEqual(\n response.json['description'],\n 'The {} header is required.'.format(self.header_name)\n )\n\n def test_not_booleans(self):\n wrong_values = [\n 'hello',\n 'TRUE',\n 'FALSE',\n 'yes',\n 'no',\n ]\n\n for value in wrong_values:\n response = self.simulate_get(self.path, headers={\n self.header_name: value,\n })\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid header value')\n self.assertEqual(\n response.json['description'],\n 'The value provided for the {} header is invalid. '\n 'Should be \"true\" or \"false\".'.format(self.header_name)\n )\n\n\nclass TestHeadersNonRequiredMixed(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/nonrequired/mixed'\n self.string_header_name = 'X-Non-Required-String'\n self.number_header_name = 'X-Non-Required-Number'\n self.integer_header_name = 'X-Non-Required-Integer'\n self.boolean_header_name = 'X-Non-Required-Boolean'\n self.string_header_value = 'hello'\n self.number_header_value = '55.66'\n self.integer_header_value = '42'\n self.boolean_header_value = 'true'\n\n def test_no_headers(self):\n response = self.simulate_get(self.path, headers={})\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_all_headers(self):\n response = self.simulate_get(self.path, headers={\n self.string_header_name: self.string_header_value,\n self.number_header_name: self.number_header_value,\n self.integer_header_name: self.integer_header_value,\n self.boolean_header_name: self.boolean_header_value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_only_string(self):\n response = self.simulate_get(self.path, headers={\n self.string_header_name: self.string_header_value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_wrong_number(self):\n response = self.simulate_get(self.path, headers={\n self.string_header_name: self.string_header_value,\n self.number_header_name: 'hey',\n self.integer_header_name: self.integer_header_value,\n self.boolean_header_name: self.boolean_header_value,\n })\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid header value')\n self.assertEqual(\n response.json['description'],\n 'The value provided for the {} header is invalid. '\n 'Should be a number.'.format(self.number_header_name)\n )\n"},{"alpha_fraction":0.8709677457809448,"alphanum_fraction":0.8709677457809448,"avg_line_length":61,"blob_id":"0701be375585cc669340cfeb4b763431e36ed241","content_id":"8c9fd6ed3295e4d81642cebd1bbb06fdb4894818","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":124,"license_type":"permissive","max_line_length":63,"num_lines":2,"path":"/falconraml/__init__.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"from falconraml.parameterchecker import ParameterChecker # noqa\nfrom falconraml.jsontranslator import JsonTranslator # noqa\n"},{"alpha_fraction":0.5351053476333618,"alphanum_fraction":0.5431494116783142,"avg_line_length":32.837276458740234,"blob_id":"bdff520221f62fc4c704b0550ba89d47c91bcf74","content_id":"850120008e3afbefbef913ac2ca93d82462a2f1c","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":11437,"license_type":"permissive","max_line_length":78,"num_lines":338,"path":"/test/tests/formbody/test.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport falcon.testing\nimport urllib.parse\nfrom tests.formbody import app\n\n\nclass TestFormBodyRequiredString(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/string'\n self.form_param_name = 'required_string'\n self.form_param_values = [\n 'hello',\n r'!@#$%^&*(){}',\n ]\n self.headers = {\n 'Content-Type': 'application/x-www-form-urlencoded',\n }\n\n def test_success(self):\n for value in self.form_param_values:\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.form_param_name: value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_content_type(self):\n response = self.simulate_post(self.path)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Bad request')\n self.assertEqual(response.json['description'], 'No content type')\n\n def test_missing_form_body(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.form_param_name)\n )\n\n def test_wrong_param(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n 'wrong_param': self.form_param_values[0],\n })\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.form_param_name)\n )\n\n def test_with_empty_string(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.form_param_name: '',\n })\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.form_param_name)\n )\n\n def test_with_number(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.form_param_name: '42',\n })\n )\n # number strings are strings too\n self.assertEqual(response.status, falcon.HTTP_200)\n\n\nclass TestFormBodyRequiredNumber(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/number'\n self.form_param_name = 'required_number'\n self.form_param_values = [\n '55.66',\n '42',\n '0',\n '-55.66',\n ]\n self.headers = {\n 'Content-Type': 'application/x-www-form-urlencoded',\n }\n\n def test_success(self):\n for value in self.form_param_values:\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.form_param_name: value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_form_body(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.form_param_name)\n )\n\n def test_not_numbers(self):\n wrong_values = [\n 'hello',\n 'a123',\n '123a',\n ]\n\n for value in wrong_values:\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.form_param_name: value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is invalid. '\n 'Should be a number.'.format(self.form_param_name)\n )\n\n\nclass TestFormBodyRequiredInteger(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/integer'\n self.form_param_name = 'required_integer'\n self.form_param_values = [\n '42',\n '0',\n '-42',\n ]\n self.headers = {\n 'Content-Type': 'application/x-www-form-urlencoded',\n }\n\n def test_success(self):\n for value in self.form_param_values:\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.form_param_name: value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_form_body(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.form_param_name)\n )\n\n def test_not_integers(self):\n wrong_values = [\n 'hello',\n '55.66',\n ]\n\n for value in wrong_values:\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.form_param_name: value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is invalid. '\n 'Should be an integer.'.format(self.form_param_name)\n )\n\n\nclass TestFormBodyRequiredBoolean(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/required/boolean'\n self.form_param_name = 'required_boolean'\n self.form_param_values = [\n 'true',\n 'false',\n ]\n self.headers = {\n 'Content-Type': 'application/x-www-form-urlencoded',\n }\n\n def test_success(self):\n for value in self.form_param_values:\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.form_param_name: value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_form_body(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.form_param_name)\n )\n\n def test_not_booleans(self):\n wrong_values = [\n 'hello',\n 'TRUE',\n 'FALSE',\n 'yes',\n 'no',\n ]\n\n for value in wrong_values:\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.form_param_name: value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is invalid. '\n 'Should be \"true\" or \"false\".'.format(self.form_param_name)\n )\n\n\nclass TestFormBodyNonRequiredMixed(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/nonrequired/mixed'\n self.string_query_param_name = 'non_required_string'\n self.number_query_param_name = 'non_required_number'\n self.integer_query_param_name = 'non_required_integer'\n self.boolean_query_param_name = 'non_required_boolean'\n self.string_query_param_value = 'hello'\n self.number_query_param_value = '55.66'\n self.integer_query_param_value = '42'\n self.boolean_query_param_value = 'true'\n self.headers = {\n 'Content-Type': 'application/x-www-form-urlencoded',\n }\n\n def test_no_form_body(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n )\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_all_params(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.string_query_param_name: self.string_query_param_value,\n self.number_query_param_name: self.number_query_param_value,\n self.integer_query_param_name: self.integer_query_param_value,\n self.boolean_query_param_name: self.boolean_query_param_value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_only_string(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.string_query_param_name: self.string_query_param_value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_wrong_number(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=urllib.parse.urlencode({\n self.string_query_param_name: self.string_query_param_value,\n self.number_query_param_name: 'hey',\n self.integer_query_param_name: self.integer_query_param_value,\n self.boolean_query_param_name: self.boolean_query_param_value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Invalid parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is invalid. '\n 'Should be a number.'.format(self.number_query_param_name)\n )\n"},{"alpha_fraction":0.688524603843689,"alphanum_fraction":0.6967213153839111,"avg_line_length":11.199999809265137,"blob_id":"2a3a73d0c8b17cd1cb5288dc9537fc07e2574c4e","content_id":"1f1ddab523ebed33e7edbefef75c10dd1b0a3519","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"INI","length_bytes":122,"license_type":"permissive","max_line_length":37,"num_lines":10,"path":"/tox.ini","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"[tox]\nenvlist = py3\n\n[testenv]\ndeps =\n pytest\n coverage\ncommands =\n coverage run -m pytest test/main.py\n coverage xml\n"},{"alpha_fraction":0.699999988079071,"alphanum_fraction":0.699999988079071,"avg_line_length":21.63157844543457,"blob_id":"b5a0d48cea855d2a063b828226423e522e41885c","content_id":"5f60f770b0a8e524880fa532747699bfe9e350db","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":430,"license_type":"permissive","max_line_length":64,"num_lines":19,"path":"/test/tests/headers/app.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport falconraml\n\n\nclass Dummy(object):\n def on_get(self, request, response):\n pass\n\n\napi = falcon.API(middleware=[\n falconraml.ParameterChecker('test/tests/headers/spec.raml'),\n])\n\n\napi.add_route('/required/string', Dummy())\napi.add_route('/required/number', Dummy())\napi.add_route('/required/integer', Dummy())\napi.add_route('/required/boolean', Dummy())\napi.add_route('/nonrequired/mixed', Dummy())\n"},{"alpha_fraction":0.5544554591178894,"alphanum_fraction":0.5614819526672363,"avg_line_length":30.9489803314209,"blob_id":"43cdacf789596f350031231695868017e7255e9d","content_id":"e8d15622a82a4a614f14e92bbbafd531cc1fc9e9","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3131,"license_type":"permissive","max_line_length":74,"num_lines":98,"path":"/test/tests/jsonbody/test.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport falcon.testing\nimport json\nfrom tests.jsonbody import app\n\n\nclass TestJsonBodySimpleString(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/simple/string'\n self.field_name = 'some_string'\n self.field_values = [\n 'hello',\n r'!@#$%^&*(){}',\n ]\n self.headers = {\n 'Content-Type': 'application/json',\n }\n\n def test_success(self):\n for value in self.field_values:\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=json.dumps({\n self.field_name: value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_not_a_string(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=json.dumps({\n self.field_name: 42,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Bad request body')\n self.assertEqual(\n response.json['description'],\n \"42 is not of type 'string'\"\n )\n\n def test_missing_content_type(self):\n response = self.simulate_post(self.path)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Bad request')\n self.assertEqual(response.json['description'], 'No content type')\n\n def test_missing_body(self):\n response = self.simulate_post(self.path, headers=self.headers)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Bad request body')\n self.assertEqual(\n response.json['description'],\n \"None is not of type 'object'\"\n )\n\n def test_empty_object(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body='{}'\n )\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Bad request body')\n self.assertEqual(\n response.json['description'],\n \"'some_string' is a required property\".format(self.field_name)\n )\n\n\nclass TestJsonBodyMalformedSchema(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/malformed/schema'\n self.field_name = 'some_string'\n self.field_value = 'hello'\n self.headers = {\n 'Content-Type': 'application/json',\n }\n\n def test_error(self):\n response = self.simulate_post(\n self.path,\n headers=self.headers,\n body=json.dumps({\n self.field_name: self.field_value,\n })\n )\n self.assertEqual(response.status, falcon.HTTP_500)\n self.assertEqual(response.json['title'], 'Internal server error')\n self.assertEqual(\n response.json['description'],\n 'Failed to parse jsonschema.'\n )\n"},{"alpha_fraction":0.6255044341087341,"alphanum_fraction":0.6303470730781555,"avg_line_length":30.769229888916016,"blob_id":"2b17a8d0517a6be054bd74f59da68a09493828f2","content_id":"e6e936dbdbcb755402e94a6658f8abf956b8502d","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1239,"license_type":"permissive","max_line_length":77,"num_lines":39,"path":"/falconraml/jsontranslator.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import json\nimport falcon\n\n\nclass JsonTranslator(object):\n \"\"\"Translate from plain text to python objects.\"\"\"\n\n def process_request(self, request, response):\n \"\"\"Translate plain text to python objects in the request.\n\n Translated JSON is stored in request.context['json'].\n\n The translation will only be done if all the following conditions are\n satisfied:\n\n 1. The content length is more than 0.\n 2. The content type is \"application/json\".\n 3. The request body can be successful parsed as a json object.\n\n Note that after this middleware, request.stream will left to EOF.\n \"\"\"\n request.context['json'] = None\n\n if request.content_length in (None, 0):\n return\n if request.content_type != 'application/json':\n return\n\n json_bytes = request.stream.read()\n\n try:\n json_str = json_bytes.decode('utf8')\n except UnicodeDecodeError:\n raise falcon.HTTPBadRequest('Bad string', 'Unicode decode error')\n\n try:\n request.context['json'] = json.loads(json_str)\n except json.decoder.JSONDecodeError:\n raise falcon.HTTPBadRequest('Bad json', 'JSON decode error')\n"},{"alpha_fraction":0.5711060762405396,"alphanum_fraction":0.6004514694213867,"avg_line_length":23.61111068725586,"blob_id":"0e2458103310ccde9f7cc2ce5cbd51e280e6caf8","content_id":"fb198c3d7247f3b69e70d9985ce23272a9d3b221","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":443,"license_type":"permissive","max_line_length":69,"num_lines":18,"path":"/setup.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"from setuptools import setup\n\nsetup(name='falcon-raml',\n version='0.1.1',\n description='Parameter checker middleware using RAML for Falcon',\n author='johnlinp',\n author_email='johnlinp@gmail.com',\n url='https://github.com/johnlinp/falcon-raml',\n license='New BSD License',\n install_requires=[\n 'falcon==1.1.0',\n 'ramlfications==0.1.9',\n 'jsonschema==4.16.0',\n ],\n packages=[\n 'falconraml',\n ],\n)\n"},{"alpha_fraction":0.7292307615280151,"alphanum_fraction":0.7292307615280151,"avg_line_length":31.5,"blob_id":"e138d0c1ce71692460bc03b6fe36f93af56a8b85","content_id":"41daf967f32b8821088280c33d7aaa5eea88b220","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":325,"license_type":"permissive","max_line_length":48,"num_lines":10,"path":"/test/main.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import unittest\nfrom tests.headers.test import * # noqa\nfrom tests.queryparams.test import * # noqa\nfrom tests.jsonbody.test import * # noqa\nfrom tests.nojsontranslator.test import * # noqa\nfrom tests.formbody.test import * # noqa\nfrom tests.templatedpath.test import * # noqa\n\nif __name__ == '__main__':\n unittest.main()\n"},{"alpha_fraction":0.626086950302124,"alphanum_fraction":0.6298136711120605,"avg_line_length":31.200000762939453,"blob_id":"9bcfa095782fe1f9c9d271f09dccd5c18d63e58a","content_id":"44f8c16406200b55a1037580001b4ebb1e2cf034","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":805,"license_type":"permissive","max_line_length":73,"num_lines":25,"path":"/test/tests/nojsontranslator/test.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport falcon.testing\nimport json\nfrom tests.nojsontranslator import app\n\n\nclass TestNoJsonTranslator(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/basic/string'\n self.field_name = 'some_string'\n self.field_value = 'hello'\n\n def test_error(self):\n response = self.simulate_post(self.path, headers={\n 'Content-Type': 'application/json',\n }, body=json.dumps({\n self.field_name: self.field_value,\n }))\n self.assertEqual(response.status, falcon.HTTP_500)\n self.assertEqual(response.json['title'], 'Internal server error')\n self.assertEqual(\n response.json['description'],\n 'Please use falconraml.JsonTranslator to parse json body.'\n )\n"},{"alpha_fraction":0.635545551776886,"alphanum_fraction":0.6467941403388977,"avg_line_length":33.19230651855469,"blob_id":"96441037725166c22ea5fcc88382da4549bc23f5","content_id":"9aff5fd5754103efd10358e71b7550bb5ce5f9b4","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":889,"license_type":"permissive","max_line_length":75,"num_lines":26,"path":"/test/tests/templatedpath/test.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport falcon.testing\nfrom tests.templatedpath import app\n\n\nclass TestTemplatedPath(falcon.testing.TestCase):\n def setUp(self):\n self.app = app.api\n self.path = '/templated/9527'\n self.query_param_name = 'required_string'\n self.query_param_value = 'hello'\n\n def test_success(self):\n response = self.simulate_get(self.path, params={\n self.query_param_name: self.query_param_value,\n })\n self.assertEqual(response.status, falcon.HTTP_200)\n\n def test_missing_query_params(self):\n response = self.simulate_get(self.path)\n self.assertEqual(response.status, falcon.HTTP_400)\n self.assertEqual(response.json['title'], 'Missing parameter')\n self.assertEqual(\n response.json['description'],\n 'The \"{}\" parameter is required.'.format(self.query_param_name)\n )\n"},{"alpha_fraction":0.5639344453811646,"alphanum_fraction":0.5665300488471985,"avg_line_length":33.36619567871094,"blob_id":"2dee8d66e5688b2f25981831df3fe5e956ed74c0","content_id":"054ee92a2be5e8c8c06d8fe8d1f54157e1afb6e2","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7320,"license_type":"permissive","max_line_length":97,"num_lines":213,"path":"/falconraml/parameterchecker.py","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"import falcon\nimport ramlfications\nimport jsonschema\n\n\nclass ParameterChecker(object):\n \"\"\"Check parameters with RAML.\"\"\"\n\n def __init__(self, raml_file_path):\n \"\"\"Load the RAML file.\"\"\"\n self._raml = ramlfications.parse(raml_file_path)\n\n def process_resource(self, request, response, falcon_resource, uri_params):\n \"\"\"Check parameters in the request.\n\n This function checks 3 parts:\n\n 1. headers\n 2. query parameters\n 3. body\n a. json body\n b. form body parameters\n\n Note that it overrides process_resource instead of process_request\n because request.uri_template maybe None in process_request.\n Please see:\n http://falcon.readthedocs.io/en/stable/api/request_and_response.html#Request.uri_template\n \"\"\"\n raml_resource = \\\n self._get_raml_resource(request.method, request.uri_template)\n\n if raml_resource is None:\n return\n\n if raml_resource.headers:\n for header in raml_resource.headers:\n self._check_header(request, header)\n\n if raml_resource.query_params:\n for query_param in raml_resource.query_params:\n self._check_param(request, query_param)\n\n if raml_resource.body:\n if request.content_type is None:\n raise falcon.HTTPBadRequest('Bad request', 'No content type')\n matched = False\n for body in raml_resource.body:\n if body.mime_type != request.content_type:\n continue\n matched = True\n if body.mime_type == 'application/json':\n self._check_json_body(request, body.schema)\n elif body.mime_type == 'application/x-www-form-urlencoded':\n for form_param_tuple in body.form_params.items():\n form_param = self._make_form_param(form_param_tuple)\n self._check_param(request, form_param)\n else:\n # No other content type is supported yet\n matched = False\n if not matched:\n raise falcon.HTTPBadRequest(\n 'Bad request',\n 'Unsupported content type: {}'.format(request.content_type)\n )\n\n def _get_raml_resource(self, method, uri_template):\n \"\"\"Get the RAML resource by method and URI template.\n\n This is the best we can do for now:\n https://github.com/spotify/ramlfications/issues/112\n \"\"\"\n for raml_resource in self._raml.resources:\n if raml_resource.method is None or raml_resource.path is None:\n continue\n if raml_resource.method.lower() != method.lower():\n continue\n if uri_template != raml_resource.path:\n continue\n return raml_resource\n return None\n\n def _check_header(self, request, raml_header):\n \"\"\"Check if the request headers are valid.\n\n It checks 2 things:\n\n 1. the existence (if required)\n 2. the type (if given)\n\n It raises HTTPMissingHeader and HTTPInvalidHeader,\n respectively.\n \"\"\"\n header = request.get_header(raml_header.display_name)\n\n if raml_header.required and header is None:\n raise falcon.HTTPMissingHeader(raml_header.display_name)\n\n if raml_header.type and header is not None:\n self.check_value_with_raml_type(\n raml_header,\n header,\n falcon.HTTPInvalidHeader\n )\n\n def _check_param(self, request, raml_param):\n \"\"\"Check if the request parameters are valid.\n\n The behavior of this function is like _check_header, but\n for query parameters and body form parameters.\n \"\"\"\n param = request.get_param(raml_param.name)\n\n if raml_param.required and param is None:\n raise falcon.HTTPMissingParam(raml_param.name)\n\n if raml_param.type and param is not None:\n self.check_value_with_raml_type(\n raml_param,\n param,\n falcon.HTTPInvalidParam\n )\n\n def _check_json_body(self, request, raml_json_body_schema):\n \"\"\"Check JSON body with jsonschema.\n\n This function works only if falconraml.JsonTranslator\n is used in the middlewares.\n \"\"\"\n if 'json' not in request.context:\n raise falcon.HTTPInternalServerError(\n 'Internal server error',\n 'Please use falconraml.JsonTranslator to parse json body.'\n )\n\n if type(raml_json_body_schema) is not dict:\n raise falcon.HTTPInternalServerError(\n 'Internal server error',\n 'Failed to parse jsonschema.'\n )\n\n request_json_body = request.context['json']\n\n try:\n jsonschema.validate(request_json_body, raml_json_body_schema)\n except jsonschema.exceptions.ValidationError as e:\n raise falcon.HTTPBadRequest('Bad request body', e.message)\n\n def check_value_with_raml_type(self, raml_spec, string, exception):\n \"\"\"Check a string value with a RAML type.\n\n The listed types are from:\n https://github.com/raml-org/raml-spec/blob/master/versions/raml-08/raml-08.md#type\n \"\"\"\n if raml_spec.type == 'string':\n # No need to check anything\n pass\n elif raml_spec.type == 'number':\n if not self._can_parse_as_python_type(float, string):\n raise exception('Should be a number.', raml_spec.name)\n elif raml_spec.type == 'integer':\n if not self._can_parse_as_python_type(int, string):\n raise exception('Should be an integer.', raml_spec.name)\n elif raml_spec.type == 'date':\n # Not supported yet\n pass\n elif raml_spec.type == 'boolean':\n if string not in ('true', 'false'):\n raise exception('Should be \"true\" or \"false\".', raml_spec.name)\n elif raml_spec.type == 'file':\n # Not supported yet\n pass\n\n def _can_parse_as_python_type(self, python_type, string):\n \"\"\"Check if a string can be converted to a python type.\n\n Currently supports float and int.\n \"\"\"\n try:\n python_type(string)\n return True\n except ValueError:\n return False\n\n def _make_form_param(self, param_tuple):\n \"\"\"Make a FormParameter from a parameter tuple.\n\n This is a workaround for weird ramlfications parsing:\n https://github.com/spotify/ramlfications/issues/122\n \"\"\"\n param_name = param_tuple[0]\n param_config = param_tuple[1]\n\n form_param = ramlfications.parameters.FormParameter(\n param_name,\n {},\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n {},\n [],\n )\n\n if 'required' in param_config:\n form_param.required = param_config['required']\n if 'type' in param_config:\n form_param.type = param_config['type']\n\n return form_param\n"},{"alpha_fraction":0.6842923760414124,"alphanum_fraction":0.7013996839523315,"avg_line_length":17.371429443359375,"blob_id":"6ad3580eee59278c5c8228a13d021aae41234991","content_id":"cf82e6bf3c263e9315dbf1a030c59cefeb9367d4","detected_licenses":["BSD-3-Clause"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":643,"license_type":"permissive","max_line_length":120,"num_lines":35,"path":"/README.md","repo_name":"johnlinp/falcon-raml","src_encoding":"UTF-8","text":"# Falcon RAML \n\nThis is a [Falcon](https://falconframework.org/) middleware for parameter checking using [RAML](http://raml.org/).\n\n\n## Requirement\n\n- Python: >= 3.4.\n- Falcon: >= 1.1.0.\n- RAML: Only 0.8 is supported since I use [ramlfications](https://github.com/spotify/ramlfications/) as the RAML parser.\n\n\n## Install\n\n```bash\npip3 install falcon-raml\n```\n\n\n## Usage\n\n```python\nimport falcon\nimport falconraml\n\napi = falcon.API(middleware=[\n falconraml.ParameterChecker('spec.raml')\n])\n```\n\n\n## Test\n\n1. `pip3 install tox`\n1. `tox`\n"}],"string":"[\n {\n \"alpha_fraction\": 0.709864616394043,\n \"alphanum_fraction\": 0.709864616394043,\n \"avg_line_length\": 21.478260040283203,\n \"blob_id\": \"6e2d97f33a07961d01cfc69ad475df9cc427cbe8\",\n \"content_id\": \"a53ba4b649a879fb30081c9ce07a07d350d68140\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 517,\n \"license_type\": \"permissive\",\n \"max_line_length\": 65,\n \"num_lines\": 23,\n \"path\": \"/test/tests/formbody/app.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport falconraml\\n\\n\\nclass Dummy(object):\\n def on_post(self, request, response):\\n pass\\n\\n\\napi = falcon.API(middleware=[\\n falconraml.JsonTranslator(),\\n falconraml.ParameterChecker('test/tests/formbody/spec.raml'),\\n])\\n\\n\\napi.req_options.auto_parse_form_urlencoded = True\\n\\n\\napi.add_route('/required/string', Dummy())\\napi.add_route('/required/number', Dummy())\\napi.add_route('/required/integer', Dummy())\\napi.add_route('/required/boolean', Dummy())\\napi.add_route('/nonrequired/mixed', Dummy())\\n\"\n },\n {\n \"alpha_fraction\": 0.7009063363075256,\n \"alphanum_fraction\": 0.7009063363075256,\n \"avg_line_length\": 18.47058868408203,\n \"blob_id\": \"00a5c57f8874eaba36e60b2f152d883495aae349\",\n \"content_id\": \"6e0c3ce3a835b72f9e893528d5b03921cb658097\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 331,\n \"license_type\": \"permissive\",\n \"max_line_length\": 65,\n \"num_lines\": 17,\n \"path\": \"/test/tests/jsonbody/app.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport falconraml\\n\\n\\nclass Dummy(object):\\n def on_post(self, request, response):\\n pass\\n\\n\\napi = falcon.API(middleware=[\\n falconraml.JsonTranslator(),\\n falconraml.ParameterChecker('test/tests/jsonbody/spec.raml'),\\n])\\n\\n\\napi.add_route('/simple/string', Dummy())\\napi.add_route('/malformed/schema', Dummy())\\n\"\n },\n {\n \"alpha_fraction\": 0.5724621415138245,\n \"alphanum_fraction\": 0.5823686718940735,\n \"avg_line_length\": 35.080322265625,\n \"blob_id\": \"487d32a4c40aa188b564749b58b5a1b1ae639c35\",\n \"content_id\": \"d50ac90f4d0cb341170d02b2bea5b06c1158ac16\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8984,\n \"license_type\": \"permissive\",\n \"max_line_length\": 76,\n \"num_lines\": 249,\n \"path\": \"/test/tests/queryparams/test.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport falcon.testing\\nfrom tests.queryparams import app\\n\\n\\nclass TestQueryParamRequiredString(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/string'\\n self.query_param_name = 'required_string'\\n self.query_param_values = [\\n 'hello',\\n r'!@#$%^&*(){}',\\n ]\\n\\n def test_success(self):\\n for value in self.query_param_values:\\n response = self.simulate_get(self.path, params={\\n self.query_param_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_query_params(self):\\n response = self.simulate_get(self.path)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.query_param_name)\\n )\\n\\n def test_wrong_param(self):\\n response = self.simulate_get(self.path, params={\\n 'wrong_param': self.query_param_values[0],\\n })\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.query_param_name)\\n )\\n\\n def test_with_empty_string(self):\\n response = self.simulate_get(self.path, params={\\n self.query_param_name: '',\\n })\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.query_param_name)\\n )\\n\\n def test_with_number(self):\\n response = self.simulate_get(self.path, params={\\n self.query_param_name: '42',\\n })\\n # number strings are strings too\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n\\nclass TestQueryParamsRequiredNumber(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/number'\\n self.query_param_name = 'required_number'\\n self.query_param_values = [\\n '55.66',\\n '42',\\n '0',\\n '-55.66',\\n ]\\n\\n def test_success(self):\\n for value in self.query_param_values:\\n response = self.simulate_get(self.path, params={\\n self.query_param_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_query_params(self):\\n response = self.simulate_get(self.path)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.query_param_name)\\n )\\n\\n def test_not_numbers(self):\\n wrong_values = [\\n 'hello',\\n 'a123',\\n '123a',\\n ]\\n\\n for value in wrong_values:\\n response = self.simulate_get(self.path, params={\\n self.query_param_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is invalid. '\\n 'Should be a number.'.format(self.query_param_name)\\n )\\n\\n\\nclass TestQueryParamsRequiredInteger(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/integer'\\n self.query_param_name = 'required_integer'\\n self.query_param_values = [\\n '42',\\n '0',\\n '-42',\\n ]\\n\\n def test_success(self):\\n for value in self.query_param_values:\\n response = self.simulate_get(self.path, params={\\n self.query_param_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_query_params(self):\\n response = self.simulate_get(self.path)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.query_param_name)\\n )\\n\\n def test_not_integers(self):\\n wrong_values = [\\n 'hello',\\n '55.66',\\n ]\\n\\n for value in wrong_values:\\n response = self.simulate_get(self.path, params={\\n self.query_param_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is invalid. '\\n 'Should be an integer.'.format(self.query_param_name)\\n )\\n\\n\\nclass TestQueryParamsRequiredBoolean(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/boolean'\\n self.query_param_name = 'required_boolean'\\n self.query_param_values = [\\n 'true',\\n 'false',\\n ]\\n\\n def test_success(self):\\n for value in self.query_param_values:\\n response = self.simulate_get(self.path, params={\\n self.query_param_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_query_params(self):\\n response = self.simulate_get(self.path)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.query_param_name)\\n )\\n\\n def test_not_booleans(self):\\n wrong_values = [\\n 'hello',\\n 'TRUE',\\n 'FALSE',\\n 'yes',\\n 'no',\\n ]\\n\\n for value in wrong_values:\\n response = self.simulate_get(self.path, params={\\n self.query_param_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is invalid. '\\n 'Should be \\\"true\\\" or \\\"false\\\".'.format(self.query_param_name)\\n )\\n\\n\\nclass TestQueryParamsNonRequiredMixed(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/nonrequired/mixed'\\n self.string_query_param_name = 'non_required_string'\\n self.number_query_param_name = 'non_required_number'\\n self.integer_query_param_name = 'non_required_integer'\\n self.boolean_query_param_name = 'non_required_boolean'\\n self.string_query_param_value = 'hello'\\n self.number_query_param_value = '55.66'\\n self.integer_query_param_value = '42'\\n self.boolean_query_param_value = 'true'\\n\\n def test_no_params(self):\\n response = self.simulate_get(self.path, params={})\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_all_params(self):\\n response = self.simulate_get(self.path, params={\\n self.string_query_param_name: self.string_query_param_value,\\n self.number_query_param_name: self.number_query_param_value,\\n self.integer_query_param_name: self.integer_query_param_value,\\n self.boolean_query_param_name: self.boolean_query_param_value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_only_string(self):\\n response = self.simulate_get(self.path, params={\\n self.string_query_param_name: self.string_query_param_value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_wrong_number(self):\\n response = self.simulate_get(self.path, params={\\n self.string_query_param_name: self.string_query_param_value,\\n self.number_query_param_name: 'hey',\\n self.integer_query_param_name: self.integer_query_param_value,\\n self.boolean_query_param_name: self.boolean_query_param_value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is invalid. '\\n 'Should be a number.'.format(self.number_query_param_name)\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.7049808502197266,\n \"alphanum_fraction\": 0.7049808502197266,\n \"avg_line_length\": 16.399999618530273,\n \"blob_id\": \"b5f25baeb4ef605c565b5b768e869a36b4f7b77f\",\n \"content_id\": \"09e1e8dab8cfbf9822f39d3b9b8b271c28028bf6\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 261,\n \"license_type\": \"permissive\",\n \"max_line_length\": 73,\n \"num_lines\": 15,\n \"path\": \"/test/tests/nojsontranslator/app.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport falconraml\\n\\n\\nclass Dummy(object):\\n def on_post(self, request, response):\\n pass\\n\\n\\napi = falcon.API(middleware=[\\n falconraml.ParameterChecker('test/tests/nojsontranslator/spec.raml'),\\n])\\n\\n\\napi.add_route('/basic/string', Dummy())\\n\"\n },\n {\n \"alpha_fraction\": 0.6959707140922546,\n \"alphanum_fraction\": 0.6959707140922546,\n \"avg_line_length\": 17.200000762939453,\n \"blob_id\": \"20bcc355d3548b6a5feb937c9ae7bb5516c73dbf\",\n \"content_id\": \"54216fa114c89151ec845466ba0665a764ce433e\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 273,\n \"license_type\": \"permissive\",\n \"max_line_length\": 70,\n \"num_lines\": 15,\n \"path\": \"/test/tests/templatedpath/app.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport falconraml\\n\\n\\nclass Dummy(object):\\n def on_get(self, request, response, some_id):\\n pass\\n\\n\\napi = falcon.API(middleware=[\\n falconraml.ParameterChecker('test/tests/templatedpath/spec.raml'),\\n])\\n\\n\\napi.add_route('/templated/{some_id}', Dummy())\\n\"\n },\n {\n \"alpha_fraction\": 0.5663115978240967,\n \"alphanum_fraction\": 0.5767294764518738,\n \"avg_line_length\": 34.01229476928711,\n \"blob_id\": \"89ade40747b82ac60786bfb162123cd501f8e0e4\",\n \"content_id\": \"bafaccf5e363d4e8296b24540530dee13cf790d4\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8543,\n \"license_type\": \"permissive\",\n \"max_line_length\": 76,\n \"num_lines\": 244,\n \"path\": \"/test/tests/headers/test.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport falcon.testing\\nfrom tests.headers import app\\n\\n\\nclass TestHeadersRequiredString(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/string'\\n self.header_name = 'X-Required-String'\\n self.header_values = [\\n 'hello',\\n r'!@#$%^&*(){}',\\n ]\\n\\n def test_success(self):\\n for value in self.header_values:\\n response = self.simulate_get(self.path, headers={\\n self.header_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_header(self):\\n response = self.simulate_get(self.path)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing header value')\\n self.assertEqual(\\n response.json['description'],\\n 'The {} header is required.'.format(self.header_name)\\n )\\n\\n def test_wrong_header(self):\\n response = self.simulate_get(self.path, headers={\\n 'X-Wrong-Header': self.header_values[0],\\n })\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing header value')\\n self.assertEqual(\\n response.json['description'],\\n 'The {} header is required.'.format(self.header_name)\\n )\\n\\n def test_with_empty_string(self):\\n response = self.simulate_get(self.path, headers={\\n self.header_name: '',\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_with_number(self):\\n response = self.simulate_get(self.path, headers={\\n self.header_name: '42',\\n })\\n # number strings are strings too\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n\\nclass TestHeadersRequiredNumber(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/number'\\n self.header_name = 'X-Required-Number'\\n self.header_values = [\\n '55.66',\\n '42',\\n '0',\\n '-55.66',\\n ]\\n\\n def test_success(self):\\n for value in self.header_values:\\n response = self.simulate_get(self.path, headers={\\n self.header_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_header(self):\\n response = self.simulate_get(self.path)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing header value')\\n self.assertEqual(\\n response.json['description'],\\n 'The {} header is required.'.format(self.header_name)\\n )\\n\\n def test_not_numbers(self):\\n wrong_values = [\\n 'hello',\\n 'a123',\\n '123a',\\n ]\\n\\n for value in wrong_values:\\n response = self.simulate_get(self.path, headers={\\n self.header_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid header value')\\n self.assertEqual(\\n response.json['description'],\\n 'The value provided for the {} header is invalid. '\\n 'Should be a number.'.format(self.header_name)\\n )\\n\\n\\nclass TestHeadersRequiredInteger(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/integer'\\n self.header_name = 'X-Required-Integer'\\n self.header_values = [\\n '42',\\n '0',\\n '-42',\\n ]\\n\\n def test_success(self):\\n for value in self.header_values:\\n response = self.simulate_get(self.path, headers={\\n self.header_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_header(self):\\n response = self.simulate_get(self.path)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing header value')\\n self.assertEqual(\\n response.json['description'],\\n 'The {} header is required.'.format(self.header_name)\\n )\\n\\n def test_not_integers(self):\\n wrong_values = [\\n 'hello',\\n '55.66',\\n ]\\n\\n for value in wrong_values:\\n response = self.simulate_get(self.path, headers={\\n self.header_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid header value')\\n self.assertEqual(\\n response.json['description'],\\n 'The value provided for the {} header is invalid. '\\n 'Should be an integer.'.format(self.header_name)\\n )\\n\\n\\nclass TestHeadersRequiredBoolean(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/boolean'\\n self.header_name = 'X-Required-Boolean'\\n self.header_values = [\\n 'true',\\n 'false',\\n ]\\n\\n def test_success(self):\\n for value in self.header_values:\\n response = self.simulate_get(self.path, headers={\\n self.header_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_header(self):\\n response = self.simulate_get(self.path)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing header value')\\n self.assertEqual(\\n response.json['description'],\\n 'The {} header is required.'.format(self.header_name)\\n )\\n\\n def test_not_booleans(self):\\n wrong_values = [\\n 'hello',\\n 'TRUE',\\n 'FALSE',\\n 'yes',\\n 'no',\\n ]\\n\\n for value in wrong_values:\\n response = self.simulate_get(self.path, headers={\\n self.header_name: value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid header value')\\n self.assertEqual(\\n response.json['description'],\\n 'The value provided for the {} header is invalid. '\\n 'Should be \\\"true\\\" or \\\"false\\\".'.format(self.header_name)\\n )\\n\\n\\nclass TestHeadersNonRequiredMixed(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/nonrequired/mixed'\\n self.string_header_name = 'X-Non-Required-String'\\n self.number_header_name = 'X-Non-Required-Number'\\n self.integer_header_name = 'X-Non-Required-Integer'\\n self.boolean_header_name = 'X-Non-Required-Boolean'\\n self.string_header_value = 'hello'\\n self.number_header_value = '55.66'\\n self.integer_header_value = '42'\\n self.boolean_header_value = 'true'\\n\\n def test_no_headers(self):\\n response = self.simulate_get(self.path, headers={})\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_all_headers(self):\\n response = self.simulate_get(self.path, headers={\\n self.string_header_name: self.string_header_value,\\n self.number_header_name: self.number_header_value,\\n self.integer_header_name: self.integer_header_value,\\n self.boolean_header_name: self.boolean_header_value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_only_string(self):\\n response = self.simulate_get(self.path, headers={\\n self.string_header_name: self.string_header_value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_wrong_number(self):\\n response = self.simulate_get(self.path, headers={\\n self.string_header_name: self.string_header_value,\\n self.number_header_name: 'hey',\\n self.integer_header_name: self.integer_header_value,\\n self.boolean_header_name: self.boolean_header_value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid header value')\\n self.assertEqual(\\n response.json['description'],\\n 'The value provided for the {} header is invalid. '\\n 'Should be a number.'.format(self.number_header_name)\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.8709677457809448,\n \"alphanum_fraction\": 0.8709677457809448,\n \"avg_line_length\": 61,\n \"blob_id\": \"0701be375585cc669340cfeb4b763431e36ed241\",\n \"content_id\": \"8c9fd6ed3295e4d81642cebd1bbb06fdb4894818\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 124,\n \"license_type\": \"permissive\",\n \"max_line_length\": 63,\n \"num_lines\": 2,\n \"path\": \"/falconraml/__init__.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from falconraml.parameterchecker import ParameterChecker # noqa\\nfrom falconraml.jsontranslator import JsonTranslator # noqa\\n\"\n },\n {\n \"alpha_fraction\": 0.5351053476333618,\n \"alphanum_fraction\": 0.5431494116783142,\n \"avg_line_length\": 32.837276458740234,\n \"blob_id\": \"bdff520221f62fc4c704b0550ba89d47c91bcf74\",\n \"content_id\": \"850120008e3afbefbef913ac2ca93d82462a2f1c\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 11437,\n \"license_type\": \"permissive\",\n \"max_line_length\": 78,\n \"num_lines\": 338,\n \"path\": \"/test/tests/formbody/test.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport falcon.testing\\nimport urllib.parse\\nfrom tests.formbody import app\\n\\n\\nclass TestFormBodyRequiredString(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/string'\\n self.form_param_name = 'required_string'\\n self.form_param_values = [\\n 'hello',\\n r'!@#$%^&*(){}',\\n ]\\n self.headers = {\\n 'Content-Type': 'application/x-www-form-urlencoded',\\n }\\n\\n def test_success(self):\\n for value in self.form_param_values:\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.form_param_name: value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_content_type(self):\\n response = self.simulate_post(self.path)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Bad request')\\n self.assertEqual(response.json['description'], 'No content type')\\n\\n def test_missing_form_body(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.form_param_name)\\n )\\n\\n def test_wrong_param(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n 'wrong_param': self.form_param_values[0],\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.form_param_name)\\n )\\n\\n def test_with_empty_string(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.form_param_name: '',\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.form_param_name)\\n )\\n\\n def test_with_number(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.form_param_name: '42',\\n })\\n )\\n # number strings are strings too\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n\\nclass TestFormBodyRequiredNumber(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/number'\\n self.form_param_name = 'required_number'\\n self.form_param_values = [\\n '55.66',\\n '42',\\n '0',\\n '-55.66',\\n ]\\n self.headers = {\\n 'Content-Type': 'application/x-www-form-urlencoded',\\n }\\n\\n def test_success(self):\\n for value in self.form_param_values:\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.form_param_name: value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_form_body(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.form_param_name)\\n )\\n\\n def test_not_numbers(self):\\n wrong_values = [\\n 'hello',\\n 'a123',\\n '123a',\\n ]\\n\\n for value in wrong_values:\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.form_param_name: value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is invalid. '\\n 'Should be a number.'.format(self.form_param_name)\\n )\\n\\n\\nclass TestFormBodyRequiredInteger(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/integer'\\n self.form_param_name = 'required_integer'\\n self.form_param_values = [\\n '42',\\n '0',\\n '-42',\\n ]\\n self.headers = {\\n 'Content-Type': 'application/x-www-form-urlencoded',\\n }\\n\\n def test_success(self):\\n for value in self.form_param_values:\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.form_param_name: value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_form_body(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.form_param_name)\\n )\\n\\n def test_not_integers(self):\\n wrong_values = [\\n 'hello',\\n '55.66',\\n ]\\n\\n for value in wrong_values:\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.form_param_name: value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is invalid. '\\n 'Should be an integer.'.format(self.form_param_name)\\n )\\n\\n\\nclass TestFormBodyRequiredBoolean(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/required/boolean'\\n self.form_param_name = 'required_boolean'\\n self.form_param_values = [\\n 'true',\\n 'false',\\n ]\\n self.headers = {\\n 'Content-Type': 'application/x-www-form-urlencoded',\\n }\\n\\n def test_success(self):\\n for value in self.form_param_values:\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.form_param_name: value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_form_body(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.form_param_name)\\n )\\n\\n def test_not_booleans(self):\\n wrong_values = [\\n 'hello',\\n 'TRUE',\\n 'FALSE',\\n 'yes',\\n 'no',\\n ]\\n\\n for value in wrong_values:\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.form_param_name: value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is invalid. '\\n 'Should be \\\"true\\\" or \\\"false\\\".'.format(self.form_param_name)\\n )\\n\\n\\nclass TestFormBodyNonRequiredMixed(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/nonrequired/mixed'\\n self.string_query_param_name = 'non_required_string'\\n self.number_query_param_name = 'non_required_number'\\n self.integer_query_param_name = 'non_required_integer'\\n self.boolean_query_param_name = 'non_required_boolean'\\n self.string_query_param_value = 'hello'\\n self.number_query_param_value = '55.66'\\n self.integer_query_param_value = '42'\\n self.boolean_query_param_value = 'true'\\n self.headers = {\\n 'Content-Type': 'application/x-www-form-urlencoded',\\n }\\n\\n def test_no_form_body(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n )\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_all_params(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.string_query_param_name: self.string_query_param_value,\\n self.number_query_param_name: self.number_query_param_value,\\n self.integer_query_param_name: self.integer_query_param_value,\\n self.boolean_query_param_name: self.boolean_query_param_value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_only_string(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.string_query_param_name: self.string_query_param_value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_wrong_number(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=urllib.parse.urlencode({\\n self.string_query_param_name: self.string_query_param_value,\\n self.number_query_param_name: 'hey',\\n self.integer_query_param_name: self.integer_query_param_value,\\n self.boolean_query_param_name: self.boolean_query_param_value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Invalid parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is invalid. '\\n 'Should be a number.'.format(self.number_query_param_name)\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.688524603843689,\n \"alphanum_fraction\": 0.6967213153839111,\n \"avg_line_length\": 11.199999809265137,\n \"blob_id\": \"2a3a73d0c8b17cd1cb5288dc9537fc07e2574c4e\",\n \"content_id\": \"1f1ddab523ebed33e7edbefef75c10dd1b0a3519\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"INI\",\n \"length_bytes\": 122,\n \"license_type\": \"permissive\",\n \"max_line_length\": 37,\n \"num_lines\": 10,\n \"path\": \"/tox.ini\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"[tox]\\nenvlist = py3\\n\\n[testenv]\\ndeps =\\n pytest\\n coverage\\ncommands =\\n coverage run -m pytest test/main.py\\n coverage xml\\n\"\n },\n {\n \"alpha_fraction\": 0.699999988079071,\n \"alphanum_fraction\": 0.699999988079071,\n \"avg_line_length\": 21.63157844543457,\n \"blob_id\": \"b5a0d48cea855d2a063b828226423e522e41885c\",\n \"content_id\": \"5f60f770b0a8e524880fa532747699bfe9e350db\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 430,\n \"license_type\": \"permissive\",\n \"max_line_length\": 64,\n \"num_lines\": 19,\n \"path\": \"/test/tests/headers/app.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport falconraml\\n\\n\\nclass Dummy(object):\\n def on_get(self, request, response):\\n pass\\n\\n\\napi = falcon.API(middleware=[\\n falconraml.ParameterChecker('test/tests/headers/spec.raml'),\\n])\\n\\n\\napi.add_route('/required/string', Dummy())\\napi.add_route('/required/number', Dummy())\\napi.add_route('/required/integer', Dummy())\\napi.add_route('/required/boolean', Dummy())\\napi.add_route('/nonrequired/mixed', Dummy())\\n\"\n },\n {\n \"alpha_fraction\": 0.5544554591178894,\n \"alphanum_fraction\": 0.5614819526672363,\n \"avg_line_length\": 30.9489803314209,\n \"blob_id\": \"43cdacf789596f350031231695868017e7255e9d\",\n \"content_id\": \"e8d15622a82a4a614f14e92bbbafd531cc1fc9e9\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3131,\n \"license_type\": \"permissive\",\n \"max_line_length\": 74,\n \"num_lines\": 98,\n \"path\": \"/test/tests/jsonbody/test.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport falcon.testing\\nimport json\\nfrom tests.jsonbody import app\\n\\n\\nclass TestJsonBodySimpleString(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/simple/string'\\n self.field_name = 'some_string'\\n self.field_values = [\\n 'hello',\\n r'!@#$%^&*(){}',\\n ]\\n self.headers = {\\n 'Content-Type': 'application/json',\\n }\\n\\n def test_success(self):\\n for value in self.field_values:\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=json.dumps({\\n self.field_name: value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_not_a_string(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=json.dumps({\\n self.field_name: 42,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Bad request body')\\n self.assertEqual(\\n response.json['description'],\\n \\\"42 is not of type 'string'\\\"\\n )\\n\\n def test_missing_content_type(self):\\n response = self.simulate_post(self.path)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Bad request')\\n self.assertEqual(response.json['description'], 'No content type')\\n\\n def test_missing_body(self):\\n response = self.simulate_post(self.path, headers=self.headers)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Bad request body')\\n self.assertEqual(\\n response.json['description'],\\n \\\"None is not of type 'object'\\\"\\n )\\n\\n def test_empty_object(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body='{}'\\n )\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Bad request body')\\n self.assertEqual(\\n response.json['description'],\\n \\\"'some_string' is a required property\\\".format(self.field_name)\\n )\\n\\n\\nclass TestJsonBodyMalformedSchema(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/malformed/schema'\\n self.field_name = 'some_string'\\n self.field_value = 'hello'\\n self.headers = {\\n 'Content-Type': 'application/json',\\n }\\n\\n def test_error(self):\\n response = self.simulate_post(\\n self.path,\\n headers=self.headers,\\n body=json.dumps({\\n self.field_name: self.field_value,\\n })\\n )\\n self.assertEqual(response.status, falcon.HTTP_500)\\n self.assertEqual(response.json['title'], 'Internal server error')\\n self.assertEqual(\\n response.json['description'],\\n 'Failed to parse jsonschema.'\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.6255044341087341,\n \"alphanum_fraction\": 0.6303470730781555,\n \"avg_line_length\": 30.769229888916016,\n \"blob_id\": \"2b17a8d0517a6be054bd74f59da68a09493828f2\",\n \"content_id\": \"e6e936dbdbcb755402e94a6658f8abf956b8502d\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1239,\n \"license_type\": \"permissive\",\n \"max_line_length\": 77,\n \"num_lines\": 39,\n \"path\": \"/falconraml/jsontranslator.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import json\\nimport falcon\\n\\n\\nclass JsonTranslator(object):\\n \\\"\\\"\\\"Translate from plain text to python objects.\\\"\\\"\\\"\\n\\n def process_request(self, request, response):\\n \\\"\\\"\\\"Translate plain text to python objects in the request.\\n\\n Translated JSON is stored in request.context['json'].\\n\\n The translation will only be done if all the following conditions are\\n satisfied:\\n\\n 1. The content length is more than 0.\\n 2. The content type is \\\"application/json\\\".\\n 3. The request body can be successful parsed as a json object.\\n\\n Note that after this middleware, request.stream will left to EOF.\\n \\\"\\\"\\\"\\n request.context['json'] = None\\n\\n if request.content_length in (None, 0):\\n return\\n if request.content_type != 'application/json':\\n return\\n\\n json_bytes = request.stream.read()\\n\\n try:\\n json_str = json_bytes.decode('utf8')\\n except UnicodeDecodeError:\\n raise falcon.HTTPBadRequest('Bad string', 'Unicode decode error')\\n\\n try:\\n request.context['json'] = json.loads(json_str)\\n except json.decoder.JSONDecodeError:\\n raise falcon.HTTPBadRequest('Bad json', 'JSON decode error')\\n\"\n },\n {\n \"alpha_fraction\": 0.5711060762405396,\n \"alphanum_fraction\": 0.6004514694213867,\n \"avg_line_length\": 23.61111068725586,\n \"blob_id\": \"0e2458103310ccde9f7cc2ce5cbd51e280e6caf8\",\n \"content_id\": \"fb198c3d7247f3b69e70d9985ce23272a9d3b221\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 443,\n \"license_type\": \"permissive\",\n \"max_line_length\": 69,\n \"num_lines\": 18,\n \"path\": \"/setup.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from setuptools import setup\\n\\nsetup(name='falcon-raml',\\n version='0.1.1',\\n description='Parameter checker middleware using RAML for Falcon',\\n author='johnlinp',\\n author_email='johnlinp@gmail.com',\\n url='https://github.com/johnlinp/falcon-raml',\\n license='New BSD License',\\n install_requires=[\\n 'falcon==1.1.0',\\n 'ramlfications==0.1.9',\\n 'jsonschema==4.16.0',\\n ],\\n packages=[\\n 'falconraml',\\n ],\\n)\\n\"\n },\n {\n \"alpha_fraction\": 0.7292307615280151,\n \"alphanum_fraction\": 0.7292307615280151,\n \"avg_line_length\": 31.5,\n \"blob_id\": \"e138d0c1ce71692460bc03b6fe36f93af56a8b85\",\n \"content_id\": \"41daf967f32b8821088280c33d7aaa5eea88b220\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 325,\n \"license_type\": \"permissive\",\n \"max_line_length\": 48,\n \"num_lines\": 10,\n \"path\": \"/test/main.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import unittest\\nfrom tests.headers.test import * # noqa\\nfrom tests.queryparams.test import * # noqa\\nfrom tests.jsonbody.test import * # noqa\\nfrom tests.nojsontranslator.test import * # noqa\\nfrom tests.formbody.test import * # noqa\\nfrom tests.templatedpath.test import * # noqa\\n\\nif __name__ == '__main__':\\n unittest.main()\\n\"\n },\n {\n \"alpha_fraction\": 0.626086950302124,\n \"alphanum_fraction\": 0.6298136711120605,\n \"avg_line_length\": 31.200000762939453,\n \"blob_id\": \"9bcfa095782fe1f9c9d271f09dccd5c18d63e58a\",\n \"content_id\": \"44f8c16406200b55a1037580001b4ebb1e2cf034\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 805,\n \"license_type\": \"permissive\",\n \"max_line_length\": 73,\n \"num_lines\": 25,\n \"path\": \"/test/tests/nojsontranslator/test.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport falcon.testing\\nimport json\\nfrom tests.nojsontranslator import app\\n\\n\\nclass TestNoJsonTranslator(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/basic/string'\\n self.field_name = 'some_string'\\n self.field_value = 'hello'\\n\\n def test_error(self):\\n response = self.simulate_post(self.path, headers={\\n 'Content-Type': 'application/json',\\n }, body=json.dumps({\\n self.field_name: self.field_value,\\n }))\\n self.assertEqual(response.status, falcon.HTTP_500)\\n self.assertEqual(response.json['title'], 'Internal server error')\\n self.assertEqual(\\n response.json['description'],\\n 'Please use falconraml.JsonTranslator to parse json body.'\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.635545551776886,\n \"alphanum_fraction\": 0.6467941403388977,\n \"avg_line_length\": 33.19230651855469,\n \"blob_id\": \"96441037725166c22ea5fcc88382da4549bc23f5\",\n \"content_id\": \"9aff5fd5754103efd10358e71b7550bb5ce5f9b4\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 889,\n \"license_type\": \"permissive\",\n \"max_line_length\": 75,\n \"num_lines\": 26,\n \"path\": \"/test/tests/templatedpath/test.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport falcon.testing\\nfrom tests.templatedpath import app\\n\\n\\nclass TestTemplatedPath(falcon.testing.TestCase):\\n def setUp(self):\\n self.app = app.api\\n self.path = '/templated/9527'\\n self.query_param_name = 'required_string'\\n self.query_param_value = 'hello'\\n\\n def test_success(self):\\n response = self.simulate_get(self.path, params={\\n self.query_param_name: self.query_param_value,\\n })\\n self.assertEqual(response.status, falcon.HTTP_200)\\n\\n def test_missing_query_params(self):\\n response = self.simulate_get(self.path)\\n self.assertEqual(response.status, falcon.HTTP_400)\\n self.assertEqual(response.json['title'], 'Missing parameter')\\n self.assertEqual(\\n response.json['description'],\\n 'The \\\"{}\\\" parameter is required.'.format(self.query_param_name)\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.5639344453811646,\n \"alphanum_fraction\": 0.5665300488471985,\n \"avg_line_length\": 33.36619567871094,\n \"blob_id\": \"2dee8d66e5688b2f25981831df3fe5e956ed74c0\",\n \"content_id\": \"054ee92a2be5e8c8c06d8fe8d1f54157e1afb6e2\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7320,\n \"license_type\": \"permissive\",\n \"max_line_length\": 97,\n \"num_lines\": 213,\n \"path\": \"/falconraml/parameterchecker.py\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import falcon\\nimport ramlfications\\nimport jsonschema\\n\\n\\nclass ParameterChecker(object):\\n \\\"\\\"\\\"Check parameters with RAML.\\\"\\\"\\\"\\n\\n def __init__(self, raml_file_path):\\n \\\"\\\"\\\"Load the RAML file.\\\"\\\"\\\"\\n self._raml = ramlfications.parse(raml_file_path)\\n\\n def process_resource(self, request, response, falcon_resource, uri_params):\\n \\\"\\\"\\\"Check parameters in the request.\\n\\n This function checks 3 parts:\\n\\n 1. headers\\n 2. query parameters\\n 3. body\\n a. json body\\n b. form body parameters\\n\\n Note that it overrides process_resource instead of process_request\\n because request.uri_template maybe None in process_request.\\n Please see:\\n http://falcon.readthedocs.io/en/stable/api/request_and_response.html#Request.uri_template\\n \\\"\\\"\\\"\\n raml_resource = \\\\\\n self._get_raml_resource(request.method, request.uri_template)\\n\\n if raml_resource is None:\\n return\\n\\n if raml_resource.headers:\\n for header in raml_resource.headers:\\n self._check_header(request, header)\\n\\n if raml_resource.query_params:\\n for query_param in raml_resource.query_params:\\n self._check_param(request, query_param)\\n\\n if raml_resource.body:\\n if request.content_type is None:\\n raise falcon.HTTPBadRequest('Bad request', 'No content type')\\n matched = False\\n for body in raml_resource.body:\\n if body.mime_type != request.content_type:\\n continue\\n matched = True\\n if body.mime_type == 'application/json':\\n self._check_json_body(request, body.schema)\\n elif body.mime_type == 'application/x-www-form-urlencoded':\\n for form_param_tuple in body.form_params.items():\\n form_param = self._make_form_param(form_param_tuple)\\n self._check_param(request, form_param)\\n else:\\n # No other content type is supported yet\\n matched = False\\n if not matched:\\n raise falcon.HTTPBadRequest(\\n 'Bad request',\\n 'Unsupported content type: {}'.format(request.content_type)\\n )\\n\\n def _get_raml_resource(self, method, uri_template):\\n \\\"\\\"\\\"Get the RAML resource by method and URI template.\\n\\n This is the best we can do for now:\\n https://github.com/spotify/ramlfications/issues/112\\n \\\"\\\"\\\"\\n for raml_resource in self._raml.resources:\\n if raml_resource.method is None or raml_resource.path is None:\\n continue\\n if raml_resource.method.lower() != method.lower():\\n continue\\n if uri_template != raml_resource.path:\\n continue\\n return raml_resource\\n return None\\n\\n def _check_header(self, request, raml_header):\\n \\\"\\\"\\\"Check if the request headers are valid.\\n\\n It checks 2 things:\\n\\n 1. the existence (if required)\\n 2. the type (if given)\\n\\n It raises HTTPMissingHeader and HTTPInvalidHeader,\\n respectively.\\n \\\"\\\"\\\"\\n header = request.get_header(raml_header.display_name)\\n\\n if raml_header.required and header is None:\\n raise falcon.HTTPMissingHeader(raml_header.display_name)\\n\\n if raml_header.type and header is not None:\\n self.check_value_with_raml_type(\\n raml_header,\\n header,\\n falcon.HTTPInvalidHeader\\n )\\n\\n def _check_param(self, request, raml_param):\\n \\\"\\\"\\\"Check if the request parameters are valid.\\n\\n The behavior of this function is like _check_header, but\\n for query parameters and body form parameters.\\n \\\"\\\"\\\"\\n param = request.get_param(raml_param.name)\\n\\n if raml_param.required and param is None:\\n raise falcon.HTTPMissingParam(raml_param.name)\\n\\n if raml_param.type and param is not None:\\n self.check_value_with_raml_type(\\n raml_param,\\n param,\\n falcon.HTTPInvalidParam\\n )\\n\\n def _check_json_body(self, request, raml_json_body_schema):\\n \\\"\\\"\\\"Check JSON body with jsonschema.\\n\\n This function works only if falconraml.JsonTranslator\\n is used in the middlewares.\\n \\\"\\\"\\\"\\n if 'json' not in request.context:\\n raise falcon.HTTPInternalServerError(\\n 'Internal server error',\\n 'Please use falconraml.JsonTranslator to parse json body.'\\n )\\n\\n if type(raml_json_body_schema) is not dict:\\n raise falcon.HTTPInternalServerError(\\n 'Internal server error',\\n 'Failed to parse jsonschema.'\\n )\\n\\n request_json_body = request.context['json']\\n\\n try:\\n jsonschema.validate(request_json_body, raml_json_body_schema)\\n except jsonschema.exceptions.ValidationError as e:\\n raise falcon.HTTPBadRequest('Bad request body', e.message)\\n\\n def check_value_with_raml_type(self, raml_spec, string, exception):\\n \\\"\\\"\\\"Check a string value with a RAML type.\\n\\n The listed types are from:\\n https://github.com/raml-org/raml-spec/blob/master/versions/raml-08/raml-08.md#type\\n \\\"\\\"\\\"\\n if raml_spec.type == 'string':\\n # No need to check anything\\n pass\\n elif raml_spec.type == 'number':\\n if not self._can_parse_as_python_type(float, string):\\n raise exception('Should be a number.', raml_spec.name)\\n elif raml_spec.type == 'integer':\\n if not self._can_parse_as_python_type(int, string):\\n raise exception('Should be an integer.', raml_spec.name)\\n elif raml_spec.type == 'date':\\n # Not supported yet\\n pass\\n elif raml_spec.type == 'boolean':\\n if string not in ('true', 'false'):\\n raise exception('Should be \\\"true\\\" or \\\"false\\\".', raml_spec.name)\\n elif raml_spec.type == 'file':\\n # Not supported yet\\n pass\\n\\n def _can_parse_as_python_type(self, python_type, string):\\n \\\"\\\"\\\"Check if a string can be converted to a python type.\\n\\n Currently supports float and int.\\n \\\"\\\"\\\"\\n try:\\n python_type(string)\\n return True\\n except ValueError:\\n return False\\n\\n def _make_form_param(self, param_tuple):\\n \\\"\\\"\\\"Make a FormParameter from a parameter tuple.\\n\\n This is a workaround for weird ramlfications parsing:\\n https://github.com/spotify/ramlfications/issues/122\\n \\\"\\\"\\\"\\n param_name = param_tuple[0]\\n param_config = param_tuple[1]\\n\\n form_param = ramlfications.parameters.FormParameter(\\n param_name,\\n {},\\n None,\\n None,\\n None,\\n None,\\n None,\\n None,\\n None,\\n None,\\n {},\\n [],\\n )\\n\\n if 'required' in param_config:\\n form_param.required = param_config['required']\\n if 'type' in param_config:\\n form_param.type = param_config['type']\\n\\n return form_param\\n\"\n },\n {\n \"alpha_fraction\": 0.6842923760414124,\n \"alphanum_fraction\": 0.7013996839523315,\n \"avg_line_length\": 17.371429443359375,\n \"blob_id\": \"6ad3580eee59278c5c8228a13d021aae41234991\",\n \"content_id\": \"cf82e6bf3c263e9315dbf1a030c59cefeb9367d4\",\n \"detected_licenses\": [\n \"BSD-3-Clause\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 643,\n \"license_type\": \"permissive\",\n \"max_line_length\": 120,\n \"num_lines\": 35,\n \"path\": \"/README.md\",\n \"repo_name\": \"johnlinp/falcon-raml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Falcon RAML \\n\\nThis is a [Falcon](https://falconframework.org/) middleware for parameter checking using [RAML](http://raml.org/).\\n\\n\\n## Requirement\\n\\n- Python: >= 3.4.\\n- Falcon: >= 1.1.0.\\n- RAML: Only 0.8 is supported since I use [ramlfications](https://github.com/spotify/ramlfications/) as the RAML parser.\\n\\n\\n## Install\\n\\n```bash\\npip3 install falcon-raml\\n```\\n\\n\\n## Usage\\n\\n```python\\nimport falcon\\nimport falconraml\\n\\napi = falcon.API(middleware=[\\n falconraml.ParameterChecker('spec.raml')\\n])\\n```\\n\\n\\n## Test\\n\\n1. `pip3 install tox`\\n1. `tox`\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":18,"string":"18"}}},{"rowIdx":2979167,"cells":{"repo_name":{"kind":"string","value":"qiaocci/django-forbidden-ip"},"repo_url":{"kind":"string","value":"https://github.com/qiaocci/django-forbidden-ip"},"snapshot_id":{"kind":"string","value":"43d3cc5b8ce31ba155b7cdad8c30d8ddd59b951a"},"revision_id":{"kind":"string","value":"dd5be0d7592d619f142a1fcdd951f9aecba36c41"},"directory_id":{"kind":"string","value":"4886ab27c5a4e935c2732f1da5ee5097a93f92bd"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-03-26T21:26:30.711229","string":"2020-03-26T21:26:30.711229"},"revision_date":{"kind":"timestamp","value":"2018-08-20T08:53:37","string":"2018-08-20T08:53:37"},"committer_date":{"kind":"timestamp","value":"2018-08-20T08:53:37","string":"2018-08-20T08:53:37"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7239709496498108,"alphanum_fraction":0.7336561679840088,"avg_line_length":23.294116973876953,"blob_id":"d9c31f838fe0c56e086c49226e163af42b88e5b4","content_id":"c39bc7887f5d61a133ec5c9d281dbc78725492c6","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":413,"license_type":"permissive","max_line_length":92,"num_lines":17,"path":"/README.md","repo_name":"qiaocci/django-forbidden-ip","src_encoding":"UTF-8","text":"django-forbidden-ip\n---\n\nThis is a django application to forbid access by IP address.\n\nInstall\n===\n\n pip install django-forbidden-ip\n \nConfig\n===\n\n1. Add `ip_interceptor` to `INSTALLED_APPS`.\n2. Add `ip_interceptor.middleware.IPInterceptorMiddleware` to `MIDDLEWARE`.\n3. Run `./manage.py makemigrations ip_interceptor` and `./manage.py migrate ip_interceptor`.\n4. Add IP you want to block to `ForbiddenIP`.\n"},{"alpha_fraction":0.6501650214195251,"alphanum_fraction":0.6666666865348816,"avg_line_length":24.25,"blob_id":"049c7a6ee4e5d2712469c5f9e1a7a993c0c76caf","content_id":"fad90f20416cecc80a1279ee6b13510777912154","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":303,"license_type":"permissive","max_line_length":59,"num_lines":12,"path":"/ip_interceptor/models.py","repo_name":"qiaocci/django-forbidden-ip","src_encoding":"UTF-8","text":"from django.db import models\n\n\nclass ForbiddenIP(models.Model):\n ip = models.CharField(max_length=18)\n reason = models.CharField(max_length=200, null=True)\n\n class Meta:\n verbose_name = verbose_name_plural = 'forbidden_ip'\n\n def __str__(self):\n return f'ForbiddenIP:{self.ip}'\n"},{"alpha_fraction":0.6881533265113831,"alphanum_fraction":0.6881533265113831,"avg_line_length":29.210525512695312,"blob_id":"36704f7076bd5cea8c69cc62cd1188efeb7e019c","content_id":"20d2690b568a33f480d46c9e7f91c6576f85c23f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":574,"license_type":"permissive","max_line_length":60,"num_lines":19,"path":"/ip_interceptor/middleware.py","repo_name":"qiaocci/django-forbidden-ip","src_encoding":"UTF-8","text":"from django.http import HttpResponseForbidden\nfrom django.utils.deprecation import MiddlewareMixin\n\nfrom .models import ForbiddenIP\n\n\nclass IPInterceptorMiddleware(MiddlewareMixin):\n def __init__(self, get_reqponse):\n self.get_response = get_reqponse\n\n def validate_ip(self, remote_ip):\n if ForbiddenIP.objects.filter(ip=remote_ip).count():\n return False\n return True\n\n def process_request(self, request):\n ip = request.META.get('REMOTE_ADDR')\n if not self.validate_ip(ip):\n return HttpResponseForbidden('')\n"},{"alpha_fraction":0.5950413346290588,"alphanum_fraction":0.6127508878707886,"avg_line_length":27.266666412353516,"blob_id":"ca8034976a21691c64a20d06e1f28ad4175e7fd5","content_id":"1d07f8c169d424e449fc2f428bdbc38d556c4a67","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":847,"license_type":"permissive","max_line_length":61,"num_lines":30,"path":"/setup.py","repo_name":"qiaocci/django-forbidden-ip","src_encoding":"UTF-8","text":"from setuptools import find_packages, setup\n\nwith open('README.md', encoding='utf8') as f:\n long_description = f.read()\n\nsetup(\n name='django-forbidden-ip',\n version=0.1,\n description='IP forbidding utilities for Django2.0',\n long_description=long_description,\n author='Jason Qiao',\n author_email='jasonqiao36@gmail.com',\n python_requires='>=3.6.0',\n url='https://github.com/jasonqiao36/django-forbidden-ip',\n packages=find_packages(),\n install_requires=[\n 'Django>=2.0',\n ],\n include_package_data=True,\n zip_safe=False,\n license='MIT',\n classifiers=[\n 'Development Status :: 3 - Alpha',\n 'Intended Audience :: Developers',\n 'Framework :: Django',\n 'Environment :: Plugins',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 3.6',\n ],\n)"}],"string":"[\n {\n \"alpha_fraction\": 0.7239709496498108,\n \"alphanum_fraction\": 0.7336561679840088,\n \"avg_line_length\": 23.294116973876953,\n \"blob_id\": \"d9c31f838fe0c56e086c49226e163af42b88e5b4\",\n \"content_id\": \"c39bc7887f5d61a133ec5c9d281dbc78725492c6\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 413,\n \"license_type\": \"permissive\",\n \"max_line_length\": 92,\n \"num_lines\": 17,\n \"path\": \"/README.md\",\n \"repo_name\": \"qiaocci/django-forbidden-ip\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"django-forbidden-ip\\n---\\n\\nThis is a django application to forbid access by IP address.\\n\\nInstall\\n===\\n\\n pip install django-forbidden-ip\\n \\nConfig\\n===\\n\\n1. Add `ip_interceptor` to `INSTALLED_APPS`.\\n2. Add `ip_interceptor.middleware.IPInterceptorMiddleware` to `MIDDLEWARE`.\\n3. Run `./manage.py makemigrations ip_interceptor` and `./manage.py migrate ip_interceptor`.\\n4. Add IP you want to block to `ForbiddenIP`.\\n\"\n },\n {\n \"alpha_fraction\": 0.6501650214195251,\n \"alphanum_fraction\": 0.6666666865348816,\n \"avg_line_length\": 24.25,\n \"blob_id\": \"049c7a6ee4e5d2712469c5f9e1a7a993c0c76caf\",\n \"content_id\": \"fad90f20416cecc80a1279ee6b13510777912154\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 303,\n \"license_type\": \"permissive\",\n \"max_line_length\": 59,\n \"num_lines\": 12,\n \"path\": \"/ip_interceptor/models.py\",\n \"repo_name\": \"qiaocci/django-forbidden-ip\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.db import models\\n\\n\\nclass ForbiddenIP(models.Model):\\n ip = models.CharField(max_length=18)\\n reason = models.CharField(max_length=200, null=True)\\n\\n class Meta:\\n verbose_name = verbose_name_plural = 'forbidden_ip'\\n\\n def __str__(self):\\n return f'ForbiddenIP:{self.ip}'\\n\"\n },\n {\n \"alpha_fraction\": 0.6881533265113831,\n \"alphanum_fraction\": 0.6881533265113831,\n \"avg_line_length\": 29.210525512695312,\n \"blob_id\": \"36704f7076bd5cea8c69cc62cd1188efeb7e019c\",\n \"content_id\": \"20d2690b568a33f480d46c9e7f91c6576f85c23f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 574,\n \"license_type\": \"permissive\",\n \"max_line_length\": 60,\n \"num_lines\": 19,\n \"path\": \"/ip_interceptor/middleware.py\",\n \"repo_name\": \"qiaocci/django-forbidden-ip\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.http import HttpResponseForbidden\\nfrom django.utils.deprecation import MiddlewareMixin\\n\\nfrom .models import ForbiddenIP\\n\\n\\nclass IPInterceptorMiddleware(MiddlewareMixin):\\n def __init__(self, get_reqponse):\\n self.get_response = get_reqponse\\n\\n def validate_ip(self, remote_ip):\\n if ForbiddenIP.objects.filter(ip=remote_ip).count():\\n return False\\n return True\\n\\n def process_request(self, request):\\n ip = request.META.get('REMOTE_ADDR')\\n if not self.validate_ip(ip):\\n return HttpResponseForbidden('')\\n\"\n },\n {\n \"alpha_fraction\": 0.5950413346290588,\n \"alphanum_fraction\": 0.6127508878707886,\n \"avg_line_length\": 27.266666412353516,\n \"blob_id\": \"ca8034976a21691c64a20d06e1f28ad4175e7fd5\",\n \"content_id\": \"1d07f8c169d424e449fc2f428bdbc38d556c4a67\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 847,\n \"license_type\": \"permissive\",\n \"max_line_length\": 61,\n \"num_lines\": 30,\n \"path\": \"/setup.py\",\n \"repo_name\": \"qiaocci/django-forbidden-ip\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from setuptools import find_packages, setup\\n\\nwith open('README.md', encoding='utf8') as f:\\n long_description = f.read()\\n\\nsetup(\\n name='django-forbidden-ip',\\n version=0.1,\\n description='IP forbidding utilities for Django2.0',\\n long_description=long_description,\\n author='Jason Qiao',\\n author_email='jasonqiao36@gmail.com',\\n python_requires='>=3.6.0',\\n url='https://github.com/jasonqiao36/django-forbidden-ip',\\n packages=find_packages(),\\n install_requires=[\\n 'Django>=2.0',\\n ],\\n include_package_data=True,\\n zip_safe=False,\\n license='MIT',\\n classifiers=[\\n 'Development Status :: 3 - Alpha',\\n 'Intended Audience :: Developers',\\n 'Framework :: Django',\\n 'Environment :: Plugins',\\n 'License :: OSI Approved :: MIT License',\\n 'Programming Language :: Python :: 3.6',\\n ],\\n)\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2979168,"cells":{"repo_name":{"kind":"string","value":"AjManzoor/WebScraping"},"repo_url":{"kind":"string","value":"https://github.com/AjManzoor/WebScraping"},"snapshot_id":{"kind":"string","value":"a6ca199f485c567e0a853eced0a0f8c2a99f262c"},"revision_id":{"kind":"string","value":"80fd7937a9528d066c2f89c864b0d7409ecfee08"},"directory_id":{"kind":"string","value":"f13913b5e027bb79d189a880c1d750af67ffbdae"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-03-22T21:36:47.276627","string":"2020-03-22T21:36:47.276627"},"revision_date":{"kind":"timestamp","value":"2018-07-12T10:54:38","string":"2018-07-12T10:54:38"},"committer_date":{"kind":"timestamp","value":"2018-07-12T10:54:38","string":"2018-07-12T10:54:38"},"github_id":{"kind":"number","value":140700556,"string":"140,700,556"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6551724076271057,"alphanum_fraction":0.6650246381759644,"avg_line_length":29.25,"blob_id":"297d41553b8c86f4434322944d370e694a232711","content_id":"57a16d8a429c5ed3cf4933778ffa92dc9090482b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1218,"license_type":"no_license","max_line_length":82,"num_lines":40,"path":"/WebScrapingReddit.py","repo_name":"AjManzoor/WebScraping","src_encoding":"UTF-8","text":"import selenium.webdriver as webdriver\nimport time\nimport os\nfrom selenium.webdriver.common.action_chains import ActionChains\nimport sys\nimport string\n\ndef clean_string(text):\n non_bmp_map = dict.fromkeys(range(0x10000, sys.maxunicode + 1), 0xfffd)\n clean_reddit_item = text\n clean_reddit_item = clean_reddit_item.translate(non_bmp_map)\n table = str.maketrans({key: None for key in string.punctuation})\n clean_reddit_item = clean_reddit_item.translate(table)\n clean_reddit_item = clean_reddit_item.lower()\n return clean_reddit_item\n\n \ndef get_results():\n\n \n all_results = []\n new_url = \"https://www.old.reddit.com/r/StarWars\"\n browser = webdriver.Firefox()\n browser.get(new_url)\n time.sleep(2)\n for x in range(0,10):\n reddit_items = browser.find_elements_by_class_name(\"title.may-blank \")\n for reddit_item in reddit_items:\n clean_text = clean_string(reddit_item.text)\n all_results.append(clean_text) \n elem = browser.find_element_by_xpath('.//span[@class = \"next-button\"]//a')\n elem.click()\n \n browser.close()\n os.system(\"taskkill /im geckodriver.exe\")\n\n return (all_results)\n\n\nprint(get_results())\n\n\n\n \n"},{"alpha_fraction":0.5920982360839844,"alphanum_fraction":0.5958355665206909,"avg_line_length":36.95918273925781,"blob_id":"12c91d8ba6ca7fd9dfed0d5a1173959473d50a6f","content_id":"b4e0be8306ef916c934122f90796f8c4a5eafae6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1873,"license_type":"no_license","max_line_length":142,"num_lines":49,"path":"/Neo4j.py","repo_name":"AjManzoor/WebScraping","src_encoding":"UTF-8","text":"from neo4j.v1 import GraphDatabase\n\nuri = \"bolt://localhost:7687\"\ndriver = GraphDatabase.driver(uri, auth=(\"neo4j\", \"password\"))\n\ndef add_activity_to_graph(Name):\n\n if not check_if_exists('Activity',Name):\n with driver.session() as session:\n with session.begin_transaction() as tx:\n result = tx.run(\"CREATE(a:Activity {name:{Name}}) RETURN a.name\", Name= Name)\n print ([record[\"a.name\"] for record in result])\n else:\n print(\"This tag already exists\")\n\n\n\ndef add_tag_to_graph(Name):\n if not check_if_exists('Tag',Name):\n with driver.session() as session:\n with session.begin_transaction() as tx:\n tx.run(\"CREATE(a:Tag {name:{Name}}) RETURN a\", Name= Name)\n\ndef add_web_tag_to_graph(Name):\n if not check_if_exists('WebTag',Name):\n with driver.session() as session:\n with session.begin_transaction() as tx:\n tx.run(\"CREATE(a:WebTag {name:{Name}}) RETURN a\", Name= Name)\n\n\ndef add_relationship_to_graph_wiki(Name,Tag,Weight):\n with driver.session() as session:\n with session.begin_transaction() as tx:\n tx.run(\"MATCH (a:Activity { name: '%s' }),(b:Tag{ name: '%s' }) MERGE (a)-[r:tag{weight:%s}]->(b) RETURN a\" % (Name, Tag, Weight))\n\ndef add_relationship_to_graph(Name,Tag,Weight):\n with driver.session() as session:\n with session.begin_transaction() as tx:\n tx.run(\"MATCH (a:Activity { name: '%s' }),(b:Tag{ name: '%s' }) MERGE (a)-[r:tag{weight:%s}]->(b) RETURN a\" % (Name, Tag, Weight))\n\n\n\n\ndef check_if_exists(Node, Name):\n with driver.session() as session:\n with session.begin_transaction() as tx:\n result = tx.run(\"MATCH(a:%s {name:'%s'}) Return a.name\" % (Node, Name))\n values = ([record[\"a.name\"] for record in result])\n return values\n \n"},{"alpha_fraction":0.4902048110961914,"alphanum_fraction":0.49287623167037964,"avg_line_length":25.104650497436523,"blob_id":"f11e4870545c0a5f57131e8893003008978cf564","content_id":"118e3ae488651c766ea46925b88acf3bf39d1788","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2246,"license_type":"no_license","max_line_length":116,"num_lines":86,"path":"/TFIDF2.py","repo_name":"AjManzoor/WebScraping","src_encoding":"UTF-8","text":"import pandas as pd\nimport math\n\ndoc_a = [\"the cat sat on my face\", \"the dog sat on my bed\", \"the rat sat on my head\", \"the elephant sat on my bike\"]\n\ndef calc_tfidf(doc_a, stop_word_list):\n bow_a = []\n for x in range(len(doc_a)):\n bow_a.append(doc_a[x].split())\n\n word_set = set()\n\n for i in range(len(bow_a)):\n word_set = word_set.union(set(bow_a[i]))\n\n word_dict_a = []\n\n for x in range(len(doc_a)):\n word_dict_a.append(dict.fromkeys(word_set,0)) \n\n\n for x in range(len(bow_a)):\n for word in bow_a[x]:\n word_dict_a[x][word] +=1\n\n\n\n def compute_TF(word_dict, bow):\n tf_dict = {}\n bow_count = len(bow)\n for word, count in word_dict.items():\n tf_dict[word] = count / float(bow_count)\n return tf_dict\n\n tf_bow_a = []\n\n for m in range(len(word_dict_a)):\n tf_bow_a.append(compute_TF(word_dict_a[m], bow_a[m]))\n \n\n def compute_IDF(doc_list):\n idf_dict = {}\n N = len(doc_list)\n\n idf_dict = dict.fromkeys(doc_list[0].keys(),0)\n for doc in doc_list:\n for word, val in doc.items():\n if val > 0:\n idf_dict[word] +=1\n\n for word, val in idf_dict.items():\n idf_dict[word] = math.log(N/float(val))\n\n return idf_dict\n \n\n idfs = compute_IDF(word_dict_a)\n\n def compute_TFIDF(tf_bow, idfs):\n tfidf = {}\n for word, val in tf_bow.items():\n tfidf[word] = val * idfs[word]\n return tfidf\n\n tfidf_bow_a = []\n for u in range(len(tf_bow_a)):\n tfidf_bow_a.append(compute_TFIDF(tf_bow_a[u], idfs))\n \n\n tfidf_set_a = set()\n final_tfidf = {}\n\n for r in range(len(tfidf_bow_a)):\n for word , val in tfidf_bow_a[r].items():\n if not word.isdigit() and word not in stop_word_list:\n if word not in final_tfidf :\n final_tfidf[word] = val\n else:\n try:\n if final_tfidf[word] < val:\n final_tfidf[word] = val\n except:\n print(word)\n print('error key') \n\n return final_tfidf\n\n"},{"alpha_fraction":0.6160374879837036,"alphanum_fraction":0.6236464977264404,"avg_line_length":25.6796875,"blob_id":"dc270eb77b5ef181587827f1a0be64785ae361bb","content_id":"a5e734bef97f8f99dd345aacc7eab593e1ec9d1b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3417,"license_type":"no_license","max_line_length":88,"num_lines":128,"path":"/WikipediaAPIScraper.py","repo_name":"AjManzoor/WebScraping","src_encoding":"UTF-8","text":"import os\nimport sys\n\nscript_paths = [\"/TFIDF2.py\", \"Neo4j.py\"]\n\nfor path in range(len(script_paths)):\n sys.path.append(os.path.abspath(script_paths[path]))\n\nimport wikipediaapi\nimport wikipedia\nimport string\nimport operator\nimport math\nimport numpy\nimport TFIDF2 as tf_idf\nimport Neo4j\n\n#print(wikipedia.summary('Coventry Transport Museum'))\n\n\ndef stop_word_list():\n stop_word_array2 = []\n F = open('stopwords.txt','r') \n stop_word_array = F.readlines()\n for word in stop_word_array:\n word = word.replace('\\n', '')\n stop_word_array2.append(word)\n\n return stop_word_array2\n\ndef activities_list():\n activites_array2 = []\n F = open('activities.txt','r') \n activities_array = F.readlines()\n for word in activities_array:\n word = word.replace('\\n', '')\n activites_array2.append(word)\n\n return activites_array2\n\ndef print_sections(sections,title_array, level=0):\n for s in sections:\n #print(s.title)\n title_array.append(s.title)\n #print(title_array)\n print_sections(s.sections, title_array, level + 1)\n return title_array\n\ndef clean_text(dirty_text):\n try:\n translator = str.maketrans(' ',' ', string.punctuation)\n dirty_text= dirty_text.translate(translator)\n dirty_text = dirty_text.replace('\\n', ' ')\n dirty_text = dirty_text.lower()\n return dirty_text\n except:\n print(' error')\n\ndef extract_key_word(sentence):\n words = ['club','soceity','volunteering','for', 'the ', 'coventry']\n sentence = sentence.lower() \n for word in words:\n if word in sentence:\n sentence = sentence.replace(word, '')\n \n sentence = sentence.strip()\n return sentence\n \n \n\n\ndef generate_tags(activity, stop_word_list):\n\n stop_word_list = stop_word_list \n data = {}\n all_documents = []\n all_documents\n \n page_search = activity\n\n wiki_wiki = wikipediaapi.Wikipedia('en')\n\n page_py = wiki_wiki.page(page_search)\n try:\n search_item = wikipedia.page(page_search)\n except wikipedia.exceptions.DisambiguationError as e:\n print(e.options[0])\n search_item = wikipedia.page(e.options[0])\n activity = e.options[0]\n \n sections_array = print_sections(page_py.sections, [])\n #print(sections_array)\n \n\n\n #print(wikipedia.summary('Coventry Transport Museum'))\n cleaned_text= clean_text(wikipedia.summary(activity))\n all_documents.append(cleaned_text)\n\n for section in range(len(sections_array)): \n cleaned_text = clean_text(search_item.section(sections_array[section]))\n if cleaned_text:\n all_documents.append(cleaned_text)\n \n #print(all_documents)\n calculated_tfidf = tf_idf.calc_tfidf(all_documents, stop_word_list)\n\n sorted_data = reversed(sorted(calculated_tfidf.items(), key=operator.itemgetter(1)))\n\n Neo4j.add_activity_to_graph(activity)\n\n x = 0\n for items in sorted_data:\n if x < 50:\n #print(items)\n Neo4j.add_tag_to_graph(items[0])\n Neo4j.add_relationship_to_graph(activity, items[0] ,items[1])\n x+=1\n else:\n break\n \n \nactivity_list = activities_list()\nfor activity in activity_list:\n key_word = extract_key_word(activity)\n print(key_word)\n generate_tags(key_word, stop_word_list())\nprint('done')\n\n\n"},{"alpha_fraction":0.7003081440925598,"alphanum_fraction":0.7033898234367371,"avg_line_length":28.045454025268555,"blob_id":"7e9554cdc38cd0d589ea51a39668cdc8133890ae","content_id":"de57a4a47859c558fb0a3341b930408ccb911a48","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1298,"license_type":"no_license","max_line_length":95,"num_lines":44,"path":"/Main.py","repo_name":"AjManzoor/WebScraping","src_encoding":"UTF-8","text":"import os\nimport sys\nimport operator\nimport string\nimport nltk\nfrom textblob import TextBlob\n\nscript_paths = [\"/TFIDF.py\",\"NewsHeadlinesScraper.py\"]\nfor path in range(len(script_paths)):\n sys.path.append(os.path.abspath(script_paths[path]))\n\nimport TFIDF\n#import Neo4j\nimport NewsHeadlinesScraper\n\n\nall_headlines = NewsHeadlinesScraper.get_results(\"ufc\")\nprint(all_headlines)\n\n\ndata = {}\nnoun_phrases = []\n\ntranslator = str.maketrans('','', string.punctuation)\n\nfor headline in range(len(all_headlines)-1):\n clean_string = all_headlines[headline].translate(translator)\n print(clean_string + \" clean string\") \n words = TextBlob(clean_string)\n noun_phrases += words.noun_phrases\n tokenised_string = clean_string.split(\" \")\n #print(tokenised_string)\n for word in range(len(tokenised_string)-1):\n tfidf_tf_val = TFIDF.term_frequency(tokenised_string[word], all_headlines[headline])\n print(all_headlines[headline])\n tfidf_idf_val = TFIDF.inverse_document_frequency(tokenised_string[word], all_headlines)\n final_val = tfidf_tf_val * tfidf_tf_val\n data[tokenised_string[word]] = final_val \nsorted_data = sorted(data.items(), key=operator.itemgetter(1))\n\nfor items in sorted_data:\n print(items)\n \nprint(noun_phrases) \n \n"},{"alpha_fraction":0.658703088760376,"alphanum_fraction":0.6666666865348816,"avg_line_length":24.676469802856445,"blob_id":"4bb55186c8e5ed8b202dcd03b0f22fd006150724","content_id":"fe983dfd0eb71764af1383d30b00a3dc36ccd936","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":879,"license_type":"no_license","max_line_length":69,"num_lines":34,"path":"/NewsHeadlinesScraper.py","repo_name":"AjManzoor/WebScraping","src_encoding":"UTF-8","text":"import selenium.webdriver as webdriver\nimport time\nimport os\n\ndef get_results(search_term):\n\n all_results = []\n url = \"https://news.google.com/news/?ned=uk&gl=GB&hl=en-GB\"\n browser = webdriver.Firefox()\n browser.get(url)\n \n search_box = browser.find_element_by_class_name(\"Ax4B8.ZAGvjd\")\n search_box.send_keys(search_term)\n search_box.send_keys(u'\\ue007')\n search_box.submit()\n new_url = browser.current_url\n print(new_url)\n browser.close()\n os.system(\"taskkill /im geckodriver.exe\")\n\n browser = webdriver.Firefox()\n browser.get(new_url)\n time.sleep(2)\n news_items = browser.find_elements_by_class_name(\"ipQwMb.Q7tWef\")\n for news_item in news_items:\n all_results.append(news_item.text)\n\n browser.close()\n os.system(\"taskkill /im geckodriver.exe\")\n\n return (all_results)\n\n\nprint(get_results(\"football\"))\n\n \n"},{"alpha_fraction":0.5585284233093262,"alphanum_fraction":0.568561851978302,"avg_line_length":26,"blob_id":"c717c3f3ebc2d310718b64b9d2259670e107bb03","content_id":"aa9cbd95966e02f356839043cb011ce79598ed5f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":299,"license_type":"no_license","max_line_length":37,"num_lines":11,"path":"/StopWords.py","repo_name":"AjManzoor/WebScraping","src_encoding":"UTF-8","text":"def stop_word_list():\n stop_word_array2 = []\n F = open('stopwords.txt','r') \n stop_word_array = F.readlines()\n for word in stop_word_array:\n print(word)\n word = word.replace('\\n', '')\n print(word)\n stop_word_array2.append(word)\n\n return stop_word_array2 \n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6551724076271057,\n \"alphanum_fraction\": 0.6650246381759644,\n \"avg_line_length\": 29.25,\n \"blob_id\": \"297d41553b8c86f4434322944d370e694a232711\",\n \"content_id\": \"57a16d8a429c5ed3cf4933778ffa92dc9090482b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1218,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 40,\n \"path\": \"/WebScrapingReddit.py\",\n \"repo_name\": \"AjManzoor/WebScraping\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import selenium.webdriver as webdriver\\nimport time\\nimport os\\nfrom selenium.webdriver.common.action_chains import ActionChains\\nimport sys\\nimport string\\n\\ndef clean_string(text):\\n non_bmp_map = dict.fromkeys(range(0x10000, sys.maxunicode + 1), 0xfffd)\\n clean_reddit_item = text\\n clean_reddit_item = clean_reddit_item.translate(non_bmp_map)\\n table = str.maketrans({key: None for key in string.punctuation})\\n clean_reddit_item = clean_reddit_item.translate(table)\\n clean_reddit_item = clean_reddit_item.lower()\\n return clean_reddit_item\\n\\n \\ndef get_results():\\n\\n \\n all_results = []\\n new_url = \\\"https://www.old.reddit.com/r/StarWars\\\"\\n browser = webdriver.Firefox()\\n browser.get(new_url)\\n time.sleep(2)\\n for x in range(0,10):\\n reddit_items = browser.find_elements_by_class_name(\\\"title.may-blank \\\")\\n for reddit_item in reddit_items:\\n clean_text = clean_string(reddit_item.text)\\n all_results.append(clean_text) \\n elem = browser.find_element_by_xpath('.//span[@class = \\\"next-button\\\"]//a')\\n elem.click()\\n \\n browser.close()\\n os.system(\\\"taskkill /im geckodriver.exe\\\")\\n\\n return (all_results)\\n\\n\\nprint(get_results())\\n\\n\\n\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.5920982360839844,\n \"alphanum_fraction\": 0.5958355665206909,\n \"avg_line_length\": 36.95918273925781,\n \"blob_id\": \"12c91d8ba6ca7fd9dfed0d5a1173959473d50a6f\",\n \"content_id\": \"b4e0be8306ef916c934122f90796f8c4a5eafae6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1873,\n \"license_type\": \"no_license\",\n \"max_line_length\": 142,\n \"num_lines\": 49,\n \"path\": \"/Neo4j.py\",\n \"repo_name\": \"AjManzoor/WebScraping\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from neo4j.v1 import GraphDatabase\\n\\nuri = \\\"bolt://localhost:7687\\\"\\ndriver = GraphDatabase.driver(uri, auth=(\\\"neo4j\\\", \\\"password\\\"))\\n\\ndef add_activity_to_graph(Name):\\n\\n if not check_if_exists('Activity',Name):\\n with driver.session() as session:\\n with session.begin_transaction() as tx:\\n result = tx.run(\\\"CREATE(a:Activity {name:{Name}}) RETURN a.name\\\", Name= Name)\\n print ([record[\\\"a.name\\\"] for record in result])\\n else:\\n print(\\\"This tag already exists\\\")\\n\\n\\n\\ndef add_tag_to_graph(Name):\\n if not check_if_exists('Tag',Name):\\n with driver.session() as session:\\n with session.begin_transaction() as tx:\\n tx.run(\\\"CREATE(a:Tag {name:{Name}}) RETURN a\\\", Name= Name)\\n\\ndef add_web_tag_to_graph(Name):\\n if not check_if_exists('WebTag',Name):\\n with driver.session() as session:\\n with session.begin_transaction() as tx:\\n tx.run(\\\"CREATE(a:WebTag {name:{Name}}) RETURN a\\\", Name= Name)\\n\\n\\ndef add_relationship_to_graph_wiki(Name,Tag,Weight):\\n with driver.session() as session:\\n with session.begin_transaction() as tx:\\n tx.run(\\\"MATCH (a:Activity { name: '%s' }),(b:Tag{ name: '%s' }) MERGE (a)-[r:tag{weight:%s}]->(b) RETURN a\\\" % (Name, Tag, Weight))\\n\\ndef add_relationship_to_graph(Name,Tag,Weight):\\n with driver.session() as session:\\n with session.begin_transaction() as tx:\\n tx.run(\\\"MATCH (a:Activity { name: '%s' }),(b:Tag{ name: '%s' }) MERGE (a)-[r:tag{weight:%s}]->(b) RETURN a\\\" % (Name, Tag, Weight))\\n\\n\\n\\n\\ndef check_if_exists(Node, Name):\\n with driver.session() as session:\\n with session.begin_transaction() as tx:\\n result = tx.run(\\\"MATCH(a:%s {name:'%s'}) Return a.name\\\" % (Node, Name))\\n values = ([record[\\\"a.name\\\"] for record in result])\\n return values\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.4902048110961914,\n \"alphanum_fraction\": 0.49287623167037964,\n \"avg_line_length\": 25.104650497436523,\n \"blob_id\": \"f11e4870545c0a5f57131e8893003008978cf564\",\n \"content_id\": \"118e3ae488651c766ea46925b88acf3bf39d1788\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2246,\n \"license_type\": \"no_license\",\n \"max_line_length\": 116,\n \"num_lines\": 86,\n \"path\": \"/TFIDF2.py\",\n \"repo_name\": \"AjManzoor/WebScraping\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import pandas as pd\\nimport math\\n\\ndoc_a = [\\\"the cat sat on my face\\\", \\\"the dog sat on my bed\\\", \\\"the rat sat on my head\\\", \\\"the elephant sat on my bike\\\"]\\n\\ndef calc_tfidf(doc_a, stop_word_list):\\n bow_a = []\\n for x in range(len(doc_a)):\\n bow_a.append(doc_a[x].split())\\n\\n word_set = set()\\n\\n for i in range(len(bow_a)):\\n word_set = word_set.union(set(bow_a[i]))\\n\\n word_dict_a = []\\n\\n for x in range(len(doc_a)):\\n word_dict_a.append(dict.fromkeys(word_set,0)) \\n\\n\\n for x in range(len(bow_a)):\\n for word in bow_a[x]:\\n word_dict_a[x][word] +=1\\n\\n\\n\\n def compute_TF(word_dict, bow):\\n tf_dict = {}\\n bow_count = len(bow)\\n for word, count in word_dict.items():\\n tf_dict[word] = count / float(bow_count)\\n return tf_dict\\n\\n tf_bow_a = []\\n\\n for m in range(len(word_dict_a)):\\n tf_bow_a.append(compute_TF(word_dict_a[m], bow_a[m]))\\n \\n\\n def compute_IDF(doc_list):\\n idf_dict = {}\\n N = len(doc_list)\\n\\n idf_dict = dict.fromkeys(doc_list[0].keys(),0)\\n for doc in doc_list:\\n for word, val in doc.items():\\n if val > 0:\\n idf_dict[word] +=1\\n\\n for word, val in idf_dict.items():\\n idf_dict[word] = math.log(N/float(val))\\n\\n return idf_dict\\n \\n\\n idfs = compute_IDF(word_dict_a)\\n\\n def compute_TFIDF(tf_bow, idfs):\\n tfidf = {}\\n for word, val in tf_bow.items():\\n tfidf[word] = val * idfs[word]\\n return tfidf\\n\\n tfidf_bow_a = []\\n for u in range(len(tf_bow_a)):\\n tfidf_bow_a.append(compute_TFIDF(tf_bow_a[u], idfs))\\n \\n\\n tfidf_set_a = set()\\n final_tfidf = {}\\n\\n for r in range(len(tfidf_bow_a)):\\n for word , val in tfidf_bow_a[r].items():\\n if not word.isdigit() and word not in stop_word_list:\\n if word not in final_tfidf :\\n final_tfidf[word] = val\\n else:\\n try:\\n if final_tfidf[word] < val:\\n final_tfidf[word] = val\\n except:\\n print(word)\\n print('error key') \\n\\n return final_tfidf\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6160374879837036,\n \"alphanum_fraction\": 0.6236464977264404,\n \"avg_line_length\": 25.6796875,\n \"blob_id\": \"dc270eb77b5ef181587827f1a0be64785ae361bb\",\n \"content_id\": \"a5e734bef97f8f99dd345aacc7eab593e1ec9d1b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3417,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 128,\n \"path\": \"/WikipediaAPIScraper.py\",\n \"repo_name\": \"AjManzoor/WebScraping\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nimport sys\\n\\nscript_paths = [\\\"/TFIDF2.py\\\", \\\"Neo4j.py\\\"]\\n\\nfor path in range(len(script_paths)):\\n sys.path.append(os.path.abspath(script_paths[path]))\\n\\nimport wikipediaapi\\nimport wikipedia\\nimport string\\nimport operator\\nimport math\\nimport numpy\\nimport TFIDF2 as tf_idf\\nimport Neo4j\\n\\n#print(wikipedia.summary('Coventry Transport Museum'))\\n\\n\\ndef stop_word_list():\\n stop_word_array2 = []\\n F = open('stopwords.txt','r') \\n stop_word_array = F.readlines()\\n for word in stop_word_array:\\n word = word.replace('\\\\n', '')\\n stop_word_array2.append(word)\\n\\n return stop_word_array2\\n\\ndef activities_list():\\n activites_array2 = []\\n F = open('activities.txt','r') \\n activities_array = F.readlines()\\n for word in activities_array:\\n word = word.replace('\\\\n', '')\\n activites_array2.append(word)\\n\\n return activites_array2\\n\\ndef print_sections(sections,title_array, level=0):\\n for s in sections:\\n #print(s.title)\\n title_array.append(s.title)\\n #print(title_array)\\n print_sections(s.sections, title_array, level + 1)\\n return title_array\\n\\ndef clean_text(dirty_text):\\n try:\\n translator = str.maketrans(' ',' ', string.punctuation)\\n dirty_text= dirty_text.translate(translator)\\n dirty_text = dirty_text.replace('\\\\n', ' ')\\n dirty_text = dirty_text.lower()\\n return dirty_text\\n except:\\n print(' error')\\n\\ndef extract_key_word(sentence):\\n words = ['club','soceity','volunteering','for', 'the ', 'coventry']\\n sentence = sentence.lower() \\n for word in words:\\n if word in sentence:\\n sentence = sentence.replace(word, '')\\n \\n sentence = sentence.strip()\\n return sentence\\n \\n \\n\\n\\ndef generate_tags(activity, stop_word_list):\\n\\n stop_word_list = stop_word_list \\n data = {}\\n all_documents = []\\n all_documents\\n \\n page_search = activity\\n\\n wiki_wiki = wikipediaapi.Wikipedia('en')\\n\\n page_py = wiki_wiki.page(page_search)\\n try:\\n search_item = wikipedia.page(page_search)\\n except wikipedia.exceptions.DisambiguationError as e:\\n print(e.options[0])\\n search_item = wikipedia.page(e.options[0])\\n activity = e.options[0]\\n \\n sections_array = print_sections(page_py.sections, [])\\n #print(sections_array)\\n \\n\\n\\n #print(wikipedia.summary('Coventry Transport Museum'))\\n cleaned_text= clean_text(wikipedia.summary(activity))\\n all_documents.append(cleaned_text)\\n\\n for section in range(len(sections_array)): \\n cleaned_text = clean_text(search_item.section(sections_array[section]))\\n if cleaned_text:\\n all_documents.append(cleaned_text)\\n \\n #print(all_documents)\\n calculated_tfidf = tf_idf.calc_tfidf(all_documents, stop_word_list)\\n\\n sorted_data = reversed(sorted(calculated_tfidf.items(), key=operator.itemgetter(1)))\\n\\n Neo4j.add_activity_to_graph(activity)\\n\\n x = 0\\n for items in sorted_data:\\n if x < 50:\\n #print(items)\\n Neo4j.add_tag_to_graph(items[0])\\n Neo4j.add_relationship_to_graph(activity, items[0] ,items[1])\\n x+=1\\n else:\\n break\\n \\n \\nactivity_list = activities_list()\\nfor activity in activity_list:\\n key_word = extract_key_word(activity)\\n print(key_word)\\n generate_tags(key_word, stop_word_list())\\nprint('done')\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7003081440925598,\n \"alphanum_fraction\": 0.7033898234367371,\n \"avg_line_length\": 28.045454025268555,\n \"blob_id\": \"7e9554cdc38cd0d589ea51a39668cdc8133890ae\",\n \"content_id\": \"de57a4a47859c558fb0a3341b930408ccb911a48\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1298,\n \"license_type\": \"no_license\",\n \"max_line_length\": 95,\n \"num_lines\": 44,\n \"path\": \"/Main.py\",\n \"repo_name\": \"AjManzoor/WebScraping\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nimport sys\\nimport operator\\nimport string\\nimport nltk\\nfrom textblob import TextBlob\\n\\nscript_paths = [\\\"/TFIDF.py\\\",\\\"NewsHeadlinesScraper.py\\\"]\\nfor path in range(len(script_paths)):\\n sys.path.append(os.path.abspath(script_paths[path]))\\n\\nimport TFIDF\\n#import Neo4j\\nimport NewsHeadlinesScraper\\n\\n\\nall_headlines = NewsHeadlinesScraper.get_results(\\\"ufc\\\")\\nprint(all_headlines)\\n\\n\\ndata = {}\\nnoun_phrases = []\\n\\ntranslator = str.maketrans('','', string.punctuation)\\n\\nfor headline in range(len(all_headlines)-1):\\n clean_string = all_headlines[headline].translate(translator)\\n print(clean_string + \\\" clean string\\\") \\n words = TextBlob(clean_string)\\n noun_phrases += words.noun_phrases\\n tokenised_string = clean_string.split(\\\" \\\")\\n #print(tokenised_string)\\n for word in range(len(tokenised_string)-1):\\n tfidf_tf_val = TFIDF.term_frequency(tokenised_string[word], all_headlines[headline])\\n print(all_headlines[headline])\\n tfidf_idf_val = TFIDF.inverse_document_frequency(tokenised_string[word], all_headlines)\\n final_val = tfidf_tf_val * tfidf_tf_val\\n data[tokenised_string[word]] = final_val \\nsorted_data = sorted(data.items(), key=operator.itemgetter(1))\\n\\nfor items in sorted_data:\\n print(items)\\n \\nprint(noun_phrases) \\n \\n\"\n },\n {\n \"alpha_fraction\": 0.658703088760376,\n \"alphanum_fraction\": 0.6666666865348816,\n \"avg_line_length\": 24.676469802856445,\n \"blob_id\": \"4bb55186c8e5ed8b202dcd03b0f22fd006150724\",\n \"content_id\": \"fe983dfd0eb71764af1383d30b00a3dc36ccd936\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 879,\n \"license_type\": \"no_license\",\n \"max_line_length\": 69,\n \"num_lines\": 34,\n \"path\": \"/NewsHeadlinesScraper.py\",\n \"repo_name\": \"AjManzoor/WebScraping\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import selenium.webdriver as webdriver\\nimport time\\nimport os\\n\\ndef get_results(search_term):\\n\\n all_results = []\\n url = \\\"https://news.google.com/news/?ned=uk&gl=GB&hl=en-GB\\\"\\n browser = webdriver.Firefox()\\n browser.get(url)\\n \\n search_box = browser.find_element_by_class_name(\\\"Ax4B8.ZAGvjd\\\")\\n search_box.send_keys(search_term)\\n search_box.send_keys(u'\\\\ue007')\\n search_box.submit()\\n new_url = browser.current_url\\n print(new_url)\\n browser.close()\\n os.system(\\\"taskkill /im geckodriver.exe\\\")\\n\\n browser = webdriver.Firefox()\\n browser.get(new_url)\\n time.sleep(2)\\n news_items = browser.find_elements_by_class_name(\\\"ipQwMb.Q7tWef\\\")\\n for news_item in news_items:\\n all_results.append(news_item.text)\\n\\n browser.close()\\n os.system(\\\"taskkill /im geckodriver.exe\\\")\\n\\n return (all_results)\\n\\n\\nprint(get_results(\\\"football\\\"))\\n\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.5585284233093262,\n \"alphanum_fraction\": 0.568561851978302,\n \"avg_line_length\": 26,\n \"blob_id\": \"c717c3f3ebc2d310718b64b9d2259670e107bb03\",\n \"content_id\": \"aa9cbd95966e02f356839043cb011ce79598ed5f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 299,\n \"license_type\": \"no_license\",\n \"max_line_length\": 37,\n \"num_lines\": 11,\n \"path\": \"/StopWords.py\",\n \"repo_name\": \"AjManzoor/WebScraping\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def stop_word_list():\\n stop_word_array2 = []\\n F = open('stopwords.txt','r') \\n stop_word_array = F.readlines()\\n for word in stop_word_array:\\n print(word)\\n word = word.replace('\\\\n', '')\\n print(word)\\n stop_word_array2.append(word)\\n\\n return stop_word_array2 \\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":7,"string":"7"}}},{"rowIdx":2979169,"cells":{"repo_name":{"kind":"string","value":"zyrxvo/GR-and-Long-term-Stability"},"repo_url":{"kind":"string","value":"https://github.com/zyrxvo/GR-and-Long-term-Stability"},"snapshot_id":{"kind":"string","value":"eff98b74ca08f2a6cc22a798e7cbb5239f978a6e"},"revision_id":{"kind":"string","value":"03a75e98de44215a261efcec9594888f144344b2"},"directory_id":{"kind":"string","value":"09a6b88fd97bb635b4a639ff6faefe27221368a9"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-04-16T20:10:48.217824","string":"2023-04-16T20:10:48.217824"},"revision_date":{"kind":"timestamp","value":"2023-04-05T15:22:44","string":"2023-04-05T15:22:44"},"committer_date":{"kind":"timestamp","value":"2023-04-05T15:22:44","string":"2023-04-05T15:22:44"},"github_id":{"kind":"number","value":597187040,"string":"597,187,040"},"star_events_count":{"kind":"number","value":3,"string":"3"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6625533699989319,"alphanum_fraction":0.7071666121482849,"avg_line_length":39.53845977783203,"blob_id":"2f10e1f6a31936813385ebf33500f39eb31d0388","content_id":"71149c03078453770f02992dc9e20c556263e370","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2107,"license_type":"no_license","max_line_length":150,"num_lines":52,"path":"/run.py","repo_name":"zyrxvo/GR-and-Long-term-Stability","src_encoding":"UTF-8","text":"import rebound\nimport reboundx # !!! Uses a modified version of REBOUNDx, https://github.com/zyrxvo/reboundx/tree/GR_Sweep !!!\nimport numpy as np\nfrom sys import argv\n\ntwopi = 2.*np.pi\ntmax = twopi * 12.5e9 # Maximum integration time.\ndt = np.sqrt(11)*twopi/365.25 # Timestep of about 3.3 days.\nsave_interval = int(2e5*twopi/dt) # save snapshots every 200,000 years\nn = int(argv[1]) # Simulation number.\n\ndelta = 0.00163677264386899684\ntaus = np.insert(np.array([10**(8 + n*delta) for n in range(1279)]), 0,0) # Assuming 1280 simulations, with taus that span from 1e8 yrs to 2.5e11 yrs.\ntau = twopi * taus[n] # Rate of change for the strength of the General Relativistic correction term.\n\nfolder = 'archives/'\nfilename = folder + 'gr_{0:04d}.bin'.format(n)\n\ntry:\n sim = rebound.Simulation(filename)\n sim.automateSimulationArchive(filename, step=save_interval, deletefile=False)\nexcept:\n try:\n sim = rebound.Simulation('ss.bin')\n sim.automateSimulationArchive(filename, step=save_interval, deletefile=True)\n except:\n sim = rebound.Simulation()\n sim.add(['sun', 'mercury', 'venus', 'earth', 'mars', 'jupiter', 'saturn', 'uranus', 'neptune'], date='2000-01-01 12:00')\n sim.save('ss.bin')\n sim.automateSimulationArchive(filename, step=save_interval, deletefile=True)\n\nsim.move_to_com()\nsim.integrator = 'whckl'\nsim.dt = dt\nsim.ri_whfast.safe_mode = 0\nsim.ri_whfast.keep_unsynchronized = True\nsim.exit_min_distance = 1e-2 # ~4x the distance between the Earth and Moon.\nsim.exit_max_distance = 1000. # 1000 AU, Unlikely that a planet is bound.\n\nrebx = reboundx.Extras(sim)\ngr = rebx.load_force('gr_potential')\nrebx.add_force(gr)\ngr.params['c'] = tau # Set the rate of change for the strength of the General Relativistic correction term.\n# !!! Uses a modified version of REBOUNDx, https://github.com/zyrxvo/reboundx/tree/GR_Sweep !!!\n\nfate = ''\ntry: sim.integrate(tmax, exact_finish_time=False)\nexcept rebound.Escape as esc: fate = 'esc'\nexcept rebound.Encounter as enc: fate = 'enc'\n\nendname = 'completed/gr_{0:04d}_{1}.bin'.format(n, fate)\nsim.save(endname)"},{"alpha_fraction":0.5571696162223816,"alphanum_fraction":0.6077788472175598,"avg_line_length":33.41935348510742,"blob_id":"e131ccfbd20f7f0e5af3d25df2ae36185d86cfe6","content_id":"4d58c9577ec92c252fccfd33916b3be8d13fe4ec","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2134,"license_type":"no_license","max_line_length":141,"num_lines":62,"path":"/model.py","repo_name":"zyrxvo/GR-and-Long-term-Stability","src_encoding":"UTF-8","text":"import numpy as np\nimport multiprocessing as mp\n\ndef model(tau, tmax=12.5, dt=0.001, withoutg1GR=False):\n '''\n Diffusion model for imitating the instability time of Mercury.\n Time is in units of Gyr.\n Use tau=1e-9 to imitate No GR and tau=1e1000 to imitate constant GR.\n '''\n # Define values for Mercury and Jupiter perihelion precession frequencies\n g1 = 5.577\n g5 = 4.257\n g1max = 5.72367\n g_GR = 0.4298\n if withoutg1GR: g1 -= g_GR\n \n # Define the diffusion constants based on the timescales for instability given by Mogavero & Laskar (2021) and Laskar & Gastineau (2009).\n tscale_GR = 27.6 # Gyr\n D_GR = (( (g1max - g5)**2.0 ) / (4.0 * tscale_GR))\n kick_GR = np.sqrt(2 * D_GR * dt)\n\n tscale_noGR = 3.216507262547807 # Gyr\n D_noGR = (( (g1max - g5)**2.0 ) / (4.0 * tscale_noGR))\n kick_noGR = np.sqrt(2 * D_noGR * dt)\n \n # Solve the advection-diffusion equation numerically.\n t = 0\n while g1 > g5 and t < tmax:\n # While GR decreases, remove additional precession from GR (advection).\n if (t < tau and (not withoutg1GR)): g1 -= g_GR * min(1, dt/tau)\n \n # Linearly interpolate between GR and no GR regimes.\n alpha = max(1. - t/tau,0)\n kick = kick_GR*alpha + (1.-alpha)*kick_noGR\n \n # Kick g1 frequency in random direction (diffusion).\n g1 += kick*np.random.normal()\n\n # Upper reflecting barrier at g1max.\n if g1 > g1max: g1 = g1max - (g1-g1max)\n t += dt\n\n\n return t\n\ndef run_model(taus, nsamples=1, tmax=12.5, dt=0.001, withoutg1GR=False, processes=None):\n '''\n Run the advection-diffusion model in parallel over the given taus.\n nsamples determines many times to run the model for each tau.\n Time is in units of Gyr.\n '''\n model_data = []\n results = lambda res: model_data.append(res)\n \n p = mp.Pool(processes=processes)\n for i in range(nsamples):\n for tau in taus:\n p.apply_async(model, args=(tau, tmax, dt, withoutg1GR,), callback=results)\n p.close()\n p.join()\n \n return np.array(model_data)\n"},{"alpha_fraction":0.7512339353561401,"alphanum_fraction":0.8055281639099121,"avg_line_length":105.57894897460938,"blob_id":"90ad7689ee433c25a52f33f49bf1aa0436aa1cce","content_id":"559020f364a651d076b5a6c281265e1c5d0b41c5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2026,"license_type":"no_license","max_line_length":223,"num_lines":19,"path":"/README.md","repo_name":"zyrxvo/GR-and-Long-term-Stability","src_encoding":"UTF-8","text":"# General relativistic precession and the long-term stability of the solar system\n[](https://zenodo.org/badge/latestdoi/597187040)\n[](https://doi.org/10.5281/zenodo.7765188)\n\nA repository containing code to reproduce figures and data for [Brown & Rein (2023)](https://academic.oup.com/mnras/advance-article/doi/10.1093/mnras/stad719/7075888?guestAccessKey=7344134d-214e-41e5-ae56-cc96c317207d).\n\nThe full SimulationArcive dataset can be found on [Zenodo](https://doi.org/10.5281/zenodo.7765188).\n\n## Abstract\nThe long-term evolution of the solar system is chaotic. \nIn some cases, chaotic diffusion caused by an overlap of secular resonances can increase the eccentricity of planets when they enter into a linear secular resonance, driving the system to instability. \nPrevious work has shown that including general relativistic contributions to the planets' precession frequency is crucial when modelling the solar system. \nIt reduces the probability that the solar system destabilizes within 5 Gyr by a factor of 60. \nWe run 1280 additional $N$-body simulations of the solar system spanning 12.5 Gyr where we allow the GR precession rate to vary with time. \nWe develop a simple, unified, Fokker-Planck advection-diffusion model that can reproduce the instability time of Mercury with, without, and with time-varying GR precession. \nWe show that while ignoring GR precession does move Mercury's precession frequency closer to a resonance with Jupiter, this alone does not explain the increased instability rate. \nIt is necessary that there is also a significant increase in the rate of diffusion. \nWe find that the system responds smoothly to a change in the precession frequency: There is no critical GR precession frequency below which the solar system becomes significantly more unstable. \nOur results show that the long-term evolution of the solar system is well described with an advection-diffusion model. \n"}],"string":"[\n {\n \"alpha_fraction\": 0.6625533699989319,\n \"alphanum_fraction\": 0.7071666121482849,\n \"avg_line_length\": 39.53845977783203,\n \"blob_id\": \"2f10e1f6a31936813385ebf33500f39eb31d0388\",\n \"content_id\": \"71149c03078453770f02992dc9e20c556263e370\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2107,\n \"license_type\": \"no_license\",\n \"max_line_length\": 150,\n \"num_lines\": 52,\n \"path\": \"/run.py\",\n \"repo_name\": \"zyrxvo/GR-and-Long-term-Stability\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import rebound\\nimport reboundx # !!! Uses a modified version of REBOUNDx, https://github.com/zyrxvo/reboundx/tree/GR_Sweep !!!\\nimport numpy as np\\nfrom sys import argv\\n\\ntwopi = 2.*np.pi\\ntmax = twopi * 12.5e9 # Maximum integration time.\\ndt = np.sqrt(11)*twopi/365.25 # Timestep of about 3.3 days.\\nsave_interval = int(2e5*twopi/dt) # save snapshots every 200,000 years\\nn = int(argv[1]) # Simulation number.\\n\\ndelta = 0.00163677264386899684\\ntaus = np.insert(np.array([10**(8 + n*delta) for n in range(1279)]), 0,0) # Assuming 1280 simulations, with taus that span from 1e8 yrs to 2.5e11 yrs.\\ntau = twopi * taus[n] # Rate of change for the strength of the General Relativistic correction term.\\n\\nfolder = 'archives/'\\nfilename = folder + 'gr_{0:04d}.bin'.format(n)\\n\\ntry:\\n sim = rebound.Simulation(filename)\\n sim.automateSimulationArchive(filename, step=save_interval, deletefile=False)\\nexcept:\\n try:\\n sim = rebound.Simulation('ss.bin')\\n sim.automateSimulationArchive(filename, step=save_interval, deletefile=True)\\n except:\\n sim = rebound.Simulation()\\n sim.add(['sun', 'mercury', 'venus', 'earth', 'mars', 'jupiter', 'saturn', 'uranus', 'neptune'], date='2000-01-01 12:00')\\n sim.save('ss.bin')\\n sim.automateSimulationArchive(filename, step=save_interval, deletefile=True)\\n\\nsim.move_to_com()\\nsim.integrator = 'whckl'\\nsim.dt = dt\\nsim.ri_whfast.safe_mode = 0\\nsim.ri_whfast.keep_unsynchronized = True\\nsim.exit_min_distance = 1e-2 # ~4x the distance between the Earth and Moon.\\nsim.exit_max_distance = 1000. # 1000 AU, Unlikely that a planet is bound.\\n\\nrebx = reboundx.Extras(sim)\\ngr = rebx.load_force('gr_potential')\\nrebx.add_force(gr)\\ngr.params['c'] = tau # Set the rate of change for the strength of the General Relativistic correction term.\\n# !!! Uses a modified version of REBOUNDx, https://github.com/zyrxvo/reboundx/tree/GR_Sweep !!!\\n\\nfate = ''\\ntry: sim.integrate(tmax, exact_finish_time=False)\\nexcept rebound.Escape as esc: fate = 'esc'\\nexcept rebound.Encounter as enc: fate = 'enc'\\n\\nendname = 'completed/gr_{0:04d}_{1}.bin'.format(n, fate)\\nsim.save(endname)\"\n },\n {\n \"alpha_fraction\": 0.5571696162223816,\n \"alphanum_fraction\": 0.6077788472175598,\n \"avg_line_length\": 33.41935348510742,\n \"blob_id\": \"e131ccfbd20f7f0e5af3d25df2ae36185d86cfe6\",\n \"content_id\": \"4d58c9577ec92c252fccfd33916b3be8d13fe4ec\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2134,\n \"license_type\": \"no_license\",\n \"max_line_length\": 141,\n \"num_lines\": 62,\n \"path\": \"/model.py\",\n \"repo_name\": \"zyrxvo/GR-and-Long-term-Stability\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport multiprocessing as mp\\n\\ndef model(tau, tmax=12.5, dt=0.001, withoutg1GR=False):\\n '''\\n Diffusion model for imitating the instability time of Mercury.\\n Time is in units of Gyr.\\n Use tau=1e-9 to imitate No GR and tau=1e1000 to imitate constant GR.\\n '''\\n # Define values for Mercury and Jupiter perihelion precession frequencies\\n g1 = 5.577\\n g5 = 4.257\\n g1max = 5.72367\\n g_GR = 0.4298\\n if withoutg1GR: g1 -= g_GR\\n \\n # Define the diffusion constants based on the timescales for instability given by Mogavero & Laskar (2021) and Laskar & Gastineau (2009).\\n tscale_GR = 27.6 # Gyr\\n D_GR = (( (g1max - g5)**2.0 ) / (4.0 * tscale_GR))\\n kick_GR = np.sqrt(2 * D_GR * dt)\\n\\n tscale_noGR = 3.216507262547807 # Gyr\\n D_noGR = (( (g1max - g5)**2.0 ) / (4.0 * tscale_noGR))\\n kick_noGR = np.sqrt(2 * D_noGR * dt)\\n \\n # Solve the advection-diffusion equation numerically.\\n t = 0\\n while g1 > g5 and t < tmax:\\n # While GR decreases, remove additional precession from GR (advection).\\n if (t < tau and (not withoutg1GR)): g1 -= g_GR * min(1, dt/tau)\\n \\n # Linearly interpolate between GR and no GR regimes.\\n alpha = max(1. - t/tau,0)\\n kick = kick_GR*alpha + (1.-alpha)*kick_noGR\\n \\n # Kick g1 frequency in random direction (diffusion).\\n g1 += kick*np.random.normal()\\n\\n # Upper reflecting barrier at g1max.\\n if g1 > g1max: g1 = g1max - (g1-g1max)\\n t += dt\\n\\n\\n return t\\n\\ndef run_model(taus, nsamples=1, tmax=12.5, dt=0.001, withoutg1GR=False, processes=None):\\n '''\\n Run the advection-diffusion model in parallel over the given taus.\\n nsamples determines many times to run the model for each tau.\\n Time is in units of Gyr.\\n '''\\n model_data = []\\n results = lambda res: model_data.append(res)\\n \\n p = mp.Pool(processes=processes)\\n for i in range(nsamples):\\n for tau in taus:\\n p.apply_async(model, args=(tau, tmax, dt, withoutg1GR,), callback=results)\\n p.close()\\n p.join()\\n \\n return np.array(model_data)\\n\"\n },\n {\n \"alpha_fraction\": 0.7512339353561401,\n \"alphanum_fraction\": 0.8055281639099121,\n \"avg_line_length\": 105.57894897460938,\n \"blob_id\": \"90ad7689ee433c25a52f33f49bf1aa0436aa1cce\",\n \"content_id\": \"559020f364a651d076b5a6c281265e1c5d0b41c5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2026,\n \"license_type\": \"no_license\",\n \"max_line_length\": 223,\n \"num_lines\": 19,\n \"path\": \"/README.md\",\n \"repo_name\": \"zyrxvo/GR-and-Long-term-Stability\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# General relativistic precession and the long-term stability of the solar system\\n[](https://zenodo.org/badge/latestdoi/597187040)\\n[](https://doi.org/10.5281/zenodo.7765188)\\n\\nA repository containing code to reproduce figures and data for [Brown & Rein (2023)](https://academic.oup.com/mnras/advance-article/doi/10.1093/mnras/stad719/7075888?guestAccessKey=7344134d-214e-41e5-ae56-cc96c317207d).\\n\\nThe full SimulationArcive dataset can be found on [Zenodo](https://doi.org/10.5281/zenodo.7765188).\\n\\n## Abstract\\nThe long-term evolution of the solar system is chaotic. \\nIn some cases, chaotic diffusion caused by an overlap of secular resonances can increase the eccentricity of planets when they enter into a linear secular resonance, driving the system to instability. \\nPrevious work has shown that including general relativistic contributions to the planets' precession frequency is crucial when modelling the solar system. \\nIt reduces the probability that the solar system destabilizes within 5 Gyr by a factor of 60. \\nWe run 1280 additional $N$-body simulations of the solar system spanning 12.5 Gyr where we allow the GR precession rate to vary with time. \\nWe develop a simple, unified, Fokker-Planck advection-diffusion model that can reproduce the instability time of Mercury with, without, and with time-varying GR precession. \\nWe show that while ignoring GR precession does move Mercury's precession frequency closer to a resonance with Jupiter, this alone does not explain the increased instability rate. \\nIt is necessary that there is also a significant increase in the rate of diffusion. \\nWe find that the system responds smoothly to a change in the precession frequency: There is no critical GR precession frequency below which the solar system becomes significantly more unstable. \\nOur results show that the long-term evolution of the solar system is well described with an advection-diffusion model. \\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979170,"cells":{"repo_name":{"kind":"string","value":"LHGames-2017/nospace"},"repo_url":{"kind":"string","value":"https://github.com/LHGames-2017/nospace"},"snapshot_id":{"kind":"string","value":"cba0b70d84b9682881fcf27d69a157c540936014"},"revision_id":{"kind":"string","value":"1f36fb980ee51cdc576b765eff2c4ad5533ea0e3"},"directory_id":{"kind":"string","value":"91453217692354c88c63aa7969f919b0070e2136"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-16T22:22:58.698362","string":"2021-01-16T22:22:58.698362"},"revision_date":{"kind":"timestamp","value":"2017-10-08T00:13:28","string":"2017-10-08T00:13:28"},"committer_date":{"kind":"timestamp","value":"2017-10-08T00:13:28","string":"2017-10-08T00:13:28"},"github_id":{"kind":"number","value":106107920,"string":"106,107,920"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.4605149030685425,"alphanum_fraction":0.5377494692802429,"avg_line_length":23.69285774230957,"blob_id":"5c0354d9991cca99c26d7f5a12ea717d34e3f158","content_id":"18f19888d799691cd362156ae0369f4b11579e29","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3457,"license_type":"permissive","max_line_length":113,"num_lines":140,"path":"/structs.py","repo_name":"LHGames-2017/nospace","src_encoding":"UTF-8","text":"import math\n# lvl,value, cost\nHEALTHLVL = {0:[5,0],\n 1:[8,10000],\n 2:[10,20000],\n 3:[15,30000],\n 4:[20,50000],\n 5:[30,100000]}\n\nATTACKLVL = {0:[1,0],\n 1:[3,15000],\n 2:[5,50000],\n 3:[7,100000],\n 4:[9,250000],\n 5:[11,500000]}\n\nDEFENSELVL = {0:[1,0],\n 1:[3,15000],\n 2:[5,50000],\n 3:[7,100000],\n 4:[9,250000],\n 5:[11,500000]}\n\nCOLSPEEDLVL = {0:[1.0,0],\n 1:[1.25,15000],\n 2:[1.5,50000],\n 3:[2,100000],\n 4:[2.5,250000],\n 5:[3.5,500000]}\n\nCAPACITYLVL = {0:[1000,0],\n 1:[1500,15000],\n 2:[2500,50000],\n 3:[5000,100000],\n 4:[10000,250000],\n 5:[25000,500000]}\n\nclass ActionTypes():\n DefaultAction, MoveAction, AttackAction, CollectAction, UpgradeAction, StealAction, PurchaseAction = range(7)\n\nclass UpgradeType():\n CarryingCapacity, AttackPower, Defence, MaximumHealth, CollectingSpeed = range(5)\n\n\nclass TileType():\n Tile, Wall, House, Lava, Resource, Shop = range(6)\n\n\nclass TileContent():\n Empty, Wall, House, Lava, Resource, Shop, Player = range(7)\n\nclass Upgrades(object):\n levels = {}\n def __init__(self, lvl=0):\n self.Lvl=0\n \n def upgrade():\n self.Lvl = self.Lvl + 1\n \n def nextLvl(currentLvl):\n if currentLvl+1<5:\n return currentLvl+1\n else:\n return -1\n def nextLvlprice(currentLvl):\n nxtlvl = nextLvl(currentLvl)\n if nxtlvl!=-1:\n return levels[nxtlvl][1]\n return -1\n def setLvls(levelDict):\n self.levels=levelDict\n\n\nclass Point(object):\n\n # Constructor\n def __init__(self, X=0, Y=0):\n self.X = X\n self.Y = Y\n\n # Overloaded operators\n def __add__(self, point):\n return Point(self.X + point.X, self.Y + point.Y)\n\n def __sub__(self, point):\n return Point(self.X - point.X, self.Y - point.Y)\n\n def __str__(self):\n return \"{{{0}, {1}}}\".format(self.X, self.Y)\n\n # Distance between two Points\n def Distance(self, p1, p2):\n delta_x = p1.X - p2.X\n delta_y = p1.Y - p2.Y\n return math.sqrt(math.pow(delta_x, 2) + math.pow(delta_y, 2))\n\n\nclass GameInfo(object):\n\n def __init__(self, json_dict):\n self.__dict__ = json_dict\n self.HouseLocation = Point(json_dict[\"HouseLocation\"])\n self.Map = None\n self.Players = dict()\n\n\nclass Tile(object):\n\n def __init__(self, content=None, x=0, y=0):\n self.Content = content\n self.X = x\n self.Y = y\n\n\nclass Player(object):\n\n def __init__(self, health, maxHealth, position, houseLocation, score, carriedRessources,\n carryingCapacity=1000):\n self.Health = health\n self.MaxHealth = maxHealth\n self.Position = position\n self.HouseLocation = houseLocation\n self.Score = score\n self.CarriedRessources = carriedRessources\n self.CarryingCapacity = carryingCapacity\n\n \n\nclass PlayerInfo(object):\n\n def __init__(self, health, maxHealth, position):\n self.Health = health\n self.MaxHealth = maxHealth\n self.Position = position\n\nclass ActionContent(object):\n\n def __init__(self, action_name, content):\n self.ActionName = action_name\n self.Content = str(content)\n"},{"alpha_fraction":0.5318267941474915,"alphanum_fraction":0.5499290227890015,"avg_line_length":30.073530197143555,"blob_id":"df69ebb6f549d940c4f3ad781a7b4bd203ee23ca","content_id":"75e9882a624cfcf705ab7744f64aca22cda52bfb","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8452,"license_type":"permissive","max_line_length":172,"num_lines":272,"path":"/ai.py","repo_name":"LHGames-2017/nospace","src_encoding":"UTF-8","text":"from flask import Flask, request\nfrom structs import *\nimport json\nimport numpy as np\nimport sys\nimport random, time\napp = Flask(__name__)\ndx=0\ndy=0\n\ndef create_action(action_type, target):\n actionContent = ActionContent(action_type, target.__dict__)\n #print(actionContent)\n return json.dumps(actionContent.__dict__)\n\ndef create_move_action(target):\n return create_action(\"MoveAction\", Point(target.X-dx,target.Y-dy))\n\ndef create_attack_action(target):\n return create_action(\"AttackAction\", Point(target.X-dx,target.Y-dy))\n\ndef create_collect_action(target):\n return create_action(\"CollectAction\", Point(target.X-dx,target.Y-dy))\n\ndef create_steal_action(target):\n return create_action(\"StealAction\", Point(target.X-dx,target.Y-dy))\n\ndef create_heal_action():\n return create_action(\"HealAction\", \"\")\n\ndef create_purchase_action(item):\n return create_action(\"PurchaseAction\", item)\n\ndef deserialize_map(serialized_map):\n \"\"\"\n Fonction utilitaire pour comprendre la map\n \"\"\"\n serialized_map = serialized_map[1:]\n rows = serialized_map.split('[')\n column = rows[0].split('{')\n deserialized_map = [[Tile() for x in range(40)] for y in range(40)]\n for i in range(len(rows) - 1):\n column = rows[i + 1].split('{')\n\n for j in range(len(column) - 1):\n infos = column[j + 1].split(',')\n end_index = infos[2].find('}')\n content = int(infos[0])\n x = int(infos[1])\n y = int(infos[2][:end_index])\n deserialized_map[i][j] = Tile(content, x, y)\n return deserialized_map\n\n#customs\ndef visual(lines,x,y):\n \n for i in lines:\n line = ''\n for j in i[:20]:\n #Empty, Wall, House, Lava, Resource, Shop, Player\n #0 1 2 3 4 5 6\n line+=str(j.Content).replace('None','N').replace('0', ' ').replace('1','#').replace('2','^').replace('3','L').replace('4','$').replace('5','S').replace('6','o')\n print(line)\n\ndef distance(p1, p2):\n return math.sqrt((p2[0]-p1[0])**2+(p2[1]-p1[1])**2)\n\n'''\ndef searchg(x,y,grid,target, at):\n if grid[x][y] == target:\n at.append([x,y]) #found\n return True\n elif grid[x][y] == 1 or grid[x][y] == 3:\n return False #wall or lava\n elif grid[x][y] == 9:\n return False #been here\n \n at.append([x,y])\n grid[x][y] == 9\n \n if ((x<len(grid)-1 and search(x+1,y,grid,target, at))\n or (y > 0 and search(x, y-1,grid,target, at))\n or (x > 0 and search(x-1,y,grid,target, at))\n or (y < len(grid)-1 and search(x, y+1,grid,target, at))):\n return True\n return False\n'''\ndef search_next(me, target,m,dx,dy):\n x=me.Position.X\n y=me.Position.Y\n if me.CarriedRessources==me.CarryingCapacity:\n print('resource')\n target=me.HouseLocation\n #if distance([target.X,target.Y],[x,y])==0:\n # return create_collect_action(Point(x+dx, x+dy))\n neighbors = [[x+1,y],[x-1,y],[x,y+1],[x,y-1]]\n tNeighbors = []\n for neighbor in neighbors:\n tNeighbors.append([distance(neighbor,[target.X, target.Y]),neighbor])\n sortedNeighbors=sorted(tNeighbors, key=lambda x:x[0])\n print(sortedNeighbors)\n for n in sortedNeighbors:\n #print(target.__dict__)\n #print(x,y)\n #print('----------',n,'--------')\n #Empty, Wall, House, Lava, Resource, Shop, Player\n #0 1 2 3 4 5 6\n tile = m[n[1][0]-dx][n[1][1]-dy]\n #print(tile.__dict__)\n content = tile.Content\n point = Point(n[1][0],n[1][1])\n if content==0 or content==2:\n print('----move----',point)\n return create_move_action(point)\n elif content==1 or content == 6:\n print('attack',point)\n return create_attack_action(point)\n elif content==4:\n return create_collect_action(point)\n else:# content==3:\n print('skip')\n continue\n \ndef route(start, end, at, best=[]):\n best.append(end)\n for i in range(len(at)-1,-1,-1):\n if compare(at[i], best[-1]):\n best.append(at[i])\n best=best[::-1]\n return best\n\ndef compare(a,b):\n if (a[0]==b[0]) and (abs(a[1]-b[1])==1):\n return True\n elif (a[1]==b[1]) and (abs(a[0]-b[0])==1):\n return True\n else:\n return False\n \ndef arr2action(c,d):\n if c[0]==d[0]:\n if c[1]<d[1]:\n return create_move_action(Point(x+1,y))\n else:\n return create_move_action(Point(x-1,y))\n elif c[0]<d[0]:\n return create_move_action(Point(x,y-1))\n else:\n return create_move_action(Point(x,y+1))\n\ndef findTargets(mapmatrix, me):\n resources = []\n enemyhouses = []\n shops = []\n for row in mapmatrix:\n for tile in row:\n if tile.Content==4:\n resources.append(tile)\n elif tile.Content==2 and tile.Content!=me.HouseLocation:\n enemyhouses.append(tile)\n elif tile.Content==5:\n shops.append(tile)\n else:\n continue\n return [resources, enemyhouses, shops]\n\ndef decide(me, closestEnemies, targets, grid):\n try:\n distEn = closestEnemy[0][0]\n enemy = closestEnemy[0][1]\n except:\n distEn=0\n enemy = []\n distTarget = targets[0][0]\n target = targets[0][1]\n best=[]\n at=[]\n \n if distEn==1:\n #print('------1-------')\n return create_attack_action(Point(enemy.X,enemy.Y))\n elif distTarget==1 and target.Content==2:\n #print('------2-------')\n return create_collect_action(Point(target.X,target.Y))\n elif distTarget==0 and target.Content==4:\n #print('------3-------')\n return create_collect_action(Point(target.X,target.Y))\n else:\n #print('------4-------')\n #t = random.choice([1,0])\n #u = (t+1)%2\n #return create_move_action(Point(me.Position.X+t,me.Position.Y+u))\n return search_next(me, target, grid)\ndef bot():\n \"\"\"\n Main de votre bot.\n \"\"\"\n map_json = request.form[\"map\"]\n\n # Player info\n\n encoded_map = map_json.encode()\n map_json = json.loads(encoded_map)\n p = map_json[\"Player\"]\n pos = p[\"Position\"]\n x = pos[\"X\"]\n y = pos[\"Y\"]\n \n house = p[\"HouseLocation\"]\n player = Player(p[\"Health\"], p[\"MaxHealth\"], Point(x,y),\n Point(house[\"X\"], house[\"Y\"]), p[\"Score\"],\n p[\"CarriedResources\"], p[\"CarryingCapacity\"])\n\n # Map\n serialized_map = map_json[\"CustomSerializedMap\"]\n deserialized_map=deserialize_map(serialized_map)\n transposed=np.transpose(deserialized_map)\n \n \n targets = findTargets(deserialized_map, player)\n visual(transposed[:20][::-1],x,y)\n otherPlayers = []\n '''\n #print(map_json)\n for player_dict in map_json[\"OtherPlayers\"]:\n #print(player_dict)\n for player_name in player_dict.keys():\n player_info = player_dict[player_name]\n #print('---------')\n #print(player_info)\n #print('---------')\n p_pos = player_info[\"Position\"]\n player_info = PlayerInfo(player_info[\"Health\"],\n player_info[\"MaxHealth\"],\n Point(p_pos[\"X\"], p_pos[\"Y\"]))\n\n otherPlayers.append({player_name: player_info })\n '''\n # return decision\n #targets = \n tTargets = []\n for target in targets[0]:#+targets[1]:\n tTargets.append([distance([x,y],[target.X,target.Y]),target])\n sortedTargets = sorted(tTargets, key=lambda x:x[0])\n \n \n tEnemies = []\n for enemy in otherPlayers:\n tEnemies.append([distance([x,y],[enemy.X,enemy.Y]),enemy])\n sortedEnemies = sorted(tEnemies, key=lambda x:x[0])\n dx,dy=0,0\n for i,line in enumerate(deserialized_map):\n for j,tile in enumerate(line):\n if tile.X==x and tile.Y==y:\n dx = x-i\n dy = y-j\n #return decide(player, sortedEnemies, sortedTargets, deserialized_map)\n print(player.__dict__,player.Position.__dict__)\n return search_next(player, sortedTargets[0][1], deserialized_map,dx,dy)\n \n \n \n@app.route(\"/\", methods=[\"POST\"])\ndef reponse():\n \"\"\"\n Point d'entree appelle par le GameServer\n \"\"\"\n sys.stdout.flush()\n return bot()\n\nif __name__ == \"__main__\":\n app.run(host=\"0.0.0.0\", port=3000)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.4605149030685425,\n \"alphanum_fraction\": 0.5377494692802429,\n \"avg_line_length\": 23.69285774230957,\n \"blob_id\": \"5c0354d9991cca99c26d7f5a12ea717d34e3f158\",\n \"content_id\": \"18f19888d799691cd362156ae0369f4b11579e29\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3457,\n \"license_type\": \"permissive\",\n \"max_line_length\": 113,\n \"num_lines\": 140,\n \"path\": \"/structs.py\",\n \"repo_name\": \"LHGames-2017/nospace\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import math\\n# lvl,value, cost\\nHEALTHLVL = {0:[5,0],\\n 1:[8,10000],\\n 2:[10,20000],\\n 3:[15,30000],\\n 4:[20,50000],\\n 5:[30,100000]}\\n\\nATTACKLVL = {0:[1,0],\\n 1:[3,15000],\\n 2:[5,50000],\\n 3:[7,100000],\\n 4:[9,250000],\\n 5:[11,500000]}\\n\\nDEFENSELVL = {0:[1,0],\\n 1:[3,15000],\\n 2:[5,50000],\\n 3:[7,100000],\\n 4:[9,250000],\\n 5:[11,500000]}\\n\\nCOLSPEEDLVL = {0:[1.0,0],\\n 1:[1.25,15000],\\n 2:[1.5,50000],\\n 3:[2,100000],\\n 4:[2.5,250000],\\n 5:[3.5,500000]}\\n\\nCAPACITYLVL = {0:[1000,0],\\n 1:[1500,15000],\\n 2:[2500,50000],\\n 3:[5000,100000],\\n 4:[10000,250000],\\n 5:[25000,500000]}\\n\\nclass ActionTypes():\\n DefaultAction, MoveAction, AttackAction, CollectAction, UpgradeAction, StealAction, PurchaseAction = range(7)\\n\\nclass UpgradeType():\\n CarryingCapacity, AttackPower, Defence, MaximumHealth, CollectingSpeed = range(5)\\n\\n\\nclass TileType():\\n Tile, Wall, House, Lava, Resource, Shop = range(6)\\n\\n\\nclass TileContent():\\n Empty, Wall, House, Lava, Resource, Shop, Player = range(7)\\n\\nclass Upgrades(object):\\n levels = {}\\n def __init__(self, lvl=0):\\n self.Lvl=0\\n \\n def upgrade():\\n self.Lvl = self.Lvl + 1\\n \\n def nextLvl(currentLvl):\\n if currentLvl+1<5:\\n return currentLvl+1\\n else:\\n return -1\\n def nextLvlprice(currentLvl):\\n nxtlvl = nextLvl(currentLvl)\\n if nxtlvl!=-1:\\n return levels[nxtlvl][1]\\n return -1\\n def setLvls(levelDict):\\n self.levels=levelDict\\n\\n\\nclass Point(object):\\n\\n # Constructor\\n def __init__(self, X=0, Y=0):\\n self.X = X\\n self.Y = Y\\n\\n # Overloaded operators\\n def __add__(self, point):\\n return Point(self.X + point.X, self.Y + point.Y)\\n\\n def __sub__(self, point):\\n return Point(self.X - point.X, self.Y - point.Y)\\n\\n def __str__(self):\\n return \\\"{{{0}, {1}}}\\\".format(self.X, self.Y)\\n\\n # Distance between two Points\\n def Distance(self, p1, p2):\\n delta_x = p1.X - p2.X\\n delta_y = p1.Y - p2.Y\\n return math.sqrt(math.pow(delta_x, 2) + math.pow(delta_y, 2))\\n\\n\\nclass GameInfo(object):\\n\\n def __init__(self, json_dict):\\n self.__dict__ = json_dict\\n self.HouseLocation = Point(json_dict[\\\"HouseLocation\\\"])\\n self.Map = None\\n self.Players = dict()\\n\\n\\nclass Tile(object):\\n\\n def __init__(self, content=None, x=0, y=0):\\n self.Content = content\\n self.X = x\\n self.Y = y\\n\\n\\nclass Player(object):\\n\\n def __init__(self, health, maxHealth, position, houseLocation, score, carriedRessources,\\n carryingCapacity=1000):\\n self.Health = health\\n self.MaxHealth = maxHealth\\n self.Position = position\\n self.HouseLocation = houseLocation\\n self.Score = score\\n self.CarriedRessources = carriedRessources\\n self.CarryingCapacity = carryingCapacity\\n\\n \\n\\nclass PlayerInfo(object):\\n\\n def __init__(self, health, maxHealth, position):\\n self.Health = health\\n self.MaxHealth = maxHealth\\n self.Position = position\\n\\nclass ActionContent(object):\\n\\n def __init__(self, action_name, content):\\n self.ActionName = action_name\\n self.Content = str(content)\\n\"\n },\n {\n \"alpha_fraction\": 0.5318267941474915,\n \"alphanum_fraction\": 0.5499290227890015,\n \"avg_line_length\": 30.073530197143555,\n \"blob_id\": \"df69ebb6f549d940c4f3ad781a7b4bd203ee23ca\",\n \"content_id\": \"75e9882a624cfcf705ab7744f64aca22cda52bfb\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8452,\n \"license_type\": \"permissive\",\n \"max_line_length\": 172,\n \"num_lines\": 272,\n \"path\": \"/ai.py\",\n \"repo_name\": \"LHGames-2017/nospace\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Flask, request\\nfrom structs import *\\nimport json\\nimport numpy as np\\nimport sys\\nimport random, time\\napp = Flask(__name__)\\ndx=0\\ndy=0\\n\\ndef create_action(action_type, target):\\n actionContent = ActionContent(action_type, target.__dict__)\\n #print(actionContent)\\n return json.dumps(actionContent.__dict__)\\n\\ndef create_move_action(target):\\n return create_action(\\\"MoveAction\\\", Point(target.X-dx,target.Y-dy))\\n\\ndef create_attack_action(target):\\n return create_action(\\\"AttackAction\\\", Point(target.X-dx,target.Y-dy))\\n\\ndef create_collect_action(target):\\n return create_action(\\\"CollectAction\\\", Point(target.X-dx,target.Y-dy))\\n\\ndef create_steal_action(target):\\n return create_action(\\\"StealAction\\\", Point(target.X-dx,target.Y-dy))\\n\\ndef create_heal_action():\\n return create_action(\\\"HealAction\\\", \\\"\\\")\\n\\ndef create_purchase_action(item):\\n return create_action(\\\"PurchaseAction\\\", item)\\n\\ndef deserialize_map(serialized_map):\\n \\\"\\\"\\\"\\n Fonction utilitaire pour comprendre la map\\n \\\"\\\"\\\"\\n serialized_map = serialized_map[1:]\\n rows = serialized_map.split('[')\\n column = rows[0].split('{')\\n deserialized_map = [[Tile() for x in range(40)] for y in range(40)]\\n for i in range(len(rows) - 1):\\n column = rows[i + 1].split('{')\\n\\n for j in range(len(column) - 1):\\n infos = column[j + 1].split(',')\\n end_index = infos[2].find('}')\\n content = int(infos[0])\\n x = int(infos[1])\\n y = int(infos[2][:end_index])\\n deserialized_map[i][j] = Tile(content, x, y)\\n return deserialized_map\\n\\n#customs\\ndef visual(lines,x,y):\\n \\n for i in lines:\\n line = ''\\n for j in i[:20]:\\n #Empty, Wall, House, Lava, Resource, Shop, Player\\n #0 1 2 3 4 5 6\\n line+=str(j.Content).replace('None','N').replace('0', ' ').replace('1','#').replace('2','^').replace('3','L').replace('4','$').replace('5','S').replace('6','o')\\n print(line)\\n\\ndef distance(p1, p2):\\n return math.sqrt((p2[0]-p1[0])**2+(p2[1]-p1[1])**2)\\n\\n'''\\ndef searchg(x,y,grid,target, at):\\n if grid[x][y] == target:\\n at.append([x,y]) #found\\n return True\\n elif grid[x][y] == 1 or grid[x][y] == 3:\\n return False #wall or lava\\n elif grid[x][y] == 9:\\n return False #been here\\n \\n at.append([x,y])\\n grid[x][y] == 9\\n \\n if ((x<len(grid)-1 and search(x+1,y,grid,target, at))\\n or (y > 0 and search(x, y-1,grid,target, at))\\n or (x > 0 and search(x-1,y,grid,target, at))\\n or (y < len(grid)-1 and search(x, y+1,grid,target, at))):\\n return True\\n return False\\n'''\\ndef search_next(me, target,m,dx,dy):\\n x=me.Position.X\\n y=me.Position.Y\\n if me.CarriedRessources==me.CarryingCapacity:\\n print('resource')\\n target=me.HouseLocation\\n #if distance([target.X,target.Y],[x,y])==0:\\n # return create_collect_action(Point(x+dx, x+dy))\\n neighbors = [[x+1,y],[x-1,y],[x,y+1],[x,y-1]]\\n tNeighbors = []\\n for neighbor in neighbors:\\n tNeighbors.append([distance(neighbor,[target.X, target.Y]),neighbor])\\n sortedNeighbors=sorted(tNeighbors, key=lambda x:x[0])\\n print(sortedNeighbors)\\n for n in sortedNeighbors:\\n #print(target.__dict__)\\n #print(x,y)\\n #print('----------',n,'--------')\\n #Empty, Wall, House, Lava, Resource, Shop, Player\\n #0 1 2 3 4 5 6\\n tile = m[n[1][0]-dx][n[1][1]-dy]\\n #print(tile.__dict__)\\n content = tile.Content\\n point = Point(n[1][0],n[1][1])\\n if content==0 or content==2:\\n print('----move----',point)\\n return create_move_action(point)\\n elif content==1 or content == 6:\\n print('attack',point)\\n return create_attack_action(point)\\n elif content==4:\\n return create_collect_action(point)\\n else:# content==3:\\n print('skip')\\n continue\\n \\ndef route(start, end, at, best=[]):\\n best.append(end)\\n for i in range(len(at)-1,-1,-1):\\n if compare(at[i], best[-1]):\\n best.append(at[i])\\n best=best[::-1]\\n return best\\n\\ndef compare(a,b):\\n if (a[0]==b[0]) and (abs(a[1]-b[1])==1):\\n return True\\n elif (a[1]==b[1]) and (abs(a[0]-b[0])==1):\\n return True\\n else:\\n return False\\n \\ndef arr2action(c,d):\\n if c[0]==d[0]:\\n if c[1]<d[1]:\\n return create_move_action(Point(x+1,y))\\n else:\\n return create_move_action(Point(x-1,y))\\n elif c[0]<d[0]:\\n return create_move_action(Point(x,y-1))\\n else:\\n return create_move_action(Point(x,y+1))\\n\\ndef findTargets(mapmatrix, me):\\n resources = []\\n enemyhouses = []\\n shops = []\\n for row in mapmatrix:\\n for tile in row:\\n if tile.Content==4:\\n resources.append(tile)\\n elif tile.Content==2 and tile.Content!=me.HouseLocation:\\n enemyhouses.append(tile)\\n elif tile.Content==5:\\n shops.append(tile)\\n else:\\n continue\\n return [resources, enemyhouses, shops]\\n\\ndef decide(me, closestEnemies, targets, grid):\\n try:\\n distEn = closestEnemy[0][0]\\n enemy = closestEnemy[0][1]\\n except:\\n distEn=0\\n enemy = []\\n distTarget = targets[0][0]\\n target = targets[0][1]\\n best=[]\\n at=[]\\n \\n if distEn==1:\\n #print('------1-------')\\n return create_attack_action(Point(enemy.X,enemy.Y))\\n elif distTarget==1 and target.Content==2:\\n #print('------2-------')\\n return create_collect_action(Point(target.X,target.Y))\\n elif distTarget==0 and target.Content==4:\\n #print('------3-------')\\n return create_collect_action(Point(target.X,target.Y))\\n else:\\n #print('------4-------')\\n #t = random.choice([1,0])\\n #u = (t+1)%2\\n #return create_move_action(Point(me.Position.X+t,me.Position.Y+u))\\n return search_next(me, target, grid)\\ndef bot():\\n \\\"\\\"\\\"\\n Main de votre bot.\\n \\\"\\\"\\\"\\n map_json = request.form[\\\"map\\\"]\\n\\n # Player info\\n\\n encoded_map = map_json.encode()\\n map_json = json.loads(encoded_map)\\n p = map_json[\\\"Player\\\"]\\n pos = p[\\\"Position\\\"]\\n x = pos[\\\"X\\\"]\\n y = pos[\\\"Y\\\"]\\n \\n house = p[\\\"HouseLocation\\\"]\\n player = Player(p[\\\"Health\\\"], p[\\\"MaxHealth\\\"], Point(x,y),\\n Point(house[\\\"X\\\"], house[\\\"Y\\\"]), p[\\\"Score\\\"],\\n p[\\\"CarriedResources\\\"], p[\\\"CarryingCapacity\\\"])\\n\\n # Map\\n serialized_map = map_json[\\\"CustomSerializedMap\\\"]\\n deserialized_map=deserialize_map(serialized_map)\\n transposed=np.transpose(deserialized_map)\\n \\n \\n targets = findTargets(deserialized_map, player)\\n visual(transposed[:20][::-1],x,y)\\n otherPlayers = []\\n '''\\n #print(map_json)\\n for player_dict in map_json[\\\"OtherPlayers\\\"]:\\n #print(player_dict)\\n for player_name in player_dict.keys():\\n player_info = player_dict[player_name]\\n #print('---------')\\n #print(player_info)\\n #print('---------')\\n p_pos = player_info[\\\"Position\\\"]\\n player_info = PlayerInfo(player_info[\\\"Health\\\"],\\n player_info[\\\"MaxHealth\\\"],\\n Point(p_pos[\\\"X\\\"], p_pos[\\\"Y\\\"]))\\n\\n otherPlayers.append({player_name: player_info })\\n '''\\n # return decision\\n #targets = \\n tTargets = []\\n for target in targets[0]:#+targets[1]:\\n tTargets.append([distance([x,y],[target.X,target.Y]),target])\\n sortedTargets = sorted(tTargets, key=lambda x:x[0])\\n \\n \\n tEnemies = []\\n for enemy in otherPlayers:\\n tEnemies.append([distance([x,y],[enemy.X,enemy.Y]),enemy])\\n sortedEnemies = sorted(tEnemies, key=lambda x:x[0])\\n dx,dy=0,0\\n for i,line in enumerate(deserialized_map):\\n for j,tile in enumerate(line):\\n if tile.X==x and tile.Y==y:\\n dx = x-i\\n dy = y-j\\n #return decide(player, sortedEnemies, sortedTargets, deserialized_map)\\n print(player.__dict__,player.Position.__dict__)\\n return search_next(player, sortedTargets[0][1], deserialized_map,dx,dy)\\n \\n \\n \\n@app.route(\\\"/\\\", methods=[\\\"POST\\\"])\\ndef reponse():\\n \\\"\\\"\\\"\\n Point d'entree appelle par le GameServer\\n \\\"\\\"\\\"\\n sys.stdout.flush()\\n return bot()\\n\\nif __name__ == \\\"__main__\\\":\\n app.run(host=\\\"0.0.0.0\\\", port=3000)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979171,"cells":{"repo_name":{"kind":"string","value":"yeguacelestial/django-microsoft-auth"},"repo_url":{"kind":"string","value":"https://github.com/yeguacelestial/django-microsoft-auth"},"snapshot_id":{"kind":"string","value":"22ced96187a12ef10486bf2a5f47d6cc8ca6bb8f"},"revision_id":{"kind":"string","value":"a3747ed0b941953cca22d23b839e4905904ad7f3"},"directory_id":{"kind":"string","value":"af01d038d9bb4fbf510f56d23a45764d838d166a"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-03-07T22:24:16.432472","string":"2023-03-07T22:24:16.432472"},"revision_date":{"kind":"timestamp","value":"2021-02-09T00:04:35","string":"2021-02-09T00:04:35"},"committer_date":{"kind":"timestamp","value":"2021-02-09T00:04:35","string":"2021-02-09T00:04:35"},"github_id":{"kind":"number","value":336100701,"string":"336,100,701"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7916666865348816,"alphanum_fraction":0.7916666865348816,"avg_line_length":23,"blob_id":"e3192b589eaeb1b00a3c2d723dee7980e33538bb","content_id":"d350677d550260a144ab7e1a0bb5c1827454920e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":24,"license_type":"no_license","max_line_length":23,"num_lines":1,"path":"/README.md","repo_name":"yeguacelestial/django-microsoft-auth","src_encoding":"UTF-8","text":"# django-microsoft-auth\n"},{"alpha_fraction":0.5555555820465088,"alphanum_fraction":0.7291666865348816,"avg_line_length":19.714284896850586,"blob_id":"e8d850280dfeb29b78fb185c84f0beb15455aad6","content_id":"da885a51efef7df3c45f5f5a07a7c5557f207b63","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":144,"license_type":"no_license","max_line_length":27,"num_lines":7,"path":"/requirements.txt","repo_name":"yeguacelestial/django-microsoft-auth","src_encoding":"UTF-8","text":"Django==3.1.5\ngunicorn==20.0.4\npython-dotenv==0.15.0\ndjango-heroku==0.3.1\ndjangorestframework==3.12.2\ndjango-allauth==0.44.0\ndj-rest-auth==2.1.3"},{"alpha_fraction":0.7741094827651978,"alphanum_fraction":0.778453528881073,"avg_line_length":33.90909194946289,"blob_id":"53a33b5fd307f9fd25d6fe6dc1639166f49b3eaa","content_id":"ac15d88522b31f1f4834f3737be14d54b46f98bd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1151,"license_type":"no_license","max_line_length":88,"num_lines":33,"path":"/api/views.py","repo_name":"yeguacelestial/django-microsoft-auth","src_encoding":"UTF-8","text":"from django.contrib.auth.models import User, Group\nfrom rest_framework import viewsets\nfrom rest_framework import permissions\nfrom api.serializers import UserSerializer, GroupSerializer\n\n# dj rest auth\nfrom allauth.socialaccount.providers.microsoft.views import MicrosoftGraphOAuth2Adapter\nfrom allauth.socialaccount.providers.oauth2.client import OAuth2Client\nfrom dj_rest_auth.registration.views import SocialLoginView\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n \"\"\"\n API endpoint that allows users to be viewed or edited.\n \"\"\"\n queryset = User.objects.all().order_by('-date_joined')\n serializer_class = UserSerializer\n permission_classes = [permissions.IsAuthenticated]\n\n\nclass GroupViewSet(viewsets.ModelViewSet):\n \"\"\"\n API endpoint that allows groups to be viewed or edited.\n \"\"\"\n queryset = Group.objects.all()\n serializer_class = GroupSerializer\n permission_classes = [permissions.IsAuthenticated]\n\n\nclass MicrosoftLogin(SocialLoginView):\n adapter_class = MicrosoftGraphOAuth2Adapter\n callback_url = 'https://django-microsoft-auth.herokuapp.com/dj-rest-auth/microsoft/'\n client_class = OAuth2Client"}],"string":"[\n {\n \"alpha_fraction\": 0.7916666865348816,\n \"alphanum_fraction\": 0.7916666865348816,\n \"avg_line_length\": 23,\n \"blob_id\": \"e3192b589eaeb1b00a3c2d723dee7980e33538bb\",\n \"content_id\": \"d350677d550260a144ab7e1a0bb5c1827454920e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 24,\n \"license_type\": \"no_license\",\n \"max_line_length\": 23,\n \"num_lines\": 1,\n \"path\": \"/README.md\",\n \"repo_name\": \"yeguacelestial/django-microsoft-auth\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# django-microsoft-auth\\n\"\n },\n {\n \"alpha_fraction\": 0.5555555820465088,\n \"alphanum_fraction\": 0.7291666865348816,\n \"avg_line_length\": 19.714284896850586,\n \"blob_id\": \"e8d850280dfeb29b78fb185c84f0beb15455aad6\",\n \"content_id\": \"da885a51efef7df3c45f5f5a07a7c5557f207b63\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 144,\n \"license_type\": \"no_license\",\n \"max_line_length\": 27,\n \"num_lines\": 7,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"yeguacelestial/django-microsoft-auth\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"Django==3.1.5\\ngunicorn==20.0.4\\npython-dotenv==0.15.0\\ndjango-heroku==0.3.1\\ndjangorestframework==3.12.2\\ndjango-allauth==0.44.0\\ndj-rest-auth==2.1.3\"\n },\n {\n \"alpha_fraction\": 0.7741094827651978,\n \"alphanum_fraction\": 0.778453528881073,\n \"avg_line_length\": 33.90909194946289,\n \"blob_id\": \"53a33b5fd307f9fd25d6fe6dc1639166f49b3eaa\",\n \"content_id\": \"ac15d88522b31f1f4834f3737be14d54b46f98bd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1151,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 33,\n \"path\": \"/api/views.py\",\n \"repo_name\": \"yeguacelestial/django-microsoft-auth\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib.auth.models import User, Group\\nfrom rest_framework import viewsets\\nfrom rest_framework import permissions\\nfrom api.serializers import UserSerializer, GroupSerializer\\n\\n# dj rest auth\\nfrom allauth.socialaccount.providers.microsoft.views import MicrosoftGraphOAuth2Adapter\\nfrom allauth.socialaccount.providers.oauth2.client import OAuth2Client\\nfrom dj_rest_auth.registration.views import SocialLoginView\\n\\n\\nclass UserViewSet(viewsets.ModelViewSet):\\n \\\"\\\"\\\"\\n API endpoint that allows users to be viewed or edited.\\n \\\"\\\"\\\"\\n queryset = User.objects.all().order_by('-date_joined')\\n serializer_class = UserSerializer\\n permission_classes = [permissions.IsAuthenticated]\\n\\n\\nclass GroupViewSet(viewsets.ModelViewSet):\\n \\\"\\\"\\\"\\n API endpoint that allows groups to be viewed or edited.\\n \\\"\\\"\\\"\\n queryset = Group.objects.all()\\n serializer_class = GroupSerializer\\n permission_classes = [permissions.IsAuthenticated]\\n\\n\\nclass MicrosoftLogin(SocialLoginView):\\n adapter_class = MicrosoftGraphOAuth2Adapter\\n callback_url = 'https://django-microsoft-auth.herokuapp.com/dj-rest-auth/microsoft/'\\n client_class = OAuth2Client\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979172,"cells":{"repo_name":{"kind":"string","value":"quakehub/discord-metro"},"repo_url":{"kind":"string","value":"https://github.com/quakehub/discord-metro"},"snapshot_id":{"kind":"string","value":"ab571c0358bb16a4e1ec4945292292c4e93f5a8f"},"revision_id":{"kind":"string","value":"2c8eeeb3f5b7f20f4bf4b57d7279bddb4d2f7ca7"},"directory_id":{"kind":"string","value":"280f83c238f90734a62b425b0f6f3bfded2295dd"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-01-31T18:48:00.512027","string":"2023-01-31T18:48:00.512027"},"revision_date":{"kind":"timestamp","value":"2020-07-13T01:32:38","string":"2020-07-13T01:32:38"},"committer_date":{"kind":"timestamp","value":"2020-07-13T01:32:38","string":"2020-07-13T01:32:38"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7636022567749023,"alphanum_fraction":0.7636022567749023,"avg_line_length":43.41666793823242,"blob_id":"b4f9dc67e7cbdbbc80ca650137496f7da7d3f660","content_id":"0a0875781f4e6d40422c5ede27ff6041ed2c53f0","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":533,"license_type":"permissive","max_line_length":131,"num_lines":12,"path":"/README.md","repo_name":"quakehub/discord-metro","src_encoding":"UTF-8","text":"# Discord Metro\n\n\n\nDiscord Metro is an experimental interaction framework for a Discord guild that emulates a city with a metro system.\n\n\n\nStatus:\n - You can configure stations and trains, and have the trains go around the stations. There's a cool map that shows up when you do.\n - No role assignments yet, you'll have to find a different way to switch roles for now.\n - I completely forgot how much this would spam the API so I guess I'll have to shelve it for now.\n"},{"alpha_fraction":0.653748095035553,"alphanum_fraction":0.6552779078483582,"avg_line_length":26.23611068725586,"blob_id":"18beab7a8ab910d3064eaf4730c3c3e1b9c47b28","content_id":"57da3360b5b0532732a25dda256269860c606b15","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1961,"license_type":"permissive","max_line_length":120,"num_lines":72,"path":"/app.py","repo_name":"quakehub/discord-metro","src_encoding":"UTF-8","text":"import discord\nimport asyncio\nimport setup\nimport json\nimport datetime\n\nfrom models import App\n\nwith open(\"bot-config.json\", encoding=\"utf8\") as o:\n botconfigs = json.loads(o.read())\n\n\nbot = discord.Client()\nmetro = setup.setup(bot)\n\n# All extra attributes defined here\nbot.metro_state = App(metro)\nbot.working_guild = None\n\n\n@bot.event\nasync def on_ready():\n print(f\"Running on {bot.user.name}#{bot.user.discriminator} ({bot.user.id})\")\n working_guild = bot.get_guild(botconfigs[\"working-guild\"])\n\n if not working_guild:\n raise ValueError(\"Working Guild not found!\")\n\n bot.working_guild = working_guild\n\n if bot.metro_state.started == None:\n bot.metro_state.started = datetime.datetime.utcnow()\n for t in bot.metro_state.metro.trains:\n bot.loop.create_task(train_loop(t))\n\n\n# Here's an idea, the train channels are voice channels instead because thats totally more immersive. Do think about it.\nasync def train_loop(train):\n metro = bot.metro_state.metro\n stations = metro.stations\n curr_ind = train.start\n curr_ind = curr_ind % len(stations)\n next_ind = curr_ind + train.direction\n while True:\n current_station = stations[curr_ind]\n next_station = stations[next_ind]\n\n await train.announce_movement(\n curr_ind, next_ind, current_station, next_station, True\n )\n await train.arrive(current_station)\n\n await asyncio.sleep(metro.wait)\n\n await train.announce_movement(\n curr_ind, next_ind, current_station, next_station, False\n )\n await train.depart(metro.cat_id)\n\n await asyncio.sleep(metro.speed)\n\n curr_ind = next_ind\n next_ind += train.direction\n\n if (not metro.looped) and (next_ind < 0 or next_ind >= len(stations)):\n train.direction *= -1\n next_ind = curr_ind + train.direction\n\n next_ind = next_ind % len(stations)\n\n\nbot.run(botconfigs[\"token\"])\n"},{"alpha_fraction":0.5992261171340942,"alphanum_fraction":0.6000552773475647,"avg_line_length":31.303571701049805,"blob_id":"edd23eb4f8bb265f10a6bf0e3da8c246fee44334","content_id":"b1e55f528d1bf16820910e8b7aed66c86e097ecc","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3618,"license_type":"permissive","max_line_length":88,"num_lines":112,"path":"/models.py","repo_name":"quakehub/discord-metro","src_encoding":"UTF-8","text":"import discord\nimport util\n\n\nclass District:\n def __init__(self, bot, cat_id, role_id, station_id):\n self.bot = bot # discord.CLient\n\n self.cat_id = cat_id # Snowflake\n self.role_id = role_id # Snowflake\n self.station_id = station_id # Snowflake\n\n async def validate(self):\n # TODO: Add stuff\n return True\n\n\nclass Train:\n def __init__(self, bot, channel_id, role_id, direction, start):\n self.bot = bot # discord.CLient\n\n self.channel_id = channel_id # Snowflake\n self.role_id = role_id # Snowflake\n\n self.direction = direction # int\n self.start = start # int index\n\n self.current_station = None # discord.CategoryChannel\n self.current_announcement = None # discord.Message\n\n async def validate(self):\n # TODO: Add stuff\n if not self.direction in [-1, 1]:\n return False\n\n return True\n\n async def announce_movement(self, curr_ind, next_ind, curr, nxt, arriving):\n curr_channel = self.bot.working_guild.get_channel(curr.cat_id)\n nxt_channel = self.bot.working_guild.get_channel(nxt.cat_id)\n\n s_map = util.draw_map(\n curr_ind,\n next_ind,\n self.direction,\n len(self.bot.metro_state.metro.stations),\n not arriving,\n self.bot.metro_state.metro.looped,\n )\n\n msg = f\"Arriving at: **{curr_channel.name}**\\nNext stop: **{nxt_channel.name}**\"\n if not arriving:\n msg = (\n f\"Departing: **{curr_channel.name}**\\nNext stop: **{nxt_channel.name}**\"\n )\n\n msg = f\"{s_map}\\n\\n{msg}\"\n msg = discord.utils.escape_mentions(msg)\n\n await self.announce(msg)\n\n async def announce(self, message):\n if not self.current_announcement:\n channel = self.bot.working_guild.get_channel(self.channel_id)\n msg = await channel.send(message)\n self.current_announcement = msg\n else:\n try:\n await self.current_announcement.edit(content=message)\n except:\n self.current_announcement = None\n await self.announce(message)\n\n async def arrive(self, dest):\n channel = self.bot.working_guild.get_channel(self.channel_id)\n role = self.bot.working_guild.get_role(self.role_id)\n dest_cat = self.bot.working_guild.get_channel(dest.cat_id)\n\n self.current_station = dest_cat\n\n await channel.edit(category=dest_cat, position=0)\n await dest_cat.set_permissions(role, read_messages=True)\n\n async def depart(self, transit_id):\n channel = self.bot.working_guild.get_channel(self.channel_id)\n role = self.bot.working_guild.get_role(self.role_id)\n transit_cat = self.bot.working_guild.get_channel(transit_id)\n\n await self.current_station.set_permissions(role, overwrite=None)\n self.current_station = None\n await channel.edit(category=transit_cat)\n\n\nclass Metro:\n def __init__(self, stations, trains, cat_id, looped, speed, wait):\n self.stations = stations # List<District>\n self.trains = trains # List<Train>\n\n self.cat_id = cat_id # Snowflake\n self.looped = looped # boolean\n self.speed = speed # int: How long it takes to go to the next station\n self.wait = wait # int: How long to wait at a station\n\n async def validate(self):\n # TODO: Add stuff\n return True\n\n\nclass App:\n def __init__(self, metro):\n self.started = None # datetime.datetime\n self.metro = metro # Metro\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7636022567749023,\n \"alphanum_fraction\": 0.7636022567749023,\n \"avg_line_length\": 43.41666793823242,\n \"blob_id\": \"b4f9dc67e7cbdbbc80ca650137496f7da7d3f660\",\n \"content_id\": \"0a0875781f4e6d40422c5ede27ff6041ed2c53f0\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 533,\n \"license_type\": \"permissive\",\n \"max_line_length\": 131,\n \"num_lines\": 12,\n \"path\": \"/README.md\",\n \"repo_name\": \"quakehub/discord-metro\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Discord Metro\\n\\n\\n\\nDiscord Metro is an experimental interaction framework for a Discord guild that emulates a city with a metro system.\\n\\n\\n\\nStatus:\\n - You can configure stations and trains, and have the trains go around the stations. There's a cool map that shows up when you do.\\n - No role assignments yet, you'll have to find a different way to switch roles for now.\\n - I completely forgot how much this would spam the API so I guess I'll have to shelve it for now.\\n\"\n },\n {\n \"alpha_fraction\": 0.653748095035553,\n \"alphanum_fraction\": 0.6552779078483582,\n \"avg_line_length\": 26.23611068725586,\n \"blob_id\": \"18beab7a8ab910d3064eaf4730c3c3e1b9c47b28\",\n \"content_id\": \"57da3360b5b0532732a25dda256269860c606b15\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1961,\n \"license_type\": \"permissive\",\n \"max_line_length\": 120,\n \"num_lines\": 72,\n \"path\": \"/app.py\",\n \"repo_name\": \"quakehub/discord-metro\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import discord\\nimport asyncio\\nimport setup\\nimport json\\nimport datetime\\n\\nfrom models import App\\n\\nwith open(\\\"bot-config.json\\\", encoding=\\\"utf8\\\") as o:\\n botconfigs = json.loads(o.read())\\n\\n\\nbot = discord.Client()\\nmetro = setup.setup(bot)\\n\\n# All extra attributes defined here\\nbot.metro_state = App(metro)\\nbot.working_guild = None\\n\\n\\n@bot.event\\nasync def on_ready():\\n print(f\\\"Running on {bot.user.name}#{bot.user.discriminator} ({bot.user.id})\\\")\\n working_guild = bot.get_guild(botconfigs[\\\"working-guild\\\"])\\n\\n if not working_guild:\\n raise ValueError(\\\"Working Guild not found!\\\")\\n\\n bot.working_guild = working_guild\\n\\n if bot.metro_state.started == None:\\n bot.metro_state.started = datetime.datetime.utcnow()\\n for t in bot.metro_state.metro.trains:\\n bot.loop.create_task(train_loop(t))\\n\\n\\n# Here's an idea, the train channels are voice channels instead because thats totally more immersive. Do think about it.\\nasync def train_loop(train):\\n metro = bot.metro_state.metro\\n stations = metro.stations\\n curr_ind = train.start\\n curr_ind = curr_ind % len(stations)\\n next_ind = curr_ind + train.direction\\n while True:\\n current_station = stations[curr_ind]\\n next_station = stations[next_ind]\\n\\n await train.announce_movement(\\n curr_ind, next_ind, current_station, next_station, True\\n )\\n await train.arrive(current_station)\\n\\n await asyncio.sleep(metro.wait)\\n\\n await train.announce_movement(\\n curr_ind, next_ind, current_station, next_station, False\\n )\\n await train.depart(metro.cat_id)\\n\\n await asyncio.sleep(metro.speed)\\n\\n curr_ind = next_ind\\n next_ind += train.direction\\n\\n if (not metro.looped) and (next_ind < 0 or next_ind >= len(stations)):\\n train.direction *= -1\\n next_ind = curr_ind + train.direction\\n\\n next_ind = next_ind % len(stations)\\n\\n\\nbot.run(botconfigs[\\\"token\\\"])\\n\"\n },\n {\n \"alpha_fraction\": 0.5992261171340942,\n \"alphanum_fraction\": 0.6000552773475647,\n \"avg_line_length\": 31.303571701049805,\n \"blob_id\": \"edd23eb4f8bb265f10a6bf0e3da8c246fee44334\",\n \"content_id\": \"b1e55f528d1bf16820910e8b7aed66c86e097ecc\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3618,\n \"license_type\": \"permissive\",\n \"max_line_length\": 88,\n \"num_lines\": 112,\n \"path\": \"/models.py\",\n \"repo_name\": \"quakehub/discord-metro\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import discord\\nimport util\\n\\n\\nclass District:\\n def __init__(self, bot, cat_id, role_id, station_id):\\n self.bot = bot # discord.CLient\\n\\n self.cat_id = cat_id # Snowflake\\n self.role_id = role_id # Snowflake\\n self.station_id = station_id # Snowflake\\n\\n async def validate(self):\\n # TODO: Add stuff\\n return True\\n\\n\\nclass Train:\\n def __init__(self, bot, channel_id, role_id, direction, start):\\n self.bot = bot # discord.CLient\\n\\n self.channel_id = channel_id # Snowflake\\n self.role_id = role_id # Snowflake\\n\\n self.direction = direction # int\\n self.start = start # int index\\n\\n self.current_station = None # discord.CategoryChannel\\n self.current_announcement = None # discord.Message\\n\\n async def validate(self):\\n # TODO: Add stuff\\n if not self.direction in [-1, 1]:\\n return False\\n\\n return True\\n\\n async def announce_movement(self, curr_ind, next_ind, curr, nxt, arriving):\\n curr_channel = self.bot.working_guild.get_channel(curr.cat_id)\\n nxt_channel = self.bot.working_guild.get_channel(nxt.cat_id)\\n\\n s_map = util.draw_map(\\n curr_ind,\\n next_ind,\\n self.direction,\\n len(self.bot.metro_state.metro.stations),\\n not arriving,\\n self.bot.metro_state.metro.looped,\\n )\\n\\n msg = f\\\"Arriving at: **{curr_channel.name}**\\\\nNext stop: **{nxt_channel.name}**\\\"\\n if not arriving:\\n msg = (\\n f\\\"Departing: **{curr_channel.name}**\\\\nNext stop: **{nxt_channel.name}**\\\"\\n )\\n\\n msg = f\\\"{s_map}\\\\n\\\\n{msg}\\\"\\n msg = discord.utils.escape_mentions(msg)\\n\\n await self.announce(msg)\\n\\n async def announce(self, message):\\n if not self.current_announcement:\\n channel = self.bot.working_guild.get_channel(self.channel_id)\\n msg = await channel.send(message)\\n self.current_announcement = msg\\n else:\\n try:\\n await self.current_announcement.edit(content=message)\\n except:\\n self.current_announcement = None\\n await self.announce(message)\\n\\n async def arrive(self, dest):\\n channel = self.bot.working_guild.get_channel(self.channel_id)\\n role = self.bot.working_guild.get_role(self.role_id)\\n dest_cat = self.bot.working_guild.get_channel(dest.cat_id)\\n\\n self.current_station = dest_cat\\n\\n await channel.edit(category=dest_cat, position=0)\\n await dest_cat.set_permissions(role, read_messages=True)\\n\\n async def depart(self, transit_id):\\n channel = self.bot.working_guild.get_channel(self.channel_id)\\n role = self.bot.working_guild.get_role(self.role_id)\\n transit_cat = self.bot.working_guild.get_channel(transit_id)\\n\\n await self.current_station.set_permissions(role, overwrite=None)\\n self.current_station = None\\n await channel.edit(category=transit_cat)\\n\\n\\nclass Metro:\\n def __init__(self, stations, trains, cat_id, looped, speed, wait):\\n self.stations = stations # List<District>\\n self.trains = trains # List<Train>\\n\\n self.cat_id = cat_id # Snowflake\\n self.looped = looped # boolean\\n self.speed = speed # int: How long it takes to go to the next station\\n self.wait = wait # int: How long to wait at a station\\n\\n async def validate(self):\\n # TODO: Add stuff\\n return True\\n\\n\\nclass App:\\n def __init__(self, metro):\\n self.started = None # datetime.datetime\\n self.metro = metro # Metro\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979173,"cells":{"repo_name":{"kind":"string","value":"BradDoddsProjects/kMeans"},"repo_url":{"kind":"string","value":"https://github.com/BradDoddsProjects/kMeans"},"snapshot_id":{"kind":"string","value":"d5a0bc13824600a052e33a169b797f959508e04f"},"revision_id":{"kind":"string","value":"307081fd856ae6188642f0d2ea75318d6b1cd1ae"},"directory_id":{"kind":"string","value":"0162f87f61c91a7d7c7eeee57b704a0a9cacd651"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-06T18:24:15.572682","string":"2021-01-06T18:24:15.572682"},"revision_date":{"kind":"timestamp","value":"2020-02-20T18:16:04","string":"2020-02-20T18:16:04"},"committer_date":{"kind":"timestamp","value":"2020-02-20T18:16:04","string":"2020-02-20T18:16:04"},"github_id":{"kind":"number","value":241438391,"string":"241,438,391"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5393848419189453,"alphanum_fraction":0.5573893189430237,"avg_line_length":21.18260955810547,"blob_id":"e97e81865b9ee66c8c046437f3853883fa2af5e7","content_id":"d01e32c4161b75fcc61f58f00e16fcff99e611f3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2666,"license_type":"no_license","max_line_length":74,"num_lines":115,"path":"/k.py","repo_name":"BradDoddsProjects/kMeans","src_encoding":"UTF-8","text":"import numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\n# INITIALIZATION number of klusters and initial centroids\r\nK = 3\r\nconvergeTotal = 10\r\ninitial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\r\n\r\ndf = pd.read_csv('file.csv', delimiter=',', header=None, names=['A', 'B'])\r\n\r\nX = np.array(df)\r\n#print('Printing data in X...')\r\n# print(X)\r\n\r\nprint('Dimensions of X', X.shape)\r\n\r\nnum_points = len(X)\r\nprint(\"Total number of points in dataset, ie. X:\", num_points)\r\n\r\n# PLOT: original data set with initial centroids\r\nplt.plot(X[:, 0], X[:, 1], 'go')\r\nplt.plot(initial_centroids[:, 0], initial_centroids[:, 1], 'rx')\r\nplt.show()\r\n\r\n# FIRST STEP: CLUSTER ASSIGNMENT\r\ndist = np.zeros((K, num_points))\r\ndistanceValues = np.zeros((num_points))\r\n\r\n\r\n# gives x value X[:, 0]\r\n# gives y value X[:, 1]\r\n\r\ndef distanceBetPointsAndCentroids(centroids):\r\n for i in range(K):\r\n for j in range(num_points):\r\n dist[i, j] = np.linalg.norm(centroids[i] - X[j])\r\n\r\n\r\ndef shortestDistance():\r\n a = []\r\n b = []\r\n c = []\r\n\r\n for i in range(num_points):\r\n\r\n num1 = dist[0, i]\r\n num2 = dist[1, i]\r\n num3 = dist[2, i]\r\n\r\n if (num1 < num2) and (num1 < num3):\r\n smallest_num = num1\r\n a.append(X[i])\r\n\r\n elif (num2 < num1) and (num2 < num3):\r\n smallest_num = num2\r\n b.append(X[i])\r\n else:\r\n smallest_num = num3\r\n c.append(X[i])\r\n # print(\"The smallest of the 3 numbers is : \", smallest_num)\r\n\r\n # point x.y values for shortest distance\r\n a = np.array(a)\r\n b = np.array(b)\r\n c = np.array(c)\r\n\r\n plt.scatter(a[:, 0], a[:, 1], color='c')\r\n plt.scatter(b[:, 0], b[:, 1], color='y')\r\n plt.scatter(c[:, 0], c[:, 1], color='b')\r\n\r\n print(\"New Centroid Values\")\r\n newCentroids = []\r\n newCentroids.append(centroids(a))\r\n newCentroids.append(centroids(b))\r\n newCentroids.append(centroids(c))\r\n print('\\n')\r\n\r\n centroidArray = np.array(newCentroids)\r\n\r\n\r\n\r\n # Comment out for no plots\r\n plt.plot(centroidArray[:, 0], centroidArray[:, 1], 'rx')\r\n plt.show()\r\n\r\n return centroidArray\r\n\r\n\r\ndef centroids(points):\r\n\r\n n = len(points)\r\n\r\n x = points[:, 0]\r\n y = points[:, 1]\r\n\r\n x = sum(x) / n\r\n y = sum(y) / n\r\n\r\n centroid = np.zeros(2)\r\n centroid[0] = x\r\n centroid[1] = y\r\n\r\n print(centroid)\r\n\r\n return centroid\r\n\r\n\r\n\r\ndistanceBetPointsAndCentroids(initial_centroids)\r\nnew_centroid = shortestDistance()\r\n\r\nfor i in range(10):\r\n distanceBetPointsAndCentroids(new_centroid)\r\n new_centroid = shortestDistance()\r\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5393848419189453,\n \"alphanum_fraction\": 0.5573893189430237,\n \"avg_line_length\": 21.18260955810547,\n \"blob_id\": \"e97e81865b9ee66c8c046437f3853883fa2af5e7\",\n \"content_id\": \"d01e32c4161b75fcc61f58f00e16fcff99e611f3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2666,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 115,\n \"path\": \"/k.py\",\n \"repo_name\": \"BradDoddsProjects/kMeans\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\r\\nimport pandas as pd\\r\\nimport matplotlib.pyplot as plt\\r\\n\\r\\n# INITIALIZATION number of klusters and initial centroids\\r\\nK = 3\\r\\nconvergeTotal = 10\\r\\ninitial_centroids = np.array([[3, 3], [6, 2], [8, 5]])\\r\\n\\r\\ndf = pd.read_csv('file.csv', delimiter=',', header=None, names=['A', 'B'])\\r\\n\\r\\nX = np.array(df)\\r\\n#print('Printing data in X...')\\r\\n# print(X)\\r\\n\\r\\nprint('Dimensions of X', X.shape)\\r\\n\\r\\nnum_points = len(X)\\r\\nprint(\\\"Total number of points in dataset, ie. X:\\\", num_points)\\r\\n\\r\\n# PLOT: original data set with initial centroids\\r\\nplt.plot(X[:, 0], X[:, 1], 'go')\\r\\nplt.plot(initial_centroids[:, 0], initial_centroids[:, 1], 'rx')\\r\\nplt.show()\\r\\n\\r\\n# FIRST STEP: CLUSTER ASSIGNMENT\\r\\ndist = np.zeros((K, num_points))\\r\\ndistanceValues = np.zeros((num_points))\\r\\n\\r\\n\\r\\n# gives x value X[:, 0]\\r\\n# gives y value X[:, 1]\\r\\n\\r\\ndef distanceBetPointsAndCentroids(centroids):\\r\\n for i in range(K):\\r\\n for j in range(num_points):\\r\\n dist[i, j] = np.linalg.norm(centroids[i] - X[j])\\r\\n\\r\\n\\r\\ndef shortestDistance():\\r\\n a = []\\r\\n b = []\\r\\n c = []\\r\\n\\r\\n for i in range(num_points):\\r\\n\\r\\n num1 = dist[0, i]\\r\\n num2 = dist[1, i]\\r\\n num3 = dist[2, i]\\r\\n\\r\\n if (num1 < num2) and (num1 < num3):\\r\\n smallest_num = num1\\r\\n a.append(X[i])\\r\\n\\r\\n elif (num2 < num1) and (num2 < num3):\\r\\n smallest_num = num2\\r\\n b.append(X[i])\\r\\n else:\\r\\n smallest_num = num3\\r\\n c.append(X[i])\\r\\n # print(\\\"The smallest of the 3 numbers is : \\\", smallest_num)\\r\\n\\r\\n # point x.y values for shortest distance\\r\\n a = np.array(a)\\r\\n b = np.array(b)\\r\\n c = np.array(c)\\r\\n\\r\\n plt.scatter(a[:, 0], a[:, 1], color='c')\\r\\n plt.scatter(b[:, 0], b[:, 1], color='y')\\r\\n plt.scatter(c[:, 0], c[:, 1], color='b')\\r\\n\\r\\n print(\\\"New Centroid Values\\\")\\r\\n newCentroids = []\\r\\n newCentroids.append(centroids(a))\\r\\n newCentroids.append(centroids(b))\\r\\n newCentroids.append(centroids(c))\\r\\n print('\\\\n')\\r\\n\\r\\n centroidArray = np.array(newCentroids)\\r\\n\\r\\n\\r\\n\\r\\n # Comment out for no plots\\r\\n plt.plot(centroidArray[:, 0], centroidArray[:, 1], 'rx')\\r\\n plt.show()\\r\\n\\r\\n return centroidArray\\r\\n\\r\\n\\r\\ndef centroids(points):\\r\\n\\r\\n n = len(points)\\r\\n\\r\\n x = points[:, 0]\\r\\n y = points[:, 1]\\r\\n\\r\\n x = sum(x) / n\\r\\n y = sum(y) / n\\r\\n\\r\\n centroid = np.zeros(2)\\r\\n centroid[0] = x\\r\\n centroid[1] = y\\r\\n\\r\\n print(centroid)\\r\\n\\r\\n return centroid\\r\\n\\r\\n\\r\\n\\r\\ndistanceBetPointsAndCentroids(initial_centroids)\\r\\nnew_centroid = shortestDistance()\\r\\n\\r\\nfor i in range(10):\\r\\n distanceBetPointsAndCentroids(new_centroid)\\r\\n new_centroid = shortestDistance()\\r\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979174,"cells":{"repo_name":{"kind":"string","value":"xingchenwan/Sentiment"},"repo_url":{"kind":"string","value":"https://github.com/xingchenwan/Sentiment"},"snapshot_id":{"kind":"string","value":"05d923e52e1b468b423e81631a034675deab850d"},"revision_id":{"kind":"string","value":"0798565fa788b969ab381920082312dc98e6f9ab"},"directory_id":{"kind":"string","value":"089591b265e53641e7d7cac88958b05dec4b9546"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-06-26T06:51:24.608816","string":"2021-06-26T06:51:24.608816"},"revision_date":{"kind":"timestamp","value":"2019-07-12T16:19:05","string":"2019-07-12T16:19:05"},"committer_date":{"kind":"timestamp","value":"2019-07-12T16:19:05","string":"2019-07-12T16:19:05"},"github_id":{"kind":"number","value":147643193,"string":"147,643,193"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5860924124717712,"alphanum_fraction":0.5963833928108215,"avg_line_length":46.52031326293945,"blob_id":"6b9a42c5ba565abc1bf9190e88eb2d361c242196","content_id":"cc41263dfc6896aa156e35fc84826172ed89681e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":30415,"license_type":"no_license","max_line_length":157,"num_lines":640,"path":"/PycharmProjects/OMI/analyser.py","repo_name":"xingchenwan/Sentiment","src_encoding":"UTF-8","text":"# Analyser: modules that perform numerical analyses on the result\n# Xingchen Wan | Xingchen.Wan@st-annes.ox.ac.uk | Oxford-Man Institute of Quantitative Finance\n\nimport numpy as np\nimport pandas as pd\nimport scipy.stats as st\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pylab as plt\nfrom pandas.tseries.offsets import BDay\nfrom processor import process_beta_time_series\n\n\ndef normalised_corr(series1, series2, max_lag=2, fix_lag=None):\n \"\"\"\n Compute the normalised cross-correlation between series1 and series2\n :param series1: Series1, Pandas series or numpy array\n :param series2: Series2, Pandas series or numpy array\n :param max_lag: Maximum lag between series1 and series2 allowed\n :param fix_lag: fixed lag - this argument overrides the max_lag argument.\n :return: res: normalised cross-correlation coefficients, max_val: maximum correlation coefficient within the\n allowed period of lag in terms of magnitude, max_idx: the lag (index) corresponding to that correlation\n coefficient\n \"\"\"\n if isinstance(series1, pd.Series): series1 = series1.values\n if isinstance(series2, pd.Series): series2 = series2.values\n series1 = (series1 - np.mean(series1)) / (np.std(series1) * len(series1))\n series2 = (series2 - np.mean(series2)) / np.std(series2)\n correl = np.correlate(series1, series2, mode='full')\n res = correl[int((len(correl)) / 2):]\n #res = correl[:]\n # print(len(res))\n if fix_lag is None:\n abs_series = np.abs(res[:max_lag + 1])\n max_idx = np.argmax(abs_series)\n max_val = res[max_idx]\n else:\n max_idx = fix_lag\n max_val = res[fix_lag]\n return res, max_val, max_idx\n\n\ndef correlate(market_data, vol_data, sentiment_med_data, sentiment_sum_data, count_data, max_lag=2,\n count_threshold=0,\n plot=True, save_csv=False, fix_lag=None,\n display_summary_stats=True,\n focus_iterable=None):\n\n assert set(sentiment_med_data.index) == set(count_data.index)\n assert set(sentiment_sum_data.index) == set(count_data.index)\n intersect_dates = market_data.index.intersection(count_data.index)\n market_data = market_data.loc[intersect_dates].astype('float64')\n benchmark_data = market_data['_ALL'] # MSCI log-return\n\n market_data = market_data.subtract(benchmark_data, axis='index')\n # Superior log return over SPX return\n vol = vol_data.loc[intersect_dates].astype('float64')\n # market_data_bin = market_data.apply(lambda x: np.sign(x-x.shift(1)))[1:]\n\n res = []\n if focus_iterable:\n include_names = [x for x in focus_iterable if x in list(market_data.columns)]\n else:\n include_names = list(market_data.columns)\n\n for single_name in include_names:\n if \".\" in single_name:\n # Special cases for BoJ and Fed where there are two underlying securities...\n sentiment_series_med = sentiment_med_data[single_name.split(\".\")[0]].astype('float64').values\n sentiment_series_sum = sentiment_sum_data[single_name.split(\".\")[0]].astype('float64').values\n count_series = count_data[single_name.split(\".\")[0]].astype('float64').values\n vol_series = vol[single_name].astype('float64').values\n market_series = market_data[single_name].astype('float64').values\n else:\n sentiment_series_med = sentiment_med_data[single_name].astype('float64').values\n sentiment_series_sum = sentiment_sum_data[single_name].astype('float64').values\n count_series = count_data[single_name].astype('float64').values\n vol_series = vol[single_name].astype('float64').values\n market_series = market_data[single_name].astype('float64').values\n\n non_nan_idx = ~np.isnan(market_series)\n if count_threshold > 0:\n non_nan_idx = np.logical_and(~np.isnan(market_series), (count_series > count_threshold))\n all_nan = True if (non_nan_idx == False).all() else False\n # Flag that the entire numpy series consists of NaN\n\n if not all_nan:\n market_series = market_series[non_nan_idx]\n count_series = count_series[non_nan_idx]\n sentiment_series_med = sentiment_series_med[non_nan_idx]\n sentiment_series_sum = sentiment_series_sum[non_nan_idx]\n vol_series = vol_series[non_nan_idx]\n # Then prune the data to get rid of the NaNs\n all_series = [market_series, count_series, sentiment_series_med, sentiment_series_sum, vol_series]\n\n\n corr_count_price, max_corr_count_price, max_corr_count_price_idx = normalised_corr(count_series, market_series, max_lag=max_lag, fix_lag=fix_lag)\n\n corr_med_sentiment_price, max_med_corr_sentiment_price, \\\n max_corr_med_sentiment_price_idx = normalised_corr(sentiment_series_med, market_series, max_lag=max_lag, fix_lag=fix_lag)\n\n corr_sum_sentiment_price, max_sum_corr_sentiment_price, \\\n max_corr_sum_sentiment_price_idx = normalised_corr(sentiment_series_sum, market_series, max_lag=max_lag, fix_lag=fix_lag)\n\n corr_count_vol, max_corr_count_vol, max_corr_count_vol_idx = normalised_corr(count_series, vol_series, max_lag=max_lag, fix_lag=fix_lag)\n\n corr_vol_sum_sentiment, max_corr_vol_sum_sentiment, \\\n max_corr_vol_sum_sentiment_idx = normalised_corr(sentiment_series_sum, vol_series, max_lag=max_lag, fix_lag=fix_lag)\n\n corr_vol_price, max_corr_vol_price, max_corr_vol_price_idx = normalised_corr(market_series, vol_series, max_lag=max_lag, fix_lag=fix_lag)\n\n this_res = {'Name': single_name,\n 'PriceCountCorr': max_corr_count_price,\n 'PriceCountLag': max_corr_count_price_idx,\n 'PriceMedSentimentCorr': max_med_corr_sentiment_price,\n 'PriceMedSentimentLag': max_corr_med_sentiment_price_idx,\n 'PriceSumSentimentCorr': max_sum_corr_sentiment_price,\n 'PriceSumSentimentLag': max_corr_sum_sentiment_price_idx,\n 'VolCountCorr': max_corr_count_vol,\n 'VolCountLag': max_corr_count_vol_idx,\n 'VolMedSentimentCorr': max_corr_vol_sum_sentiment,\n 'VolMedSentimentLag': max_corr_vol_sum_sentiment_idx,\n 'SpotVolCorr': max_corr_vol_price,\n 'SpotVolLag': max_corr_vol_price_idx,\n }\n print(this_res)\n else:\n this_res = {'Name': single_name,\n 'PriceCountCorr': np.nan,\n 'PriceCountLag': np.nan,\n 'PriceMedSentimentCorr': np.nan,\n 'PriceMedSentimentLag': np.nan,\n 'PriceSumSentimentCorr': np.nan,\n 'PriceSumSentimentLag': np.nan,\n 'VolCountCorr': np.nan,\n 'VolCountLag': np.nan,\n 'VolMedSentimentCorr': np.nan,\n 'VolMedSentimentLag': np.nan,\n 'SpotVolCorr': np.nan,\n 'SpotVolLag': np.nan,\n }\n print('Empty series occurred at ', single_name)\n res.append(this_res)\n\n if plot and not all_nan:\n # Perform the cross-correlation\n plt.subplot(231)\n plt.title('Count-Price Correlation')\n plt.plot(corr_count_price)\n plt.subplot(232)\n plt.title('MedSentiment-Price Correlation')\n plt.plot(corr_med_sentiment_price)\n plt.subplot(233)\n plt.title('SumSentiment-Price Correlation')\n plt.plot(corr_sum_sentiment_price)\n plt.subplot(234)\n plt.title('Count-Vol Correlation')\n plt.plot(corr_count_vol)\n plt.subplot(235)\n plt.title('Spot-Vol Correlation')\n plt.plot(corr_vol_price)\n plt.show()\n\n res = pd.DataFrame(res)\n\n if display_summary_stats: print(res.describe())\n\n if save_csv:\n pd.DataFrame(res).to_csv('CorrRes.csv')\n print('Done')\n return res\n\n\n\ndef stat_tests(data, threshold=0.05):\n \"\"\"\n Perform a series of statistic tests: normal distribution test, t-test and confidence interval\n :param data: array-like\n :param threshold: threshold to reject null hypothesis. Default value is 0.05\n :return:\n \"\"\"\n if not isinstance(data, pd.Series) and not isinstance(data, np.ndarray):\n raise TypeError('The input data must be a pandas series or a numpy array')\n elif isinstance(data, pd.Series):\n data = data.values\n\n data = data[~np.isnan(data)]\n # Clear NaNs from the data-set\n # print(data)\n t, prob = st.ttest_1samp(data, 0)\n wilcox, prob2 = st.wilcoxon(data)\n # Return the t-statistics and p-value on whether the data mean is significantly different from 0\n mean_5, mean_95 = st.t.interval(1-threshold, len(data)-1, loc=np.mean(data), scale=st.sem(data))\n print(\n 'T-statistic', t,\n 'T-test p-value', prob,\n 'WilCox', prob2,\n '95th Percentile', mean_95,\n '5th Percentile', mean_5)\n return t, prob, mean_5, mean_95\n\n\ndef rolling_correlate(name, roll_window_size, *input_frames, frame_names=[], fix_lag=None, max_lag=2,\n start_date=None, end_date=None, exclude_pairs=None, include_pairs=None,\n count_threshold={}):\n\n if include_pairs:\n if not isinstance(include_pairs, list):\n raise TypeError(\"include_pairs need to be list of tuples with length 2\")\n elif not isinstance(include_pairs[0], tuple):\n raise TypeError(\"include_pairs need to be list of tuples with length 2\")\n elif len(include_pairs[0]) != 2:\n raise TypeError(\"include_pairs need to be list of tuples with length 2\")\n\n if exclude_pairs:\n if not isinstance(exclude_pairs, list):\n raise TypeError(\"Exclude_pairs need to be list of tuples with length 2\")\n elif not isinstance(exclude_pairs[0], tuple):\n raise TypeError(\"Exclude_pairs need to be list of tuples with length 2\")\n elif len(exclude_pairs[0]) != 2:\n raise TypeError(\"Exclude_pairs need to be list of tuples with length 2\")\n if frame_names and len(frame_names) != len(input_frames):\n raise ValueError(\"Mismatch between data length and data_names length.\")\n\n series_collection = []\n i = 0\n for frame in input_frames:\n try:\n series = frame[name].astype('float64')\n series.name = frame_names[i]\n series_collection.append(series)\n except KeyError:\n print(name, \" is not found in data argument position \", input_frames.index(frame))\n i += 1\n data = pd.concat(series_collection, axis=1, join='inner')\n # data only contains where the dates of the different data frames intersect\n if start_date:\n start_date = pd.to_datetime(start_date)\n if start_date > data.index[0]:\n data = data[data.index >= start_date]\n else: start_date = data.index[0]\n else: start_date = data.index[0]\n\n if end_date:\n end_date = pd.to_datetime(end_date)\n if end_date < data.index[-1]:\n data = data[data.index <= end_date]\n else: end_date = data.index[-1]\n else: end_date = data.index[-1]\n # Truncate the the data frame in accordance to the specified start and end dates\n res = {}\n\n roll_window_start = start_date+pd.to_timedelta(roll_window_size, unit='d')\n\n for i in range(len(data.columns)):\n for j in range(i, len(data.columns)):\n col_name1 = data.columns[i]\n col_name2 = data.columns[j]\n if exclude_pairs and ((col_name1, col_name2) in exclude_pairs or (col_name2, col_name1) in exclude_pairs):\n continue\n if include_pairs and ((col_name1, col_name2) not in include_pairs and (col_name2, col_name1) not in include_pairs):\n continue\n local_data1 = data[col_name1]\n local_data2 = data[col_name2]\n this_res = pd.Series(0, index=data.index[data.index >= roll_window_start])\n this_res.name = \"Corr\" + col_name1 + \"To\" + col_name2\n\n k = 0\n for dt in this_res.index:\n series1 = local_data1[dt-pd.to_timedelta(roll_window_size, unit='d'):dt].values\n series2 = local_data2[dt-pd.to_timedelta(roll_window_size, unit='d'):dt].values\n if count_threshold:\n if col_name1 in count_threshold.keys():\n series1 = series1[series1 > count_threshold[col_name1]]\n series2 = series2[series1 > count_threshold[col_name1]]\n if col_name2 in count_threshold.keys():\n series1 = series1[series2 > count_threshold[col_name2]]\n series2 = series2[series2 > count_threshold[col_name2]]\n # Select appropriate date range\n if fix_lag is not None:\n _, this_res.iloc[k], _ = normalised_corr(series1, series2, fix_lag=fix_lag)\n else:\n _, this_res.iloc[k], _ = normalised_corr(series1, series2, max_lag=max_lag)\n k += 1\n res[this_res.name] = this_res\n return pd.DataFrame(res)\n\n\ndef px_correlate(name1, *names, px_frame, correlation_period=90, start_date=None, end_date=None):\n if start_date is None: start_date = px_frame.index[0]\n if end_date is None: end_date = px_frame.index[-1]\n px_frame = px_frame[(px_frame.index >= start_date) & (px_frame.index <= end_date)]\n res = pd.DataFrame(np.nan, columns=names, index=px_frame.index)\n for dt in px_frame.index:\n if dt >= start_date + pd.to_timedelta(correlation_period):\n sub_frame = px_frame[(px_frame.index >= dt - pd.to_timedelta(correlation_period, 'D')) & (px_frame.index < dt)]\n px1 = sub_frame[name1]\n for name in names:\n px2 = sub_frame[name]\n res.loc[dt, name] = np.corrcoef(px1, px2)[0, 1]\n return res\n\n\n#######################################################\n# Event-based analysis tools:\n\n\ndef _get_z_score(frame, window):\n r = frame.rolling(window=window)\n m = r.mean()\n s = r.std(ddof=0)\n return (frame-m)/s\n\n\ndef _get_event_series(z_score_series, threshold, ignore_window=10):\n event_series = pd.Series(0, index=z_score_series.index)\n ignore_cnt = 0\n for i in range(len(z_score_series)):\n if ignore_cnt != 0:\n ignore_cnt -= 1\n pt = z_score_series[i]\n if pt > threshold: status = 1\n elif pt < -threshold: status = -1\n else: status = 0\n\n if ignore_cnt: status = 0\n if status != 0 and ignore_cnt == 0:\n ignore_cnt = ignore_window\n event_series.iloc[i] = status\n return event_series\n\n\ndef get_abnormal_return(name, log_return_frame, date, window, beta_path='data/^GSPC.csv'):\n # The normal return is given by the Market Model over MSCI return over the last window\n open_date = date - pd.to_timedelta(window, 'D')\n hist_returns = log_return_frame[['_ALL', name]]\n hist_returns = hist_returns[(hist_returns.index >= open_date) & (hist_returns.index < date)]\n\n mkt_return_today = log_return_frame.loc[date, '_ALL']\n stock_return_today = log_return_frame.loc[date, name]\n\n # Estimate alpha and beta using simple OLS\n X = hist_returns['_ALL'].values\n y = hist_returns[name].values\n beta, alpha, r_val, p_val, std_err = st.linregress(X, y)\n # disturb_var = 1/(window - 2)*np.sum(np.square(y - alpha - beta*X))\n # print(beta)\n abnormal_return = stock_return_today - alpha - beta * mkt_return_today\n return abnormal_return\n\n\ndef get_event_cumulative_abnormal_return(name, event_series, z_series, daily_abnormal_return_series, max_lag):\n event_cnt = event_series[event_series == 1].count() + event_series[event_series == -1].count()\n day_range = [i for i in range(-max_lag, max_lag + 1)]\n res = pd.DataFrame(0, index=[i for i in range(event_cnt)], columns=['Name','EventDate', 'Type'] + day_range)\n z_res = pd.DataFrame(0, index=res.index, columns=res.columns)\n i = 0\n for day in range(len(event_series)):\n if event_series.iloc[day] != 0:\n dt = event_series.index[day]\n res.loc[i, 'Name'] = name\n res.loc[i, 'EventDate'] = dt\n res.loc[i, 'Type'] = event_series.iloc[day]\n\n z_res.loc[i, 'Name'] = name\n z_res.loc[i, 'EventDate'] = dt\n z_res.loc[i, 'Type'] = event_series.iloc[day]\n\n cum_rtn = 0\n for lag in range(-max_lag, max_lag+1):\n try:\n cum_rtn += daily_abnormal_return_series[dt+BDay(lag)]\n except KeyError:\n pass\n res.loc[i, lag] = cum_rtn\n try:\n z_res.loc[i, lag] = z_series[dt+BDay(lag)]\n except KeyError:\n z_res.loc[i, lag] = np.nan\n i += 1\n return res, z_res\n\n\ndef get_event_vol(name, event_series, z_series, vol_series, max_lag):\n event_cnt = event_series[event_series == 1].count() + event_series[event_series == -1].count()\n day_range = [i for i in range(-max_lag, max_lag + 1)]\n res = pd.DataFrame(np.nan, index=[i for i in range(event_cnt)], columns=['Name', 'EventDate'] + day_range)\n z_res = pd.DataFrame(np.nan, index=res.index, columns=res.columns)\n i = 0\n for day in range(len(event_series)):\n if event_series.iloc[day] != 0:\n dt = event_series.index[day]\n res.loc[i, 'Name'] = name\n res.loc[i, 'EventDate'] = dt\n res.loc[i, 'Type'] = event_series.iloc[day]\n\n #z_res.loc[i, 'Name'] = name\n #z_res.loc[i, 'EventDate'] = dt\n #z_res.loc[i, 'Type'] = event_series.iloc[day]\n\n for lag in day_range:\n try:\n res.loc[i, lag] = vol_series[dt+BDay(lag)] if vol_series[dt+BDay(lag)] != 0 else np.nan\n except KeyError:\n res.loc[i, lag] = np.nan\n # try:\n # z_res.loc[i, lag] = z_series[dt+BDay(lag)]\n # except KeyError:\n # z_res.loc[i, lag] = np.nan\n i += 1\n return res, z_res\n\n\ndef get_avg_col(names, market_frame):\n # Get the average volatility of a single name / a collection of names (e.g. all companies within a sector\n res = pd.Series(np.nan, index=names)\n for name in names:\n # Retrieve all volatility\n res[name] = np.nanmean(market_frame[name])\n all_average = np.nanmean(res)\n print(\"Average Vol\", all_average)\n return res, all_average\n\n\ndef event_analyser(names, market_frame, exogen_frame, rolling_window=180, detection_threshold=2.5, max_lag=5,\n save_csv=True, mode='rtn', start_date=None, end_date=None):\n if mode == 'rtn':\n day_range = [i for i in range(-max_lag, max_lag+1)]\n elif mode == 'vol':\n day_range = [i for i in range(-max_lag, max_lag+1)]\n else:\n raise ValueError(\"Unrecognised mode argument: rtn or vol allowed\")\n\n res = pd.DataFrame(columns=['Name','EventDate', 'Type'] + day_range)\n z_res = pd.DataFrame(columns=res.columns)\n\n start_date = pd.Timestamp(start_date) if start_date and pd.Timestamp(start_date) > market_frame.index[0] else market_frame.index[0]\n end_date = pd.Timestamp(end_date) if end_date and pd.Timestamp(end_date) < market_frame.index[-1] else market_frame.index[-1]\n market_frame = market_frame[(market_frame.index >= start_date) & (market_frame.index <= end_date)]\n exogen_frame = exogen_frame[(exogen_frame.index >= start_date) & (exogen_frame.index <= end_date)]\n\n for name in names:\n if mode == 'rtn':\n daily_abnormal_return = pd.Series(np.nan, index=market_frame.index)\n for date in market_frame.index:\n if date < start_date + pd.to_timedelta(rolling_window, 'D'):\n continue\n elif date > end_date:\n break\n daily_abnormal_return[date] = get_abnormal_return(name, market_frame, date, window=180)\n\n exogen_series = exogen_frame[name]\n exogen_z_score = _get_z_score(exogen_series, rolling_window)\n event = _get_event_series(exogen_z_score, detection_threshold)\n\n #pos_event_idx = event[event == 1].index\n #neg_event_idx = event[event == -1].index\n #plt.scatter(pos_event_idx, exogen_series[pos_event_idx], marker='^', color = 'r', label='Positive Events')\n #plt.scatter(neg_event_idx, exogen_series[neg_event_idx], marker='v', color='r', label='Negative Events')\n #exogen_series.plot()\n #plt.ylabel('SumSentiment')\n #plt.show()\n\n if mode == 'rtn':\n this_res, this_z_res = get_event_cumulative_abnormal_return(name, event, exogen_z_score, daily_abnormal_return, max_lag)\n else:\n this_res, this_z_res = get_event_vol(name, event, exogen_z_score, market_frame[name], max_lag)\n res = res.append(this_res, ignore_index=True)\n z_res = z_res.append(this_z_res, ignore_index=True)\n pos_res = res[res['Type'] == 1]\n neg_res = res[res['Type'] == -1]\n\n pos_z_score = z_res[z_res.Type == 1].mean(axis=0)\n pos_z_score.name = 'AvgZScore'\n neg_z_score = z_res[z_res.Type == -1].mean(axis=0)\n neg_z_score.name = 'AvgZScore'\n\n pos_summary_stat = pos_res.describe()\n neg_summary_stat = neg_res.describe()\n\n # Append t-test and Wilcoxon rank test p-values to the bottom of the result data frames\n pos_res_stat_tests = pos_res[day_range]\n neg_res_stat_tests = neg_res[day_range]\n\n # if mode is vol, we use the days leading up to the event day as the benchmark and conduct two-sample, one-tailed\n # t-test to ascertain whether the volatilities after the event days are significantly higher than before the event.\n if mode == 'vol':\n # T-test\n\n\n # Wilcoxon Rank-sum test\n #pos_wilcoxon = st.wilcoxon(pos_res_stat_tests.loc[:, list(range(0, max_lag))].values,\n # pos_res_stat_tests.loc[:, list(range(-max_lag, 0))].values,\n # alternative='greater') # one-tailed test here\n #neg_wilcoxon = st.wilcoxon(neg_res_stat_tests.loc[:, list(range(0, max_lag))].values,\n # neg_res_stat_tests.loc[:, list(range(-max_lag, 0))].values,\n # alternative='greater') # one-tailed test here\n #\n\n # Convert two tailed p-value to one-tailed p-value\n pos_pval = pd.Series(1., name='t-test', index=pos_res_stat_tests.columns)\n neg_pval = pd.Series(1., name='t-test', index=neg_res_stat_tests.columns)\n\n for i in day_range:\n if i >= -int(max_lag / 2):\n pos_t_test = st.ttest_1samp(pos_res_stat_tests.loc[:, i].values,\n popmean=np.nanmean(pos_res_stat_tests.loc[:, list(range(-max_lag, -int(max_lag/2)))].values),\n nan_policy='omit')\n neg_t_test = st.ttest_1samp(neg_res_stat_tests.loc[:, i].values,\n popmean=np.nanmean(neg_res_stat_tests.loc[:, list(range(-max_lag, -int(max_lag/2)))].values),\n nan_policy='omit')\n\n pos_pval[i] = pos_t_test[1]/2 if pos_t_test[0] > 0 else 1 - pos_t_test[1] / 2\n neg_pval[i] = neg_t_test[1]/2 if neg_t_test[0] > 0 else 1 - neg_t_test[1] / 2\n\n pos_summary_stat = pos_summary_stat.append(pos_pval)\n # here we divide the p-value by 2 to obtain the one tailed p value\n\n #wilcoxon_pos = pd.Series({i: st.wilcoxon(pos_res_stat_tests[i] - pos_pop_mean)[1]\n # for i in day_range}, name='wilcoxon', index=pos_res_stat_tests.columns)\n\n neg_summary_stat = neg_summary_stat.append(neg_pval)\n\n else:\n # 2-tailed t-test against the hypothesis that the abnormal return should be 0\n pos_summary_stat = pos_summary_stat.append(pd.Series(\n st.ttest_1samp(pos_res_stat_tests, popmean=0, nan_policy='omit')[1], name='t-test', index=pos_res_stat_tests.columns))\n #wilcoxon_pos = pd.Series({i: st.wilcoxon(pos_res_stat_tests[i] - 0)[1]\n # for i in day_range}, name='wilcoxon', index=pos_res_stat_tests.columns)\n # pos_summary_stat = pos_summary_stat.append(wilcoxon_pos)\n # pos_summary_stat = pos_summary_stat.append(pos_z_score)\n neg_summary_stat = neg_summary_stat.append(pd.Series(\n st.ttest_1samp(neg_res_stat_tests, popmean=0, nan_policy='omit')[1], name='t-test', index=neg_res_stat_tests.columns))\n #wilcoxon_neg = pd.Series({i: st.wilcoxon(neg_res_stat_tests[i] - 0)[1] for i in day_range}, name='wilcoxon'\n # , index=neg_res_stat_tests.columns)\n #neg_summary_stat = neg_summary_stat.append(wilcoxon_neg)\n neg_summary_stat = neg_summary_stat.append(neg_z_score)\n\n if save_csv:\n file_appendix = str(names[0]) + \"_rollWindow\" + str(rolling_window) + \\\n \"_maxLag\" + str(max_lag) + \"_mode\" + str(mode) + \"_\" + str(start_date.date()) + \\\n \"_\" + str(end_date.date()) + \".csv\"\n pos_summary_stat.to_csv('output/posEvents' + file_appendix)\n neg_summary_stat.to_csv('output/negEvents' + file_appendix)\n\n return pos_summary_stat, neg_summary_stat, pos_res, neg_res\n\n\ndef network_event_analyser(leading_name, lagging_names, market_frame, exogen_frame, rolling_window=90,\n detection_threshold=2.5, max_lag=5, mode='rtn', start_date=None, end_date=None):\n\n def process_sub_result(df, z_df):\n # print(z_df)\n groupby = df.groupby('Name')\n groupby_z = z_df.groupby('Name')\n res = {}\n for name, frame in groupby:\n data_stat_test = frame[day_rng]\n mean_z = groupby_z[day_rng].get_group(name).mean(axis=0)\n mean_z.name = 'AvgZScore'\n summary_stat = frame.describe()\n pop_mean = np.nanmean(data_stat_test.loc[:, list(range(-2*max_lag, -1))].values) if mode == 'vol' else 0\n ttest = pd.Series(st.ttest_1samp(data_stat_test, popmean=pop_mean, nan_policy='omit')[1], name='t-test', index=day_rng)\n wilcoxon = pd.Series({i: st.wilcoxon(data_stat_test[i] - pop_mean)[1] for i in day_rng}, name='wilcoxon', index=day_rng)\n summary_stat = summary_stat.append(ttest)\n summary_stat = summary_stat.append(wilcoxon)\n # print(mean_z)\n summary_stat = summary_stat.append(mean_z)\n res[name] = summary_stat\n\n # Average exclude leading name:\n df = df[df.Name != leading_name]\n data_stat_test = df[day_rng]\n mean_z = data_stat_test.mean(axis=0)\n mean_z.name = 'AvgZScore'\n summary_stat = df.describe()\n\n pop_mean = np.nanmean(data_stat_test.loc[:, list(range(-2 * max_lag, -1))].values) if mode == 'vol' else 0\n ttest = pd.Series(st.ttest_1samp(data_stat_test, popmean=pop_mean, nan_policy='omit')[1], name='t-test', index=day_rng)\n tstats = pd.Series(st.ttest_1samp(data_stat_test, popmean=pop_mean, nan_policy='omit')[0], name='t-stat', index=day_rng)\n wilcoxon = pd.Series({i: st.wilcoxon(data_stat_test[i]-pop_mean)[1] for i in day_rng}, name='wilcoxon', index=day_rng)\n wstats = pd.Series({i: st.wilcoxon(data_stat_test[i]-pop_mean)[0] for i in day_rng}, name='wstats', index=day_rng)\n summary_stat = summary_stat.append(ttest)\n summary_stat = summary_stat.append(wilcoxon)\n summary_stat = summary_stat.append(mean_z)\n summary_stat = summary_stat.append(tstats)\n summary_stat = summary_stat.append(wstats)\n\n res['Average'] = summary_stat\n return res\n\n if mode == 'rtn':\n day_rng = [i for i in range(-max_lag, max_lag+1)]\n else:\n day_rng = [i for i in range(-2*max_lag, max_lag)]\n\n start_date = pd.Timestamp(start_date) if start_date and pd.Timestamp(start_date) > market_frame.index[0] else market_frame.index[0]\n end_date = pd.Timestamp(end_date) if end_date and pd.Timestamp(end_date) < market_frame.index[-1] else market_frame.index[-1]\n\n market_frame = market_frame[(market_frame.index >= start_date) & (market_frame.index <= end_date)]\n exogen_frame = exogen_frame[(exogen_frame.index >= start_date) & (exogen_frame.index <= end_date)]\n\n exogen_series = exogen_frame[leading_name]\n exogen_z_score = _get_z_score(exogen_series, rolling_window)\n event = _get_event_series(exogen_z_score, detection_threshold)\n # Get the sentiment event for the LEADING NAME\n res = pd.DataFrame(columns=['Name', 'EventDate', 'Type'] + day_rng)\n z_res = pd.DataFrame(columns=res.columns)\n\n #print(market_frame)\n #print(exogen_frame)\n\n for lagging_name in lagging_names:\n daily_abnormal_return = pd.Series(np.nan, index=market_frame.index)\n z_score = _get_z_score(exogen_frame[lagging_name], rolling_window)\n if mode == 'rtn':\n for date in market_frame.index:\n if date < start_date + pd.to_timedelta(rolling_window, 'D'):\n continue\n daily_abnormal_return[date] = get_abnormal_return(lagging_name, market_frame, date, window=180)\n this_res, this_z_res = get_event_cumulative_abnormal_return(lagging_name, event, z_score, daily_abnormal_return, max_lag)\n else:\n this_res, this_z_res = get_event_vol(lagging_name, event, z_score, market_frame[lagging_name], max_lag)\n res = res.append(this_res, ignore_index=True)\n # print(this_z_res)\n z_res = z_res.append(this_z_res, ignore_index=True)\n\n pos = res[res['Type'] == 1]\n neg = res[res['Type'] == -1]\n pos_z = z_res[z_res.Type == 1]\n neg_z = z_res[z_res.Type == -1]\n print(pos)\n print(neg)\n\n pos_res = process_sub_result(pos, pos_z)\n neg_res = process_sub_result(neg, neg_z)\n print(pos_res)\n print(neg_res)\n\n return pos_res, neg_res\n\n\n"},{"alpha_fraction":0.6579012274742126,"alphanum_fraction":0.659375786781311,"avg_line_length":41.375,"blob_id":"d4876be0c3e43fca37a31dd11d6f87b13c8dcb6d","content_id":"0182a121b46ce78f6bb9547434e0fbea85af1172","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4069,"license_type":"no_license","max_line_length":113,"num_lines":96,"path":"/PycharmProjects/OMI/utilities.py","repo_name":"xingchenwan/Sentiment","src_encoding":"UTF-8","text":"# Utilities module: provide some utilities functions\n# Xingchen Wan | Xingchen.Wan@st-annes.ox.ac.uk | Oxford-Man Institute of Quantitative Finance\n\nimport datetime\nfrom source.all_classes import *\n\n\ndef fix_fulldata(full_data):\n \"\"\"\n A temporary patch to resolve the incompatibility issue between Python 2.x and 3.x pickling\n :param full_data:\n :return:\n \"\"\"\n\n def _fix_object(obj):\n \"\"\"\n Decode the byte data type back to strings due to Python 3.x un-pickling\n :param obj: all_data object\n :return: None\n \"\"\"\n obj.__dict__ = dict((k.decode(\"utf8\"), v) for k, v in obj.__dict__.items())\n\n def _fix_iterables(obj):\n \"\"\"\n Decode iterables\n :param obj: An iterable, dict or list (other iterables are not included as they were not included in the\n original data type design\n :return: Fixed iterables\n \"\"\"\n if isinstance(obj, list):\n return [x.decode('utf8') if isinstance(x, bytes) else x for x in obj]\n elif isinstance(obj, dict):\n return {k.decode('utf8'): v for k, v in obj.items()}\n\n _fix_object(full_data)\n full_data.entity_occur_interval = _fix_iterables(full_data.entity_occur_interval)\n full_data.entity_sentiment_interval = _fix_iterables(full_data.entity_sentiment_interval)\n\n for day in full_data.days:\n _fix_object(day)\n day.entity_sentiment_day = _fix_iterables(day.entity_sentiment_day)\n day.entity_occur_day = _fix_iterables(day.entity_occur_day)\n for news in day.day_news:\n _fix_object(news)\n news.entity_occur = _fix_iterables(news.entity_occur)\n news.entity_sentiment = _fix_iterables(news.entity_sentiment)\n\n\ndef create_sub_obj(parent_obj, start_date=None, end_date=None):\n \"\"\"\n Create a new sub FullData object from an existing FullData object with a narrower range of dates.\n :param parent_obj: FullData object. The parent object\n :param start_date: datetime.date type. Start date of the sub object. Must be later than the parent start date\n :param end_date: datetime.date type. End date fo the sub object. Must be earlier than the parent end date.\n :return:\n \"\"\"\n if not isinstance(parent_obj, FullData):\n raise TypeError(\"The parent_obj must be f FullData Type\")\n if start_date and not isinstance(start_date, datetime.date):\n raise ValueError(\"Start date needs to be of date type\")\n if end_date and not isinstance(end_date, datetime.date):\n raise ValueError(\"End date needs to be of date type\")\n\n if start_date is None: start_date = parent_obj.start_date\n if end_date is None: end_date = parent_obj.end_date\n\n start_date = max(start_date, parent_obj.start_date)\n end_date = min(end_date, parent_obj.end_date)\n\n if start_date == parent_obj.start_date and end_date == parent_obj.end_date:\n return parent_obj\n copied_obj = copy.deepcopy(parent_obj)\n # Create a copied object of the parent object first\n\n copied_obj.entity_occur_interval = {}\n copied_obj.entity_sentiment_interval = {}\n copied_obj.start_date = start_date\n copied_obj.end_date = end_date\n # Reset various properties of the copied object\n\n copied_obj.days = [day for day in parent_obj.days if day.date > start_date and day.date < end_date]\n total_sentiment = {}\n for each_day in copied_obj.days:\n for entity, num in each_day.entity_occur_day.items():\n if entity in copied_obj.entity_occur_interval:\n copied_obj.entity_occur_interval[entity] += num\n total_sentiment[entity] += num * each_day.entity_sentiment_day[entity]\n else:\n copied_obj.entity_occur_interval[entity] = num\n total_sentiment[entity] = num * each_day.entity_sentiment_day[entity]\n for entity, num in copied_obj.entity_occur_interval.items():\n copied_obj.entity_sentiment_interval[entity] = total_sentiment[entity] / num\n return copied_obj\n\ndef normalise(series):\n return (series - series.mean()) / (series.max() - series.min())\n\n"},{"alpha_fraction":0.5100798606872559,"alphanum_fraction":0.5533155798912048,"avg_line_length":44.56647491455078,"blob_id":"16bcd19f121236416c4523f73e7e5987c40ff4c8","content_id":"5b6060dad78395a42d1d1503d3c5a55a9352287b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7887,"license_type":"no_license","max_line_length":133,"num_lines":173,"path":"/PycharmProjects/OMI/main.py","repo_name":"xingchenwan/Sentiment","src_encoding":"UTF-8","text":"# Xingchen Wan | Oxford-Man Institute of Quantitative Finance | Updated 6 Sep 2018\n\nimport processor, grouper, analyser, visualiser, utilities\nfrom source.all_classes import *\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pylab as plt\nimport pandas as pd\n\n\nNAME = 'USDJPY'\nMODE = 'vol'\n# Sector representatives:\n# Financials: AXP\n# Tech: AAPL\n# Retail: WMT\n# Industrials: BA\nSTART_DATE = datetime.date(2007, 1, 1)\nEND_DATE = datetime.date(2007, 12, 31)\nMAX_LAG = 10\n\nPATH_POS = \"output/\" + \"posEvents\" + NAME + \"_rollWindow180_maxLag\" + str(MAX_LAG) + \"_mode\" + MODE + \"_\" + \\\n str(START_DATE) + \"_\" + str(END_DATE) + \".csv\"\nPATH_NEG = \"output/\" + \"negEvents\" + NAME + \"_rollWindow180_maxLag\" + str(MAX_LAG) + \"_mode\" + MODE + \"_\" + \\\n str(START_DATE) + \"_\" + str(END_DATE) + \".csv\"\nFIGURE_PATH = \"figures/\" + NAME + \"_rollWindow180_maxLag\" + str(MAX_LAG) + \"_mode\" + MODE + \"_\" + \\\n str(START_DATE) + \"_\" + str(END_DATE) + \".png\"\n\n\ndef plot_from_csv():\n pos_df = pd.read_csv(PATH_POS, index_col=0)\n neg_df = pd.read_csv(PATH_NEG, index_col=0)\n visualiser.plot_events(pos_df, neg_df, descps=['Positive Events', 'Negative Events'], mode=MODE)\n plt.xlabel(\"Number of days to events\")\n # plt.savefig(FIGURE_PATH)\n\n\ndef plot_multiple_from_csv(paths: dict, plot_range=None):\n \"\"\"\n Plot multiple graphs from a collection of csv paths. the paths should be organised in a dictionary ordered in a\n chronological order. The key is the year and the value is the path\n :param paths:\n :return:\n \"\"\"\n\n from collections import OrderedDict\n\n # setting colours\n\n def plot_event(event_summary, year, mode='vol', alpha=0.1, plot_range=None):\n ubound = 0.1\n lbound = 0\n\n\n event_summary = event_summary[[i for i in list(event_summary.columns) if i.lstrip('-').isdigit()]]\n event_summary.columns = event_summary.columns.astype(int)\n\n if plot_range is None:\n xi = np.array((event_summary.columns))\n\n else:\n xi = np.array([x for x in range(plot_range[0], plot_range[1])])\n\n mean_val = event_summary.loc['mean', xi]\n offset = mean_val.iloc[0] if mode != 'vol' else 0\n p = plt.plot(xi + 1 , event_summary.loc['mean', xi] - offset, 'o-', linewidth=1, alpha=1, label=str(year), markersize=2)\n for i in range(len(xi)):\n if event_summary.loc['mean', xi[i]] - offset > ubound:\n plt.plot(xi[i] + 1, ubound * 0.97, marker=\"^\", color=p[0].get_color())\n elif event_summary.loc['mean', xi[i]] - offset < lbound:\n plt.plot(xi[i] + 1, lbound * 0.97, marker='v', color=p[0].get_color())\n #test_stats = event_summary.loc['t-test', :]\n\n #sig_idx = np.argwhere((test_stats < 0.05) & (test_stats > 0.01)).reshape(-1) + mean_val.index[0]\n #v_sig_idx = np.argwhere(test_stats <= 0.01).reshape(-1).reshape(-1)+ mean_val.index[0]\n\n #if len(sig_idx):\n # plt.plot(sig_idx, mean_val[sig_idx] - offset, 'o', color='orange', label='p < 0.05')\n #if len(v_sig_idx):\n # plt.plot(v_sig_idx, mean_val[v_sig_idx] - offset, 'o', color='red', label='p < 0.01')\n if mode == 'rtn':\n plt.ylabel('Cumulative Abnormal Return (CAR)')\n elif mode == 'vol':\n plt.ylabel('Volatility 1D')\n\n handles, labels = plt.gca().get_legend_handles_labels()\n by_label = OrderedDict(zip(labels, handles))\n plt.grid(True)\n plt.gca().set_ylim(bottom=lbound)\n plt.gca().set_ylim(top=ubound)\n plt.legend(by_label.values(), by_label.keys(), prop={'size': 6}, loc='upper right')\n\n i = 0\n plt.axvspan(-1, 0, alpha=0.2, color='gray')\n plt.rcParams.update({'font.size': 8})\n\n for year, path_pos in paths.items():\n i += 1\n pos_df = pd.read_csv(path_pos, index_col=0)\n plot_event(pos_df, year, mode=MODE, plot_range=plot_range)\n plt.xlabel(\"Number of days to events\")\n plt.show()\n\n\n\ndef event(start_date=START_DATE, end_date=END_DATE):\n\n full_data_obj = 'data/full.date.20061020-20131120'\n market_data_path = 'data/market_data.xlsx'\n market_data_sheet = 'Price1'\n volume_data_sheet = 'Volatility1'\n sector_path = \"source/sector.csv\"\n pd.options.display.max_rows = 10000\n\n with open(full_data_obj, 'rb') as f:\n all_data = pickle.load(f, fix_imports=True, encoding='bytes')\n utilities.fix_fulldata(all_data)\n\n sub_all_data = utilities.create_sub_obj(all_data, start_date, end_date)\n price, daily_rtn, log_return, vol = processor.process_market_time_series(market_data_path, market_data_sheet,\n start_date=pd.to_datetime(start_date),\n end_date=pd.to_datetime(end_date),\n )\n volume, _, _, _ = processor.process_market_time_series(market_data_path,volume_data_sheet,\n start_date=pd.to_datetime(start_date),\n end_date= pd.to_datetime(end_date))\n\n all_names = list(price.columns)[:-1]\n sector = grouper.get_sector_grouping(all_names, sector_path, 0, 2)\n this_sector = grouper.get_sector_peer(sector, NAME)\n\n count, med_sentiment, sum_sentiment = processor.process_count_sentiment(sub_all_data,\n start_date=pd.to_datetime(start_date),\n end_date=pd.to_datetime(end_date),\n focus_iterable=all_names,\n rolling=True,\n rolling_smoothing_factor=0.7)\n\n pos, neg, _, _ = analyser.event_analyser(all_names, market_frame=vol, exogen_frame=sum_sentiment, mode=MODE,\n start_date=start_date,\n end_date=end_date,\n max_lag=MAX_LAG)\n\n\ndef event_by_year(start_date, end_date):\n for _ in range(7):\n print(start_date, end_date)\n event(start_date, end_date)\n start_date += datetime.timedelta(days=365)\n end_date += datetime.timedelta(days=365)\n\n\n\nif __name__ == \"__main__\":\n #event_by_year(START_DATE, END_DATE)\n\n event_type = \"neg\"\n\n paths_dict = {\n '2007': \"output/\" + event_type + \"Events\" + NAME + \"_rollWindow180_maxLag10_mode\" + MODE + \"_2007-01-01_2007-12-31\" + \".csv\",\n '2008': \"output/\" + event_type + \"Events\" + NAME + \"_rollWindow180_maxLag10_mode\" + MODE + \"_2008-01-01_2008-12-30\" + \".csv\",\n '2009': \"output/\" + event_type + \"Events\" + NAME + \"_rollWindow180_maxLag10_mode\" + MODE + \"_2008-12-31_2009-12-30\" + \".csv\",\n '2010': \"output/\" + event_type + \"Events\" + NAME + \"_rollWindow180_maxLag10_mode\" + MODE + \"_2009-12-31_2010-12-30\" + \".csv\",\n '2011': \"output/\" + event_type + \"Events\" + NAME + \"_rollWindow180_maxLag10_mode\" + MODE + \"_2010-12-31_2011-12-30\" + \".csv\",\n '2012': \"output/\" + event_type + \"Events\" + NAME + \"_rollWindow180_maxLag10_mode\" + MODE + \"_2012-01-02_2012-12-28\" + \".csv\",\n '2013': \"output/\" + event_type + \"Events\" + NAME + \"_rollWindow180_maxLag10_mode\" + MODE + \"_2012-12-31_2013-12-27\" + \".csv\"\n }\n\n paths_dict2 = {\n 'Positive': \"output/\" + \"pos\" + \"Events\" + NAME + \"_rollWindow180_maxLag10_modevol_2006-10-30_2013-12-31\" + \".csv\",\n 'Negative': \"output/\" + \"neg\" + \"Events\" + NAME + \"_rollWindow180_maxLag10_modevol_2006-10-30_2013-12-31\" + \".csv\",\n }\n plot_multiple_from_csv(paths_dict, plot_range=[-10, 5])\n\n\n\n\n"},{"alpha_fraction":0.5800870656967163,"alphanum_fraction":0.5868335366249084,"avg_line_length":45.882652282714844,"blob_id":"8455ee09ac48c23d41ddf7d1d7b7e3239884d45d","content_id":"a0cadccc1215f68caa78becdd109bbbae1cc8a7d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":9190,"license_type":"no_license","max_line_length":132,"num_lines":196,"path":"/PycharmProjects/OMI/networkAnalyser.py","repo_name":"xingchenwan/Sentiment","src_encoding":"UTF-8","text":"import utilities\nfrom source.utils import cosine_distance\nimport networkx as nx\nimport community\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom visualiser import get_color_map\n\n\nclass NetworkAnalyser:\n def __init__(self, full_data_object, names, start_date=None, end_date=None):\n self.names = names\n self.start_date = start_date if start_date and start_date >= full_data_object.start_date else full_data_object.start_date\n self.end_date = end_date if end_date and end_date <= full_data_object.end_date else full_data_object.end_date\n if self.start_date != full_data_object.start_date or self.end_date != full_data_object.end_date:\n self.full_data = utilities.create_sub_obj(full_data_object, self.start_date, self.end_date)\n else:\n self.full_data = full_data_object\n self.G = self.full_data.build_occurrence_network_graph(focus_iterable=self.names)\n self.partition = {}\n self.node_pos = None\n\n def get_centrality(self, names=None, type='katz', ordered=True):\n if type == 'katz':\n centrality = nx.katz_centrality(self.G, weight='weight')\n elif type == 'eigen':\n centrality = nx.eigenvector_centrality(self.G, weight='weight')\n else:\n raise NotImplementedError('Unrecognised centrality mode name.')\n if ordered:\n centrality = sorted(centrality.items(), key=lambda kv: -kv[1])\n if names is not None:\n res = [x for x in centrality if x[0] in names]\n return res\n else: return centrality\n\n def cluster(self, resolution=None):\n if resolution:\n self.partition = community.best_partition(self.G, weight='weight', resolution=resolution)\n else:\n self.partition = community.best_partition(self.G, weight='weight')\n return self.partition\n\n def get_cluster_peers(self, name):\n if self.partition == {}:\n self.cluster()\n peers = [key for key, value in self.partition.items() if value == self.partition[name]]\n peer_centrality = self.get_centrality(names=peers)\n res = {peer[0]: peer[1] for peer in peer_centrality}\n return res\n\n def get_category(self, name):\n if self.partition == {}:\n self.cluster()\n return self.partition[name]\n\n def get_neighbours(self, name, max_neighbour=-1, weight_threshold=-1, exclude=None):\n neighbours = self.G[name]\n sorted_neighbours = sorted(neighbours.items(), key=lambda kv: -kv[1]['weight'])\n #print(sorted_neighbours)\n if weight_threshold > 0:\n if isinstance(weight_threshold, float):\n res = [item for item in sorted_neighbours if item[1]['weight'] >= weight_threshold]\n elif isinstance(weight_threshold, int):\n threshold = np.percentile(np.array([x[1]['weight'] for x in sorted_neighbours]), weight_threshold)\n res = [item for item in sorted_neighbours if item[1]['weight'] >= threshold]\n else:\n raise TypeError(\"percentile threshold needs to be a positive integer or float\")\n else:\n res = sorted_neighbours\n if neighbours != -1:\n res = res[:min(max_neighbour, len(sorted_neighbours))]\n if exclude is not None:\n res = [item for item in res if item[0] not in exclude]\n names = [name] + [each_res[0] for each_res in res]\n weight = [1] + [each_res[1]['weight'] for each_res in res]\n category = [self.get_category(name)] + [self.get_category(each_name) for each_name in names]\n centrality = [self.get_centrality(name)[0][1]] + [self.get_centrality(each_name)[0][1] for each_name in names]\n res = pd.DataFrame([names, weight, category, centrality]).transpose()\n res.columns = ['Names', 'CosineDistance', 'Category', 'Centrality']\n res.dropna(axis=0, inplace=True, how='any')\n return res\n\n def plot_network(self, group_by='category', threshold=90):\n if self.partition == {}: self.cluster()\n color_map = get_color_map(self.names, self.partition)\n c = [color_map[name] for name in self.G.nodes()]\n if self.node_pos is None:\n pos = nx.spring_layout(self.G)\n self.node_pos = pos\n edgelist = self.G.edges(data=True)\n if group_by == 'centrality':\n centrality = list(self.get_centrality().values())\n vmin = np.min(centrality)\n vmax = np.max(centrality)\n\n if threshold:\n if isinstance(threshold, int):\n weight_list = np.array([x[2]['weight'] for x in edgelist])\n threshold = np.percentile(weight_list, threshold)\n edgelist = [x for x in edgelist if x[2]['weight'] >= threshold]\n else:\n edgelist = [x for x in edgelist if x[2]['weight'] >= threshold]\n if group_by == 'category':\n nx.draw(self.G, self.node_pos, edgelist=edgelist, node_color=c, with_labels=True)\n elif group_by == 'centrality':\n nx.draw(self.G, self.node_pos, edgelist=edgelist,\n cmap=plt.plasma(),\n node_color=centrality,\n with_labels=True)\n sm = plt.cm.ScalarMappable(cmap=plt.plasma(), norm=plt.Normalize(vmin=vmin, vmax=vmax))\n sm.set_array([])\n plt.colorbar(sm)\n else:\n raise NotImplemented()\n\n def plot_single_name(self, name, max_neighbour=-1, weight_threshold=-1):\n if self.partition == {}: self.cluster()\n data = self.get_neighbours(name, max_neighbour, weight_threshold)\n nodes = data.iloc[:, 0]\n G = nx.Graph()\n G.add_nodes_from(nodes)\n nx.draw(G)\n\n def get_cos_dist_time_series(self, name1, *names, correlation_period=90):\n entity_list = [name1] + list(names)\n name2id = {entity_list[i]: i for i in range(len(entity_list))}\n occurrence_array = np.zeros((len(entity_list), self.full_data.news_number))\n date_array = np.zeros((self.full_data.news_number, ))\n start_date = self.start_date + pd.to_timedelta(correlation_period)\n res = {entity: [] for entity in names}\n date_frame = []\n news_id = 0\n day_id = 0\n for each_day in self.full_data.days:\n for news in each_day.day_news:\n for entity in entity_list:\n if entity in news.entity_occur:\n occurrence_array[name2id[entity], news_id] = news.entity_occur[entity]\n date_array[news_id] = day_id\n news_id += 1\n if each_day.date >= start_date + pd.to_timedelta(correlation_period):\n date_frame.append(each_day.date)\n included_news = (date_array >= day_id - correlation_period) & (date_array < day_id)\n sub_occurrence_array = occurrence_array[:, included_news]\n for name in names:\n res[name].append(cosine_distance(sub_occurrence_array[name2id[name1]],\n sub_occurrence_array[name2id[name]]))\n day_id += 1\n date_array = pd.Series(date_frame)\n res = pd.DataFrame(res, index=date_array)\n return res\n\n def get_dynamic_neighbors(self, name, date, period=180, weight_threshold=-1, max_neighbour=-1, exclude=None):\n \"\"\"\n Get top n neighbours in based on cosine distance in the past n days\n :param name:\n :param time:\n :param period:\n :param weight_threshold:\n :param max_neighbour:\n :param exclude:\n :return:\n \"\"\"\n if date < self.start_date + pd.to_timedelta(period):\n return np.nan\n #print(date - pd.to_timedelta(period, 'D', ), date)\n day_objs = [day for day in self.full_data.days if ((day.date > date - pd.to_timedelta(period, 'D')) and (day.date <= date))]\n name2id = {self.names[i]: i for i in range(len(self.names))}\n news_cnt = sum([day.news_number for day in day_objs])\n res = {entity: 0 for entity in self.names}\n occurrence_array = np.zeros((len(self.names), news_cnt))\n news_id = 0\n for each_day in day_objs:\n for news in each_day.day_news:\n for entity in self.names:\n if entity in news.entity_occur:\n occurrence_array[name2id[entity], news_id] = news.entity_occur[entity]\n news_id += 1\n\n for each_name in self.names:\n res[each_name] = cosine_distance(occurrence_array[name2id[name]], occurrence_array[name2id[each_name]])\n res = sorted(res.items(), key=lambda v: -v[1])[1:]\n\n if weight_threshold != -1:\n res = [x for x in res if x[1] > weight_threshold]\n if max_neighbour != -1 and max_neighbour < len(res):\n thres = res[max_neighbour][1]\n res = [x for x in res if x[1] > thres]\n if exclude is not None:\n res = [x for x in res if x[0] not in exclude]\n print(res)\n return res\n\n"},{"alpha_fraction":0.5869565010070801,"alphanum_fraction":0.5966865420341492,"avg_line_length":39.450355529785156,"blob_id":"4569b5dad96a3bbb92bd0d73ca999017807aaa7a","content_id":"0caf2d557b79d7238d15c407b05fd8dcf0931791","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":11408,"license_type":"no_license","max_line_length":121,"num_lines":282,"path":"/PycharmProjects/OMI/visualiser.py","repo_name":"xingchenwan/Sentiment","src_encoding":"UTF-8","text":"# Visualiser: Plot functions\n# Xingchen Wan | Xingchen.Wan@st-annes.ox.ac.uk | Oxford-Man Institute of Quantitative Finance\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nimport matplotlib.pylab as plt\nfrom analyser import _get_z_score, _get_event_series\nimport datetime\nimport math\nimport networkx as nx\nfrom utilities import create_sub_obj\nfrom collections import OrderedDict\n\n\ndef get_color_map(data_labels, categories):\n color_map_num2text = ['b', 'g', 'r', 'c', 'm', 'y', 'olive', 'coral', 'brown', 'gray', 'indigo']\n color_map ={ x:'k' for x in data_labels}\n assert isinstance(categories, dict)\n color_map_keys = list(set(categories.values()))\n color_map_values = [color_map_num2text[i] if i < len(color_map_num2text) else 'k' for i in\n range(len(color_map_keys))]\n color_map_dict = dict(zip(color_map_keys, color_map_values))\n for name in data_labels:\n try:\n color_map[name] = color_map_dict[categories[name]]\n except KeyError:\n pass\n return color_map\n\n\ndef plot_scatter(data, data_labels, x_label=None, y_label=None, categories=None):\n \"\"\"\n Plot the scatter diagram of the correlation of different single names\n :param data: input data. pandas dataframe or numpy ndarray\n :param data_labels: label of each data point - usually the ticker or name of the entities in data. Should be of\n the same length as the data\n :param x_label: Optional. x-axis label of the scatter plot.\n :param y_label: Optional. y-axis label of the scatter plot\n :param categories: Optional {}. Sectorial or dynamic categorisation of the entities. Must be of the same length\n as the number of entities in data.\n :return:\n \"\"\"\n\n assert len(data) == len(data_labels)\n # Default colour scheme\n\n c = ['k']*len(data_labels)\n if categories:\n color_map = get_color_map(data_labels, categories)\n c = [color_map[name] for name in data_labels]\n\n axis = np.linspace(0, 100, len(data))\n fig, ax = plt.subplots()\n ax.scatter(axis, data, c=c, s=10)\n plt.axhline(0, color=\"gray\")\n plt.axhline(np.nanmean(data), color=\"red\")\n plt.axhline(np.nanmedian(data), color=\"blue\")\n if x_label: plt.xlabel(x_label)\n if y_label: plt.ylabel(y_label)\n if categories:\n labels = list(categories.values())\n\n for i in range(len(data)):\n if categories:\n ax.scatter(axis[i], data[i], c=c[i], label=labels[i] if labels[i] not in\n plt.gca().get_legend_handles_labels()[1] else '')\n else:\n ax.scatter(axis[i], data[i], c=c[i])\n ax.annotate(data_labels.iloc[i], (axis[i], data[i]), fontsize=6)\n plt.legend(prop={'size': 6})\n\n\ndef plot_single_name(name, *args, arg_names=[], start_date=None, end_date=None):\n if arg_names and len(arg_names) != len(args):\n raise ValueError(\"Length mismatch between arg_names and number of series supplied.\")\n start_date = start_date if start_date and isinstance(start_date, datetime.date) else datetime.datetime(2007, 1, 1)\n end_date = end_date if end_date and isinstance(end_date, datetime.date) else datetime.datetime(2014, 1, 1)\n\n fig = plt.figure(figsize=(5, 5))\n subplot_width = 2\n subplot_height = 1\n i = 0\n for arg in args:\n if not isinstance(arg, pd.DataFrame) and not isinstance(arg, pd.Series):\n raise TypeError(\"Input arguments need to be Pandas DataFrames\")\n elif not isinstance(arg.index, pd.DatetimeIndex):\n raise TypeError(\"Input series index needs to be DatetimeIndex\")\n\n for arg in args:\n if isinstance(arg, pd.Series):\n series = arg\n elif name in list(arg.columns):\n series = arg[name]\n else:\n print(name, \" not found in argument of position\", args.index(arg))\n continue\n series = series[series.index >= start_date]\n series = series[series.index < end_date]\n ax = plt.subplot(subplot_width, subplot_height, i+1)\n if 'Sentiment' in arg_names[i]:\n z_score = _get_z_score(series, window=90)\n events = _get_event_series(z_score, threshold=2.5,)\n neg_event_days = events.index[events == -1]\n pos_event_days = events.index[events == 1]\n for day in neg_event_days:\n plt.axvline(day, color='r', alpha=0.5)\n for day in pos_event_days:\n plt.axvline(day, color='g', alpha=0.5)\n plt.plot(series.index, series)\n #plt.axhline(np.nanmean(series), color=\"red\")\n #plt.axhline(np.nanmedian(series), color=\"blue\")\n #plt.axhline(0, color=\"gray\")\n\n if arg_names:\n ax.set_title(arg_names[i])\n i += 1\n\n plt.subplot(211)\n for day in neg_event_days:\n plt.axvline(day, color='r', alpha=0.5)\n for day in pos_event_days:\n plt.axvline(day, color='g', alpha=0.5)\n\n fig.suptitle(name)\n\n\ndef plot_network(full_data_obj, names=[], start_date=None, end_date=None, categories=None, threshold=90,\n group_by=None):\n\n if not group_by:\n group_by = 'category'\n sub_obj = create_sub_obj(full_data_obj, start_date=start_date, end_date=end_date)\n G = sub_obj.build_occurrence_network_graph(focus_iterable=names)\n color_map = get_color_map(names, categories)\n c = [color_map[name] for name in G.nodes()] if categories else ['k']*len(names)\n pos = nx.spring_layout(G)\n edgelist = G.edges(data=True)\n if group_by == 'centrality':\n centrality = list(nx.katz_centrality(G, weight='weight').values())\n vmin = np.min(centrality)\n vmax = np.max(centrality)\n\n if threshold:\n if isinstance(threshold, int):\n weight_list = np.array([x[2]['weight'] for x in edgelist])\n threshold = np.percentile(weight_list, threshold)\n edgelist = [x for x in edgelist if x[2]['weight'] >= threshold]\n else:\n edgelist = [x for x in edgelist if x[2]['weight'] >= threshold]\n if group_by == 'category':\n nx.draw(G, pos, edgelist=edgelist, node_color=c, with_labels=True)\n elif group_by == 'centrality':\n nx.draw(G, pos, edgelist=edgelist,\n cmap=plt.plasma(),\n node_color=centrality,\n with_labels=True)\n sm = plt.cm.ScalarMappable(cmap=plt.plasma(), norm=plt.Normalize(vmin=vmin, vmax=vmax))\n sm.set_array([])\n plt.colorbar(sm)\n else:\n raise NotImplemented()\n\n\ndef plot_distribution_around_events(df):\n import matplotlib.mlab as mlab\n\n def gen_caption(days_to_event):\n if days_to_event < 0:\n return str(abs(days_to_event))+'d before event'\n elif days_to_event == 0:\n return \"Event day\"\n else:\n return str(days_to_event)+'d after event'\n\n numeric_col_names = [i for i in list(df.columns) if isinstance(i, int)]\n subplot_width = 2\n subplot_height = math.ceil(len(numeric_col_names) / subplot_width)\n df = df[numeric_col_names]\n i = 0\n for col in numeric_col_names:\n ax = plt.subplot(subplot_width, subplot_height, i+1)\n data = df[col].values\n data = data[~np.isnan(data)]\n mu = np.mean(data)\n sigma = np.std(data)\n n, bins, patches = plt.hist(data, 50, normed=1)\n y = mlab.normpdf(bins, mu, sigma)\n l = plt.plot(bins, y, 'r--', linewidth=1)\n plt.xlabel('CAR')\n plt.axvline(mu, color='red')\n ax.set_title(gen_caption(col))\n i += 1\n\n\ndef plot_events(*event_summaries, descps, test_chosen='t-test', mode='vol', special_line=False, plot_special_only=False,\n plot_sentiment=False):\n plt.figure(figsize=(8, 6))\n\n def plot_event(event_summary, descp, test_chosen, ):\n event_summary = event_summary[[i for i in list(event_summary.columns) if i.lstrip('-').isdigit()]]\n\n xi = [int(x) for x in list(event_summary.columns)]\n mean_val = event_summary.loc['mean', :].values\n # z_val = event_summary.loc['AvgZScore',:].values\n if plot_sentiment:\n plt.subplot(211)\n if not special_line:\n if plot_special_only:\n pass\n else:\n plt.plot(xi, mean_val, linewidth=1, label=descp)\n # plt.plot(xi, mean_val, label=descp)\n # plt.fill_between(xi, mean_val-std_devs, mean_val+std_devs, alpha=0.2)\n else:\n plt.plot(xi, mean_val, linewidth=3.0, label=descp)\n if (not special_line) and (plot_special_only):\n pass\n else:\n test_stats = event_summary.loc[test_chosen, :].values\n sig_idx = np.argwhere((test_stats < 0.05) & (test_stats > 0.01))\n v_sig_idx = np.argwhere(test_stats <= 0.01)\n if len(sig_idx):\n plt.plot(sig_idx+xi[0], mean_val[[sig_idx]], 'o', color='orange', label='p < 0.05')\n if len(v_sig_idx):\n plt.plot(v_sig_idx+xi[0], mean_val[[v_sig_idx]], 'o', color='red', label='p < 0.01')\n\n if plot_sentiment:\n plt.subplot(212)\n if not special_line:\n if plot_special_only:\n pass\n else:\n plt.plot(list(event_summary.columns), np.abs(z_val), linewidth=1, label=descp)\n else:\n plt.plot(list(event_summary.columns), np.abs(z_val), linewidth=3.0, label=descp)\n\n if len(descps) != len(event_summaries):\n raise ValueError(\"Descp length must match number of event summaries passed!\")\n for i in range(len(event_summaries)):\n plot_event(event_summaries[i], descps[i], test_chosen=test_chosen,)\n\n if plot_sentiment:\n plt.subplot(211)\n if mode == 'rtn':\n #plt.axhline(0, color='k',)\n #plt.axvline(0, color='k',)\n plt.ylabel('Cumulative Abnormal Return (CAR)')\n elif mode == 'vol':\n #plt.axhline(0, color='k', linestyle='dashed')\n #plt.axvline(0, color='k', linestyle='dashed')\n plt.ylabel('Volatility 1D')\n if plot_sentiment:\n plt.subplot(212)\n #plt.axvline(0, color='k', linestyle='dashed')\n plt.xlabel('Days to the event')\n plt.ylabel('Sentiment abs. z-score')\n handles, labels = plt.gca().get_legend_handles_labels()\n by_label = OrderedDict(zip(labels, handles))\n plt.grid(True)\n plt.legend(by_label.values(), by_label.keys(), prop={'size': 6}, loc='upper right')\n\n\ndef plot_x_events(leading_name, group_summary, sector_summary=None, test_chosen='t-test', specialOnly=False, mode='rtn'):\n for name, summary in group_summary.items():\n if name == leading_name: isSpecial = True\n elif name == 'Average':\n isSpecial = True\n name = 'GroupAverage'\n else: isSpecial = False\n plot_events(summary, descps=[name], test_chosen=test_chosen, mode=mode,\n special_line=isSpecial, plot_special_only=specialOnly)\n if sector_summary is not None:\n for name, summary in sector_summary.items():\n if name == 'Average':\n isSpecial = True\n name = 'SectorAverage'\n else:\n isSpecial = False\n plot_events(summary, descps=[name], test_chosen=test_chosen, mode=mode,\n special_line=isSpecial, plot_special_only=True)\n\n"},{"alpha_fraction":0.705568253993988,"alphanum_fraction":0.7058225274085999,"avg_line_length":47.567901611328125,"blob_id":"0ca6ea2c37aa328ac24f2413ca0e021a4ee4cbc3","content_id":"eec2f25f0f6e3218021ab0239d0c8bc2fe5e3217","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3933,"license_type":"no_license","max_line_length":120,"num_lines":81,"path":"/PycharmProjects/OMI/grouper.py","repo_name":"xingchenwan/Sentiment","src_encoding":"UTF-8","text":"# Grouper module: generate different classification of the entities, either based on dynamic cosine distance in relevant\n# co-occurrence matrices or by static sector categorisation\n# Xingchen Wan | Xingchen.Wan@st-annes.ox.ac.uk | Oxford-Man Institute of Quantitative Finance\n\nimport datetime\nimport pandas as pd\nfrom source.all_classes import *\nimport community\nfrom utilities import *\n\n\ndef get_dynamic_grouping(names, full_data_obj, start_date=None, end_date=None,save_to_csv=True):\n \"\"\"\n Compute the dynamic grouping dictionary of a series of single names based on the cosine-distance of the entities\n within the specified period of time\n :param start_date: optional. If none, the start date will be taken to be the same as the starting date of the\n full_data object\n :param end_date: optional. Same as above\n :return: Dict mapping names to their dynamic grouping\n \"\"\"\n if start_date and not isinstance(start_date, datetime.date):\n raise TypeError(\"Start time needs to be a date object\")\n if end_date and not isinstance(end_date, datetime.date):\n raise TypeError(\"End time needs to be a date object\")\n if not isinstance(full_data_obj, FullData):\n raise TypeError(\"full_data_obj needs to be a FullData object\")\n if isinstance(names, pd.Series):\n names = names.values.tolist()\n start_date = start_date if start_date and start_date >= full_data_obj.start_date else full_data_obj.start_date\n end_date = end_date if end_date and end_date <= full_data_obj.end_date else full_data_obj.end_date\n # Sanity Checks\n\n full_data_sub = create_sub_obj(full_data_obj, start_date, end_date)\n G = full_data_sub.build_occurrence_network_graph(focus_iterable=names,)\n partition = community.best_partition(G, weight='weight') #, resolution=7)\n if save_to_csv:\n pd.Series(partition).to_csv('dynamic_grouping'+str(start_date)+str(end_date)+'.csv')\n return partition\n\n\ndef get_sector_grouping(names, path, name_col, sector_col):\n \"\"\"\n Get the ground truth static sector grouping dictionary of a series of single names. If there are any names in\n 'names' argument that do not have a sector grouping in the lookup csv file, these names will be assigned an\n \"unclassified\" grouping in the output dictionary\n :param path: str. path string of the csv file containing the sector information\n :param name_col: str or int. If str, this argument is interpreted as the name of the column. If int,\n this arguement is interpreted as the column number\n :param sector_col: Same as above for the sector column\n :return: Dict mapping names to their sector grouping\n \"\"\"\n data = pd.read_csv(path, header=None)\n if isinstance(name_col, str):\n assert name_col in list(data.columns)\n name_column = data[name_col]\n elif isinstance(name_col, int):\n assert name_col <= len(data.columns)\n name_column = data.iloc[:, name_col]\n else:\n raise TypeError(\"Invalid name column data type\")\n\n if isinstance(sector_col, str):\n assert sector_col in list(data.columns)\n sector_column = data[sector_col]\n elif isinstance(sector_col, int):\n assert sector_col <= len(data.columns)\n sector_column = data.iloc[:, sector_col]\n else:\n raise TypeError(\"Invalid sector column data type\")\n\n sector_lookup = pd.Series(sector_column.values, index=name_column).to_dict()\n # print(\"Names not found in the lookup:\", [i for i in names if i not in sector_lookup.keys()])\n return {name: sector_lookup[name] if name in sector_lookup.keys() else 'Unclassified' for name in names}\n\n\ndef get_sector_peer(sector_dict, name):\n if not isinstance(sector_dict, dict):\n raise TypeError(\"sector_dict argument needs to be a dictionary.\")\n if name not in sector_dict:\n raise ValueError(\"name not found in sector_dict\")\n return [key for key, value in sector_dict.items() if value == sector_dict[name]]"},{"alpha_fraction":0.5569639801979065,"alphanum_fraction":0.5712571740150452,"avg_line_length":43.58139419555664,"blob_id":"d5ecdaf349d05fc7d6d2a4538211b786fb2242a1","content_id":"92a6dff438e044f5d6655eede9522d88975cfc47","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":19170,"license_type":"no_license","max_line_length":192,"num_lines":430,"path":"/PycharmProjects/OMI/source/all_classes.py","repo_name":"xingchenwan/Sentiment","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n# @Author: Jie Yang\n# @Date: 2018-08-20 14:52:25\n# @Last Modified by: Jie Yang, Contact: jieynlp@gmail.com\n# @Last Modified time: 2018-08-23 23:58:10\n\n# Modified by Xingchen Wan on 3 Sep to make the file Python 3 compatible\n\nimport os\n\n# import community\nimport networkx as nx\nimport numpy as np\nimport copy\n\nfrom source.utils import *\n\nignore_entity_dict = {\"ADDITIONAL\", \"REUTERS\", \"JOURNAL\", \"U\", \"CO.\", \"ALISTER BULL\", \"EDITING\", \"REUTERS ESTIMATES\",\n \"REPORTING\", \"COMPILED BY ASIA COMMODITIES\"}\n\n\nclass News:\n def __init__(self, news_dir):\n self.null_news = False\n self.title = \"\"\n self.dir = news_dir\n self.newsID = -1\n self.date = datetime.date(1929, 10, 12)\n self.date_format = \"EDT\" # EDT/EST\n self.time = datetime.time(0, 0, 0)\n self.entity_occur = {}\n self.entity_sentiment = {}\n self.url = \"\"\n # self.token_sentence = []\n self.load_file(news_dir)\n\n def load_file(self, news_dir):\n if os.path.isfile(news_dir):\n the_news = read_news_file(news_dir)\n if the_news == []:\n self.null_news = True\n else:\n self.title = the_news['title']\n self.date = the_news['date']\n self.time = the_news['time']\n self.url = the_news['url']\n # self.token_sentence = the_news['sentences']\n\n def extract_token_sentences(self):\n if os.path.isfile(self.dir):\n the_news = read_news_file(self.dir)\n if the_news == []:\n return []\n else:\n return the_news['sentences']\n return []\n\n def build_entity_sentiment(self, entity_sentiment_list):\n \"\"\"\n input:\n entity_sentiment_list in one news: [[entity1, sentiment1], [entity2, sentiment2],[entity3, sentiment3]], entity may duplicate, and sentiment = {-1,0,1}\n \"\"\"\n entity_dict = {}\n for each_list in entity_sentiment_list:\n entity, sentiment = each_list\n sentiment = int(sentiment)\n # # entity = company_name_normalize(entity)\n # if (len(name_mapping_dict) > 0) and (entity in name_mapping_dict):\n # \tentity = company_name_normalize(name_mapping_dict[entity])\n # entity = entity.upper()\n if entity in entity_dict:\n entity_dict[entity][0] += 1\n entity_dict[entity][1] += sentiment\n else:\n entity_dict[entity] = [1, sentiment]\n for entity, num_sent in entity_dict.items():\n avg_sentiment = (num_sent[1] + 0.) / num_sent[0]\n avg_sentiment = round(avg_sentiment, 3)\n self.entity_occur[entity] = num_sent[0]\n self.entity_sentiment[entity] = avg_sentiment\n\n\nclass Day:\n def __init__(self, day_folder, each_date):\n self.null_today = False\n self.date = each_date\n self.day_news = []\n self.news_number = 0\n self.load_folder(day_folder)\n self.entity_occur_day = {}\n self.entity_sentiment_day = {}\n\n def load_folder(self, day_folder):\n for path, subdirs, files in os.walk(day_folder):\n for name in files:\n the_file = os.path.join(path, name)\n the_news = News(the_file)\n if (the_news.null_news == False) and (the_news.date == self.date):\n self.day_news.append(the_news)\n if len(self.day_news) == 0:\n self.null_today = True\n self.news_number = len(self.day_news)\n\n def assign_sentence_to_news(self, sentence_sentiment):\n \"\"\"\n input:\n sentence_sentiment in one day: [[sentence, entity, sentiment1], [sentence, entity, sentiment1]...], entity may duplicate, and sentiment = {-1,0,1}\n name_mapping_dict: convert entity names\n \"\"\"\n # load sentence dict\n sentence_dict = {}\n entity_sentiment_for_news = []\n for idx in range(len(self.day_news)):\n entity_sentiment_for_news.append([])\n token_sentences = self.day_news[idx].extract_token_sentences()\n for each_sent in token_sentences:\n sentence_dict[each_sent] = idx\n\n ### assign entity/sentiment pair to news based on sentence\n count_assigned_sentence = 0\n for sentence, entity, sentiment in sentence_sentiment:\n if sentence in sentence_dict:\n sentence_pos = sentence_dict[sentence]\n entity_sentiment_for_news[sentence_pos].append([entity, sentiment])\n count_assigned_sentence += 1\n\n # print(\"Total sentiment sentence: %s, assigned sentence: %s, assign rate: %s\"%(len(sentence_sentiment), count_assigned_sentence, (count_assigned_sentence+0.)/len(sentence_sentiment)))\n # print(\"Total sentence in date: %s is %s\"%(self.date.strftime('%Y%m%d'), len(sentence_dict)))\n ### for each news, calculate entity_occur and entity_sentiment\n for idx in range(len(self.day_news)):\n self.day_news[idx].build_entity_sentiment(entity_sentiment_for_news[idx])\n\n ## calculate entity occur num and avg sentiment within one day\n total_sentiment = {}\n for each_news in self.day_news:\n for entity, num in each_news.entity_occur.items():\n if entity in self.entity_occur_day:\n self.entity_occur_day[entity] += num\n total_sentiment[entity] += num * each_news.entity_sentiment[entity]\n else:\n self.entity_occur_day[entity] = num\n total_sentiment[entity] = num * each_news.entity_sentiment[entity]\n for entity, num in self.entity_occur_day.items():\n self.entity_sentiment_day[entity] = total_sentiment[entity] / num\n\n\nclass FullData:\n def __init__(self, data_folder, start_date=datetime.date(1929, 10, 12), end_date=datetime.date(2029, 10, 12)):\n self.start_date = start_date\n self.end_date = end_date\n self.entity2id = {}\n self.id2entity = {}\n self.days = []\n self.date2daysID = {}\n self.news_number = 0\n self.data_folder = data_folder\n self.load_full_data(data_folder)\n self.entity_occur_interval = {}\n self.entity_sentiment_interval = {}\n\n\n def load_full_data(self, date_folder):\n '''\n input:\n\t\t\tdate_folder: dir of the whole data, e.g. \"../../ReutersNews106521/\"\n\t\t'''\n print(\"Load folder: %s, from [date %s to date %s)\" % (\n date_folder, self.start_date.strftime('%Y/%m/%d'), self.end_date.strftime('%Y/%m/%d')))\n date_list = []\n for path, subdirs, files in os.walk(date_folder):\n if files == [\".DS_Store\"]:\n date_list = subdirs\n break\n if len(date_list) == 0:\n print(\"No subfolder founded!\")\n exit(0)\n\n count_days = 0\n for each_date_string in date_list:\n each_date = string2date(each_date_string)\n if each_date >= self.start_date and each_date < self.end_date:\n sub_folder_dir = os.path.join(path, each_date_string)\n the_day = Day(sub_folder_dir, each_date)\n self.days.append(the_day)\n self.date2daysID[the_day.date] = len(self.days) - 1\n self.news_number += the_day.news_number\n if count_days % 30 == 0:\n print(\"\tLoading... loaded days:\", count_days)\n count_days += 1\n print(\"Days number: %s\" % (len(self.days)))\n\n def load_sentiment_result(self, sentiment_in, sentiment_out, sentence_mapping_file, entity_mapping_file):\n # load files\n abbreviation_dict = reform_abbreviate_name_dict(entity_mapping_file)\n sentiment_map_dict = load_sentence_map_file(sentence_mapping_file)\n sentiment_in_lines = open(sentiment_in, 'r').readlines()\n sentiment_out_lines = open(sentiment_out, 'r').readlines()\n line_num = len(sentiment_in_lines)\n assert (line_num == len(sentiment_out_lines))\n # read each line and assign [sentence, entity, sentiment] to each Day\n sentence_entity_sentiment_in_all = []\n for idx in range(len(self.days)):\n sentence_entity_sentiment_in_all.append([])\n\n current_date = datetime.date(1929, 10, 12)\n for idx in range(line_num):\n line = sentiment_in_lines[idx]\n if \"#DATE:data/\" in line:\n current_date = string2date(file_name_alphabet2number(line.split(\" \")[0][-8:]))\n continue\n else:\n sentence, entity = read_sentiment_in_line(line)\n if current_date >= self.start_date and current_date < self.end_date:\n if (\"(Additional reporting by\" not in sentence) and (\"(Reporting by\" not in sentence) and (\n \"COMPILED BY\" not in sentence.upper()):\n ## convert sentence\n if sentence in sentiment_map_dict:\n sentence = sentiment_map_dict[sentence]\n entity = \" \".join(entity)\n if entity in abbreviation_dict:\n entity = abbreviation_dict[entity]\n if \" \" not in entity:\n entity = remove_stock_abbre_suffix(entity)\n entity = entity.upper()\n if entity == \"LEHMAN\":\n entity = \"LEHMQ\"\n if (entity not in ignore_entity_dict) and len(entity) > 1 and (\"000\" not in entity):\n days_id = self.date2daysID[current_date]\n sentiment = int(sentiment_out_lines[idx].strip(\"\\n\"))\n sentence_entity_sentiment_in_all[days_id].append([sentence, entity, sentiment])\n\n ### for each day, calculate the entity/sentiment\n for idx in range(len(self.days)):\n # print self.days[idx].date\n self.days[idx].assign_sentence_to_news(sentence_entity_sentiment_in_all[idx])\n ### extract entity_avg_sentiment for time interval\n total_sentiment = {}\n for each_day in self.days:\n for entity, num in each_day.entity_occur_day.items():\n if entity in self.entity_occur_interval:\n self.entity_occur_interval[entity] += num\n total_sentiment[entity] += num * each_day.entity_sentiment_day[entity]\n else:\n self.entity_occur_interval[entity] = num\n total_sentiment[entity] = num * each_day.entity_sentiment_day[entity]\n for entity, num in self.entity_occur_interval.items():\n self.entity_sentiment_interval[entity] = total_sentiment[entity] / num\n\n def show_information(self):\n for each_day in self.days:\n for each_news in each_day.day_news:\n print(each_day.date, each_news.entity_occur, each_news.entity_sentiment)\n\n def count_entity_occurance_news(self, top=-1):\n # count the number of entity occurs in news. Notice it only count how many news mentioned entity, not count the occurance time\n entity_count_dict = {}\n for each_day in self.days:\n for each_news in each_day.day_news:\n for entity, occ in each_news.entity_occur.items():\n if entity in entity_count_dict:\n entity_count_dict[entity] += 1\n else:\n entity_count_dict[entity] = 1\n # print_dict(entity_count_dict)\n # print(\"Total news number:\", self.news_number)\n d_view = [(v, k) for k, v in entity_count_dict.items()]\n d_view.sort(reverse=True) # natively sort tuples by first element\n if top > 0:\n if len(d_view) > top:\n return d_view[:top]\n else:\n return d_view\n else:\n return d_view\n\n def build_occurrence_network_graph(self, top=-1, focus_iterable=None, weight_threshold=None):\n entity_occ_list = self.count_entity_occurance_news(top)\n entity_list = []\n for num, entity in entity_occ_list:\n if len(focus_iterable) > 0:\n if entity in focus_iterable:\n entity_list.append(entity)\n else:\n entity_list.append(entity)\n ## entity_occ_list: [[num, entity], [num, entity]]\n entity_num = len(entity_list)\n occurance_array = np.zeros((entity_num, self.news_number))\n sentiment_array = np.zeros((entity_num, self.news_number))\n entity2ID = {}\n ID2entity = {}\n for idx in range(entity_num):\n entity = entity_list[idx]\n entity2ID[entity] = idx\n ID2entity[idx] = entity\n\n ## build the entity-news matrix\n newsID = 0\n for each_day in self.days:\n for each_news in each_day.day_news:\n overlap_entity_set = set(each_news.entity_occur.keys()) & set(entity_list)\n for each_entity in overlap_entity_set:\n the_occ = each_news.entity_occur[each_entity]\n the_sentiment = each_news.entity_sentiment[each_entity]\n enity_id = entity2ID[each_entity]\n occurance_array[enity_id, newsID] = the_occ\n sentiment_array[enity_id, newsID] = the_sentiment\n newsID += 1\n print(\"Occurrence full shape:\", occurance_array.shape)\n # print occurance_array\n # print sentiment_array\n # print np.count_nonzero(occurance_array)\n # print np.count_nonzero(sentiment_array)\n\n ## calculate degree/centrality/community\n # build networkX graph\n occ_edge_list = []\n\n for s_id in range(entity_num - 1):\n for t_id in range(s_id + 1, entity_num):\n weight = cosine_distance(occurance_array[s_id], occurance_array[t_id])\n if weight > 0:\n s_entity = ID2entity[s_id]\n t_entity = ID2entity[t_id]\n occ_edge_list.append((s_entity, t_entity, weight))\n occ_graph = nx.Graph()\n occ_graph.add_weighted_edges_from(occ_edge_list)\n return occ_graph\n\n def get_group_avg_sentiment(self, start_date, end_date, entity_list):\n if start_date < self.start_date or end_date >= self.end_date:\n print(\"Error: Date exceed the interval!\")\n exit(0)\n\n if len(self.entity_occur_interval) == 0:\n print(\"Error: Empty entity sentiment information in interval! Please run load_sentiment_result first!\")\n exit(0)\n ## convert to list if entity_list is dict\n if isinstance(entity_list, dict):\n entity_list = entity_list.keys()\n\n ### extract the entity/sentiment in focus date interval\n focus_total_sentiment = {}\n focus_entity_occur_interval = {}\n focus_entity_sentiment_interval = {}\n for single_date in daterange(start_date, end_date):\n if single_date not in self.date2daysID: ## 2013/08/18 does not have news\n continue\n the_day_idx = self.date2daysID[single_date]\n each_day = self.days[the_day_idx]\n for entity, num in each_day.entity_occur_day.items():\n if entity in focus_entity_occur_interval:\n focus_entity_occur_interval[entity] += num\n focus_total_sentiment[entity] += num * each_day.entity_sentiment_day[entity]\n else:\n focus_entity_occur_interval[entity] = num\n focus_total_sentiment[entity] = num * each_day.entity_sentiment_day[entity]\n for entity, num in focus_entity_occur_interval.items():\n focus_entity_sentiment_interval[entity] = focus_total_sentiment[entity] / num\n\n ## calculate group sentiment\n total_sentiment = 0\n total_number = 0\n for entity in entity_list:\n if entity not in focus_entity_occur_interval:\n print(\"Error: Cannot get entity sentiment information! Entity:\", entity)\n continue\n else:\n total_sentiment += focus_entity_occur_interval[entity] * focus_entity_sentiment_interval[entity]\n total_number += focus_entity_occur_interval[entity]\n if total_number == 0:\n return -2, 0\n return total_sentiment / total_number, total_number\n\n\ndef get_coexisting_entity(start_date, month_interval, top):\n sentiment_in = \"../data/2006-2013.content.filter.senti.in\"\n sentiment_out = \"../data/2006-2013.content.filter.senti.out\"\n sentence_map = \"../data/2006-2013.content.ner.out.filter.map\"\n abbreviation_file = \"../data/abbreviation_pairs.pkl\"\n all_dict = []\n for idx in range(30):\n end_date = add_months(start_date, month_interval)\n if end_date > datetime.date(2013, 10, 20):\n break\n full_date = FullData(\"../../ReutersNews106521/\", start_date, end_date)\n full_date.load_sentiment_result(sentiment_in, sentiment_out, sentence_map, abbreviation_file)\n current_list = full_date.count_entity_occurance_news(top)\n current_dict = {}\n current_set = set()\n for num, ent in current_list:\n current_dict[ent] = num\n current_set.add(ent)\n\n if idx == 0:\n entity_set = current_set\n else:\n entity_set = entity_set & current_set\n all_dict.append(current_dict)\n start_date = end_date\n print(\"current accumulate entity set size: \", len(entity_set))\n print(entity_set)\n print(len(entity_set))\n entity_interval_occ = {}\n for entity in entity_set:\n entity_interval_occ[entity] = []\n for each_dict in all_dict:\n entity_interval_occ[entity].append(each_dict[entity])\n print(entity_interval_occ)\n print(len(entity_interval_occ))\n fout = open(\"coexisting_entity.\" + str(month_interval) + \".txt\", 'w')\n for k, v in entity_interval_occ.items():\n fout.write(k + \": \" + ' '.join(str(e) for e in v) + \"\\n\")\n\n\nif __name__ == '__main__':\n sentiment_in = \"../data/2006-2013.content.filter.senti.in\"\n sentiment_out = \"../data/2006-2013.content.filter.senti.out\"\n sentence_map = \"../data/2006-2013.content.ner.out.filter.map\"\n abbreviation_file = \"../data/abbreviation_pairs.pkl\"\n\n # the_day = Day(\"../../ReutersNews106521/20061020/\")\n # print the_day.date\n # print len(the_day.day_news)\n full_date = FullData(\"../../ReutersNews106521/\", datetime.date(2007, 1, 1), datetime.date(2007, 4, 1))\n\n # get_coexisting_entity(datetime.date(2007,1,1), 3, 1000)\n full_date.load_sentiment_result(sentiment_in, sentiment_out, sentence_map, abbreviation_file)\n focus_dict = load_focus_dict(\"coexisting_entity.3month_clean.sector.manual.txt\")\n full_date.build_occurence_network(-1, focus_dict)\n # print full_date.count_entity_occurance_news(200)\n"},{"alpha_fraction":0.8036072254180908,"alphanum_fraction":0.8156312704086304,"avg_line_length":51.52631759643555,"blob_id":"6ec7dabf4b186cbcd6f7eda7017cc9d5a3ed301d","content_id":"2beb417ec01ba79306b77d311d65dd7fa78f72fa","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":998,"license_type":"no_license","max_line_length":446,"num_lines":19,"path":"/README.md","repo_name":"xingchenwan/Sentiment","src_encoding":"UTF-8","text":"# Sentiment\nSummer Internship Project\n\nA project aiming to explore the relationships between sentiment of the news (Reuter news for this case, via NLP analysis of the textual information) and market reactions, including but not limited to:\n1) Price\n\n2) Volatilities\n\nInvestigations include both 1) relationship between market reaction of the entity from its *own* sentiment and 2) the relationship between market reaction to sentiment of its peers.\n\nThe peer relationship was established via both the static ground-truth sector grouping and the co-occurrence in the news. Entities co-appearing frequently will subsequently have a higher cosine distance and a higher similarities, and will be regarded as neighbours in the networks subsequently generated. The market reactions due to sentiment events of the central entities (judged upon their centrality position) were given particular attention.\n\nXingchen Wan | xingchen.wan@st-annes.ox.ac.uk\n\nOxford-Man Institute of Quantitative Finance,\n\nUniversity of Oxford \n\n2018 - 2019\n"},{"alpha_fraction":0.5450525879859924,"alphanum_fraction":0.5688316822052002,"avg_line_length":34.708770751953125,"blob_id":"c7af772189c99316938b7e6a1d216fa98d0d331f","content_id":"691d67d29ba8a1a431fb1458b10583c7d1f3a9e6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":10177,"license_type":"no_license","max_line_length":119,"num_lines":285,"path":"/PycharmProjects/OMI/source/utils.py","repo_name":"xingchenwan/Sentiment","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n# @Author: Jie Yang\n# @Date: 2018-08-20 14:52:25\n# @Last Modified by: Jie Yang, Contact: jieynlp@gmail.com\n# @Last Modified time: 2018-08-23 14:51:54\n\n# Modified by Xingchen Wan on 3 Sep to make the file Python 3 compatible\n\nimport datetime\nimport nltk\nimport re\nfrom os.path import isfile, join\nimport numpy as np\nfrom numpy.linalg import norm\n# import cPickle as pickle\nimport pickle\nimport calendar\n\nmonth_to_num = {\"Jan\": 1, \"Feb\": 2, \"Mar\": 3, \"Apr\": 4, \"May\": 5, \"Jun\": 6, \"Jul\": 7, \"Aug\": 8, \"Sep\": 9, \"Oct\": 10,\n \"Nov\": 11, \"Dec\": 12}\n\n\ndef read_news_file(news_file):\n fins = open(news_file, 'r').readlines()\n news = {}\n line_num = len(fins)\n content = \"\"\n if line_num < 1:\n return []\n for idx in range(line_num):\n if idx == 0:\n news[\"title\"] = [re.sub('\\d', '0', fins[idx].strip('-- \\n'))]\n elif idx == 1:\n continue\n elif idx == 2:\n # print type(fins[idx])\n alltimes = fins[idx].strip('-- \\n').split() ## 'Mon Oct 30, 2006 1:55pm EST'.split()\n assert (len(alltimes) == 6)\n if alltimes[1] not in month_to_num:\n print(\"Month convert error!\", \"Month string: \", alltimes[1], \" at file:\", news_file)\n exit(0)\n month = month_to_num[alltimes[1]]\n day = int(alltimes[2].strip(','))\n year = int(alltimes[3])\n news['date'] = datetime.date(year, month, day)\n hour = int(alltimes[4].split(\":\")[0])\n if alltimes[4][-2:] == \"pm\":\n if hour < 12:\n hour += 12\n minute = int(alltimes[4].split(\":\")[1][:-2])\n news['time'] = datetime.time(hour, minute)\n\n elif idx == 3:\n news[\"url\"] = fins[idx].strip('-- \\n')\n else:\n each_line = fins[idx].strip('-- \\n')\n if \"(Reuters) -\" in each_line:\n each_line = each_line.split(\"(Reuters) -\")[-1]\n if len(each_line) > 0:\n content += each_line + \" \"\n origin_content = nltk.sent_tokenize(re.sub('\\d', '0', content))\n sentence = news['title'] + origin_content\n token_sentences = []\n for sent in sentence:\n new_sent = \" \".join(nltk.word_tokenize(sent))\n token_sentences.append(new_sent)\n news[\"sentences\"] = token_sentences\n return news\n\n\ndef add_months(sourcedate, months):\n month = sourcedate.month - 1 + months\n year = sourcedate.year + month // 12\n month = month % 12 + 1\n day = min(sourcedate.day, calendar.monthrange(year, month)[1])\n return datetime.date(year, month, day)\n\n\ndef cosine_distance(np_a, np_b):\n # print np_a, np_b\n cos_sim = np.inner(np_a, np_b) / (norm(np_a) * norm(np_b))\n return cos_sim\n\n\ndef print_dict(input_dict):\n d_view = [(v, k) for k, v in input_dict.iteritems()]\n d_view.sort(reverse=True) # natively sort tuples by first element\n for v, k in d_view:\n print(\"%s: %d\" % (k, v))\n\n\ndef file_name_alphabet2number(input_string):\n convert_dict = {\"A\": \"0\", \"B\": \"1\", \"C\": \"2\", \"D\": \"3\", \"E\": \"4\", \"F\": \"5\", \"G\": \"6\", \"H\": \"7\", \"J\": \"8\", \"K\": \"9\"}\n # convert_dict = {\"0\":\"A\", \"1\":\"B\", \"2\":\"C\", \"3\":\"D\", \"4\":\"E\", \"5\":\"F\", \"6\":\"G\", \"7\":\"H\", \"8\":\"J\", \"9\":\"K\", }\n\n if len(input_string) < 8:\n print(\"wrong name:\", input_string)\n exit(0)\n out = input_string[0:-8]\n for c in input_string[-8:]:\n out += convert_dict[c]\n return out\n\n\ndef load_company_mapping(company_name_file):\n print(\"Load company mapping:\", company_name_file)\n name_mapping = {}\n fins = open(company_name_file, 'r').readlines()\n for line in fins:\n pair = line.strip('\\n').split(': ', 1)\n name_mapping[pair[0]] = pair[1]\n print(\"Company mapping size:\", len(name_mapping))\n return name_mapping\n\n\ndef string2date(input_string):\n assert (len(input_string) == 8)\n year = int(input_string[:4])\n month = int(input_string[4:6])\n day = int(input_string[6:])\n return datetime.date(year, month, day)\n\n\ndef company_name_normalize(input_company_name):\n input_company_name = sepecial_stock_token(input_company_name).upper()\n\n if \", INC\" in input_company_name:\n input_company_name = input_company_name.split(', INC')[0]\n elif \" INC\" in input_company_name:\n input_company_name = input_company_name.split(' INC')[0]\n if \" 'S\" in input_company_name:\n input_company_name = input_company_name.split(\" 'S\")[0]\n if \" CORP.\" in input_company_name:\n input_company_name = input_company_name.split(\" CORP.\")[0]\n elif \" Corp\" == input_company_name[-5:]:\n input_company_name = input_company_name[:-5]\n elif \" CORPORATION\" in input_company_name:\n input_company_name = input_company_name.split(\" CORPORATION\")[0]\n elif \" CO.\" in input_company_name:\n input_company_name = input_company_name.split(\" CO.\")[0]\n elif input_company_name[-3:] == \" CO\":\n input_company_name = input_company_name[:-3]\n elif \" COS.\" in input_company_name:\n input_company_name = input_company_name.split(\" COS.\")[0]\n if \" COMPANY\" in input_company_name:\n input_company_name = input_company_name.rsplit(\" COMPANY\", 1)[0]\n elif \" GROUP\" in input_company_name:\n input_company_name = input_company_name.rsplit(\" GROUP\", 1)[0]\n if \"&\" in input_company_name:\n input_company_name = input_company_name.replace(\"&\", \"&\")\n if \" P.L.C.\" == input_company_name[-7:]:\n input_company_name = input_company_name[:-7]\n elif \" PLC\" == input_company_name[-4:]:\n input_company_name = input_company_name[:-4]\n elif \" LIMITED\" == input_company_name[-8:]:\n input_company_name = input_company_name[:-8]\n elif \" LTD\" == input_company_name[-4:]:\n input_company_name = input_company_name[:-4]\n elif \" LTD/PLC\" == input_company_name[-8:]:\n input_company_name = input_company_name[:-8]\n\n if \" &\" == input_company_name[-2:]:\n input_company_name = input_company_name[:-2]\n elif \" & CO\" == input_company_name[-5:]:\n input_company_name = input_company_name[:-5]\n elif \" LP\" == input_company_name[-3:]:\n input_company_name = input_company_name[:-3]\n elif \" LLC\" == input_company_name[-4:]:\n input_company_name = input_company_name[:-4]\n elif \" PLC\" == input_company_name[-4:]:\n input_company_name = input_company_name[:-4]\n elif \" LP\" == input_company_name[-3:]:\n input_company_name = input_company_name[:-3]\n if \" AND \" in input_company_name:\n input_company_name = input_company_name.replace(\" AND \", \" & \")\n return input_company_name\n\n\ndef sepecial_stock_token(input_token):\n if len(input_token) < 4:\n return input_token\n elif input_token[-1].isupper() and (input_token[-2] == \".\") and (input_token[:-2].isupper()):\n return input_token[:-2]\n elif input_token[-1].isupper() and (input_token[-2] == \"^\") and (input_token[:-2].isupper()):\n return input_token[:-2]\n return input_token\n\n\ndef reform_abbreviate_name_dict(input_pairs_pkl):\n abbre_dict = pickle.load(open(input_pairs_pkl))\n long2short_dict = {}\n print(\"Total abbreviate pair num:\", len(abbre_dict))\n conflict_num = 0\n for K, V in abbre_dict.iteritems():\n if K in [\"Reporting\", \"Additional\", \"Writing\", \"Editing\"] or (\"000\" in K) or (')' in K):\n continue\n K = remove_stock_abbre_suffix(K)\n for v in V:\n if v in long2short_dict:\n if long2short_dict[v] != K:\n conflict_num += 1\n # print v, \";\",long2short_dict[v],\";\", K\n long2short_dict[v] = K\n print(\"Reformed abbreviate dict size:\", len(long2short_dict), \"conflict_num:\", conflict_num)\n return long2short_dict\n\n\ndef remove_stock_abbre_suffix(input_string):\n if input_string.isupper():\n if \".\" in input_string:\n return input_string.split('.')[0]\n else:\n return input_string\n return input_string.upper()\n\n\ndef load_sentence_map_file(sent_map_file):\n fins = open(sent_map_file, 'r').readlines()\n new = \"\"\n old = \"\"\n map_dict = {}\n for idx in range(len(fins)):\n if (idx + 1) % 3 == 1:\n new = fins[idx].strip('\\n')\n elif (idx + 1) % 3 == 2:\n old = fins[idx].strip('\\n')\n else:\n map_dict[new] = old\n print(\"Sentence map size:\", len(map_dict))\n return map_dict\n\n\ndef read_sentiment_in_line(line):\n \"\"\"\n input: line \"Wells Fargo to pay Citi $ 000 million over Wachovia ||| 0 1 ||| 0\"\n output: sentence, entity\n \"\"\"\n splits = line.split('|||')\n sent = splits[0].strip(\" \")\n [entity_start, entity_end] = splits[1].strip(\" \").split()\n entity = sent.split(\" \")[int(entity_start):(int(entity_end) + 1)]\n return sent, entity\n\n\ndef load_focus_dict(focus_file):\n fins = open(focus_file, 'r').readlines()\n focus_dict = {}\n for line in fins:\n entity = line.split(\":\")[0]\n full_name = line.split(\":\")[1]\n if entity not in focus_dict:\n focus_dict[entity] = full_name\n return focus_dict\n\n\ndef daterange(start_date, end_date):\n for n in range(int((end_date - start_date).days)):\n yield start_date + datetime.timedelta(n)\n\n\ndef community_to_dicts(community_file):\n fins = open(community_file, 'r').readlines()\n entity2id = {}\n id2community = {}\n id2noncommunity = {}\n all_entity_set = set()\n for line in fins:\n line = line.strip(\"\\n\")\n pair = line.split(\": \")\n community_id = pair[0]\n entity_list = pair[1].split(\",\")\n for entity in entity_list:\n entity2id[entity] = community_id\n id2community[community_id] = entity_list\n all_entity_set = all_entity_set | set(entity_list)\n for c_id, community in id2community.items():\n noncommunity = list(all_entity_set - set(community))\n id2noncommunity[c_id] = noncommunity\n return entity2id, id2community, id2noncommunity\n\n\nif __name__ == '__main__':\n news = read_news_file(\"../../ReutersNews106521/20061030/idUSL3090279820061030\")\n for k, v in news.items():\n print(k, v)\n"},{"alpha_fraction":0.6431381702423096,"alphanum_fraction":0.6473138928413391,"avg_line_length":47.45525360107422,"blob_id":"2112d0c0629512018b8b28d3420c79ff93229bf5","content_id":"9aaafca7ef8512d6cc8374690a9a1c8006236de7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":12453,"license_type":"no_license","max_line_length":129,"num_lines":257,"path":"/PycharmProjects/OMI/processor.py","repo_name":"xingchenwan/Sentiment","src_encoding":"UTF-8","text":"# Processor module: process relevant market and sentiment data for subsequent analyses\n# Xingchen Wan | Xingchen.Wan@st-annes.ox.ac.uk | Oxford-Man Institute of Quantitative Finance\n\nimport pandas as pd\nimport numpy as np\nfrom collections import Counter\nimport datetime\n\n\ndef process_beta_time_series(path, start_date=None, end_date=None, period=\"weekly\"):\n df = pd.read_csv(path)\n assert all(string in df.columns for string in ['Date', 'Close'])\n df = df[['Date', 'Close']]\n # Initialise an datetime index\n df_start_date = df['Date'].iloc[0]\n df_end_date = df['Date'].iloc[-1]\n\n # Change the datetime string to pandas datetime object\n df['Date'] = pd.to_datetime(df['Date']).dt.date\n df = df.set_index('Date')\n\n # Set the start and end dates. The start date the later date between the \"start_date\" argument and the dataframe\n # start date. The end date is the earlier date between the \"end_Date\" argument and the dataframe end date\n start_date = max(df_start_date, start_date) if start_date is not None else df_start_date\n end_date = min(df_end_date, end_date) if end_date is not None else df_end_date\n\n # Create the pandas date index\n date_idx = pd.date_range(start_date, end_date, freq='D')\n\n # Initialise the series to return\n data = df.reindex(date_idx)\n data = data.fillna(method='ffill') # Forward fill the data entries\n\n # Compute log return, period vol, daily_vol and etc\n daily_log_return = data.apply(lambda x: np.log(x.shift(-1)) - np.log(x))\n if period == 'weekly':\n period_log_return = data.apply(lambda x: np.log(x.shift(-5)) - np.log(x))\n else:\n raise TypeError()\n vol = daily_log_return.abs()\n return data, daily_log_return, period_log_return, vol\n\n\ndef process_market_time_series(path, sheet=None, start_date=None, end_date=None, period=\"weekly\",\n focus_iterable=None):\n \"\"\"\n Process the raw price series from Bloomberg\n :param path: path string of the price series document\n :param sheet: Sheet name in the excel document\n :param start_date: datetime.date. Start date of the processed series\n :param end_date: datetime.date. End date of the processed series\n :param period: Valid str literal or int. Period to calculate log return. Default is \"7D\". Recommend to use 7D\n rather than 7 due to the difference in convention between calendar days and trading days.\n :return:\n data: Array-like. the unprocessed price time series\n daily_log_return: Array-like. the daily log return series\n period_log_return: Array-like. the log return series in accordance to the set period. Default is weekly log return\n vol: Array-like. volatility of daily log return in period time. Default is weekly realised volatility\n \"\"\"\n import xlrd\n\n def read_date(date):\n \"\"\"\n In case conversion from the Excel-style datetime is needed - not currently in use\n :param date: Excel datetime object\n :return: Pandas-compliant datetime data type\n \"\"\"\n return xlrd.xldate.xldate_as_datetime(date, 0)\n if period == 'weekly':\n roll_period = '7D'\n shift_period = 5 # Trading week\n elif period == 'daily':\n roll_period = '1D'\n shift_period = 1\n elif period == '2d':\n roll_period = '2d'\n shift_period = 2\n elif period == 'monthly':\n roll_period = '30D'\n shift_period = 21 # Trading month\n else:\n roll_period = str(period) + 'D'\n shift_period = int(period / 7 * 5)\n\n raw = pd.read_excel(path, sheetname=sheet)\n #print(raw)\n start_date = start_date if start_date and start_date >= raw.index[1] else raw.index[1]\n end_date = end_date if end_date and end_date <= raw.index[-1] else raw.index[-1]\n raw = raw[raw.index >= start_date]\n data = raw[raw.index <= end_date].astype('float64')\n if focus_iterable:\n focus_securities = [x for x in data.columns if x in focus_iterable]\n data = data[focus_securities]\n # Truncate the price time series to be consistent with the count and sentiment time series\n daily_log_return = data.apply(lambda x: np.log(x.shift(-1)) - np.log(x))\n period_log_return = data.apply(lambda x: np.log(x.shift(-shift_period)) - np.log(x))\n #daily_log_return = data.apply(lambda x: np.log(x) - np.log(x.shift(1)))\n #period_log_return = data.apply(lambda x: np.log(x) - np.log(x.shift(shift_period)))\n #vol = daily_log_return.rolling(roll_period).std().shift(-shift_period) # Forward looking period volatility\n vol = daily_log_return.abs()\n #abs_daily_log_return = np.abs(daily_log_return)\n #abs_period_log_return = np.abs(period_log_return)\n return data, daily_log_return, period_log_return, vol\n\n\ndef process_count_sentiment(full_data_obj, start_date=None, end_date=None, mode='weekly',\n rolling=False, rolling_smoothing_factor=0.2, focus_iterable=None):\n \"\"\"\n Process the FullData object to obtain corresponding count and sentiment time series\n :param full_data_obj: The FullData object containing all the unprocessed count and sentiment information.\n :param start_date: datetime, optional - start time of data. To be a valid argument, the start_date\n must be later than the start date of the FullData object\n :param end_date: datetime, optional - end time of the data. To be a valid argument, the end_date must be earlier\n than the end date of the FullData object\n :param mode: the period for aggregation. 'weekly','monthly', 'daily'\n :param focus_iterable: optional [] or {} - only entities in the list will be considered. If blank all entities\n will be considered\n :return:\n \"\"\"\n\n def is_end_of_period(day, mode):\n from pandas.tseries.offsets import MonthEnd\n\n if mode == 'monthly': return True if day == day + MonthEnd(0) else False\n elif mode == 'daily': return True\n elif mode == 'weekly': return True if day.weekday() == 4 else False # Snap to nearest Friday\n else: raise ValueError(\"Unrecognised mode. Only weekly, daily and monthly modes are currently supported.\")\n\n def non_zero_median(list_obj):\n v = np.asarray(list_obj)\n #v = v[np.nonzero(v)]\n #return np.nanmedian(v) if len(v) else 0\n return np.nanmean(v)\n\n def moving_avg(list, alpha=rolling_smoothing_factor):\n series = pd.Series(list).fillna(0, inplace=True)\n ewm_series = series.ewm(alpha=alpha)\n return ewm_series.iloc[-1]\n\n # Misc sanity checks...\n if isinstance(start_date, datetime.datetime):\n start_date = start_date.date() if start_date.date() > full_data_obj.start_date else full_data_obj.start_date\n elif isinstance(start_date, datetime.date):\n start_date = start_date if start_date > full_data_obj.start_date else full_data_obj.start_date\n else:\n if start_date is not None:\n print('Invalid start date. Using the FullData object start date.')\n start_date = full_data_obj.start_date\n\n if isinstance(end_date, datetime.datetime):\n end_date = end_date.date() if end_date.date() < full_data_obj.end_date else full_data_obj.end_date\n elif isinstance(end_date, datetime.date):\n end_date = end_date if end_date < full_data_obj.end_date else full_data_obj.end_date\n else:\n if end_date is not None:\n print('Invalid end date. Using the FullData object end date')\n end_date = full_data_obj.end_date\n\n if focus_iterable:\n if isinstance(focus_iterable, dict):\n focus_iterable = focus_iterable.keys()\n elif isinstance(focus_iterable, list):\n pass\n else:\n raise TypeError(\"include_list needs to be an iterable of type either list or dictionary\")\n else:\n focus_iterable = full_data_obj.entity_occur_interval.keys()\n\n # Initialise the aggregate list and counter\n aggregate_count_sum = []\n aggregate_sentiment_med = []\n aggregate_sentiment_sum = []\n date_time_series = []\n\n # Initialise the weekly counters\n period_count_sum = Counter({})\n period_sentiment_sum = Counter({})\n period_sentiment_med = Counter({})\n\n # Setting appropriate period in terms of CALENDAR days\n if mode == 'daily': period = 1\n elif mode == 'weekly': period = 7\n elif mode == 'monthly': period = 30\n else: period = 7\n\n if rolling:\n # Rolling weekly counters\n for day in full_data_obj.days:\n if day.date >= start_date and day.date <= end_date:\n date_time_series.append(day.date)\n day_count_sum = Counter(dict((name, 0) for name in focus_iterable))\n day_sentiment_med = Counter(dict((name, 0) for name in focus_iterable))\n day_sentiment_sum = Counter(dict((name, 0) for name in focus_iterable))\n for name in focus_iterable:\n if name in day.entity_occur_day.keys():\n day_count_sum[name] = day.entity_occur_day[name]\n day_sentiment_med[name] = day.entity_sentiment_day[name]\n day_sentiment_sum[name] = day.entity_occur_day[name] * day.entity_sentiment_day[name]\n aggregate_count_sum.append(day_count_sum)\n aggregate_sentiment_med.append(day_sentiment_med)\n aggregate_sentiment_sum.append(day_sentiment_sum)\n\n else:\n # Non-rolling counters - weekly counter set at every Friday\n i = 0\n for day in full_data_obj.days:\n if day.date < start_date:\n continue\n elif day.date == start_date:\n i = 0\n period_count_sum = Counter(dict((name, 0) for name in focus_iterable))\n period_sentiment_med = Counter(dict((name, []) for name in focus_iterable))\n period_sentiment_sum = Counter(dict((name, 0) for name in focus_iterable))\n elif day.date > end_date:\n break\n\n if is_end_of_period(day.date, mode):\n aggregate_count_sum.append(period_count_sum)\n aggregate_sentiment_med.append({k: non_zero_median(v) if len(v) else 0 for k, v in period_sentiment_med.items()})\n aggregate_sentiment_sum.append(period_sentiment_sum)\n # Re-initialise the periodical sub-totals for the next period\n date_time_series.append(day.date)\n period_count_sum = Counter(dict((name, 0) for name in focus_iterable))\n period_sentiment_med = Counter(dict((name, []) for name in focus_iterable))\n period_sentiment_sum = Counter(dict((name, 0) for name in focus_iterable))\n\n else:\n for news in day.day_news:\n for entity in focus_iterable:\n if entity in news.entity_occur.keys():\n period_count_sum[entity] += news.entity_occur[entity]\n period_sentiment_med[entity].append(news.entity_sentiment[entity])\n period_sentiment_sum[entity] += news.entity_sentiment[entity]*news.entity_occur[entity]\n i += 1\n\n date_time_series = pd.Series(date_time_series)\n count_time_series = pd.DataFrame(aggregate_count_sum).dropna(how='all')\n count_time_series[\"Time\"] = date_time_series\n count_time_series.set_index('Time', inplace=True)\n count_time_series.index = pd.to_datetime(count_time_series.index)\n\n sentiment_med_time_series = pd.DataFrame(aggregate_sentiment_med).dropna(how='all')\n sentiment_med_time_series[\"Time\"] = date_time_series\n sentiment_med_time_series.set_index('Time', inplace=True)\n sentiment_med_time_series.index = pd.to_datetime(sentiment_med_time_series.index)\n # print(sentiment_med_time_series)\n\n sentiment_sum_time_series = pd.DataFrame(aggregate_sentiment_sum).dropna(how='all')\n sentiment_sum_time_series[\"Time\"] = date_time_series\n sentiment_sum_time_series.set_index('Time', inplace=True)\n sentiment_sum_time_series.index = pd.to_datetime(sentiment_sum_time_series.index)\n\n if rolling:\n sentiment_sum_time_series = sentiment_sum_time_series.ewm(alpha=rolling_smoothing_factor).mean()\n count_time_series = count_time_series.rolling(window=period).sum()\n sentiment_med_time_series = sentiment_med_time_series.ewm(alpha=rolling_smoothing_factor).mean()\n return count_time_series, sentiment_med_time_series, sentiment_sum_time_series\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5860924124717712,\n \"alphanum_fraction\": 0.5963833928108215,\n \"avg_line_length\": 46.52031326293945,\n \"blob_id\": \"6b9a42c5ba565abc1bf9190e88eb2d361c242196\",\n \"content_id\": \"cc41263dfc6896aa156e35fc84826172ed89681e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 30415,\n \"license_type\": \"no_license\",\n \"max_line_length\": 157,\n \"num_lines\": 640,\n \"path\": \"/PycharmProjects/OMI/analyser.py\",\n \"repo_name\": \"xingchenwan/Sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Analyser: modules that perform numerical analyses on the result\\n# Xingchen Wan | Xingchen.Wan@st-annes.ox.ac.uk | Oxford-Man Institute of Quantitative Finance\\n\\nimport numpy as np\\nimport pandas as pd\\nimport scipy.stats as st\\nimport matplotlib\\nmatplotlib.use(\\\"TkAgg\\\")\\nimport matplotlib.pylab as plt\\nfrom pandas.tseries.offsets import BDay\\nfrom processor import process_beta_time_series\\n\\n\\ndef normalised_corr(series1, series2, max_lag=2, fix_lag=None):\\n \\\"\\\"\\\"\\n Compute the normalised cross-correlation between series1 and series2\\n :param series1: Series1, Pandas series or numpy array\\n :param series2: Series2, Pandas series or numpy array\\n :param max_lag: Maximum lag between series1 and series2 allowed\\n :param fix_lag: fixed lag - this argument overrides the max_lag argument.\\n :return: res: normalised cross-correlation coefficients, max_val: maximum correlation coefficient within the\\n allowed period of lag in terms of magnitude, max_idx: the lag (index) corresponding to that correlation\\n coefficient\\n \\\"\\\"\\\"\\n if isinstance(series1, pd.Series): series1 = series1.values\\n if isinstance(series2, pd.Series): series2 = series2.values\\n series1 = (series1 - np.mean(series1)) / (np.std(series1) * len(series1))\\n series2 = (series2 - np.mean(series2)) / np.std(series2)\\n correl = np.correlate(series1, series2, mode='full')\\n res = correl[int((len(correl)) / 2):]\\n #res = correl[:]\\n # print(len(res))\\n if fix_lag is None:\\n abs_series = np.abs(res[:max_lag + 1])\\n max_idx = np.argmax(abs_series)\\n max_val = res[max_idx]\\n else:\\n max_idx = fix_lag\\n max_val = res[fix_lag]\\n return res, max_val, max_idx\\n\\n\\ndef correlate(market_data, vol_data, sentiment_med_data, sentiment_sum_data, count_data, max_lag=2,\\n count_threshold=0,\\n plot=True, save_csv=False, fix_lag=None,\\n display_summary_stats=True,\\n focus_iterable=None):\\n\\n assert set(sentiment_med_data.index) == set(count_data.index)\\n assert set(sentiment_sum_data.index) == set(count_data.index)\\n intersect_dates = market_data.index.intersection(count_data.index)\\n market_data = market_data.loc[intersect_dates].astype('float64')\\n benchmark_data = market_data['_ALL'] # MSCI log-return\\n\\n market_data = market_data.subtract(benchmark_data, axis='index')\\n # Superior log return over SPX return\\n vol = vol_data.loc[intersect_dates].astype('float64')\\n # market_data_bin = market_data.apply(lambda x: np.sign(x-x.shift(1)))[1:]\\n\\n res = []\\n if focus_iterable:\\n include_names = [x for x in focus_iterable if x in list(market_data.columns)]\\n else:\\n include_names = list(market_data.columns)\\n\\n for single_name in include_names:\\n if \\\".\\\" in single_name:\\n # Special cases for BoJ and Fed where there are two underlying securities...\\n sentiment_series_med = sentiment_med_data[single_name.split(\\\".\\\")[0]].astype('float64').values\\n sentiment_series_sum = sentiment_sum_data[single_name.split(\\\".\\\")[0]].astype('float64').values\\n count_series = count_data[single_name.split(\\\".\\\")[0]].astype('float64').values\\n vol_series = vol[single_name].astype('float64').values\\n market_series = market_data[single_name].astype('float64').values\\n else:\\n sentiment_series_med = sentiment_med_data[single_name].astype('float64').values\\n sentiment_series_sum = sentiment_sum_data[single_name].astype('float64').values\\n count_series = count_data[single_name].astype('float64').values\\n vol_series = vol[single_name].astype('float64').values\\n market_series = market_data[single_name].astype('float64').values\\n\\n non_nan_idx = ~np.isnan(market_series)\\n if count_threshold > 0:\\n non_nan_idx = np.logical_and(~np.isnan(market_series), (count_series > count_threshold))\\n all_nan = True if (non_nan_idx == False).all() else False\\n # Flag that the entire numpy series consists of NaN\\n\\n if not all_nan:\\n market_series = market_series[non_nan_idx]\\n count_series = count_series[non_nan_idx]\\n sentiment_series_med = sentiment_series_med[non_nan_idx]\\n sentiment_series_sum = sentiment_series_sum[non_nan_idx]\\n vol_series = vol_series[non_nan_idx]\\n # Then prune the data to get rid of the NaNs\\n all_series = [market_series, count_series, sentiment_series_med, sentiment_series_sum, vol_series]\\n\\n\\n corr_count_price, max_corr_count_price, max_corr_count_price_idx = normalised_corr(count_series, market_series, max_lag=max_lag, fix_lag=fix_lag)\\n\\n corr_med_sentiment_price, max_med_corr_sentiment_price, \\\\\\n max_corr_med_sentiment_price_idx = normalised_corr(sentiment_series_med, market_series, max_lag=max_lag, fix_lag=fix_lag)\\n\\n corr_sum_sentiment_price, max_sum_corr_sentiment_price, \\\\\\n max_corr_sum_sentiment_price_idx = normalised_corr(sentiment_series_sum, market_series, max_lag=max_lag, fix_lag=fix_lag)\\n\\n corr_count_vol, max_corr_count_vol, max_corr_count_vol_idx = normalised_corr(count_series, vol_series, max_lag=max_lag, fix_lag=fix_lag)\\n\\n corr_vol_sum_sentiment, max_corr_vol_sum_sentiment, \\\\\\n max_corr_vol_sum_sentiment_idx = normalised_corr(sentiment_series_sum, vol_series, max_lag=max_lag, fix_lag=fix_lag)\\n\\n corr_vol_price, max_corr_vol_price, max_corr_vol_price_idx = normalised_corr(market_series, vol_series, max_lag=max_lag, fix_lag=fix_lag)\\n\\n this_res = {'Name': single_name,\\n 'PriceCountCorr': max_corr_count_price,\\n 'PriceCountLag': max_corr_count_price_idx,\\n 'PriceMedSentimentCorr': max_med_corr_sentiment_price,\\n 'PriceMedSentimentLag': max_corr_med_sentiment_price_idx,\\n 'PriceSumSentimentCorr': max_sum_corr_sentiment_price,\\n 'PriceSumSentimentLag': max_corr_sum_sentiment_price_idx,\\n 'VolCountCorr': max_corr_count_vol,\\n 'VolCountLag': max_corr_count_vol_idx,\\n 'VolMedSentimentCorr': max_corr_vol_sum_sentiment,\\n 'VolMedSentimentLag': max_corr_vol_sum_sentiment_idx,\\n 'SpotVolCorr': max_corr_vol_price,\\n 'SpotVolLag': max_corr_vol_price_idx,\\n }\\n print(this_res)\\n else:\\n this_res = {'Name': single_name,\\n 'PriceCountCorr': np.nan,\\n 'PriceCountLag': np.nan,\\n 'PriceMedSentimentCorr': np.nan,\\n 'PriceMedSentimentLag': np.nan,\\n 'PriceSumSentimentCorr': np.nan,\\n 'PriceSumSentimentLag': np.nan,\\n 'VolCountCorr': np.nan,\\n 'VolCountLag': np.nan,\\n 'VolMedSentimentCorr': np.nan,\\n 'VolMedSentimentLag': np.nan,\\n 'SpotVolCorr': np.nan,\\n 'SpotVolLag': np.nan,\\n }\\n print('Empty series occurred at ', single_name)\\n res.append(this_res)\\n\\n if plot and not all_nan:\\n # Perform the cross-correlation\\n plt.subplot(231)\\n plt.title('Count-Price Correlation')\\n plt.plot(corr_count_price)\\n plt.subplot(232)\\n plt.title('MedSentiment-Price Correlation')\\n plt.plot(corr_med_sentiment_price)\\n plt.subplot(233)\\n plt.title('SumSentiment-Price Correlation')\\n plt.plot(corr_sum_sentiment_price)\\n plt.subplot(234)\\n plt.title('Count-Vol Correlation')\\n plt.plot(corr_count_vol)\\n plt.subplot(235)\\n plt.title('Spot-Vol Correlation')\\n plt.plot(corr_vol_price)\\n plt.show()\\n\\n res = pd.DataFrame(res)\\n\\n if display_summary_stats: print(res.describe())\\n\\n if save_csv:\\n pd.DataFrame(res).to_csv('CorrRes.csv')\\n print('Done')\\n return res\\n\\n\\n\\ndef stat_tests(data, threshold=0.05):\\n \\\"\\\"\\\"\\n Perform a series of statistic tests: normal distribution test, t-test and confidence interval\\n :param data: array-like\\n :param threshold: threshold to reject null hypothesis. Default value is 0.05\\n :return:\\n \\\"\\\"\\\"\\n if not isinstance(data, pd.Series) and not isinstance(data, np.ndarray):\\n raise TypeError('The input data must be a pandas series or a numpy array')\\n elif isinstance(data, pd.Series):\\n data = data.values\\n\\n data = data[~np.isnan(data)]\\n # Clear NaNs from the data-set\\n # print(data)\\n t, prob = st.ttest_1samp(data, 0)\\n wilcox, prob2 = st.wilcoxon(data)\\n # Return the t-statistics and p-value on whether the data mean is significantly different from 0\\n mean_5, mean_95 = st.t.interval(1-threshold, len(data)-1, loc=np.mean(data), scale=st.sem(data))\\n print(\\n 'T-statistic', t,\\n 'T-test p-value', prob,\\n 'WilCox', prob2,\\n '95th Percentile', mean_95,\\n '5th Percentile', mean_5)\\n return t, prob, mean_5, mean_95\\n\\n\\ndef rolling_correlate(name, roll_window_size, *input_frames, frame_names=[], fix_lag=None, max_lag=2,\\n start_date=None, end_date=None, exclude_pairs=None, include_pairs=None,\\n count_threshold={}):\\n\\n if include_pairs:\\n if not isinstance(include_pairs, list):\\n raise TypeError(\\\"include_pairs need to be list of tuples with length 2\\\")\\n elif not isinstance(include_pairs[0], tuple):\\n raise TypeError(\\\"include_pairs need to be list of tuples with length 2\\\")\\n elif len(include_pairs[0]) != 2:\\n raise TypeError(\\\"include_pairs need to be list of tuples with length 2\\\")\\n\\n if exclude_pairs:\\n if not isinstance(exclude_pairs, list):\\n raise TypeError(\\\"Exclude_pairs need to be list of tuples with length 2\\\")\\n elif not isinstance(exclude_pairs[0], tuple):\\n raise TypeError(\\\"Exclude_pairs need to be list of tuples with length 2\\\")\\n elif len(exclude_pairs[0]) != 2:\\n raise TypeError(\\\"Exclude_pairs need to be list of tuples with length 2\\\")\\n if frame_names and len(frame_names) != len(input_frames):\\n raise ValueError(\\\"Mismatch between data length and data_names length.\\\")\\n\\n series_collection = []\\n i = 0\\n for frame in input_frames:\\n try:\\n series = frame[name].astype('float64')\\n series.name = frame_names[i]\\n series_collection.append(series)\\n except KeyError:\\n print(name, \\\" is not found in data argument position \\\", input_frames.index(frame))\\n i += 1\\n data = pd.concat(series_collection, axis=1, join='inner')\\n # data only contains where the dates of the different data frames intersect\\n if start_date:\\n start_date = pd.to_datetime(start_date)\\n if start_date > data.index[0]:\\n data = data[data.index >= start_date]\\n else: start_date = data.index[0]\\n else: start_date = data.index[0]\\n\\n if end_date:\\n end_date = pd.to_datetime(end_date)\\n if end_date < data.index[-1]:\\n data = data[data.index <= end_date]\\n else: end_date = data.index[-1]\\n else: end_date = data.index[-1]\\n # Truncate the the data frame in accordance to the specified start and end dates\\n res = {}\\n\\n roll_window_start = start_date+pd.to_timedelta(roll_window_size, unit='d')\\n\\n for i in range(len(data.columns)):\\n for j in range(i, len(data.columns)):\\n col_name1 = data.columns[i]\\n col_name2 = data.columns[j]\\n if exclude_pairs and ((col_name1, col_name2) in exclude_pairs or (col_name2, col_name1) in exclude_pairs):\\n continue\\n if include_pairs and ((col_name1, col_name2) not in include_pairs and (col_name2, col_name1) not in include_pairs):\\n continue\\n local_data1 = data[col_name1]\\n local_data2 = data[col_name2]\\n this_res = pd.Series(0, index=data.index[data.index >= roll_window_start])\\n this_res.name = \\\"Corr\\\" + col_name1 + \\\"To\\\" + col_name2\\n\\n k = 0\\n for dt in this_res.index:\\n series1 = local_data1[dt-pd.to_timedelta(roll_window_size, unit='d'):dt].values\\n series2 = local_data2[dt-pd.to_timedelta(roll_window_size, unit='d'):dt].values\\n if count_threshold:\\n if col_name1 in count_threshold.keys():\\n series1 = series1[series1 > count_threshold[col_name1]]\\n series2 = series2[series1 > count_threshold[col_name1]]\\n if col_name2 in count_threshold.keys():\\n series1 = series1[series2 > count_threshold[col_name2]]\\n series2 = series2[series2 > count_threshold[col_name2]]\\n # Select appropriate date range\\n if fix_lag is not None:\\n _, this_res.iloc[k], _ = normalised_corr(series1, series2, fix_lag=fix_lag)\\n else:\\n _, this_res.iloc[k], _ = normalised_corr(series1, series2, max_lag=max_lag)\\n k += 1\\n res[this_res.name] = this_res\\n return pd.DataFrame(res)\\n\\n\\ndef px_correlate(name1, *names, px_frame, correlation_period=90, start_date=None, end_date=None):\\n if start_date is None: start_date = px_frame.index[0]\\n if end_date is None: end_date = px_frame.index[-1]\\n px_frame = px_frame[(px_frame.index >= start_date) & (px_frame.index <= end_date)]\\n res = pd.DataFrame(np.nan, columns=names, index=px_frame.index)\\n for dt in px_frame.index:\\n if dt >= start_date + pd.to_timedelta(correlation_period):\\n sub_frame = px_frame[(px_frame.index >= dt - pd.to_timedelta(correlation_period, 'D')) & (px_frame.index < dt)]\\n px1 = sub_frame[name1]\\n for name in names:\\n px2 = sub_frame[name]\\n res.loc[dt, name] = np.corrcoef(px1, px2)[0, 1]\\n return res\\n\\n\\n#######################################################\\n# Event-based analysis tools:\\n\\n\\ndef _get_z_score(frame, window):\\n r = frame.rolling(window=window)\\n m = r.mean()\\n s = r.std(ddof=0)\\n return (frame-m)/s\\n\\n\\ndef _get_event_series(z_score_series, threshold, ignore_window=10):\\n event_series = pd.Series(0, index=z_score_series.index)\\n ignore_cnt = 0\\n for i in range(len(z_score_series)):\\n if ignore_cnt != 0:\\n ignore_cnt -= 1\\n pt = z_score_series[i]\\n if pt > threshold: status = 1\\n elif pt < -threshold: status = -1\\n else: status = 0\\n\\n if ignore_cnt: status = 0\\n if status != 0 and ignore_cnt == 0:\\n ignore_cnt = ignore_window\\n event_series.iloc[i] = status\\n return event_series\\n\\n\\ndef get_abnormal_return(name, log_return_frame, date, window, beta_path='data/^GSPC.csv'):\\n # The normal return is given by the Market Model over MSCI return over the last window\\n open_date = date - pd.to_timedelta(window, 'D')\\n hist_returns = log_return_frame[['_ALL', name]]\\n hist_returns = hist_returns[(hist_returns.index >= open_date) & (hist_returns.index < date)]\\n\\n mkt_return_today = log_return_frame.loc[date, '_ALL']\\n stock_return_today = log_return_frame.loc[date, name]\\n\\n # Estimate alpha and beta using simple OLS\\n X = hist_returns['_ALL'].values\\n y = hist_returns[name].values\\n beta, alpha, r_val, p_val, std_err = st.linregress(X, y)\\n # disturb_var = 1/(window - 2)*np.sum(np.square(y - alpha - beta*X))\\n # print(beta)\\n abnormal_return = stock_return_today - alpha - beta * mkt_return_today\\n return abnormal_return\\n\\n\\ndef get_event_cumulative_abnormal_return(name, event_series, z_series, daily_abnormal_return_series, max_lag):\\n event_cnt = event_series[event_series == 1].count() + event_series[event_series == -1].count()\\n day_range = [i for i in range(-max_lag, max_lag + 1)]\\n res = pd.DataFrame(0, index=[i for i in range(event_cnt)], columns=['Name','EventDate', 'Type'] + day_range)\\n z_res = pd.DataFrame(0, index=res.index, columns=res.columns)\\n i = 0\\n for day in range(len(event_series)):\\n if event_series.iloc[day] != 0:\\n dt = event_series.index[day]\\n res.loc[i, 'Name'] = name\\n res.loc[i, 'EventDate'] = dt\\n res.loc[i, 'Type'] = event_series.iloc[day]\\n\\n z_res.loc[i, 'Name'] = name\\n z_res.loc[i, 'EventDate'] = dt\\n z_res.loc[i, 'Type'] = event_series.iloc[day]\\n\\n cum_rtn = 0\\n for lag in range(-max_lag, max_lag+1):\\n try:\\n cum_rtn += daily_abnormal_return_series[dt+BDay(lag)]\\n except KeyError:\\n pass\\n res.loc[i, lag] = cum_rtn\\n try:\\n z_res.loc[i, lag] = z_series[dt+BDay(lag)]\\n except KeyError:\\n z_res.loc[i, lag] = np.nan\\n i += 1\\n return res, z_res\\n\\n\\ndef get_event_vol(name, event_series, z_series, vol_series, max_lag):\\n event_cnt = event_series[event_series == 1].count() + event_series[event_series == -1].count()\\n day_range = [i for i in range(-max_lag, max_lag + 1)]\\n res = pd.DataFrame(np.nan, index=[i for i in range(event_cnt)], columns=['Name', 'EventDate'] + day_range)\\n z_res = pd.DataFrame(np.nan, index=res.index, columns=res.columns)\\n i = 0\\n for day in range(len(event_series)):\\n if event_series.iloc[day] != 0:\\n dt = event_series.index[day]\\n res.loc[i, 'Name'] = name\\n res.loc[i, 'EventDate'] = dt\\n res.loc[i, 'Type'] = event_series.iloc[day]\\n\\n #z_res.loc[i, 'Name'] = name\\n #z_res.loc[i, 'EventDate'] = dt\\n #z_res.loc[i, 'Type'] = event_series.iloc[day]\\n\\n for lag in day_range:\\n try:\\n res.loc[i, lag] = vol_series[dt+BDay(lag)] if vol_series[dt+BDay(lag)] != 0 else np.nan\\n except KeyError:\\n res.loc[i, lag] = np.nan\\n # try:\\n # z_res.loc[i, lag] = z_series[dt+BDay(lag)]\\n # except KeyError:\\n # z_res.loc[i, lag] = np.nan\\n i += 1\\n return res, z_res\\n\\n\\ndef get_avg_col(names, market_frame):\\n # Get the average volatility of a single name / a collection of names (e.g. all companies within a sector\\n res = pd.Series(np.nan, index=names)\\n for name in names:\\n # Retrieve all volatility\\n res[name] = np.nanmean(market_frame[name])\\n all_average = np.nanmean(res)\\n print(\\\"Average Vol\\\", all_average)\\n return res, all_average\\n\\n\\ndef event_analyser(names, market_frame, exogen_frame, rolling_window=180, detection_threshold=2.5, max_lag=5,\\n save_csv=True, mode='rtn', start_date=None, end_date=None):\\n if mode == 'rtn':\\n day_range = [i for i in range(-max_lag, max_lag+1)]\\n elif mode == 'vol':\\n day_range = [i for i in range(-max_lag, max_lag+1)]\\n else:\\n raise ValueError(\\\"Unrecognised mode argument: rtn or vol allowed\\\")\\n\\n res = pd.DataFrame(columns=['Name','EventDate', 'Type'] + day_range)\\n z_res = pd.DataFrame(columns=res.columns)\\n\\n start_date = pd.Timestamp(start_date) if start_date and pd.Timestamp(start_date) > market_frame.index[0] else market_frame.index[0]\\n end_date = pd.Timestamp(end_date) if end_date and pd.Timestamp(end_date) < market_frame.index[-1] else market_frame.index[-1]\\n market_frame = market_frame[(market_frame.index >= start_date) & (market_frame.index <= end_date)]\\n exogen_frame = exogen_frame[(exogen_frame.index >= start_date) & (exogen_frame.index <= end_date)]\\n\\n for name in names:\\n if mode == 'rtn':\\n daily_abnormal_return = pd.Series(np.nan, index=market_frame.index)\\n for date in market_frame.index:\\n if date < start_date + pd.to_timedelta(rolling_window, 'D'):\\n continue\\n elif date > end_date:\\n break\\n daily_abnormal_return[date] = get_abnormal_return(name, market_frame, date, window=180)\\n\\n exogen_series = exogen_frame[name]\\n exogen_z_score = _get_z_score(exogen_series, rolling_window)\\n event = _get_event_series(exogen_z_score, detection_threshold)\\n\\n #pos_event_idx = event[event == 1].index\\n #neg_event_idx = event[event == -1].index\\n #plt.scatter(pos_event_idx, exogen_series[pos_event_idx], marker='^', color = 'r', label='Positive Events')\\n #plt.scatter(neg_event_idx, exogen_series[neg_event_idx], marker='v', color='r', label='Negative Events')\\n #exogen_series.plot()\\n #plt.ylabel('SumSentiment')\\n #plt.show()\\n\\n if mode == 'rtn':\\n this_res, this_z_res = get_event_cumulative_abnormal_return(name, event, exogen_z_score, daily_abnormal_return, max_lag)\\n else:\\n this_res, this_z_res = get_event_vol(name, event, exogen_z_score, market_frame[name], max_lag)\\n res = res.append(this_res, ignore_index=True)\\n z_res = z_res.append(this_z_res, ignore_index=True)\\n pos_res = res[res['Type'] == 1]\\n neg_res = res[res['Type'] == -1]\\n\\n pos_z_score = z_res[z_res.Type == 1].mean(axis=0)\\n pos_z_score.name = 'AvgZScore'\\n neg_z_score = z_res[z_res.Type == -1].mean(axis=0)\\n neg_z_score.name = 'AvgZScore'\\n\\n pos_summary_stat = pos_res.describe()\\n neg_summary_stat = neg_res.describe()\\n\\n # Append t-test and Wilcoxon rank test p-values to the bottom of the result data frames\\n pos_res_stat_tests = pos_res[day_range]\\n neg_res_stat_tests = neg_res[day_range]\\n\\n # if mode is vol, we use the days leading up to the event day as the benchmark and conduct two-sample, one-tailed\\n # t-test to ascertain whether the volatilities after the event days are significantly higher than before the event.\\n if mode == 'vol':\\n # T-test\\n\\n\\n # Wilcoxon Rank-sum test\\n #pos_wilcoxon = st.wilcoxon(pos_res_stat_tests.loc[:, list(range(0, max_lag))].values,\\n # pos_res_stat_tests.loc[:, list(range(-max_lag, 0))].values,\\n # alternative='greater') # one-tailed test here\\n #neg_wilcoxon = st.wilcoxon(neg_res_stat_tests.loc[:, list(range(0, max_lag))].values,\\n # neg_res_stat_tests.loc[:, list(range(-max_lag, 0))].values,\\n # alternative='greater') # one-tailed test here\\n #\\n\\n # Convert two tailed p-value to one-tailed p-value\\n pos_pval = pd.Series(1., name='t-test', index=pos_res_stat_tests.columns)\\n neg_pval = pd.Series(1., name='t-test', index=neg_res_stat_tests.columns)\\n\\n for i in day_range:\\n if i >= -int(max_lag / 2):\\n pos_t_test = st.ttest_1samp(pos_res_stat_tests.loc[:, i].values,\\n popmean=np.nanmean(pos_res_stat_tests.loc[:, list(range(-max_lag, -int(max_lag/2)))].values),\\n nan_policy='omit')\\n neg_t_test = st.ttest_1samp(neg_res_stat_tests.loc[:, i].values,\\n popmean=np.nanmean(neg_res_stat_tests.loc[:, list(range(-max_lag, -int(max_lag/2)))].values),\\n nan_policy='omit')\\n\\n pos_pval[i] = pos_t_test[1]/2 if pos_t_test[0] > 0 else 1 - pos_t_test[1] / 2\\n neg_pval[i] = neg_t_test[1]/2 if neg_t_test[0] > 0 else 1 - neg_t_test[1] / 2\\n\\n pos_summary_stat = pos_summary_stat.append(pos_pval)\\n # here we divide the p-value by 2 to obtain the one tailed p value\\n\\n #wilcoxon_pos = pd.Series({i: st.wilcoxon(pos_res_stat_tests[i] - pos_pop_mean)[1]\\n # for i in day_range}, name='wilcoxon', index=pos_res_stat_tests.columns)\\n\\n neg_summary_stat = neg_summary_stat.append(neg_pval)\\n\\n else:\\n # 2-tailed t-test against the hypothesis that the abnormal return should be 0\\n pos_summary_stat = pos_summary_stat.append(pd.Series(\\n st.ttest_1samp(pos_res_stat_tests, popmean=0, nan_policy='omit')[1], name='t-test', index=pos_res_stat_tests.columns))\\n #wilcoxon_pos = pd.Series({i: st.wilcoxon(pos_res_stat_tests[i] - 0)[1]\\n # for i in day_range}, name='wilcoxon', index=pos_res_stat_tests.columns)\\n # pos_summary_stat = pos_summary_stat.append(wilcoxon_pos)\\n # pos_summary_stat = pos_summary_stat.append(pos_z_score)\\n neg_summary_stat = neg_summary_stat.append(pd.Series(\\n st.ttest_1samp(neg_res_stat_tests, popmean=0, nan_policy='omit')[1], name='t-test', index=neg_res_stat_tests.columns))\\n #wilcoxon_neg = pd.Series({i: st.wilcoxon(neg_res_stat_tests[i] - 0)[1] for i in day_range}, name='wilcoxon'\\n # , index=neg_res_stat_tests.columns)\\n #neg_summary_stat = neg_summary_stat.append(wilcoxon_neg)\\n neg_summary_stat = neg_summary_stat.append(neg_z_score)\\n\\n if save_csv:\\n file_appendix = str(names[0]) + \\\"_rollWindow\\\" + str(rolling_window) + \\\\\\n \\\"_maxLag\\\" + str(max_lag) + \\\"_mode\\\" + str(mode) + \\\"_\\\" + str(start_date.date()) + \\\\\\n \\\"_\\\" + str(end_date.date()) + \\\".csv\\\"\\n pos_summary_stat.to_csv('output/posEvents' + file_appendix)\\n neg_summary_stat.to_csv('output/negEvents' + file_appendix)\\n\\n return pos_summary_stat, neg_summary_stat, pos_res, neg_res\\n\\n\\ndef network_event_analyser(leading_name, lagging_names, market_frame, exogen_frame, rolling_window=90,\\n detection_threshold=2.5, max_lag=5, mode='rtn', start_date=None, end_date=None):\\n\\n def process_sub_result(df, z_df):\\n # print(z_df)\\n groupby = df.groupby('Name')\\n groupby_z = z_df.groupby('Name')\\n res = {}\\n for name, frame in groupby:\\n data_stat_test = frame[day_rng]\\n mean_z = groupby_z[day_rng].get_group(name).mean(axis=0)\\n mean_z.name = 'AvgZScore'\\n summary_stat = frame.describe()\\n pop_mean = np.nanmean(data_stat_test.loc[:, list(range(-2*max_lag, -1))].values) if mode == 'vol' else 0\\n ttest = pd.Series(st.ttest_1samp(data_stat_test, popmean=pop_mean, nan_policy='omit')[1], name='t-test', index=day_rng)\\n wilcoxon = pd.Series({i: st.wilcoxon(data_stat_test[i] - pop_mean)[1] for i in day_rng}, name='wilcoxon', index=day_rng)\\n summary_stat = summary_stat.append(ttest)\\n summary_stat = summary_stat.append(wilcoxon)\\n # print(mean_z)\\n summary_stat = summary_stat.append(mean_z)\\n res[name] = summary_stat\\n\\n # Average exclude leading name:\\n df = df[df.Name != leading_name]\\n data_stat_test = df[day_rng]\\n mean_z = data_stat_test.mean(axis=0)\\n mean_z.name = 'AvgZScore'\\n summary_stat = df.describe()\\n\\n pop_mean = np.nanmean(data_stat_test.loc[:, list(range(-2 * max_lag, -1))].values) if mode == 'vol' else 0\\n ttest = pd.Series(st.ttest_1samp(data_stat_test, popmean=pop_mean, nan_policy='omit')[1], name='t-test', index=day_rng)\\n tstats = pd.Series(st.ttest_1samp(data_stat_test, popmean=pop_mean, nan_policy='omit')[0], name='t-stat', index=day_rng)\\n wilcoxon = pd.Series({i: st.wilcoxon(data_stat_test[i]-pop_mean)[1] for i in day_rng}, name='wilcoxon', index=day_rng)\\n wstats = pd.Series({i: st.wilcoxon(data_stat_test[i]-pop_mean)[0] for i in day_rng}, name='wstats', index=day_rng)\\n summary_stat = summary_stat.append(ttest)\\n summary_stat = summary_stat.append(wilcoxon)\\n summary_stat = summary_stat.append(mean_z)\\n summary_stat = summary_stat.append(tstats)\\n summary_stat = summary_stat.append(wstats)\\n\\n res['Average'] = summary_stat\\n return res\\n\\n if mode == 'rtn':\\n day_rng = [i for i in range(-max_lag, max_lag+1)]\\n else:\\n day_rng = [i for i in range(-2*max_lag, max_lag)]\\n\\n start_date = pd.Timestamp(start_date) if start_date and pd.Timestamp(start_date) > market_frame.index[0] else market_frame.index[0]\\n end_date = pd.Timestamp(end_date) if end_date and pd.Timestamp(end_date) < market_frame.index[-1] else market_frame.index[-1]\\n\\n market_frame = market_frame[(market_frame.index >= start_date) & (market_frame.index <= end_date)]\\n exogen_frame = exogen_frame[(exogen_frame.index >= start_date) & (exogen_frame.index <= end_date)]\\n\\n exogen_series = exogen_frame[leading_name]\\n exogen_z_score = _get_z_score(exogen_series, rolling_window)\\n event = _get_event_series(exogen_z_score, detection_threshold)\\n # Get the sentiment event for the LEADING NAME\\n res = pd.DataFrame(columns=['Name', 'EventDate', 'Type'] + day_rng)\\n z_res = pd.DataFrame(columns=res.columns)\\n\\n #print(market_frame)\\n #print(exogen_frame)\\n\\n for lagging_name in lagging_names:\\n daily_abnormal_return = pd.Series(np.nan, index=market_frame.index)\\n z_score = _get_z_score(exogen_frame[lagging_name], rolling_window)\\n if mode == 'rtn':\\n for date in market_frame.index:\\n if date < start_date + pd.to_timedelta(rolling_window, 'D'):\\n continue\\n daily_abnormal_return[date] = get_abnormal_return(lagging_name, market_frame, date, window=180)\\n this_res, this_z_res = get_event_cumulative_abnormal_return(lagging_name, event, z_score, daily_abnormal_return, max_lag)\\n else:\\n this_res, this_z_res = get_event_vol(lagging_name, event, z_score, market_frame[lagging_name], max_lag)\\n res = res.append(this_res, ignore_index=True)\\n # print(this_z_res)\\n z_res = z_res.append(this_z_res, ignore_index=True)\\n\\n pos = res[res['Type'] == 1]\\n neg = res[res['Type'] == -1]\\n pos_z = z_res[z_res.Type == 1]\\n neg_z = z_res[z_res.Type == -1]\\n print(pos)\\n print(neg)\\n\\n pos_res = process_sub_result(pos, pos_z)\\n neg_res = process_sub_result(neg, neg_z)\\n print(pos_res)\\n print(neg_res)\\n\\n return pos_res, neg_res\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6579012274742126,\n \"alphanum_fraction\": 0.659375786781311,\n \"avg_line_length\": 41.375,\n \"blob_id\": \"d4876be0c3e43fca37a31dd11d6f87b13c8dcb6d\",\n \"content_id\": \"0182a121b46ce78f6bb9547434e0fbea85af1172\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4069,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 96,\n \"path\": \"/PycharmProjects/OMI/utilities.py\",\n \"repo_name\": \"xingchenwan/Sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Utilities module: provide some utilities functions\\n# Xingchen Wan | Xingchen.Wan@st-annes.ox.ac.uk | Oxford-Man Institute of Quantitative Finance\\n\\nimport datetime\\nfrom source.all_classes import *\\n\\n\\ndef fix_fulldata(full_data):\\n \\\"\\\"\\\"\\n A temporary patch to resolve the incompatibility issue between Python 2.x and 3.x pickling\\n :param full_data:\\n :return:\\n \\\"\\\"\\\"\\n\\n def _fix_object(obj):\\n \\\"\\\"\\\"\\n Decode the byte data type back to strings due to Python 3.x un-pickling\\n :param obj: all_data object\\n :return: None\\n \\\"\\\"\\\"\\n obj.__dict__ = dict((k.decode(\\\"utf8\\\"), v) for k, v in obj.__dict__.items())\\n\\n def _fix_iterables(obj):\\n \\\"\\\"\\\"\\n Decode iterables\\n :param obj: An iterable, dict or list (other iterables are not included as they were not included in the\\n original data type design\\n :return: Fixed iterables\\n \\\"\\\"\\\"\\n if isinstance(obj, list):\\n return [x.decode('utf8') if isinstance(x, bytes) else x for x in obj]\\n elif isinstance(obj, dict):\\n return {k.decode('utf8'): v for k, v in obj.items()}\\n\\n _fix_object(full_data)\\n full_data.entity_occur_interval = _fix_iterables(full_data.entity_occur_interval)\\n full_data.entity_sentiment_interval = _fix_iterables(full_data.entity_sentiment_interval)\\n\\n for day in full_data.days:\\n _fix_object(day)\\n day.entity_sentiment_day = _fix_iterables(day.entity_sentiment_day)\\n day.entity_occur_day = _fix_iterables(day.entity_occur_day)\\n for news in day.day_news:\\n _fix_object(news)\\n news.entity_occur = _fix_iterables(news.entity_occur)\\n news.entity_sentiment = _fix_iterables(news.entity_sentiment)\\n\\n\\ndef create_sub_obj(parent_obj, start_date=None, end_date=None):\\n \\\"\\\"\\\"\\n Create a new sub FullData object from an existing FullData object with a narrower range of dates.\\n :param parent_obj: FullData object. The parent object\\n :param start_date: datetime.date type. Start date of the sub object. Must be later than the parent start date\\n :param end_date: datetime.date type. End date fo the sub object. Must be earlier than the parent end date.\\n :return:\\n \\\"\\\"\\\"\\n if not isinstance(parent_obj, FullData):\\n raise TypeError(\\\"The parent_obj must be f FullData Type\\\")\\n if start_date and not isinstance(start_date, datetime.date):\\n raise ValueError(\\\"Start date needs to be of date type\\\")\\n if end_date and not isinstance(end_date, datetime.date):\\n raise ValueError(\\\"End date needs to be of date type\\\")\\n\\n if start_date is None: start_date = parent_obj.start_date\\n if end_date is None: end_date = parent_obj.end_date\\n\\n start_date = max(start_date, parent_obj.start_date)\\n end_date = min(end_date, parent_obj.end_date)\\n\\n if start_date == parent_obj.start_date and end_date == parent_obj.end_date:\\n return parent_obj\\n copied_obj = copy.deepcopy(parent_obj)\\n # Create a copied object of the parent object first\\n\\n copied_obj.entity_occur_interval = {}\\n copied_obj.entity_sentiment_interval = {}\\n copied_obj.start_date = start_date\\n copied_obj.end_date = end_date\\n # Reset various properties of the copied object\\n\\n copied_obj.days = [day for day in parent_obj.days if day.date > start_date and day.date < end_date]\\n total_sentiment = {}\\n for each_day in copied_obj.days:\\n for entity, num in each_day.entity_occur_day.items():\\n if entity in copied_obj.entity_occur_interval:\\n copied_obj.entity_occur_interval[entity] += num\\n total_sentiment[entity] += num * each_day.entity_sentiment_day[entity]\\n else:\\n copied_obj.entity_occur_interval[entity] = num\\n total_sentiment[entity] = num * each_day.entity_sentiment_day[entity]\\n for entity, num in copied_obj.entity_occur_interval.items():\\n copied_obj.entity_sentiment_interval[entity] = total_sentiment[entity] / num\\n return copied_obj\\n\\ndef normalise(series):\\n return (series - series.mean()) / (series.max() - series.min())\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5100798606872559,\n \"alphanum_fraction\": 0.5533155798912048,\n \"avg_line_length\": 44.56647491455078,\n \"blob_id\": \"16bcd19f121236416c4523f73e7e5987c40ff4c8\",\n \"content_id\": \"5b6060dad78395a42d1d1503d3c5a55a9352287b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7887,\n \"license_type\": \"no_license\",\n \"max_line_length\": 133,\n \"num_lines\": 173,\n \"path\": \"/PycharmProjects/OMI/main.py\",\n \"repo_name\": \"xingchenwan/Sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Xingchen Wan | Oxford-Man Institute of Quantitative Finance | Updated 6 Sep 2018\\n\\nimport processor, grouper, analyser, visualiser, utilities\\nfrom source.all_classes import *\\nimport matplotlib\\nmatplotlib.use(\\\"TkAgg\\\")\\nimport matplotlib.pylab as plt\\nimport pandas as pd\\n\\n\\nNAME = 'USDJPY'\\nMODE = 'vol'\\n# Sector representatives:\\n# Financials: AXP\\n# Tech: AAPL\\n# Retail: WMT\\n# Industrials: BA\\nSTART_DATE = datetime.date(2007, 1, 1)\\nEND_DATE = datetime.date(2007, 12, 31)\\nMAX_LAG = 10\\n\\nPATH_POS = \\\"output/\\\" + \\\"posEvents\\\" + NAME + \\\"_rollWindow180_maxLag\\\" + str(MAX_LAG) + \\\"_mode\\\" + MODE + \\\"_\\\" + \\\\\\n str(START_DATE) + \\\"_\\\" + str(END_DATE) + \\\".csv\\\"\\nPATH_NEG = \\\"output/\\\" + \\\"negEvents\\\" + NAME + \\\"_rollWindow180_maxLag\\\" + str(MAX_LAG) + \\\"_mode\\\" + MODE + \\\"_\\\" + \\\\\\n str(START_DATE) + \\\"_\\\" + str(END_DATE) + \\\".csv\\\"\\nFIGURE_PATH = \\\"figures/\\\" + NAME + \\\"_rollWindow180_maxLag\\\" + str(MAX_LAG) + \\\"_mode\\\" + MODE + \\\"_\\\" + \\\\\\n str(START_DATE) + \\\"_\\\" + str(END_DATE) + \\\".png\\\"\\n\\n\\ndef plot_from_csv():\\n pos_df = pd.read_csv(PATH_POS, index_col=0)\\n neg_df = pd.read_csv(PATH_NEG, index_col=0)\\n visualiser.plot_events(pos_df, neg_df, descps=['Positive Events', 'Negative Events'], mode=MODE)\\n plt.xlabel(\\\"Number of days to events\\\")\\n # plt.savefig(FIGURE_PATH)\\n\\n\\ndef plot_multiple_from_csv(paths: dict, plot_range=None):\\n \\\"\\\"\\\"\\n Plot multiple graphs from a collection of csv paths. the paths should be organised in a dictionary ordered in a\\n chronological order. The key is the year and the value is the path\\n :param paths:\\n :return:\\n \\\"\\\"\\\"\\n\\n from collections import OrderedDict\\n\\n # setting colours\\n\\n def plot_event(event_summary, year, mode='vol', alpha=0.1, plot_range=None):\\n ubound = 0.1\\n lbound = 0\\n\\n\\n event_summary = event_summary[[i for i in list(event_summary.columns) if i.lstrip('-').isdigit()]]\\n event_summary.columns = event_summary.columns.astype(int)\\n\\n if plot_range is None:\\n xi = np.array((event_summary.columns))\\n\\n else:\\n xi = np.array([x for x in range(plot_range[0], plot_range[1])])\\n\\n mean_val = event_summary.loc['mean', xi]\\n offset = mean_val.iloc[0] if mode != 'vol' else 0\\n p = plt.plot(xi + 1 , event_summary.loc['mean', xi] - offset, 'o-', linewidth=1, alpha=1, label=str(year), markersize=2)\\n for i in range(len(xi)):\\n if event_summary.loc['mean', xi[i]] - offset > ubound:\\n plt.plot(xi[i] + 1, ubound * 0.97, marker=\\\"^\\\", color=p[0].get_color())\\n elif event_summary.loc['mean', xi[i]] - offset < lbound:\\n plt.plot(xi[i] + 1, lbound * 0.97, marker='v', color=p[0].get_color())\\n #test_stats = event_summary.loc['t-test', :]\\n\\n #sig_idx = np.argwhere((test_stats < 0.05) & (test_stats > 0.01)).reshape(-1) + mean_val.index[0]\\n #v_sig_idx = np.argwhere(test_stats <= 0.01).reshape(-1).reshape(-1)+ mean_val.index[0]\\n\\n #if len(sig_idx):\\n # plt.plot(sig_idx, mean_val[sig_idx] - offset, 'o', color='orange', label='p < 0.05')\\n #if len(v_sig_idx):\\n # plt.plot(v_sig_idx, mean_val[v_sig_idx] - offset, 'o', color='red', label='p < 0.01')\\n if mode == 'rtn':\\n plt.ylabel('Cumulative Abnormal Return (CAR)')\\n elif mode == 'vol':\\n plt.ylabel('Volatility 1D')\\n\\n handles, labels = plt.gca().get_legend_handles_labels()\\n by_label = OrderedDict(zip(labels, handles))\\n plt.grid(True)\\n plt.gca().set_ylim(bottom=lbound)\\n plt.gca().set_ylim(top=ubound)\\n plt.legend(by_label.values(), by_label.keys(), prop={'size': 6}, loc='upper right')\\n\\n i = 0\\n plt.axvspan(-1, 0, alpha=0.2, color='gray')\\n plt.rcParams.update({'font.size': 8})\\n\\n for year, path_pos in paths.items():\\n i += 1\\n pos_df = pd.read_csv(path_pos, index_col=0)\\n plot_event(pos_df, year, mode=MODE, plot_range=plot_range)\\n plt.xlabel(\\\"Number of days to events\\\")\\n plt.show()\\n\\n\\n\\ndef event(start_date=START_DATE, end_date=END_DATE):\\n\\n full_data_obj = 'data/full.date.20061020-20131120'\\n market_data_path = 'data/market_data.xlsx'\\n market_data_sheet = 'Price1'\\n volume_data_sheet = 'Volatility1'\\n sector_path = \\\"source/sector.csv\\\"\\n pd.options.display.max_rows = 10000\\n\\n with open(full_data_obj, 'rb') as f:\\n all_data = pickle.load(f, fix_imports=True, encoding='bytes')\\n utilities.fix_fulldata(all_data)\\n\\n sub_all_data = utilities.create_sub_obj(all_data, start_date, end_date)\\n price, daily_rtn, log_return, vol = processor.process_market_time_series(market_data_path, market_data_sheet,\\n start_date=pd.to_datetime(start_date),\\n end_date=pd.to_datetime(end_date),\\n )\\n volume, _, _, _ = processor.process_market_time_series(market_data_path,volume_data_sheet,\\n start_date=pd.to_datetime(start_date),\\n end_date= pd.to_datetime(end_date))\\n\\n all_names = list(price.columns)[:-1]\\n sector = grouper.get_sector_grouping(all_names, sector_path, 0, 2)\\n this_sector = grouper.get_sector_peer(sector, NAME)\\n\\n count, med_sentiment, sum_sentiment = processor.process_count_sentiment(sub_all_data,\\n start_date=pd.to_datetime(start_date),\\n end_date=pd.to_datetime(end_date),\\n focus_iterable=all_names,\\n rolling=True,\\n rolling_smoothing_factor=0.7)\\n\\n pos, neg, _, _ = analyser.event_analyser(all_names, market_frame=vol, exogen_frame=sum_sentiment, mode=MODE,\\n start_date=start_date,\\n end_date=end_date,\\n max_lag=MAX_LAG)\\n\\n\\ndef event_by_year(start_date, end_date):\\n for _ in range(7):\\n print(start_date, end_date)\\n event(start_date, end_date)\\n start_date += datetime.timedelta(days=365)\\n end_date += datetime.timedelta(days=365)\\n\\n\\n\\nif __name__ == \\\"__main__\\\":\\n #event_by_year(START_DATE, END_DATE)\\n\\n event_type = \\\"neg\\\"\\n\\n paths_dict = {\\n '2007': \\\"output/\\\" + event_type + \\\"Events\\\" + NAME + \\\"_rollWindow180_maxLag10_mode\\\" + MODE + \\\"_2007-01-01_2007-12-31\\\" + \\\".csv\\\",\\n '2008': \\\"output/\\\" + event_type + \\\"Events\\\" + NAME + \\\"_rollWindow180_maxLag10_mode\\\" + MODE + \\\"_2008-01-01_2008-12-30\\\" + \\\".csv\\\",\\n '2009': \\\"output/\\\" + event_type + \\\"Events\\\" + NAME + \\\"_rollWindow180_maxLag10_mode\\\" + MODE + \\\"_2008-12-31_2009-12-30\\\" + \\\".csv\\\",\\n '2010': \\\"output/\\\" + event_type + \\\"Events\\\" + NAME + \\\"_rollWindow180_maxLag10_mode\\\" + MODE + \\\"_2009-12-31_2010-12-30\\\" + \\\".csv\\\",\\n '2011': \\\"output/\\\" + event_type + \\\"Events\\\" + NAME + \\\"_rollWindow180_maxLag10_mode\\\" + MODE + \\\"_2010-12-31_2011-12-30\\\" + \\\".csv\\\",\\n '2012': \\\"output/\\\" + event_type + \\\"Events\\\" + NAME + \\\"_rollWindow180_maxLag10_mode\\\" + MODE + \\\"_2012-01-02_2012-12-28\\\" + \\\".csv\\\",\\n '2013': \\\"output/\\\" + event_type + \\\"Events\\\" + NAME + \\\"_rollWindow180_maxLag10_mode\\\" + MODE + \\\"_2012-12-31_2013-12-27\\\" + \\\".csv\\\"\\n }\\n\\n paths_dict2 = {\\n 'Positive': \\\"output/\\\" + \\\"pos\\\" + \\\"Events\\\" + NAME + \\\"_rollWindow180_maxLag10_modevol_2006-10-30_2013-12-31\\\" + \\\".csv\\\",\\n 'Negative': \\\"output/\\\" + \\\"neg\\\" + \\\"Events\\\" + NAME + \\\"_rollWindow180_maxLag10_modevol_2006-10-30_2013-12-31\\\" + \\\".csv\\\",\\n }\\n plot_multiple_from_csv(paths_dict, plot_range=[-10, 5])\\n\\n\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5800870656967163,\n \"alphanum_fraction\": 0.5868335366249084,\n \"avg_line_length\": 45.882652282714844,\n \"blob_id\": \"8455ee09ac48c23d41ddf7d1d7b7e3239884d45d\",\n \"content_id\": \"a0cadccc1215f68caa78becdd109bbbae1cc8a7d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 9190,\n \"license_type\": \"no_license\",\n \"max_line_length\": 132,\n \"num_lines\": 196,\n \"path\": \"/PycharmProjects/OMI/networkAnalyser.py\",\n \"repo_name\": \"xingchenwan/Sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import utilities\\nfrom source.utils import cosine_distance\\nimport networkx as nx\\nimport community\\nimport matplotlib\\nmatplotlib.use(\\\"TkAgg\\\")\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport pandas as pd\\nfrom visualiser import get_color_map\\n\\n\\nclass NetworkAnalyser:\\n def __init__(self, full_data_object, names, start_date=None, end_date=None):\\n self.names = names\\n self.start_date = start_date if start_date and start_date >= full_data_object.start_date else full_data_object.start_date\\n self.end_date = end_date if end_date and end_date <= full_data_object.end_date else full_data_object.end_date\\n if self.start_date != full_data_object.start_date or self.end_date != full_data_object.end_date:\\n self.full_data = utilities.create_sub_obj(full_data_object, self.start_date, self.end_date)\\n else:\\n self.full_data = full_data_object\\n self.G = self.full_data.build_occurrence_network_graph(focus_iterable=self.names)\\n self.partition = {}\\n self.node_pos = None\\n\\n def get_centrality(self, names=None, type='katz', ordered=True):\\n if type == 'katz':\\n centrality = nx.katz_centrality(self.G, weight='weight')\\n elif type == 'eigen':\\n centrality = nx.eigenvector_centrality(self.G, weight='weight')\\n else:\\n raise NotImplementedError('Unrecognised centrality mode name.')\\n if ordered:\\n centrality = sorted(centrality.items(), key=lambda kv: -kv[1])\\n if names is not None:\\n res = [x for x in centrality if x[0] in names]\\n return res\\n else: return centrality\\n\\n def cluster(self, resolution=None):\\n if resolution:\\n self.partition = community.best_partition(self.G, weight='weight', resolution=resolution)\\n else:\\n self.partition = community.best_partition(self.G, weight='weight')\\n return self.partition\\n\\n def get_cluster_peers(self, name):\\n if self.partition == {}:\\n self.cluster()\\n peers = [key for key, value in self.partition.items() if value == self.partition[name]]\\n peer_centrality = self.get_centrality(names=peers)\\n res = {peer[0]: peer[1] for peer in peer_centrality}\\n return res\\n\\n def get_category(self, name):\\n if self.partition == {}:\\n self.cluster()\\n return self.partition[name]\\n\\n def get_neighbours(self, name, max_neighbour=-1, weight_threshold=-1, exclude=None):\\n neighbours = self.G[name]\\n sorted_neighbours = sorted(neighbours.items(), key=lambda kv: -kv[1]['weight'])\\n #print(sorted_neighbours)\\n if weight_threshold > 0:\\n if isinstance(weight_threshold, float):\\n res = [item for item in sorted_neighbours if item[1]['weight'] >= weight_threshold]\\n elif isinstance(weight_threshold, int):\\n threshold = np.percentile(np.array([x[1]['weight'] for x in sorted_neighbours]), weight_threshold)\\n res = [item for item in sorted_neighbours if item[1]['weight'] >= threshold]\\n else:\\n raise TypeError(\\\"percentile threshold needs to be a positive integer or float\\\")\\n else:\\n res = sorted_neighbours\\n if neighbours != -1:\\n res = res[:min(max_neighbour, len(sorted_neighbours))]\\n if exclude is not None:\\n res = [item for item in res if item[0] not in exclude]\\n names = [name] + [each_res[0] for each_res in res]\\n weight = [1] + [each_res[1]['weight'] for each_res in res]\\n category = [self.get_category(name)] + [self.get_category(each_name) for each_name in names]\\n centrality = [self.get_centrality(name)[0][1]] + [self.get_centrality(each_name)[0][1] for each_name in names]\\n res = pd.DataFrame([names, weight, category, centrality]).transpose()\\n res.columns = ['Names', 'CosineDistance', 'Category', 'Centrality']\\n res.dropna(axis=0, inplace=True, how='any')\\n return res\\n\\n def plot_network(self, group_by='category', threshold=90):\\n if self.partition == {}: self.cluster()\\n color_map = get_color_map(self.names, self.partition)\\n c = [color_map[name] for name in self.G.nodes()]\\n if self.node_pos is None:\\n pos = nx.spring_layout(self.G)\\n self.node_pos = pos\\n edgelist = self.G.edges(data=True)\\n if group_by == 'centrality':\\n centrality = list(self.get_centrality().values())\\n vmin = np.min(centrality)\\n vmax = np.max(centrality)\\n\\n if threshold:\\n if isinstance(threshold, int):\\n weight_list = np.array([x[2]['weight'] for x in edgelist])\\n threshold = np.percentile(weight_list, threshold)\\n edgelist = [x for x in edgelist if x[2]['weight'] >= threshold]\\n else:\\n edgelist = [x for x in edgelist if x[2]['weight'] >= threshold]\\n if group_by == 'category':\\n nx.draw(self.G, self.node_pos, edgelist=edgelist, node_color=c, with_labels=True)\\n elif group_by == 'centrality':\\n nx.draw(self.G, self.node_pos, edgelist=edgelist,\\n cmap=plt.plasma(),\\n node_color=centrality,\\n with_labels=True)\\n sm = plt.cm.ScalarMappable(cmap=plt.plasma(), norm=plt.Normalize(vmin=vmin, vmax=vmax))\\n sm.set_array([])\\n plt.colorbar(sm)\\n else:\\n raise NotImplemented()\\n\\n def plot_single_name(self, name, max_neighbour=-1, weight_threshold=-1):\\n if self.partition == {}: self.cluster()\\n data = self.get_neighbours(name, max_neighbour, weight_threshold)\\n nodes = data.iloc[:, 0]\\n G = nx.Graph()\\n G.add_nodes_from(nodes)\\n nx.draw(G)\\n\\n def get_cos_dist_time_series(self, name1, *names, correlation_period=90):\\n entity_list = [name1] + list(names)\\n name2id = {entity_list[i]: i for i in range(len(entity_list))}\\n occurrence_array = np.zeros((len(entity_list), self.full_data.news_number))\\n date_array = np.zeros((self.full_data.news_number, ))\\n start_date = self.start_date + pd.to_timedelta(correlation_period)\\n res = {entity: [] for entity in names}\\n date_frame = []\\n news_id = 0\\n day_id = 0\\n for each_day in self.full_data.days:\\n for news in each_day.day_news:\\n for entity in entity_list:\\n if entity in news.entity_occur:\\n occurrence_array[name2id[entity], news_id] = news.entity_occur[entity]\\n date_array[news_id] = day_id\\n news_id += 1\\n if each_day.date >= start_date + pd.to_timedelta(correlation_period):\\n date_frame.append(each_day.date)\\n included_news = (date_array >= day_id - correlation_period) & (date_array < day_id)\\n sub_occurrence_array = occurrence_array[:, included_news]\\n for name in names:\\n res[name].append(cosine_distance(sub_occurrence_array[name2id[name1]],\\n sub_occurrence_array[name2id[name]]))\\n day_id += 1\\n date_array = pd.Series(date_frame)\\n res = pd.DataFrame(res, index=date_array)\\n return res\\n\\n def get_dynamic_neighbors(self, name, date, period=180, weight_threshold=-1, max_neighbour=-1, exclude=None):\\n \\\"\\\"\\\"\\n Get top n neighbours in based on cosine distance in the past n days\\n :param name:\\n :param time:\\n :param period:\\n :param weight_threshold:\\n :param max_neighbour:\\n :param exclude:\\n :return:\\n \\\"\\\"\\\"\\n if date < self.start_date + pd.to_timedelta(period):\\n return np.nan\\n #print(date - pd.to_timedelta(period, 'D', ), date)\\n day_objs = [day for day in self.full_data.days if ((day.date > date - pd.to_timedelta(period, 'D')) and (day.date <= date))]\\n name2id = {self.names[i]: i for i in range(len(self.names))}\\n news_cnt = sum([day.news_number for day in day_objs])\\n res = {entity: 0 for entity in self.names}\\n occurrence_array = np.zeros((len(self.names), news_cnt))\\n news_id = 0\\n for each_day in day_objs:\\n for news in each_day.day_news:\\n for entity in self.names:\\n if entity in news.entity_occur:\\n occurrence_array[name2id[entity], news_id] = news.entity_occur[entity]\\n news_id += 1\\n\\n for each_name in self.names:\\n res[each_name] = cosine_distance(occurrence_array[name2id[name]], occurrence_array[name2id[each_name]])\\n res = sorted(res.items(), key=lambda v: -v[1])[1:]\\n\\n if weight_threshold != -1:\\n res = [x for x in res if x[1] > weight_threshold]\\n if max_neighbour != -1 and max_neighbour < len(res):\\n thres = res[max_neighbour][1]\\n res = [x for x in res if x[1] > thres]\\n if exclude is not None:\\n res = [x for x in res if x[0] not in exclude]\\n print(res)\\n return res\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5869565010070801,\n \"alphanum_fraction\": 0.5966865420341492,\n \"avg_line_length\": 39.450355529785156,\n \"blob_id\": \"4569b5dad96a3bbb92bd0d73ca999017807aaa7a\",\n \"content_id\": \"0caf2d557b79d7238d15c407b05fd8dcf0931791\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 11408,\n \"license_type\": \"no_license\",\n \"max_line_length\": 121,\n \"num_lines\": 282,\n \"path\": \"/PycharmProjects/OMI/visualiser.py\",\n \"repo_name\": \"xingchenwan/Sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Visualiser: Plot functions\\n# Xingchen Wan | Xingchen.Wan@st-annes.ox.ac.uk | Oxford-Man Institute of Quantitative Finance\\n\\nimport numpy as np\\nimport pandas as pd\\nimport matplotlib\\nmatplotlib.use(\\\"TkAgg\\\")\\nimport matplotlib.pylab as plt\\nfrom analyser import _get_z_score, _get_event_series\\nimport datetime\\nimport math\\nimport networkx as nx\\nfrom utilities import create_sub_obj\\nfrom collections import OrderedDict\\n\\n\\ndef get_color_map(data_labels, categories):\\n color_map_num2text = ['b', 'g', 'r', 'c', 'm', 'y', 'olive', 'coral', 'brown', 'gray', 'indigo']\\n color_map ={ x:'k' for x in data_labels}\\n assert isinstance(categories, dict)\\n color_map_keys = list(set(categories.values()))\\n color_map_values = [color_map_num2text[i] if i < len(color_map_num2text) else 'k' for i in\\n range(len(color_map_keys))]\\n color_map_dict = dict(zip(color_map_keys, color_map_values))\\n for name in data_labels:\\n try:\\n color_map[name] = color_map_dict[categories[name]]\\n except KeyError:\\n pass\\n return color_map\\n\\n\\ndef plot_scatter(data, data_labels, x_label=None, y_label=None, categories=None):\\n \\\"\\\"\\\"\\n Plot the scatter diagram of the correlation of different single names\\n :param data: input data. pandas dataframe or numpy ndarray\\n :param data_labels: label of each data point - usually the ticker or name of the entities in data. Should be of\\n the same length as the data\\n :param x_label: Optional. x-axis label of the scatter plot.\\n :param y_label: Optional. y-axis label of the scatter plot\\n :param categories: Optional {}. Sectorial or dynamic categorisation of the entities. Must be of the same length\\n as the number of entities in data.\\n :return:\\n \\\"\\\"\\\"\\n\\n assert len(data) == len(data_labels)\\n # Default colour scheme\\n\\n c = ['k']*len(data_labels)\\n if categories:\\n color_map = get_color_map(data_labels, categories)\\n c = [color_map[name] for name in data_labels]\\n\\n axis = np.linspace(0, 100, len(data))\\n fig, ax = plt.subplots()\\n ax.scatter(axis, data, c=c, s=10)\\n plt.axhline(0, color=\\\"gray\\\")\\n plt.axhline(np.nanmean(data), color=\\\"red\\\")\\n plt.axhline(np.nanmedian(data), color=\\\"blue\\\")\\n if x_label: plt.xlabel(x_label)\\n if y_label: plt.ylabel(y_label)\\n if categories:\\n labels = list(categories.values())\\n\\n for i in range(len(data)):\\n if categories:\\n ax.scatter(axis[i], data[i], c=c[i], label=labels[i] if labels[i] not in\\n plt.gca().get_legend_handles_labels()[1] else '')\\n else:\\n ax.scatter(axis[i], data[i], c=c[i])\\n ax.annotate(data_labels.iloc[i], (axis[i], data[i]), fontsize=6)\\n plt.legend(prop={'size': 6})\\n\\n\\ndef plot_single_name(name, *args, arg_names=[], start_date=None, end_date=None):\\n if arg_names and len(arg_names) != len(args):\\n raise ValueError(\\\"Length mismatch between arg_names and number of series supplied.\\\")\\n start_date = start_date if start_date and isinstance(start_date, datetime.date) else datetime.datetime(2007, 1, 1)\\n end_date = end_date if end_date and isinstance(end_date, datetime.date) else datetime.datetime(2014, 1, 1)\\n\\n fig = plt.figure(figsize=(5, 5))\\n subplot_width = 2\\n subplot_height = 1\\n i = 0\\n for arg in args:\\n if not isinstance(arg, pd.DataFrame) and not isinstance(arg, pd.Series):\\n raise TypeError(\\\"Input arguments need to be Pandas DataFrames\\\")\\n elif not isinstance(arg.index, pd.DatetimeIndex):\\n raise TypeError(\\\"Input series index needs to be DatetimeIndex\\\")\\n\\n for arg in args:\\n if isinstance(arg, pd.Series):\\n series = arg\\n elif name in list(arg.columns):\\n series = arg[name]\\n else:\\n print(name, \\\" not found in argument of position\\\", args.index(arg))\\n continue\\n series = series[series.index >= start_date]\\n series = series[series.index < end_date]\\n ax = plt.subplot(subplot_width, subplot_height, i+1)\\n if 'Sentiment' in arg_names[i]:\\n z_score = _get_z_score(series, window=90)\\n events = _get_event_series(z_score, threshold=2.5,)\\n neg_event_days = events.index[events == -1]\\n pos_event_days = events.index[events == 1]\\n for day in neg_event_days:\\n plt.axvline(day, color='r', alpha=0.5)\\n for day in pos_event_days:\\n plt.axvline(day, color='g', alpha=0.5)\\n plt.plot(series.index, series)\\n #plt.axhline(np.nanmean(series), color=\\\"red\\\")\\n #plt.axhline(np.nanmedian(series), color=\\\"blue\\\")\\n #plt.axhline(0, color=\\\"gray\\\")\\n\\n if arg_names:\\n ax.set_title(arg_names[i])\\n i += 1\\n\\n plt.subplot(211)\\n for day in neg_event_days:\\n plt.axvline(day, color='r', alpha=0.5)\\n for day in pos_event_days:\\n plt.axvline(day, color='g', alpha=0.5)\\n\\n fig.suptitle(name)\\n\\n\\ndef plot_network(full_data_obj, names=[], start_date=None, end_date=None, categories=None, threshold=90,\\n group_by=None):\\n\\n if not group_by:\\n group_by = 'category'\\n sub_obj = create_sub_obj(full_data_obj, start_date=start_date, end_date=end_date)\\n G = sub_obj.build_occurrence_network_graph(focus_iterable=names)\\n color_map = get_color_map(names, categories)\\n c = [color_map[name] for name in G.nodes()] if categories else ['k']*len(names)\\n pos = nx.spring_layout(G)\\n edgelist = G.edges(data=True)\\n if group_by == 'centrality':\\n centrality = list(nx.katz_centrality(G, weight='weight').values())\\n vmin = np.min(centrality)\\n vmax = np.max(centrality)\\n\\n if threshold:\\n if isinstance(threshold, int):\\n weight_list = np.array([x[2]['weight'] for x in edgelist])\\n threshold = np.percentile(weight_list, threshold)\\n edgelist = [x for x in edgelist if x[2]['weight'] >= threshold]\\n else:\\n edgelist = [x for x in edgelist if x[2]['weight'] >= threshold]\\n if group_by == 'category':\\n nx.draw(G, pos, edgelist=edgelist, node_color=c, with_labels=True)\\n elif group_by == 'centrality':\\n nx.draw(G, pos, edgelist=edgelist,\\n cmap=plt.plasma(),\\n node_color=centrality,\\n with_labels=True)\\n sm = plt.cm.ScalarMappable(cmap=plt.plasma(), norm=plt.Normalize(vmin=vmin, vmax=vmax))\\n sm.set_array([])\\n plt.colorbar(sm)\\n else:\\n raise NotImplemented()\\n\\n\\ndef plot_distribution_around_events(df):\\n import matplotlib.mlab as mlab\\n\\n def gen_caption(days_to_event):\\n if days_to_event < 0:\\n return str(abs(days_to_event))+'d before event'\\n elif days_to_event == 0:\\n return \\\"Event day\\\"\\n else:\\n return str(days_to_event)+'d after event'\\n\\n numeric_col_names = [i for i in list(df.columns) if isinstance(i, int)]\\n subplot_width = 2\\n subplot_height = math.ceil(len(numeric_col_names) / subplot_width)\\n df = df[numeric_col_names]\\n i = 0\\n for col in numeric_col_names:\\n ax = plt.subplot(subplot_width, subplot_height, i+1)\\n data = df[col].values\\n data = data[~np.isnan(data)]\\n mu = np.mean(data)\\n sigma = np.std(data)\\n n, bins, patches = plt.hist(data, 50, normed=1)\\n y = mlab.normpdf(bins, mu, sigma)\\n l = plt.plot(bins, y, 'r--', linewidth=1)\\n plt.xlabel('CAR')\\n plt.axvline(mu, color='red')\\n ax.set_title(gen_caption(col))\\n i += 1\\n\\n\\ndef plot_events(*event_summaries, descps, test_chosen='t-test', mode='vol', special_line=False, plot_special_only=False,\\n plot_sentiment=False):\\n plt.figure(figsize=(8, 6))\\n\\n def plot_event(event_summary, descp, test_chosen, ):\\n event_summary = event_summary[[i for i in list(event_summary.columns) if i.lstrip('-').isdigit()]]\\n\\n xi = [int(x) for x in list(event_summary.columns)]\\n mean_val = event_summary.loc['mean', :].values\\n # z_val = event_summary.loc['AvgZScore',:].values\\n if plot_sentiment:\\n plt.subplot(211)\\n if not special_line:\\n if plot_special_only:\\n pass\\n else:\\n plt.plot(xi, mean_val, linewidth=1, label=descp)\\n # plt.plot(xi, mean_val, label=descp)\\n # plt.fill_between(xi, mean_val-std_devs, mean_val+std_devs, alpha=0.2)\\n else:\\n plt.plot(xi, mean_val, linewidth=3.0, label=descp)\\n if (not special_line) and (plot_special_only):\\n pass\\n else:\\n test_stats = event_summary.loc[test_chosen, :].values\\n sig_idx = np.argwhere((test_stats < 0.05) & (test_stats > 0.01))\\n v_sig_idx = np.argwhere(test_stats <= 0.01)\\n if len(sig_idx):\\n plt.plot(sig_idx+xi[0], mean_val[[sig_idx]], 'o', color='orange', label='p < 0.05')\\n if len(v_sig_idx):\\n plt.plot(v_sig_idx+xi[0], mean_val[[v_sig_idx]], 'o', color='red', label='p < 0.01')\\n\\n if plot_sentiment:\\n plt.subplot(212)\\n if not special_line:\\n if plot_special_only:\\n pass\\n else:\\n plt.plot(list(event_summary.columns), np.abs(z_val), linewidth=1, label=descp)\\n else:\\n plt.plot(list(event_summary.columns), np.abs(z_val), linewidth=3.0, label=descp)\\n\\n if len(descps) != len(event_summaries):\\n raise ValueError(\\\"Descp length must match number of event summaries passed!\\\")\\n for i in range(len(event_summaries)):\\n plot_event(event_summaries[i], descps[i], test_chosen=test_chosen,)\\n\\n if plot_sentiment:\\n plt.subplot(211)\\n if mode == 'rtn':\\n #plt.axhline(0, color='k',)\\n #plt.axvline(0, color='k',)\\n plt.ylabel('Cumulative Abnormal Return (CAR)')\\n elif mode == 'vol':\\n #plt.axhline(0, color='k', linestyle='dashed')\\n #plt.axvline(0, color='k', linestyle='dashed')\\n plt.ylabel('Volatility 1D')\\n if plot_sentiment:\\n plt.subplot(212)\\n #plt.axvline(0, color='k', linestyle='dashed')\\n plt.xlabel('Days to the event')\\n plt.ylabel('Sentiment abs. z-score')\\n handles, labels = plt.gca().get_legend_handles_labels()\\n by_label = OrderedDict(zip(labels, handles))\\n plt.grid(True)\\n plt.legend(by_label.values(), by_label.keys(), prop={'size': 6}, loc='upper right')\\n\\n\\ndef plot_x_events(leading_name, group_summary, sector_summary=None, test_chosen='t-test', specialOnly=False, mode='rtn'):\\n for name, summary in group_summary.items():\\n if name == leading_name: isSpecial = True\\n elif name == 'Average':\\n isSpecial = True\\n name = 'GroupAverage'\\n else: isSpecial = False\\n plot_events(summary, descps=[name], test_chosen=test_chosen, mode=mode,\\n special_line=isSpecial, plot_special_only=specialOnly)\\n if sector_summary is not None:\\n for name, summary in sector_summary.items():\\n if name == 'Average':\\n isSpecial = True\\n name = 'SectorAverage'\\n else:\\n isSpecial = False\\n plot_events(summary, descps=[name], test_chosen=test_chosen, mode=mode,\\n special_line=isSpecial, plot_special_only=True)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.705568253993988,\n \"alphanum_fraction\": 0.7058225274085999,\n \"avg_line_length\": 47.567901611328125,\n \"blob_id\": \"0ca6ea2c37aa328ac24f2413ca0e021a4ee4cbc3\",\n \"content_id\": \"eec2f25f0f6e3218021ab0239d0c8bc2fe5e3217\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3933,\n \"license_type\": \"no_license\",\n \"max_line_length\": 120,\n \"num_lines\": 81,\n \"path\": \"/PycharmProjects/OMI/grouper.py\",\n \"repo_name\": \"xingchenwan/Sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Grouper module: generate different classification of the entities, either based on dynamic cosine distance in relevant\\n# co-occurrence matrices or by static sector categorisation\\n# Xingchen Wan | Xingchen.Wan@st-annes.ox.ac.uk | Oxford-Man Institute of Quantitative Finance\\n\\nimport datetime\\nimport pandas as pd\\nfrom source.all_classes import *\\nimport community\\nfrom utilities import *\\n\\n\\ndef get_dynamic_grouping(names, full_data_obj, start_date=None, end_date=None,save_to_csv=True):\\n \\\"\\\"\\\"\\n Compute the dynamic grouping dictionary of a series of single names based on the cosine-distance of the entities\\n within the specified period of time\\n :param start_date: optional. If none, the start date will be taken to be the same as the starting date of the\\n full_data object\\n :param end_date: optional. Same as above\\n :return: Dict mapping names to their dynamic grouping\\n \\\"\\\"\\\"\\n if start_date and not isinstance(start_date, datetime.date):\\n raise TypeError(\\\"Start time needs to be a date object\\\")\\n if end_date and not isinstance(end_date, datetime.date):\\n raise TypeError(\\\"End time needs to be a date object\\\")\\n if not isinstance(full_data_obj, FullData):\\n raise TypeError(\\\"full_data_obj needs to be a FullData object\\\")\\n if isinstance(names, pd.Series):\\n names = names.values.tolist()\\n start_date = start_date if start_date and start_date >= full_data_obj.start_date else full_data_obj.start_date\\n end_date = end_date if end_date and end_date <= full_data_obj.end_date else full_data_obj.end_date\\n # Sanity Checks\\n\\n full_data_sub = create_sub_obj(full_data_obj, start_date, end_date)\\n G = full_data_sub.build_occurrence_network_graph(focus_iterable=names,)\\n partition = community.best_partition(G, weight='weight') #, resolution=7)\\n if save_to_csv:\\n pd.Series(partition).to_csv('dynamic_grouping'+str(start_date)+str(end_date)+'.csv')\\n return partition\\n\\n\\ndef get_sector_grouping(names, path, name_col, sector_col):\\n \\\"\\\"\\\"\\n Get the ground truth static sector grouping dictionary of a series of single names. If there are any names in\\n 'names' argument that do not have a sector grouping in the lookup csv file, these names will be assigned an\\n \\\"unclassified\\\" grouping in the output dictionary\\n :param path: str. path string of the csv file containing the sector information\\n :param name_col: str or int. If str, this argument is interpreted as the name of the column. If int,\\n this arguement is interpreted as the column number\\n :param sector_col: Same as above for the sector column\\n :return: Dict mapping names to their sector grouping\\n \\\"\\\"\\\"\\n data = pd.read_csv(path, header=None)\\n if isinstance(name_col, str):\\n assert name_col in list(data.columns)\\n name_column = data[name_col]\\n elif isinstance(name_col, int):\\n assert name_col <= len(data.columns)\\n name_column = data.iloc[:, name_col]\\n else:\\n raise TypeError(\\\"Invalid name column data type\\\")\\n\\n if isinstance(sector_col, str):\\n assert sector_col in list(data.columns)\\n sector_column = data[sector_col]\\n elif isinstance(sector_col, int):\\n assert sector_col <= len(data.columns)\\n sector_column = data.iloc[:, sector_col]\\n else:\\n raise TypeError(\\\"Invalid sector column data type\\\")\\n\\n sector_lookup = pd.Series(sector_column.values, index=name_column).to_dict()\\n # print(\\\"Names not found in the lookup:\\\", [i for i in names if i not in sector_lookup.keys()])\\n return {name: sector_lookup[name] if name in sector_lookup.keys() else 'Unclassified' for name in names}\\n\\n\\ndef get_sector_peer(sector_dict, name):\\n if not isinstance(sector_dict, dict):\\n raise TypeError(\\\"sector_dict argument needs to be a dictionary.\\\")\\n if name not in sector_dict:\\n raise ValueError(\\\"name not found in sector_dict\\\")\\n return [key for key, value in sector_dict.items() if value == sector_dict[name]]\"\n },\n {\n \"alpha_fraction\": 0.5569639801979065,\n \"alphanum_fraction\": 0.5712571740150452,\n \"avg_line_length\": 43.58139419555664,\n \"blob_id\": \"d5ecdaf349d05fc7d6d2a4538211b786fb2242a1\",\n \"content_id\": \"92a6dff438e044f5d6655eede9522d88975cfc47\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 19170,\n \"license_type\": \"no_license\",\n \"max_line_length\": 192,\n \"num_lines\": 430,\n \"path\": \"/PycharmProjects/OMI/source/all_classes.py\",\n \"repo_name\": \"xingchenwan/Sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n# @Author: Jie Yang\\n# @Date: 2018-08-20 14:52:25\\n# @Last Modified by: Jie Yang, Contact: jieynlp@gmail.com\\n# @Last Modified time: 2018-08-23 23:58:10\\n\\n# Modified by Xingchen Wan on 3 Sep to make the file Python 3 compatible\\n\\nimport os\\n\\n# import community\\nimport networkx as nx\\nimport numpy as np\\nimport copy\\n\\nfrom source.utils import *\\n\\nignore_entity_dict = {\\\"ADDITIONAL\\\", \\\"REUTERS\\\", \\\"JOURNAL\\\", \\\"U\\\", \\\"CO.\\\", \\\"ALISTER BULL\\\", \\\"EDITING\\\", \\\"REUTERS ESTIMATES\\\",\\n \\\"REPORTING\\\", \\\"COMPILED BY ASIA COMMODITIES\\\"}\\n\\n\\nclass News:\\n def __init__(self, news_dir):\\n self.null_news = False\\n self.title = \\\"\\\"\\n self.dir = news_dir\\n self.newsID = -1\\n self.date = datetime.date(1929, 10, 12)\\n self.date_format = \\\"EDT\\\" # EDT/EST\\n self.time = datetime.time(0, 0, 0)\\n self.entity_occur = {}\\n self.entity_sentiment = {}\\n self.url = \\\"\\\"\\n # self.token_sentence = []\\n self.load_file(news_dir)\\n\\n def load_file(self, news_dir):\\n if os.path.isfile(news_dir):\\n the_news = read_news_file(news_dir)\\n if the_news == []:\\n self.null_news = True\\n else:\\n self.title = the_news['title']\\n self.date = the_news['date']\\n self.time = the_news['time']\\n self.url = the_news['url']\\n # self.token_sentence = the_news['sentences']\\n\\n def extract_token_sentences(self):\\n if os.path.isfile(self.dir):\\n the_news = read_news_file(self.dir)\\n if the_news == []:\\n return []\\n else:\\n return the_news['sentences']\\n return []\\n\\n def build_entity_sentiment(self, entity_sentiment_list):\\n \\\"\\\"\\\"\\n input:\\n entity_sentiment_list in one news: [[entity1, sentiment1], [entity2, sentiment2],[entity3, sentiment3]], entity may duplicate, and sentiment = {-1,0,1}\\n \\\"\\\"\\\"\\n entity_dict = {}\\n for each_list in entity_sentiment_list:\\n entity, sentiment = each_list\\n sentiment = int(sentiment)\\n # # entity = company_name_normalize(entity)\\n # if (len(name_mapping_dict) > 0) and (entity in name_mapping_dict):\\n # \\tentity = company_name_normalize(name_mapping_dict[entity])\\n # entity = entity.upper()\\n if entity in entity_dict:\\n entity_dict[entity][0] += 1\\n entity_dict[entity][1] += sentiment\\n else:\\n entity_dict[entity] = [1, sentiment]\\n for entity, num_sent in entity_dict.items():\\n avg_sentiment = (num_sent[1] + 0.) / num_sent[0]\\n avg_sentiment = round(avg_sentiment, 3)\\n self.entity_occur[entity] = num_sent[0]\\n self.entity_sentiment[entity] = avg_sentiment\\n\\n\\nclass Day:\\n def __init__(self, day_folder, each_date):\\n self.null_today = False\\n self.date = each_date\\n self.day_news = []\\n self.news_number = 0\\n self.load_folder(day_folder)\\n self.entity_occur_day = {}\\n self.entity_sentiment_day = {}\\n\\n def load_folder(self, day_folder):\\n for path, subdirs, files in os.walk(day_folder):\\n for name in files:\\n the_file = os.path.join(path, name)\\n the_news = News(the_file)\\n if (the_news.null_news == False) and (the_news.date == self.date):\\n self.day_news.append(the_news)\\n if len(self.day_news) == 0:\\n self.null_today = True\\n self.news_number = len(self.day_news)\\n\\n def assign_sentence_to_news(self, sentence_sentiment):\\n \\\"\\\"\\\"\\n input:\\n sentence_sentiment in one day: [[sentence, entity, sentiment1], [sentence, entity, sentiment1]...], entity may duplicate, and sentiment = {-1,0,1}\\n name_mapping_dict: convert entity names\\n \\\"\\\"\\\"\\n # load sentence dict\\n sentence_dict = {}\\n entity_sentiment_for_news = []\\n for idx in range(len(self.day_news)):\\n entity_sentiment_for_news.append([])\\n token_sentences = self.day_news[idx].extract_token_sentences()\\n for each_sent in token_sentences:\\n sentence_dict[each_sent] = idx\\n\\n ### assign entity/sentiment pair to news based on sentence\\n count_assigned_sentence = 0\\n for sentence, entity, sentiment in sentence_sentiment:\\n if sentence in sentence_dict:\\n sentence_pos = sentence_dict[sentence]\\n entity_sentiment_for_news[sentence_pos].append([entity, sentiment])\\n count_assigned_sentence += 1\\n\\n # print(\\\"Total sentiment sentence: %s, assigned sentence: %s, assign rate: %s\\\"%(len(sentence_sentiment), count_assigned_sentence, (count_assigned_sentence+0.)/len(sentence_sentiment)))\\n # print(\\\"Total sentence in date: %s is %s\\\"%(self.date.strftime('%Y%m%d'), len(sentence_dict)))\\n ### for each news, calculate entity_occur and entity_sentiment\\n for idx in range(len(self.day_news)):\\n self.day_news[idx].build_entity_sentiment(entity_sentiment_for_news[idx])\\n\\n ## calculate entity occur num and avg sentiment within one day\\n total_sentiment = {}\\n for each_news in self.day_news:\\n for entity, num in each_news.entity_occur.items():\\n if entity in self.entity_occur_day:\\n self.entity_occur_day[entity] += num\\n total_sentiment[entity] += num * each_news.entity_sentiment[entity]\\n else:\\n self.entity_occur_day[entity] = num\\n total_sentiment[entity] = num * each_news.entity_sentiment[entity]\\n for entity, num in self.entity_occur_day.items():\\n self.entity_sentiment_day[entity] = total_sentiment[entity] / num\\n\\n\\nclass FullData:\\n def __init__(self, data_folder, start_date=datetime.date(1929, 10, 12), end_date=datetime.date(2029, 10, 12)):\\n self.start_date = start_date\\n self.end_date = end_date\\n self.entity2id = {}\\n self.id2entity = {}\\n self.days = []\\n self.date2daysID = {}\\n self.news_number = 0\\n self.data_folder = data_folder\\n self.load_full_data(data_folder)\\n self.entity_occur_interval = {}\\n self.entity_sentiment_interval = {}\\n\\n\\n def load_full_data(self, date_folder):\\n '''\\n input:\\n\\t\\t\\tdate_folder: dir of the whole data, e.g. \\\"../../ReutersNews106521/\\\"\\n\\t\\t'''\\n print(\\\"Load folder: %s, from [date %s to date %s)\\\" % (\\n date_folder, self.start_date.strftime('%Y/%m/%d'), self.end_date.strftime('%Y/%m/%d')))\\n date_list = []\\n for path, subdirs, files in os.walk(date_folder):\\n if files == [\\\".DS_Store\\\"]:\\n date_list = subdirs\\n break\\n if len(date_list) == 0:\\n print(\\\"No subfolder founded!\\\")\\n exit(0)\\n\\n count_days = 0\\n for each_date_string in date_list:\\n each_date = string2date(each_date_string)\\n if each_date >= self.start_date and each_date < self.end_date:\\n sub_folder_dir = os.path.join(path, each_date_string)\\n the_day = Day(sub_folder_dir, each_date)\\n self.days.append(the_day)\\n self.date2daysID[the_day.date] = len(self.days) - 1\\n self.news_number += the_day.news_number\\n if count_days % 30 == 0:\\n print(\\\"\\tLoading... loaded days:\\\", count_days)\\n count_days += 1\\n print(\\\"Days number: %s\\\" % (len(self.days)))\\n\\n def load_sentiment_result(self, sentiment_in, sentiment_out, sentence_mapping_file, entity_mapping_file):\\n # load files\\n abbreviation_dict = reform_abbreviate_name_dict(entity_mapping_file)\\n sentiment_map_dict = load_sentence_map_file(sentence_mapping_file)\\n sentiment_in_lines = open(sentiment_in, 'r').readlines()\\n sentiment_out_lines = open(sentiment_out, 'r').readlines()\\n line_num = len(sentiment_in_lines)\\n assert (line_num == len(sentiment_out_lines))\\n # read each line and assign [sentence, entity, sentiment] to each Day\\n sentence_entity_sentiment_in_all = []\\n for idx in range(len(self.days)):\\n sentence_entity_sentiment_in_all.append([])\\n\\n current_date = datetime.date(1929, 10, 12)\\n for idx in range(line_num):\\n line = sentiment_in_lines[idx]\\n if \\\"#DATE:data/\\\" in line:\\n current_date = string2date(file_name_alphabet2number(line.split(\\\" \\\")[0][-8:]))\\n continue\\n else:\\n sentence, entity = read_sentiment_in_line(line)\\n if current_date >= self.start_date and current_date < self.end_date:\\n if (\\\"(Additional reporting by\\\" not in sentence) and (\\\"(Reporting by\\\" not in sentence) and (\\n \\\"COMPILED BY\\\" not in sentence.upper()):\\n ## convert sentence\\n if sentence in sentiment_map_dict:\\n sentence = sentiment_map_dict[sentence]\\n entity = \\\" \\\".join(entity)\\n if entity in abbreviation_dict:\\n entity = abbreviation_dict[entity]\\n if \\\" \\\" not in entity:\\n entity = remove_stock_abbre_suffix(entity)\\n entity = entity.upper()\\n if entity == \\\"LEHMAN\\\":\\n entity = \\\"LEHMQ\\\"\\n if (entity not in ignore_entity_dict) and len(entity) > 1 and (\\\"000\\\" not in entity):\\n days_id = self.date2daysID[current_date]\\n sentiment = int(sentiment_out_lines[idx].strip(\\\"\\\\n\\\"))\\n sentence_entity_sentiment_in_all[days_id].append([sentence, entity, sentiment])\\n\\n ### for each day, calculate the entity/sentiment\\n for idx in range(len(self.days)):\\n # print self.days[idx].date\\n self.days[idx].assign_sentence_to_news(sentence_entity_sentiment_in_all[idx])\\n ### extract entity_avg_sentiment for time interval\\n total_sentiment = {}\\n for each_day in self.days:\\n for entity, num in each_day.entity_occur_day.items():\\n if entity in self.entity_occur_interval:\\n self.entity_occur_interval[entity] += num\\n total_sentiment[entity] += num * each_day.entity_sentiment_day[entity]\\n else:\\n self.entity_occur_interval[entity] = num\\n total_sentiment[entity] = num * each_day.entity_sentiment_day[entity]\\n for entity, num in self.entity_occur_interval.items():\\n self.entity_sentiment_interval[entity] = total_sentiment[entity] / num\\n\\n def show_information(self):\\n for each_day in self.days:\\n for each_news in each_day.day_news:\\n print(each_day.date, each_news.entity_occur, each_news.entity_sentiment)\\n\\n def count_entity_occurance_news(self, top=-1):\\n # count the number of entity occurs in news. Notice it only count how many news mentioned entity, not count the occurance time\\n entity_count_dict = {}\\n for each_day in self.days:\\n for each_news in each_day.day_news:\\n for entity, occ in each_news.entity_occur.items():\\n if entity in entity_count_dict:\\n entity_count_dict[entity] += 1\\n else:\\n entity_count_dict[entity] = 1\\n # print_dict(entity_count_dict)\\n # print(\\\"Total news number:\\\", self.news_number)\\n d_view = [(v, k) for k, v in entity_count_dict.items()]\\n d_view.sort(reverse=True) # natively sort tuples by first element\\n if top > 0:\\n if len(d_view) > top:\\n return d_view[:top]\\n else:\\n return d_view\\n else:\\n return d_view\\n\\n def build_occurrence_network_graph(self, top=-1, focus_iterable=None, weight_threshold=None):\\n entity_occ_list = self.count_entity_occurance_news(top)\\n entity_list = []\\n for num, entity in entity_occ_list:\\n if len(focus_iterable) > 0:\\n if entity in focus_iterable:\\n entity_list.append(entity)\\n else:\\n entity_list.append(entity)\\n ## entity_occ_list: [[num, entity], [num, entity]]\\n entity_num = len(entity_list)\\n occurance_array = np.zeros((entity_num, self.news_number))\\n sentiment_array = np.zeros((entity_num, self.news_number))\\n entity2ID = {}\\n ID2entity = {}\\n for idx in range(entity_num):\\n entity = entity_list[idx]\\n entity2ID[entity] = idx\\n ID2entity[idx] = entity\\n\\n ## build the entity-news matrix\\n newsID = 0\\n for each_day in self.days:\\n for each_news in each_day.day_news:\\n overlap_entity_set = set(each_news.entity_occur.keys()) & set(entity_list)\\n for each_entity in overlap_entity_set:\\n the_occ = each_news.entity_occur[each_entity]\\n the_sentiment = each_news.entity_sentiment[each_entity]\\n enity_id = entity2ID[each_entity]\\n occurance_array[enity_id, newsID] = the_occ\\n sentiment_array[enity_id, newsID] = the_sentiment\\n newsID += 1\\n print(\\\"Occurrence full shape:\\\", occurance_array.shape)\\n # print occurance_array\\n # print sentiment_array\\n # print np.count_nonzero(occurance_array)\\n # print np.count_nonzero(sentiment_array)\\n\\n ## calculate degree/centrality/community\\n # build networkX graph\\n occ_edge_list = []\\n\\n for s_id in range(entity_num - 1):\\n for t_id in range(s_id + 1, entity_num):\\n weight = cosine_distance(occurance_array[s_id], occurance_array[t_id])\\n if weight > 0:\\n s_entity = ID2entity[s_id]\\n t_entity = ID2entity[t_id]\\n occ_edge_list.append((s_entity, t_entity, weight))\\n occ_graph = nx.Graph()\\n occ_graph.add_weighted_edges_from(occ_edge_list)\\n return occ_graph\\n\\n def get_group_avg_sentiment(self, start_date, end_date, entity_list):\\n if start_date < self.start_date or end_date >= self.end_date:\\n print(\\\"Error: Date exceed the interval!\\\")\\n exit(0)\\n\\n if len(self.entity_occur_interval) == 0:\\n print(\\\"Error: Empty entity sentiment information in interval! Please run load_sentiment_result first!\\\")\\n exit(0)\\n ## convert to list if entity_list is dict\\n if isinstance(entity_list, dict):\\n entity_list = entity_list.keys()\\n\\n ### extract the entity/sentiment in focus date interval\\n focus_total_sentiment = {}\\n focus_entity_occur_interval = {}\\n focus_entity_sentiment_interval = {}\\n for single_date in daterange(start_date, end_date):\\n if single_date not in self.date2daysID: ## 2013/08/18 does not have news\\n continue\\n the_day_idx = self.date2daysID[single_date]\\n each_day = self.days[the_day_idx]\\n for entity, num in each_day.entity_occur_day.items():\\n if entity in focus_entity_occur_interval:\\n focus_entity_occur_interval[entity] += num\\n focus_total_sentiment[entity] += num * each_day.entity_sentiment_day[entity]\\n else:\\n focus_entity_occur_interval[entity] = num\\n focus_total_sentiment[entity] = num * each_day.entity_sentiment_day[entity]\\n for entity, num in focus_entity_occur_interval.items():\\n focus_entity_sentiment_interval[entity] = focus_total_sentiment[entity] / num\\n\\n ## calculate group sentiment\\n total_sentiment = 0\\n total_number = 0\\n for entity in entity_list:\\n if entity not in focus_entity_occur_interval:\\n print(\\\"Error: Cannot get entity sentiment information! Entity:\\\", entity)\\n continue\\n else:\\n total_sentiment += focus_entity_occur_interval[entity] * focus_entity_sentiment_interval[entity]\\n total_number += focus_entity_occur_interval[entity]\\n if total_number == 0:\\n return -2, 0\\n return total_sentiment / total_number, total_number\\n\\n\\ndef get_coexisting_entity(start_date, month_interval, top):\\n sentiment_in = \\\"../data/2006-2013.content.filter.senti.in\\\"\\n sentiment_out = \\\"../data/2006-2013.content.filter.senti.out\\\"\\n sentence_map = \\\"../data/2006-2013.content.ner.out.filter.map\\\"\\n abbreviation_file = \\\"../data/abbreviation_pairs.pkl\\\"\\n all_dict = []\\n for idx in range(30):\\n end_date = add_months(start_date, month_interval)\\n if end_date > datetime.date(2013, 10, 20):\\n break\\n full_date = FullData(\\\"../../ReutersNews106521/\\\", start_date, end_date)\\n full_date.load_sentiment_result(sentiment_in, sentiment_out, sentence_map, abbreviation_file)\\n current_list = full_date.count_entity_occurance_news(top)\\n current_dict = {}\\n current_set = set()\\n for num, ent in current_list:\\n current_dict[ent] = num\\n current_set.add(ent)\\n\\n if idx == 0:\\n entity_set = current_set\\n else:\\n entity_set = entity_set & current_set\\n all_dict.append(current_dict)\\n start_date = end_date\\n print(\\\"current accumulate entity set size: \\\", len(entity_set))\\n print(entity_set)\\n print(len(entity_set))\\n entity_interval_occ = {}\\n for entity in entity_set:\\n entity_interval_occ[entity] = []\\n for each_dict in all_dict:\\n entity_interval_occ[entity].append(each_dict[entity])\\n print(entity_interval_occ)\\n print(len(entity_interval_occ))\\n fout = open(\\\"coexisting_entity.\\\" + str(month_interval) + \\\".txt\\\", 'w')\\n for k, v in entity_interval_occ.items():\\n fout.write(k + \\\": \\\" + ' '.join(str(e) for e in v) + \\\"\\\\n\\\")\\n\\n\\nif __name__ == '__main__':\\n sentiment_in = \\\"../data/2006-2013.content.filter.senti.in\\\"\\n sentiment_out = \\\"../data/2006-2013.content.filter.senti.out\\\"\\n sentence_map = \\\"../data/2006-2013.content.ner.out.filter.map\\\"\\n abbreviation_file = \\\"../data/abbreviation_pairs.pkl\\\"\\n\\n # the_day = Day(\\\"../../ReutersNews106521/20061020/\\\")\\n # print the_day.date\\n # print len(the_day.day_news)\\n full_date = FullData(\\\"../../ReutersNews106521/\\\", datetime.date(2007, 1, 1), datetime.date(2007, 4, 1))\\n\\n # get_coexisting_entity(datetime.date(2007,1,1), 3, 1000)\\n full_date.load_sentiment_result(sentiment_in, sentiment_out, sentence_map, abbreviation_file)\\n focus_dict = load_focus_dict(\\\"coexisting_entity.3month_clean.sector.manual.txt\\\")\\n full_date.build_occurence_network(-1, focus_dict)\\n # print full_date.count_entity_occurance_news(200)\\n\"\n },\n {\n \"alpha_fraction\": 0.8036072254180908,\n \"alphanum_fraction\": 0.8156312704086304,\n \"avg_line_length\": 51.52631759643555,\n \"blob_id\": \"6ec7dabf4b186cbcd6f7eda7017cc9d5a3ed301d\",\n \"content_id\": \"2beb417ec01ba79306b77d311d65dd7fa78f72fa\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 998,\n \"license_type\": \"no_license\",\n \"max_line_length\": 446,\n \"num_lines\": 19,\n \"path\": \"/README.md\",\n \"repo_name\": \"xingchenwan/Sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Sentiment\\nSummer Internship Project\\n\\nA project aiming to explore the relationships between sentiment of the news (Reuter news for this case, via NLP analysis of the textual information) and market reactions, including but not limited to:\\n1) Price\\n\\n2) Volatilities\\n\\nInvestigations include both 1) relationship between market reaction of the entity from its *own* sentiment and 2) the relationship between market reaction to sentiment of its peers.\\n\\nThe peer relationship was established via both the static ground-truth sector grouping and the co-occurrence in the news. Entities co-appearing frequently will subsequently have a higher cosine distance and a higher similarities, and will be regarded as neighbours in the networks subsequently generated. The market reactions due to sentiment events of the central entities (judged upon their centrality position) were given particular attention.\\n\\nXingchen Wan | xingchen.wan@st-annes.ox.ac.uk\\n\\nOxford-Man Institute of Quantitative Finance,\\n\\nUniversity of Oxford \\n\\n2018 - 2019\\n\"\n },\n {\n \"alpha_fraction\": 0.5450525879859924,\n \"alphanum_fraction\": 0.5688316822052002,\n \"avg_line_length\": 34.708770751953125,\n \"blob_id\": \"c7af772189c99316938b7e6a1d216fa98d0d331f\",\n \"content_id\": \"691d67d29ba8a1a431fb1458b10583c7d1f3a9e6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 10177,\n \"license_type\": \"no_license\",\n \"max_line_length\": 119,\n \"num_lines\": 285,\n \"path\": \"/PycharmProjects/OMI/source/utils.py\",\n \"repo_name\": \"xingchenwan/Sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n# @Author: Jie Yang\\n# @Date: 2018-08-20 14:52:25\\n# @Last Modified by: Jie Yang, Contact: jieynlp@gmail.com\\n# @Last Modified time: 2018-08-23 14:51:54\\n\\n# Modified by Xingchen Wan on 3 Sep to make the file Python 3 compatible\\n\\nimport datetime\\nimport nltk\\nimport re\\nfrom os.path import isfile, join\\nimport numpy as np\\nfrom numpy.linalg import norm\\n# import cPickle as pickle\\nimport pickle\\nimport calendar\\n\\nmonth_to_num = {\\\"Jan\\\": 1, \\\"Feb\\\": 2, \\\"Mar\\\": 3, \\\"Apr\\\": 4, \\\"May\\\": 5, \\\"Jun\\\": 6, \\\"Jul\\\": 7, \\\"Aug\\\": 8, \\\"Sep\\\": 9, \\\"Oct\\\": 10,\\n \\\"Nov\\\": 11, \\\"Dec\\\": 12}\\n\\n\\ndef read_news_file(news_file):\\n fins = open(news_file, 'r').readlines()\\n news = {}\\n line_num = len(fins)\\n content = \\\"\\\"\\n if line_num < 1:\\n return []\\n for idx in range(line_num):\\n if idx == 0:\\n news[\\\"title\\\"] = [re.sub('\\\\d', '0', fins[idx].strip('-- \\\\n'))]\\n elif idx == 1:\\n continue\\n elif idx == 2:\\n # print type(fins[idx])\\n alltimes = fins[idx].strip('-- \\\\n').split() ## 'Mon Oct 30, 2006 1:55pm EST'.split()\\n assert (len(alltimes) == 6)\\n if alltimes[1] not in month_to_num:\\n print(\\\"Month convert error!\\\", \\\"Month string: \\\", alltimes[1], \\\" at file:\\\", news_file)\\n exit(0)\\n month = month_to_num[alltimes[1]]\\n day = int(alltimes[2].strip(','))\\n year = int(alltimes[3])\\n news['date'] = datetime.date(year, month, day)\\n hour = int(alltimes[4].split(\\\":\\\")[0])\\n if alltimes[4][-2:] == \\\"pm\\\":\\n if hour < 12:\\n hour += 12\\n minute = int(alltimes[4].split(\\\":\\\")[1][:-2])\\n news['time'] = datetime.time(hour, minute)\\n\\n elif idx == 3:\\n news[\\\"url\\\"] = fins[idx].strip('-- \\\\n')\\n else:\\n each_line = fins[idx].strip('-- \\\\n')\\n if \\\"(Reuters) -\\\" in each_line:\\n each_line = each_line.split(\\\"(Reuters) -\\\")[-1]\\n if len(each_line) > 0:\\n content += each_line + \\\" \\\"\\n origin_content = nltk.sent_tokenize(re.sub('\\\\d', '0', content))\\n sentence = news['title'] + origin_content\\n token_sentences = []\\n for sent in sentence:\\n new_sent = \\\" \\\".join(nltk.word_tokenize(sent))\\n token_sentences.append(new_sent)\\n news[\\\"sentences\\\"] = token_sentences\\n return news\\n\\n\\ndef add_months(sourcedate, months):\\n month = sourcedate.month - 1 + months\\n year = sourcedate.year + month // 12\\n month = month % 12 + 1\\n day = min(sourcedate.day, calendar.monthrange(year, month)[1])\\n return datetime.date(year, month, day)\\n\\n\\ndef cosine_distance(np_a, np_b):\\n # print np_a, np_b\\n cos_sim = np.inner(np_a, np_b) / (norm(np_a) * norm(np_b))\\n return cos_sim\\n\\n\\ndef print_dict(input_dict):\\n d_view = [(v, k) for k, v in input_dict.iteritems()]\\n d_view.sort(reverse=True) # natively sort tuples by first element\\n for v, k in d_view:\\n print(\\\"%s: %d\\\" % (k, v))\\n\\n\\ndef file_name_alphabet2number(input_string):\\n convert_dict = {\\\"A\\\": \\\"0\\\", \\\"B\\\": \\\"1\\\", \\\"C\\\": \\\"2\\\", \\\"D\\\": \\\"3\\\", \\\"E\\\": \\\"4\\\", \\\"F\\\": \\\"5\\\", \\\"G\\\": \\\"6\\\", \\\"H\\\": \\\"7\\\", \\\"J\\\": \\\"8\\\", \\\"K\\\": \\\"9\\\"}\\n # convert_dict = {\\\"0\\\":\\\"A\\\", \\\"1\\\":\\\"B\\\", \\\"2\\\":\\\"C\\\", \\\"3\\\":\\\"D\\\", \\\"4\\\":\\\"E\\\", \\\"5\\\":\\\"F\\\", \\\"6\\\":\\\"G\\\", \\\"7\\\":\\\"H\\\", \\\"8\\\":\\\"J\\\", \\\"9\\\":\\\"K\\\", }\\n\\n if len(input_string) < 8:\\n print(\\\"wrong name:\\\", input_string)\\n exit(0)\\n out = input_string[0:-8]\\n for c in input_string[-8:]:\\n out += convert_dict[c]\\n return out\\n\\n\\ndef load_company_mapping(company_name_file):\\n print(\\\"Load company mapping:\\\", company_name_file)\\n name_mapping = {}\\n fins = open(company_name_file, 'r').readlines()\\n for line in fins:\\n pair = line.strip('\\\\n').split(': ', 1)\\n name_mapping[pair[0]] = pair[1]\\n print(\\\"Company mapping size:\\\", len(name_mapping))\\n return name_mapping\\n\\n\\ndef string2date(input_string):\\n assert (len(input_string) == 8)\\n year = int(input_string[:4])\\n month = int(input_string[4:6])\\n day = int(input_string[6:])\\n return datetime.date(year, month, day)\\n\\n\\ndef company_name_normalize(input_company_name):\\n input_company_name = sepecial_stock_token(input_company_name).upper()\\n\\n if \\\", INC\\\" in input_company_name:\\n input_company_name = input_company_name.split(', INC')[0]\\n elif \\\" INC\\\" in input_company_name:\\n input_company_name = input_company_name.split(' INC')[0]\\n if \\\" 'S\\\" in input_company_name:\\n input_company_name = input_company_name.split(\\\" 'S\\\")[0]\\n if \\\" CORP.\\\" in input_company_name:\\n input_company_name = input_company_name.split(\\\" CORP.\\\")[0]\\n elif \\\" Corp\\\" == input_company_name[-5:]:\\n input_company_name = input_company_name[:-5]\\n elif \\\" CORPORATION\\\" in input_company_name:\\n input_company_name = input_company_name.split(\\\" CORPORATION\\\")[0]\\n elif \\\" CO.\\\" in input_company_name:\\n input_company_name = input_company_name.split(\\\" CO.\\\")[0]\\n elif input_company_name[-3:] == \\\" CO\\\":\\n input_company_name = input_company_name[:-3]\\n elif \\\" COS.\\\" in input_company_name:\\n input_company_name = input_company_name.split(\\\" COS.\\\")[0]\\n if \\\" COMPANY\\\" in input_company_name:\\n input_company_name = input_company_name.rsplit(\\\" COMPANY\\\", 1)[0]\\n elif \\\" GROUP\\\" in input_company_name:\\n input_company_name = input_company_name.rsplit(\\\" GROUP\\\", 1)[0]\\n if \\\"&\\\" in input_company_name:\\n input_company_name = input_company_name.replace(\\\"&\\\", \\\"&\\\")\\n if \\\" P.L.C.\\\" == input_company_name[-7:]:\\n input_company_name = input_company_name[:-7]\\n elif \\\" PLC\\\" == input_company_name[-4:]:\\n input_company_name = input_company_name[:-4]\\n elif \\\" LIMITED\\\" == input_company_name[-8:]:\\n input_company_name = input_company_name[:-8]\\n elif \\\" LTD\\\" == input_company_name[-4:]:\\n input_company_name = input_company_name[:-4]\\n elif \\\" LTD/PLC\\\" == input_company_name[-8:]:\\n input_company_name = input_company_name[:-8]\\n\\n if \\\" &\\\" == input_company_name[-2:]:\\n input_company_name = input_company_name[:-2]\\n elif \\\" & CO\\\" == input_company_name[-5:]:\\n input_company_name = input_company_name[:-5]\\n elif \\\" LP\\\" == input_company_name[-3:]:\\n input_company_name = input_company_name[:-3]\\n elif \\\" LLC\\\" == input_company_name[-4:]:\\n input_company_name = input_company_name[:-4]\\n elif \\\" PLC\\\" == input_company_name[-4:]:\\n input_company_name = input_company_name[:-4]\\n elif \\\" LP\\\" == input_company_name[-3:]:\\n input_company_name = input_company_name[:-3]\\n if \\\" AND \\\" in input_company_name:\\n input_company_name = input_company_name.replace(\\\" AND \\\", \\\" & \\\")\\n return input_company_name\\n\\n\\ndef sepecial_stock_token(input_token):\\n if len(input_token) < 4:\\n return input_token\\n elif input_token[-1].isupper() and (input_token[-2] == \\\".\\\") and (input_token[:-2].isupper()):\\n return input_token[:-2]\\n elif input_token[-1].isupper() and (input_token[-2] == \\\"^\\\") and (input_token[:-2].isupper()):\\n return input_token[:-2]\\n return input_token\\n\\n\\ndef reform_abbreviate_name_dict(input_pairs_pkl):\\n abbre_dict = pickle.load(open(input_pairs_pkl))\\n long2short_dict = {}\\n print(\\\"Total abbreviate pair num:\\\", len(abbre_dict))\\n conflict_num = 0\\n for K, V in abbre_dict.iteritems():\\n if K in [\\\"Reporting\\\", \\\"Additional\\\", \\\"Writing\\\", \\\"Editing\\\"] or (\\\"000\\\" in K) or (')' in K):\\n continue\\n K = remove_stock_abbre_suffix(K)\\n for v in V:\\n if v in long2short_dict:\\n if long2short_dict[v] != K:\\n conflict_num += 1\\n # print v, \\\";\\\",long2short_dict[v],\\\";\\\", K\\n long2short_dict[v] = K\\n print(\\\"Reformed abbreviate dict size:\\\", len(long2short_dict), \\\"conflict_num:\\\", conflict_num)\\n return long2short_dict\\n\\n\\ndef remove_stock_abbre_suffix(input_string):\\n if input_string.isupper():\\n if \\\".\\\" in input_string:\\n return input_string.split('.')[0]\\n else:\\n return input_string\\n return input_string.upper()\\n\\n\\ndef load_sentence_map_file(sent_map_file):\\n fins = open(sent_map_file, 'r').readlines()\\n new = \\\"\\\"\\n old = \\\"\\\"\\n map_dict = {}\\n for idx in range(len(fins)):\\n if (idx + 1) % 3 == 1:\\n new = fins[idx].strip('\\\\n')\\n elif (idx + 1) % 3 == 2:\\n old = fins[idx].strip('\\\\n')\\n else:\\n map_dict[new] = old\\n print(\\\"Sentence map size:\\\", len(map_dict))\\n return map_dict\\n\\n\\ndef read_sentiment_in_line(line):\\n \\\"\\\"\\\"\\n input: line \\\"Wells Fargo to pay Citi $ 000 million over Wachovia ||| 0 1 ||| 0\\\"\\n output: sentence, entity\\n \\\"\\\"\\\"\\n splits = line.split('|||')\\n sent = splits[0].strip(\\\" \\\")\\n [entity_start, entity_end] = splits[1].strip(\\\" \\\").split()\\n entity = sent.split(\\\" \\\")[int(entity_start):(int(entity_end) + 1)]\\n return sent, entity\\n\\n\\ndef load_focus_dict(focus_file):\\n fins = open(focus_file, 'r').readlines()\\n focus_dict = {}\\n for line in fins:\\n entity = line.split(\\\":\\\")[0]\\n full_name = line.split(\\\":\\\")[1]\\n if entity not in focus_dict:\\n focus_dict[entity] = full_name\\n return focus_dict\\n\\n\\ndef daterange(start_date, end_date):\\n for n in range(int((end_date - start_date).days)):\\n yield start_date + datetime.timedelta(n)\\n\\n\\ndef community_to_dicts(community_file):\\n fins = open(community_file, 'r').readlines()\\n entity2id = {}\\n id2community = {}\\n id2noncommunity = {}\\n all_entity_set = set()\\n for line in fins:\\n line = line.strip(\\\"\\\\n\\\")\\n pair = line.split(\\\": \\\")\\n community_id = pair[0]\\n entity_list = pair[1].split(\\\",\\\")\\n for entity in entity_list:\\n entity2id[entity] = community_id\\n id2community[community_id] = entity_list\\n all_entity_set = all_entity_set | set(entity_list)\\n for c_id, community in id2community.items():\\n noncommunity = list(all_entity_set - set(community))\\n id2noncommunity[c_id] = noncommunity\\n return entity2id, id2community, id2noncommunity\\n\\n\\nif __name__ == '__main__':\\n news = read_news_file(\\\"../../ReutersNews106521/20061030/idUSL3090279820061030\\\")\\n for k, v in news.items():\\n print(k, v)\\n\"\n },\n {\n \"alpha_fraction\": 0.6431381702423096,\n \"alphanum_fraction\": 0.6473138928413391,\n \"avg_line_length\": 47.45525360107422,\n \"blob_id\": \"2112d0c0629512018b8b28d3420c79ff93229bf5\",\n \"content_id\": \"9aaafca7ef8512d6cc8374690a9a1c8006236de7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 12453,\n \"license_type\": \"no_license\",\n \"max_line_length\": 129,\n \"num_lines\": 257,\n \"path\": \"/PycharmProjects/OMI/processor.py\",\n \"repo_name\": \"xingchenwan/Sentiment\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Processor module: process relevant market and sentiment data for subsequent analyses\\n# Xingchen Wan | Xingchen.Wan@st-annes.ox.ac.uk | Oxford-Man Institute of Quantitative Finance\\n\\nimport pandas as pd\\nimport numpy as np\\nfrom collections import Counter\\nimport datetime\\n\\n\\ndef process_beta_time_series(path, start_date=None, end_date=None, period=\\\"weekly\\\"):\\n df = pd.read_csv(path)\\n assert all(string in df.columns for string in ['Date', 'Close'])\\n df = df[['Date', 'Close']]\\n # Initialise an datetime index\\n df_start_date = df['Date'].iloc[0]\\n df_end_date = df['Date'].iloc[-1]\\n\\n # Change the datetime string to pandas datetime object\\n df['Date'] = pd.to_datetime(df['Date']).dt.date\\n df = df.set_index('Date')\\n\\n # Set the start and end dates. The start date the later date between the \\\"start_date\\\" argument and the dataframe\\n # start date. The end date is the earlier date between the \\\"end_Date\\\" argument and the dataframe end date\\n start_date = max(df_start_date, start_date) if start_date is not None else df_start_date\\n end_date = min(df_end_date, end_date) if end_date is not None else df_end_date\\n\\n # Create the pandas date index\\n date_idx = pd.date_range(start_date, end_date, freq='D')\\n\\n # Initialise the series to return\\n data = df.reindex(date_idx)\\n data = data.fillna(method='ffill') # Forward fill the data entries\\n\\n # Compute log return, period vol, daily_vol and etc\\n daily_log_return = data.apply(lambda x: np.log(x.shift(-1)) - np.log(x))\\n if period == 'weekly':\\n period_log_return = data.apply(lambda x: np.log(x.shift(-5)) - np.log(x))\\n else:\\n raise TypeError()\\n vol = daily_log_return.abs()\\n return data, daily_log_return, period_log_return, vol\\n\\n\\ndef process_market_time_series(path, sheet=None, start_date=None, end_date=None, period=\\\"weekly\\\",\\n focus_iterable=None):\\n \\\"\\\"\\\"\\n Process the raw price series from Bloomberg\\n :param path: path string of the price series document\\n :param sheet: Sheet name in the excel document\\n :param start_date: datetime.date. Start date of the processed series\\n :param end_date: datetime.date. End date of the processed series\\n :param period: Valid str literal or int. Period to calculate log return. Default is \\\"7D\\\". Recommend to use 7D\\n rather than 7 due to the difference in convention between calendar days and trading days.\\n :return:\\n data: Array-like. the unprocessed price time series\\n daily_log_return: Array-like. the daily log return series\\n period_log_return: Array-like. the log return series in accordance to the set period. Default is weekly log return\\n vol: Array-like. volatility of daily log return in period time. Default is weekly realised volatility\\n \\\"\\\"\\\"\\n import xlrd\\n\\n def read_date(date):\\n \\\"\\\"\\\"\\n In case conversion from the Excel-style datetime is needed - not currently in use\\n :param date: Excel datetime object\\n :return: Pandas-compliant datetime data type\\n \\\"\\\"\\\"\\n return xlrd.xldate.xldate_as_datetime(date, 0)\\n if period == 'weekly':\\n roll_period = '7D'\\n shift_period = 5 # Trading week\\n elif period == 'daily':\\n roll_period = '1D'\\n shift_period = 1\\n elif period == '2d':\\n roll_period = '2d'\\n shift_period = 2\\n elif period == 'monthly':\\n roll_period = '30D'\\n shift_period = 21 # Trading month\\n else:\\n roll_period = str(period) + 'D'\\n shift_period = int(period / 7 * 5)\\n\\n raw = pd.read_excel(path, sheetname=sheet)\\n #print(raw)\\n start_date = start_date if start_date and start_date >= raw.index[1] else raw.index[1]\\n end_date = end_date if end_date and end_date <= raw.index[-1] else raw.index[-1]\\n raw = raw[raw.index >= start_date]\\n data = raw[raw.index <= end_date].astype('float64')\\n if focus_iterable:\\n focus_securities = [x for x in data.columns if x in focus_iterable]\\n data = data[focus_securities]\\n # Truncate the price time series to be consistent with the count and sentiment time series\\n daily_log_return = data.apply(lambda x: np.log(x.shift(-1)) - np.log(x))\\n period_log_return = data.apply(lambda x: np.log(x.shift(-shift_period)) - np.log(x))\\n #daily_log_return = data.apply(lambda x: np.log(x) - np.log(x.shift(1)))\\n #period_log_return = data.apply(lambda x: np.log(x) - np.log(x.shift(shift_period)))\\n #vol = daily_log_return.rolling(roll_period).std().shift(-shift_period) # Forward looking period volatility\\n vol = daily_log_return.abs()\\n #abs_daily_log_return = np.abs(daily_log_return)\\n #abs_period_log_return = np.abs(period_log_return)\\n return data, daily_log_return, period_log_return, vol\\n\\n\\ndef process_count_sentiment(full_data_obj, start_date=None, end_date=None, mode='weekly',\\n rolling=False, rolling_smoothing_factor=0.2, focus_iterable=None):\\n \\\"\\\"\\\"\\n Process the FullData object to obtain corresponding count and sentiment time series\\n :param full_data_obj: The FullData object containing all the unprocessed count and sentiment information.\\n :param start_date: datetime, optional - start time of data. To be a valid argument, the start_date\\n must be later than the start date of the FullData object\\n :param end_date: datetime, optional - end time of the data. To be a valid argument, the end_date must be earlier\\n than the end date of the FullData object\\n :param mode: the period for aggregation. 'weekly','monthly', 'daily'\\n :param focus_iterable: optional [] or {} - only entities in the list will be considered. If blank all entities\\n will be considered\\n :return:\\n \\\"\\\"\\\"\\n\\n def is_end_of_period(day, mode):\\n from pandas.tseries.offsets import MonthEnd\\n\\n if mode == 'monthly': return True if day == day + MonthEnd(0) else False\\n elif mode == 'daily': return True\\n elif mode == 'weekly': return True if day.weekday() == 4 else False # Snap to nearest Friday\\n else: raise ValueError(\\\"Unrecognised mode. Only weekly, daily and monthly modes are currently supported.\\\")\\n\\n def non_zero_median(list_obj):\\n v = np.asarray(list_obj)\\n #v = v[np.nonzero(v)]\\n #return np.nanmedian(v) if len(v) else 0\\n return np.nanmean(v)\\n\\n def moving_avg(list, alpha=rolling_smoothing_factor):\\n series = pd.Series(list).fillna(0, inplace=True)\\n ewm_series = series.ewm(alpha=alpha)\\n return ewm_series.iloc[-1]\\n\\n # Misc sanity checks...\\n if isinstance(start_date, datetime.datetime):\\n start_date = start_date.date() if start_date.date() > full_data_obj.start_date else full_data_obj.start_date\\n elif isinstance(start_date, datetime.date):\\n start_date = start_date if start_date > full_data_obj.start_date else full_data_obj.start_date\\n else:\\n if start_date is not None:\\n print('Invalid start date. Using the FullData object start date.')\\n start_date = full_data_obj.start_date\\n\\n if isinstance(end_date, datetime.datetime):\\n end_date = end_date.date() if end_date.date() < full_data_obj.end_date else full_data_obj.end_date\\n elif isinstance(end_date, datetime.date):\\n end_date = end_date if end_date < full_data_obj.end_date else full_data_obj.end_date\\n else:\\n if end_date is not None:\\n print('Invalid end date. Using the FullData object end date')\\n end_date = full_data_obj.end_date\\n\\n if focus_iterable:\\n if isinstance(focus_iterable, dict):\\n focus_iterable = focus_iterable.keys()\\n elif isinstance(focus_iterable, list):\\n pass\\n else:\\n raise TypeError(\\\"include_list needs to be an iterable of type either list or dictionary\\\")\\n else:\\n focus_iterable = full_data_obj.entity_occur_interval.keys()\\n\\n # Initialise the aggregate list and counter\\n aggregate_count_sum = []\\n aggregate_sentiment_med = []\\n aggregate_sentiment_sum = []\\n date_time_series = []\\n\\n # Initialise the weekly counters\\n period_count_sum = Counter({})\\n period_sentiment_sum = Counter({})\\n period_sentiment_med = Counter({})\\n\\n # Setting appropriate period in terms of CALENDAR days\\n if mode == 'daily': period = 1\\n elif mode == 'weekly': period = 7\\n elif mode == 'monthly': period = 30\\n else: period = 7\\n\\n if rolling:\\n # Rolling weekly counters\\n for day in full_data_obj.days:\\n if day.date >= start_date and day.date <= end_date:\\n date_time_series.append(day.date)\\n day_count_sum = Counter(dict((name, 0) for name in focus_iterable))\\n day_sentiment_med = Counter(dict((name, 0) for name in focus_iterable))\\n day_sentiment_sum = Counter(dict((name, 0) for name in focus_iterable))\\n for name in focus_iterable:\\n if name in day.entity_occur_day.keys():\\n day_count_sum[name] = day.entity_occur_day[name]\\n day_sentiment_med[name] = day.entity_sentiment_day[name]\\n day_sentiment_sum[name] = day.entity_occur_day[name] * day.entity_sentiment_day[name]\\n aggregate_count_sum.append(day_count_sum)\\n aggregate_sentiment_med.append(day_sentiment_med)\\n aggregate_sentiment_sum.append(day_sentiment_sum)\\n\\n else:\\n # Non-rolling counters - weekly counter set at every Friday\\n i = 0\\n for day in full_data_obj.days:\\n if day.date < start_date:\\n continue\\n elif day.date == start_date:\\n i = 0\\n period_count_sum = Counter(dict((name, 0) for name in focus_iterable))\\n period_sentiment_med = Counter(dict((name, []) for name in focus_iterable))\\n period_sentiment_sum = Counter(dict((name, 0) for name in focus_iterable))\\n elif day.date > end_date:\\n break\\n\\n if is_end_of_period(day.date, mode):\\n aggregate_count_sum.append(period_count_sum)\\n aggregate_sentiment_med.append({k: non_zero_median(v) if len(v) else 0 for k, v in period_sentiment_med.items()})\\n aggregate_sentiment_sum.append(period_sentiment_sum)\\n # Re-initialise the periodical sub-totals for the next period\\n date_time_series.append(day.date)\\n period_count_sum = Counter(dict((name, 0) for name in focus_iterable))\\n period_sentiment_med = Counter(dict((name, []) for name in focus_iterable))\\n period_sentiment_sum = Counter(dict((name, 0) for name in focus_iterable))\\n\\n else:\\n for news in day.day_news:\\n for entity in focus_iterable:\\n if entity in news.entity_occur.keys():\\n period_count_sum[entity] += news.entity_occur[entity]\\n period_sentiment_med[entity].append(news.entity_sentiment[entity])\\n period_sentiment_sum[entity] += news.entity_sentiment[entity]*news.entity_occur[entity]\\n i += 1\\n\\n date_time_series = pd.Series(date_time_series)\\n count_time_series = pd.DataFrame(aggregate_count_sum).dropna(how='all')\\n count_time_series[\\\"Time\\\"] = date_time_series\\n count_time_series.set_index('Time', inplace=True)\\n count_time_series.index = pd.to_datetime(count_time_series.index)\\n\\n sentiment_med_time_series = pd.DataFrame(aggregate_sentiment_med).dropna(how='all')\\n sentiment_med_time_series[\\\"Time\\\"] = date_time_series\\n sentiment_med_time_series.set_index('Time', inplace=True)\\n sentiment_med_time_series.index = pd.to_datetime(sentiment_med_time_series.index)\\n # print(sentiment_med_time_series)\\n\\n sentiment_sum_time_series = pd.DataFrame(aggregate_sentiment_sum).dropna(how='all')\\n sentiment_sum_time_series[\\\"Time\\\"] = date_time_series\\n sentiment_sum_time_series.set_index('Time', inplace=True)\\n sentiment_sum_time_series.index = pd.to_datetime(sentiment_sum_time_series.index)\\n\\n if rolling:\\n sentiment_sum_time_series = sentiment_sum_time_series.ewm(alpha=rolling_smoothing_factor).mean()\\n count_time_series = count_time_series.rolling(window=period).sum()\\n sentiment_med_time_series = sentiment_med_time_series.ewm(alpha=rolling_smoothing_factor).mean()\\n return count_time_series, sentiment_med_time_series, sentiment_sum_time_series\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":10,"string":"10"}}},{"rowIdx":2979175,"cells":{"repo_name":{"kind":"string","value":"HenryJobst/bowling_ml"},"repo_url":{"kind":"string","value":"https://github.com/HenryJobst/bowling_ml"},"snapshot_id":{"kind":"string","value":"706e5c70edf90f57b01fcacd27bdf230d75b4daf"},"revision_id":{"kind":"string","value":"f52bff897c823dc6f43913414c2ec21dcb921b35"},"directory_id":{"kind":"string","value":"374ea3aa01ca1d9d20e41136fc9c392f94008f2b"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-04-21T22:15:36.634297","string":"2020-04-21T22:15:36.634297"},"revision_date":{"kind":"timestamp","value":"2019-02-09T19:35:57","string":"2019-02-09T19:35:57"},"committer_date":{"kind":"timestamp","value":"2019-02-09T19:35:57","string":"2019-02-09T19:35:57"},"github_id":{"kind":"number","value":169905223,"string":"169,905,223"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6952277421951294,"alphanum_fraction":0.7201735377311707,"avg_line_length":31.85714340209961,"blob_id":"9864eeaa418dfe672397e969d17147617c66d85f","content_id":"eb7c0afa85552ffa858feb75576a1a3ea4f4abbc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":922,"license_type":"no_license","max_line_length":82,"num_lines":28,"path":"/bowling_learn.py","repo_name":"HenryJobst/bowling_ml","src_encoding":"UTF-8","text":"import numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom load_data import load_data\n\n(XTrain, YTrain) = load_data('bowling_data_5000.csv')\n(XTest, YTest) = load_data('bowling_data_3000.csv')\n\nmodel = Sequential()\nmodel.add(Dense(21, input_dim=21, kernel_initializer='normal', activation='relu'))\nmodel.add(Dense(21, kernel_initializer='normal', activation='relu'))\nmodel.add(Dense(10, kernel_initializer='normal', activation='sigmoid'))\nmodel.add(Dense(10, kernel_initializer='random_uniform', activation='relu'))\nmodel.add(Dense(1, kernel_initializer='normal', activation='linear'))\nmodel.compile(loss='logcosh', optimizer='adam')\n\nprint(XTrain)\nprint(YTrain)\n\nmodel.fit(XTrain, YTrain, epochs=500, verbose=True)\n\nev = model.evaluate(XTest, YTest)\nprint(ev)\n\nyP = model.predict(XTest)\n\nfor i in range(5):\n print(\"{}: {} Ist: {} <-> Soll: {}\\n\".format(i, XTest[i], yP[i], YTest[i]))\n\n\n"},{"alpha_fraction":0.7028985619544983,"alphanum_fraction":0.70652174949646,"avg_line_length":29.77777862548828,"blob_id":"d49d19665e3f95c6daae1e239624174215b2b3e8","content_id":"a1af9205251c3afaca4eb0a7a0a38b27a34e8383","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":276,"license_type":"no_license","max_line_length":65,"num_lines":9,"path":"/load_data.py","repo_name":"HenryJobst/bowling_ml","src_encoding":"UTF-8","text":"import pandas as pd\nfrom bowling_rolls_transformations.transformations import convert\n\n\ndef load_data(path):\n df = pd.read_csv(path, header=0, names=['rolls', 'scores'])\n rolls = df['rolls'].apply(convert).values\n scores = df['scores'].values\n return rolls, scores"},{"alpha_fraction":0.6432797908782959,"alphanum_fraction":0.6999154686927795,"avg_line_length":27.16666603088379,"blob_id":"8c7b66f70bc03427bbe102cba5bf79cc2c30350f","content_id":"3702a9b42d69ced677dffdeeaba9ef66a97065cd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1183,"license_type":"no_license","max_line_length":92,"num_lines":42,"path":"/bowling_learn_lstm.py","repo_name":"HenryJobst/bowling_ml","src_encoding":"UTF-8","text":"from keras.models import Sequential\nfrom keras.layers import LSTM, Bidirectional, Dense\nfrom load_data import load_data\n\n(XTrain, YTrain) = load_data('bowling_data_5000.csv')\n(XTest, YTest) = load_data('bowling_data_3000.csv')\n(XVal, YVal) = load_data('bowling_data_3000_val.csv')\n\n# set up input dimension\ndata_dim = 1\ntimesteps = 22\nnum_classes = 1\n\n# reshape our data to use in the model\nXTrain = XTrain.reshape(5000, 21, 1)\nYTrain = YTrain.reshape(5000, 1)\n\nXTest = XTest.reshape(3000, 21, 1)\nYTest = YTest.reshape(3000, 1)\n\nXVal = XVal.reshape(3000, 21, 1)\nYVal = YVal.reshape(3000, 1)\n\nmodel = Sequential()\nmodel.add(Bidirectional(LSTM(32, return_sequences=True, input_shape=(timesteps, data_dim))))\nmodel.add(Bidirectional(LSTM(32, return_sequences=True)))\nmodel.add(Bidirectional(LSTM(32)))\nmodel.add(Dense(1, activation='linear'))\nmodel.compile(loss='logcosh', optimizer='adam')\n\n# start training\nmodel.fit(XTrain, YTrain,\n epochs=100, batch_size=1000,\n validation_data=(XVal, YVal))\n\nev = model.evaluate(XTest, YTest)\nprint(ev)\n\nyP = model.predict(XTest)\n\nfor i in range(5):\n print(\"{}: {} Ist: {} <-> Soll: {}\\n\".format(i, XTest[i], yP[i], YTest[i]))\n"},{"alpha_fraction":0.6221498250961304,"alphanum_fraction":0.6221498250961304,"avg_line_length":21,"blob_id":"d7b650dad0463bf02d42f14cc4e8c89a3a3899e9","content_id":"4ee9e575e575ea13dc9a787083e926a2de0a6599","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":307,"license_type":"no_license","max_line_length":72,"num_lines":14,"path":"/bowling_rolls_transformations/tokenize.py","repo_name":"HenryJobst/bowling_ml","src_encoding":"UTF-8","text":"from typing import List, Any\n\n\ndef tokenize_seq(seq):\n \"\"\"\n To tokenize the sequence of rolls input from user to single letters.\n\n Returns:\n -------\n rolls: list\n A list of rolls in the sequence input by the users.\n \"\"\"\n rolls: List[Any] = [roll for roll in seq]\n return rolls"},{"alpha_fraction":0.8333333134651184,"alphanum_fraction":0.8333333134651184,"avg_line_length":50,"blob_id":"f6c1695b2024e3ac2364293c1278bedb2b0c8f32","content_id":"9348a4b56788d30e4399011eb37171e4c400c169","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":102,"license_type":"no_license","max_line_length":88,"num_lines":2,"path":"/README.md","repo_name":"HenryJobst/bowling_ml","src_encoding":"UTF-8","text":"# bowling_ml\ntry to calculate bowling score with machine learning based on precalculated game results\n"},{"alpha_fraction":0.5441803932189941,"alphanum_fraction":0.5575868487358093,"avg_line_length":25.88524627685547,"blob_id":"e683cb6132e2dda96ce91759ed85171f91577478","content_id":"3ebb467ecf074530ec965504e681d54ef9c90693","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1641,"license_type":"no_license","max_line_length":81,"num_lines":61,"path":"/bowling_rolls_transformations/transformations.py","repo_name":"HenryJobst/bowling_ml","src_encoding":"UTF-8","text":"from bowling_rolls_transformations.tokenize import tokenize_seq\nimport pandas as pd\n\n\ndef transform_symbol(rolls):\n \"\"\"\n Transform the rolls to scores based on the annotation of the symbols.\n Annotation of the symbols:\n \"X\" indicates a strike, \"/\" indicates a spare, \"-\" indicates a miss,\n and a number indicates the number of pins knocked down in the roll.\n\n For symbols:\n 'X' -> 10\n '-' -> 0\n '/' -> '/' This will be kept the same to differentiate spare from strike.\n For numbers:\n Will transform the number in str to int.\n\n Parameters:\n -------\n rolls: list of str\n The rolls in a list that is retrieved from the console.\n\n Returns:\n -------\n rolls: list of str and int\n A list of transformed rolls.\n\n \"\"\"\n for i in range(len(rolls)):\n # If it's 'X', it's strike. Set the score to 10.\n if rolls[i] == 'X':\n rolls[i] = 10\n # If it's '-', it's missed. Set the score to 0.\n elif rolls[i] == '-':\n rolls[i] = 0\n # If it's '/', it's spare, keep it for the record.\n elif rolls[i] == '/':\n rolls[i] = 10 - rolls[i - 1]\n else:\n rolls[i] = int(rolls[i])\n return rolls\n\n\ndef fill(seq):\n filled_seq = []\n for i in range(len(seq)):\n filled_seq.append(seq[i])\n if seq[i] == 10 and len(filled_seq) < 18 and len(filled_seq) % 2 != 0:\n filled_seq.append(0)\n\n seq = filled_seq\n\n for i in range(21-len(seq)):\n seq.append(0)\n\n return pd.Series(seq)\n\n\ndef convert(x):\n return fill(transform_symbol(tokenize_seq(x)))\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6952277421951294,\n \"alphanum_fraction\": 0.7201735377311707,\n \"avg_line_length\": 31.85714340209961,\n \"blob_id\": \"9864eeaa418dfe672397e969d17147617c66d85f\",\n \"content_id\": \"eb7c0afa85552ffa858feb75576a1a3ea4f4abbc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 922,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 28,\n \"path\": \"/bowling_learn.py\",\n \"repo_name\": \"HenryJobst/bowling_ml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nfrom keras.models import Sequential\\nfrom keras.layers import Dense\\nfrom load_data import load_data\\n\\n(XTrain, YTrain) = load_data('bowling_data_5000.csv')\\n(XTest, YTest) = load_data('bowling_data_3000.csv')\\n\\nmodel = Sequential()\\nmodel.add(Dense(21, input_dim=21, kernel_initializer='normal', activation='relu'))\\nmodel.add(Dense(21, kernel_initializer='normal', activation='relu'))\\nmodel.add(Dense(10, kernel_initializer='normal', activation='sigmoid'))\\nmodel.add(Dense(10, kernel_initializer='random_uniform', activation='relu'))\\nmodel.add(Dense(1, kernel_initializer='normal', activation='linear'))\\nmodel.compile(loss='logcosh', optimizer='adam')\\n\\nprint(XTrain)\\nprint(YTrain)\\n\\nmodel.fit(XTrain, YTrain, epochs=500, verbose=True)\\n\\nev = model.evaluate(XTest, YTest)\\nprint(ev)\\n\\nyP = model.predict(XTest)\\n\\nfor i in range(5):\\n print(\\\"{}: {} Ist: {} <-> Soll: {}\\\\n\\\".format(i, XTest[i], yP[i], YTest[i]))\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7028985619544983,\n \"alphanum_fraction\": 0.70652174949646,\n \"avg_line_length\": 29.77777862548828,\n \"blob_id\": \"d49d19665e3f95c6daae1e239624174215b2b3e8\",\n \"content_id\": \"a1af9205251c3afaca4eb0a7a0a38b27a34e8383\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 276,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 9,\n \"path\": \"/load_data.py\",\n \"repo_name\": \"HenryJobst/bowling_ml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import pandas as pd\\nfrom bowling_rolls_transformations.transformations import convert\\n\\n\\ndef load_data(path):\\n df = pd.read_csv(path, header=0, names=['rolls', 'scores'])\\n rolls = df['rolls'].apply(convert).values\\n scores = df['scores'].values\\n return rolls, scores\"\n },\n {\n \"alpha_fraction\": 0.6432797908782959,\n \"alphanum_fraction\": 0.6999154686927795,\n \"avg_line_length\": 27.16666603088379,\n \"blob_id\": \"8c7b66f70bc03427bbe102cba5bf79cc2c30350f\",\n \"content_id\": \"3702a9b42d69ced677dffdeeaba9ef66a97065cd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1183,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 42,\n \"path\": \"/bowling_learn_lstm.py\",\n \"repo_name\": \"HenryJobst/bowling_ml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from keras.models import Sequential\\nfrom keras.layers import LSTM, Bidirectional, Dense\\nfrom load_data import load_data\\n\\n(XTrain, YTrain) = load_data('bowling_data_5000.csv')\\n(XTest, YTest) = load_data('bowling_data_3000.csv')\\n(XVal, YVal) = load_data('bowling_data_3000_val.csv')\\n\\n# set up input dimension\\ndata_dim = 1\\ntimesteps = 22\\nnum_classes = 1\\n\\n# reshape our data to use in the model\\nXTrain = XTrain.reshape(5000, 21, 1)\\nYTrain = YTrain.reshape(5000, 1)\\n\\nXTest = XTest.reshape(3000, 21, 1)\\nYTest = YTest.reshape(3000, 1)\\n\\nXVal = XVal.reshape(3000, 21, 1)\\nYVal = YVal.reshape(3000, 1)\\n\\nmodel = Sequential()\\nmodel.add(Bidirectional(LSTM(32, return_sequences=True, input_shape=(timesteps, data_dim))))\\nmodel.add(Bidirectional(LSTM(32, return_sequences=True)))\\nmodel.add(Bidirectional(LSTM(32)))\\nmodel.add(Dense(1, activation='linear'))\\nmodel.compile(loss='logcosh', optimizer='adam')\\n\\n# start training\\nmodel.fit(XTrain, YTrain,\\n epochs=100, batch_size=1000,\\n validation_data=(XVal, YVal))\\n\\nev = model.evaluate(XTest, YTest)\\nprint(ev)\\n\\nyP = model.predict(XTest)\\n\\nfor i in range(5):\\n print(\\\"{}: {} Ist: {} <-> Soll: {}\\\\n\\\".format(i, XTest[i], yP[i], YTest[i]))\\n\"\n },\n {\n \"alpha_fraction\": 0.6221498250961304,\n \"alphanum_fraction\": 0.6221498250961304,\n \"avg_line_length\": 21,\n \"blob_id\": \"d7b650dad0463bf02d42f14cc4e8c89a3a3899e9\",\n \"content_id\": \"4ee9e575e575ea13dc9a787083e926a2de0a6599\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 307,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 14,\n \"path\": \"/bowling_rolls_transformations/tokenize.py\",\n \"repo_name\": \"HenryJobst/bowling_ml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from typing import List, Any\\n\\n\\ndef tokenize_seq(seq):\\n \\\"\\\"\\\"\\n To tokenize the sequence of rolls input from user to single letters.\\n\\n Returns:\\n -------\\n rolls: list\\n A list of rolls in the sequence input by the users.\\n \\\"\\\"\\\"\\n rolls: List[Any] = [roll for roll in seq]\\n return rolls\"\n },\n {\n \"alpha_fraction\": 0.8333333134651184,\n \"alphanum_fraction\": 0.8333333134651184,\n \"avg_line_length\": 50,\n \"blob_id\": \"f6c1695b2024e3ac2364293c1278bedb2b0c8f32\",\n \"content_id\": \"9348a4b56788d30e4399011eb37171e4c400c169\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 102,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"HenryJobst/bowling_ml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# bowling_ml\\ntry to calculate bowling score with machine learning based on precalculated game results\\n\"\n },\n {\n \"alpha_fraction\": 0.5441803932189941,\n \"alphanum_fraction\": 0.5575868487358093,\n \"avg_line_length\": 25.88524627685547,\n \"blob_id\": \"e683cb6132e2dda96ce91759ed85171f91577478\",\n \"content_id\": \"3ebb467ecf074530ec965504e681d54ef9c90693\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1641,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 61,\n \"path\": \"/bowling_rolls_transformations/transformations.py\",\n \"repo_name\": \"HenryJobst/bowling_ml\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from bowling_rolls_transformations.tokenize import tokenize_seq\\nimport pandas as pd\\n\\n\\ndef transform_symbol(rolls):\\n \\\"\\\"\\\"\\n Transform the rolls to scores based on the annotation of the symbols.\\n Annotation of the symbols:\\n \\\"X\\\" indicates a strike, \\\"/\\\" indicates a spare, \\\"-\\\" indicates a miss,\\n and a number indicates the number of pins knocked down in the roll.\\n\\n For symbols:\\n 'X' -> 10\\n '-' -> 0\\n '/' -> '/' This will be kept the same to differentiate spare from strike.\\n For numbers:\\n Will transform the number in str to int.\\n\\n Parameters:\\n -------\\n rolls: list of str\\n The rolls in a list that is retrieved from the console.\\n\\n Returns:\\n -------\\n rolls: list of str and int\\n A list of transformed rolls.\\n\\n \\\"\\\"\\\"\\n for i in range(len(rolls)):\\n # If it's 'X', it's strike. Set the score to 10.\\n if rolls[i] == 'X':\\n rolls[i] = 10\\n # If it's '-', it's missed. Set the score to 0.\\n elif rolls[i] == '-':\\n rolls[i] = 0\\n # If it's '/', it's spare, keep it for the record.\\n elif rolls[i] == '/':\\n rolls[i] = 10 - rolls[i - 1]\\n else:\\n rolls[i] = int(rolls[i])\\n return rolls\\n\\n\\ndef fill(seq):\\n filled_seq = []\\n for i in range(len(seq)):\\n filled_seq.append(seq[i])\\n if seq[i] == 10 and len(filled_seq) < 18 and len(filled_seq) % 2 != 0:\\n filled_seq.append(0)\\n\\n seq = filled_seq\\n\\n for i in range(21-len(seq)):\\n seq.append(0)\\n\\n return pd.Series(seq)\\n\\n\\ndef convert(x):\\n return fill(transform_symbol(tokenize_seq(x)))\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":2979176,"cells":{"repo_name":{"kind":"string","value":"manuelbenedicto/Emotions_Towards_COVID-19_on_Reddit"},"repo_url":{"kind":"string","value":"https://github.com/manuelbenedicto/Emotions_Towards_COVID-19_on_Reddit"},"snapshot_id":{"kind":"string","value":"922dc41cd524c172dd0add9b34509e60bf405b57"},"revision_id":{"kind":"string","value":"f1ec64e35ee47c9da0dfb34a9a9ec105f7f709de"},"directory_id":{"kind":"string","value":"30b741c2c5c69ebbe628ff072c6f8843e40dbf89"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-06-11T17:21:51.546083","string":"2022-06-11T17:21:51.546083"},"revision_date":{"kind":"timestamp","value":"2020-05-06T11:18:16","string":"2020-05-06T11:18:16"},"committer_date":{"kind":"timestamp","value":"2020-05-06T11:18:16","string":"2020-05-06T11:18:16"},"github_id":{"kind":"number","value":255926824,"string":"255,926,824"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":2,"string":"2"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6060842275619507,"alphanum_fraction":0.6310452222824097,"avg_line_length":39.69841384887695,"blob_id":"c1a44382c0368233742f9466a49da51a877db2c1","content_id":"72b2058349ec18257e5260af4ba798948ab5c5f7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2564,"license_type":"no_license","max_line_length":126,"num_lines":63,"path":"/reddit_comments.py","repo_name":"manuelbenedicto/Emotions_Towards_COVID-19_on_Reddit","src_encoding":"UTF-8","text":"# Let's import all the necessary libraries to retrieve and preprocess the data\nimport requests\nimport numpy as np\nimport pandas as pd\nimport json\nfrom datetime import datetime\nimport textblob\nfrom textblob.classifiers import NaiveBayesClassifier\n\nterms = ['coronavirus', 'corona virus', 'COVID-19', 'COVID19', 'covid-19', 'covid19']\n\ndef pushshift(base_url, **kwargs):\n base_url = 'https://api.pushshift.io/reddit/search/comment/?'\n params = kwargs\n request = requests.get(base_url, params=kwargs)\n return request.json()\n\ndef get_pushshift_data(terms):\n df_final = pd.DataFrame()\n for term in terms:\n start_date = 1577750400 # 1st January 2020\n now = datetime.now()\n now_epoch = now.timestamp()\n end_date = int(now_epoch)\n while start_date <= end_date:\n data = pushshift(base_url = 'https://api.pushshift.io/reddit/search/comment/?',\n q = term,\n after=str(start_date),\n before=str(start_date+43200), #3600 seconds == 1 hour; 43200s = 12h\n size=500,\n sort_type='score',\n sort='desc').get('data')\n if data == None:\n start_date = start_date + 43200\n else:\n df = pd.DataFrame.from_records(data)\n df_final= df_final.append(df)\n start_date = start_date + 43200 \n return df_final\n \ndf_final = get_pushshift_data(terms)\n \n# Let's keep only some columns for our final database\ndf_final = df_final[['created_utc', 'author', 'subreddit', 'body', 'score', 'permalink']]\n\n# Let's create a new column with all the epoch dates converted\ndf_final['date'] = pd.to_datetime(df_final['created_utc'], unit='s')\n\nimport textblob\n\n# create a column with sentiment polarity\ndf_final[\"sentiment_polarity\"] = df_final.apply(lambda row: textblob.TextBlob(row[\"body\"]).sentiment.polarity, axis=1)\n\n# create a column with sentiment subjectivity\ndf_final[\"sentiment_subjectivity\"] = df_final.apply(lambda row: textblob.TextBlob(row[\"body\"]).sentiment.subjectivity, axis=1)\n\n# create a column with 'positive' or 'negative' depending on sentiment_polarity\ndf_final[\"sentiment\"] = df_final.apply(lambda row: \"positive\" if row[\"sentiment_polarity\"] >= 0 else \"negative\", axis=1)\n\n# create a column with a text preview that shows the first 50 characters\ndf_final[\"preview\"] = df_final[\"body\"].str[0:50]\n\ndf_final.to_csv(path_or_buf='covid19_comments.csv')\n"},{"alpha_fraction":0.7775628566741943,"alphanum_fraction":0.8085106611251831,"avg_line_length":46,"blob_id":"50f4515e418bd531ee8897471e5fa73f70d8d1ee","content_id":"05397689ef75ed4ae5239090a620869a8bb9ff2b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":517,"license_type":"no_license","max_line_length":141,"num_lines":11,"path":"/README.md","repo_name":"manuelbenedicto/Emotions_Towards_COVID-19_on_Reddit","src_encoding":"UTF-8","text":"# Emotions Towards COVID-19 on Reddit\n\nUpdated Jupyter Notebook: https://www.kaggle.com/manuelbenedicto/emotions-towards-covid-19-on-reddit\n\n## Interest on COVID-19 among Reddit users\n\n\n\n## Emotion Trends of Comments Related to COVID-19\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6060842275619507,\n \"alphanum_fraction\": 0.6310452222824097,\n \"avg_line_length\": 39.69841384887695,\n \"blob_id\": \"c1a44382c0368233742f9466a49da51a877db2c1\",\n \"content_id\": \"72b2058349ec18257e5260af4ba798948ab5c5f7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2564,\n \"license_type\": \"no_license\",\n \"max_line_length\": 126,\n \"num_lines\": 63,\n \"path\": \"/reddit_comments.py\",\n \"repo_name\": \"manuelbenedicto/Emotions_Towards_COVID-19_on_Reddit\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Let's import all the necessary libraries to retrieve and preprocess the data\\nimport requests\\nimport numpy as np\\nimport pandas as pd\\nimport json\\nfrom datetime import datetime\\nimport textblob\\nfrom textblob.classifiers import NaiveBayesClassifier\\n\\nterms = ['coronavirus', 'corona virus', 'COVID-19', 'COVID19', 'covid-19', 'covid19']\\n\\ndef pushshift(base_url, **kwargs):\\n base_url = 'https://api.pushshift.io/reddit/search/comment/?'\\n params = kwargs\\n request = requests.get(base_url, params=kwargs)\\n return request.json()\\n\\ndef get_pushshift_data(terms):\\n df_final = pd.DataFrame()\\n for term in terms:\\n start_date = 1577750400 # 1st January 2020\\n now = datetime.now()\\n now_epoch = now.timestamp()\\n end_date = int(now_epoch)\\n while start_date <= end_date:\\n data = pushshift(base_url = 'https://api.pushshift.io/reddit/search/comment/?',\\n q = term,\\n after=str(start_date),\\n before=str(start_date+43200), #3600 seconds == 1 hour; 43200s = 12h\\n size=500,\\n sort_type='score',\\n sort='desc').get('data')\\n if data == None:\\n start_date = start_date + 43200\\n else:\\n df = pd.DataFrame.from_records(data)\\n df_final= df_final.append(df)\\n start_date = start_date + 43200 \\n return df_final\\n \\ndf_final = get_pushshift_data(terms)\\n \\n# Let's keep only some columns for our final database\\ndf_final = df_final[['created_utc', 'author', 'subreddit', 'body', 'score', 'permalink']]\\n\\n# Let's create a new column with all the epoch dates converted\\ndf_final['date'] = pd.to_datetime(df_final['created_utc'], unit='s')\\n\\nimport textblob\\n\\n# create a column with sentiment polarity\\ndf_final[\\\"sentiment_polarity\\\"] = df_final.apply(lambda row: textblob.TextBlob(row[\\\"body\\\"]).sentiment.polarity, axis=1)\\n\\n# create a column with sentiment subjectivity\\ndf_final[\\\"sentiment_subjectivity\\\"] = df_final.apply(lambda row: textblob.TextBlob(row[\\\"body\\\"]).sentiment.subjectivity, axis=1)\\n\\n# create a column with 'positive' or 'negative' depending on sentiment_polarity\\ndf_final[\\\"sentiment\\\"] = df_final.apply(lambda row: \\\"positive\\\" if row[\\\"sentiment_polarity\\\"] >= 0 else \\\"negative\\\", axis=1)\\n\\n# create a column with a text preview that shows the first 50 characters\\ndf_final[\\\"preview\\\"] = df_final[\\\"body\\\"].str[0:50]\\n\\ndf_final.to_csv(path_or_buf='covid19_comments.csv')\\n\"\n },\n {\n \"alpha_fraction\": 0.7775628566741943,\n \"alphanum_fraction\": 0.8085106611251831,\n \"avg_line_length\": 46,\n \"blob_id\": \"50f4515e418bd531ee8897471e5fa73f70d8d1ee\",\n \"content_id\": \"05397689ef75ed4ae5239090a620869a8bb9ff2b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 517,\n \"license_type\": \"no_license\",\n \"max_line_length\": 141,\n \"num_lines\": 11,\n \"path\": \"/README.md\",\n \"repo_name\": \"manuelbenedicto/Emotions_Towards_COVID-19_on_Reddit\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Emotions Towards COVID-19 on Reddit\\n\\nUpdated Jupyter Notebook: https://www.kaggle.com/manuelbenedicto/emotions-towards-covid-19-on-reddit\\n\\n## Interest on COVID-19 among Reddit users\\n\\n\\n\\n## Emotion Trends of Comments Related to COVID-19\\n\\n\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":2,"string":"2"}}},{"rowIdx":2979177,"cells":{"repo_name":{"kind":"string","value":"CuTePsYcHo110/AI_Robotic_Arm_Code"},"repo_url":{"kind":"string","value":"https://github.com/CuTePsYcHo110/AI_Robotic_Arm_Code"},"snapshot_id":{"kind":"string","value":"3748e979c538bf5320b45d8c71bc67449fb0d4a3"},"revision_id":{"kind":"string","value":"40d2f08b55fd92b9a2e20150a74ee38241cb2378"},"directory_id":{"kind":"string","value":"9e3635402fd4b8e09d1aa74d9be207ed2c216eed"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-07-18T16:40:40.836740","string":"2023-07-18T16:40:40.836740"},"revision_date":{"kind":"timestamp","value":"2021-09-21T16:49:39","string":"2021-09-21T16:49:39"},"committer_date":{"kind":"timestamp","value":"2021-09-21T16:49:39","string":"2021-09-21T16:49:39"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5632549524307251,"alphanum_fraction":0.6001271605491638,"avg_line_length":22.83333396911621,"blob_id":"53576e48040a39e30f61b30c79567bb826793d8c","content_id":"6a10261eac484d5316c067c61668f760640b5ad4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"C++","length_bytes":1573,"license_type":"no_license","max_line_length":86,"num_lines":66,"path":"/ArduinoCode.cpp","repo_name":"CuTePsYcHo110/AI_Robotic_Arm_Code","src_encoding":"UTF-8","text":"#include <Servo.h>\n#define numOfValsRec 5\n#define digitsPerValsRec 1\n\nServo servoThumb;\nServo servoIndex;\nServo servoMiddle;\nServo servoRing;\nServo servoPinky;\n\nint valsRec[numOfValsRec];\nint stringLenght = numOfValsRec * digitsPerValsRec + 1 //$00000\nint counter = 0;\nbool counterStart = false;\nstr receivedString;\n\n\nvoid setup (){\n Serial.begin(9600);\n servoThumb.attach(7);\n servoIndex.attach(9);\n servoMiddle.attach(11);\n servoRing.attach(8);\n servoPinky.attach(10);\n}\n\nvoid receiveData() {\n while (Serial.available())\n {\n char c = serial.read();\n \n if (c=='$') {\n counterStart= true \n }\n if (counterStart) {\n if (counter < stringLenght) {\n receivedString = String(receivedString+c)\n counter++;\n }\n if (counter >= stringLenght) {\n //$00000\n for (int i = 0 ; i<numOfValsRec; i++)\n {\n int num = ( i * digitsPerValRec)+1 ;\n valsReceived[i] = receivedString.substring(num, num+digitsPerValsRec).toInt();\n }\n receivedString = \"\";\n counter = 0 ;\n counterStart = false\n }\n }\n \n }\n\n}\n\nvoid loop () {\n \n receiveData();\n if (valsRec [0]==1) {servoThumb.write(180); else {servoThumb.write(0);}\n if (valsRec [0]==1) {servoIndex.write(180); else {servoIndex.write(0);}\n if (valsRec [0]==1) {servoMiddle.write(180); else {servoMiddle.write(0);}\n if (valsRec [0]==1) {servoRing.write(180); else {servoRing.write(0);}\n if (valsRec [0]==1) {servoPinky.write(180); else {servoPinky.write(0);\n \n}\n"},{"alpha_fraction":0.6858513355255127,"alphanum_fraction":0.714628279209137,"avg_line_length":25.0625,"blob_id":"db5298accb0840b3c4028b2b18a7bc0d0e4bd4f8","content_id":"3e03d8a98b15e47cec657c07afc833610fa1ae2c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":417,"license_type":"no_license","max_line_length":60,"num_lines":16,"path":"/HandDetection.py","repo_name":"CuTePsYcHo110/AI_Robotic_Arm_Code","src_encoding":"UTF-8","text":"import cvzone\nimport cv2\n\ncap= cv.VideoCapture(1)\ndetector= cvzone.HandDetector(maxHands=1 , detectionCon=0.7)\nmySerial= cvzone.SerialObject(\"COM3\",9600,1)\nwhile True:\n success , img = cap.read()\n img = detector.findHands(img)\n lmlist , bbox = detector.findPosition(img)\n if lmlist:\n fingers = detector.fingersUp()\n #print(fingers)\n mySerial.sendData(fingers)\n cv.imshow(\"image\" , img )\n cv.waitKey(1)\n"},{"alpha_fraction":0.7592592835426331,"alphanum_fraction":0.7592592835426331,"avg_line_length":25.5,"blob_id":"b2439a7a67fb74cf601ceb7c69a656982f568021","content_id":"16dd97dee21a920a82bb306c5bfd370c2095a111","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":54,"license_type":"no_license","max_line_length":30,"num_lines":2,"path":"/README.md","repo_name":"CuTePsYcHo110/AI_Robotic_Arm_Code","src_encoding":"UTF-8","text":"# AI_Robotic_Arm_Code\nSource code for AI Robotic Arm \n"}],"string":"[\n {\n \"alpha_fraction\": 0.5632549524307251,\n \"alphanum_fraction\": 0.6001271605491638,\n \"avg_line_length\": 22.83333396911621,\n \"blob_id\": \"53576e48040a39e30f61b30c79567bb826793d8c\",\n \"content_id\": \"6a10261eac484d5316c067c61668f760640b5ad4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"C++\",\n \"length_bytes\": 1573,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 66,\n \"path\": \"/ArduinoCode.cpp\",\n \"repo_name\": \"CuTePsYcHo110/AI_Robotic_Arm_Code\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#include <Servo.h>\\n#define numOfValsRec 5\\n#define digitsPerValsRec 1\\n\\nServo servoThumb;\\nServo servoIndex;\\nServo servoMiddle;\\nServo servoRing;\\nServo servoPinky;\\n\\nint valsRec[numOfValsRec];\\nint stringLenght = numOfValsRec * digitsPerValsRec + 1 //$00000\\nint counter = 0;\\nbool counterStart = false;\\nstr receivedString;\\n\\n\\nvoid setup (){\\n Serial.begin(9600);\\n servoThumb.attach(7);\\n servoIndex.attach(9);\\n servoMiddle.attach(11);\\n servoRing.attach(8);\\n servoPinky.attach(10);\\n}\\n\\nvoid receiveData() {\\n while (Serial.available())\\n {\\n char c = serial.read();\\n \\n if (c=='$') {\\n counterStart= true \\n }\\n if (counterStart) {\\n if (counter < stringLenght) {\\n receivedString = String(receivedString+c)\\n counter++;\\n }\\n if (counter >= stringLenght) {\\n //$00000\\n for (int i = 0 ; i<numOfValsRec; i++)\\n {\\n int num = ( i * digitsPerValRec)+1 ;\\n valsReceived[i] = receivedString.substring(num, num+digitsPerValsRec).toInt();\\n }\\n receivedString = \\\"\\\";\\n counter = 0 ;\\n counterStart = false\\n }\\n }\\n \\n }\\n\\n}\\n\\nvoid loop () {\\n \\n receiveData();\\n if (valsRec [0]==1) {servoThumb.write(180); else {servoThumb.write(0);}\\n if (valsRec [0]==1) {servoIndex.write(180); else {servoIndex.write(0);}\\n if (valsRec [0]==1) {servoMiddle.write(180); else {servoMiddle.write(0);}\\n if (valsRec [0]==1) {servoRing.write(180); else {servoRing.write(0);}\\n if (valsRec [0]==1) {servoPinky.write(180); else {servoPinky.write(0);\\n \\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.6858513355255127,\n \"alphanum_fraction\": 0.714628279209137,\n \"avg_line_length\": 25.0625,\n \"blob_id\": \"db5298accb0840b3c4028b2b18a7bc0d0e4bd4f8\",\n \"content_id\": \"3e03d8a98b15e47cec657c07afc833610fa1ae2c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 417,\n \"license_type\": \"no_license\",\n \"max_line_length\": 60,\n \"num_lines\": 16,\n \"path\": \"/HandDetection.py\",\n \"repo_name\": \"CuTePsYcHo110/AI_Robotic_Arm_Code\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import cvzone\\nimport cv2\\n\\ncap= cv.VideoCapture(1)\\ndetector= cvzone.HandDetector(maxHands=1 , detectionCon=0.7)\\nmySerial= cvzone.SerialObject(\\\"COM3\\\",9600,1)\\nwhile True:\\n success , img = cap.read()\\n img = detector.findHands(img)\\n lmlist , bbox = detector.findPosition(img)\\n if lmlist:\\n fingers = detector.fingersUp()\\n #print(fingers)\\n mySerial.sendData(fingers)\\n cv.imshow(\\\"image\\\" , img )\\n cv.waitKey(1)\\n\"\n },\n {\n \"alpha_fraction\": 0.7592592835426331,\n \"alphanum_fraction\": 0.7592592835426331,\n \"avg_line_length\": 25.5,\n \"blob_id\": \"b2439a7a67fb74cf601ceb7c69a656982f568021\",\n \"content_id\": \"16dd97dee21a920a82bb306c5bfd370c2095a111\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 54,\n \"license_type\": \"no_license\",\n \"max_line_length\": 30,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"CuTePsYcHo110/AI_Robotic_Arm_Code\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# AI_Robotic_Arm_Code\\nSource code for AI Robotic Arm \\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":3,"string":"3"}}},{"rowIdx":2979178,"cells":{"repo_name":{"kind":"string","value":"franzpoeschel/openPMD-pipe"},"repo_url":{"kind":"string","value":"https://github.com/franzpoeschel/openPMD-pipe"},"snapshot_id":{"kind":"string","value":"c7817a01645381410d0e4f3d17bc35720421cdfd"},"revision_id":{"kind":"string","value":"d4a56a2be647ccccb85f752bb84632093e8b6f1d"},"directory_id":{"kind":"string","value":"b8fa870f757a5c944313ba26d2449431be9f14dd"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-01-08T23:46:18.822490","string":"2023-01-08T23:46:18.822490"},"revision_date":{"kind":"timestamp","value":"2020-11-12T14:21:06","string":"2020-11-12T14:21:06"},"committer_date":{"kind":"timestamp","value":"2020-11-12T14:23:52","string":"2020-11-12T14:23:52"},"github_id":{"kind":"number","value":306111080,"string":"306,111,080"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5182367563247681,"alphanum_fraction":0.5212575793266296,"avg_line_length":39.261260986328125,"blob_id":"945f37fb13103edfde3cd3b6ff323851650b04b5","content_id":"59d568d322a9e918aac37b189a44b89e304f175e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":8938,"license_type":"no_license","max_line_length":80,"num_lines":222,"path":"/openpmd-pipe.py","repo_name":"franzpoeschel/openPMD-pipe","src_encoding":"UTF-8","text":"#!/usr/bin/env python3\n\nfrom mpi4py import MPI\nimport openpmd_api as io\nimport argparse\nimport sys # sys.stderr.write\n\ndebug = False\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='openPMD Pipe')\n\n parser.add_argument('--infile', type=str, help='In file')\n parser.add_argument('--outfile', type=str, help='Out file')\n parser.add_argument('--inconfig',\n type=str,\n default='{}',\n help='JSON config for the in file')\n parser.add_argument('--outconfig',\n type=str,\n default='{}',\n help='JSON config for the out file')\n\n return parser.parse_args()\n\n\nclass Chunk:\n \"\"\"\n A Chunk is an n-dimensional hypercube, defined by an offset and an extent.\n Offset and extent must be of the same dimensionality (Chunk.__len__).\n \"\"\"\n def __init__(self, offset, extent):\n assert (len(offset) == len(extent))\n self.offset = offset\n self.extent = extent\n\n def __len__(self):\n return len(self.offset)\n\n def slice1D(self, mpi_rank, mpi_size, dimension=None):\n \"\"\"\n Slice this chunk into mpi_size hypercubes along one of its n dimensions.\n The dimension is given through the 'dimension' parameter. If None, the\n dimension with the largest extent on this hypercube is automatically\n picked.\n Returns the mpi_rank'th of the sliced chunks.\n \"\"\"\n if dimension is None:\n # pick that dimension which has the highest count of items\n dimension = 0\n maximum = self.extent[0]\n for k, v in enumerate(self.extent):\n if v > maximum:\n dimension = k\n assert (dimension < len(self))\n # no offset\n assert (self.offset == [0 for _ in range(len(self))])\n offset = [0 for _ in range(len(self))]\n stride = self.extent[dimension] // mpi_size\n rest = self.extent[dimension] % mpi_size\n\n # local function f computes the offset of a rank\n # for more equal balancing, we want the start index\n # at the upper gaussian bracket of (N/n*rank)\n # where N the size of the dataset in dimension dim\n # and n the MPI size\n # for avoiding integer overflow, this is the same as:\n # (N div n)*rank + round((N%n)/n*rank)\n def f(rank):\n res = stride * rank\n padDivident = rest * rank\n pad = padDivident // mpi_size\n if pad * mpi_size < padDivident:\n pad += 1\n return res + pad\n\n offset[dimension] = f(mpi_rank)\n extent = self.extent.copy()\n if mpi_rank >= mpi_size - 1:\n extent[dimension] -= offset[dimension]\n else:\n extent[dimension] = f(mpi_rank + 1) - offset[dimension]\n return Chunk(offset, extent)\n\n\nclass pipe:\n \"\"\"\n Represents the configuration of one \"pipe\" pass.\n \"\"\"\n def __init__(self, infile, outfile, inconfig, outconfig, comm):\n self.infile = infile\n self.outfile = outfile\n self.inconfig = inconfig\n self.outconfig = outconfig\n self.chunks = []\n self.comm = comm\n\n def run(self):\n if self.comm.size == 1:\n print(\"Opening data source\")\n sys.stdout.flush()\n inseries = io.Series(self.infile, io.Access_Type.read_only,\n self.inconfig)\n print(\"Opening data sink\")\n sys.stdout.flush()\n outseries = io.Series(self.outfile, io.Access_Type.create,\n self.outconfig)\n print(\"Opened input and output\")\n sys.stdout.flush()\n else:\n print(\"Opening data source\")\n sys.stdout.flush()\n inseries = io.Series(self.infile, io.Access_Type.read_only,\n self.comm, self.inconfig)\n print(\"Opening data sink\")\n sys.stdout.flush()\n outseries = io.Series(self.outfile, io.Access_Type.create,\n self.comm, self.outconfig)\n print(\"Opened input and output\")\n sys.stdout.flush()\n self.__copy(inseries, outseries)\n\n def __copy(self, src, dest, current_path=\"/data/\"):\n \"\"\"\n Worker method.\n Copies data from src to dest. May represent any point in the openPMD\n hierarchy, but src and dest must both represent the same layer.\n \"\"\"\n if (type(src) != type(dest)\n and not isinstance(src, io.IndexedIteration)\n and not isinstance(dest, io.Iteration)):\n raise RuntimeError(\n \"Internal error: Trying to copy mismatching types\")\n for key in src.attributes:\n if key == \"openPMDextension\":\n # this sets the wrong datatype otherwise\n dest.set_openPMD_extension(src.openPMD_extension)\n else:\n attr = src.get_attribute(key)\n if key == \"unitDimension\":\n if hasattr(dest, 'unit_dimension'):\n dest.unit_dimension = {\n io.Unit_Dimension.L: attr[0],\n io.Unit_Dimension.M: attr[1],\n io.Unit_Dimension.T: attr[2],\n io.Unit_Dimension.I: attr[3],\n io.Unit_Dimension.theta: attr[4],\n io.Unit_Dimension.N: attr[5],\n io.Unit_Dimension.J: attr[6]\n }\n else:\n dest.set_attribute(key, attr)\n container_types = [\n io.Mesh_Container, io.Particle_Container, io.ParticleSpecies,\n io.Record, io.Mesh\n ]\n if isinstance(src, io.Series):\n # main loop: read iterations of src, write to dest\n write_iterations = dest.write_iterations()\n for in_iteration in src.read_iterations():\n print(\"Iteration {0} contains {1} meshes:\".format(\n in_iteration.iteration_index, len(in_iteration.meshes)))\n for m in in_iteration.meshes:\n print(\"\\t {0}\".format(m))\n print(\"\")\n print(\"Iteration {0} contains {1} particle species:\".format(\n in_iteration.iteration_index, len(in_iteration.particles)))\n for ps in in_iteration.particles:\n print(\"\\t {0}\".format(ps))\n print(\"With records:\")\n for r in in_iteration.particles[ps]:\n print(\"\\t {0}\".format(r))\n out_iteration = write_iterations[in_iteration.iteration_index]\n sys.stdout.flush()\n self.__copy(\n in_iteration, out_iteration,\n current_path + str(in_iteration.iteration_index) + \"/\")\n in_iteration.close()\n out_iteration.close()\n self.chunks.clear()\n sys.stdout.flush()\n elif isinstance(src, io.Record_Component):\n shape = src.shape\n offset = [0 for _ in shape]\n dtype = src.dtype\n dest.reset_dataset(io.Dataset(dtype, shape))\n if src.empty:\n pass # empty record component automatically created by\n # dest.reset_dataset()\n elif src.constant:\n dest.make_constant(src.get_attribute(\"value\"))\n else:\n chunk = Chunk(offset, shape)\n local_chunk = chunk.slice1D(self.comm.rank, self.comm.size)\n if debug:\n end = local_chunk.offset.copy()\n for i in range(len(end)):\n end[i] += local_chunk.extent[i]\n print(\"{}\\t{}/{}:\\t{} -- {}\".format(\n current_path, self.comm.rank, self.comm.size,\n local_chunk.offset, end))\n chunk = src.load_chunk(local_chunk.offset, local_chunk.extent)\n self.chunks.append(chunk)\n dest.store_chunk(chunk, local_chunk.offset, local_chunk.extent)\n elif isinstance(src, io.Iteration):\n self.__copy(src.meshes, dest.meshes, current_path + \"meshes/\")\n self.__copy(src.particles, dest.particles,\n current_path + \"particles/\")\n elif any([\n isinstance(src, container_type)\n for container_type in container_types\n ]):\n for key in src:\n self.__copy(src[key], dest[key], current_path + key + \"/\")\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n pipe = pipe(args.infile, args.outfile, args.inconfig, args.outconfig,\n MPI.COMM_WORLD)\n pipe.run()\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5182367563247681,\n \"alphanum_fraction\": 0.5212575793266296,\n \"avg_line_length\": 39.261260986328125,\n \"blob_id\": \"945f37fb13103edfde3cd3b6ff323851650b04b5\",\n \"content_id\": \"59d568d322a9e918aac37b189a44b89e304f175e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 8938,\n \"license_type\": \"no_license\",\n \"max_line_length\": 80,\n \"num_lines\": 222,\n \"path\": \"/openpmd-pipe.py\",\n \"repo_name\": \"franzpoeschel/openPMD-pipe\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python3\\n\\nfrom mpi4py import MPI\\nimport openpmd_api as io\\nimport argparse\\nimport sys # sys.stderr.write\\n\\ndebug = False\\n\\n\\ndef parse_args():\\n parser = argparse.ArgumentParser(description='openPMD Pipe')\\n\\n parser.add_argument('--infile', type=str, help='In file')\\n parser.add_argument('--outfile', type=str, help='Out file')\\n parser.add_argument('--inconfig',\\n type=str,\\n default='{}',\\n help='JSON config for the in file')\\n parser.add_argument('--outconfig',\\n type=str,\\n default='{}',\\n help='JSON config for the out file')\\n\\n return parser.parse_args()\\n\\n\\nclass Chunk:\\n \\\"\\\"\\\"\\n A Chunk is an n-dimensional hypercube, defined by an offset and an extent.\\n Offset and extent must be of the same dimensionality (Chunk.__len__).\\n \\\"\\\"\\\"\\n def __init__(self, offset, extent):\\n assert (len(offset) == len(extent))\\n self.offset = offset\\n self.extent = extent\\n\\n def __len__(self):\\n return len(self.offset)\\n\\n def slice1D(self, mpi_rank, mpi_size, dimension=None):\\n \\\"\\\"\\\"\\n Slice this chunk into mpi_size hypercubes along one of its n dimensions.\\n The dimension is given through the 'dimension' parameter. If None, the\\n dimension with the largest extent on this hypercube is automatically\\n picked.\\n Returns the mpi_rank'th of the sliced chunks.\\n \\\"\\\"\\\"\\n if dimension is None:\\n # pick that dimension which has the highest count of items\\n dimension = 0\\n maximum = self.extent[0]\\n for k, v in enumerate(self.extent):\\n if v > maximum:\\n dimension = k\\n assert (dimension < len(self))\\n # no offset\\n assert (self.offset == [0 for _ in range(len(self))])\\n offset = [0 for _ in range(len(self))]\\n stride = self.extent[dimension] // mpi_size\\n rest = self.extent[dimension] % mpi_size\\n\\n # local function f computes the offset of a rank\\n # for more equal balancing, we want the start index\\n # at the upper gaussian bracket of (N/n*rank)\\n # where N the size of the dataset in dimension dim\\n # and n the MPI size\\n # for avoiding integer overflow, this is the same as:\\n # (N div n)*rank + round((N%n)/n*rank)\\n def f(rank):\\n res = stride * rank\\n padDivident = rest * rank\\n pad = padDivident // mpi_size\\n if pad * mpi_size < padDivident:\\n pad += 1\\n return res + pad\\n\\n offset[dimension] = f(mpi_rank)\\n extent = self.extent.copy()\\n if mpi_rank >= mpi_size - 1:\\n extent[dimension] -= offset[dimension]\\n else:\\n extent[dimension] = f(mpi_rank + 1) - offset[dimension]\\n return Chunk(offset, extent)\\n\\n\\nclass pipe:\\n \\\"\\\"\\\"\\n Represents the configuration of one \\\"pipe\\\" pass.\\n \\\"\\\"\\\"\\n def __init__(self, infile, outfile, inconfig, outconfig, comm):\\n self.infile = infile\\n self.outfile = outfile\\n self.inconfig = inconfig\\n self.outconfig = outconfig\\n self.chunks = []\\n self.comm = comm\\n\\n def run(self):\\n if self.comm.size == 1:\\n print(\\\"Opening data source\\\")\\n sys.stdout.flush()\\n inseries = io.Series(self.infile, io.Access_Type.read_only,\\n self.inconfig)\\n print(\\\"Opening data sink\\\")\\n sys.stdout.flush()\\n outseries = io.Series(self.outfile, io.Access_Type.create,\\n self.outconfig)\\n print(\\\"Opened input and output\\\")\\n sys.stdout.flush()\\n else:\\n print(\\\"Opening data source\\\")\\n sys.stdout.flush()\\n inseries = io.Series(self.infile, io.Access_Type.read_only,\\n self.comm, self.inconfig)\\n print(\\\"Opening data sink\\\")\\n sys.stdout.flush()\\n outseries = io.Series(self.outfile, io.Access_Type.create,\\n self.comm, self.outconfig)\\n print(\\\"Opened input and output\\\")\\n sys.stdout.flush()\\n self.__copy(inseries, outseries)\\n\\n def __copy(self, src, dest, current_path=\\\"/data/\\\"):\\n \\\"\\\"\\\"\\n Worker method.\\n Copies data from src to dest. May represent any point in the openPMD\\n hierarchy, but src and dest must both represent the same layer.\\n \\\"\\\"\\\"\\n if (type(src) != type(dest)\\n and not isinstance(src, io.IndexedIteration)\\n and not isinstance(dest, io.Iteration)):\\n raise RuntimeError(\\n \\\"Internal error: Trying to copy mismatching types\\\")\\n for key in src.attributes:\\n if key == \\\"openPMDextension\\\":\\n # this sets the wrong datatype otherwise\\n dest.set_openPMD_extension(src.openPMD_extension)\\n else:\\n attr = src.get_attribute(key)\\n if key == \\\"unitDimension\\\":\\n if hasattr(dest, 'unit_dimension'):\\n dest.unit_dimension = {\\n io.Unit_Dimension.L: attr[0],\\n io.Unit_Dimension.M: attr[1],\\n io.Unit_Dimension.T: attr[2],\\n io.Unit_Dimension.I: attr[3],\\n io.Unit_Dimension.theta: attr[4],\\n io.Unit_Dimension.N: attr[5],\\n io.Unit_Dimension.J: attr[6]\\n }\\n else:\\n dest.set_attribute(key, attr)\\n container_types = [\\n io.Mesh_Container, io.Particle_Container, io.ParticleSpecies,\\n io.Record, io.Mesh\\n ]\\n if isinstance(src, io.Series):\\n # main loop: read iterations of src, write to dest\\n write_iterations = dest.write_iterations()\\n for in_iteration in src.read_iterations():\\n print(\\\"Iteration {0} contains {1} meshes:\\\".format(\\n in_iteration.iteration_index, len(in_iteration.meshes)))\\n for m in in_iteration.meshes:\\n print(\\\"\\\\t {0}\\\".format(m))\\n print(\\\"\\\")\\n print(\\\"Iteration {0} contains {1} particle species:\\\".format(\\n in_iteration.iteration_index, len(in_iteration.particles)))\\n for ps in in_iteration.particles:\\n print(\\\"\\\\t {0}\\\".format(ps))\\n print(\\\"With records:\\\")\\n for r in in_iteration.particles[ps]:\\n print(\\\"\\\\t {0}\\\".format(r))\\n out_iteration = write_iterations[in_iteration.iteration_index]\\n sys.stdout.flush()\\n self.__copy(\\n in_iteration, out_iteration,\\n current_path + str(in_iteration.iteration_index) + \\\"/\\\")\\n in_iteration.close()\\n out_iteration.close()\\n self.chunks.clear()\\n sys.stdout.flush()\\n elif isinstance(src, io.Record_Component):\\n shape = src.shape\\n offset = [0 for _ in shape]\\n dtype = src.dtype\\n dest.reset_dataset(io.Dataset(dtype, shape))\\n if src.empty:\\n pass # empty record component automatically created by\\n # dest.reset_dataset()\\n elif src.constant:\\n dest.make_constant(src.get_attribute(\\\"value\\\"))\\n else:\\n chunk = Chunk(offset, shape)\\n local_chunk = chunk.slice1D(self.comm.rank, self.comm.size)\\n if debug:\\n end = local_chunk.offset.copy()\\n for i in range(len(end)):\\n end[i] += local_chunk.extent[i]\\n print(\\\"{}\\\\t{}/{}:\\\\t{} -- {}\\\".format(\\n current_path, self.comm.rank, self.comm.size,\\n local_chunk.offset, end))\\n chunk = src.load_chunk(local_chunk.offset, local_chunk.extent)\\n self.chunks.append(chunk)\\n dest.store_chunk(chunk, local_chunk.offset, local_chunk.extent)\\n elif isinstance(src, io.Iteration):\\n self.__copy(src.meshes, dest.meshes, current_path + \\\"meshes/\\\")\\n self.__copy(src.particles, dest.particles,\\n current_path + \\\"particles/\\\")\\n elif any([\\n isinstance(src, container_type)\\n for container_type in container_types\\n ]):\\n for key in src:\\n self.__copy(src[key], dest[key], current_path + key + \\\"/\\\")\\n\\n\\nif __name__ == \\\"__main__\\\":\\n args = parse_args()\\n pipe = pipe(args.infile, args.outfile, args.inconfig, args.outconfig,\\n MPI.COMM_WORLD)\\n pipe.run()\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979179,"cells":{"repo_name":{"kind":"string","value":"jeneigabor/github"},"repo_url":{"kind":"string","value":"https://github.com/jeneigabor/github"},"snapshot_id":{"kind":"string","value":"3462b967c5ebb6d38a341d3c19536eef52698c56"},"revision_id":{"kind":"string","value":"74543e04988d43a766523db40f3faf2e8f3a3f71"},"directory_id":{"kind":"string","value":"6507d565f7cd4bb89d7cc03b92366e304d78891e"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-06-13T14:20:08.855163","string":"2020-06-13T14:20:08.855163"},"revision_date":{"kind":"timestamp","value":"2017-02-02T17:24:44","string":"2017-02-02T17:24:44"},"committer_date":{"kind":"timestamp","value":"2017-02-02T17:24:44","string":"2017-02-02T17:24:44"},"github_id":{"kind":"number","value":75368427,"string":"75,368,427"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5461081862449646,"alphanum_fraction":0.5601750612258911,"avg_line_length":21.87313461303711,"blob_id":"9c164da88f4b0dc91547ef7da7fd3d49dd02845a","content_id":"2b8eb93134eeb1573e8ae65cbf403bd68b6142ce","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3309,"license_type":"no_license","max_line_length":71,"num_lines":134,"path":"/Túrázás.py","repo_name":"jeneigabor/github","src_encoding":"UTF-8","text":"#1. verzió\r\névszak = input('Nyár van, vagy ősz? (ny/ő) ')\r\nesik = input('Esik? (i/n) ')\r\nszél = input('Fúj a szél? (i/n) ')\r\n \r\nif évszak == 'ny' and szél == 'n':\r\n print('megyünk')\r\nif évszak == 'ő' and esik == 'n' and szél == 'n':\r\n print('megyünk')\r\n \r\n#2. verzió\r\névszak = input('Nyár van, vagy ősz? (ny/ő) ')\r\nesik = input('Esik? (i/n) ')\r\nszél = input('Fúj a szél? (i/n) ')\r\n \r\nif évszak == 'ny' and szél == 'n':\r\n print('megyünk')\r\nelif évszak == 'ő' and esik == 'n' and szél == 'n':\r\n print('megyünk')\r\nelse:\r\n print('maradunk')\r\n\r\n#3. verzió\r\névszak = input('Melyik évszak van? (ny/ő) ')\r\nesik = input('Esik? (i/n) ')\r\nszél = input('Fúj a szél? (i/n) ')\r\n \r\nif (évszak == 'ny' and szél == 'n') or (évszak == 'ő' and esik == 'n' \\\r\n and szél == 'n'):\r\n print('megyünk')\r\nelse:\r\n print('maradunk') \r\n\r\n#4. verzió\r\névszak = input('Nyár van, vagy ősz? (ny/ő) ')\r\nesik = input('Esik? (i/n) ')\r\nszél = input('Fúj a szél? (i/n) ')\r\n \r\nif szél == 'n' and (évszak == 'ny' or (évszak == 'ő' and esik == 'n')):\r\n print('megyünk')\r\nelse:\r\n print('maradunk')\r\n\r\n#1. feladat\r\n\r\nszam1 = int(input(\"Kérek egy számot: \"))\r\nszam2 = int(input(\"Kérek egy másik számot:\"))\r\nprint(min(szam1,szam2))\r\n\r\n#2. feladat\r\nszam1 = int(input(\"Kérek egy számot: \"))\r\nszam2 = int(input(\"Kérek egy másik számot:\"))\r\nprint(min(szam1,szam2))\r\n\r\n#3. feladat\r\nhelyezes=int(input(\"Hányadik helyezést érted el? \"))\r\nif helyezes<=3:\r\n print(\"Dobogós!\")\r\nelse:\r\n print(helyezes)\r\n\r\n#4. feladat\r\neredmeny=int(input(\"Hanyadik helyezést érted el? \"))\r\nif eredmeny == 3:\r\n print(\"Bronz.\")\r\nelif eredmeny == 2:\r\n print(\"Ezüst.\")\r\nelif eredmeny == 1:\r\n print(\"Arany.\")\r\nelse:\r\n print(\"Nem kaptám érmet.\")\r\n\r\n#5. feladat\r\na=int(input(\"Egyik oldal nagysága: \"))\r\nb=int(input(\"Másik oldal nagysága: \"))\r\nc=int(input(\"Harmadik oldal nagysága: \"))\r\nif a+b>c and a+c>b and b+c>a:\r\n print(\"A háromszög megszerkeszthető.\")\r\nelse:\r\n print(\"A háromszög nem szerkeszthető meg.\")\r\n\r\n#6. feladat\r\n\"\"\"\r\nHa darab > 500, akkor\r\n ki: ár * 0,7\r\nKülönben ha darab > 100, akkor\r\n ki: ár * 0,9\r\nElágazás vége\r\n\"\"\"\r\n\r\n#7. feladat\r\nnev = input(\"Mi a neve? \")\r\nnem = input(\"Lány, vagy fiú vagy? (l/f)\")\r\nif nem ==\"l\":\r\n elotag = \"Ms.\"\r\nelif nem == \"f\":\r\n elotag == \"Mr.\"\r\nelse:\r\n print(\"Elég furcsa szerzet vagy te.\")\r\n elotag == \"???\"\r\nnapszak = input(\"Milyen napszak van? (r,du,e,é)\")\r\nif napszak == \"r\":\r\n angol = \"morning\"\r\nelif napszak == \"du\":\r\n angol = \"afternoon\"\r\nelif napszak == \"e\":\r\n angol = \"evening\"\r\nelif napszak == \"é\":\r\n angol = \"night\"\r\nelse:\r\n print(\"Nem igazán ismerek ilyen napszakot, de te tudod.\")\r\n angol = \"I don\\'t know\"\r\nprint(\"Good \", angol, \", \", elotag, \" \",nev, \"!\", sep=\"\")\r\n\r\n\r\n#8. feladat\r\ngondolt=4\r\ntipp=int(input(\"Mire gondolok 1 és 5 között? \"))\r\nif gondolt == tipp:\r\n print(\"Ez az.\")\r\nelif abs(tipp - gondolt) == 1:\r\n print(\"Majdnem...\")\r\nelse:\r\n print(\"Nem sikerült\")\r\n\r\n\r\n#9. feladat\r\nkor=int(input(\"Hány éves vagy? \"))\r\nif kor<6:\r\n print(\"Nézzél Piroska és a farkast!\")\r\nelif 6<= kor <=16:\r\n print(\"Nézzél Zootropoliszt!\")\r\nelif kor>16:\r\n print(\"Azt nézel, amit akarsz!\")\r\n"},{"alpha_fraction":0.6237288117408752,"alphanum_fraction":0.6305084824562073,"avg_line_length":19.071428298950195,"blob_id":"5b3a9731e876da7b62a3fba4af16d3ee65a746b7","content_id":"13924ce89b8aa4de9516067e7c632f016fdd5525","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1226,"license_type":"no_license","max_line_length":50,"num_lines":56,"path":"/Változók.py","repo_name":"jeneigabor/github","src_encoding":"UTF-8","text":"refrén = 'Csillárom, haj!'\r\nprint('Csizmadialegények')\r\nprint('\\nCsizmadialegények')\r\nprint(refrén) \r\nprint('Nyolcan ülnek egy lóra')\r\nprint(refrén)\r\nprint('Úgy mennek a jó borra')\r\nprint(refrén)\r\n# a következő sorban felülírjuk a változó értékét\r\nrefrén = 'Sej, ripityom!'\r\nprint('Parancsolnak kocsmárosnak;')\r\nprint('Mérjen egy decit nyócuknak!')\r\nprint(refrén) # itt már Sej, ripityom! lesz\r\nprint('Azt se tudják kifizetni.')\r\nprint('Az ablakon szekdesnek ki.')\r\nprint(refrén)\r\n\r\n\r\n\r\n\r\n#Feladatok\r\n#1. feladat megoldása: az \"egért nem fogja kiírni\"\r\ncica = 'kutya'\r\nkutya = 'egér'\r\negér = 'cica'\r\nprint(cica)\r\nprint('kutya')\r\nprint(egér)\r\nprint('cica')\r\n\r\n#2. feladat megoldása: a \"tyúk\" lesz kiírva\r\ncica = 'kutya'\r\nprint(cica)\r\nkutya = 'egér'\r\negér = 'cica'\r\nprint('kutya')\r\ncica = 'tyúk'\r\nprint(egér)\r\nkutya = cica\r\nprint('kutya')\r\nprint(kutya)\r\n\r\n#3. feladat megoldása: 11 sor\r\nprint('Első sor\\n')\r\nprint('\\nNos?')\r\nprint('Azaz?\\n')\r\nprint('\\nAkkor most mennyi?\\n\\n')\r\nprint('Utolsó sor')\r\n\r\n#4. feladat megoldása:\r\nprint(\" /\\n /\\n /\\n /\\n/\")\r\n#5. feladat megoldása:\r\nprint(\"\\\\\\n \\\\\\n \\\\\\n \\\\\\n \\\\\\n \\\\\")\r\n\r\n#6. feladat megoldása:\r\nprint(\"\\\\ /\\n \\\\ /\\n \\\\/\")\r\n"},{"alpha_fraction":0.6457564830780029,"alphanum_fraction":0.6457564830780029,"avg_line_length":43.16666793823242,"blob_id":"e7b98a203d71dd8fcf24c8d765beb9e776dae307","content_id":"9a7ffd987a39b27214dfc88723db6226d95d80b1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":285,"license_type":"no_license","max_line_length":86,"num_lines":6,"path":"/Írás a képernyőre.py","repo_name":"jeneigabor/github","src_encoding":"UTF-8","text":"print(\"Szia\")\r\n#print('...Hogyha én ezt tudtam volna) --- nem fogja kiírni, mert nincs lezárva ' -lel\r\nprint('...Hogyha én ezt tudtam volna') #--- így helyes\r\nprint('Köpönyeget hoztam vóna'!)\r\nprint('Rásír a sok kis malac is!')\r\nprint('\\\"Én is, én is, én is, én is!\\\"')\r\n"},{"alpha_fraction":0.6108266115188599,"alphanum_fraction":0.6320409774780273,"avg_line_length":18.68181800842285,"blob_id":"43e8ed49a8f3c3856bb39798015328155844cffe","content_id":"c523127705720468517237688dad0b050b886c0f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1428,"license_type":"no_license","max_line_length":68,"num_lines":66,"path":"/Gondoltam egy számra 2.py","repo_name":"jeneigabor/github","src_encoding":"UTF-8","text":"gondolt_szám = 4\r\n \r\nkitalálta = False \r\n \r\nwhile not kitalálta:\r\n tipp = input('Melyik számra gondoltam 1 és 5 között? ')\r\n tipp = int(tipp)\r\n if tipp == gondolt_szám:\r\n kitalálta = True\r\nprint('Pápá!')\r\n\r\n\r\n#1. feladat\r\nszam = 1\r\nwhile szam <= 100:\r\n print(szam**2)\r\n szam += 1\r\n\r\n#2. feladat\r\nszam=0\r\nwhile szam<100:\r\n print(szam)\r\n szam +=2\r\n\r\n#3. feladat\r\nszam=101\r\nwhile szam <= 999:\r\n print(szam)\r\n szam+=2\r\n\r\n#4. feladat\r\nvalasz = None \r\nwhile valasz != \"Szeretlek\":\r\n valasz = input(\"De ugye szeretsz? \")\r\n\r\n#5. feladat\r\nkezdet=int(input(\"Hánytól írjam? \"))\r\nveg=int(input(\"Meddig írjam? \"))\r\nwhile kezdet<=veg:\r\n print(kezdet)\r\n kezdet+=1\r\n\r\n#6. feladat\r\nkezdet=int(input(\"Hánytól írjam? \"))\r\nveg=int(input(\"Meddig írjam? \"))\r\nhanyasaval=int(input(\"Hányasával számoljak? \"))\r\nwhile kezdet<=veg:\r\n print(kezdet)\r\n kezdet+=hanyasaval\r\n\r\n#7. feladat\r\nmettől = input('Honnan kezdve írjam ki a számokat? ')\r\nmeddig = input('Meddig írjam ki a számokat? ')\r\nhányasával = input('Hányasával írjam ki a számokat? ')\r\nhatványkitevő = input('Hányadik hatványukat írjam a számok mellé? ')\r\n \r\nmettől = int(mettől)\r\nmeddig = int(meddig)\r\nhányasával = int(hányasával)\r\nhatványkitevő = int(hatványkitevő)\r\n \r\nszám = mettől \r\n \r\nwhile szám <= meddig:\r\n print(szám, szám**hatványkitevő)\r\n szám = szám + hányasával\r\n\r\n"},{"alpha_fraction":0.6458333134651184,"alphanum_fraction":0.6594827771186829,"avg_line_length":33.69230651855469,"blob_id":"2fc1c149dfaca0a002e0c5f9bfddd852d6e34e38","content_id":"686dd36bdd9762c08b38ad3733b44845b637c198","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1496,"license_type":"no_license","max_line_length":84,"num_lines":39,"path":"/Születési év.py","repo_name":"jeneigabor/github","src_encoding":"UTF-8","text":"név = input('Hogy híjják kendet? ')\r\nkor = input('És hány éves kend? ') # itt még stringet kapunk\r\nkor = int(kor) # átalakítjuk egész számmá, azaz int-té\r\nszületési_év = 2016 - kor\r\nprint(név, ', kend ', születési_év, '-ban született.', sep='')\r\n\r\n#Feladatok:\r\n#1. feladat megoldása:\r\nnév = input('Hogy híjják kendet? ')\r\nkor = input('És hány éves kend? ') # itt még stringet kapunk\r\nkor = int(kor) # átalakítjuk egész számmá, azaz int-té\r\név = input('Melyik évben járunk?')\r\név = int(év)\r\nszületési_év = év - kor\r\nprint(név, ', kend ', születési_év, '-ban született.', sep='')\r\n\r\n#2. feladat megoldása:\r\nnév = input('Hogy híjják kendet? ')\r\nkor = input('És hány éves kend? ') # itt még stringet kapunk\r\nkor = int(kor) # átalakítjuk egész számmá, azaz int-té\r\név = input('Melyik évben járunk? ')\r\név = int(év)\r\nszületési_év = év - kor\r\nprint(név, ', kend ', születési_év, '-ban született.', sep='')\r\nérettségi_év = születési_év + 18\r\nprint(név, ', kend ', érettségi_év, '-ban érettségizik.', sep='')\r\n\r\n#3. feladat megoldása:\r\na = int(input(\"Egyik szám:\"))\r\nb = int(input(\"Másik szám\"))\r\nc = a*b\r\nprint(c)\r\n\r\n#4. feladat megoldása:\r\nmagyar_merfold = input(\"Hány magyar mérföldre van a sárkány barlangja? \")\r\nmagyar_merfold = int(magyar_merfold)\r\nkm = magyar_merfold * 8.3536\r\ntengeri_merfold = km / 1.852\r\nprint(\"Ez a táv\", km, \"kilométert és\", tengeri_merfold, \"tengeri mérföldet jelent.\")\r\n"},{"alpha_fraction":0.6024464964866638,"alphanum_fraction":0.621814489364624,"avg_line_length":23.153846740722656,"blob_id":"412e8bc575bd3a75f4361a2067d31cae03958fba","content_id":"1dfbe93ddd51b39f245f7b9f23ddaa797e31d542","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1015,"license_type":"no_license","max_line_length":93,"num_lines":39,"path":"/Gondoltam egy számra.py","repo_name":"jeneigabor/github","src_encoding":"UTF-8","text":"gondolt_szám = 4\r\ntipp = input('Szerinted melyik számra gondolok 1 és 5 között? ')\r\ntipp = int(tipp)\r\nif gondolt_szám == tipp: # a gondolt szám egyenlő a tippel?\r\n print('Eltaláltad!')\r\n print('Kis ügyes!')\r\nelse: #különben\r\n print('Ez most sajnos nem sikerült...')\r\n print('De majd legközelebb... talán... ha nagyobb szerencséd lesz...')\r\nprint('Pápá!')\r\n\r\n\r\n#1 feladat\r\nprint(\"Megoldás linkje: https://pythonidomar.wordpress.com/2016/09/15/gondoltam-egy-szamra/\")\r\n\r\n#2. feladat\r\njelszo=\"valami\"\r\ntipp=input(\"Írja be a jelszót:\")\r\nif tipp==jelszo:\r\n print(\"Jelszó elfogadva.\")\r\nelse:\r\n print(\"Belépés megtagadva.\")\r\n\r\n#3. feladat\r\na=10\r\nb=3\r\nosszeg=a+b\r\ntipp=int(input(\"Mennyi \" + str(a) + \" + \" + str(b) + \" ? \"))\r\nif tipp == osszeg:\r\n print(\"Helyes megoldás\")\r\nelse:\r\n print(\"Ülj le, egyes!\")\r\n\r\n#4. feladat\r\na=int(input(\"Írj be egy egész számot: \"))\r\nif a >= 0:\r\n print(\"A\", a, \"természetes szám.\")\r\nelse:\r\n print(\"A\", a, \"negatív egész szám.\")\r\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5461081862449646,\n \"alphanum_fraction\": 0.5601750612258911,\n \"avg_line_length\": 21.87313461303711,\n \"blob_id\": \"9c164da88f4b0dc91547ef7da7fd3d49dd02845a\",\n \"content_id\": \"2b8eb93134eeb1573e8ae65cbf403bd68b6142ce\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3309,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 134,\n \"path\": \"/Túrázás.py\",\n \"repo_name\": \"jeneigabor/github\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#1. verzió\\r\\névszak = input('Nyár van, vagy ősz? (ny/ő) ')\\r\\nesik = input('Esik? (i/n) ')\\r\\nszél = input('Fúj a szél? (i/n) ')\\r\\n \\r\\nif évszak == 'ny' and szél == 'n':\\r\\n print('megyünk')\\r\\nif évszak == 'ő' and esik == 'n' and szél == 'n':\\r\\n print('megyünk')\\r\\n \\r\\n#2. verzió\\r\\névszak = input('Nyár van, vagy ősz? (ny/ő) ')\\r\\nesik = input('Esik? (i/n) ')\\r\\nszél = input('Fúj a szél? (i/n) ')\\r\\n \\r\\nif évszak == 'ny' and szél == 'n':\\r\\n print('megyünk')\\r\\nelif évszak == 'ő' and esik == 'n' and szél == 'n':\\r\\n print('megyünk')\\r\\nelse:\\r\\n print('maradunk')\\r\\n\\r\\n#3. verzió\\r\\névszak = input('Melyik évszak van? (ny/ő) ')\\r\\nesik = input('Esik? (i/n) ')\\r\\nszél = input('Fúj a szél? (i/n) ')\\r\\n \\r\\nif (évszak == 'ny' and szél == 'n') or (évszak == 'ő' and esik == 'n' \\\\\\r\\n and szél == 'n'):\\r\\n print('megyünk')\\r\\nelse:\\r\\n print('maradunk') \\r\\n\\r\\n#4. verzió\\r\\névszak = input('Nyár van, vagy ősz? (ny/ő) ')\\r\\nesik = input('Esik? (i/n) ')\\r\\nszél = input('Fúj a szél? (i/n) ')\\r\\n \\r\\nif szél == 'n' and (évszak == 'ny' or (évszak == 'ő' and esik == 'n')):\\r\\n print('megyünk')\\r\\nelse:\\r\\n print('maradunk')\\r\\n\\r\\n#1. feladat\\r\\n\\r\\nszam1 = int(input(\\\"Kérek egy számot: \\\"))\\r\\nszam2 = int(input(\\\"Kérek egy másik számot:\\\"))\\r\\nprint(min(szam1,szam2))\\r\\n\\r\\n#2. feladat\\r\\nszam1 = int(input(\\\"Kérek egy számot: \\\"))\\r\\nszam2 = int(input(\\\"Kérek egy másik számot:\\\"))\\r\\nprint(min(szam1,szam2))\\r\\n\\r\\n#3. feladat\\r\\nhelyezes=int(input(\\\"Hányadik helyezést érted el? \\\"))\\r\\nif helyezes<=3:\\r\\n print(\\\"Dobogós!\\\")\\r\\nelse:\\r\\n print(helyezes)\\r\\n\\r\\n#4. feladat\\r\\neredmeny=int(input(\\\"Hanyadik helyezést érted el? \\\"))\\r\\nif eredmeny == 3:\\r\\n print(\\\"Bronz.\\\")\\r\\nelif eredmeny == 2:\\r\\n print(\\\"Ezüst.\\\")\\r\\nelif eredmeny == 1:\\r\\n print(\\\"Arany.\\\")\\r\\nelse:\\r\\n print(\\\"Nem kaptám érmet.\\\")\\r\\n\\r\\n#5. feladat\\r\\na=int(input(\\\"Egyik oldal nagysága: \\\"))\\r\\nb=int(input(\\\"Másik oldal nagysága: \\\"))\\r\\nc=int(input(\\\"Harmadik oldal nagysága: \\\"))\\r\\nif a+b>c and a+c>b and b+c>a:\\r\\n print(\\\"A háromszög megszerkeszthető.\\\")\\r\\nelse:\\r\\n print(\\\"A háromszög nem szerkeszthető meg.\\\")\\r\\n\\r\\n#6. feladat\\r\\n\\\"\\\"\\\"\\r\\nHa darab > 500, akkor\\r\\n ki: ár * 0,7\\r\\nKülönben ha darab > 100, akkor\\r\\n ki: ár * 0,9\\r\\nElágazás vége\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n#7. feladat\\r\\nnev = input(\\\"Mi a neve? \\\")\\r\\nnem = input(\\\"Lány, vagy fiú vagy? (l/f)\\\")\\r\\nif nem ==\\\"l\\\":\\r\\n elotag = \\\"Ms.\\\"\\r\\nelif nem == \\\"f\\\":\\r\\n elotag == \\\"Mr.\\\"\\r\\nelse:\\r\\n print(\\\"Elég furcsa szerzet vagy te.\\\")\\r\\n elotag == \\\"???\\\"\\r\\nnapszak = input(\\\"Milyen napszak van? (r,du,e,é)\\\")\\r\\nif napszak == \\\"r\\\":\\r\\n angol = \\\"morning\\\"\\r\\nelif napszak == \\\"du\\\":\\r\\n angol = \\\"afternoon\\\"\\r\\nelif napszak == \\\"e\\\":\\r\\n angol = \\\"evening\\\"\\r\\nelif napszak == \\\"é\\\":\\r\\n angol = \\\"night\\\"\\r\\nelse:\\r\\n print(\\\"Nem igazán ismerek ilyen napszakot, de te tudod.\\\")\\r\\n angol = \\\"I don\\\\'t know\\\"\\r\\nprint(\\\"Good \\\", angol, \\\", \\\", elotag, \\\" \\\",nev, \\\"!\\\", sep=\\\"\\\")\\r\\n\\r\\n\\r\\n#8. feladat\\r\\ngondolt=4\\r\\ntipp=int(input(\\\"Mire gondolok 1 és 5 között? \\\"))\\r\\nif gondolt == tipp:\\r\\n print(\\\"Ez az.\\\")\\r\\nelif abs(tipp - gondolt) == 1:\\r\\n print(\\\"Majdnem...\\\")\\r\\nelse:\\r\\n print(\\\"Nem sikerült\\\")\\r\\n\\r\\n\\r\\n#9. feladat\\r\\nkor=int(input(\\\"Hány éves vagy? \\\"))\\r\\nif kor<6:\\r\\n print(\\\"Nézzél Piroska és a farkast!\\\")\\r\\nelif 6<= kor <=16:\\r\\n print(\\\"Nézzél Zootropoliszt!\\\")\\r\\nelif kor>16:\\r\\n print(\\\"Azt nézel, amit akarsz!\\\")\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6237288117408752,\n \"alphanum_fraction\": 0.6305084824562073,\n \"avg_line_length\": 19.071428298950195,\n \"blob_id\": \"5b3a9731e876da7b62a3fba4af16d3ee65a746b7\",\n \"content_id\": \"13924ce89b8aa4de9516067e7c632f016fdd5525\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1226,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 56,\n \"path\": \"/Változók.py\",\n \"repo_name\": \"jeneigabor/github\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"refrén = 'Csillárom, haj!'\\r\\nprint('Csizmadialegények')\\r\\nprint('\\\\nCsizmadialegények')\\r\\nprint(refrén) \\r\\nprint('Nyolcan ülnek egy lóra')\\r\\nprint(refrén)\\r\\nprint('Úgy mennek a jó borra')\\r\\nprint(refrén)\\r\\n# a következő sorban felülírjuk a változó értékét\\r\\nrefrén = 'Sej, ripityom!'\\r\\nprint('Parancsolnak kocsmárosnak;')\\r\\nprint('Mérjen egy decit nyócuknak!')\\r\\nprint(refrén) # itt már Sej, ripityom! lesz\\r\\nprint('Azt se tudják kifizetni.')\\r\\nprint('Az ablakon szekdesnek ki.')\\r\\nprint(refrén)\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n#Feladatok\\r\\n#1. feladat megoldása: az \\\"egért nem fogja kiírni\\\"\\r\\ncica = 'kutya'\\r\\nkutya = 'egér'\\r\\negér = 'cica'\\r\\nprint(cica)\\r\\nprint('kutya')\\r\\nprint(egér)\\r\\nprint('cica')\\r\\n\\r\\n#2. feladat megoldása: a \\\"tyúk\\\" lesz kiírva\\r\\ncica = 'kutya'\\r\\nprint(cica)\\r\\nkutya = 'egér'\\r\\negér = 'cica'\\r\\nprint('kutya')\\r\\ncica = 'tyúk'\\r\\nprint(egér)\\r\\nkutya = cica\\r\\nprint('kutya')\\r\\nprint(kutya)\\r\\n\\r\\n#3. feladat megoldása: 11 sor\\r\\nprint('Első sor\\\\n')\\r\\nprint('\\\\nNos?')\\r\\nprint('Azaz?\\\\n')\\r\\nprint('\\\\nAkkor most mennyi?\\\\n\\\\n')\\r\\nprint('Utolsó sor')\\r\\n\\r\\n#4. feladat megoldása:\\r\\nprint(\\\" /\\\\n /\\\\n /\\\\n /\\\\n/\\\")\\r\\n#5. feladat megoldása:\\r\\nprint(\\\"\\\\\\\\\\\\n \\\\\\\\\\\\n \\\\\\\\\\\\n \\\\\\\\\\\\n \\\\\\\\\\\\n \\\\\\\\\\\")\\r\\n\\r\\n#6. feladat megoldása:\\r\\nprint(\\\"\\\\\\\\ /\\\\n \\\\\\\\ /\\\\n \\\\\\\\/\\\")\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6457564830780029,\n \"alphanum_fraction\": 0.6457564830780029,\n \"avg_line_length\": 43.16666793823242,\n \"blob_id\": \"e7b98a203d71dd8fcf24c8d765beb9e776dae307\",\n \"content_id\": \"9a7ffd987a39b27214dfc88723db6226d95d80b1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 285,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 6,\n \"path\": \"/Írás a képernyőre.py\",\n \"repo_name\": \"jeneigabor/github\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"print(\\\"Szia\\\")\\r\\n#print('...Hogyha én ezt tudtam volna) --- nem fogja kiírni, mert nincs lezárva ' -lel\\r\\nprint('...Hogyha én ezt tudtam volna') #--- így helyes\\r\\nprint('Köpönyeget hoztam vóna'!)\\r\\nprint('Rásír a sok kis malac is!')\\r\\nprint('\\\\\\\"Én is, én is, én is, én is!\\\\\\\"')\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6108266115188599,\n \"alphanum_fraction\": 0.6320409774780273,\n \"avg_line_length\": 18.68181800842285,\n \"blob_id\": \"43e8ed49a8f3c3856bb39798015328155844cffe\",\n \"content_id\": \"c523127705720468517237688dad0b050b886c0f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1428,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 66,\n \"path\": \"/Gondoltam egy számra 2.py\",\n \"repo_name\": \"jeneigabor/github\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"gondolt_szám = 4\\r\\n \\r\\nkitalálta = False \\r\\n \\r\\nwhile not kitalálta:\\r\\n tipp = input('Melyik számra gondoltam 1 és 5 között? ')\\r\\n tipp = int(tipp)\\r\\n if tipp == gondolt_szám:\\r\\n kitalálta = True\\r\\nprint('Pápá!')\\r\\n\\r\\n\\r\\n#1. feladat\\r\\nszam = 1\\r\\nwhile szam <= 100:\\r\\n print(szam**2)\\r\\n szam += 1\\r\\n\\r\\n#2. feladat\\r\\nszam=0\\r\\nwhile szam<100:\\r\\n print(szam)\\r\\n szam +=2\\r\\n\\r\\n#3. feladat\\r\\nszam=101\\r\\nwhile szam <= 999:\\r\\n print(szam)\\r\\n szam+=2\\r\\n\\r\\n#4. feladat\\r\\nvalasz = None \\r\\nwhile valasz != \\\"Szeretlek\\\":\\r\\n valasz = input(\\\"De ugye szeretsz? \\\")\\r\\n\\r\\n#5. feladat\\r\\nkezdet=int(input(\\\"Hánytól írjam? \\\"))\\r\\nveg=int(input(\\\"Meddig írjam? \\\"))\\r\\nwhile kezdet<=veg:\\r\\n print(kezdet)\\r\\n kezdet+=1\\r\\n\\r\\n#6. feladat\\r\\nkezdet=int(input(\\\"Hánytól írjam? \\\"))\\r\\nveg=int(input(\\\"Meddig írjam? \\\"))\\r\\nhanyasaval=int(input(\\\"Hányasával számoljak? \\\"))\\r\\nwhile kezdet<=veg:\\r\\n print(kezdet)\\r\\n kezdet+=hanyasaval\\r\\n\\r\\n#7. feladat\\r\\nmettől = input('Honnan kezdve írjam ki a számokat? ')\\r\\nmeddig = input('Meddig írjam ki a számokat? ')\\r\\nhányasával = input('Hányasával írjam ki a számokat? ')\\r\\nhatványkitevő = input('Hányadik hatványukat írjam a számok mellé? ')\\r\\n \\r\\nmettől = int(mettől)\\r\\nmeddig = int(meddig)\\r\\nhányasával = int(hányasával)\\r\\nhatványkitevő = int(hatványkitevő)\\r\\n \\r\\nszám = mettől \\r\\n \\r\\nwhile szám <= meddig:\\r\\n print(szám, szám**hatványkitevő)\\r\\n szám = szám + hányasával\\r\\n\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6458333134651184,\n \"alphanum_fraction\": 0.6594827771186829,\n \"avg_line_length\": 33.69230651855469,\n \"blob_id\": \"2fc1c149dfaca0a002e0c5f9bfddd852d6e34e38\",\n \"content_id\": \"686dd36bdd9762c08b38ad3733b44845b637c198\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1496,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 39,\n \"path\": \"/Születési év.py\",\n \"repo_name\": \"jeneigabor/github\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"név = input('Hogy híjják kendet? ')\\r\\nkor = input('És hány éves kend? ') # itt még stringet kapunk\\r\\nkor = int(kor) # átalakítjuk egész számmá, azaz int-té\\r\\nszületési_év = 2016 - kor\\r\\nprint(név, ', kend ', születési_év, '-ban született.', sep='')\\r\\n\\r\\n#Feladatok:\\r\\n#1. feladat megoldása:\\r\\nnév = input('Hogy híjják kendet? ')\\r\\nkor = input('És hány éves kend? ') # itt még stringet kapunk\\r\\nkor = int(kor) # átalakítjuk egész számmá, azaz int-té\\r\\név = input('Melyik évben járunk?')\\r\\név = int(év)\\r\\nszületési_év = év - kor\\r\\nprint(név, ', kend ', születési_év, '-ban született.', sep='')\\r\\n\\r\\n#2. feladat megoldása:\\r\\nnév = input('Hogy híjják kendet? ')\\r\\nkor = input('És hány éves kend? ') # itt még stringet kapunk\\r\\nkor = int(kor) # átalakítjuk egész számmá, azaz int-té\\r\\név = input('Melyik évben járunk? ')\\r\\név = int(év)\\r\\nszületési_év = év - kor\\r\\nprint(név, ', kend ', születési_év, '-ban született.', sep='')\\r\\nérettségi_év = születési_év + 18\\r\\nprint(név, ', kend ', érettségi_év, '-ban érettségizik.', sep='')\\r\\n\\r\\n#3. feladat megoldása:\\r\\na = int(input(\\\"Egyik szám:\\\"))\\r\\nb = int(input(\\\"Másik szám\\\"))\\r\\nc = a*b\\r\\nprint(c)\\r\\n\\r\\n#4. feladat megoldása:\\r\\nmagyar_merfold = input(\\\"Hány magyar mérföldre van a sárkány barlangja? \\\")\\r\\nmagyar_merfold = int(magyar_merfold)\\r\\nkm = magyar_merfold * 8.3536\\r\\ntengeri_merfold = km / 1.852\\r\\nprint(\\\"Ez a táv\\\", km, \\\"kilométert és\\\", tengeri_merfold, \\\"tengeri mérföldet jelent.\\\")\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6024464964866638,\n \"alphanum_fraction\": 0.621814489364624,\n \"avg_line_length\": 23.153846740722656,\n \"blob_id\": \"412e8bc575bd3a75f4361a2067d31cae03958fba\",\n \"content_id\": \"1dfbe93ddd51b39f245f7b9f23ddaa797e31d542\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1015,\n \"license_type\": \"no_license\",\n \"max_line_length\": 93,\n \"num_lines\": 39,\n \"path\": \"/Gondoltam egy számra.py\",\n \"repo_name\": \"jeneigabor/github\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"gondolt_szám = 4\\r\\ntipp = input('Szerinted melyik számra gondolok 1 és 5 között? ')\\r\\ntipp = int(tipp)\\r\\nif gondolt_szám == tipp: # a gondolt szám egyenlő a tippel?\\r\\n print('Eltaláltad!')\\r\\n print('Kis ügyes!')\\r\\nelse: #különben\\r\\n print('Ez most sajnos nem sikerült...')\\r\\n print('De majd legközelebb... talán... ha nagyobb szerencséd lesz...')\\r\\nprint('Pápá!')\\r\\n\\r\\n\\r\\n#1 feladat\\r\\nprint(\\\"Megoldás linkje: https://pythonidomar.wordpress.com/2016/09/15/gondoltam-egy-szamra/\\\")\\r\\n\\r\\n#2. feladat\\r\\njelszo=\\\"valami\\\"\\r\\ntipp=input(\\\"Írja be a jelszót:\\\")\\r\\nif tipp==jelszo:\\r\\n print(\\\"Jelszó elfogadva.\\\")\\r\\nelse:\\r\\n print(\\\"Belépés megtagadva.\\\")\\r\\n\\r\\n#3. feladat\\r\\na=10\\r\\nb=3\\r\\nosszeg=a+b\\r\\ntipp=int(input(\\\"Mennyi \\\" + str(a) + \\\" + \\\" + str(b) + \\\" ? \\\"))\\r\\nif tipp == osszeg:\\r\\n print(\\\"Helyes megoldás\\\")\\r\\nelse:\\r\\n print(\\\"Ülj le, egyes!\\\")\\r\\n\\r\\n#4. feladat\\r\\na=int(input(\\\"Írj be egy egész számot: \\\"))\\r\\nif a >= 0:\\r\\n print(\\\"A\\\", a, \\\"természetes szám.\\\")\\r\\nelse:\\r\\n print(\\\"A\\\", a, \\\"negatív egész szám.\\\")\\r\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":2979180,"cells":{"repo_name":{"kind":"string","value":"kelajin/djangochat"},"repo_url":{"kind":"string","value":"https://github.com/kelajin/djangochat"},"snapshot_id":{"kind":"string","value":"6b15fab6c26f8f67bbd5aebe3ede3d2c037bd54b"},"revision_id":{"kind":"string","value":"08fc1dcfd0e15adee194f1788e829598d4781545"},"directory_id":{"kind":"string","value":"49158bb1726173cbf1bad4463b30175088bfdb94"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2018-09-28T10:33:55.439405","string":"2018-09-28T10:33:55.439405"},"revision_date":{"kind":"timestamp","value":"2018-06-18T06:48:48","string":"2018-06-18T06:48:48"},"committer_date":{"kind":"timestamp","value":"2018-06-18T06:48:48","string":"2018-06-18T06:48:48"},"github_id":{"kind":"number","value":133458789,"string":"133,458,789"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5901639461517334,"alphanum_fraction":0.6090794205665588,"avg_line_length":29.5,"blob_id":"fcdebd4a3357cc1ac3fcb69c5a085fae64a12977","content_id":"66e8391d715fd10a3e0e39dddadc54e136d2df90","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":793,"license_type":"no_license","max_line_length":114,"num_lines":26,"path":"/chatbot/migrations/0001_initial.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"# Generated by Django 2.0.5 on 2018-05-31 10:28\n\nimport datetime\nfrom django.db import migrations, models\nimport djongo.models.fields\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Conversation',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('statements', djongo.models.fields.ListField()),\n ('train_on', models.DateTimeField(default=datetime.datetime.now)),\n ('create_on', models.DateTimeField(default=datetime.datetime.now)),\n ('update_on', models.DateTimeField(default=datetime.datetime.now)),\n ],\n ),\n ]\n"},{"alpha_fraction":0.5089108943939209,"alphanum_fraction":0.5702970027923584,"avg_line_length":21.954545974731445,"blob_id":"6f4154985b617c62c74bf869c172a20b05a95645","content_id":"fedb8dcbc2c09dae0066a6811111729b045e73a3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":505,"license_type":"no_license","max_line_length":53,"num_lines":22,"path":"/chatbot/migrations/0005_auto_20180531_1431.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"# Generated by Django 2.0.5 on 2018-05-31 14:31\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('chatbot', '0004_auto_20180531_1419'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='conversation',\n name='train_on',\n ),\n migrations.AddField(\n model_name='conversation',\n name='trained',\n field=models.BooleanField(default=False),\n ),\n ]\n"},{"alpha_fraction":0.6058897376060486,"alphanum_fraction":0.6102756857872009,"avg_line_length":26.517240524291992,"blob_id":"a708a556fd3d378ffbe390edee9a8e97b8f7d53e","content_id":"df24b58da3d8f1160e86945c239dfa4a4b1a4526","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1596,"license_type":"no_license","max_line_length":82,"num_lines":58,"path":"/chatbot/sync_corpus.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"import datetime\n\nfrom django.utils import timezone\n\nfrom case.models import CaseEntity\nfrom chatbot.models import Conversation\nfrom djangochat import settings\nfrom mongodb.mongo_utils import MongoDB\n\n\ndef f_func(case: CaseEntity) -> bool:\n # print(case)\n result = not case['question'] == '' and not case['answers'] == []\n return result\n\n\ndef m_func(case: CaseEntity):\n a = [[case['question'], a['answer']] for a in case['answers']]\n statements = [y for x in a for y in x]\n print(statements)\n con = Conversation()\n con.statements = statements\n con.create_on = timezone.now()\n con.update_on = timezone.now()\n return con\n\n\ndef auto_refresh_corpus():\n case_col = settings.MONGODB['CASE_COL']\n conv_col = settings.MONGODB['CONV_COL']\n pipeline = [\n {'$match': {'update_on': {'$exists': 1}}},\n {'$sort': {'update_on': -1}},\n {'$limit': 1}\n ]\n last_one = list(MongoDB().aggregate(collection=conv_col, pipeline=pipeline))\n # print(midnight)\n # last_date = last_one[0].get('update_on', midnight) if last_one else midnight\n pipeline1 = [\n {'$match': {'update_on': {\n # '$gt': last_date,\n '$lte': timezone.now()}}},\n ]\n last = MongoDB().aggregate(collection=case_col, pipeline=pipeline1)\n corpus = list(map(m_func, filter(f_func, last)))\n if not corpus:\n print('corpus is empty')\n return\n print(type(corpus[0]))\n Conversation.objects.bulk_create(corpus)\n\n\ndef run():\n try:\n auto_refresh_corpus()\n except Exception as e:\n # print(e)\n raise e\n"},{"alpha_fraction":0.6520211100578308,"alphanum_fraction":0.671353280544281,"avg_line_length":34.5625,"blob_id":"b2b397c57cc3aa007fabffba200e732195ec92b3","content_id":"878dfc3a3d85faabce52f06adfc4d4e8c69750eb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":569,"license_type":"no_license","max_line_length":73,"num_lines":16,"path":"/djangochat/csrf.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"try:\n from django.utils.deprecation import MiddlewareMixin # Django 1.10.x\nexcept ImportError:\n MiddlewareMixin = object # Django 1.4.x - Django 1.9.x\n\n\nclass CSRFMiddleware(MiddlewareMixin):\n def process_request(self, request):\n return None\n\n def process_response(self, request, response):\n response['Access-Control-Allow-Origin'] = '*'\n response['Access-Control-Allow-Methods'] = 'POST, GET, OPTIONS'\n response[\"Access-Control-Max-Age\"] = \"1000\"\n response[\"Access-Control-Allow-Headers\"] = \"*\"\n return response\n"},{"alpha_fraction":0.5296609997749329,"alphanum_fraction":0.5300847291946411,"avg_line_length":26.44186019897461,"blob_id":"453e0cb4e596725da771704cfadf11011abafa1d","content_id":"acdae2595bcf9ffd5d0e551a9844ee53da835645","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2360,"license_type":"no_license","max_line_length":81,"num_lines":86,"path":"/mongodb/mongo_utils.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"import pymongo\nfrom django.conf import settings\n\nfrom chatbot.models import Conversation\n\n\nclass CorpusDataRetriever(object):\n @staticmethod\n def get_all():\n pass\n\n @staticmethod\n def get_last():\n pass\n\n\nclass LawCorpusDataRetriever(CorpusDataRetriever):\n @staticmethod\n def get_last(**kwargs) -> list:\n data = Conversation.objects.mongo_aggregate([\n {\n '$match': {\n 'statements': {\n '$exists': 1\n },\n 'trained': {\n '$ne': True\n }\n },\n\n },\n ])\n for i in data:\n Conversation.objects.filter(id=i['id']).update(trained=True)\n yield i['statements']\n\n @staticmethod\n def get_all(**kwargs) -> list:\n pass\n\n\nclass CorpusData:\n def __init__(self):\n self.__CORPUS_MAP = {\n 'law': {\n 'last': LawCorpusDataRetriever.get_last,\n 'all': LawCorpusDataRetriever.get_all,\n },\n None: LawCorpusDataRetriever.get_last\n }\n\n def get_corpus_data(self, corpus_type: str, data_type: str) -> list:\n return self.__CORPUS_MAP[corpus_type][data_type]()\n\n\nclass MongoDB(object):\n def __init__(self, db=None):\n self.__settings = settings\n self.__mongo_uri = self.__settings.MONGODB['MONGO_URI']\n self.__mongo_db = db if db else self.__settings.MONGODB['MONGO_DATABASE']\n self.__client = pymongo.MongoClient(self.__mongo_uri)\n self.__db = self.__client[self.__mongo_db]\n\n def aggregate(self, collection: str, pipeline: list):\n try:\n return self.__db[collection].aggregate(pipeline=pipeline)\n finally:\n self.__client.close()\n\n def distinct(self, collection: str, key: str, query: dict) -> list:\n try:\n return self.__db[collection].distinct(key, query)\n finally:\n self.__client.close()\n\n def insert_many(self, col: str, items: list) -> None:\n try:\n return self.__db[col].insert_many(documents=items, ordered=False)\n finally:\n self.__client.close()\n\n def get_all(self, col: str):\n try:\n return self.__db[col].find()\n finally:\n self.__client.close()\n"},{"alpha_fraction":0.7630854249000549,"alphanum_fraction":0.7630854249000549,"avg_line_length":32,"blob_id":"b245d639eaf0ce81abdd60d8a5edea1abb4e0cd1","content_id":"8d18d918d95810efeca0bd3b26c00e9536ed567f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":363,"license_type":"no_license","max_line_length":58,"num_lines":11,"path":"/chatbot/models.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"from django.utils import timezone\nfrom djongo import models\n\n\n# Create your models here.\nclass Conversation(models.Model):\n statements = models.ListField()\n trained = models.BooleanField(default=False)\n create_on = models.DateTimeField(default=timezone.now)\n update_on = models.DateTimeField(default=timezone.now)\n objects = models.DjongoManager()\n"},{"alpha_fraction":0.5583657622337341,"alphanum_fraction":0.5817120671272278,"avg_line_length":33.266666412353516,"blob_id":"d78ebc1f787c4e3d966cea62db781c798b549172","content_id":"ea800d569b9c18f6d7ff4fa06c6c78453affcfec","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1028,"license_type":"no_license","max_line_length":114,"num_lines":30,"path":"/case/migrations/0001_initial.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"# Generated by Django 2.0.5 on 2018-05-31 10:28\n\nimport datetime\nfrom django.db import migrations, models\nimport djongo.models.fields\n\n\nclass Migration(migrations.Migration):\n\n initial = True\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='CaseEntity',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ('url', models.CharField(default='default', max_length=500)),\n ('question', models.TextField()),\n ('desc', models.TextField()),\n ('category', models.CharField(default='default', max_length=255)),\n ('questioner', models.CharField(max_length=255)),\n ('answers', djongo.models.fields.ListField()),\n ('create_on', models.DateTimeField(default=datetime.datetime.now)),\n ('update_on', models.DateTimeField(default=datetime.datetime.now)),\n ],\n ),\n ]\n"},{"alpha_fraction":0.6084005236625671,"alphanum_fraction":0.6100975871086121,"avg_line_length":26.4069766998291,"blob_id":"fceeab607dd418df0f20c42e65707b7288d6811e","content_id":"2843d03cbb24903559fbc14ab879534459481936","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2357,"license_type":"no_license","max_line_length":85,"num_lines":86,"path":"/case/sync_case.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"import logging\nfrom datetime import datetime\n\nfrom case.models import CaseEntity\nfrom djangochat import settings\nfrom mongodb.mongo_utils import MongoDB\n\nlogger = logging.getLogger('django')\nCASE_COL = settings.MONGODB['CASE_COL']\nSCRAPY_DB = settings.MONGODB['SCRAPY_DB']\nSCRAPY_COL = settings.MONGODB['SCRAPY_COL']\n\n\ndef get_last_update_on():\n case_db = MongoDB()\n pipeline = [\n {\n '$sort': {\n 'update_on': -1\n }\n },\n {\n '$limit': 1\n }\n ]\n last_list = list(case_db.aggregate(collection=CASE_COL, pipeline=pipeline))\n if not last_list:\n return datetime.now()\n result = last_list[0]\n return result['update_on'] if result else datetime.now()\n\n\ndef get_last_scrapy_list():\n scrapy_db = MongoDB(SCRAPY_DB)\n update_on = get_last_update_on()\n print('last_update_on:%s', update_on)\n pipeline = [\n {'$match': {\"update_on\": {'$gte': update_on}}},\n {'$project': {'_id': 0}}\n ]\n return scrapy_db.aggregate(SCRAPY_COL, pipeline=pipeline)\n\n\ndef load_case(item):\n case = dict()\n case['url'] = item.get('url', 'default')\n case['category'] = item.get('category', 'default')\n case['question'] = item.get('question', 'default')\n case['questioner'] = item.get('questioner', 'default')\n case['desc'] = item.get('desc', 'default')\n case['answers'] = item.get('answers', [])\n case['create_on'] = item.get('create_on', datetime.now())\n case['update_on'] = item.get('update_on', datetime.now())\n case['id'] = datetime.now().timestamp()\n return case\n\n\ndef get_one_case():\n return CaseEntity.objects.first()\n\n\ndef get_all_scrapy_list():\n return MongoDB(SCRAPY_DB).get_all(SCRAPY_COL)\n\n\ndef case_filter(item):\n logger.info(item)\n return not item or not item.get('question', 'default') == 'default'\n\n\ndef auto_refresh_case():\n last_list = get_last_scrapy_list() if get_one_case() else get_all_scrapy_list()\n item_list = list(map(load_case, filter(case_filter, last_list)))\n if not item_list:\n print('case list is empty')\n return\n MongoDB().insert_many(CASE_COL, item_list)\n print('auto_refresh_case at %s with %d items' % (datetime.now(), len(item_list)))\n\n\ndef run():\n try:\n auto_refresh_case()\n except Exception as e:\n logger.error(e)\n print(e)\n"},{"alpha_fraction":0.562881588935852,"alphanum_fraction":0.6007326245307922,"avg_line_length":27.241378784179688,"blob_id":"2ad444b01a6195fec8503fb0fed7fa30c2d1eb58","content_id":"aa0bb0b1ceaf2e9507ccf433534c58bcea111239","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":819,"license_type":"no_license","max_line_length":74,"num_lines":29,"path":"/chatbot/migrations/0004_auto_20180531_1419.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"# Generated by Django 2.0.5 on 2018-05-31 14:19\n\nfrom django.db import migrations, models\nimport django.utils.timezone\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('chatbot', '0003_auto_20180531_1400'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='conversation',\n name='create_on',\n field=models.DateTimeField(default=django.utils.timezone.now),\n ),\n migrations.AlterField(\n model_name='conversation',\n name='train_on',\n field=models.DateTimeField(default=django.utils.timezone.now),\n ),\n migrations.AlterField(\n model_name='conversation',\n name='update_on',\n field=models.DateTimeField(default=django.utils.timezone.now),\n ),\n ]\n"},{"alpha_fraction":0.6907630562782288,"alphanum_fraction":0.6907630562782288,"avg_line_length":21.636363983154297,"blob_id":"79456df3336580a00fc2c2ce84f3cddf11ee1585","content_id":"66af492c38829dc698865d1eb02aa6b4ea7689ad","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":249,"license_type":"no_license","max_line_length":70,"num_lines":11,"path":"/chatbot/tests.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"import os, django\n\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"djangochat.settings\")\ndjango.setup()\nfrom chatbot import sync_corpus, auto_train\n\n# Create your tests here.\n\nif __name__ == \"__main__\":\n # sync_corpus.run()\n auto_train.run()\n"},{"alpha_fraction":0.5770114660263062,"alphanum_fraction":0.6206896305084229,"avg_line_length":21.894737243652344,"blob_id":"7a71d2ccba67fab57604453bcab811d9d59cb927","content_id":"4995b269382288d2cf139693abc2bc5751a07d96","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":435,"license_type":"no_license","max_line_length":88,"num_lines":19,"path":"/chatbot/migrations/0002_auto_20180531_1359.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"# Generated by Django 2.0.5 on 2018-05-31 13:59\n\nfrom django.db import migrations\nimport djongo.models.fields\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('chatbot', '0001_initial'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='conversation',\n name='id',\n field=djongo.models.fields.ObjectIdField(primary_key=True, serialize=False),\n ),\n ]\n"},{"alpha_fraction":0.8942307829856873,"alphanum_fraction":0.8942307829856873,"avg_line_length":14,"blob_id":"b7e1b0c99c65b83af88de6036f69ea3c291154bb","content_id":"59736a226fb20d3424294c51181163399853d231","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":104,"license_type":"no_license","max_line_length":24,"num_lines":7,"path":"/requirements.txt","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"django\nchatterbot\ndjongo\ndjango_crontab\ndjangorestframework\ndjango-rest-swagger\ndjango-simple-serializer"},{"alpha_fraction":0.6614173054695129,"alphanum_fraction":0.6771653294563293,"avg_line_length":29.479999542236328,"blob_id":"2db8998ef315698bd135857290138a1331d07e24","content_id":"5c7459246474d0a053b88c4d8cd8f0954a5c5789","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":762,"license_type":"no_license","max_line_length":68,"num_lines":25,"path":"/api/models.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"# from datetime import datetime\n#\n# from djongo import models\n#\n#\n# class AnswerEntity(models.Model):\n# answer = models.TextField()\n# answerer = models.CharField(max_length=255)\n# create_on = models.DateTimeField()\n#\n# class Meta:\n# abstract = True\n#\n#\n# class CaseEntity(models.Model):\n# url = models.CharField(max_length=500, default='default')\n# question = models.TextField()\n# desc = models.TextField()\n# category = models.CharField(max_length=255, default='default')\n# questioner = models.CharField(max_length=255)\n# answers = models.ListField()\n# create_on = models.DateTimeField(default=datetime.now)\n# update_on = models.DateTimeField(default=datetime.now)\n#\n# objects = models.DjongoManager()\n"},{"alpha_fraction":0.6906077265739441,"alphanum_fraction":0.6906077265739441,"avg_line_length":19.11111068725586,"blob_id":"4f0eefb08fb146b1f936bd356600c1ca593447ae","content_id":"5a2fee49eaf80f2633ee01663ec9af8967a25254","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":181,"license_type":"no_license","max_line_length":70,"num_lines":9,"path":"/case/tests.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"import os, django\n\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"djangochat.settings\")\ndjango.setup()\n\nfrom case import sync_case\n\nif __name__ == \"__main__\":\n sync_case.run()\n"},{"alpha_fraction":0.6768292784690857,"alphanum_fraction":0.6859756112098694,"avg_line_length":24.230770111083984,"blob_id":"c655341017389248eb1dbdc0be31c067cd0cd4a5","content_id":"5c4f9c46d002d9dbecf875a6132031c9f5d2faf1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":328,"license_type":"no_license","max_line_length":56,"num_lines":13,"path":"/utils/date.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"import datetime\n\n\ndef timestamp_datetime(time_stamp: any):\n if isinstance(time_stamp, int):\n time_stamp = str(time_stamp)\n time_stamp = int(time_stamp[0:10])\n print(time_stamp)\n return datetime.datetime.fromtimestamp(time_stamp)\n\n\ndef now_timestamp():\n return datetime.datetime.now().timestamp().__int__()\n"},{"alpha_fraction":0.5347368717193604,"alphanum_fraction":0.5347368717193604,"avg_line_length":22.75,"blob_id":"ffafb02b2585370ccebdeea96861f0d1c9805b71","content_id":"d17df40f219610ba897ac2374d2e07cbe3e279af","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":998,"license_type":"no_license","max_line_length":56,"num_lines":40,"path":"/api/serializers.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"from django.contrib.auth.models import User\nfrom rest_framework import serializers\n\nfrom case.models import CaseEntity\n\n\nclass CaseEntitySerializer(serializers.ModelSerializer):\n # ModelSerializer和Django中ModelForm功能相似\n # Serializer和Django中Form功能相似\n class Meta:\n model = CaseEntity\n fields = (\n 'id',\n 'url',\n 'question',\n 'questioner',\n 'desc',\n 'answers',\n 'create_on',\n 'update_on'\n )\n\n\nclass UserSerializer(serializers.ModelSerializer):\n # ModelSerializer和Django中ModelForm功能相似\n # Serializer和Django中Form功能相似\n class Meta:\n model = User\n fields = (\n 'password',\n 'last_login',\n 'is_superuser',\n 'username',\n 'first_name',\n 'email',\n 'is_staff',\n 'is_active',\n 'date_joined',\n 'last_name',\n )\n"},{"alpha_fraction":0.6165911555290222,"alphanum_fraction":0.624782145023346,"avg_line_length":25.938966751098633,"blob_id":"6a449ad02605333cde101f0aaf21444c830de9ed","content_id":"fe8cf1cf2be93423c6f5948bf36d10e754a47ab9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5738,"license_type":"no_license","max_line_length":91,"num_lines":213,"path":"/djangochat/settings.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"\"\"\"\nDjango settings for djangochat project.\n\nGenerated by 'django-admin startproject' using Django 2.0.5.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/2.0/topics/settings/\n\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/2.0/ref/settings/\n\"\"\"\n\nimport os\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\n# Quick-start development settings - unsuitable for production\n# See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = 'c_p^%$qjn)$w^zi%=e69mk193a^^%t_u$^_w1^%uu%h#5q1!1j'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True\n\nALLOWED_HOSTS = [\n 'localhost',\n 'http://localhost:4200/'\n]\nCORS_ORIGIN_ALLOW_ALL = False\nCORS_ALLOW_CREDENTIALS = True\nCORS_ORIGIN_WHITELIST = (\n 'http://localhost:4200'\n)\n\n# Application definition\n\nINSTALLED_APPS = [\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'chatterbot.ext.django_chatterbot',\n 'chatbot.apps.ChatbotConfig',\n 'mongodb.apps.MongodbConfig',\n 'api.apps.ApiConfig',\n 'case.apps.CaseConfig',\n 'django_crontab',\n 'rest_framework_swagger',\n 'rest_framework',\n]\n\nCHATTERBOT = {\n 'name': 'djangochat',\n 'logic_adapters': [\n 'chatterbot.logic.BestMatch'\n ],\n 'storage_adapter': \"chatterbot.storage.SQLStorageAdapter\",\n 'database': 'chatterbot',\n 'read_only': True\n}\n\nSESSION_SERIALIZER = 'django.contrib.sessions.serializers.PickleSerializer'\n\nMIDDLEWARE = [\n 'django.middleware.security.SecurityMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n # 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n 'djangochat.csrf.CSRFMiddleware'\n]\n\nROOT_URLCONF = 'djangochat.urls'\n\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': [os.path.join(BASE_DIR, 'templates')]\n ,\n 'APP_DIRS': True,\n 'OPTIONS': {\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.contrib.messages.context_processors.messages',\n ],\n },\n },\n]\n\nWSGI_APPLICATION = 'djangochat.wsgi.application'\n\n# Database\n# https://docs.djangoproject.com/en/2.0/ref/settings/#databases\n\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': os.path.join(BASE_DIR, 'chatterbot'),\n },\n 'mongodb': {\n 'ENGINE': 'djongo',\n 'NAME': 'corpus',\n }\n}\nMONGODB = {\n 'MONGO_URI': 'mongodb://127.0.0.1:27017',\n 'MONGO_DATABASE': 'corpus',\n 'CASE_COL': 'case_caseentity',\n 'CONV_COL': 'chatbot_conversation',\n 'SCRAPY_DB': 'scrapy_items',\n 'SCRAPY_COL': 'findlaw_item',\n\n}\nDATABASE_ROUTERS = ['djangochat.database_router.DatabaseAppsRouter']\nDATABASE_APPS_MAPPING = {\n 'djangochat': 'default',\n 'chatbot': 'mongodb',\n 'case': 'mongodb'\n # 'mongodb': 'mongodb',\n # 'api': 'mongodb',\n}\n# Password validation\n# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\n },\n {\n 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\n },\n]\n\n# Internationalization\n# https://docs.djangoproject.com/en/2.0/topics/i18n/\n\nLANGUAGE_CODE = 'en-us'\n\nTIME_ZONE = 'UTC'\n\nUSE_I18N = True\n\nUSE_L10N = True\n\nUSE_TZ = True\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/2.0/howto/static-files/\n\nSTATIC_URL = '/static/'\n\nCRONJOBS = [\n ('* */1 * * *', 'chatbot.auto_train.run', '>>/tmp/chat_train.log'),\n ('* */1 * * *', 'chattbot.sync_corpus.run', '>>/tmp/chat_corpus.log'),\n ('* */1 * * *', 'case.sync_case.run', '>>/tmp/chat_case.log'),\n]\n\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': {\n 'verbose': {\n 'format': '[%(asctime)s] [%(levelname)s] %(message)s'\n },\n },\n 'handlers': {\n 'console': {\n 'level': 'INFO',\n 'class': 'logging.StreamHandler',\n 'formatter': 'verbose'\n },\n 'file': {\n 'level': 'INFO',\n 'class': 'logging.FileHandler',\n 'filename': '/tmp/djangochat.log',\n 'formatter': 'verbose'\n },\n 'email': {\n 'level': 'ERROR',\n 'class': 'django.utils.log.AdminEmailHandler',\n 'include_html': True,\n }\n },\n 'loggers': {\n 'django': {\n 'handlers': ['console', 'file', 'email'],\n 'level': 'INFO',\n 'propagate': True,\n },\n },\n}\n\nREST_FRAMEWORK = {\n # Use Django's standard `django.contrib.auth` permissions,\n # or allow read-only access for unauthenticated users.\n # 'DEFAULT_PERMISSION_CLASSES': [\n # 'rest_framework.permissions.DjangoModelPermissionsOrAnonReadOnly'\n # ]\n}\n"},{"alpha_fraction":0.5898215174674988,"alphanum_fraction":0.5928598642349243,"avg_line_length":24.8137264251709,"blob_id":"3c6ed0de9f6541c34e90a889c82f8d37e40fc8af","content_id":"fac315ddb03f861dc764c2cf731fa81679d466dc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2649,"license_type":"no_license","max_line_length":83,"num_lines":102,"path":"/api/views.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"import datetime\nimport json\nimport logging\n\nfrom django.contrib.auth.models import User\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n\nfrom api.serializers import UserSerializer\nfrom case.models import CaseEntity\nfrom djangochat import settings\nfrom mongodb.mongo_utils import MongoDB\nfrom utils.date import timestamp_datetime, now_timestamp\n\nlogger = logging.getLogger(__name__)\n\n\n@api_view(['GET'])\ndef index(request):\n return Response(\"Hello, world.\")\n\n\n@api_view(['GET'])\ndef get_case(request):\n m = request.GET\n q = m.get('q', 'default')\n result = CaseEntity.objects.mongo_aggregate([\n {'$match': {'url': q}},\n {'$project': {'_id': 0}},\n {'$limit': 1}\n ])\n return Response(result)\n\n\n@api_view(['GET'])\ndef get_case_list(request):\n m = request.GET\n ca = m.get('category', '全部')\n co = m.get('create_on', now_timestamp())\n co = timestamp_datetime(co)\n match = {\n 'create_on': {'$lte': co},\n 'question': {'$not': {'$eq': 'default'}},\n 'answers': {'$not': {'$eq': []}},\n }\n if ca and not ca == '全部':\n match['category'] = ca\n\n limit = int(m.get('limit', 10))\n skip = int(m.get('skip', 0))\n result = CaseEntity.objects.mongo_aggregate([\n {'$match': match},\n {'$sort': {'create_on': -1}},\n {'$skip': skip},\n {'$limit': limit},\n {'$project': {'_id': 0}}\n ])\n return Response(result)\n\n\n@api_view(['GET'])\ndef get_total(request):\n m = request.GET\n ca = m.get('category', '全部')\n print(ca)\n co = m.get('create_on', now_timestamp())\n co = timestamp_datetime(co)\n match = dict()\n if ca and not ca == '全部':\n match['category'] = ca\n if co:\n match['create_on'] = {'$lte': co}\n total_list = CaseEntity.objects \\\n .mongo_aggregate([{'$match': match}])\n total = list(total_list).__len__()\n print(total)\n return Response(total)\n\n\n@api_view(['GET'])\ndef get_case_category_list(req):\n case_col = settings.MONGODB['CASE_COL']\n result = MongoDB().distinct(case_col, 'category', {'category': {'$exists': 1}})\n logger.info(result)\n return Response(result)\n\n\n@api_view(['POST'])\ndef register(req):\n m = json.loads(req.body.decode())\n print(m)\n username = m.get('un')\n password = m.get('pw')\n print(username)\n print(password)\n if not User.objects.get(username=username):\n user = User.objects.create_user(username=username, password=password)\n user.save()\n else:\n print('user exists')\n user = User()\n return Response(UserSerializer(user).data)\n"},{"alpha_fraction":0.7862069010734558,"alphanum_fraction":0.7862069010734558,"avg_line_length":23.16666603088379,"blob_id":"28504f0f51b1e8515af3a784997aea522f86eb95","content_id":"e5cd5755ffd02d61d3075040c67f5b4eb6ddae9a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":145,"license_type":"no_license","max_line_length":45,"num_lines":6,"path":"/mongodb/admin.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"from django.contrib import admin\n\n# Register your models here.\n# from mongodb.models.models import LawCorpus\n\n# admin.site.register([LawCorpus])\n"},{"alpha_fraction":0.6612903475761414,"alphanum_fraction":0.6612903475761414,"avg_line_length":24.83333396911621,"blob_id":"b9905f8670e300408553cd59de666ac986afe281","content_id":"34896a356e772dccf6c8e3e5c2c10d94c2a2585b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":310,"license_type":"no_license","max_line_length":57,"num_lines":12,"path":"/api/urls.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"from django.urls import path\n\nfrom api import views\n\nurlpatterns = [\n path('', views.index),\n path('case', views.get_case),\n path('cases', views.get_case_list),\n path('cases/category', views.get_case_category_list),\n path('cases/total', views.get_total),\n path('register', views.register),\n]\n"},{"alpha_fraction":0.6459107995033264,"alphanum_fraction":0.6477695107460022,"avg_line_length":20.959182739257812,"blob_id":"c697df0782595a8405c40c70fefcc687ed1e91b9","content_id":"6c53e749d7aabb64dcfd115ab3b7527514d0f99a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1076,"license_type":"no_license","max_line_length":73,"num_lines":49,"path":"/chatbot/auto_train.py","repo_name":"kelajin/djangochat","src_encoding":"UTF-8","text":"from datetime import datetime\n\nfrom chatterbot import ChatBot\nfrom chatterbot.trainers import ListTrainer\nfrom mongodb.mongo_utils import CorpusData\nimport logging\n\nlogger = logging.getLogger('django')\n\n__corpus_data = CorpusData()\n__chatterbot = ChatBot(\n 'djangochat',\n logic_adapters=[\n 'chatterbot.logic.BestMatch'\n ],\n storage_adapter=\"chatterbot.storage.SQLStorageAdapter\",\n database='../chatterbot',\n)\n__chatterbot.set_trainer(ListTrainer)\n\n\ndef train_by_db():\n \"\"\"train chatterbot\n this function try to train chatterbot\n with database, so that make a\n catch-store-train loop\n :return:\n \"\"\"\n\n new_data = __corpus_data \\\n .get_corpus_data(corpus_type='law', data_type='last')\n count = 0\n for data in new_data:\n count += 1\n __chatterbot.train(data)\n print('train chatbot at %s with %d corpus' % (datetime.now(), count))\n\n\ndef train_manually():\n arr = ['hello']\n __chatterbot.train(arr)\n\n\ndef run():\n try:\n train_by_db()\n except Exception as e:\n # print(e)\n raise e\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5901639461517334,\n \"alphanum_fraction\": 0.6090794205665588,\n \"avg_line_length\": 29.5,\n \"blob_id\": \"fcdebd4a3357cc1ac3fcb69c5a085fae64a12977\",\n \"content_id\": \"66e8391d715fd10a3e0e39dddadc54e136d2df90\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 793,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 26,\n \"path\": \"/chatbot/migrations/0001_initial.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.0.5 on 2018-05-31 10:28\\n\\nimport datetime\\nfrom django.db import migrations, models\\nimport djongo.models.fields\\n\\n\\nclass Migration(migrations.Migration):\\n\\n initial = True\\n\\n dependencies = [\\n ]\\n\\n operations = [\\n migrations.CreateModel(\\n name='Conversation',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('statements', djongo.models.fields.ListField()),\\n ('train_on', models.DateTimeField(default=datetime.datetime.now)),\\n ('create_on', models.DateTimeField(default=datetime.datetime.now)),\\n ('update_on', models.DateTimeField(default=datetime.datetime.now)),\\n ],\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.5089108943939209,\n \"alphanum_fraction\": 0.5702970027923584,\n \"avg_line_length\": 21.954545974731445,\n \"blob_id\": \"6f4154985b617c62c74bf869c172a20b05a95645\",\n \"content_id\": \"fedb8dcbc2c09dae0066a6811111729b045e73a3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 505,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 22,\n \"path\": \"/chatbot/migrations/0005_auto_20180531_1431.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.0.5 on 2018-05-31 14:31\\n\\nfrom django.db import migrations, models\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('chatbot', '0004_auto_20180531_1419'),\\n ]\\n\\n operations = [\\n migrations.RemoveField(\\n model_name='conversation',\\n name='train_on',\\n ),\\n migrations.AddField(\\n model_name='conversation',\\n name='trained',\\n field=models.BooleanField(default=False),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6058897376060486,\n \"alphanum_fraction\": 0.6102756857872009,\n \"avg_line_length\": 26.517240524291992,\n \"blob_id\": \"a708a556fd3d378ffbe390edee9a8e97b8f7d53e\",\n \"content_id\": \"df24b58da3d8f1160e86945c239dfa4a4b1a4526\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1596,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 58,\n \"path\": \"/chatbot/sync_corpus.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import datetime\\n\\nfrom django.utils import timezone\\n\\nfrom case.models import CaseEntity\\nfrom chatbot.models import Conversation\\nfrom djangochat import settings\\nfrom mongodb.mongo_utils import MongoDB\\n\\n\\ndef f_func(case: CaseEntity) -> bool:\\n # print(case)\\n result = not case['question'] == '' and not case['answers'] == []\\n return result\\n\\n\\ndef m_func(case: CaseEntity):\\n a = [[case['question'], a['answer']] for a in case['answers']]\\n statements = [y for x in a for y in x]\\n print(statements)\\n con = Conversation()\\n con.statements = statements\\n con.create_on = timezone.now()\\n con.update_on = timezone.now()\\n return con\\n\\n\\ndef auto_refresh_corpus():\\n case_col = settings.MONGODB['CASE_COL']\\n conv_col = settings.MONGODB['CONV_COL']\\n pipeline = [\\n {'$match': {'update_on': {'$exists': 1}}},\\n {'$sort': {'update_on': -1}},\\n {'$limit': 1}\\n ]\\n last_one = list(MongoDB().aggregate(collection=conv_col, pipeline=pipeline))\\n # print(midnight)\\n # last_date = last_one[0].get('update_on', midnight) if last_one else midnight\\n pipeline1 = [\\n {'$match': {'update_on': {\\n # '$gt': last_date,\\n '$lte': timezone.now()}}},\\n ]\\n last = MongoDB().aggregate(collection=case_col, pipeline=pipeline1)\\n corpus = list(map(m_func, filter(f_func, last)))\\n if not corpus:\\n print('corpus is empty')\\n return\\n print(type(corpus[0]))\\n Conversation.objects.bulk_create(corpus)\\n\\n\\ndef run():\\n try:\\n auto_refresh_corpus()\\n except Exception as e:\\n # print(e)\\n raise e\\n\"\n },\n {\n \"alpha_fraction\": 0.6520211100578308,\n \"alphanum_fraction\": 0.671353280544281,\n \"avg_line_length\": 34.5625,\n \"blob_id\": \"b2b397c57cc3aa007fabffba200e732195ec92b3\",\n \"content_id\": \"878dfc3a3d85faabce52f06adfc4d4e8c69750eb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 569,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 16,\n \"path\": \"/djangochat/csrf.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"try:\\n from django.utils.deprecation import MiddlewareMixin # Django 1.10.x\\nexcept ImportError:\\n MiddlewareMixin = object # Django 1.4.x - Django 1.9.x\\n\\n\\nclass CSRFMiddleware(MiddlewareMixin):\\n def process_request(self, request):\\n return None\\n\\n def process_response(self, request, response):\\n response['Access-Control-Allow-Origin'] = '*'\\n response['Access-Control-Allow-Methods'] = 'POST, GET, OPTIONS'\\n response[\\\"Access-Control-Max-Age\\\"] = \\\"1000\\\"\\n response[\\\"Access-Control-Allow-Headers\\\"] = \\\"*\\\"\\n return response\\n\"\n },\n {\n \"alpha_fraction\": 0.5296609997749329,\n \"alphanum_fraction\": 0.5300847291946411,\n \"avg_line_length\": 26.44186019897461,\n \"blob_id\": \"453e0cb4e596725da771704cfadf11011abafa1d\",\n \"content_id\": \"acdae2595bcf9ffd5d0e551a9844ee53da835645\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2360,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 86,\n \"path\": \"/mongodb/mongo_utils.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import pymongo\\nfrom django.conf import settings\\n\\nfrom chatbot.models import Conversation\\n\\n\\nclass CorpusDataRetriever(object):\\n @staticmethod\\n def get_all():\\n pass\\n\\n @staticmethod\\n def get_last():\\n pass\\n\\n\\nclass LawCorpusDataRetriever(CorpusDataRetriever):\\n @staticmethod\\n def get_last(**kwargs) -> list:\\n data = Conversation.objects.mongo_aggregate([\\n {\\n '$match': {\\n 'statements': {\\n '$exists': 1\\n },\\n 'trained': {\\n '$ne': True\\n }\\n },\\n\\n },\\n ])\\n for i in data:\\n Conversation.objects.filter(id=i['id']).update(trained=True)\\n yield i['statements']\\n\\n @staticmethod\\n def get_all(**kwargs) -> list:\\n pass\\n\\n\\nclass CorpusData:\\n def __init__(self):\\n self.__CORPUS_MAP = {\\n 'law': {\\n 'last': LawCorpusDataRetriever.get_last,\\n 'all': LawCorpusDataRetriever.get_all,\\n },\\n None: LawCorpusDataRetriever.get_last\\n }\\n\\n def get_corpus_data(self, corpus_type: str, data_type: str) -> list:\\n return self.__CORPUS_MAP[corpus_type][data_type]()\\n\\n\\nclass MongoDB(object):\\n def __init__(self, db=None):\\n self.__settings = settings\\n self.__mongo_uri = self.__settings.MONGODB['MONGO_URI']\\n self.__mongo_db = db if db else self.__settings.MONGODB['MONGO_DATABASE']\\n self.__client = pymongo.MongoClient(self.__mongo_uri)\\n self.__db = self.__client[self.__mongo_db]\\n\\n def aggregate(self, collection: str, pipeline: list):\\n try:\\n return self.__db[collection].aggregate(pipeline=pipeline)\\n finally:\\n self.__client.close()\\n\\n def distinct(self, collection: str, key: str, query: dict) -> list:\\n try:\\n return self.__db[collection].distinct(key, query)\\n finally:\\n self.__client.close()\\n\\n def insert_many(self, col: str, items: list) -> None:\\n try:\\n return self.__db[col].insert_many(documents=items, ordered=False)\\n finally:\\n self.__client.close()\\n\\n def get_all(self, col: str):\\n try:\\n return self.__db[col].find()\\n finally:\\n self.__client.close()\\n\"\n },\n {\n \"alpha_fraction\": 0.7630854249000549,\n \"alphanum_fraction\": 0.7630854249000549,\n \"avg_line_length\": 32,\n \"blob_id\": \"b245d639eaf0ce81abdd60d8a5edea1abb4e0cd1\",\n \"content_id\": \"8d18d918d95810efeca0bd3b26c00e9536ed567f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 363,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 11,\n \"path\": \"/chatbot/models.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.utils import timezone\\nfrom djongo import models\\n\\n\\n# Create your models here.\\nclass Conversation(models.Model):\\n statements = models.ListField()\\n trained = models.BooleanField(default=False)\\n create_on = models.DateTimeField(default=timezone.now)\\n update_on = models.DateTimeField(default=timezone.now)\\n objects = models.DjongoManager()\\n\"\n },\n {\n \"alpha_fraction\": 0.5583657622337341,\n \"alphanum_fraction\": 0.5817120671272278,\n \"avg_line_length\": 33.266666412353516,\n \"blob_id\": \"d78ebc1f787c4e3d966cea62db781c798b549172\",\n \"content_id\": \"ea800d569b9c18f6d7ff4fa06c6c78453affcfec\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1028,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 30,\n \"path\": \"/case/migrations/0001_initial.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.0.5 on 2018-05-31 10:28\\n\\nimport datetime\\nfrom django.db import migrations, models\\nimport djongo.models.fields\\n\\n\\nclass Migration(migrations.Migration):\\n\\n initial = True\\n\\n dependencies = [\\n ]\\n\\n operations = [\\n migrations.CreateModel(\\n name='CaseEntity',\\n fields=[\\n ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\\n ('url', models.CharField(default='default', max_length=500)),\\n ('question', models.TextField()),\\n ('desc', models.TextField()),\\n ('category', models.CharField(default='default', max_length=255)),\\n ('questioner', models.CharField(max_length=255)),\\n ('answers', djongo.models.fields.ListField()),\\n ('create_on', models.DateTimeField(default=datetime.datetime.now)),\\n ('update_on', models.DateTimeField(default=datetime.datetime.now)),\\n ],\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6084005236625671,\n \"alphanum_fraction\": 0.6100975871086121,\n \"avg_line_length\": 26.4069766998291,\n \"blob_id\": \"fceeab607dd418df0f20c42e65707b7288d6811e\",\n \"content_id\": \"2843d03cbb24903559fbc14ab879534459481936\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2357,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 86,\n \"path\": \"/case/sync_case.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import logging\\nfrom datetime import datetime\\n\\nfrom case.models import CaseEntity\\nfrom djangochat import settings\\nfrom mongodb.mongo_utils import MongoDB\\n\\nlogger = logging.getLogger('django')\\nCASE_COL = settings.MONGODB['CASE_COL']\\nSCRAPY_DB = settings.MONGODB['SCRAPY_DB']\\nSCRAPY_COL = settings.MONGODB['SCRAPY_COL']\\n\\n\\ndef get_last_update_on():\\n case_db = MongoDB()\\n pipeline = [\\n {\\n '$sort': {\\n 'update_on': -1\\n }\\n },\\n {\\n '$limit': 1\\n }\\n ]\\n last_list = list(case_db.aggregate(collection=CASE_COL, pipeline=pipeline))\\n if not last_list:\\n return datetime.now()\\n result = last_list[0]\\n return result['update_on'] if result else datetime.now()\\n\\n\\ndef get_last_scrapy_list():\\n scrapy_db = MongoDB(SCRAPY_DB)\\n update_on = get_last_update_on()\\n print('last_update_on:%s', update_on)\\n pipeline = [\\n {'$match': {\\\"update_on\\\": {'$gte': update_on}}},\\n {'$project': {'_id': 0}}\\n ]\\n return scrapy_db.aggregate(SCRAPY_COL, pipeline=pipeline)\\n\\n\\ndef load_case(item):\\n case = dict()\\n case['url'] = item.get('url', 'default')\\n case['category'] = item.get('category', 'default')\\n case['question'] = item.get('question', 'default')\\n case['questioner'] = item.get('questioner', 'default')\\n case['desc'] = item.get('desc', 'default')\\n case['answers'] = item.get('answers', [])\\n case['create_on'] = item.get('create_on', datetime.now())\\n case['update_on'] = item.get('update_on', datetime.now())\\n case['id'] = datetime.now().timestamp()\\n return case\\n\\n\\ndef get_one_case():\\n return CaseEntity.objects.first()\\n\\n\\ndef get_all_scrapy_list():\\n return MongoDB(SCRAPY_DB).get_all(SCRAPY_COL)\\n\\n\\ndef case_filter(item):\\n logger.info(item)\\n return not item or not item.get('question', 'default') == 'default'\\n\\n\\ndef auto_refresh_case():\\n last_list = get_last_scrapy_list() if get_one_case() else get_all_scrapy_list()\\n item_list = list(map(load_case, filter(case_filter, last_list)))\\n if not item_list:\\n print('case list is empty')\\n return\\n MongoDB().insert_many(CASE_COL, item_list)\\n print('auto_refresh_case at %s with %d items' % (datetime.now(), len(item_list)))\\n\\n\\ndef run():\\n try:\\n auto_refresh_case()\\n except Exception as e:\\n logger.error(e)\\n print(e)\\n\"\n },\n {\n \"alpha_fraction\": 0.562881588935852,\n \"alphanum_fraction\": 0.6007326245307922,\n \"avg_line_length\": 27.241378784179688,\n \"blob_id\": \"2ad444b01a6195fec8503fb0fed7fa30c2d1eb58\",\n \"content_id\": \"aa0bb0b1ceaf2e9507ccf433534c58bcea111239\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 819,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 29,\n \"path\": \"/chatbot/migrations/0004_auto_20180531_1419.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.0.5 on 2018-05-31 14:19\\n\\nfrom django.db import migrations, models\\nimport django.utils.timezone\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('chatbot', '0003_auto_20180531_1400'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='conversation',\\n name='create_on',\\n field=models.DateTimeField(default=django.utils.timezone.now),\\n ),\\n migrations.AlterField(\\n model_name='conversation',\\n name='train_on',\\n field=models.DateTimeField(default=django.utils.timezone.now),\\n ),\\n migrations.AlterField(\\n model_name='conversation',\\n name='update_on',\\n field=models.DateTimeField(default=django.utils.timezone.now),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.6907630562782288,\n \"alphanum_fraction\": 0.6907630562782288,\n \"avg_line_length\": 21.636363983154297,\n \"blob_id\": \"79456df3336580a00fc2c2ce84f3cddf11ee1585\",\n \"content_id\": \"66af492c38829dc698865d1eb02aa6b4ea7689ad\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 249,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 11,\n \"path\": \"/chatbot/tests.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os, django\\n\\nos.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"djangochat.settings\\\")\\ndjango.setup()\\nfrom chatbot import sync_corpus, auto_train\\n\\n# Create your tests here.\\n\\nif __name__ == \\\"__main__\\\":\\n # sync_corpus.run()\\n auto_train.run()\\n\"\n },\n {\n \"alpha_fraction\": 0.5770114660263062,\n \"alphanum_fraction\": 0.6206896305084229,\n \"avg_line_length\": 21.894737243652344,\n \"blob_id\": \"7a71d2ccba67fab57604453bcab811d9d59cb927\",\n \"content_id\": \"4995b269382288d2cf139693abc2bc5751a07d96\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 435,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 19,\n \"path\": \"/chatbot/migrations/0002_auto_20180531_1359.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Generated by Django 2.0.5 on 2018-05-31 13:59\\n\\nfrom django.db import migrations\\nimport djongo.models.fields\\n\\n\\nclass Migration(migrations.Migration):\\n\\n dependencies = [\\n ('chatbot', '0001_initial'),\\n ]\\n\\n operations = [\\n migrations.AlterField(\\n model_name='conversation',\\n name='id',\\n field=djongo.models.fields.ObjectIdField(primary_key=True, serialize=False),\\n ),\\n ]\\n\"\n },\n {\n \"alpha_fraction\": 0.8942307829856873,\n \"alphanum_fraction\": 0.8942307829856873,\n \"avg_line_length\": 14,\n \"blob_id\": \"b7e1b0c99c65b83af88de6036f69ea3c291154bb\",\n \"content_id\": \"59736a226fb20d3424294c51181163399853d231\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 104,\n \"license_type\": \"no_license\",\n \"max_line_length\": 24,\n \"num_lines\": 7,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"django\\nchatterbot\\ndjongo\\ndjango_crontab\\ndjangorestframework\\ndjango-rest-swagger\\ndjango-simple-serializer\"\n },\n {\n \"alpha_fraction\": 0.6614173054695129,\n \"alphanum_fraction\": 0.6771653294563293,\n \"avg_line_length\": 29.479999542236328,\n \"blob_id\": \"2db8998ef315698bd135857290138a1331d07e24\",\n \"content_id\": \"5c7459246474d0a053b88c4d8cd8f0954a5c5789\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 762,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 25,\n \"path\": \"/api/models.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# from datetime import datetime\\n#\\n# from djongo import models\\n#\\n#\\n# class AnswerEntity(models.Model):\\n# answer = models.TextField()\\n# answerer = models.CharField(max_length=255)\\n# create_on = models.DateTimeField()\\n#\\n# class Meta:\\n# abstract = True\\n#\\n#\\n# class CaseEntity(models.Model):\\n# url = models.CharField(max_length=500, default='default')\\n# question = models.TextField()\\n# desc = models.TextField()\\n# category = models.CharField(max_length=255, default='default')\\n# questioner = models.CharField(max_length=255)\\n# answers = models.ListField()\\n# create_on = models.DateTimeField(default=datetime.now)\\n# update_on = models.DateTimeField(default=datetime.now)\\n#\\n# objects = models.DjongoManager()\\n\"\n },\n {\n \"alpha_fraction\": 0.6906077265739441,\n \"alphanum_fraction\": 0.6906077265739441,\n \"avg_line_length\": 19.11111068725586,\n \"blob_id\": \"4f0eefb08fb146b1f936bd356600c1ca593447ae\",\n \"content_id\": \"5a2fee49eaf80f2633ee01663ec9af8967a25254\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 181,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 9,\n \"path\": \"/case/tests.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os, django\\n\\nos.environ.setdefault(\\\"DJANGO_SETTINGS_MODULE\\\", \\\"djangochat.settings\\\")\\ndjango.setup()\\n\\nfrom case import sync_case\\n\\nif __name__ == \\\"__main__\\\":\\n sync_case.run()\\n\"\n },\n {\n \"alpha_fraction\": 0.6768292784690857,\n \"alphanum_fraction\": 0.6859756112098694,\n \"avg_line_length\": 24.230770111083984,\n \"blob_id\": \"c655341017389248eb1dbdc0be31c067cd0cd4a5\",\n \"content_id\": \"5c4f9c46d002d9dbecf875a6132031c9f5d2faf1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 328,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 13,\n \"path\": \"/utils/date.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import datetime\\n\\n\\ndef timestamp_datetime(time_stamp: any):\\n if isinstance(time_stamp, int):\\n time_stamp = str(time_stamp)\\n time_stamp = int(time_stamp[0:10])\\n print(time_stamp)\\n return datetime.datetime.fromtimestamp(time_stamp)\\n\\n\\ndef now_timestamp():\\n return datetime.datetime.now().timestamp().__int__()\\n\"\n },\n {\n \"alpha_fraction\": 0.5347368717193604,\n \"alphanum_fraction\": 0.5347368717193604,\n \"avg_line_length\": 22.75,\n \"blob_id\": \"ffafb02b2585370ccebdeea96861f0d1c9805b71\",\n \"content_id\": \"d17df40f219610ba897ac2374d2e07cbe3e279af\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 998,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 40,\n \"path\": \"/api/serializers.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib.auth.models import User\\nfrom rest_framework import serializers\\n\\nfrom case.models import CaseEntity\\n\\n\\nclass CaseEntitySerializer(serializers.ModelSerializer):\\n # ModelSerializer和Django中ModelForm功能相似\\n # Serializer和Django中Form功能相似\\n class Meta:\\n model = CaseEntity\\n fields = (\\n 'id',\\n 'url',\\n 'question',\\n 'questioner',\\n 'desc',\\n 'answers',\\n 'create_on',\\n 'update_on'\\n )\\n\\n\\nclass UserSerializer(serializers.ModelSerializer):\\n # ModelSerializer和Django中ModelForm功能相似\\n # Serializer和Django中Form功能相似\\n class Meta:\\n model = User\\n fields = (\\n 'password',\\n 'last_login',\\n 'is_superuser',\\n 'username',\\n 'first_name',\\n 'email',\\n 'is_staff',\\n 'is_active',\\n 'date_joined',\\n 'last_name',\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.6165911555290222,\n \"alphanum_fraction\": 0.624782145023346,\n \"avg_line_length\": 25.938966751098633,\n \"blob_id\": \"6a449ad02605333cde101f0aaf21444c830de9ed\",\n \"content_id\": \"fe8cf1cf2be93423c6f5948bf36d10e754a47ab9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5738,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 213,\n \"path\": \"/djangochat/settings.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nDjango settings for djangochat project.\\n\\nGenerated by 'django-admin startproject' using Django 2.0.5.\\n\\nFor more information on this file, see\\nhttps://docs.djangoproject.com/en/2.0/topics/settings/\\n\\nFor the full list of settings and their values, see\\nhttps://docs.djangoproject.com/en/2.0/ref/settings/\\n\\\"\\\"\\\"\\n\\nimport os\\n\\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\\n\\n# Quick-start development settings - unsuitable for production\\n# See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/\\n\\n# SECURITY WARNING: keep the secret key used in production secret!\\nSECRET_KEY = 'c_p^%$qjn)$w^zi%=e69mk193a^^%t_u$^_w1^%uu%h#5q1!1j'\\n\\n# SECURITY WARNING: don't run with debug turned on in production!\\nDEBUG = True\\n\\nALLOWED_HOSTS = [\\n 'localhost',\\n 'http://localhost:4200/'\\n]\\nCORS_ORIGIN_ALLOW_ALL = False\\nCORS_ALLOW_CREDENTIALS = True\\nCORS_ORIGIN_WHITELIST = (\\n 'http://localhost:4200'\\n)\\n\\n# Application definition\\n\\nINSTALLED_APPS = [\\n 'django.contrib.admin',\\n 'django.contrib.auth',\\n 'django.contrib.contenttypes',\\n 'django.contrib.sessions',\\n 'django.contrib.messages',\\n 'django.contrib.staticfiles',\\n 'chatterbot.ext.django_chatterbot',\\n 'chatbot.apps.ChatbotConfig',\\n 'mongodb.apps.MongodbConfig',\\n 'api.apps.ApiConfig',\\n 'case.apps.CaseConfig',\\n 'django_crontab',\\n 'rest_framework_swagger',\\n 'rest_framework',\\n]\\n\\nCHATTERBOT = {\\n 'name': 'djangochat',\\n 'logic_adapters': [\\n 'chatterbot.logic.BestMatch'\\n ],\\n 'storage_adapter': \\\"chatterbot.storage.SQLStorageAdapter\\\",\\n 'database': 'chatterbot',\\n 'read_only': True\\n}\\n\\nSESSION_SERIALIZER = 'django.contrib.sessions.serializers.PickleSerializer'\\n\\nMIDDLEWARE = [\\n 'django.middleware.security.SecurityMiddleware',\\n 'django.contrib.sessions.middleware.SessionMiddleware',\\n 'django.middleware.common.CommonMiddleware',\\n # 'django.middleware.csrf.CsrfViewMiddleware',\\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\\n 'django.contrib.messages.middleware.MessageMiddleware',\\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\\n 'djangochat.csrf.CSRFMiddleware'\\n]\\n\\nROOT_URLCONF = 'djangochat.urls'\\n\\nTEMPLATES = [\\n {\\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\\n 'DIRS': [os.path.join(BASE_DIR, 'templates')]\\n ,\\n 'APP_DIRS': True,\\n 'OPTIONS': {\\n 'context_processors': [\\n 'django.template.context_processors.debug',\\n 'django.template.context_processors.request',\\n 'django.contrib.auth.context_processors.auth',\\n 'django.contrib.messages.context_processors.messages',\\n ],\\n },\\n },\\n]\\n\\nWSGI_APPLICATION = 'djangochat.wsgi.application'\\n\\n# Database\\n# https://docs.djangoproject.com/en/2.0/ref/settings/#databases\\n\\nDATABASES = {\\n 'default': {\\n 'ENGINE': 'django.db.backends.sqlite3',\\n 'NAME': os.path.join(BASE_DIR, 'chatterbot'),\\n },\\n 'mongodb': {\\n 'ENGINE': 'djongo',\\n 'NAME': 'corpus',\\n }\\n}\\nMONGODB = {\\n 'MONGO_URI': 'mongodb://127.0.0.1:27017',\\n 'MONGO_DATABASE': 'corpus',\\n 'CASE_COL': 'case_caseentity',\\n 'CONV_COL': 'chatbot_conversation',\\n 'SCRAPY_DB': 'scrapy_items',\\n 'SCRAPY_COL': 'findlaw_item',\\n\\n}\\nDATABASE_ROUTERS = ['djangochat.database_router.DatabaseAppsRouter']\\nDATABASE_APPS_MAPPING = {\\n 'djangochat': 'default',\\n 'chatbot': 'mongodb',\\n 'case': 'mongodb'\\n # 'mongodb': 'mongodb',\\n # 'api': 'mongodb',\\n}\\n# Password validation\\n# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators\\n\\nAUTH_PASSWORD_VALIDATORS = [\\n {\\n 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',\\n },\\n {\\n 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',\\n },\\n {\\n 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',\\n },\\n {\\n 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',\\n },\\n]\\n\\n# Internationalization\\n# https://docs.djangoproject.com/en/2.0/topics/i18n/\\n\\nLANGUAGE_CODE = 'en-us'\\n\\nTIME_ZONE = 'UTC'\\n\\nUSE_I18N = True\\n\\nUSE_L10N = True\\n\\nUSE_TZ = True\\n\\n# Static files (CSS, JavaScript, Images)\\n# https://docs.djangoproject.com/en/2.0/howto/static-files/\\n\\nSTATIC_URL = '/static/'\\n\\nCRONJOBS = [\\n ('* */1 * * *', 'chatbot.auto_train.run', '>>/tmp/chat_train.log'),\\n ('* */1 * * *', 'chattbot.sync_corpus.run', '>>/tmp/chat_corpus.log'),\\n ('* */1 * * *', 'case.sync_case.run', '>>/tmp/chat_case.log'),\\n]\\n\\nLOGGING = {\\n 'version': 1,\\n 'disable_existing_loggers': False,\\n 'formatters': {\\n 'verbose': {\\n 'format': '[%(asctime)s] [%(levelname)s] %(message)s'\\n },\\n },\\n 'handlers': {\\n 'console': {\\n 'level': 'INFO',\\n 'class': 'logging.StreamHandler',\\n 'formatter': 'verbose'\\n },\\n 'file': {\\n 'level': 'INFO',\\n 'class': 'logging.FileHandler',\\n 'filename': '/tmp/djangochat.log',\\n 'formatter': 'verbose'\\n },\\n 'email': {\\n 'level': 'ERROR',\\n 'class': 'django.utils.log.AdminEmailHandler',\\n 'include_html': True,\\n }\\n },\\n 'loggers': {\\n 'django': {\\n 'handlers': ['console', 'file', 'email'],\\n 'level': 'INFO',\\n 'propagate': True,\\n },\\n },\\n}\\n\\nREST_FRAMEWORK = {\\n # Use Django's standard `django.contrib.auth` permissions,\\n # or allow read-only access for unauthenticated users.\\n # 'DEFAULT_PERMISSION_CLASSES': [\\n # 'rest_framework.permissions.DjangoModelPermissionsOrAnonReadOnly'\\n # ]\\n}\\n\"\n },\n {\n \"alpha_fraction\": 0.5898215174674988,\n \"alphanum_fraction\": 0.5928598642349243,\n \"avg_line_length\": 24.8137264251709,\n \"blob_id\": \"3c6ed0de9f6541c34e90a889c82f8d37e40fc8af\",\n \"content_id\": \"fac315ddb03f861dc764c2cf731fa81679d466dc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2649,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 102,\n \"path\": \"/api/views.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import datetime\\nimport json\\nimport logging\\n\\nfrom django.contrib.auth.models import User\\nfrom rest_framework.decorators import api_view\\nfrom rest_framework.response import Response\\n\\nfrom api.serializers import UserSerializer\\nfrom case.models import CaseEntity\\nfrom djangochat import settings\\nfrom mongodb.mongo_utils import MongoDB\\nfrom utils.date import timestamp_datetime, now_timestamp\\n\\nlogger = logging.getLogger(__name__)\\n\\n\\n@api_view(['GET'])\\ndef index(request):\\n return Response(\\\"Hello, world.\\\")\\n\\n\\n@api_view(['GET'])\\ndef get_case(request):\\n m = request.GET\\n q = m.get('q', 'default')\\n result = CaseEntity.objects.mongo_aggregate([\\n {'$match': {'url': q}},\\n {'$project': {'_id': 0}},\\n {'$limit': 1}\\n ])\\n return Response(result)\\n\\n\\n@api_view(['GET'])\\ndef get_case_list(request):\\n m = request.GET\\n ca = m.get('category', '全部')\\n co = m.get('create_on', now_timestamp())\\n co = timestamp_datetime(co)\\n match = {\\n 'create_on': {'$lte': co},\\n 'question': {'$not': {'$eq': 'default'}},\\n 'answers': {'$not': {'$eq': []}},\\n }\\n if ca and not ca == '全部':\\n match['category'] = ca\\n\\n limit = int(m.get('limit', 10))\\n skip = int(m.get('skip', 0))\\n result = CaseEntity.objects.mongo_aggregate([\\n {'$match': match},\\n {'$sort': {'create_on': -1}},\\n {'$skip': skip},\\n {'$limit': limit},\\n {'$project': {'_id': 0}}\\n ])\\n return Response(result)\\n\\n\\n@api_view(['GET'])\\ndef get_total(request):\\n m = request.GET\\n ca = m.get('category', '全部')\\n print(ca)\\n co = m.get('create_on', now_timestamp())\\n co = timestamp_datetime(co)\\n match = dict()\\n if ca and not ca == '全部':\\n match['category'] = ca\\n if co:\\n match['create_on'] = {'$lte': co}\\n total_list = CaseEntity.objects \\\\\\n .mongo_aggregate([{'$match': match}])\\n total = list(total_list).__len__()\\n print(total)\\n return Response(total)\\n\\n\\n@api_view(['GET'])\\ndef get_case_category_list(req):\\n case_col = settings.MONGODB['CASE_COL']\\n result = MongoDB().distinct(case_col, 'category', {'category': {'$exists': 1}})\\n logger.info(result)\\n return Response(result)\\n\\n\\n@api_view(['POST'])\\ndef register(req):\\n m = json.loads(req.body.decode())\\n print(m)\\n username = m.get('un')\\n password = m.get('pw')\\n print(username)\\n print(password)\\n if not User.objects.get(username=username):\\n user = User.objects.create_user(username=username, password=password)\\n user.save()\\n else:\\n print('user exists')\\n user = User()\\n return Response(UserSerializer(user).data)\\n\"\n },\n {\n \"alpha_fraction\": 0.7862069010734558,\n \"alphanum_fraction\": 0.7862069010734558,\n \"avg_line_length\": 23.16666603088379,\n \"blob_id\": \"28504f0f51b1e8515af3a784997aea522f86eb95\",\n \"content_id\": \"e5cd5755ffd02d61d3075040c67f5b4eb6ddae9a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 145,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 6,\n \"path\": \"/mongodb/admin.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib import admin\\n\\n# Register your models here.\\n# from mongodb.models.models import LawCorpus\\n\\n# admin.site.register([LawCorpus])\\n\"\n },\n {\n \"alpha_fraction\": 0.6612903475761414,\n \"alphanum_fraction\": 0.6612903475761414,\n \"avg_line_length\": 24.83333396911621,\n \"blob_id\": \"b9905f8670e300408553cd59de666ac986afe281\",\n \"content_id\": \"34896a356e772dccf6c8e3e5c2c10d94c2a2585b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 310,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 12,\n \"path\": \"/api/urls.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.urls import path\\n\\nfrom api import views\\n\\nurlpatterns = [\\n path('', views.index),\\n path('case', views.get_case),\\n path('cases', views.get_case_list),\\n path('cases/category', views.get_case_category_list),\\n path('cases/total', views.get_total),\\n path('register', views.register),\\n]\\n\"\n },\n {\n \"alpha_fraction\": 0.6459107995033264,\n \"alphanum_fraction\": 0.6477695107460022,\n \"avg_line_length\": 20.959182739257812,\n \"blob_id\": \"c697df0782595a8405c40c70fefcc687ed1e91b9\",\n \"content_id\": \"6c53e749d7aabb64dcfd115ab3b7527514d0f99a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1076,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 49,\n \"path\": \"/chatbot/auto_train.py\",\n \"repo_name\": \"kelajin/djangochat\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\n\\nfrom chatterbot import ChatBot\\nfrom chatterbot.trainers import ListTrainer\\nfrom mongodb.mongo_utils import CorpusData\\nimport logging\\n\\nlogger = logging.getLogger('django')\\n\\n__corpus_data = CorpusData()\\n__chatterbot = ChatBot(\\n 'djangochat',\\n logic_adapters=[\\n 'chatterbot.logic.BestMatch'\\n ],\\n storage_adapter=\\\"chatterbot.storage.SQLStorageAdapter\\\",\\n database='../chatterbot',\\n)\\n__chatterbot.set_trainer(ListTrainer)\\n\\n\\ndef train_by_db():\\n \\\"\\\"\\\"train chatterbot\\n this function try to train chatterbot\\n with database, so that make a\\n catch-store-train loop\\n :return:\\n \\\"\\\"\\\"\\n\\n new_data = __corpus_data \\\\\\n .get_corpus_data(corpus_type='law', data_type='last')\\n count = 0\\n for data in new_data:\\n count += 1\\n __chatterbot.train(data)\\n print('train chatbot at %s with %d corpus' % (datetime.now(), count))\\n\\n\\ndef train_manually():\\n arr = ['hello']\\n __chatterbot.train(arr)\\n\\n\\ndef run():\\n try:\\n train_by_db()\\n except Exception as e:\\n # print(e)\\n raise e\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":21,"string":"21"}}},{"rowIdx":2979181,"cells":{"repo_name":{"kind":"string","value":"dattv/RetinaNet_TF14"},"repo_url":{"kind":"string","value":"https://github.com/dattv/RetinaNet_TF14"},"snapshot_id":{"kind":"string","value":"2350d33038430057626a748ff6f0e30e058e7b1d"},"revision_id":{"kind":"string","value":"03631421078b2340da1387298542188d6a0014b0"},"directory_id":{"kind":"string","value":"967458b1638f7e6005e471c29e8450c24d3496b4"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-06-19T05:50:54.488309","string":"2023-06-19T05:50:54.488309"},"revision_date":{"kind":"timestamp","value":"2021-07-14T16:57:58","string":"2021-07-14T16:57:58"},"committer_date":{"kind":"timestamp","value":"2021-07-14T16:57:58","string":"2021-07-14T16:57:58"},"github_id":{"kind":"number","value":347784170,"string":"347,784,170"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6914119124412537,"alphanum_fraction":0.7423580884933472,"avg_line_length":28.869565963745117,"blob_id":"3f472f8a2c957b777cbb91d8c0b9ce38d57c3bb2","content_id":"bf6fe0adabe5bb97c7fa52edb4ffa29b183bd694","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":687,"license_type":"no_license","max_line_length":85,"num_lines":23,"path":"/README.md","repo_name":"dattv/RetinaNet_TF14","src_encoding":"UTF-8","text":"# RetinaNet_TF14\n\n# Dataset\n\nUse tfrecord coco2017 \n\n## Matching Anchor boxes\n\n\n\n\n\n\n\n\n\n\n\n\n\n# Model\n\n\n"},{"alpha_fraction":0.5689198970794678,"alphanum_fraction":0.5886456370353699,"avg_line_length":37.49074172973633,"blob_id":"e0b8aa381cdcd86ddf79f56552f0cac71b642342","content_id":"149ed1338c3c6ff15b351a13d6978b171b97e66e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4157,"license_type":"no_license","max_line_length":85,"num_lines":108,"path":"/anchor_generator.py","repo_name":"dattv/RetinaNet_TF14","src_encoding":"UTF-8","text":"\"\"\"\n\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\nimport numpy as np\n\n\nclass AnchorBox(object):\n \"\"\"Generates anchor boxes.\n\n This class has operations to generate anchor boxes for feature maps at\n strides `[8, 16, 32, 64, 128]`. Where each anchor each box is of the\n format `[x, y, width, height]`.\n\n Attributes:\n aspect_ratios: A list of float values representing the aspect ratios of\n the anchor boxes at each location on the feature map\n scales: A list of float values representing the scale of the anchor boxes\n at each location on the feature map.\n num_anchors: The number of anchor boxes at each location on feature map\n areas: A list of float values representing the areas of the anchor\n boxes for each feature map in the feature pyramid.\n strides: A list of float value representing the strides for each feature\n map in the feature pyramid.\n \"\"\"\n\n def __init__(self, mode='tf'):\n self.mode = mode\n self.aspect_ratios = [0.5, 1.0, 2.0]\n self.scales = [2 ** x for x in [0, 1 / 3, 2 / 3]]\n\n self._num_anchors = len(self.aspect_ratios) * len(self.scales)\n self._strides = [2 ** i for i in range(3, 8)]\n self._areas = [x ** 2 for x in [32.0, 64.0, 128.0, 256.0, 512.0]]\n self._anchor_dims = self._compute_dims()\n\n def _compute_dims(self):\n \"\"\"Computes anchor box dimensions for all ratios and scales at all levels\n of the feature pyramid.\n \"\"\"\n anchor_dims_all = []\n for area in self._areas:\n anchor_dims = []\n for ratio in self.aspect_ratios:\n anchor_height = np.sqrt(area / ratio)\n anchor_width = area / anchor_height\n dims = np.reshape(\n np.stack([anchor_width, anchor_height], axis=-1), [1, 1, 2]\n )\n for scale in self.scales:\n anchor_dims.append(scale * dims)\n anchor_dims_all.append(np.stack(anchor_dims, axis=-2).astype(np.float32))\n return anchor_dims_all\n\n def _get_anchors(self, feature_height, feature_width, level):\n \"\"\"Generates anchor boxes for a given feature map size and level\n\n Arguments:\n feature_height: An integer representing the height of the feature map.\n feature_width: An integer representing the width of the feature map.\n level: An integer representing the level of the feature map in the\n feature pyramid.\n\n Returns:\n anchor boxes with the shape\n `(feature_height * feature_width * num_anchors, 4)`\n \"\"\"\n rx = np.arange(feature_width, dtype=np.float32) + 0.5\n ry = np.arange(feature_height, dtype=np.float32) + 0.5\n centers = np.stack(np.meshgrid(rx, ry), axis=-1) * self._strides[level - 3]\n centers = np.expand_dims(centers, axis=-2)\n\n centers = np.tile(centers, [1, 1, self._num_anchors, 1])\n dims = np.tile(\n self._anchor_dims[level - 3], [feature_height, feature_width, 1, 1]\n )\n anchors = np.concatenate([centers, dims], axis=-1)\n return np.reshape(\n anchors, [feature_height * feature_width * self._num_anchors, 4]\n )\n\n def get_anchors(self, image_height, image_width):\n \"\"\"Generates anchor boxes for all the feature maps of the feature pyramid.\n\n Arguments:\n image_height: Height of the input image.\n image_width: Width of the input image.\n\n Returns:\n anchor boxes for all the feature maps, stacked as a single tensor\n with shape `(total_anchors, 4)`\n \"\"\"\n anchors = [\n self._get_anchors(\n np.ceil(image_height / 2 ** i).astype(np.int32),\n np.ceil(image_width / 2 ** i).astype(np.int32),\n i,\n )\n for i in range(3, 8)\n ]\n if self.mode.lower() == 'tf':\n return tf.concat(anchors, axis=0)\n else:\n return np.concatenate(anchors, axis=0)\n"},{"alpha_fraction":0.5610252022743225,"alphanum_fraction":0.5868592262268066,"avg_line_length":28.799999237060547,"blob_id":"b94b97a8b3420e93ceafc2f2e1e493f452a539fd","content_id":"43109c1c858dff7f14c25d12f42b3edb3ad514b8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4916,"license_type":"no_license","max_line_length":112,"num_lines":165,"path":"/utility.py","repo_name":"dattv/RetinaNet_TF14","src_encoding":"UTF-8","text":"\"\"\"\n\n\"\"\"\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\n\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nkeras = tf.keras\n\n\ndef swap_xy(boxes):\n \"\"\"Swaps order the of x and y coordinates of the boxes.\n\n Arguments:\n boxes: A tensor with shape `(num_boxes, 4)` representing bounding boxes.\n\n Returns:\n swapped boxes with shape same as that of boxes.\n \"\"\"\n return tf.stack([boxes[:, 1], boxes[:, 0], boxes[:, 3], boxes[:, 2]], axis=-1)\n\n\ndef convert_to_xywh(boxes):\n \"\"\"Changes the box format to center, width and height.\n\n Arguments:\n boxes: A tensor of rank 2 or higher with a shape of `(..., num_boxes, 4)`\n representing bounding boxes where each box is of the format\n `[xmin, ymin, xmax, ymax]`.\n\n Returns:\n converted boxes with shape same as that of boxes.\n \"\"\"\n return tf.concat(\n [(boxes[..., :2] + boxes[..., 2:]) / 2.0, boxes[..., 2:] - boxes[..., :2]],\n axis=-1,\n )\n\n\ndef convert_to_corners(boxes):\n \"\"\"Changes the box format to corner coordinates\n\n Arguments:\n boxes: A tensor of rank 2 or higher with a shape of `(..., num_boxes, 4)`\n representing bounding boxes where each box is of the format\n `[x, y, width, height]`.\n\n Returns:\n converted boxes with shape same as that of boxes.\n \"\"\"\n return tf.concat(\n [boxes[..., :2] - boxes[..., 2:] / 2.0, boxes[..., :2] + boxes[..., 2:] / 2.0],\n axis=-1,\n )\n\ndef convert_to_corners_np(boxes):\n \"\"\"\n\n :param boxes:\n :return:\n \"\"\"\n return np.concatenate(\n [boxes[..., :2] - boxes[..., 2:] / 2.0, boxes[..., :2] + boxes[..., 2:] / 2.0],\n axis=-1,\n )\n\n\ndef compute_iou(boxes1, boxes2):\n \"\"\"Computes pairwise IOU matrix for given two sets of boxes\n\n Arguments:\n boxes1: A tensor with shape `(N, 4)` representing bounding boxes\n where each box is of the format `[x, y, width, height]`.\n boxes2: A tensor with shape `(M, 4)` representing bounding boxes\n where each box is of the format `[x, y, width, height]`.\n\n Returns:\n pairwise IOU matrix with shape `(N, M)`, where the value at ith row\n jth column holds the IOU between ith box and jth box from\n boxes1 and boxes2 respectively.\n \"\"\"\n boxes1_corners = convert_to_corners(boxes1)\n boxes2_corners = convert_to_corners(boxes2)\n lu = tf.maximum(boxes1_corners[:, None, :2], boxes2_corners[:, :2])\n rd = tf.minimum(boxes1_corners[:, None, 2:], boxes2_corners[:, 2:])\n intersection = tf.maximum(0.0, rd - lu)\n intersection_area = intersection[:, :, 0] * intersection[:, :, 1]\n boxes1_area = boxes1[:, 2] * boxes1[:, 3]\n boxes2_area = boxes2[:, 2] * boxes2[:, 3]\n union_area = tf.maximum(\n boxes1_area[:, None] + boxes2_area - intersection_area, 1e-8\n )\n return tf.clip_by_value(intersection_area / union_area, 0.0, 1.0)\n\n\ndef visualize_detections(\n image, boxes, classes, scores, figsize=(7, 7), linewidth=1, color=[0, 0, 1]\n):\n \"\"\"Visualize Detections\"\"\"\n image = np.array(image, dtype=np.uint8)\n plt.figure(figsize=figsize)\n plt.axis(\"off\")\n plt.imshow(image)\n ax = plt.gca()\n for box, _cls, score in zip(boxes, classes, scores):\n text = \"{}: {:.2f}\".format(_cls, score)\n x1, y1, x2, y2 = box\n w, h = x2 - x1, y2 - y1\n patch = plt.Rectangle(\n [x1, y1], w, h, fill=False, edgecolor=color, linewidth=linewidth\n )\n ax.add_patch(patch)\n ax.text(\n x1,\n y1,\n text,\n bbox={\"facecolor\": color, \"alpha\": 0.4},\n clip_box=ax.clipbox,\n clip_on=True,\n )\n plt.show()\n return ax\n\n\"\"\"\n\n\"\"\"\ndef resize_img_keeping_ar(img_tf, target_height, target_width):\n \"\"\"\n\n :param img_tf:\n :param target_height:\n :param target_width:\n :return:\n \"\"\"\n new_img_shape = tf.convert_to_tensor([target_height, target_width], dtype=tf.int32)\n\n org_img_shape = tf.shape(img_tf)\n org_img_shape = tf.unstack(org_img_shape, axis=-1)\n org_img_shape = tf.stack([org_img_shape[0], org_img_shape[1]], axis=-1)\n\n ratio = tf.reduce_min(\n tf.cast(new_img_shape, dtype=tf.float32) / tf.cast(org_img_shape, tf.float32)\n )\n\n size_ratio = ratio * tf.cast(org_img_shape, tf.float32)\n\n img_tf = tf.squeeze(\n tf.image.resize_bilinear(\n tf.expand_dims(img_tf, axis=0), size=tf.cast(size_ratio, tf.int32)\n ), axis=0\n )\n\n org_img_shape = tf.shape(img_tf)[0:2]\n\n pad_number = tf.clip_by_value(\n new_img_shape - org_img_shape, clip_value_min=0, clip_value_max=tf.maximum(target_height, target_width))\n\n img_tf = tf.pad(img_tf, [[0, pad_number[0]], [0, pad_number[1]], [0, 0]])\n\n\n return tf.cast(img_tf, dtype=tf.uint8), size_ratio, tf.cast(new_img_shape, tf.float32)"},{"alpha_fraction":0.44040989875793457,"alphanum_fraction":0.45823124051094055,"avg_line_length":35.495933532714844,"blob_id":"eece85deeb6cd8360188d006b708384c7d98ae3a","content_id":"23b18d31f6679927afd94e43b36ca9d06e339ce8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4489,"license_type":"no_license","max_line_length":160,"num_lines":123,"path":"/test_dataset.py","repo_name":"dattv/RetinaNet_TF14","src_encoding":"UTF-8","text":"\"\"\"\n\n\"\"\"\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\n\nimport unittest\nimport tensorflow as tf\n\nfrom dataset import data_input_pipeline\nfrom data_augmentation import preprocess_data\nimport cv2 as cv\nimport numpy as np\nimport coco_map\nimport yaml\nfrom encoding import LabelEncoder\nfrom decoding import wraper_decode_np\n\n\nclass test_dataset(unittest.TestCase):\n def test_coco_2007(self):\n \"\"\"\n\n :return:\n \"\"\"\n font = cv.FONT_HERSHEY_SIMPLEX\n color = (255, 0, 0)\n\n config_file = './configs/retina_net_mobilenetV1.yaml'\n\n with open(config_file) as file:\n cfg = yaml.load(file, Loader=yaml.FullLoader)\n\n dataset_dir = cfg['DATASET_DIR']\n output_dir = cfg['OUTPUT_DIR']\n log_dir = cfg['LOGS_DIR']\n\n train_config = cfg['TRAIN']\n model_config = cfg['MODEL']\n solver_config = cfg['SOLVER']\n\n train_generator = data_input_pipeline(\n mode=tf.estimator.ModeKeys.TRAIN,\n dataset_dir=dataset_dir,\n preprocess_data=preprocess_data,\n batch_size=train_config['BATCH_SIZE'],\n label_encoder=LabelEncoder,\n config=cfg\n )\n\n iterator = train_generator.make_one_shot_iterator()\n next_element = iterator.get_next()\n\n decode_layer_np = wraper_decode_np(config=cfg)\n\n with tf.Session() as sess:\n for k in range(100):\n data = sess.run(\n next_element\n )\n img_data = data[0]\n labels = data[1]\n # sample = data[2]\n for i in range(len(img_data)):\n file_name = str(k) + \"_\" + str(i)\n img = cv.cvtColor(img_data[i], cv.COLOR_RGB2BGR)\n\n label = labels[i]\n\n bbox = label[:, :4]\n\n cls = label[:, 4]\n\n id = cls >= 0\n new_cls = cls[id]\n new_bbox = bbox[id]\n\n new_bbox = decode_layer_np._decode_box_predictions(id, new_bbox)\n new_anchor = decode_layer_np.anchor_boxes[id]\n for j in range(len(new_bbox)):\n id = new_cls[j]\n box = new_bbox[j]\n box_int = box.astype(np.int32)\n\n anchor = new_anchor[j]\n # cv.rectangle(img, pt1=(box_int[0], box_int[1]),\n # pt2=(box_int[2], box_int[3]), color=coco_map.COLOR_MAP[id], thickness=1)\n\n cv.rectangle(img,\n pt1=(int(anchor[0] - anchor[2] / 2),\n int(anchor[1] - anchor[3] / 2)),\n pt2=(int(anchor[0] + anchor[2] / 2),\n int(anchor[1] + anchor[3] / 2)),\n color=coco_map.COLOR_MAP[id], thickness=1)\n\n text = coco_map.LABEL_MAP[new_cls[j]]\n\n # cv.putText(img, text, (box_int[0], box_int[1] + 10), font, fontScale=0.5,\n # color=coco_map.COLOR_MAP[id], thickness=1)\n\n # n_object = sample['objects']['n_object'][i]\n # label = sample['objects']['label'][i]\n # bbox = sample['objects']['bbox'][i]\n #\n # for j in range(n_object):\n # temp_labels = label[j]\n # text = coco_map.LABEL_MAP[temp_labels]\n # temp_box = bbox[j].astype(np.int32)\n #\n # cv.rectangle(img, pt1=(temp_box[0] - temp_box[2] // 2, temp_box[1] - temp_box[3] // 2),\n # pt2=(temp_box[0] + temp_box[2] // 2, temp_box[1] + temp_box[3] // 2), color=coco_map.COLOR_MAP[temp_labels], thickness=1)\n #\n # cv.putText(img, text, (temp_box[0] - temp_box[2] // 2, temp_box[1] - temp_box[3] // 2 + 10), font, fontScale=0.5,\n # color=coco_map.COLOR_MAP[temp_labels], thickness=2)\n\n name = './test_dataset_results/' + file_name + '.jpg'\n cv.imwrite(name, img)\n print(name)\n\n\nif __name__ == '__main__':\n unittest.main()\n"},{"alpha_fraction":0.6225315928459167,"alphanum_fraction":0.6455163359642029,"avg_line_length":28.419048309326172,"blob_id":"26d1477e7802b14ef53d424e023876be0ce973bc","content_id":"e65b52ed8835170d18f3d4c1092020000b131f4a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3089,"license_type":"no_license","max_line_length":87,"num_lines":105,"path":"/train.py","repo_name":"dattv/RetinaNet_TF14","src_encoding":"UTF-8","text":"\"\"\"\n\n\"\"\"\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\n\nimport os\nfrom datetime import datetime\nimport tensorflow as tf\nimport numpy as np\nfrom nets import RetinaNet_fn\nfrom dataset import data_input_pipeline\nfrom data_augmentation import preprocess_data\nfrom encoding import LabelEncoder\nfrom losser import RetinaNetLoss\nimport yaml\n\n\ndef train(cfg=None):\n \"\"\"\n\n :param cfg:\n :return:\n \"\"\"\n dataset_dir = cfg['DATASET_DIR']\n output_dir = cfg['OUTPUT_DIR']\n log_dir = cfg['LOGS_DIR']\n\n train_config = cfg['TRAIN']\n test_config = cfg['TEST']\n model_config = cfg['MODEL']\n solver_config = cfg['SOLVER']\n\n n_classes = model_config['NUM_CLASSES']\n\n train_data_generator = data_input_pipeline(\n mode=tf.estimator.ModeKeys.TRAIN,\n dataset_dir=dataset_dir,\n preprocess_data=preprocess_data,\n batch_size=train_config['BATCH_SIZE'],\n label_encoder=LabelEncoder,\n config=cfg\n )\n\n test_data_generator = data_input_pipeline(\n mode=tf.estimator.ModeKeys.EVAL,\n dataset_dir=dataset_dir,\n batch_size=test_config['BATCH_SIZE'],\n label_encoder=LabelEncoder,\n config=config\n )\n\n retina_model = RetinaNet_fn(\n input_shape=[model_config['INPUT_HEIGHT'], model_config['INPUT_WIDTH'], 3],\n back_bone=model_config['BACKBONE'], num_classes=n_classes, training=True\n )\n\n print(retina_model.summary())\n tf.keras.utils.plot_model(retina_model, to_file='retina.png', show_shapes=True)\n\n retina_model.save('./{}.h5'.format(retina_model.name))\n\n loss_fn = RetinaNetLoss(num_classes=n_classes)\n\n learning_rates = [2.5e-06, 0.000625, 0.00125, 0.0025, 0.00025, 2.5e-05]\n learning_rate_boundaries = [125., 250., 500., 240000., 360000.]\n learning_rate_fn = tf.compat.v2.optimizers.schedules.PiecewiseConstantDecay(\n boundaries=learning_rate_boundaries, values=learning_rates\n )\n\n optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate_fn, momentum=0.9)\n retina_model.compile(loss=loss_fn, optimizer=optimizer)\n\n logdir = os.path.join(config['LOGS_DIR'], datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n callbacks_list = [\n tf.keras.callbacks.ModelCheckpoint(\n filepath=os.path.join(cfg['OUTPUT_DIR'], \"weights\" + \"_epoch_{epoch}\"),\n monitor=\"loss\",\n save_best_only=False,\n save_weights_only=True,\n verbose=1,\n ),\n tf.keras.callbacks.TensorBoard(\n log_dir=logdir, write_images=True, profile_batch=10\n ),\n ]\n\n retina_model.fit(\n train_data_generator,\n epochs=train_config['N_EPOCH'],\n callbacks=callbacks_list,\n verbose=1,\n steps_per_epoch=118200 // train_config['BATCH_SIZE'],\n validation_data=test_data_generator.take(50)\n )\n\n\nif __name__ == '__main__':\n config_file = './configs/retina_net_mobilenetV1.yaml'\n\n with open(config_file) as file:\n config = yaml.load(file, Loader=yaml.FullLoader)\n\n train(config)\n"},{"alpha_fraction":0.5936641693115234,"alphanum_fraction":0.6114264726638794,"avg_line_length":29.853107452392578,"blob_id":"2560aa767955bff9659e9657e3fee6a3540ab324","content_id":"7771f17f759917588624b280866a823201eb85e3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5461,"license_type":"no_license","max_line_length":85,"num_lines":177,"path":"/main.py","repo_name":"dattv/RetinaNet_TF14","src_encoding":"UTF-8","text":"\"\"\"\n\n\"\"\"\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\n\nimport os\nimport yaml\n\nimport tensorflow as tf\nfrom encoding import LabelEncoder\nfrom decoding import DecodePredictions\nfrom dataset import data_input_pipeline\nfrom data_augmentation import preprocess_data\nfrom nets import get_backbone\nfrom nets import RetinaNet\nfrom losser import RetinaNetLoss\n\n\ndef train(data_dir=None,\n log_dir='./logs',\n checkpoints='./checkpoints',\n config=None):\n \"\"\"\n\n :param data_dir:\n :param log_dir:\n :param checkpoints:\n :return:\n \"\"\"\n\n assert data_dir != None, '{} data_dir must be a folder'.format(data_dir)\n assert config != None, '{} config must not be None'.format(config)\n\n train_config = config['TRAIN']\n test_config = config['TEST']\n model_config = config['MODEL']\n solver_config = config['SOLVER']\n train_config['PREPROCESS'] = preprocess_data\n\n if os.path.isdir(log_dir) == False:\n os.mkdir(log_dir)\n if os.path.isdir(checkpoints) == False:\n os.mkdir(checkpoints)\n\n model_dir = os.path.join(config['OUTPUT_DIR'], \"retinanet\")\n label_encoder = LabelEncoder()\n num_classes = 80\n\n train_dataset = data_input_pipeline(mode=tf.estimator.ModeKeys.TRAIN,\n dataset_dir=config['DATASET_DIR'],\n preprocess_data=preprocess_data,\n batch_size=train_config['BATCH_SIZE'],\n label_encoder=label_encoder)\n\n val_dataset = data_input_pipeline(mode=tf.estimator.ModeKeys.EVAL,\n dataset_dir=config['DATASET_DIR'],\n preprocess_data=test_config['PREPROCESS'],\n batch_size=test_config['BATCH_SIZE'],\n label_encoder=label_encoder)\n\n learning_rates = [2.5e-06, 0.000625, 0.00125, 0.0025, 0.00025, 2.5e-05]\n learning_rate_boundaries = [125, 250, 500, 240000, 360000]\n learning_rate_fn = tf.keras.optimizers.schedules.PiecewiseConstantDecay(\n boundaries=learning_rate_boundaries, values=learning_rates\n )\n\n backbone = get_backbone(\n model_name=model_config['BACKBONE']\n )\n\n loss_fn = RetinaNetLoss(num_classes)\n model = RetinaNet(num_classes, backbone)\n\n optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate_fn, momentum=0.9)\n model.compile(loss=loss_fn, optimizer=optimizer)\n\n callbacks_list = [\n tf.keras.callbacks.ModelCheckpoint(\n filepath=os.path.join(model_dir, \"weights\" + \"_epoch_{epoch}\"),\n monitor=\"loss\",\n save_best_only=False,\n save_weights_only=True,\n verbose=1,\n )\n ]\n\n # Uncomment the following lines, when training on full dataset\n # train_steps_per_epoch = dataset_info.splits[\"train\"].num_examples // batch_size\n # val_steps_per_epoch = \\\n # dataset_info.splits[\"validation\"].num_examples // batch_size\n #\n # train_steps = 4 * 100000\n # epochs = train_steps // train_steps_per_epoch\n #\n epochs = 1\n\n # Running 100 training and 50 validation steps,\n # remove `.take` when training on the full dataset\n\n model.fit(\n train_dataset.take(100),\n validation_data=val_dataset.take(50),\n epochs=epochs,\n callbacks=callbacks_list,\n verbose=1,\n initial_epoch=0\n )\n\n\ndef evaluation(data_dir=None,\n log_dir='./logs',\n checkpoints='./checkpoints'):\n \"\"\"\n\n :param data_dir:\n :param log_dir:\n :param checkpoints:\n :return:\n \"\"\"\n num_classes = 80\n batch_size = 8\n\n lastest_checkpoint = tf.train.latest_checkpoint(checkpoints)\n\n resnet50_backbone = get_backbone()\n model = RetinaNet(num_classes, resnet50_backbone)\n\n model.load_weights(lastest_checkpoint)\n\n # building inference model\n image = tf.keras.Input(shape=[None, None, 3], name=\"image\")\n predictions = model(image, training=False)\n detections = DecodePredictions(confidence_threshold=0.5)(image, predictions)\n inference_model = tf.keras.Model(inputs=image, outputs=detections)\n\n def prepare_image(image):\n image, _, ratio = resize_and_pad_image(image, jitter=None)\n image = tf.keras.applications.resnet.preprocess_input(image)\n return tf.expand_dims(image, axis=0), ratio\n\n val_dataset = tfds.load(\"coco/2017\", split=\"validation\", data_dir=\"data\")\n int2str = dataset_info.features[\"objects\"][\"label\"].int2str\n\n\ndef main(data_dir=None,\n mode=tf.estimator.ModeKeys.TRAIN,\n config=None):\n \"\"\"\n\n :param mode:\n :return:\n \"\"\"\n assert data_dir != None, '{} dataset must not be None'\n assert config != None, '{} config must not be None'\n\n if mode == tf.estimator.ModeKeys.TRAIN:\n train(data_dir=data_dir,\n log_dir='./logs',\n checkpoints='./checkpoints',\n config=config)\n elif mode == tf.estimator.ModeKeys.EVAL:\n print('Not support yet')\n elif mode == tf.estimator.ModeKeys.PREDICT:\n print('Not support yet')\n\n\nif __name__ == '__main__':\n\n # read config file\n f = open('./configs/retina_net_resnet50.yaml')\n config = yaml.load(f)\n\n main(data_dir=config['DATASET_DIR'],\n mode=tf.estimator.ModeKeys.TRAIN,\n config=config)\n"},{"alpha_fraction":0.5850669145584106,"alphanum_fraction":0.6168360114097595,"avg_line_length":33.25967025756836,"blob_id":"1eae0c08a45299118dca55167c5acd7a83443a69","content_id":"828fd94bd323936012971ad2e5417bdbb573816c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6201,"license_type":"no_license","max_line_length":104,"num_lines":181,"path":"/nets.py","repo_name":"dattv/RetinaNet_TF14","src_encoding":"UTF-8","text":"\"\"\"\n\n\"\"\"\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\n\nimport numpy as np\nimport tensorflow as tf\nfrom encoding import AnchorBox\nfrom utility import convert_to_corners\n\nfrom mobilenet import MobileNet\nkeras = tf.keras\n\ndef RetinaNet_fn(input_shape=[224, 224, 3], back_bone='resnet50', num_classes=90, training=True):\n \"\"\"\n\n :param input_shape:\n :param back_bone:\n :param training:\n :return:\n \"\"\"\n # Backbone\n if back_bone.lower() == 'resnet50':\n \"\"\"Builds ResNet50 with pre-trained imagenet weights\"\"\"\n backbone = keras.applications.ResNet50(\n include_top=False, input_shape=input_shape\n )\n\n layer_names = [\"conv3_block4_out\", \"conv4_block6_out\", \"conv5_block3_out\"]\n\n else:\n \"\"\"Builds mobilenetV1 with pre-trained imagenet weights\"\"\"\n backbone = keras.applications.MobileNet(\n include_top=False, input_shape=input_shape\n )\n layer_names = [\"conv_pw_5_relu\", \"conv_pw_11_relu\", \"conv_pw_13_relu\"]\n\n # backbone = MobileNet(shape=(480, 640, 3), batch_size=2, num_classes=1000, include_top=False)\n\n for layer in backbone.layers:\n layer.trainable = training\n\n c3_output, c4_output, c5_output = [\n backbone.get_layer(layer_name).output\n for layer_name in layer_names\n ]\n\n input = backbone.input\n\n # FeaturePyramid\n p3_output = tf.keras.layers.Conv2D(256, 1, 1, \"same\")(c3_output)\n p4_output = tf.keras.layers.Conv2D(256, 1, 1, \"same\")(c4_output)\n p5_output = tf.keras.layers.Conv2D(256, 1, 1, \"same\")(c5_output)\n\n p4_output = tf.keras.layers.add(\n [p4_output, tf.keras.layers.UpSampling2D(2)(p5_output)]\n )\n p3_output = tf.keras.layers.add(\n [p3_output, tf.keras.layers.UpSampling2D(2)(p4_output)]\n )\n p3_output = tf.keras.layers.Conv2D(256, 3, 1, \"same\")(p3_output)\n p4_output = tf.keras.layers.Conv2D(256, 3, 1, \"same\")(p4_output)\n p5_output = tf.keras.layers.Conv2D(256, 3, 1, \"same\")(p5_output)\n\n p6_output = tf.keras.layers.Conv2D(256, 3, 2, \"same\")(c5_output)\n p7_output = tf.keras.layers.Conv2D(256, 3, 2, \"same\")(\n tf.keras.layers.Activation('relu')(p6_output)\n )\n\n FPN = [p3_output, p4_output, p5_output, p6_output, p7_output]\n\n # Head\n prior_probability = tf.constant_initializer(-np.log((1 - 0.01) / 0.01))\n\n\n cls_outputs = []\n box_outputs = []\n for feature in FPN:\n box_outputs.append(\n tf.keras.layers.Reshape(target_shape=[-1, 4])(\n build_head_fn(feature, 9 * 4, prior_probability)))\n cls_outputs.append(\n tf.keras.layers.Reshape(target_shape=[-1, num_classes])(\n build_head_fn(feature, 9 * num_classes, \"zeros\"))\n )\n\n cls_outputs = tf.keras.layers.Concatenate(axis=1)(cls_outputs)\n box_outputs = tf.keras.layers.Concatenate(axis=1)(box_outputs)\n\n output = tf.keras.layers.Concatenate(axis=-1)([cls_outputs, box_outputs])\n return tf.keras.Model(inputs=input, outputs=output, name='RetinaNet')\n\ndef build_head_fn(input, output_filters, bias_init):\n \"\"\"\n\n :param input:\n :param output_filters:\n :param bias_init:\n :return:\n \"\"\"\n output = input\n kernel_init = tf.initializers.random_normal(0.0, 0.01)\n for _ in range(4):\n output = tf.keras.layers.Conv2D(256, 3, padding=\"same\", kernel_initializer=kernel_init)(output)\n output = tf.keras.layers.Activation('relu')(output)\n\n output = tf.keras.layers.Conv2D(\n output_filters, 3, 1, padding=\"same\", kernel_initializer=kernel_init, bias_initializer=bias_init\n )(output)\n\n return output\n\n\nclass DecodePredictions(tf.keras.layers.Layer):\n \"\"\"A Keras layer that decodes predictions of the RetinaNet model.\n\n Attributes:\n num_classes: Number of classes in the dataset\n confidence_threshold: Minimum class probability, below which detections\n are pruned.\n nms_iou_threshold: IOU threshold for the NMS operation\n max_detections_per_class: Maximum number of detections to retain per\n class.\n max_detections: Maximum number of detections to retain across all\n classes.\n box_variance: The scaling factors used to scale the bounding box\n predictions.\n \"\"\"\n\n def __init__(\n self,\n num_classes=80,\n confidence_threshold=0.05,\n nms_iou_threshold=0.5,\n max_detections_per_class=100,\n max_detections=100,\n box_variance=[0.1, 0.1, 0.2, 0.2],\n **kwargs\n ):\n super(DecodePredictions, self).__init__(**kwargs)\n self.num_classes = num_classes\n self.confidence_threshold = confidence_threshold\n self.nms_iou_threshold = nms_iou_threshold\n self.max_detections_per_class = max_detections_per_class\n self.max_detections = max_detections\n\n self._anchor_box = AnchorBox()\n self._box_variance = tf.convert_to_tensor(\n [0.1, 0.1, 0.2, 0.2], dtype=tf.float32\n )\n\n def _decode_box_predictions(self, anchor_boxes, box_predictions):\n boxes = box_predictions * self._box_variance\n boxes = tf.concat(\n [\n boxes[:, :, :2] * anchor_boxes[:, :, 2:] + anchor_boxes[:, :, :2],\n tf.math.exp(boxes[:, :, 2:]) * anchor_boxes[:, :, 2:],\n ],\n axis=-1,\n )\n boxes_transformed = convert_to_corners(boxes)\n return boxes_transformed\n\n def call(self, images, predictions):\n image_shape = tf.cast(tf.shape(images), dtype=tf.float32)\n anchor_boxes = self._anchor_box.get_anchors(image_shape[1], image_shape[2])\n box_predictions = predictions[:, :, :4]\n cls_predictions = tf.nn.sigmoid(predictions[:, :, 4:])\n boxes = self._decode_box_predictions(anchor_boxes[None, ...], box_predictions)\n\n return tf.image.combined_non_max_suppression(\n tf.expand_dims(boxes, axis=2),\n cls_predictions,\n self.max_detections_per_class,\n self.max_detections,\n self.nms_iou_threshold,\n self.confidence_threshold,\n clip_boxes=False,\n )\n"},{"alpha_fraction":0.5448275804519653,"alphanum_fraction":0.559586226940155,"avg_line_length":36.37113571166992,"blob_id":"1901148f1d3dd017993b424fa34b82feb0ad6d72","content_id":"6498a0c7aac3d3945e6d12f22a2f903708be9f8d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7250,"license_type":"no_license","max_line_length":113,"num_lines":194,"path":"/dataset.py","repo_name":"dattv/RetinaNet_TF14","src_encoding":"UTF-8","text":"\"\"\"\n\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport zipfile\nimport tensorflow as tf\nfrom data_augmentation import preprocess_data\nfrom encoding import LabelEncoder\nfrom utility import resize_img_keeping_ar\nfrom encoding import wraper_encode\nfrom anchor_generator import AnchorBox\n\nMAXIMUM_OBJECTS = 100\nINPUT_RESOLUTION = {'height': 480,\n 'width': 640}\nkeras = tf.keras\n\n\ndef tiny_coco_2017():\n \"\"\"\n\n :return:\n \"\"\"\n url = \"https://github.com/srihari-humbarwadi/datasets/releases/download/v0.1.0/data.zip\"\n filename = os.path.join(os.getcwd(), \"data.zip\")\n keras.utils.get_file(filename, url)\n\n with zipfile.ZipFile(\"data.zip\", \"r\") as z_fp:\n z_fp.extractall(\"/media/dat/68fa98f8-9d03-4c1e-9bdb-c71ea72ab6fa/dat/dataset/tiny_coco_2017/\")\n\n\ndef data_input_pipeline(mode=tf.estimator.ModeKeys.TRAIN,\n dataset_dir='./',\n preprocess_data=preprocess_data,\n batch_size=8,\n label_encoder=LabelEncoder(),\n config=None):\n \"\"\"\n\n :param mode:\n :param dataset:\n :param preprocess_data:\n :param batch_size:\n :param label_encoder:\n :return:\n \"\"\"\n if config is not None:\n INPUT_RESOLUTION['height'] = config['MODEL']['INPUT_HEIGHT']\n INPUT_RESOLUTION['width'] = config['MODEL']['INPUT_WIDTH']\n\n\n def parse(feature):\n features = tf.io.parse_single_example(\n feature,\n features={\n 'image/encoded':\n tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/format':\n tf.FixedLenFeature((), tf.string, default_value='jpeg'),\n 'image/filename':\n tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/key/sha256':\n tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/source_id':\n tf.FixedLenFeature((), tf.string, default_value=''),\n 'image/height':\n tf.FixedLenFeature((), tf.int64, 1),\n 'image/width':\n tf.FixedLenFeature((), tf.int64, 1),\n # Object boxes and classes.\n 'image/object/bbox/xmin':\n tf.VarLenFeature(tf.float32),\n 'image/object/bbox/xmax':\n tf.VarLenFeature(tf.float32),\n 'image/object/bbox/ymin':\n tf.VarLenFeature(tf.float32),\n 'image/object/bbox/ymax':\n tf.VarLenFeature(tf.float32),\n 'image/object/class/label':\n tf.VarLenFeature(tf.int64),\n 'image/object/class/text':\n tf.VarLenFeature(tf.string),\n 'image/object/area':\n tf.VarLenFeature(tf.float32),\n 'image/object/is_crowd':\n tf.VarLenFeature(tf.int64),\n 'image/object/difficult':\n tf.VarLenFeature(tf.int64),\n 'image/object/group_of':\n tf.VarLenFeature(tf.int64),\n 'image/object/weight':\n tf.VarLenFeature(tf.float32),\n\n }\n )\n\n # image = features['image/encoded']\n image = tf.image.decode_jpeg(features['image/encoded'], channels=3)\n image = tf.reshape(image, shape=[features['image/height'], features['image/width'], 3])\n\n bbox = tf.concat(\n [\n tf.expand_dims(tf.sparse.to_dense(features['image/object/bbox/ymin']), axis=-1),\n tf.expand_dims(tf.sparse.to_dense(features['image/object/bbox/xmin']), axis=-1),\n tf.expand_dims(tf.sparse.to_dense(features['image/object/bbox/ymax']), axis=-1),\n tf.expand_dims(tf.sparse.to_dense(features['image/object/bbox/xmax']), axis=-1),\n ], axis=-1\n )\n\n\n image, size_ratio, new_shape = resize_img_keeping_ar(image, target_height=INPUT_RESOLUTION['height'],\n target_width=INPUT_RESOLUTION['width'])\n\n image_id = features['image/object/class/text']\n image_name = features['image/filename']\n image_height = features['image/height']\n image_width = features['image/width']\n\n size_ratio = tf.unstack(size_ratio, axis=-1)\n size_ratio = tf.stack([size_ratio[0], size_ratio[1], size_ratio[0], size_ratio[1]], axis=-1)\n bbox *= size_ratio\n\n \"\"\"\n Cause number of of bounding boxes in each img is variable, so we need to pad it to be a fixed size tensor\n \"\"\"\n label = tf.sparse.to_dense(features['image/object/class/label'])\n object_num = tf.shape(bbox)[0]\n paddings = [[0, MAXIMUM_OBJECTS - object_num], [0, 0]]\n\n bbox = tf.pad(bbox, paddings)\n label = tf.pad(label, [[0, MAXIMUM_OBJECTS - object_num]])\n\n is_crowd = tf.sparse.to_dense(features['image/object/is_crowd'])\n is_crowd = tf.pad(is_crowd, [[0, MAXIMUM_OBJECTS - object_num]])\n\n source_id = features['image/source_id']\n tensor_dict = {\n 'image': image,\n 'height': image_height,\n 'width': image_width,\n 'new_shape': new_shape,\n 'image_id': image_id,\n 'filename': image_name,\n 'source_id': source_id,\n 'size_ratio': size_ratio,\n 'objects': {\n 'bbox': bbox,\n 'n_object': object_num,\n 'label': label,\n 'is_crowd': is_crowd,\n },\n }\n\n return tensor_dict\n\n autotune = tf.data.experimental.AUTOTUNE\n dataset = tf.data.TFRecordDataset(tf.io.gfile.glob(dataset_dir + '/*{}-*.tfrecord'.format(mode.lower())))\n dataset = dataset.repeat()\n dataset = dataset.map(parse)\n\n if preprocess_data is not None:\n dataset = dataset.map(preprocess_data, num_parallel_calls=autotune)\n\n if mode == tf.estimator.ModeKeys.TRAIN:\n dataset = dataset.map(\n wraper_encode(config), num_parallel_calls=autotune\n )\n dataset = dataset.batch(batch_size)\n dataset = dataset.shuffle(8 * batch_size)\n # dataset = dataset.map(\n # encode_batch, num_parallel_calls=autotune\n # )\n # dataset = dataset.apply(tf.data.experimental.ignore_errors())\n dataset = dataset.prefetch(autotune)\n elif mode == tf.estimator.ModeKeys.EVAL:\n dataset = dataset.map(label_encoder.encode_batch, num_parallel_calls=autotune)\n dataset = dataset.apply(tf.data.experimental.ignore_errors())\n dataset = dataset.prefetch(autotune)\n\n else:\n dataset = dataset.map(label_encoder.encode_batch, num_parallel_calls=autotune)\n dataset = dataset.apply(tf.data.experimental.ignore_errors())\n dataset = dataset.prefetch(autotune)\n return dataset\n\n\nif __name__ == '__main__':\n # tiny_coco_2017()\n data_input_pipeline(mode=tf.estimator.ModeKeys.TRAIN,\n dataset_dir='/media/dat/68fa98f8-9d03-4c1e-9bdb-c71ea72ab6fa/dat/dataset/COCO_2', )\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6914119124412537,\n \"alphanum_fraction\": 0.7423580884933472,\n \"avg_line_length\": 28.869565963745117,\n \"blob_id\": \"3f472f8a2c957b777cbb91d8c0b9ce38d57c3bb2\",\n \"content_id\": \"bf6fe0adabe5bb97c7fa52edb4ffa29b183bd694\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 687,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 23,\n \"path\": \"/README.md\",\n \"repo_name\": \"dattv/RetinaNet_TF14\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# RetinaNet_TF14\\n\\n# Dataset\\n\\nUse tfrecord coco2017 \\n\\n## Matching Anchor boxes\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n# Model\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5689198970794678,\n \"alphanum_fraction\": 0.5886456370353699,\n \"avg_line_length\": 37.49074172973633,\n \"blob_id\": \"e0b8aa381cdcd86ddf79f56552f0cac71b642342\",\n \"content_id\": \"149ed1338c3c6ff15b351a13d6978b171b97e66e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4157,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 108,\n \"path\": \"/anchor_generator.py\",\n \"repo_name\": \"dattv/RetinaNet_TF14\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n\\n\\\"\\\"\\\"\\nfrom __future__ import absolute_import\\nfrom __future__ import division\\nfrom __future__ import print_function\\n\\nimport tensorflow as tf\\nimport numpy as np\\n\\n\\nclass AnchorBox(object):\\n \\\"\\\"\\\"Generates anchor boxes.\\n\\n This class has operations to generate anchor boxes for feature maps at\\n strides `[8, 16, 32, 64, 128]`. Where each anchor each box is of the\\n format `[x, y, width, height]`.\\n\\n Attributes:\\n aspect_ratios: A list of float values representing the aspect ratios of\\n the anchor boxes at each location on the feature map\\n scales: A list of float values representing the scale of the anchor boxes\\n at each location on the feature map.\\n num_anchors: The number of anchor boxes at each location on feature map\\n areas: A list of float values representing the areas of the anchor\\n boxes for each feature map in the feature pyramid.\\n strides: A list of float value representing the strides for each feature\\n map in the feature pyramid.\\n \\\"\\\"\\\"\\n\\n def __init__(self, mode='tf'):\\n self.mode = mode\\n self.aspect_ratios = [0.5, 1.0, 2.0]\\n self.scales = [2 ** x for x in [0, 1 / 3, 2 / 3]]\\n\\n self._num_anchors = len(self.aspect_ratios) * len(self.scales)\\n self._strides = [2 ** i for i in range(3, 8)]\\n self._areas = [x ** 2 for x in [32.0, 64.0, 128.0, 256.0, 512.0]]\\n self._anchor_dims = self._compute_dims()\\n\\n def _compute_dims(self):\\n \\\"\\\"\\\"Computes anchor box dimensions for all ratios and scales at all levels\\n of the feature pyramid.\\n \\\"\\\"\\\"\\n anchor_dims_all = []\\n for area in self._areas:\\n anchor_dims = []\\n for ratio in self.aspect_ratios:\\n anchor_height = np.sqrt(area / ratio)\\n anchor_width = area / anchor_height\\n dims = np.reshape(\\n np.stack([anchor_width, anchor_height], axis=-1), [1, 1, 2]\\n )\\n for scale in self.scales:\\n anchor_dims.append(scale * dims)\\n anchor_dims_all.append(np.stack(anchor_dims, axis=-2).astype(np.float32))\\n return anchor_dims_all\\n\\n def _get_anchors(self, feature_height, feature_width, level):\\n \\\"\\\"\\\"Generates anchor boxes for a given feature map size and level\\n\\n Arguments:\\n feature_height: An integer representing the height of the feature map.\\n feature_width: An integer representing the width of the feature map.\\n level: An integer representing the level of the feature map in the\\n feature pyramid.\\n\\n Returns:\\n anchor boxes with the shape\\n `(feature_height * feature_width * num_anchors, 4)`\\n \\\"\\\"\\\"\\n rx = np.arange(feature_width, dtype=np.float32) + 0.5\\n ry = np.arange(feature_height, dtype=np.float32) + 0.5\\n centers = np.stack(np.meshgrid(rx, ry), axis=-1) * self._strides[level - 3]\\n centers = np.expand_dims(centers, axis=-2)\\n\\n centers = np.tile(centers, [1, 1, self._num_anchors, 1])\\n dims = np.tile(\\n self._anchor_dims[level - 3], [feature_height, feature_width, 1, 1]\\n )\\n anchors = np.concatenate([centers, dims], axis=-1)\\n return np.reshape(\\n anchors, [feature_height * feature_width * self._num_anchors, 4]\\n )\\n\\n def get_anchors(self, image_height, image_width):\\n \\\"\\\"\\\"Generates anchor boxes for all the feature maps of the feature pyramid.\\n\\n Arguments:\\n image_height: Height of the input image.\\n image_width: Width of the input image.\\n\\n Returns:\\n anchor boxes for all the feature maps, stacked as a single tensor\\n with shape `(total_anchors, 4)`\\n \\\"\\\"\\\"\\n anchors = [\\n self._get_anchors(\\n np.ceil(image_height / 2 ** i).astype(np.int32),\\n np.ceil(image_width / 2 ** i).astype(np.int32),\\n i,\\n )\\n for i in range(3, 8)\\n ]\\n if self.mode.lower() == 'tf':\\n return tf.concat(anchors, axis=0)\\n else:\\n return np.concatenate(anchors, axis=0)\\n\"\n },\n {\n \"alpha_fraction\": 0.5610252022743225,\n \"alphanum_fraction\": 0.5868592262268066,\n \"avg_line_length\": 28.799999237060547,\n \"blob_id\": \"b94b97a8b3420e93ceafc2f2e1e493f452a539fd\",\n \"content_id\": \"43109c1c858dff7f14c25d12f42b3edb3ad514b8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4916,\n \"license_type\": \"no_license\",\n \"max_line_length\": 112,\n \"num_lines\": 165,\n \"path\": \"/utility.py\",\n \"repo_name\": \"dattv/RetinaNet_TF14\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n\\n\\\"\\\"\\\"\\nfrom __future__ import print_function\\nfrom __future__ import division\\nfrom __future__ import absolute_import\\n\\nimport tensorflow as tf\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\n\\nkeras = tf.keras\\n\\n\\ndef swap_xy(boxes):\\n \\\"\\\"\\\"Swaps order the of x and y coordinates of the boxes.\\n\\n Arguments:\\n boxes: A tensor with shape `(num_boxes, 4)` representing bounding boxes.\\n\\n Returns:\\n swapped boxes with shape same as that of boxes.\\n \\\"\\\"\\\"\\n return tf.stack([boxes[:, 1], boxes[:, 0], boxes[:, 3], boxes[:, 2]], axis=-1)\\n\\n\\ndef convert_to_xywh(boxes):\\n \\\"\\\"\\\"Changes the box format to center, width and height.\\n\\n Arguments:\\n boxes: A tensor of rank 2 or higher with a shape of `(..., num_boxes, 4)`\\n representing bounding boxes where each box is of the format\\n `[xmin, ymin, xmax, ymax]`.\\n\\n Returns:\\n converted boxes with shape same as that of boxes.\\n \\\"\\\"\\\"\\n return tf.concat(\\n [(boxes[..., :2] + boxes[..., 2:]) / 2.0, boxes[..., 2:] - boxes[..., :2]],\\n axis=-1,\\n )\\n\\n\\ndef convert_to_corners(boxes):\\n \\\"\\\"\\\"Changes the box format to corner coordinates\\n\\n Arguments:\\n boxes: A tensor of rank 2 or higher with a shape of `(..., num_boxes, 4)`\\n representing bounding boxes where each box is of the format\\n `[x, y, width, height]`.\\n\\n Returns:\\n converted boxes with shape same as that of boxes.\\n \\\"\\\"\\\"\\n return tf.concat(\\n [boxes[..., :2] - boxes[..., 2:] / 2.0, boxes[..., :2] + boxes[..., 2:] / 2.0],\\n axis=-1,\\n )\\n\\ndef convert_to_corners_np(boxes):\\n \\\"\\\"\\\"\\n\\n :param boxes:\\n :return:\\n \\\"\\\"\\\"\\n return np.concatenate(\\n [boxes[..., :2] - boxes[..., 2:] / 2.0, boxes[..., :2] + boxes[..., 2:] / 2.0],\\n axis=-1,\\n )\\n\\n\\ndef compute_iou(boxes1, boxes2):\\n \\\"\\\"\\\"Computes pairwise IOU matrix for given two sets of boxes\\n\\n Arguments:\\n boxes1: A tensor with shape `(N, 4)` representing bounding boxes\\n where each box is of the format `[x, y, width, height]`.\\n boxes2: A tensor with shape `(M, 4)` representing bounding boxes\\n where each box is of the format `[x, y, width, height]`.\\n\\n Returns:\\n pairwise IOU matrix with shape `(N, M)`, where the value at ith row\\n jth column holds the IOU between ith box and jth box from\\n boxes1 and boxes2 respectively.\\n \\\"\\\"\\\"\\n boxes1_corners = convert_to_corners(boxes1)\\n boxes2_corners = convert_to_corners(boxes2)\\n lu = tf.maximum(boxes1_corners[:, None, :2], boxes2_corners[:, :2])\\n rd = tf.minimum(boxes1_corners[:, None, 2:], boxes2_corners[:, 2:])\\n intersection = tf.maximum(0.0, rd - lu)\\n intersection_area = intersection[:, :, 0] * intersection[:, :, 1]\\n boxes1_area = boxes1[:, 2] * boxes1[:, 3]\\n boxes2_area = boxes2[:, 2] * boxes2[:, 3]\\n union_area = tf.maximum(\\n boxes1_area[:, None] + boxes2_area - intersection_area, 1e-8\\n )\\n return tf.clip_by_value(intersection_area / union_area, 0.0, 1.0)\\n\\n\\ndef visualize_detections(\\n image, boxes, classes, scores, figsize=(7, 7), linewidth=1, color=[0, 0, 1]\\n):\\n \\\"\\\"\\\"Visualize Detections\\\"\\\"\\\"\\n image = np.array(image, dtype=np.uint8)\\n plt.figure(figsize=figsize)\\n plt.axis(\\\"off\\\")\\n plt.imshow(image)\\n ax = plt.gca()\\n for box, _cls, score in zip(boxes, classes, scores):\\n text = \\\"{}: {:.2f}\\\".format(_cls, score)\\n x1, y1, x2, y2 = box\\n w, h = x2 - x1, y2 - y1\\n patch = plt.Rectangle(\\n [x1, y1], w, h, fill=False, edgecolor=color, linewidth=linewidth\\n )\\n ax.add_patch(patch)\\n ax.text(\\n x1,\\n y1,\\n text,\\n bbox={\\\"facecolor\\\": color, \\\"alpha\\\": 0.4},\\n clip_box=ax.clipbox,\\n clip_on=True,\\n )\\n plt.show()\\n return ax\\n\\n\\\"\\\"\\\"\\n\\n\\\"\\\"\\\"\\ndef resize_img_keeping_ar(img_tf, target_height, target_width):\\n \\\"\\\"\\\"\\n\\n :param img_tf:\\n :param target_height:\\n :param target_width:\\n :return:\\n \\\"\\\"\\\"\\n new_img_shape = tf.convert_to_tensor([target_height, target_width], dtype=tf.int32)\\n\\n org_img_shape = tf.shape(img_tf)\\n org_img_shape = tf.unstack(org_img_shape, axis=-1)\\n org_img_shape = tf.stack([org_img_shape[0], org_img_shape[1]], axis=-1)\\n\\n ratio = tf.reduce_min(\\n tf.cast(new_img_shape, dtype=tf.float32) / tf.cast(org_img_shape, tf.float32)\\n )\\n\\n size_ratio = ratio * tf.cast(org_img_shape, tf.float32)\\n\\n img_tf = tf.squeeze(\\n tf.image.resize_bilinear(\\n tf.expand_dims(img_tf, axis=0), size=tf.cast(size_ratio, tf.int32)\\n ), axis=0\\n )\\n\\n org_img_shape = tf.shape(img_tf)[0:2]\\n\\n pad_number = tf.clip_by_value(\\n new_img_shape - org_img_shape, clip_value_min=0, clip_value_max=tf.maximum(target_height, target_width))\\n\\n img_tf = tf.pad(img_tf, [[0, pad_number[0]], [0, pad_number[1]], [0, 0]])\\n\\n\\n return tf.cast(img_tf, dtype=tf.uint8), size_ratio, tf.cast(new_img_shape, tf.float32)\"\n },\n {\n \"alpha_fraction\": 0.44040989875793457,\n \"alphanum_fraction\": 0.45823124051094055,\n \"avg_line_length\": 35.495933532714844,\n \"blob_id\": \"eece85deeb6cd8360188d006b708384c7d98ae3a\",\n \"content_id\": \"23b18d31f6679927afd94e43b36ca9d06e339ce8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4489,\n \"license_type\": \"no_license\",\n \"max_line_length\": 160,\n \"num_lines\": 123,\n \"path\": \"/test_dataset.py\",\n \"repo_name\": \"dattv/RetinaNet_TF14\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n\\n\\\"\\\"\\\"\\nfrom __future__ import print_function\\nfrom __future__ import division\\nfrom __future__ import absolute_import\\n\\nimport unittest\\nimport tensorflow as tf\\n\\nfrom dataset import data_input_pipeline\\nfrom data_augmentation import preprocess_data\\nimport cv2 as cv\\nimport numpy as np\\nimport coco_map\\nimport yaml\\nfrom encoding import LabelEncoder\\nfrom decoding import wraper_decode_np\\n\\n\\nclass test_dataset(unittest.TestCase):\\n def test_coco_2007(self):\\n \\\"\\\"\\\"\\n\\n :return:\\n \\\"\\\"\\\"\\n font = cv.FONT_HERSHEY_SIMPLEX\\n color = (255, 0, 0)\\n\\n config_file = './configs/retina_net_mobilenetV1.yaml'\\n\\n with open(config_file) as file:\\n cfg = yaml.load(file, Loader=yaml.FullLoader)\\n\\n dataset_dir = cfg['DATASET_DIR']\\n output_dir = cfg['OUTPUT_DIR']\\n log_dir = cfg['LOGS_DIR']\\n\\n train_config = cfg['TRAIN']\\n model_config = cfg['MODEL']\\n solver_config = cfg['SOLVER']\\n\\n train_generator = data_input_pipeline(\\n mode=tf.estimator.ModeKeys.TRAIN,\\n dataset_dir=dataset_dir,\\n preprocess_data=preprocess_data,\\n batch_size=train_config['BATCH_SIZE'],\\n label_encoder=LabelEncoder,\\n config=cfg\\n )\\n\\n iterator = train_generator.make_one_shot_iterator()\\n next_element = iterator.get_next()\\n\\n decode_layer_np = wraper_decode_np(config=cfg)\\n\\n with tf.Session() as sess:\\n for k in range(100):\\n data = sess.run(\\n next_element\\n )\\n img_data = data[0]\\n labels = data[1]\\n # sample = data[2]\\n for i in range(len(img_data)):\\n file_name = str(k) + \\\"_\\\" + str(i)\\n img = cv.cvtColor(img_data[i], cv.COLOR_RGB2BGR)\\n\\n label = labels[i]\\n\\n bbox = label[:, :4]\\n\\n cls = label[:, 4]\\n\\n id = cls >= 0\\n new_cls = cls[id]\\n new_bbox = bbox[id]\\n\\n new_bbox = decode_layer_np._decode_box_predictions(id, new_bbox)\\n new_anchor = decode_layer_np.anchor_boxes[id]\\n for j in range(len(new_bbox)):\\n id = new_cls[j]\\n box = new_bbox[j]\\n box_int = box.astype(np.int32)\\n\\n anchor = new_anchor[j]\\n # cv.rectangle(img, pt1=(box_int[0], box_int[1]),\\n # pt2=(box_int[2], box_int[3]), color=coco_map.COLOR_MAP[id], thickness=1)\\n\\n cv.rectangle(img,\\n pt1=(int(anchor[0] - anchor[2] / 2),\\n int(anchor[1] - anchor[3] / 2)),\\n pt2=(int(anchor[0] + anchor[2] / 2),\\n int(anchor[1] + anchor[3] / 2)),\\n color=coco_map.COLOR_MAP[id], thickness=1)\\n\\n text = coco_map.LABEL_MAP[new_cls[j]]\\n\\n # cv.putText(img, text, (box_int[0], box_int[1] + 10), font, fontScale=0.5,\\n # color=coco_map.COLOR_MAP[id], thickness=1)\\n\\n # n_object = sample['objects']['n_object'][i]\\n # label = sample['objects']['label'][i]\\n # bbox = sample['objects']['bbox'][i]\\n #\\n # for j in range(n_object):\\n # temp_labels = label[j]\\n # text = coco_map.LABEL_MAP[temp_labels]\\n # temp_box = bbox[j].astype(np.int32)\\n #\\n # cv.rectangle(img, pt1=(temp_box[0] - temp_box[2] // 2, temp_box[1] - temp_box[3] // 2),\\n # pt2=(temp_box[0] + temp_box[2] // 2, temp_box[1] + temp_box[3] // 2), color=coco_map.COLOR_MAP[temp_labels], thickness=1)\\n #\\n # cv.putText(img, text, (temp_box[0] - temp_box[2] // 2, temp_box[1] - temp_box[3] // 2 + 10), font, fontScale=0.5,\\n # color=coco_map.COLOR_MAP[temp_labels], thickness=2)\\n\\n name = './test_dataset_results/' + file_name + '.jpg'\\n cv.imwrite(name, img)\\n print(name)\\n\\n\\nif __name__ == '__main__':\\n unittest.main()\\n\"\n },\n {\n \"alpha_fraction\": 0.6225315928459167,\n \"alphanum_fraction\": 0.6455163359642029,\n \"avg_line_length\": 28.419048309326172,\n \"blob_id\": \"26d1477e7802b14ef53d424e023876be0ce973bc\",\n \"content_id\": \"e65b52ed8835170d18f3d4c1092020000b131f4a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3089,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 105,\n \"path\": \"/train.py\",\n \"repo_name\": \"dattv/RetinaNet_TF14\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n\\n\\\"\\\"\\\"\\nfrom __future__ import print_function\\nfrom __future__ import division\\nfrom __future__ import absolute_import\\n\\nimport os\\nfrom datetime import datetime\\nimport tensorflow as tf\\nimport numpy as np\\nfrom nets import RetinaNet_fn\\nfrom dataset import data_input_pipeline\\nfrom data_augmentation import preprocess_data\\nfrom encoding import LabelEncoder\\nfrom losser import RetinaNetLoss\\nimport yaml\\n\\n\\ndef train(cfg=None):\\n \\\"\\\"\\\"\\n\\n :param cfg:\\n :return:\\n \\\"\\\"\\\"\\n dataset_dir = cfg['DATASET_DIR']\\n output_dir = cfg['OUTPUT_DIR']\\n log_dir = cfg['LOGS_DIR']\\n\\n train_config = cfg['TRAIN']\\n test_config = cfg['TEST']\\n model_config = cfg['MODEL']\\n solver_config = cfg['SOLVER']\\n\\n n_classes = model_config['NUM_CLASSES']\\n\\n train_data_generator = data_input_pipeline(\\n mode=tf.estimator.ModeKeys.TRAIN,\\n dataset_dir=dataset_dir,\\n preprocess_data=preprocess_data,\\n batch_size=train_config['BATCH_SIZE'],\\n label_encoder=LabelEncoder,\\n config=cfg\\n )\\n\\n test_data_generator = data_input_pipeline(\\n mode=tf.estimator.ModeKeys.EVAL,\\n dataset_dir=dataset_dir,\\n batch_size=test_config['BATCH_SIZE'],\\n label_encoder=LabelEncoder,\\n config=config\\n )\\n\\n retina_model = RetinaNet_fn(\\n input_shape=[model_config['INPUT_HEIGHT'], model_config['INPUT_WIDTH'], 3],\\n back_bone=model_config['BACKBONE'], num_classes=n_classes, training=True\\n )\\n\\n print(retina_model.summary())\\n tf.keras.utils.plot_model(retina_model, to_file='retina.png', show_shapes=True)\\n\\n retina_model.save('./{}.h5'.format(retina_model.name))\\n\\n loss_fn = RetinaNetLoss(num_classes=n_classes)\\n\\n learning_rates = [2.5e-06, 0.000625, 0.00125, 0.0025, 0.00025, 2.5e-05]\\n learning_rate_boundaries = [125., 250., 500., 240000., 360000.]\\n learning_rate_fn = tf.compat.v2.optimizers.schedules.PiecewiseConstantDecay(\\n boundaries=learning_rate_boundaries, values=learning_rates\\n )\\n\\n optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate_fn, momentum=0.9)\\n retina_model.compile(loss=loss_fn, optimizer=optimizer)\\n\\n logdir = os.path.join(config['LOGS_DIR'], datetime.now().strftime(\\\"%Y%m%d-%H%M%S\\\"))\\n callbacks_list = [\\n tf.keras.callbacks.ModelCheckpoint(\\n filepath=os.path.join(cfg['OUTPUT_DIR'], \\\"weights\\\" + \\\"_epoch_{epoch}\\\"),\\n monitor=\\\"loss\\\",\\n save_best_only=False,\\n save_weights_only=True,\\n verbose=1,\\n ),\\n tf.keras.callbacks.TensorBoard(\\n log_dir=logdir, write_images=True, profile_batch=10\\n ),\\n ]\\n\\n retina_model.fit(\\n train_data_generator,\\n epochs=train_config['N_EPOCH'],\\n callbacks=callbacks_list,\\n verbose=1,\\n steps_per_epoch=118200 // train_config['BATCH_SIZE'],\\n validation_data=test_data_generator.take(50)\\n )\\n\\n\\nif __name__ == '__main__':\\n config_file = './configs/retina_net_mobilenetV1.yaml'\\n\\n with open(config_file) as file:\\n config = yaml.load(file, Loader=yaml.FullLoader)\\n\\n train(config)\\n\"\n },\n {\n \"alpha_fraction\": 0.5936641693115234,\n \"alphanum_fraction\": 0.6114264726638794,\n \"avg_line_length\": 29.853107452392578,\n \"blob_id\": \"2560aa767955bff9659e9657e3fee6a3540ab324\",\n \"content_id\": \"7771f17f759917588624b280866a823201eb85e3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5461,\n \"license_type\": \"no_license\",\n \"max_line_length\": 85,\n \"num_lines\": 177,\n \"path\": \"/main.py\",\n \"repo_name\": \"dattv/RetinaNet_TF14\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n\\n\\\"\\\"\\\"\\nfrom __future__ import print_function\\nfrom __future__ import division\\nfrom __future__ import absolute_import\\n\\nimport os\\nimport yaml\\n\\nimport tensorflow as tf\\nfrom encoding import LabelEncoder\\nfrom decoding import DecodePredictions\\nfrom dataset import data_input_pipeline\\nfrom data_augmentation import preprocess_data\\nfrom nets import get_backbone\\nfrom nets import RetinaNet\\nfrom losser import RetinaNetLoss\\n\\n\\ndef train(data_dir=None,\\n log_dir='./logs',\\n checkpoints='./checkpoints',\\n config=None):\\n \\\"\\\"\\\"\\n\\n :param data_dir:\\n :param log_dir:\\n :param checkpoints:\\n :return:\\n \\\"\\\"\\\"\\n\\n assert data_dir != None, '{} data_dir must be a folder'.format(data_dir)\\n assert config != None, '{} config must not be None'.format(config)\\n\\n train_config = config['TRAIN']\\n test_config = config['TEST']\\n model_config = config['MODEL']\\n solver_config = config['SOLVER']\\n train_config['PREPROCESS'] = preprocess_data\\n\\n if os.path.isdir(log_dir) == False:\\n os.mkdir(log_dir)\\n if os.path.isdir(checkpoints) == False:\\n os.mkdir(checkpoints)\\n\\n model_dir = os.path.join(config['OUTPUT_DIR'], \\\"retinanet\\\")\\n label_encoder = LabelEncoder()\\n num_classes = 80\\n\\n train_dataset = data_input_pipeline(mode=tf.estimator.ModeKeys.TRAIN,\\n dataset_dir=config['DATASET_DIR'],\\n preprocess_data=preprocess_data,\\n batch_size=train_config['BATCH_SIZE'],\\n label_encoder=label_encoder)\\n\\n val_dataset = data_input_pipeline(mode=tf.estimator.ModeKeys.EVAL,\\n dataset_dir=config['DATASET_DIR'],\\n preprocess_data=test_config['PREPROCESS'],\\n batch_size=test_config['BATCH_SIZE'],\\n label_encoder=label_encoder)\\n\\n learning_rates = [2.5e-06, 0.000625, 0.00125, 0.0025, 0.00025, 2.5e-05]\\n learning_rate_boundaries = [125, 250, 500, 240000, 360000]\\n learning_rate_fn = tf.keras.optimizers.schedules.PiecewiseConstantDecay(\\n boundaries=learning_rate_boundaries, values=learning_rates\\n )\\n\\n backbone = get_backbone(\\n model_name=model_config['BACKBONE']\\n )\\n\\n loss_fn = RetinaNetLoss(num_classes)\\n model = RetinaNet(num_classes, backbone)\\n\\n optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate_fn, momentum=0.9)\\n model.compile(loss=loss_fn, optimizer=optimizer)\\n\\n callbacks_list = [\\n tf.keras.callbacks.ModelCheckpoint(\\n filepath=os.path.join(model_dir, \\\"weights\\\" + \\\"_epoch_{epoch}\\\"),\\n monitor=\\\"loss\\\",\\n save_best_only=False,\\n save_weights_only=True,\\n verbose=1,\\n )\\n ]\\n\\n # Uncomment the following lines, when training on full dataset\\n # train_steps_per_epoch = dataset_info.splits[\\\"train\\\"].num_examples // batch_size\\n # val_steps_per_epoch = \\\\\\n # dataset_info.splits[\\\"validation\\\"].num_examples // batch_size\\n #\\n # train_steps = 4 * 100000\\n # epochs = train_steps // train_steps_per_epoch\\n #\\n epochs = 1\\n\\n # Running 100 training and 50 validation steps,\\n # remove `.take` when training on the full dataset\\n\\n model.fit(\\n train_dataset.take(100),\\n validation_data=val_dataset.take(50),\\n epochs=epochs,\\n callbacks=callbacks_list,\\n verbose=1,\\n initial_epoch=0\\n )\\n\\n\\ndef evaluation(data_dir=None,\\n log_dir='./logs',\\n checkpoints='./checkpoints'):\\n \\\"\\\"\\\"\\n\\n :param data_dir:\\n :param log_dir:\\n :param checkpoints:\\n :return:\\n \\\"\\\"\\\"\\n num_classes = 80\\n batch_size = 8\\n\\n lastest_checkpoint = tf.train.latest_checkpoint(checkpoints)\\n\\n resnet50_backbone = get_backbone()\\n model = RetinaNet(num_classes, resnet50_backbone)\\n\\n model.load_weights(lastest_checkpoint)\\n\\n # building inference model\\n image = tf.keras.Input(shape=[None, None, 3], name=\\\"image\\\")\\n predictions = model(image, training=False)\\n detections = DecodePredictions(confidence_threshold=0.5)(image, predictions)\\n inference_model = tf.keras.Model(inputs=image, outputs=detections)\\n\\n def prepare_image(image):\\n image, _, ratio = resize_and_pad_image(image, jitter=None)\\n image = tf.keras.applications.resnet.preprocess_input(image)\\n return tf.expand_dims(image, axis=0), ratio\\n\\n val_dataset = tfds.load(\\\"coco/2017\\\", split=\\\"validation\\\", data_dir=\\\"data\\\")\\n int2str = dataset_info.features[\\\"objects\\\"][\\\"label\\\"].int2str\\n\\n\\ndef main(data_dir=None,\\n mode=tf.estimator.ModeKeys.TRAIN,\\n config=None):\\n \\\"\\\"\\\"\\n\\n :param mode:\\n :return:\\n \\\"\\\"\\\"\\n assert data_dir != None, '{} dataset must not be None'\\n assert config != None, '{} config must not be None'\\n\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n train(data_dir=data_dir,\\n log_dir='./logs',\\n checkpoints='./checkpoints',\\n config=config)\\n elif mode == tf.estimator.ModeKeys.EVAL:\\n print('Not support yet')\\n elif mode == tf.estimator.ModeKeys.PREDICT:\\n print('Not support yet')\\n\\n\\nif __name__ == '__main__':\\n\\n # read config file\\n f = open('./configs/retina_net_resnet50.yaml')\\n config = yaml.load(f)\\n\\n main(data_dir=config['DATASET_DIR'],\\n mode=tf.estimator.ModeKeys.TRAIN,\\n config=config)\\n\"\n },\n {\n \"alpha_fraction\": 0.5850669145584106,\n \"alphanum_fraction\": 0.6168360114097595,\n \"avg_line_length\": 33.25967025756836,\n \"blob_id\": \"1eae0c08a45299118dca55167c5acd7a83443a69\",\n \"content_id\": \"828fd94bd323936012971ad2e5417bdbb573816c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6201,\n \"license_type\": \"no_license\",\n \"max_line_length\": 104,\n \"num_lines\": 181,\n \"path\": \"/nets.py\",\n \"repo_name\": \"dattv/RetinaNet_TF14\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n\\n\\\"\\\"\\\"\\nfrom __future__ import print_function\\nfrom __future__ import division\\nfrom __future__ import absolute_import\\n\\nimport numpy as np\\nimport tensorflow as tf\\nfrom encoding import AnchorBox\\nfrom utility import convert_to_corners\\n\\nfrom mobilenet import MobileNet\\nkeras = tf.keras\\n\\ndef RetinaNet_fn(input_shape=[224, 224, 3], back_bone='resnet50', num_classes=90, training=True):\\n \\\"\\\"\\\"\\n\\n :param input_shape:\\n :param back_bone:\\n :param training:\\n :return:\\n \\\"\\\"\\\"\\n # Backbone\\n if back_bone.lower() == 'resnet50':\\n \\\"\\\"\\\"Builds ResNet50 with pre-trained imagenet weights\\\"\\\"\\\"\\n backbone = keras.applications.ResNet50(\\n include_top=False, input_shape=input_shape\\n )\\n\\n layer_names = [\\\"conv3_block4_out\\\", \\\"conv4_block6_out\\\", \\\"conv5_block3_out\\\"]\\n\\n else:\\n \\\"\\\"\\\"Builds mobilenetV1 with pre-trained imagenet weights\\\"\\\"\\\"\\n backbone = keras.applications.MobileNet(\\n include_top=False, input_shape=input_shape\\n )\\n layer_names = [\\\"conv_pw_5_relu\\\", \\\"conv_pw_11_relu\\\", \\\"conv_pw_13_relu\\\"]\\n\\n # backbone = MobileNet(shape=(480, 640, 3), batch_size=2, num_classes=1000, include_top=False)\\n\\n for layer in backbone.layers:\\n layer.trainable = training\\n\\n c3_output, c4_output, c5_output = [\\n backbone.get_layer(layer_name).output\\n for layer_name in layer_names\\n ]\\n\\n input = backbone.input\\n\\n # FeaturePyramid\\n p3_output = tf.keras.layers.Conv2D(256, 1, 1, \\\"same\\\")(c3_output)\\n p4_output = tf.keras.layers.Conv2D(256, 1, 1, \\\"same\\\")(c4_output)\\n p5_output = tf.keras.layers.Conv2D(256, 1, 1, \\\"same\\\")(c5_output)\\n\\n p4_output = tf.keras.layers.add(\\n [p4_output, tf.keras.layers.UpSampling2D(2)(p5_output)]\\n )\\n p3_output = tf.keras.layers.add(\\n [p3_output, tf.keras.layers.UpSampling2D(2)(p4_output)]\\n )\\n p3_output = tf.keras.layers.Conv2D(256, 3, 1, \\\"same\\\")(p3_output)\\n p4_output = tf.keras.layers.Conv2D(256, 3, 1, \\\"same\\\")(p4_output)\\n p5_output = tf.keras.layers.Conv2D(256, 3, 1, \\\"same\\\")(p5_output)\\n\\n p6_output = tf.keras.layers.Conv2D(256, 3, 2, \\\"same\\\")(c5_output)\\n p7_output = tf.keras.layers.Conv2D(256, 3, 2, \\\"same\\\")(\\n tf.keras.layers.Activation('relu')(p6_output)\\n )\\n\\n FPN = [p3_output, p4_output, p5_output, p6_output, p7_output]\\n\\n # Head\\n prior_probability = tf.constant_initializer(-np.log((1 - 0.01) / 0.01))\\n\\n\\n cls_outputs = []\\n box_outputs = []\\n for feature in FPN:\\n box_outputs.append(\\n tf.keras.layers.Reshape(target_shape=[-1, 4])(\\n build_head_fn(feature, 9 * 4, prior_probability)))\\n cls_outputs.append(\\n tf.keras.layers.Reshape(target_shape=[-1, num_classes])(\\n build_head_fn(feature, 9 * num_classes, \\\"zeros\\\"))\\n )\\n\\n cls_outputs = tf.keras.layers.Concatenate(axis=1)(cls_outputs)\\n box_outputs = tf.keras.layers.Concatenate(axis=1)(box_outputs)\\n\\n output = tf.keras.layers.Concatenate(axis=-1)([cls_outputs, box_outputs])\\n return tf.keras.Model(inputs=input, outputs=output, name='RetinaNet')\\n\\ndef build_head_fn(input, output_filters, bias_init):\\n \\\"\\\"\\\"\\n\\n :param input:\\n :param output_filters:\\n :param bias_init:\\n :return:\\n \\\"\\\"\\\"\\n output = input\\n kernel_init = tf.initializers.random_normal(0.0, 0.01)\\n for _ in range(4):\\n output = tf.keras.layers.Conv2D(256, 3, padding=\\\"same\\\", kernel_initializer=kernel_init)(output)\\n output = tf.keras.layers.Activation('relu')(output)\\n\\n output = tf.keras.layers.Conv2D(\\n output_filters, 3, 1, padding=\\\"same\\\", kernel_initializer=kernel_init, bias_initializer=bias_init\\n )(output)\\n\\n return output\\n\\n\\nclass DecodePredictions(tf.keras.layers.Layer):\\n \\\"\\\"\\\"A Keras layer that decodes predictions of the RetinaNet model.\\n\\n Attributes:\\n num_classes: Number of classes in the dataset\\n confidence_threshold: Minimum class probability, below which detections\\n are pruned.\\n nms_iou_threshold: IOU threshold for the NMS operation\\n max_detections_per_class: Maximum number of detections to retain per\\n class.\\n max_detections: Maximum number of detections to retain across all\\n classes.\\n box_variance: The scaling factors used to scale the bounding box\\n predictions.\\n \\\"\\\"\\\"\\n\\n def __init__(\\n self,\\n num_classes=80,\\n confidence_threshold=0.05,\\n nms_iou_threshold=0.5,\\n max_detections_per_class=100,\\n max_detections=100,\\n box_variance=[0.1, 0.1, 0.2, 0.2],\\n **kwargs\\n ):\\n super(DecodePredictions, self).__init__(**kwargs)\\n self.num_classes = num_classes\\n self.confidence_threshold = confidence_threshold\\n self.nms_iou_threshold = nms_iou_threshold\\n self.max_detections_per_class = max_detections_per_class\\n self.max_detections = max_detections\\n\\n self._anchor_box = AnchorBox()\\n self._box_variance = tf.convert_to_tensor(\\n [0.1, 0.1, 0.2, 0.2], dtype=tf.float32\\n )\\n\\n def _decode_box_predictions(self, anchor_boxes, box_predictions):\\n boxes = box_predictions * self._box_variance\\n boxes = tf.concat(\\n [\\n boxes[:, :, :2] * anchor_boxes[:, :, 2:] + anchor_boxes[:, :, :2],\\n tf.math.exp(boxes[:, :, 2:]) * anchor_boxes[:, :, 2:],\\n ],\\n axis=-1,\\n )\\n boxes_transformed = convert_to_corners(boxes)\\n return boxes_transformed\\n\\n def call(self, images, predictions):\\n image_shape = tf.cast(tf.shape(images), dtype=tf.float32)\\n anchor_boxes = self._anchor_box.get_anchors(image_shape[1], image_shape[2])\\n box_predictions = predictions[:, :, :4]\\n cls_predictions = tf.nn.sigmoid(predictions[:, :, 4:])\\n boxes = self._decode_box_predictions(anchor_boxes[None, ...], box_predictions)\\n\\n return tf.image.combined_non_max_suppression(\\n tf.expand_dims(boxes, axis=2),\\n cls_predictions,\\n self.max_detections_per_class,\\n self.max_detections,\\n self.nms_iou_threshold,\\n self.confidence_threshold,\\n clip_boxes=False,\\n )\\n\"\n },\n {\n \"alpha_fraction\": 0.5448275804519653,\n \"alphanum_fraction\": 0.559586226940155,\n \"avg_line_length\": 36.37113571166992,\n \"blob_id\": \"1901148f1d3dd017993b424fa34b82feb0ad6d72\",\n \"content_id\": \"6498a0c7aac3d3945e6d12f22a2f903708be9f8d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7250,\n \"license_type\": \"no_license\",\n \"max_line_length\": 113,\n \"num_lines\": 194,\n \"path\": \"/dataset.py\",\n \"repo_name\": \"dattv/RetinaNet_TF14\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\n\\n\\\"\\\"\\\"\\nfrom __future__ import absolute_import\\nfrom __future__ import division\\nfrom __future__ import print_function\\n\\nimport os\\nimport zipfile\\nimport tensorflow as tf\\nfrom data_augmentation import preprocess_data\\nfrom encoding import LabelEncoder\\nfrom utility import resize_img_keeping_ar\\nfrom encoding import wraper_encode\\nfrom anchor_generator import AnchorBox\\n\\nMAXIMUM_OBJECTS = 100\\nINPUT_RESOLUTION = {'height': 480,\\n 'width': 640}\\nkeras = tf.keras\\n\\n\\ndef tiny_coco_2017():\\n \\\"\\\"\\\"\\n\\n :return:\\n \\\"\\\"\\\"\\n url = \\\"https://github.com/srihari-humbarwadi/datasets/releases/download/v0.1.0/data.zip\\\"\\n filename = os.path.join(os.getcwd(), \\\"data.zip\\\")\\n keras.utils.get_file(filename, url)\\n\\n with zipfile.ZipFile(\\\"data.zip\\\", \\\"r\\\") as z_fp:\\n z_fp.extractall(\\\"/media/dat/68fa98f8-9d03-4c1e-9bdb-c71ea72ab6fa/dat/dataset/tiny_coco_2017/\\\")\\n\\n\\ndef data_input_pipeline(mode=tf.estimator.ModeKeys.TRAIN,\\n dataset_dir='./',\\n preprocess_data=preprocess_data,\\n batch_size=8,\\n label_encoder=LabelEncoder(),\\n config=None):\\n \\\"\\\"\\\"\\n\\n :param mode:\\n :param dataset:\\n :param preprocess_data:\\n :param batch_size:\\n :param label_encoder:\\n :return:\\n \\\"\\\"\\\"\\n if config is not None:\\n INPUT_RESOLUTION['height'] = config['MODEL']['INPUT_HEIGHT']\\n INPUT_RESOLUTION['width'] = config['MODEL']['INPUT_WIDTH']\\n\\n\\n def parse(feature):\\n features = tf.io.parse_single_example(\\n feature,\\n features={\\n 'image/encoded':\\n tf.FixedLenFeature((), tf.string, default_value=''),\\n 'image/format':\\n tf.FixedLenFeature((), tf.string, default_value='jpeg'),\\n 'image/filename':\\n tf.FixedLenFeature((), tf.string, default_value=''),\\n 'image/key/sha256':\\n tf.FixedLenFeature((), tf.string, default_value=''),\\n 'image/source_id':\\n tf.FixedLenFeature((), tf.string, default_value=''),\\n 'image/height':\\n tf.FixedLenFeature((), tf.int64, 1),\\n 'image/width':\\n tf.FixedLenFeature((), tf.int64, 1),\\n # Object boxes and classes.\\n 'image/object/bbox/xmin':\\n tf.VarLenFeature(tf.float32),\\n 'image/object/bbox/xmax':\\n tf.VarLenFeature(tf.float32),\\n 'image/object/bbox/ymin':\\n tf.VarLenFeature(tf.float32),\\n 'image/object/bbox/ymax':\\n tf.VarLenFeature(tf.float32),\\n 'image/object/class/label':\\n tf.VarLenFeature(tf.int64),\\n 'image/object/class/text':\\n tf.VarLenFeature(tf.string),\\n 'image/object/area':\\n tf.VarLenFeature(tf.float32),\\n 'image/object/is_crowd':\\n tf.VarLenFeature(tf.int64),\\n 'image/object/difficult':\\n tf.VarLenFeature(tf.int64),\\n 'image/object/group_of':\\n tf.VarLenFeature(tf.int64),\\n 'image/object/weight':\\n tf.VarLenFeature(tf.float32),\\n\\n }\\n )\\n\\n # image = features['image/encoded']\\n image = tf.image.decode_jpeg(features['image/encoded'], channels=3)\\n image = tf.reshape(image, shape=[features['image/height'], features['image/width'], 3])\\n\\n bbox = tf.concat(\\n [\\n tf.expand_dims(tf.sparse.to_dense(features['image/object/bbox/ymin']), axis=-1),\\n tf.expand_dims(tf.sparse.to_dense(features['image/object/bbox/xmin']), axis=-1),\\n tf.expand_dims(tf.sparse.to_dense(features['image/object/bbox/ymax']), axis=-1),\\n tf.expand_dims(tf.sparse.to_dense(features['image/object/bbox/xmax']), axis=-1),\\n ], axis=-1\\n )\\n\\n\\n image, size_ratio, new_shape = resize_img_keeping_ar(image, target_height=INPUT_RESOLUTION['height'],\\n target_width=INPUT_RESOLUTION['width'])\\n\\n image_id = features['image/object/class/text']\\n image_name = features['image/filename']\\n image_height = features['image/height']\\n image_width = features['image/width']\\n\\n size_ratio = tf.unstack(size_ratio, axis=-1)\\n size_ratio = tf.stack([size_ratio[0], size_ratio[1], size_ratio[0], size_ratio[1]], axis=-1)\\n bbox *= size_ratio\\n\\n \\\"\\\"\\\"\\n Cause number of of bounding boxes in each img is variable, so we need to pad it to be a fixed size tensor\\n \\\"\\\"\\\"\\n label = tf.sparse.to_dense(features['image/object/class/label'])\\n object_num = tf.shape(bbox)[0]\\n paddings = [[0, MAXIMUM_OBJECTS - object_num], [0, 0]]\\n\\n bbox = tf.pad(bbox, paddings)\\n label = tf.pad(label, [[0, MAXIMUM_OBJECTS - object_num]])\\n\\n is_crowd = tf.sparse.to_dense(features['image/object/is_crowd'])\\n is_crowd = tf.pad(is_crowd, [[0, MAXIMUM_OBJECTS - object_num]])\\n\\n source_id = features['image/source_id']\\n tensor_dict = {\\n 'image': image,\\n 'height': image_height,\\n 'width': image_width,\\n 'new_shape': new_shape,\\n 'image_id': image_id,\\n 'filename': image_name,\\n 'source_id': source_id,\\n 'size_ratio': size_ratio,\\n 'objects': {\\n 'bbox': bbox,\\n 'n_object': object_num,\\n 'label': label,\\n 'is_crowd': is_crowd,\\n },\\n }\\n\\n return tensor_dict\\n\\n autotune = tf.data.experimental.AUTOTUNE\\n dataset = tf.data.TFRecordDataset(tf.io.gfile.glob(dataset_dir + '/*{}-*.tfrecord'.format(mode.lower())))\\n dataset = dataset.repeat()\\n dataset = dataset.map(parse)\\n\\n if preprocess_data is not None:\\n dataset = dataset.map(preprocess_data, num_parallel_calls=autotune)\\n\\n if mode == tf.estimator.ModeKeys.TRAIN:\\n dataset = dataset.map(\\n wraper_encode(config), num_parallel_calls=autotune\\n )\\n dataset = dataset.batch(batch_size)\\n dataset = dataset.shuffle(8 * batch_size)\\n # dataset = dataset.map(\\n # encode_batch, num_parallel_calls=autotune\\n # )\\n # dataset = dataset.apply(tf.data.experimental.ignore_errors())\\n dataset = dataset.prefetch(autotune)\\n elif mode == tf.estimator.ModeKeys.EVAL:\\n dataset = dataset.map(label_encoder.encode_batch, num_parallel_calls=autotune)\\n dataset = dataset.apply(tf.data.experimental.ignore_errors())\\n dataset = dataset.prefetch(autotune)\\n\\n else:\\n dataset = dataset.map(label_encoder.encode_batch, num_parallel_calls=autotune)\\n dataset = dataset.apply(tf.data.experimental.ignore_errors())\\n dataset = dataset.prefetch(autotune)\\n return dataset\\n\\n\\nif __name__ == '__main__':\\n # tiny_coco_2017()\\n data_input_pipeline(mode=tf.estimator.ModeKeys.TRAIN,\\n dataset_dir='/media/dat/68fa98f8-9d03-4c1e-9bdb-c71ea72ab6fa/dat/dataset/COCO_2', )\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":8,"string":"8"}}},{"rowIdx":2979182,"cells":{"repo_name":{"kind":"string","value":"mertoenjosh/Lux-Tech-BootCamp"},"repo_url":{"kind":"string","value":"https://github.com/mertoenjosh/Lux-Tech-BootCamp"},"snapshot_id":{"kind":"string","value":"1929b708e2b39f9aba68e065573dee742244fe0a"},"revision_id":{"kind":"string","value":"4150dcafd6dd6013b2bca6058a9ae95373f84bb7"},"directory_id":{"kind":"string","value":"75387bd21aab148a3f91fce58c87fc2b61ed89f8"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-07-11T10:20:43.465616","string":"2023-07-11T10:20:43.465616"},"revision_date":{"kind":"timestamp","value":"2021-08-15T02:23:27","string":"2021-08-15T02:23:27"},"committer_date":{"kind":"timestamp","value":"2021-08-15T02:23:27","string":"2021-08-15T02:23:27"},"github_id":{"kind":"number","value":388427743,"string":"388,427,743"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6166666746139526,"alphanum_fraction":0.6743589639663696,"avg_line_length":27.851852416992188,"blob_id":"4d69a5ebdcd43f13806afd071e16f0a71523928c","content_id":"36dc09d8afbb05e6d8b4b13e83abc86da64f42a5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":780,"license_type":"no_license","max_line_length":120,"num_lines":27,"path":"/week2/youtube-downloader.py","repo_name":"mertoenjosh/Lux-Tech-BootCamp","src_encoding":"UTF-8","text":"#! /usr/bin/python3\n\n# importing of libraries\nfrom tkinter import *\nfrom pytube import YouTube\n\nroot =Tk()\nroot.geometry('500x300')\nroot.resizable(0,0)\nroot.title(\"Youtube Downloader\")\n\nLabel(root, text = 'Youtube Video Downloader', font='arial 20 bold').pack()\n\nlink = StringVar()\n\nLabel(root, text = 'Paste Link Here: ', font='arial 15 bold').place(x=160, y=60)\ntext_box = Entry(root, width = 50, textvariable = link).place(x = 32, y = 100)\n\ndef download():\n url = YouTube(str(link.get()))\n video = url.streams.get_by_resolution(\"720p\")\n video.download()\n Label(root, text = 'DOWNLOADED', font='arial 15').place(x=180, y=210)\n\nButton(root,text = 'DOWNLOAD', font='arial 15 bold', bg='skyblue', padx = 2, command = download).place(x = 180, y = 150)\n\nroot.mainloop()\n\n"},{"alpha_fraction":0.6848381757736206,"alphanum_fraction":0.7308347821235657,"avg_line_length":22.479999542236328,"blob_id":"45ce05281118ef86dd13eb0e7b83bde500df4d03","content_id":"f35ad44e0e1ef4f7bb0ef83ba7330bfde6b7d9be","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":587,"license_type":"no_license","max_line_length":141,"num_lines":25,"path":"/README.md","repo_name":"mertoenjosh/Lux-Tech-BootCamp","src_encoding":"UTF-8","text":"# Python Boot Camp by LuxTech\n\n## Week 1 (Technical skills): Random password generator\n\n**The project is to test on python technical skills.**\n\nTask:\nUse a python script to generate a random password of 8 characters long and will have to include any of the following characters in any order:\n\n- [x] 2 upperCase letters A-Z\n- [x] 2 lowerCase letter a-z\n- [x] 2 digits 0-9\n- [x] 2 symbols\n\n## Testing the script\n\n\n\n## Week 2: Youtube Downloader\n\n\n\n## Week 3: Flask application\n\n\n"},{"alpha_fraction":0.6236429214477539,"alphanum_fraction":0.6236429214477539,"avg_line_length":24.90625,"blob_id":"8d6f34a7d4ceaf4b343aeb1f57668b799a865bd2","content_id":"39a4647a93b7ee14544feb0ec3f70431fd5aac40","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":829,"license_type":"no_license","max_line_length":108,"num_lines":32,"path":"/week3/app.py","repo_name":"mertoenjosh/Lux-Tech-BootCamp","src_encoding":"UTF-8","text":"from flask import Flask, render_template, request\nfrom flask_mysqldb import MySQL\n\napp = Flask(__name__)\n\napp.config.from_pyfile('settings.py')\n\nmysql = MySQL(app)\n\n\n# set database cursor\n\n# set the routes\n@app.route('/')\ndef form():\n return render_template('form.html')\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n if request.method != \"POST\":\n return \"Use the sign up form\"\n \n if request.method == 'POST':\n details = request.form\n name = details['name']\n email = details['email']\n message = details['message']\n cursor = mysql.connection.cursor()\n cursor.execute(\"INSERT INTO users(name, email, message) VALUES (%s, %s, %s)\",(name, email, message))\n cursor.connection.commit()\n cursor.close()\n return f'Created user {name}'\n"},{"alpha_fraction":0.7246963381767273,"alphanum_fraction":0.7476383447647095,"avg_line_length":27.538461685180664,"blob_id":"229358e5a07bdbe845010a587f08da4d83e5cc62","content_id":"9972ee30b95476186075b55282ee1e35e845d59d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":741,"license_type":"no_license","max_line_length":92,"num_lines":26,"path":"/week1/password-generator.py","repo_name":"mertoenjosh/Lux-Tech-BootCamp","src_encoding":"UTF-8","text":"#! /usr/bin/python3\nimport string, random\n\n# A helper function to split a string\ndef split(all):\n return [char for char in all]\n\nall_strings = split(string.printable)\n\n# create lists to store each type of string\ndigits = all_strings[0:10]\nlowerCase = all_strings[10:36]\nupperCase = all_strings[36:62]\nsyms = all_strings[62:94]\n\n# A helper that returns two random numbers from a collection passed\ndef twoRandoms(coll):\n return random.choices(coll, k=2)\n\n# concatenate the returned strings in to one string\ntemp = twoRandoms(lowerCase) + twoRandoms(upperCase) + twoRandoms(digits) + twoRandoms(syms)\nrandom.shuffle(temp) # shuffle the final string\n\n# join the random list elements into a string\npassword = \"\".join(temp)\nprint (password)"}],"string":"[\n {\n \"alpha_fraction\": 0.6166666746139526,\n \"alphanum_fraction\": 0.6743589639663696,\n \"avg_line_length\": 27.851852416992188,\n \"blob_id\": \"4d69a5ebdcd43f13806afd071e16f0a71523928c\",\n \"content_id\": \"36dc09d8afbb05e6d8b4b13e83abc86da64f42a5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 780,\n \"license_type\": \"no_license\",\n \"max_line_length\": 120,\n \"num_lines\": 27,\n \"path\": \"/week2/youtube-downloader.py\",\n \"repo_name\": \"mertoenjosh/Lux-Tech-BootCamp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#! /usr/bin/python3\\n\\n# importing of libraries\\nfrom tkinter import *\\nfrom pytube import YouTube\\n\\nroot =Tk()\\nroot.geometry('500x300')\\nroot.resizable(0,0)\\nroot.title(\\\"Youtube Downloader\\\")\\n\\nLabel(root, text = 'Youtube Video Downloader', font='arial 20 bold').pack()\\n\\nlink = StringVar()\\n\\nLabel(root, text = 'Paste Link Here: ', font='arial 15 bold').place(x=160, y=60)\\ntext_box = Entry(root, width = 50, textvariable = link).place(x = 32, y = 100)\\n\\ndef download():\\n url = YouTube(str(link.get()))\\n video = url.streams.get_by_resolution(\\\"720p\\\")\\n video.download()\\n Label(root, text = 'DOWNLOADED', font='arial 15').place(x=180, y=210)\\n\\nButton(root,text = 'DOWNLOAD', font='arial 15 bold', bg='skyblue', padx = 2, command = download).place(x = 180, y = 150)\\n\\nroot.mainloop()\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6848381757736206,\n \"alphanum_fraction\": 0.7308347821235657,\n \"avg_line_length\": 22.479999542236328,\n \"blob_id\": \"45ce05281118ef86dd13eb0e7b83bde500df4d03\",\n \"content_id\": \"f35ad44e0e1ef4f7bb0ef83ba7330bfde6b7d9be\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 587,\n \"license_type\": \"no_license\",\n \"max_line_length\": 141,\n \"num_lines\": 25,\n \"path\": \"/README.md\",\n \"repo_name\": \"mertoenjosh/Lux-Tech-BootCamp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Python Boot Camp by LuxTech\\n\\n## Week 1 (Technical skills): Random password generator\\n\\n**The project is to test on python technical skills.**\\n\\nTask:\\nUse a python script to generate a random password of 8 characters long and will have to include any of the following characters in any order:\\n\\n- [x] 2 upperCase letters A-Z\\n- [x] 2 lowerCase letter a-z\\n- [x] 2 digits 0-9\\n- [x] 2 symbols\\n\\n## Testing the script\\n\\n\\n\\n## Week 2: Youtube Downloader\\n\\n\\n\\n## Week 3: Flask application\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6236429214477539,\n \"alphanum_fraction\": 0.6236429214477539,\n \"avg_line_length\": 24.90625,\n \"blob_id\": \"8d6f34a7d4ceaf4b343aeb1f57668b799a865bd2\",\n \"content_id\": \"39a4647a93b7ee14544feb0ec3f70431fd5aac40\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 829,\n \"license_type\": \"no_license\",\n \"max_line_length\": 108,\n \"num_lines\": 32,\n \"path\": \"/week3/app.py\",\n \"repo_name\": \"mertoenjosh/Lux-Tech-BootCamp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from flask import Flask, render_template, request\\nfrom flask_mysqldb import MySQL\\n\\napp = Flask(__name__)\\n\\napp.config.from_pyfile('settings.py')\\n\\nmysql = MySQL(app)\\n\\n\\n# set database cursor\\n\\n# set the routes\\n@app.route('/')\\ndef form():\\n return render_template('form.html')\\n\\n@app.route('/register', methods=['GET', 'POST'])\\ndef register():\\n if request.method != \\\"POST\\\":\\n return \\\"Use the sign up form\\\"\\n \\n if request.method == 'POST':\\n details = request.form\\n name = details['name']\\n email = details['email']\\n message = details['message']\\n cursor = mysql.connection.cursor()\\n cursor.execute(\\\"INSERT INTO users(name, email, message) VALUES (%s, %s, %s)\\\",(name, email, message))\\n cursor.connection.commit()\\n cursor.close()\\n return f'Created user {name}'\\n\"\n },\n {\n \"alpha_fraction\": 0.7246963381767273,\n \"alphanum_fraction\": 0.7476383447647095,\n \"avg_line_length\": 27.538461685180664,\n \"blob_id\": \"229358e5a07bdbe845010a587f08da4d83e5cc62\",\n \"content_id\": \"9972ee30b95476186075b55282ee1e35e845d59d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 741,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 26,\n \"path\": \"/week1/password-generator.py\",\n \"repo_name\": \"mertoenjosh/Lux-Tech-BootCamp\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#! /usr/bin/python3\\nimport string, random\\n\\n# A helper function to split a string\\ndef split(all):\\n return [char for char in all]\\n\\nall_strings = split(string.printable)\\n\\n# create lists to store each type of string\\ndigits = all_strings[0:10]\\nlowerCase = all_strings[10:36]\\nupperCase = all_strings[36:62]\\nsyms = all_strings[62:94]\\n\\n# A helper that returns two random numbers from a collection passed\\ndef twoRandoms(coll):\\n return random.choices(coll, k=2)\\n\\n# concatenate the returned strings in to one string\\ntemp = twoRandoms(lowerCase) + twoRandoms(upperCase) + twoRandoms(digits) + twoRandoms(syms)\\nrandom.shuffle(temp) # shuffle the final string\\n\\n# join the random list elements into a string\\npassword = \\\"\\\".join(temp)\\nprint (password)\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2979183,"cells":{"repo_name":{"kind":"string","value":"arjangroen/corona_dynamics"},"repo_url":{"kind":"string","value":"https://github.com/arjangroen/corona_dynamics"},"snapshot_id":{"kind":"string","value":"27f751837508c91a3954a0918cfb6d4714c4b57e"},"revision_id":{"kind":"string","value":"bd09eb08b75f2bb0274a4013a071ff170de16a5e"},"directory_id":{"kind":"string","value":"7ade0a5687a9420a8285d802ad60fa20876e3a5d"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-03-21T16:42:34.790363","string":"2021-03-21T16:42:34.790363"},"revision_date":{"kind":"timestamp","value":"2020-03-19T17:15:28","string":"2020-03-19T17:15:28"},"committer_date":{"kind":"timestamp","value":"2020-03-19T17:15:28","string":"2020-03-19T17:15:28"},"github_id":{"kind":"number","value":247312584,"string":"247,312,584"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.626724123954773,"alphanum_fraction":0.6594827771186829,"avg_line_length":27.975000381469727,"blob_id":"a5e3b6e4e1ef8abfd0a548fcd222665fc2050b79","content_id":"3994049fed2f1247ada46905f69d214b6c0240f6","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1160,"license_type":"permissive","max_line_length":90,"num_lines":40,"path":"/README.md","repo_name":"arjangroen/corona_dynamics","src_encoding":"UTF-8","text":"# Corona Dynamics\n\n\n##### author: Arjan Groen\n### Usage:\n\n#### Running a simulation \n```python\nfrom environment.population import Population\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nparams = {\n \"R_free\": 3, # the basic reproduction number\n \"R_control\": 1.5, # the basic reproduction number after government measures.\n \"incubation_time_mu\" : 5, # days, poisson distributed\n \"contagious_time_mu\" : 6, # days, poisson distributed\n \"death_rt\": 0.01, # mortality rate (1%)\n \"increased_death_rt\": 0.05, # mortality rate for over-capacity (1%)\n \"duration_mu\": 15, # diseasue duration, poisson distributed.\n \"infection_to_death_mu\": 22, # duration from infection to death, poisson distributed.\n \"popsize\": 17000000, # pop of NL\n \"carry_cap\": 10000, # ICU beds / 10%\n \"test_rt\" : 0.1, # percentage that gets tested.\n \"dur\": 365 # simulation duration \n}\n\np = Population(params)\nfor x in range(params['dur']):\n p.step()\n```\n\n### Plotting the results\n```python\nplt.figure()\nplt.plot(infected_2,color='r')\nplt.plot(np.ones(shape=(params['dur']))*params['carry_cap'],color='blue')\nplt.show()\n```\n\n"},{"alpha_fraction":0.6189747452735901,"alphanum_fraction":0.6231828331947327,"avg_line_length":43.305084228515625,"blob_id":"c07c76311c55fa94684e276869adcdeec3543c2f","content_id":"a36a2e37b1d45f6da31e7f415f2bc62158c77c47","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2614,"license_type":"permissive","max_line_length":120,"num_lines":59,"path":"/environment/population.py","repo_name":"arjangroen/corona_dynamics","src_encoding":"UTF-8","text":"\"\"\"Class for managing a single population\"\"\"\nfrom environment.utils import get_logistic_growth_rate\nimport numpy as np\nfrom scipy.stats import poisson\n\n\nclass Population(object):\n\n def __init__(self, params):\n self.t = 0\n self.infected = np.zeros(\n shape=(params['dur'])) # Number of infected persons ordered from most recent to historic.\n self.infected[0] = 100 # Initial immigration of infections\n self.confirmed = np.zeros(shape=(params['dur'])) # Estimated confirmed cases\n self.suspectible = [] # Susceptible population over time.\n self.deaths = np.zeros(shape=(params['dur'])) # Deceased population over time\n self.susceptible_popsize = params['popsize']\n self.R = params['R_free']\n\n # Initial transmission rate. Poisson distribution scaled by reproductive number.\n self.transmission_rate = np.array(\n [self.R * poisson.pmf(k=k, mu=params['contagious_time_mu']) for k in range(params['dur'])])\n self.params = params\n\n def step(self):\n \"\"\"\n Simulate 1 day of Corona pandemic.\n \"\"\"\n\n # The fraction of the population that can still get the disease\n susceptible_pop_rt = self.susceptible_popsize / self.params['popsize']\n\n # the number of new cases is proportional to the susceptible population\n new_cases = np.sum(self.infected[:self.params['dur']] * self.transmission_rate * susceptible_pop_rt)\n\n # new cases can no longer get the disease\n self.susceptible_popsize -= new_cases\n self.suspectible.append(self.susceptible_popsize)\n\n # Shift the infection timeline\n self.infected[1:] = self.infected[:-1]\n self.infected[0] = new_cases\n\n # Calculate the deaths\n if self.t >= self.params['infection_to_death_mu']:\n self.deaths[self.t] = min(self.infected[self.params['infection_to_death_mu']], self.params['carry_cap']) * \\\n self.params['death_rt'] + max(\n self.infected[self.params['infection_to_death_mu']] - self.params['carry_cap'], 0) * \\\n self.params['increased_death_rt']\n\n # Update the transmission rates\n self.transmission_rate = np.array(\n [self.R * poisson.pmf(k=k, mu=self.params['contagious_time_mu']) for k in range(self.params['dur'])])\n\n # Update the confirmed case count\n if self.t > self.params['incubation_time_mu']:\n self.confirmed[self.t] = self.infected[self.params['contagious_time_mu']] * self.params['test_rt']\n\n self.t += 1\n"},{"alpha_fraction":0.6374452114105225,"alphanum_fraction":0.668127715587616,"avg_line_length":31.612245559692383,"blob_id":"4abb9b03252d9ee76b65792ff331c6550fbdad0a","content_id":"fde8e537c175d0bc747ef0075c8cec7ce3e3f620","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1597,"license_type":"permissive","max_line_length":114,"num_lines":49,"path":"/simulation/rutte2.py","repo_name":"arjangroen/corona_dynamics","src_encoding":"UTF-8","text":"\"\"\"Plan Rutte 1 is to let the virus spread.\"\"\"\nfrom environment.population import Population\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nparams = {\n \"R_free\": 3, # the basic reproduction rate\n \"R_control\": 3, # the basic reproduction rate after government measures in the period 12 march - 8 april 2020\n \"incubation_time_mu\" : 5, # days, poisson distributed\n \"contagious_time_mu\" : 6, # days, poisson distributed\n \"death_rt\": 0.01, # mortality rate (1%)\n \"increased_death_rt\": 0.05, # mortality rate for over-capacity (1%)\n \"duration_mu\": 15, # disease duration, poisson distributed.\n \"infection_to_death_mu\": 22, # duration from infection to death, poisson distributed.\n \"popsize\": 17000000, # pop of NL\n \"carry_cap\": 10000, # ICU beds / 10%\n \"test_rt\" : 0.1, # percentage that gets tested.\n \"dur\": 365\n}\n\n\nsimdur = params['dur']\np = Population(params)\nfor x in range(simdur):\n p.step()\n\n\ninfected_2 = p.infected[::-1].copy()\n\nplt.figure()\nplt.plot(infected_2,color='r')\nplt.plot(np.ones(shape=(simdur))*params['carry_cap'],color='blue')\nplt.show()\n\ndf = pd.DataFrame()\ndf['infections'] = p.infected[::-1]\ndf['deaths'] = p.deaths\ndf['confirmed_cases'] = p.confirmed\ndf['suspectible_pop'] = p.suspectible\ndf['popsize'] = params['popsize']\ndf['vulnerable_pop_pct'] = df['suspectible_pop'] / df['popsize']\ndf['disease_prevalence'] = df['infections'] / df['popsize']\ndf['carry_capacity_abs'] = params['carry_cap']\ndf['carry_capacity_pct'] = params['carry_cap'] / params['popsize']\n\n\n\ndf.to_excel('../data/sim_rutte2.xlsx')"},{"alpha_fraction":0.5757575631141663,"alphanum_fraction":0.5930736064910889,"avg_line_length":24.77777862548828,"blob_id":"e44c7dd95550f1c1fd6168a2be5484018b593677","content_id":"e5c6de2ba62bb6aaa072f330ec543f64a78ac1a5","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":231,"license_type":"permissive","max_line_length":38,"num_lines":9,"path":"/environment/utils.py","repo_name":"arjangroen/corona_dynamics","src_encoding":"UTF-8","text":"def get_logistic_growth_rate(r, N, K):\n \"\"\"\n Logistic growth equation, 2.14\n :param r: growth rate\n :param N: Population size\n :param K: Carrying capacity\n :return: growth rate\n \"\"\"\n return r * N * (1-(N/K))"}],"string":"[\n {\n \"alpha_fraction\": 0.626724123954773,\n \"alphanum_fraction\": 0.6594827771186829,\n \"avg_line_length\": 27.975000381469727,\n \"blob_id\": \"a5e3b6e4e1ef8abfd0a548fcd222665fc2050b79\",\n \"content_id\": \"3994049fed2f1247ada46905f69d214b6c0240f6\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1160,\n \"license_type\": \"permissive\",\n \"max_line_length\": 90,\n \"num_lines\": 40,\n \"path\": \"/README.md\",\n \"repo_name\": \"arjangroen/corona_dynamics\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Corona Dynamics\\n\\n\\n##### author: Arjan Groen\\n### Usage:\\n\\n#### Running a simulation \\n```python\\nfrom environment.population import Population\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport pandas as pd\\n\\nparams = {\\n \\\"R_free\\\": 3, # the basic reproduction number\\n \\\"R_control\\\": 1.5, # the basic reproduction number after government measures.\\n \\\"incubation_time_mu\\\" : 5, # days, poisson distributed\\n \\\"contagious_time_mu\\\" : 6, # days, poisson distributed\\n \\\"death_rt\\\": 0.01, # mortality rate (1%)\\n \\\"increased_death_rt\\\": 0.05, # mortality rate for over-capacity (1%)\\n \\\"duration_mu\\\": 15, # diseasue duration, poisson distributed.\\n \\\"infection_to_death_mu\\\": 22, # duration from infection to death, poisson distributed.\\n \\\"popsize\\\": 17000000, # pop of NL\\n \\\"carry_cap\\\": 10000, # ICU beds / 10%\\n \\\"test_rt\\\" : 0.1, # percentage that gets tested.\\n \\\"dur\\\": 365 # simulation duration \\n}\\n\\np = Population(params)\\nfor x in range(params['dur']):\\n p.step()\\n```\\n\\n### Plotting the results\\n```python\\nplt.figure()\\nplt.plot(infected_2,color='r')\\nplt.plot(np.ones(shape=(params['dur']))*params['carry_cap'],color='blue')\\nplt.show()\\n```\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6189747452735901,\n \"alphanum_fraction\": 0.6231828331947327,\n \"avg_line_length\": 43.305084228515625,\n \"blob_id\": \"c07c76311c55fa94684e276869adcdeec3543c2f\",\n \"content_id\": \"a36a2e37b1d45f6da31e7f415f2bc62158c77c47\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2614,\n \"license_type\": \"permissive\",\n \"max_line_length\": 120,\n \"num_lines\": 59,\n \"path\": \"/environment/population.py\",\n \"repo_name\": \"arjangroen/corona_dynamics\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Class for managing a single population\\\"\\\"\\\"\\nfrom environment.utils import get_logistic_growth_rate\\nimport numpy as np\\nfrom scipy.stats import poisson\\n\\n\\nclass Population(object):\\n\\n def __init__(self, params):\\n self.t = 0\\n self.infected = np.zeros(\\n shape=(params['dur'])) # Number of infected persons ordered from most recent to historic.\\n self.infected[0] = 100 # Initial immigration of infections\\n self.confirmed = np.zeros(shape=(params['dur'])) # Estimated confirmed cases\\n self.suspectible = [] # Susceptible population over time.\\n self.deaths = np.zeros(shape=(params['dur'])) # Deceased population over time\\n self.susceptible_popsize = params['popsize']\\n self.R = params['R_free']\\n\\n # Initial transmission rate. Poisson distribution scaled by reproductive number.\\n self.transmission_rate = np.array(\\n [self.R * poisson.pmf(k=k, mu=params['contagious_time_mu']) for k in range(params['dur'])])\\n self.params = params\\n\\n def step(self):\\n \\\"\\\"\\\"\\n Simulate 1 day of Corona pandemic.\\n \\\"\\\"\\\"\\n\\n # The fraction of the population that can still get the disease\\n susceptible_pop_rt = self.susceptible_popsize / self.params['popsize']\\n\\n # the number of new cases is proportional to the susceptible population\\n new_cases = np.sum(self.infected[:self.params['dur']] * self.transmission_rate * susceptible_pop_rt)\\n\\n # new cases can no longer get the disease\\n self.susceptible_popsize -= new_cases\\n self.suspectible.append(self.susceptible_popsize)\\n\\n # Shift the infection timeline\\n self.infected[1:] = self.infected[:-1]\\n self.infected[0] = new_cases\\n\\n # Calculate the deaths\\n if self.t >= self.params['infection_to_death_mu']:\\n self.deaths[self.t] = min(self.infected[self.params['infection_to_death_mu']], self.params['carry_cap']) * \\\\\\n self.params['death_rt'] + max(\\n self.infected[self.params['infection_to_death_mu']] - self.params['carry_cap'], 0) * \\\\\\n self.params['increased_death_rt']\\n\\n # Update the transmission rates\\n self.transmission_rate = np.array(\\n [self.R * poisson.pmf(k=k, mu=self.params['contagious_time_mu']) for k in range(self.params['dur'])])\\n\\n # Update the confirmed case count\\n if self.t > self.params['incubation_time_mu']:\\n self.confirmed[self.t] = self.infected[self.params['contagious_time_mu']] * self.params['test_rt']\\n\\n self.t += 1\\n\"\n },\n {\n \"alpha_fraction\": 0.6374452114105225,\n \"alphanum_fraction\": 0.668127715587616,\n \"avg_line_length\": 31.612245559692383,\n \"blob_id\": \"4abb9b03252d9ee76b65792ff331c6550fbdad0a\",\n \"content_id\": \"fde8e537c175d0bc747ef0075c8cec7ce3e3f620\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1597,\n \"license_type\": \"permissive\",\n \"max_line_length\": 114,\n \"num_lines\": 49,\n \"path\": \"/simulation/rutte2.py\",\n \"repo_name\": \"arjangroen/corona_dynamics\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"Plan Rutte 1 is to let the virus spread.\\\"\\\"\\\"\\nfrom environment.population import Population\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport pandas as pd\\n\\nparams = {\\n \\\"R_free\\\": 3, # the basic reproduction rate\\n \\\"R_control\\\": 3, # the basic reproduction rate after government measures in the period 12 march - 8 april 2020\\n \\\"incubation_time_mu\\\" : 5, # days, poisson distributed\\n \\\"contagious_time_mu\\\" : 6, # days, poisson distributed\\n \\\"death_rt\\\": 0.01, # mortality rate (1%)\\n \\\"increased_death_rt\\\": 0.05, # mortality rate for over-capacity (1%)\\n \\\"duration_mu\\\": 15, # disease duration, poisson distributed.\\n \\\"infection_to_death_mu\\\": 22, # duration from infection to death, poisson distributed.\\n \\\"popsize\\\": 17000000, # pop of NL\\n \\\"carry_cap\\\": 10000, # ICU beds / 10%\\n \\\"test_rt\\\" : 0.1, # percentage that gets tested.\\n \\\"dur\\\": 365\\n}\\n\\n\\nsimdur = params['dur']\\np = Population(params)\\nfor x in range(simdur):\\n p.step()\\n\\n\\ninfected_2 = p.infected[::-1].copy()\\n\\nplt.figure()\\nplt.plot(infected_2,color='r')\\nplt.plot(np.ones(shape=(simdur))*params['carry_cap'],color='blue')\\nplt.show()\\n\\ndf = pd.DataFrame()\\ndf['infections'] = p.infected[::-1]\\ndf['deaths'] = p.deaths\\ndf['confirmed_cases'] = p.confirmed\\ndf['suspectible_pop'] = p.suspectible\\ndf['popsize'] = params['popsize']\\ndf['vulnerable_pop_pct'] = df['suspectible_pop'] / df['popsize']\\ndf['disease_prevalence'] = df['infections'] / df['popsize']\\ndf['carry_capacity_abs'] = params['carry_cap']\\ndf['carry_capacity_pct'] = params['carry_cap'] / params['popsize']\\n\\n\\n\\ndf.to_excel('../data/sim_rutte2.xlsx')\"\n },\n {\n \"alpha_fraction\": 0.5757575631141663,\n \"alphanum_fraction\": 0.5930736064910889,\n \"avg_line_length\": 24.77777862548828,\n \"blob_id\": \"e44c7dd95550f1c1fd6168a2be5484018b593677\",\n \"content_id\": \"e5c6de2ba62bb6aaa072f330ec543f64a78ac1a5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 231,\n \"license_type\": \"permissive\",\n \"max_line_length\": 38,\n \"num_lines\": 9,\n \"path\": \"/environment/utils.py\",\n \"repo_name\": \"arjangroen/corona_dynamics\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def get_logistic_growth_rate(r, N, K):\\n \\\"\\\"\\\"\\n Logistic growth equation, 2.14\\n :param r: growth rate\\n :param N: Population size\\n :param K: Carrying capacity\\n :return: growth rate\\n \\\"\\\"\\\"\\n return r * N * (1-(N/K))\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2979184,"cells":{"repo_name":{"kind":"string","value":"jinhw1989/Leetcode_Python"},"repo_url":{"kind":"string","value":"https://github.com/jinhw1989/Leetcode_Python"},"snapshot_id":{"kind":"string","value":"d02d4e81b7dd5a4048de1707370ffe8f06641cb7"},"revision_id":{"kind":"string","value":"a6243e8b7b108a6dbee7c559feda7745c78d7281"},"directory_id":{"kind":"string","value":"3475848e0c253a8db0be3dbdbe356aae0c2b190d"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2016-08-06T21:46:37.774395","string":"2016-08-06T21:46:37.774395"},"revision_date":{"kind":"timestamp","value":"2015-06-11T21:34:12","string":"2015-06-11T21:34:12"},"committer_date":{"kind":"timestamp","value":"2015-06-11T21:34:12","string":"2015-06-11T21:34:12"},"github_id":{"kind":"number","value":21712065,"string":"21,712,065"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.739534854888916,"alphanum_fraction":0.7441860437393188,"avg_line_length":32,"blob_id":"f9dcb2dddaa98525eefe00248ffd00f078bf1713","content_id":"c052e0b135463c823de82fbb5db503ca88eccea3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":438,"license_type":"no_license","max_line_length":198,"num_lines":13,"path":"/Hints/159_Longest Substring with At Most Two Distinct Characters.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\nGiven a string, find the longest substring that contains only two unique characters. For example, given \"abcbbbbcccbdddadacb\", the longest substring that contains 2 unique character is \"bcbbbbcccb\".\n\nGiven a string S, find the length of the longest substring T that contains at most two distinct characters.\nFor example,\nGiven S = “eceba”,\nT is “ece” which its length is 3.\n\n### Hints:\n\n#### References:\n\n#### Tags:\n\n"},{"alpha_fraction":0.42252397537231445,"alphanum_fraction":0.4480830729007721,"avg_line_length":29.560976028442383,"blob_id":"a050edbcd5a61e2ed7d9bbdff72443aca40ed526","content_id":"c4d9c4574c17da011639a23e7af6d1fff16896d8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1252,"license_type":"no_license","max_line_length":74,"num_lines":41,"path":"/Solutions/188_Best Time to Buy and Sell Stock IV.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return an integer as the maximum profit \n # def maxProfit(self, k, prices):\n # if len(prices) < 1:\n # return 0\n # low = prices[0]\n # high = prices[0]\n # profit = []\n # for i in range(len(prices)):\n # if prices[i]>=high:\n # high = prices[i]\n # else:\n # profit.append(high-low)\n # low = prices[i]\n # high = prices[i]\n # profit.append(high-low)\n # print profit\n # profit = sorted(profit, reverse=True)\n # return sum(profit[:k])\n # \n\n def maxProfit(self, k, prices):\n size = len(prices)\n if k > size / 2:\n return self.quickSolve(size, prices)\n dp = [None] * (2 * k + 1)\n dp[0] = 0\n for i in range(size):\n for j in range(min(2 * k, i + 1) , 0 , -1):\n dp[j] = max(dp[j], dp[j - 1] + prices[i] * [1, -1][j % 2])\n return max(dp)\n\n def quickSolve(self, size, prices):\n sum = 0\n for x in range(size - 1):\n if prices[x + 1] > prices[x]:\n sum += prices[x + 1] - prices[x]\n return sum\n\ns = Solution()\nprint s.maxProfit(2, [1,2,4,2,5,7,2,4,9,0])"},{"alpha_fraction":0.3533724248409271,"alphanum_fraction":0.3631476163864136,"avg_line_length":35.55356979370117,"blob_id":"9f2c22777c8825a926c1de95baa79477016b1a47","content_id":"3cd8b933506590b1eced2abf7837447c751faa4a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2046,"license_type":"no_license","max_line_length":81,"num_lines":56,"path":"/Solutions/017_4Sum.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# class Solution:\n # @return a list of lists of length 4, [[val1,val2,val3,val4]]\n\n # def fourSum(self, num, target):\n # numLen, res, dict = len(num), set(), {}\n # if numLen < 4:\n # return []\n # num.sort()\n # for p in range(numLen):\n # for q in range(p + 1, numLen):\n # if num[p] + num[q] not in dict:\n # dict[num[p] + num[q]] = [(p, q)]\n # else:\n # dict[num[p] + num[q]].append((p, q))\n # print dict\n # for i in range(numLen):\n # for j in range(i + 1, numLen - 2):\n # T = target - num[i] - num[j]\n # if T in dict:\n # for k in dict[T]:\n # if k[0] > j:\n # res.add((num[i], num[j], num[k[0]], num[k[1]]))\n # return [list(i) for i in res]\n\n\nclass Solution:\n # @return a list of lists of length 4, [[val1,val2,val3,val4]]\n\n def fourSum(self, num, target):\n numLen, dic = len(num), {}\n res = []\n if numLen < 4:\n return []\n # num.sort()\n for p in range(numLen):\n for q in range(p + 1, numLen):\n if num[p] + num[q] not in dic:\n dic[num[p] + num[q]] = [(p, q)]\n else:\n dic[num[p] + num[q]].append((p, q))\n keyList = dic.keys()\n for i in dic.keys():\n if target-i in dic.keys():\n for j in dic[i]:\n for k in dic[target-i]:\n if len(set(list(j) + list(k))) == 4:\n s = list(j) + list(k)\n # print s \n l = [num[n] for n in s]\n l.sort()\n res.append(l)\n result = []\n for i in res:\n if i not in result:\n result.append(i)\n return result"},{"alpha_fraction":0.5732683539390564,"alphanum_fraction":0.5885833501815796,"avg_line_length":34.92499923706055,"blob_id":"c61d7aade9f2828b6ccffd3340344efb6907a6d5","content_id":"b377b3c85eb0fbd213848a9d0ada5f899a1ab40e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2873,"license_type":"no_license","max_line_length":222,"num_lines":80,"path":"/Hints/001_Two Sum.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n> Given an array of integers, find two numbers such that they add up to a specific target number.\n>\n> The function twoSum should return indices of the two numbers such that they add up to the target, where index1 must be less than index2. Please note that your returned answers (both index1 and index2) are not zero-based.\n>\n> You may assume that each input would have exactly one solution.\n>\n> Input: numbers={2, 7, 11, 15}, target=9\n>\n> Output: index1=1, index2=2 \n\n### Hints:\nAn naive way is pick one _number1_ and then find the number _target - number1_. This will cost O(n^2).\n\n```python\nclass Solution:\n # @return a tuple, (index1, index2)\n\n def twoSum(self, num, target):\n for i in num:\n if target - i in num:\n return (num.index(i) + 1, num.index(target - i) + 1)\n return (-1, -1)\n```\n__ISSUE__: Time Limit Exceeded, since the array will be large enough, and you cannot find from its beginning without have a proper order of that array.\n\nSo before compare the sum and the target, we need to sort the list first.\n\n```python\nclass Solution:\n # @return a tuple, (index1, index2)\n\n def twoSum(self, num, target):\n for i in sorted(num, reverse=True):\n if target - i in num:\n return (num.index(i) + 1, num.index(target - i) + 1)\n return (-1, -1)\n```\n\nAnother way is to think about using two pointers, one is from beginning, and another one from the end of the SORTED list. And then return the index of its \n\n```python\nclass Solution:\n # @return a tuple, (index1, index2)\n\n def twoSum(self, num, target):\n index = []\n numtosort = num[:] \t# deep copy\n numtosort.sort()\t# sort list\n i = 0\t\t\t\t# pointer at beginning\n j = len(numtosort) - 1 # pointer at end\n while i < j:\n if numtosort[i] + numtosort[j] == target:\n for k in range(0, len(num)):\n if num[k] == numtosort[i]:\n index.append(k)\n break\n for k in range(len(num) - 1, -1, -1):\n if num[k] == numtosort[j]:\n index.append(k)\n break\n index.sort()\n break\n elif numtosort[i] + numtosort[j] < target:\n i = i + 1\n elif numtosort[i] + numtosort[j] > target:\n j = j - 1\n return (index[0] + 1, index[1] + 1)\n```\n\n#### Note:\n* the index should plus one.\n* the possible range of target should between the first two sum of sorted list, and last two sum of sorted list.\n* we could assume that the list number are all non-negatives.\n* there may exist two or more number which are the same, be careful when handling the index. Especially do not use `list.index(x)`. \n\n#### References:\n\n#### Tags:\n**list**, **sorted list**"},{"alpha_fraction":0.5729984045028687,"alphanum_fraction":0.5839874148368835,"avg_line_length":21.75,"blob_id":"e9ec72b4f4ea57378a01e9410907a503fd7ed0f0","content_id":"42181fd42558596520a575512787f4d87e32d468","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":637,"license_type":"no_license","max_line_length":108,"num_lines":28,"path":"/Hints/028_Divide Two Integers.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\nDivide two integers without using multiplication, division and mod operator.\n\nIf it is overflow, return MAX_INT.\n\n### Hints:\n\n1. like Eular's method\n2. what if the divident or the divisor is negative?\n3. consider time complexity\n\nInstead of minus divisor one by one, use a faster way to minus its polynomial powers. like x^1, x^2, x^4,...\n'''\n while a >= b:\n summing = b\n count = 1\n while summing + summing <= a:\n summing += summing\n count += count\n a -= summing\n res += count\n'''\n#### References:\n\n\n#### Tags:\nMath, Binary Search\n"},{"alpha_fraction":0.3583333194255829,"alphanum_fraction":0.363095223903656,"avg_line_length":25.28125,"blob_id":"96e4b829d51b3f00515cba73b2244a574387dc46","content_id":"6027046eebf15412823d0ecddda44b89fe05cf04","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":840,"license_type":"no_license","max_line_length":63,"num_lines":32,"path":"/Solutions/020_Valid Parentheses.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return a boolean\n\n def isValid(self, s):\n lookup, stack = {\")\": \"(\", \"}\": \"{\", \"]\": \"[\"}, []\n for i in s:\n if i not in lookup:\n stack.append(i)\n else:\n if len(stack) == 0 or stack.pop() != lookup[i]:\n return False\n return len(stack) == 0\n\n \"\"\"\n naive way\n \"\"\"\n # def isValid(self, s):\n # # match = [(\"(\",\")\"),(\"[\",\"]\"),(\"{\",\"}\")]\n # match = {\")\": \"(\", \"]\": \"[\", \"}\": \"{\"}\n # stack = []\n # for c in s:\n # if c not in match:\n # stack.append(c)\n # else:\n # if len(stack) == 0:\n # return False\n # elif stack.pop() != match[c]:\n # return False\n # if len(stack) == 0:\n # return True\n # else:\n # return False"},{"alpha_fraction":0.6992882490158081,"alphanum_fraction":0.709964394569397,"avg_line_length":25.714284896850586,"blob_id":"098627b454154803e98b2efedf22883c1beccbf3","content_id":"f7b3a8a439e6b4596d9dc3e68393f1de6fadd985","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":562,"license_type":"no_license","max_line_length":94,"num_lines":21,"path":"/Hints/170_Two Sum III.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\nDesign and implement a TwoSum class. It should support the following operations: add and find.\n\nadd - Add the number to an internal data structure.\nfind - Find if there exists any pair of numbers which sum is equal to the value.\n\nFor example,\nadd(1); add(3); add(5);\nfind(4) -> true\nfind(7) -> false\n\n### Hints:\nInternal data strucuture? -> dict\n1. use dict to store data\n\nIs there any duplicated values? \nWhat is the maximum range of its size? \n -> if it is too large, you need to calculate the range of matched sum\n#### References:\n\n#### Tags:\n\n"},{"alpha_fraction":0.5127701163291931,"alphanum_fraction":0.5474787354469299,"avg_line_length":30.14285659790039,"blob_id":"16af241a8e6a973d64f48efc7e70e197660cab91","content_id":"616e113a91cc61f52c6d3380fcc34e0558e65663","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1527,"license_type":"no_license","max_line_length":209,"num_lines":49,"path":"/Hints/004_Add Two Numbers.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n>You are given two linked lists representing two non-negative numbers. The digits are stored in reverse order and each of their nodes contain a single digit. Add the two numbers and return it as a linked list.\n>\n>Input: (2 -> 4 -> 3) + (5 -> 6 -> 4)\n>Output: 7 -> 0 -> 8\n\n### Hints:\nJust operation of linked list.\n\n```python \n# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution:\n # @return a ListNode\n def addTwoNumbers(self, l1, l2):\n if l1 == None: return l2\n if l2 == None: return l1\n flag = 0\n dummy = ListNode(0); p = dummy\n while l1 and l2:\n p.next = ListNode((l1.val+l2.val+flag) % 10)\n flag = (l1.val+l2.val+flag) / 10\n l1 = l1.next; l2 = l2.next; p = p.next\n if l2:\n while l2:\n p.next = ListNode((l2.val+flag) % 10)\n flag = (l2.val+flag) / 10\n l2 = l2.next; p = p.next\n if l1:\n while l1:\n p.next = ListNode((l1.val+flag) % 10)\n flag = (l1.val+flag) / 10\n l1 = l1.next; p = p.next\n if flag == 1: p.next = ListNode(1)\n return dummy.next\n```\n#### Note:\n* If one of the list is none, then return the other one as result.\n\n* If the size of them are not the same, calculate one by one and use flag to continue calculate until all the values are none.\n\n#### References:\n\n#### Tags:\n**linked list**, \n"},{"alpha_fraction":0.5074626803398132,"alphanum_fraction":0.5223880410194397,"avg_line_length":29.545454025268555,"blob_id":"fd889c43095916142daf6cc0b12eda2332b25fda","content_id":"ddbb282a61e66b93f9b74c7c4c02a0b70bfb2e86","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":335,"license_type":"no_license","max_line_length":66,"num_lines":11,"path":"/Solutions/164_Maximum Gap.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param num, a list of integer\n # @return an integer\n\n def maximumGap(self, num):\n num = sorted(num)\n if len(num) in [0, 1]:\n return 0\n new_num = [num[0]] + [num[i] for i in range(len(num) - 1)]\n sub_num = [a-b for a, b in zip(num, new_num)]\n return max(sub_num)"},{"alpha_fraction":0.7577937841415405,"alphanum_fraction":0.7577937841415405,"avg_line_length":51.125,"blob_id":"a7ef052d017058b8ca778e81520f7225e0b6780c","content_id":"7c5d139f1790bca4f6b4caf00f30a7149eaadbfc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"SQL","length_bytes":417,"license_type":"no_license","max_line_length":109,"num_lines":8,"path":"/Solutions/183_Customers Who Never Order.sql","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# Write your MySQL query statement below\nSELECT Name FROM Customers c WHERE c.Id NOT IN (SELECT CustomerId FROM Orders o);\n\n-- # Write your MySQL query statement below\n-- SELECT Name FROM Customers c WHERE NOT EXISTS (SELECT CustomerId FROM Orders o WHERE o.CustomerId = c.id);\n\n-- # Write your MySQL query statement below\n-- SELECT Name FROM Customers c LEFT JOIN Orders o ON c.Id = o.CustomerId WHERE o.Id IS NULL;\n"},{"alpha_fraction":0.4098939895629883,"alphanum_fraction":0.45229682326316833,"avg_line_length":27.299999237060547,"blob_id":"324b7abde22672f804f5adba73a4d24d3fba2661","content_id":"19cf318461d7b8039d1e119641a1636bcde30c93","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":283,"license_type":"no_license","max_line_length":68,"num_lines":10,"path":"/Solutions/007_Reverse Integer.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return an integer\n\n def reverse(self, x):\n tmp = str(x)\n tmp = \"-\" + tmp[:0:-1] if tmp.startswith(\"-\") else tmp[::-1]\n if int(tmp) > 2 ** 31 - 1 or int(tmp) < -2 ** 31 + 1:\n return 0\n else:\n return int(tmp)\n"},{"alpha_fraction":0.521327018737793,"alphanum_fraction":0.521327018737793,"avg_line_length":29.14285659790039,"blob_id":"84ed24c37216915ad738bb5cf094e0c72403916b","content_id":"df9527338574bd2547e86c2822cfe5b45c304865","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":844,"license_type":"no_license","max_line_length":87,"num_lines":28,"path":"/Solutions/113_Path Sum II.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\n\nclass Solution:\n # @param root, a tree node\n # @param sum, an integer\n # @return a list of lists of integers\n\n def pathSum(self, root, sum):\n def dfs(root, currsum, valuelist):\n if root.left == None and root.right == None:\n if currsum == sum:\n res.append(valuelist)\n if root.left:\n dfs(root.left, currsum + root.left.val, valuelist + [root.left.val])\n if root.right:\n dfs(root.right, currsum + root.right.val, valuelist + [root.right.val])\n\n res = []\n if root == None:\n return []\n dfs(root, root.val, [root.val])\n return res\n"},{"alpha_fraction":0.5057034492492676,"alphanum_fraction":0.5285171270370483,"avg_line_length":31.875,"blob_id":"79035777b76af0704e88b59696919f74cb414eeb","content_id":"e40748b9186e084f9f7560d17f65a7743a140819","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":263,"license_type":"no_license","max_line_length":68,"num_lines":8,"path":"/Solutions/001_Two Sum.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return a tuple, (index1, index2)\n\n def twoSum(self, num, target):\n for i in sorted(num, reverse=True):\n if target - i in num:\n return (num.index(i) + 1, num.index(target - i) + 1)\n return (-1, -1)\n"},{"alpha_fraction":0.7414075136184692,"alphanum_fraction":0.7528641819953918,"avg_line_length":46,"blob_id":"9cb3a2407f6a041b6c936d852065a247ca851a08","content_id":"e11c7a57b6c10a815a2ec2b03585ffb6af144787","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1833,"license_type":"no_license","max_line_length":294,"num_lines":39,"path":"/Hints/008_String to Integer (atoi).md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\nImplement atoi to convert a string to an integer.\n\n**Hint**: Carefully consider all possible input cases. If you want a challenge, please do not see below and ask yourself what are the possible input cases.\n\n**Notes**: It is intended for this problem to be specified vaguely (ie, no given input specs). You are responsible to gather all the input requirements up front.\n\n**Requirements for atoi**:\nThe function first discards as many whitespace characters as necessary until the first non-whitespace character is found. Then, starting from this character, takes an optional initial plus or minus sign followed by as many numerical digits as possible, and interprets them as a numerical value.\n\nThe string can contain additional characters after those that form the integral number, which are ignored and have no effect on the behavior of this function.\n\nIf the first sequence of non-whitespace characters in str is not a valid integral number, or if no such sequence exists because either str is empty or it contains only whitespace characters, no conversion is performed.\n\nIf no valid conversion could be performed, a zero value is returned. If the correct value is out of the range of representable values, INT_MAX (2147483647) or INT_MIN (-2147483648) is returned.\n\n### Hints:\n\n* remove whitespaces: `strip()`\n* check first char of the left string: + or -\n* check the following char one by one, until to the end or meet char is not a digit: `isdigit()`\n* convert to int(), check the size of the number meet its ranges.\n\nANOTHER approach:\nif we can use packages, then use `re` to search string, and convert it to int. \n```python\nimport re\n\n\nclass Solution:\n # @return an integer\n\n def atoi(self, str):\n return int(re.search('[+-]?\\d+', str.strip()).group(0))\n```\n#### References:\n\n#### Tags:\nMath, String\n"},{"alpha_fraction":0.45679011940956116,"alphanum_fraction":0.4722222089767456,"avg_line_length":26,"blob_id":"2664fade02441fd22158359c2851ebb41967f014","content_id":"a0b37ad9cce4a8c7b321a84f1470a1cd3ff59019","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":324,"license_type":"no_license","max_line_length":55,"num_lines":12,"path":"/Solutions/052_Maximum Subarray.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param A, a list of integers\n # @return an integer\n\n def maxSubArray(self, A):\n length = len(A)\n max_dp = [0] * (length)\n if A:\n max_dp[0] = A[0]\n for i in range(1, length):\n max_dp[i] = max(max_dp[i - 1] + A[i], A[i])\n return max(max_dp)\n"},{"alpha_fraction":0.38823530077934265,"alphanum_fraction":0.42352941632270813,"avg_line_length":29.058822631835938,"blob_id":"cae10500a79881f2e1ab5f9af12159ea5c4cfb4e","content_id":"6323486af6e8a34c7dbf6b85a24205cc3a90a233","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":510,"license_type":"no_license","max_line_length":62,"num_lines":17,"path":"/Solutions/167_Two Sum II.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n def twoSum(self, lst, target):\n for i in range(len(lst)):\n j = target - lst[i]\n for ii in range(len(lst)-1, i, -1):\n if lst[ii]<j:\n break\n if lst[ii] == j:\n return [i+1, ii+1]\n return [-1,-1]\n # for i in lst:\n # if target - i in lst:\n # return [lst.index(i)+1, lst.index(target-i)+1]\n # return [-1, -1]\n\ns = Solution()\nprint s.twoSum([2,2,11,15], 26)"},{"alpha_fraction":0.6248612403869629,"alphanum_fraction":0.6304106712341309,"avg_line_length":41.9523811340332,"blob_id":"abfc5275e9dc0188ecdd4e52f328f90fec3b48f4","content_id":"a4fa2156fa5760b462de9d66b368a8032fcd1fbb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"SQL","length_bytes":901,"license_type":"no_license","max_line_length":87,"num_lines":21,"path":"/Solutions/185_Department Top Three Salaries.sql","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"SELECT d.NAME AS Department, t.NAME AS Employee, Salary FROM (\n SELECT DepartmentId,\n NAME,\n Salary, \n @rank := IF(@prevDeptId != DepartmentId, 1, \n IF(@prevSalary = Salary, @rank, @rank + 1) ) AS Rank,\n @prevDeptId := DepartmentId AS prevDeptId,\n @prevSalary := Salary AS prevSalary\n FROM Employee e, (SELECT @rank := 0, @prevDeptId := NULL, @prevSalary := NULL) r\n ORDER BY DepartmentId ASC, Salary DESC\n) t INNER JOIN Department d ON t.DepartmentId = d.ID\nWHERE t.rank <= 3\n\n\n-- # Solution in Discuss\n-- SELECT D.Name AS Department, E.Name AS Employee, E.Salary AS Salary \n-- FROM Employee E, Department D\n-- WHERE (SELECT COUNT(DISTINCT(Salary)) FROM Employee \n-- WHERE DepartmentId = E.DepartmentId AND Salary > E.Salary) < 3\n-- AND E.DepartmentId = D.Id \n-- ORDER by E.DepartmentId, E.Salary DESC;"},{"alpha_fraction":0.32452574372291565,"alphanum_fraction":0.3414634168148041,"avg_line_length":33.32558059692383,"blob_id":"010029f04a6ebd06448dc29ff394fceacb22b92c","content_id":"f5717e52e12733086a79ee185fb4e91c628295b0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1476,"license_type":"no_license","max_line_length":61,"num_lines":43,"path":"/Solutions/015_3Sum.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return a list of lists of length 3, [[val1,val2,val3]]\n\n def threeSum(self, num):\n \"\"\" TLE version \"\"\"\n # result_list = []\n # length = len(num)\n # if length < 3:\n # return []\n # origin = list(num)\n # for i in range(length):\n # for j in range(i + 1, length):\n # m = num[i]\n # n = num[j]\n # seek = 0 - m - n\n # num.pop(j)\n # num.pop(i)\n # if seek in num:\n # result = sorted([m, n, seek])\n # if result not in result_list:\n # result_list.append(result)\n # num = list(origin)\n # return result_list\n\n num, result, i = sorted(num), [], 0\n while i < len(num) - 2:\n j, k = i + 1, len(num) - 1\n while j < k:\n if num[i] + num[j] + num[k] < 0:\n j += 1\n elif num[i] + num[j] + num[k] > 0:\n k -= 1\n else:\n result.append([num[i], num[j], num[k]])\n j, k = j + 1, k - 1\n while j < k and num[j] == num[j - 1]:\n j += 1\n while j < k and num[k] == num[k + 1]:\n k -= 1\n i += 1\n while i < len(num) - 2 and num[i] == num[i - 1]:\n i += 1\n return result\n"},{"alpha_fraction":0.6251511573791504,"alphanum_fraction":0.6590084433555603,"avg_line_length":28.535715103149414,"blob_id":"8a8ed2214381d6f2e64f877fb361ea05b1d46fc7","content_id":"5e11e06fb51ddb1c5e766f78c4c955c9543753dc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":833,"license_type":"no_license","max_line_length":176,"num_lines":28,"path":"/Hints/017_4Sum.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\nGiven an array S of n integers, are there elements a, b, c, and d in S such that a + b + c + d = target? Find all unique quadruplets in the array which gives the sum of target.\n\nNote:\nElements in a quadruplet (a,b,c,d) must be in non-descending order. (ie, a ≤ b ≤ c ≤ d)\nThe solution set must not contain duplicate quadruplets.\n For example, given array S = {1 0 -1 0 -2 2}, and target = 0.\n\n A solution set is:\n (-1, 0, 0, 1)\n (-2, -1, 1, 2)\n (-2, 0, 0, 2)\n\n### Hints:\n\n\nQ:\n1. len(numbers) < 4\n2. create a dic to store all possible sum of two\n3. pick one from dic and find its remaining in dic as well \n4. match if there two picked out tuple have no common values \n5. return the unduplicate list (quadruplets)\n\n#### References:\n3Sum, closet 3Sum, 2Sum\n#### Tags:\nArray, Hash Table, Two Pointer\n"},{"alpha_fraction":0.6809045076370239,"alphanum_fraction":0.6997487545013428,"avg_line_length":29.615385055541992,"blob_id":"0dffd3bb452bbc69ec12da3700ff15cbe1c8eab3","content_id":"c664f04f4c3ad1f94bfb07399a65d4ded81c1e67","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":796,"license_type":"no_license","max_line_length":120,"num_lines":26,"path":"/Hints/019_Remove Nth Node From End of List.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\nGiven a linked list, remove the nth node from the end of list and return its head.\n\nFor example,\n\n Given linked list: 1->2->3->4->5, and n = 2.\n\n After removing the second node from the end, the linked list becomes 1->2->3->5.\n\nNote: \nGiven n will always be valid. \nTry to do this in one pass. \n\n### Hints:\n1. use two pointers, one in front one in after. Both pointed to head; \n2. first use the front one move to the nth node, and then let both of them move together; \n3. until the first one move to the end of the linked list, then the later one is pointed to the nth node from the end; \n4. need another pointer to point the previous node of the later one; \n5. let the prev one pointer to the next node of later one.\n\n\n#### References:\n \n#### Tags:\npointer, linked-list\n"},{"alpha_fraction":0.5165048837661743,"alphanum_fraction":0.5339806079864502,"avg_line_length":27.61111068725586,"blob_id":"271fc196a469a26e5ab03ee42b5a9bbfc90f590c","content_id":"3aa3d195244109e28d25d335a03c19870ed35d2d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":515,"license_type":"no_license","max_line_length":70,"num_lines":18,"path":"/Solutions/187_Repeated DNA Sequences.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param s, a string\n # @return a list of strings\n\n def findRepeatedDnaSequences(self, s):\n seq = {}\n res = []\n for i in range(len(s)):\n if s[i:i + 10] in seq:\n if s[i:i + 10] not in res:\n res.append(s[i:i + 10])\n else:\n seq[s[i:i + 10]] = 1\n return res\n\ns = Solution()\n# print s.findRepeatedDnaSequences(\"AAAAACCCCCAAAAACCCCCCAAAAAGGGTTT\")\nprint s.findRepeatedDnaSequences(\"AAAAAAAAAAAA\")\n"},{"alpha_fraction":0.5747126340866089,"alphanum_fraction":0.5785440802574158,"avg_line_length":28,"blob_id":"9ebd746612374894e2f9d02ef417349b5f0af3d9","content_id":"881aa6dadb64f60b9ffd697d86555b46ee944f9c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":261,"license_type":"no_license","max_line_length":45,"num_lines":9,"path":"/Solutions/067_Plus One.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param digits, a list of integer digits\n # @return a list of integer digits\n\n def plusOne(self, digits):\n \tdigits = [str(i) for i in digits]\n st = \"\".join(digits)\n val = int(st) + 1\n return map(int, str(val))\n"},{"alpha_fraction":0.7151162624359131,"alphanum_fraction":0.7238371968269348,"avg_line_length":23.571428298950195,"blob_id":"73692544f6111b192e06e1e580957f50665e7789","content_id":"8460e9e8e147ec2c17a440af0f76bba95479c50a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":344,"license_type":"no_license","max_line_length":96,"num_lines":14,"path":"/Hints/014_Longest Common Prefix.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\nWrite a function to find the longest common prefix string amongst an array of strings.\n\n### Hints:\nAssumptions: \n1. no spaces; \n2. two functions like `min` and `max` of string list will retune the minimum and maximum string;\n3. instead of comparing all strings, compare min and max instead;\n\n\n#### References:\n\n#### Tags:\nString\n"},{"alpha_fraction":0.7280966639518738,"alphanum_fraction":0.7507553100585938,"avg_line_length":46.21428680419922,"blob_id":"72e12b435c29856a42e989e7674c57732b04b376","content_id":"5e80b4cdc70801f0371acc517c90ffb374d5162e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":662,"license_type":"no_license","max_line_length":222,"num_lines":14,"path":"/Hints/167_Two Sum II.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\nGiven an array of integers that is already sorted in ascending order, find two numbers such that they add up to a specific target number. \nThe function twoSum should return indices of the two numbers such that they add up to the target, where index1 must be less than index2. Please note that your returned answers (both index1 and index2) are not zero-based. \nYou may assume that each input would have exactly one solution.\nInput: numbers={2, 7, 11, 15}, target=9\nOutput: index1=1, index2=2\n\n### Hints:\nIs there any duplicated values? If yes the index are consecutive. \nHow to take the advantage of sorted list. \n\n#### References:\n\n#### Tags:\n\n"},{"alpha_fraction":0.6417391300201416,"alphanum_fraction":0.6486956477165222,"avg_line_length":34.9375,"blob_id":"eb599fd38373cc110e311d3608934bd79d7e3abf","content_id":"6df0c443c95011f2225b53ce063c319cf1ccc94e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":575,"license_type":"no_license","max_line_length":118,"num_lines":16,"path":"/Hints/020_Valid Parentheses.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\nGiven a string containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.\n\nThe brackets must close in the correct order, \"()\" and \"()[]{}\" are all valid but \"(]\" and \"([)]\" are not.\n\n### Hints:\nStack to push in and pop out to match brackets.\n1. define dict of brackets, one trick to define them like {\")\":\"(\"} \n2. like a compiler push into stack if not in keys \n3. pop out when it is a key, and match them together \n4. check the size of stack at the end of processing. \n\n#### References:\n\n#### Tags:\nStack String\n"},{"alpha_fraction":0.4482758641242981,"alphanum_fraction":0.4482758641242981,"avg_line_length":5.333333492279053,"blob_id":"c66cc93e5267222f838813433c1a7799d737daa1","content_id":"48b3ef506f50a320894c617fd1ad9482748b31ea","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":58,"license_type":"no_license","max_line_length":16,"num_lines":9,"path":"/Hints/096_Unique Binary Search Trees II.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\n\n\n### Hints:\n\n#### References:\n\n#### Tags:\n\n"},{"alpha_fraction":0.7338709831237793,"alphanum_fraction":0.7338709831237793,"avg_line_length":23.733333587646484,"blob_id":"499f0a24bf581407b5461761fa05968fa18f1d5b","content_id":"516efdbb76392eccfd2a2b5e38d81ed269dab76e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":372,"license_type":"no_license","max_line_length":107,"num_lines":15,"path":"/Hints/100_Same Tree.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\nGiven two binary trees, write a function to check if they are equal or not.\n\nTwo binary trees are considered equal if they are structurally identical and the nodes have the same value.\n\n### Hints:\n\nRecursive way:\nif both val to be none: True\none is None while the other is not: False\nBoth are not None: compare their value and \n#### References:\n\n#### Tags:\n\n"},{"alpha_fraction":0.7086494565010071,"alphanum_fraction":0.7374810576438904,"avg_line_length":24.384614944458008,"blob_id":"38584408886e73d3fd1e998d6765d81c4ed3a44a","content_id":"507b707fac414749370417dbb96dec6cf2b7d2a8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":659,"license_type":"no_license","max_line_length":193,"num_lines":26,"path":"/Hints/187_Repeated DNA Sequences.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\nAll DNA is composed of a series of nucleotides abbreviated as A, C, G, and T, for example: \"ACGAATTCCG\". When studying DNA, it is sometimes useful to identify repeated sequences within the DNA.\n\nWrite a function to find all the 10-letter-long sequences (substrings) that occur more than once in a DNA molecule.\n\nFor example,\n\nGiven s = \"AAAAACCCCCAAAAACCCCCCAAAAAGGGTTT\",\n\nReturn:\n[\"AAAAACCCCC\", \"CCCCCAAAAA\"].\n\n### Hints:\nMy approach: \nstore 10-letter-string to dict, and then check strings. \n\n1. check duplicated string in result.\n\nTime cost O(N), Memory too much\n\n#### References:\n78 136 137 187 169 \n\n#### Tags:\nDictionary, bit manuplate"},{"alpha_fraction":0.669767439365387,"alphanum_fraction":0.6837209463119507,"avg_line_length":21.63157844543457,"blob_id":"009bb8304f7a8b2f5f64d14552ff8e0820c4cea8","content_id":"01f0712f00c1131fce7df740435c6f3d582092fc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":430,"license_type":"no_license","max_line_length":150,"num_lines":19,"path":"/Hints/047_Rotate Image.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\nYou are given an n x n 2D matrix representing an image.\n\nRotate the image by 90 degrees (clockwise).\n\nFollow up:\nCould you do this in-place?\n\n### Hints:\n1. replace with [i][j] with [j][i] \n2. reverse elements in each line \n\nQ:\nThink about using a matrix multiplication way to do a rotation, what matrix to choose in that problem (sin \\theta cos \\theta ...) let \\theta == pi/2 \n#### References:\n\n#### Tags:\nArray\n"},{"alpha_fraction":0.42450493574142456,"alphanum_fraction":0.43440595269203186,"avg_line_length":34.130435943603516,"blob_id":"7507357f3999547478827baef556a154a6995e1d","content_id":"5a1d1629a231effcc6891831621b95a993079932","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":808,"license_type":"no_license","max_line_length":114,"num_lines":23,"path":"/Solutions/010_Regular Expression Matching.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return a boolean\n\n def isMatch(self, s, p):\n previousRow = [True]\n for i in range(0, len(p)):\n if p[i] == '*':\n previousRow.append(previousRow[i - 1])\n else:\n previousRow.append(False)\n\n for letter in range(0, len(s)):\n actualRow = [False]\n for i in range(0, len(p)):\n if p[i] == '*':\n temp = actualRow[i - 1] or (previousRow[i + 1] and (p[i - 1] == s[letter] or p[i - 1] == '.'))\n elif p[i] == '.':\n temp = previousRow[i]\n else:\n temp = previousRow[i] and p[i] == s[letter]\n actualRow.append(temp)\n previousRow = actualRow\n return previousRow[len(p)]\n"},{"alpha_fraction":0.44974446296691895,"alphanum_fraction":0.4846678078174591,"avg_line_length":28.350000381469727,"blob_id":"ac93fe0c25517fd6c36d0a6fb83d920aeb7d41e8","content_id":"51fae4737e9dc65a2af8a63845b3e9a4fcdc0833","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1174,"license_type":"no_license","max_line_length":78,"num_lines":40,"path":"/Solutions/198_House Robber.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param num, a list of integer\n # @return an integer\n\n def rob(self, num):\n # Approach 1: Greedy algorithm: NOT CORRECT!!!\n # length = len(num)\n # if length == 0:\n # return 0\n # elif length == 1:\n # return num[0]\n\n # max_num = max(num)\n # max_index = num.index(max_num)\n # if max_index == 0:\n # return max_num + self.rob(num[2:])\n # elif max_index == length - 1:\n # return max_num + self.rob(num[:max_index-1])\n # else:\n # return max_num + self.rob(num[:max_index-1] + num[max_index+2:])\n\n # Approach 2: sum of odd and even number: NOT CORRECT!!!\n # return max(sum(num[1::2]), sum(num[::2]))\n\n # Approach 3: Dynanmic programming\n length = len(num)\n dp = [0] * (length + 1)\n if length:\n dp[1] = num[0]\n for i in range(2, length + 1):\n dp[i] = max(dp[i - 1], dp[i - 2] + num[i - 1])\n print dp\n return dp[length]\n\ns = Solution()\nprint s.rob([1, 1])\nprint s.rob([2, 3, 2])\nprint s.rob([2, 1, 1, 2])\nprint s.rob([0, 0, 0])\nprint s.rob([1, 7, 9, 4])\n"},{"alpha_fraction":0.6908665299415588,"alphanum_fraction":0.7400468587875366,"avg_line_length":30.629629135131836,"blob_id":"657612135124258c172841adc350baf5da36322a","content_id":"a4c77d7a0467998b505d11c56db0246fed4c7a17","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":854,"license_type":"no_license","max_line_length":170,"num_lines":27,"path":"/Hints/007_Reverse Integer.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\nReverse digits of an integer.\n\nExample1: x = 123, return 321\n\nExample2: x = -123, return -321\n\nHere are some good questions to ask before coding. Bonus points for you if you have already thought through this!\n\nIf the integer's last digit is 0, what should the output be? ie, cases such as 10, 100.\n\nDid you notice that the reversed integer might overflow? Assume the input is a 32-bit integer, then the reverse of 1000000003 overflows. How should you handle such cases?\n\nFor the purpose of this problem, assume that your function returns 0 when the reversed integer overflows.\n\n### Hints:\n* Thinking about negative numbers\n* Thinking about overflow problems `-2**31+1 < x < 2**31-1`\n* Thinking about begin with zeros\n\nTake advantage of python, convert int to str, convert str to int, reverse str `[::-1]`\n\n#### References:\n\n#### Tags:\nMath\n"},{"alpha_fraction":0.33253589272499084,"alphanum_fraction":0.3564593195915222,"avg_line_length":22.22222137451172,"blob_id":"3aa4a49ae030d858602e7538c507414d05ecbb45","content_id":"248b6b9d2306a1dd9acbff62f618cde9f612652f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":418,"license_type":"no_license","max_line_length":33,"num_lines":18,"path":"/Solutions/069_Sqrt(x).py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param x, an integer\n # @return an integer\n\n def sqrt(self, x):\n if x == 0:\n return 0\n i = 1\n j = x / 2 + 1\n while(i <= j):\n center = (i + j) / 2\n if center ** 2 == x:\n return center\n elif center ** 2 > x:\n j = center - 1\n else:\n i = center + 1\n return j\n"},{"alpha_fraction":0.4157509207725525,"alphanum_fraction":0.46886447072029114,"avg_line_length":25,"blob_id":"c521bff59c77b0e4100280833769c22f66695170","content_id":"ec76c36313afda1d8fd6661e3e6c22b306443539","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":546,"license_type":"no_license","max_line_length":50,"num_lines":21,"path":"/Solutions/165_Compare Version Numbers.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param version1, a string\n # @param version2, a string\n # @return an integer\n\n def compareVersion(self, version1, version2):\n v1 = [int(x) for x in version1.split('.')]\n v2 = [int(x) for x in version2.split('.')]\n rv1, rv2 = 0, 0\n for i in range(len(v1)):\n rv1 += v1[i] * 0.1 ** i\n\n for j in range(len(v2)):\n rv2 += v2[j] * 0.1 ** j\n\n if rv1 > rv2:\n return 1\n elif rv1 < rv2:\n return -1\n else:\n return 0\n"},{"alpha_fraction":0.3679775297641754,"alphanum_fraction":0.40449437499046326,"avg_line_length":24.428571701049805,"blob_id":"235964fa6e4098ecd0b1d7177bb66d8de389c5bf","content_id":"c8d46eec16dbd06464667665d4581cecd09b3213","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":356,"license_type":"no_license","max_line_length":47,"num_lines":14,"path":"/Solutions/168_Excel Sheet Column Title.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return a string\n\n def convertToTitle(self, num):\n alpha = [chr(i) for i in range(65, 91)]\n res = []\n while num > 0:\n t = num % 26\n print t\n res.append(alpha[t - 1])\n num = (num / 26)\n if t == 0:\n num -= 1\n return ''.join(res[::-1])\n"},{"alpha_fraction":0.5932203531265259,"alphanum_fraction":0.6033898591995239,"avg_line_length":36,"blob_id":"05a07276b6e040f9310c7b9272e607ca886bd777","content_id":"96e660788c10155489d192244de22034b6e8efeb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"SQL","length_bytes":295,"license_type":"no_license","max_line_length":77,"num_lines":8,"path":"/Solutions/178_Rank Scores.sql","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# Write your MySQL query statement below\nSELECT Score, Rank FROM(\n SELECT Score,\n @curRank := @curRank + IF(@prevScore = Score, 0, 1) AS Rank,\n @prevScore := Score\n FROM Scores s, (SELECT @curRank := 0) r, (SELECT @prevScore := NULL) p\n ORDER BY Score DESC\n) t;"},{"alpha_fraction":0.35548171401023865,"alphanum_fraction":0.3698781728744507,"avg_line_length":31.25,"blob_id":"7f2c9c3dcea82a5d5a9c97d54359640f54aaf472","content_id":"347ff086639593c3e1e19dc5ad8ab411ca76db59","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":903,"license_type":"no_license","max_line_length":70,"num_lines":28,"path":"/Solutions/035_Valid Sudoku.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param board, a 9x9 2D array\n # @return a boolean\n\n def isValidSudoku(self, board):\n def isValid(x, y, tmp):\n for i in range(9):\n if board[i][y] == tmp:\n return False\n for i in range(9):\n if board[x][i] == tmp:\n return False\n for i in range(3):\n for j in range(3):\n if board[(x / 3) * 3 + i][(y / 3) * 3 + j] == tmp:\n return False\n return True\n for i in range(9):\n for j in range(9):\n if board[i][j] == '.':\n continue\n tmp = board[i][j]\n board[i][j] = 'D'\n if isValid(i, j, tmp) == False:\n return False\n else:\n board[i][j] = tmp\n return True\n"},{"alpha_fraction":0.3467000722885132,"alphanum_fraction":0.3583959937095642,"avg_line_length":30.5,"blob_id":"bb96fc1b5ea77413d5c1f20df7f3f00b69dd66f9","content_id":"c198503602a54f387c8f78264b6e383a6b6ff65a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1197,"license_type":"no_license","max_line_length":82,"num_lines":38,"path":"/Solutions/068_Text Justification.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param words, a list of strings\n # @param L, an integer\n # @return a list of strings\n\n def fullJustify(self, words, L):\n res = []\n i = 0\n while i < len(words):\n size = 0\n begin = i\n while i < len(words):\n newsize = len(words[i]) if size == 0 else size + len(words[i]) + 1\n if newsize <= L:\n size = newsize\n else:\n break\n i += 1\n spaceCount = L - size\n if i - begin - 1 > 0 and i < len(words):\n everyCount = spaceCount / (i - begin - 1)\n spaceCount %= i - begin - 1\n else:\n everyCount = 0\n j = begin\n while j < i:\n if j == begin:\n s = words[j]\n else:\n s += ' ' * (everyCount + 1)\n if spaceCount > 0 and i < len(words):\n s += ' '\n spaceCount -= 1\n s += words[j]\n j += 1\n s += ' ' * spaceCount\n res.append(s)\n return res\n"},{"alpha_fraction":0.7432098984718323,"alphanum_fraction":0.7432098984718323,"avg_line_length":22.823530197143555,"blob_id":"3c897faf251633ce5b2294844f1578ee5e9811ba","content_id":"8abb4501e0a856584d1eb0f82c7f1d5313a912c9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":405,"license_type":"no_license","max_line_length":112,"num_lines":17,"path":"/Hints/188_Best Time to Buy and Sell Stock IV.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\nSay you have an array for which the ith element is the price of a given stock on day i.\n\nDesign an algorithm to find the maximum profit. You may complete at most k transactions.\n\nNote:\nYou may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again).\n\n### Hints:\n\n#### References:\n\nOther three related questions\n\n#### Tags:\nDynamic Programming\n"},{"alpha_fraction":0.42509135603904724,"alphanum_fraction":0.4409256875514984,"avg_line_length":29.407407760620117,"blob_id":"2d3e5fdba308090d93e9cbf6c626c7219d263bd7","content_id":"c558254d0677292a086ae446f55ae3482d98576a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":821,"license_type":"no_license","max_line_length":67,"num_lines":27,"path":"/Solutions/030_Next Permutation.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param num, a list of integer\n # @return a list of integer\n\n def nextPermutation(self, num):\n if len(num) <= 1:\n return num\n partition = -1\n for i in range(len(num) - 2, -1, -1):\n if num[i] < num[i + 1]:\n partition = i\n break\n if partition == -1:\n num.reverse()\n return num\n else:\n for i in range(len(num) - 1, partition, -1):\n if num[i] > num[partition]:\n num[i], num[partition] = num[partition], num[i]\n break\n left = partition + 1\n right = len(num) - 1\n while left < right:\n num[left], num[right] = num[right], num[left]\n left += 1\n right -= 1\n return num\n"},{"alpha_fraction":0.4509246051311493,"alphanum_fraction":0.4694167971611023,"avg_line_length":27.1200008392334,"blob_id":"670f51b5740065e13976cf564528830d5f50d945","content_id":"3e50f52f64e71e8d2f0dea7a5a78ffdf3c7ae7ab","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":703,"license_type":"no_license","max_line_length":81,"num_lines":25,"path":"/Solutions/014_Longest Common Prefix.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n\n def longestCommonPrefix(self, strs):\n \"Given a list of pathnames, returns the longest common leading component\"\n if not strs:\n return ''\n s1 = min(strs)\n s2 = max(strs)\n print s1, s2\n for i, c in enumerate(s1):\n if c != s2[i]:\n return s1[:i]\n return s1\n\n\n # def longestCommonPrefix(self, strs):\n # if len(strs) == 0:\n # return \"\"\n # longest = strs[0]\n # for str in strs[1:]:\n # i = 0\n # while i < len(str) and i < len(longest) and str[i] == longest[i]:\n # i += 1\n # longest = longest[:i]\n # return longest\n"},{"alpha_fraction":0.5034013390541077,"alphanum_fraction":0.5034013390541077,"avg_line_length":22.210525512695312,"blob_id":"8dd46e41ba8e8926cb46dcaf4f9b0ecdd91a5203","content_id":"86059284aee6e13e0448d1b2e563fe2b7fc4c693","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":441,"license_type":"no_license","max_line_length":51,"num_lines":19,"path":"/Solutions/141_Linked List Cycle.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\n\nclass Solution:\n # @param head, a ListNode\n # @return a boolean\n\n def hasCycle(self, head):\n fastP = head\n slowP = head\n while fastP != None and fastP.next != None:\n fastP = fastP.next.next\n slowP = slowP.next\n if fastP == slowP:\n return True\n return False\n"},{"alpha_fraction":0.4438040256500244,"alphanum_fraction":0.45437079668045044,"avg_line_length":40.63999938964844,"blob_id":"ebefa4c5423a690c1b86dc1c22e4d2fac657a281","content_id":"c4f60fdd04e0920d0225a11f531bc6fb3fab13b8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1041,"license_type":"no_license","max_line_length":110,"num_lines":25,"path":"/Solutions/150_Evaluate Reverse Polish Notation.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param tokens, a list of string\n # @return an integer\n\n # def evalRPN(self, tokens):\n # while len(tokens) > 2:\n # for i in tokens:\n # if i in [\"+\", \"-\", \"*\", \"/\"]:\n # index = tokens.index(i)\n # res = eval(\n # tokens[index - 2] + tokens[index] + tokens[index - 1])\n # tokens.pop(index - 2)\n # tokens.pop(index - 2)\n # tokens.pop(index - 2)\n # tokens.insert(index - 2, str(res))\n # return tokens[0]\n def evalRPN(self, tokens):\n numerals, operators = [], {\"+\": operator.add, \"-\": operator.sub, \"*\": operator.mul, \"/\": operator.div}\n for token in tokens:\n if token.isdigit() or token[1:].isdigit():\n numerals.append(int(token))\n else:\n x, y = numerals.pop(), numerals.pop()\n numerals.append(int(operators[token](y * 1.0, x)))\n return numerals.pop()\n"},{"alpha_fraction":0.34840425848960876,"alphanum_fraction":0.36968085169792175,"avg_line_length":22.5,"blob_id":"469913479df61626c2379796f3c422feb7654a2e","content_id":"0148af4aff513baeb932a88b6f0c61b7f847815b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":376,"license_type":"no_license","max_line_length":36,"num_lines":16,"path":"/Solutions/154_Find Minimum in Rotated Sorted Array II.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param num, a list of integer\n # @return an integer\n\n def findMin(self, num):\n i = 0\n length = len(num)\n m = num[0]\n while(i < length-1):\n if num[i + 1] <= num[i]:\n if num[i+1]<m:\n m = num[i+1]\n i += 1\n else:\n i += 1\n return m\n"},{"alpha_fraction":0.46000000834465027,"alphanum_fraction":0.48235294222831726,"avg_line_length":35.956520080566406,"blob_id":"66940edcc9e516d761bdc19d256db66a44a5e4ba","content_id":"17a522e557d2a67edc574e207695ca4172f07e46","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":850,"license_type":"no_license","max_line_length":77,"num_lines":23,"path":"/Solutions/012_Integer to Roman.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# wiki: Roman Number: http://en.wikipedia.org/wiki/Roman_numerals\nclass Solution:\n # @return a string\n\n def intToRoman(self, num):\n result = \"\"\n transTable = {\n 1: \"I\", 5: \"V\", 10: \"X\", 50: \"L\", 100: \"C\", 500: \"D\", 1000: \"M\"}\n for trans in sorted(transTable.keys())[::-1]:\n while num != 0 and num >= trans:\n num -= trans\n result += transTable[trans]\n if num == 0:\n break\n\n simplifyTable = {\"DCCCC\": \"CM\", \"CCCC\": \"CD\", \"LXXXX\":\n \"XC\", \"XXXX\": \"XL\", \"VIIII\": \"IX\", \"IIII\": \"IV\"}\n simplifyContent = [\"DCCCC\", \"CCCC\", \"LXXXX\", \"XXXX\", \"VIIII\", \"IIII\"]\n for item in simplifyContent:\n if item in result:\n result = result.replace(item, simplifyTable[item])\n\n return result\n"},{"alpha_fraction":0.6651480793952942,"alphanum_fraction":0.7061503529548645,"avg_line_length":23.38888931274414,"blob_id":"0c13fd1ac63617f3040e261f326d02c3b6377953","content_id":"77038a33589f8cea9437d45b416877cb3df5a901","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":439,"license_type":"no_license","max_line_length":150,"num_lines":18,"path":"/Hints/041_Trapping Rain Water.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\nGiven n non-negative integers representing an elevation map where the width of each bar is 1, compute how much water it is able to trap after raining.\n\nFor example, \nGiven [0,1,0,2,1,0,1,3,2,1,2,1], return 6.\n\n### Hints:\n\n1. scan from right to left to find max\n2. scan from left to right to find max\n3. compare the min of the list to the exact value\n4. sum all gaps up\n\n#### References:\n\n#### Tags:\nArray, Stack, Two Pointer\n"},{"alpha_fraction":0.5243902206420898,"alphanum_fraction":0.5383275151252747,"avg_line_length":27.75,"blob_id":"4109bc26614a8f357346ed63fa4378f5bec2e8df","content_id":"abcd1ae07b2153dbe4aaa850836de2a1c448af83","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":574,"license_type":"no_license","max_line_length":58,"num_lines":20,"path":"/Solutions/136_Single Number.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"import operator\nclass Solution:\n # @param A, a list of integer\n # @return an integer\n\n def singleNumber(self, A):\n ## This is a dic implementation\n # dic = {}\n # for i in A:\n # dic[i]=dic.get(i, 0) + 1\n # for k in dic:\n # if dic[k] == 1:\n # return k\n ## This is the implementation in lambda\n # return reduce(lambda x, y: operator.xor(x,y), A)\n ## This is the most efficient way using reduce\n return reduce(operator.xor, A)\n\ns = Solution()\nprint s.singleNumber([1,2,2,3,3])"},{"alpha_fraction":0.4881889820098877,"alphanum_fraction":0.5059055089950562,"avg_line_length":35.28571319580078,"blob_id":"75e4770be9c7fa3e5ee9209165e136e5b8068b99","content_id":"0b1973563ab3ed48d66ddb37c61d2ea6cc5fcf9f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":508,"license_type":"no_license","max_line_length":73,"num_lines":14,"path":"/Solutions/162_Find Peak Element.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param num, a list of integer\n # @return an integer\n\n def findPeakElement(self, num):\n pre_num = [num[i] for i in range(1, len(num))] + [float('-inf')]\n suf_num = [float('-inf')] + [num[i] for i in range(len(num) - 1)]\n\n sub_num_1 = [n - p for n, p in zip(num, pre_num)]\n sub_num_2 = [n - s for n, s in zip(num, suf_num)]\n for i in range(len(num)):\n if sub_num_1[i] > 0 and sub_num_2[i] > 0:\n return i\n return 0\n"},{"alpha_fraction":0.5196078419685364,"alphanum_fraction":0.5220588445663452,"avg_line_length":23.058822631835938,"blob_id":"9e79ff6de6528df7052af35c31b4ec6baadbd2b4","content_id":"c2962d00b0b655a1e16aeb5b19af1a057e12b671","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":408,"license_type":"no_license","max_line_length":57,"num_lines":17,"path":"/Solutions/151_Reverse Words in a String.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# Note: Done\nclass Solution:\n # @param s, a string\n # @return a string\n def reverseWords(self, s):\n if s.isspace():\n return \"\"\n else:\n sList = s.split()\n re = sList[::-1]\n return \" \".join(re).strip\n \n# Given an input string, reverse the string word by word.\n# \n# For example,\n# Given s = \"the sky is blue\",\n# return \"blue is sky the\"."},{"alpha_fraction":0.38808372616767883,"alphanum_fraction":0.4138486385345459,"avg_line_length":23.84000015258789,"blob_id":"8c5402ac2189dd6aeb6f5c78f5bfc91cd16bba31","content_id":"b3032728c89847b9dcfbdbde35c3faaa23011943","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":621,"license_type":"no_license","max_line_length":47,"num_lines":25,"path":"/Solutions/009_Palindrome Number.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return a boolean\n\n def isPalindrome(self, x):\n if x < 0:\n return False\n divider = 1\n while(x / divider >= 10):\n divider *= 10\n while(x != 0):\n left = x / divider\n right = x % 10\n if left != right:\n return False\n x %= divider\n x /= 10\n divider /= 100\n return True\n\n # def isPalindrome(self, x):\n # if str(x).startswith(\"-\"):\n # return False\n # y = str(x)[::-1]\n # res = True if y == y[::-1] else False\n # return res\n"},{"alpha_fraction":0.4513692259788513,"alphanum_fraction":0.47119924426078796,"avg_line_length":30.909090042114258,"blob_id":"078642458ab4e993d34d3b4416863c07e10ef1dd","content_id":"9eccb8f98035b511cda2ea05f0963852f26f1161","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1059,"license_type":"no_license","max_line_length":72,"num_lines":33,"path":"/Solutions/095_Unique Binary Search Trees.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return an integer\n\n def numTrees(self, n):\n ## Recursive way\n # TIME LIMITATION ERROR\n # if n == 0:\n # return 1\n # if n == 1:\n # return 1\n # if n == 2:\n # return 2\n # count = 0\n # for i in range(n):\n # count += self.numTrees(i)*self.numTrees(n-i-1)\n # return count\n \n # numOfTrees[i] == j means for n is i, there are j unique\n # binary search trees for it.\n ## Iterative way\n numOfTrees = [1, 1, 2]\n nextTrying = len(numOfTrees)\n while nextTrying <= n:\n # Comptute how many unique binary search trees are there for\n # n == nextTrying.\n # f(3) = f(0)*f(2) + f(1)*f(1) + f(2)*f(0)\n count = 0\n for center in xrange(nextTrying):\n count += numOfTrees[center] * \\\n numOfTrees[nextTrying - center - 1]\n numOfTrees.append(count)\n nextTrying += 1\n return numOfTrees[n]\n \n\n"},{"alpha_fraction":0.581818163394928,"alphanum_fraction":0.585454523563385,"avg_line_length":20.153846740722656,"blob_id":"e881d9369010ffc0c5400ca196c299447b3e275b","content_id":"99843dfea08df26d38895b3312836405ca3095b9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":275,"license_type":"no_license","max_line_length":103,"num_lines":13,"path":"/Hints/021_Generate Parentheses.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\nGiven n pairs of parentheses, write a function to generate all combinations of well-formed parentheses.\n\nFor example, given n = 3, a solution set is:\n\n\"((()))\", \"(()())\", \"(())()\", \"()(())\", \"()()()\"\n\n### Hints:\n\n#### References:\n\n#### Tags:\nBacktracking String\n"},{"alpha_fraction":0.7119155526161194,"alphanum_fraction":0.7315233945846558,"avg_line_length":27.782608032226562,"blob_id":"eec55d3feaf8334c290365d6870a7d90ab9ec8a5","content_id":"65011bccc785e3afb32d8dc806e9215702d9643c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":663,"license_type":"no_license","max_line_length":170,"num_lines":23,"path":"/Hints/054_Jump Game.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\nGiven an array of non-negative integers, you are initially positioned at the first index of the array.\n\nEach element in the array represents your maximum jump length at that position.\n\nDetermine if you are able to reach the last index.\n\nFor example:\nA = [2,3,1,1,4], return true.\n\nA = [3,2,1,0,4], return false.\n\n### Hints:\n\n1. what if the value is 0?\n2. define a variable named reachable, which is the maximum index you can reach at this moment\n\nfrom its first index, make the reachable to be the max of reachable and index + value. If the next index is less than reachable, then reture false, otherwise return true.\n\n#### References:\n\n#### Tags:\n\n"},{"alpha_fraction":0.3695652186870575,"alphanum_fraction":0.37826088070869446,"avg_line_length":27.75,"blob_id":"9c2c13a1d491914dab088e5a6289f3c9f7dc44a3","content_id":"a1cb0ea6f1acd30a9ee33770a4de83f8e1746659","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":920,"license_type":"no_license","max_line_length":67,"num_lines":32,"path":"/Solutions/029_Substring with Concatenation of All Words.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param S, a string\n # @param L, a list of string\n # @return a list of integer\n\n def findSubstring(self, S, L):\n words = {}\n wordNum = len(L)\n for i in L:\n if i not in words:\n words[i] = 1\n else:\n words[i] += 1\n wordLen = len(L[0])\n res = []\n for i in range(len(S) + 1 - wordLen * wordNum):\n curr = {}\n j = 0\n while j < wordNum:\n word = S[i + j * wordLen:i + j * wordLen + wordLen]\n if word not in words:\n break\n if word not in curr:\n curr[word] = 1\n else:\n curr[word] += 1\n if curr[word] > words[word]:\n break\n j += 1\n if j == wordNum:\n res.append(i)\n return res\n"},{"alpha_fraction":0.4684014916419983,"alphanum_fraction":0.49814125895500183,"avg_line_length":28.88888931274414,"blob_id":"adb7985672b79c1c1ee1bc549fafac4afaedd5ab","content_id":"31f85b4d003b62287785cdd402a625b79ccf8936","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":269,"license_type":"no_license","max_line_length":72,"num_lines":9,"path":"/Solutions/002_Median of Two Sorted Arrays.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return a float\n\n def findMedianSortedArrays(self, A, B):\n temp = sorted(A + B)\n if len(temp) % 2 == 0:\n return (temp[len(temp) / 2] + temp[len(temp) / 2 - 1]) / 2.0\n else:\n return temp[len(temp) / 2]\n"},{"alpha_fraction":0.5043478012084961,"alphanum_fraction":0.5093167424201965,"avg_line_length":27.75,"blob_id":"c96f8ef720889b3429630bff37b5a06af46de114","content_id":"ff4aa4cf3ef0fbf608352564b33dd3fb46c2ba50","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":805,"license_type":"no_license","max_line_length":56,"num_lines":28,"path":"/Solutions/096_Unique Binary Search Trees II.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\n\nclass Solution:\n # @return a list of tree node\n\n def generateTrees(self, n):\n return self.generateTreesRecur(1, n)\n\n def generateTreesRecur(self, low, high):\n result = []\n if low > high:\n result.append(None)\n for i in range(low, high + 1):\n left = self.generateTreesRecur(low, i - 1)\n right = self.generateTreesRecur(i + 1, high)\n for j in left:\n for k in right:\n current = TreeNode(i)\n current.left = j\n current.right = k\n result.append(current)\n return result\n"},{"alpha_fraction":0.5966348052024841,"alphanum_fraction":0.6066393852233887,"avg_line_length":41.28845977783203,"blob_id":"3052e4de7bc8cc2e5064a7231ea49ef58dfc338d","content_id":"c9c48385f46a0ed673f4a62c8270034fd40e5d51","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2199,"license_type":"no_license","max_line_length":535,"num_lines":52,"path":"/Hints/003_Longest Substring Without Repeating Characters.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n> Given a string, find the length of the longest substring without repeating characters. For example, the longest substring without repeating letters for \"abcabcbb\" is \"abc\", which the length is 3. For \"bbbbb\" the longest substring is \"b\", with the length of 1.\n\n### Hints:\nFirst find a char, if it has been visited before, then compare the substring from its last appearance to the position it visited with the substring from this position to the same length substrings. For example, \"dabcabcd\", visited from the beginning, and then at the 5th position, it finds out \"a\" has been explored before, then get the substring from \"a\" to \"a\" which is \"abc\", now get the same length string from the second \"a\", which is \"abc\", then compare them whether they are the same or not. Update the maximum length variable. \n\nUse hashtable to indicate whether a char has been visited or not.\n\nUse a `ord` build-in function to find out its Unicode value.\n\n```python\nclass Solution:\n # @return an integer\n def lengthOfLongestSubstring(self, s):\n start = 0\n maxlen = 0\n hashtable = [-1 for i in range(256)]\n for i in range(len(s)):\n if hashtable[ord(s[i])] != -1:\n while start <= hashtable[ord(s[i])]:\n hashtable[ord(s[start])] = -1\n start += 1\n if i - start + 1 > maxlen: maxlen = i - start + 1\n hashtable[ord(s[i])] = i\n return maxlen\n```\n```python\nclass Solution:\n # @return an integer\n def lengthOfLongestSubstring(self, s):\n start = 0\n maxlen = 0\n dict = {}\n for i in range(len(s)):\n dict[s[i]] = -1\n for i in range(len(s)):\n if dict[s[i]] != -1:\n while start <= dict[s[i]]:\n dict[s[start]] = -1\n start += 1\n if i - start + 1 > maxlen: maxlen = i - start + 1\n dict[s[i]] = i\n return maxlen\n```\n#### Note:\n* What if a char appears in several places, and how can you compare them? For example: \"abcbaabcba\", the maximum substring is \"abcba\".\n\n* How to update the hashtable?\n#### References:\n\n#### Tags:\n**hashtable**, **string**\n"},{"alpha_fraction":0.3715847134590149,"alphanum_fraction":0.3825136721134186,"avg_line_length":31.294116973876953,"blob_id":"0d72948493936805fb84bc8cc515be0fd7f426c9","content_id":"6612e3ad02569abe31768cf06ac2929b022c7e28","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":549,"license_type":"no_license","max_line_length":109,"num_lines":17,"path":"/Solutions/125_Valid Palindrome.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param s, a string\n # @return a boolean\n\n def isPalindrome(self, s):\n if s == '':\n return True\n else:\n sTmp = ''\n for i in range(0, len(s)):\n if s[i] >= 'a' and s[i] <= 'z' or s[i] >= '0' and s[i] <= '9' or s[i] >= 'A' and s[i] <= 'Z':\n sTmp += s[i]\n sTmp = sTmp.lower()\n for i in range(0, len(sTmp) / 2):\n if sTmp[i] != sTmp[len(sTmp) - 1 - i]:\n return False\n return True\n"},{"alpha_fraction":0.4508928656578064,"alphanum_fraction":0.4776785671710968,"avg_line_length":24,"blob_id":"bf141d37ff04fa4c71427d90bd6f78bf498ec350","content_id":"fe729f7557c811cbed28d9256f692995edf2869f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":224,"license_type":"no_license","max_line_length":45,"num_lines":9,"path":"/Solutions/171_Excel Sheet Column Number.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param s, a string\n # @return an integer\n def titleToNumber(self, s):\n res = 0\n l = len(s)\n for i in range(l):\n res += 26**i*(ord(s[l-i-1]) - 64)\n return res"},{"alpha_fraction":0.479353666305542,"alphanum_fraction":0.48833033442497253,"avg_line_length":26.850000381469727,"blob_id":"d1ae552a026196118bfef63f1fefb04d0f18dec4","content_id":"cb708f9201f40e0933fc92ad6f68145551a205f4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":557,"license_type":"no_license","max_line_length":61,"num_lines":20,"path":"/Solutions/054_Jump Game.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param A, a list of integers\n # @return a boolean\n\n # def canJump(self, A):\n # reachable = 0\n # while reachable < len(A) - 1 and A[reachable] != 0:\n # reachable = reachable + A[reachable]\n # if reachable == len(A) - 1:\n # return True\n # else:\n # return False\n\n def canJump(self, A):\n reachable = 0\n for i in range(len(A)):\n if i > reachable:\n return False\n reachable = max(reachable, i + A[i])\n return True\n"},{"alpha_fraction":0.683582067489624,"alphanum_fraction":0.6895522475242615,"avg_line_length":19.875,"blob_id":"85f1cdd6d6b6593bf9cfff4d383f8b86c8d28b9f","content_id":"a34336717b72d3250ba8e8868a9725ad901da7bc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":335,"license_type":"no_license","max_line_length":55,"num_lines":16,"path":"/Hints/186_Reverse Words in a String II.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\nGiven an input string, reverse the string word by word.\nFor example,\nGiven s = \"the sky is blue\",\nreturn \"blue is sky the\".\n\n### Hints:\n1. split string to list \n2. join the reversed list to a new string.\n\nOption:\nwhat if there is a period sign? \nwhat if the first letter should be captial?\n#### References:\n\n#### Tags:\n\n"},{"alpha_fraction":0.570762038230896,"alphanum_fraction":0.5816485285758972,"avg_line_length":28.227272033691406,"blob_id":"02400f795d051a631c43a371985035bcb092b599","content_id":"6b1b3ea2480deb1f2eb465d95ea7b302b443c28c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":643,"license_type":"no_license","max_line_length":92,"num_lines":22,"path":"/Solutions/110_Balanced Binary Tree.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\n\nclass Solution:\n # @param root, a tree node\n # @return a boolean\n\n def isBalanced(self, root):\n return self.getHeight(root) != -1\n\n def getHeight(self, root):\n if root == None:\n return 0\n left_height, right_height = self.getHeight(root.left), self.getHeight(root.right)\n if left_height < 0 or right_height < 0 or math.fabs(left_height - right_height) > 1:\n return -1\n return max(left_height, right_height) + 1\n"},{"alpha_fraction":0.33595284819602966,"alphanum_fraction":0.34774067997932434,"avg_line_length":21.130434036254883,"blob_id":"85057256c44ae768f71df7dc7c4dbe01150b8db2","content_id":"7a4991c69aa6580d08cc68027c6072a3eefaa42e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":509,"license_type":"no_license","max_line_length":42,"num_lines":23,"path":"/Solutions/037_Count and Say.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return a string\n\n def count(self, s):\n t = ''\n count = 0\n curr = '#'\n for i in s:\n if i != curr:\n if curr != '#':\n t += str(count) + curr\n curr = i\n count = 1\n else:\n count += 1\n t += str(count) + curr\n return t\n\n def countAndSay(self, n):\n s = '1'\n for i in range(2, n + 1):\n s = self.count(s)\n return s\n"},{"alpha_fraction":0.4523507058620453,"alphanum_fraction":0.4866581857204437,"avg_line_length":42.72222137451172,"blob_id":"367bd35685d5bd4cc547de142a31bc7e2a3e9864","content_id":"5cec0fe32700e0cf4330986b28048c42d6c3e3ff","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":787,"license_type":"no_license","max_line_length":83,"num_lines":18,"path":"/Solutions/174_Dungeon Game.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param dungeon, a list of lists of integers\n # @return a integer\n\n def calculateMinimumHP(self, dungeon):\n m = len(dungeon)\n n = len(dungeon[0])\n dp = [[0 for index in range(n)] for index in range(m)]\n dp[m - 1][n - 1] = max(1, 1 - dungeon[m - 1][n - 1])\n for row in reversed(range(m - 1)):\n dp[row][n - 1] = max(1, dp[row + 1][n - 1] - dungeon[row][n - 1])\n for col in reversed(range(n - 1)):\n dp[m - 1][col] = max(1, dp[m - 1][col + 1] - dungeon[m - 1][col])\n for row in reversed(range(m - 1)):\n for col in reversed(range(n - 1)):\n dp[row][col] = max(\n 1, min(dp[row + 1][col], dp[row][col + 1]) - dungeon[row][col])\n return dp[0][0]\n"},{"alpha_fraction":0.7068965435028076,"alphanum_fraction":0.7210031151771545,"avg_line_length":30.899999618530273,"blob_id":"0499064e6fc272d69b81a416efeefaa75dce7e50","content_id":"1c1d0c165afdaa4c33d2201c7aba01f64ca9205f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":638,"license_type":"no_license","max_line_length":281,"num_lines":20,"path":"/Hints/122_Best Time to Buy and Sell Stock II.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n\nSay you have an array for which the ith element is the price of a given stock on day i.\n\nDesign an algorithm to find the maximum profit. You may complete as many transactions as you like (ie, buy one and sell one share of the stock multiple times). However, you may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again).\n\n### Hints:\n\nThis problem you can buy stock several times, so the maximum profit is to buy it at lower price and sell it every higher price.\n\n1,2,3,4,5,2,3,4,5\n^, , , ,|,^, , ,|\n\n\n#### References:\n\nOther stockpricing problems:\n\n#### Tags:\nArray, Greedy\n"},{"alpha_fraction":0.6883116960525513,"alphanum_fraction":0.6935064792633057,"avg_line_length":18.25,"blob_id":"74f4a9e3f626b438f4ed81462205cde39ba440a0","content_id":"ca28cb938efd4369a989454b8f6776a2213271a0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":385,"license_type":"no_license","max_line_length":114,"num_lines":20,"path":"/Hints/104_Maximum Depth of Binary Tree.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n Given a binary tree, find its maximum depth.\n\nThe maximum depth is the number of nodes along the longest path from the root node down to the farthest leaf node.\n\n### Hints:\n\nDo it in a recursive way:\nD_{node} = max{D_{node.left}, D_{node.right}} + 1\n\nwhere 1 is the node itself\n\n\nQ:\nHow to implement it in a iterative way.\n\n#### References:\n\n#### Tags:\nDepth-fisrt, Tree\n"},{"alpha_fraction":0.7055016160011292,"alphanum_fraction":0.7135922312736511,"avg_line_length":43.07143020629883,"blob_id":"d7c021eb69b8bd85415eaa6906869589e69ac421","content_id":"509b48e6f1242d005b28eb7faeffcbfbc8993ebe","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":626,"license_type":"no_license","max_line_length":177,"num_lines":14,"path":"/Hints/161_One Edit Distance.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\nGiven two strings S and T, determine if they are both one edit distance apart.\n\nHint:\n1. If | n – m | is greater than 1, we know immediately both are not one-edit distance apart.\n2. It might help if you consider these cases separately, m == n and m ≠ n.\n3. Assume that m is always ≤ n, which greatly simplifies the conditional statements. If m > n, we could just simply swap S and T.\n4. If m == n, it becomes finding if there is exactly one modified operation. If m ≠ n, you do not have to consider the delete operation. Just consider the insert operation in T.\n\n### Hints:\n\n#### References:\n\n#### Tags:\n\n"},{"alpha_fraction":0.7757575511932373,"alphanum_fraction":0.7757575511932373,"avg_line_length":40.375,"blob_id":"0e029464a39cca248e1853f9054d229fe879d1f7","content_id":"07ab8ab4a81c6739088fc88f8b039c76d93194cd","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"SQL","length_bytes":330,"license_type":"no_license","max_line_length":72,"num_lines":8,"path":"/Solutions/175_Combine Two Tables.sql","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# Write your MySQL query statement below\nSELECT p.FirstName, p.LastName, a.City, a.State\nFROM Person p LEFT OUTER JOIN Address a USING (PersonId);\n\n# Write your MySQL query statement below\n-- SELECT Person.FirstName, Person.LastName, Address.City, Address.State\n-- FROM Person, Address\n-- WHERE Person.PersonId = Address.PersonId;"},{"alpha_fraction":0.44788485765457153,"alphanum_fraction":0.6990841627120972,"avg_line_length":62.72222137451172,"blob_id":"486fc45c10dbc726d3243057274925f860b9d688","content_id":"de95e2579561d809d81ca9a8db823f1c57e0eecc","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2301,"license_type":"no_license","max_line_length":453,"num_lines":36,"path":"/README.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# Leetcode_Python\n* This is a repository for solutions to [Leetcode Online Judge Problems](https://oj.leetcode.com/). It helps you to prepare the interview of tech companies.\n\n* Folder [Solutions](https://github.com/jinhw1989/Leetcode_Python/tree/master/Solutions) contains all the Python approaches to the problems, and [Hints](https://github.com/jinhw1989/Leetcode_Python/tree/master/Hints) contains explainations to the problems respectively. \n\n* All the problems are ordered by the created time, not by the difficulties of them. Some problems are still under consideration, and may not be the best approaches.\n\n\t\n## TODO List:\n* Not implemented problems(Time Limit Exceeded):\n\t005, 036, 132, 148\n\n* Add hint to each problem.\n\n* ~~PEP8 Format~~.\n\n\n## Resources:\n* [Cracking the Coding Interview: 150 Programming Questions and Solutions](http://www.amazon.com/Cracking-Coding-Interview-Programming-Questions/dp/098478280X)\n\n* [The Google Resume: How to Prepare for a Career and Land a Job at Apple, Microsoft, Google, or any Top Tech Company](http://www.amazon.com/The-Google-Resume-Prepare-Microsoft/dp/0470927623)\n\n* [CareerCup](http://www.careercup.com/)\n\nYou can also look into [jw2013/Leetcode](https://github.com/jw2013/Leetcode) for references. And it also refers to parts of work of [南郭子綦](http://www.cnblogs.com/zuoyuan/tag/leetcode/). Thanks to both of them.\n\n## Difficulty\n\n### Easy: \n006, 007, 008, 009, 013, 014, 019, 020, 021, 026, 027, 028, 036, 038, 058, 066, 067, 070, 083, 088, 100, 101, 102, 104, 107, 110, 111, 112, 118, 119, 125, 155, 157, 160, 165, 168, 169, 170, 171, 172\n\n### Medium:\n001, 002, 003, 005, 011, 012, 015, 016, 017, 018, 022, 024, 029, 031, 034, 035, 039, 040, 043, 046, 048, 049, 050, 053, 054, 055, 059, 060, 061, 062, 063, 064, 069, 071, 073, 074, 075, 077, 078, 079, 080, 081, 082, 086, 089, 090, 091, 092, 093, 094, 095, 096, 098, 103, 105, 106, 108, 109, 113, 114, 116, 120, 121, 122, 127, 129, 130, 131, 133, 134, 136, 137, 139, 141, 142, 143, 144, 147, 148, 150, 151, 152, 153, 156, 161, 162, 163, 166, 167, 173, 179\n\n### Hard:\n004, 010, 023, 025, 030, 032, 033, 037, 041, 042, 044, 045, 047, 051, 052, 056, 057, 065, 068, 072, 076, 084, 085, 087, 097, 099, 115, 117, 123, 124, 126, 128, 132, 135, 138, 140, 145, 146, 149, 154, 158, 159, 164, 174"},{"alpha_fraction":0.49569496512413025,"alphanum_fraction":0.5018450021743774,"avg_line_length":31.520000457763672,"blob_id":"6d3206de88f96adc1731fd84df200ebe6ab47834","content_id":"e290ea5fc8511c710bc226e3327b85f5170f76ff","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":813,"license_type":"no_license","max_line_length":70,"num_lines":25,"path":"/Solutions/041_Trapping Rain Water.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param A, a list of integers\n # @return an integer\n\n def trap(self, A):\n # from right to left\n leftmosthigh = [0 for i in range(len(A))]\n leftmax = 0\n for i in range(len(A)):\n if A[i] > leftmax:\n leftmax = A[i]\n leftmosthigh[i] = leftmax\n # from left to right \n rightmosthigh = [0 for i in range(len(A))]\n rightmax = 0\n for i in reversed(range(len(A))):\n if A[i] > rightmax:\n rightmax = A[i]\n rightmosthigh[i] = rightmax\n # print rightmosthigh\n total = 0\n for i in range(len(A)):\n if min(rightmosthigh[i], leftmosthigh[i]) > A[i]:\n total += min(rightmosthigh[i], leftmosthigh[i]) - A[i]\n return total\n"},{"alpha_fraction":0.57505863904953,"alphanum_fraction":0.6032056212425232,"avg_line_length":43.10344696044922,"blob_id":"4646f8ebb2be6dfaf200dead85f7e1dc794ed9f5","content_id":"95575f440538fc2d2bc13ec26f4f760c5326977b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2558,"license_type":"no_license","max_line_length":526,"num_lines":58,"path":"/Hints/002_Median of Two Sorted Arrays.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\n > There are two sorted arrays A and B of size m and n respectively. Find the median of the two sorted arrays. The overall run time complexity should be O(log (m+n)).\n\n### Hints:\nFirst we need to know what is median. \n> The median of a finite list of numbers can be found by arranging all the observations from lowest value to highest value and picking the middle one (e.g., the median of {3, 3, 5, 9, 11} is 5). If there is an even number of observations, then there is no single middle value; the median is then usually defined to be the mean of the two middle values (the median of {3, 5, 7, 9} is (5 + 7) / 2 = 6), which corresponds to interpreting the median as the fully trimmed mid-range. [wikipedia](http://en.wikipedia.org/wiki/Median)\n\nThen a simple implementation should be like this:\n```python\nclass Solution:\n # @return a float\n\n def findMedianSortedArrays(self, A, B):\n temp = sorted(A + B)\n if len(temp) % 2 == 0:\n return (temp[len(temp) / 2] + temp[len(temp) / 2 - 1]) / 2.0\n else:\n return temp[len(temp) / 2]\n```\nHowever we should think about the complexity of this implementation, it seems OK, but the `sorted()` function cost O(n log (n)), which is not the required operation cost.\n\nThink about recursive way to solve this problem\nFor example, if we want to find the 7th number of two list A = [1,3,4,5]; B = [2,4,6,8,9,10]. Let x be the 7th number, then choose the third number of each list, and then compare them. 4<6, it means 1,3,4 are not the 7th number, then compare A=[5], B = [2,4,6,8,9,10], and find 4(7-3)th number. And do the recursive way.\n\n```python\nclass Solution:\n # @return a float\n\n def getKth(self, A, B, k):\n lenA = len(A)\n lenB = len(B)\n if lenA > lenB:\n return self.getKth(B, A, k)\n if lenA == 0:\n return B[k - 1]\n if k == 1:\n return min(A[0], B[0])\n pa = min(k / 2, lenA)\n pb = k - pa\n if A[pa - 1] <= B[pb - 1]:\n return self.getKth(A[pa:], B, pb)\n else:\n return self.getKth(A, B[pb:], pa)\n\n def findMedianSortedArrays(self, A, B):\n lenA = len(A)\n lenB = len(B)\n if (lenA + lenB) % 2 == 1:\n return self.getKth(A, B, (lenA + lenB) / 2 + 1)\n else:\n # must multiply 0.5 for return a float else it will return an int\n return (self.getKth(A, B, (lenA + lenB) / 2) + self.getKth(A, B, (lenA + lenB) / 2 + 1)) * 0.5\n```\n\n#### References:\n\n#### Tags:\n**list**, **recursion**\n"},{"alpha_fraction":0.47999998927116394,"alphanum_fraction":0.4959999918937683,"avg_line_length":17.75,"blob_id":"bb8ff811194e760a4708eb80717a615deda035e6","content_id":"c841efa87f0a2bca124fdd82addb0ac635ff93c4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":375,"license_type":"no_license","max_line_length":48,"num_lines":20,"path":"/Solutions/170_Two Sum III.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class TwoSum:\n\n def __init__(self):\n self.table = dict()\n\n def add(self, n):\n self.table[n] = self.table.get(n, 0) + 1\n\n def find(self, value):\n for i in self.table:\n c = value - i\n if c in self.table or c == i:\n return True\n return False\n\nts = TwoSum()\nts.add(1)\nts.add(2)\nts.add(4)\nprint ts.find(7)\n"},{"alpha_fraction":0.43431636691093445,"alphanum_fraction":0.4611259996891022,"avg_line_length":22.3125,"blob_id":"68597bcaf6a097bc861f7d51fc011d8ebae191d9","content_id":"25f8be7244c345435b6fa47c7539d4ac3592e657","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":373,"license_type":"no_license","max_line_length":43,"num_lines":16,"path":"/Solutions/201_Happy Number.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param {integer} n\n # @return {boolean}\n\n def isHappy(self, n):\n l = [i for i in str(n)]\n s = 0\n count = 0\n while s != 1 and count < 10:\n s = sum([int(i)**2 for i in l])\n l = [i for i in str(s)]\n count += 1\n return True if s == 1 else False\n\ns = Solution()\nprint s.isHappy(19)\n"},{"alpha_fraction":0.5570032596588135,"alphanum_fraction":0.6753528714179993,"avg_line_length":33.074073791503906,"blob_id":"72e4d8f8517c9e5532f5aa6354780faa1c6f0e08","content_id":"d46dbde5b3807693b375f245c670836d250caa2f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":921,"license_type":"no_license","max_line_length":120,"num_lines":27,"path":"/Hints/013_Roman to Integer.md","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"### Problem:\nGiven a roman numeral, convert it to an integer.\n\nInput is guaranteed to be within the range from 1 to 3999.\n\n### Hints:\nRead the wikipedia first: [Roman Numerals](http://en.wikipedia.org/wiki/Roman_numerals)\n\nI: 1, V: 5, X: 10, L: 50, C: 100, D: 500, M: 1000.\nFor example: \n2015 = 1000 + 1000 + 10 + 5 = MMXV \n2014 = 1000 + 1000 + 10 + 5 - 1 = MMXIV \n2013 = 1000 + 1000 + 10 + 1 + 1 + 1 = MMXIII\n\nNotice that 4 = IV = V-I \nGenerally, Roman Numerals are represent by descending order. If a pair of the two letters are in ascending order, \nthe result is the substraction the smaller one from the larger one.\n\nFirst make some assumptions:\n1. The number is an integer; \n2. The range of the integers is from 1 to 3999, which is I to 3999 = 1000 + 1000 + 1000 + 1000 - 100 + 100 - 10 + 10 - 1\n= MMMCMXCIX \n3. Question: what's the maximum number Roman numerals can represent? \n\n#### References:\n\n#### Tags:\n\n"},{"alpha_fraction":0.526392936706543,"alphanum_fraction":0.526392936706543,"avg_line_length":24.259260177612305,"blob_id":"8243502986d72ae887bc4aabb9073a8c4f7d0e05","content_id":"f30e28143161688e87521a5a48ea6e6bafb9b4b4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":682,"license_type":"no_license","max_line_length":49,"num_lines":27,"path":"/Solutions/099_Recover Binary Search Tree.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"# Definition for a binary tree node\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\n\nclass Solution:\n # @param root, a tree node\n # @return a tree node\n\n def inorder(self, root, list, listp):\n if root:\n self.inorder(root.left, list, listp)\n list.append(root.val)\n listp.append(root)\n self.inorder(root.right, list, listp)\n\n def recoverTree(self, root):\n list = []\n listp = []\n self.inorder(root, list, listp)\n list.sort()\n for i in range(len(list)):\n listp[i].val = list[i]\n return root\n"},{"alpha_fraction":0.406815767288208,"alphanum_fraction":0.42492014169692993,"avg_line_length":29.29032325744629,"blob_id":"8ce1c341b4c3e3bfae9d7c408a80e022e91cc2b7","content_id":"4f28984d5641deedcf4fe1e5900c4ddf36dc79b9","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":939,"license_type":"no_license","max_line_length":75,"num_lines":31,"path":"/Solutions/059_Permutation Sequence.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return a string\n # def dfs(self, n, k, string, stringlist):\n # if len(stringlist) == n:\n # Solution.count += 1\n # if Solution.count == k:\n # print stringlist\n # return\n # for i in range(len(string)):\n # self.dfs(n, k, string[:i]+string[i+1:], stringlist+string[i])\n\n # def getPermutation(self, n, k):\n # string = ''\n # for i in range(n): string += str(i+1)\n # Solution.count = 0\n # self.dfs(n, k, string, '')\n def getPermutation(self, n, k):\n res = ''\n k -= 1\n fac = 1\n for i in range(1, n):\n fac *= i\n num = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n for i in reversed(range(n)):\n curr = num[k / fac]\n res += str(curr)\n num.remove(curr)\n if i != 0:\n k %= fac\n fac /= i\n return res\n"},{"alpha_fraction":0.3598130941390991,"alphanum_fraction":0.3995327055454254,"avg_line_length":20.9743595123291,"blob_id":"62dc7253804489de58224be5c07b5860af6a6c59","content_id":"78d03c8a132ef4f7a5bf25c020ceb888dccdbe45","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":856,"license_type":"no_license","max_line_length":65,"num_lines":39,"path":"/Solutions/008_String to Integer (atoi).py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @return an integer\n\n def atoi(self, str):\n s = str.strip()\n if len(s) == 0:\n return 0\n INT_MAX, INT_MIN = 2147483647, -2147483648\n sign = 1\n if s[0] in '+-':\n if s[0] == '-':\n sign = -1\n s = s[1:]\n if s.isdigit():\n res = int(s)\n else:\n i = 0\n while s[i].isdigit():\n i += 1\n if i != 0:\n s = s[0:i]\n res = int(s)\n else:\n return 0\n if res > INT_MAX:\n return INT_MIN if sign == -1 else INT_MAX\n return sign * res\n\n\n\n# Option, if can use packages\n# import re\n\n\n# class Solution:\n# # @return an integer\n\n# def atoi(self, str):\n# return int(re.search('[+-]?\\d+', str.strip()).group(0))"},{"alpha_fraction":0.5227272510528564,"alphanum_fraction":0.5397727489471436,"avg_line_length":22.46666717529297,"blob_id":"4a77a2e874de2136170ac3e0d3d9e41c7f8bc145","content_id":"5e1b25ec653fe37346f3484f8dec40b2f5cb4c41","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":352,"license_type":"no_license","max_line_length":73,"num_lines":15,"path":"/Solutions/191_Numbers of 1 Bits.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n # @param n, an integer\n # @return an integer\n\n def hammingWeight(self, n):\n # return bin(n)[2:].count('1')\n # Bit Manuplation, the performance is worse than string operation\n ans = 0\n while n:\n ans += n & 1\n n >>= 1\n return ans\n\ns = Solution()\nprint s.hammingWeight(3)\n"},{"alpha_fraction":0.7291666865348816,"alphanum_fraction":0.7291666865348816,"avg_line_length":15.166666984558105,"blob_id":"182bf660d4cc6bfd643492e8f86a9d931bc458dc","content_id":"c42452856ad14a05143904eb055820ef93b00344","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":96,"license_type":"no_license","max_line_length":35,"num_lines":6,"path":"/Solutions/161_One Edit Distance.py","repo_name":"jinhw1989/Leetcode_Python","src_encoding":"UTF-8","text":"class Solution:\n\tdef isOneEditDistance(self, s, t):\n\t\tpass\n\ns = Solution()\ns.isOneEditDistance()"}],"string":"[\n {\n \"alpha_fraction\": 0.739534854888916,\n \"alphanum_fraction\": 0.7441860437393188,\n \"avg_line_length\": 32,\n \"blob_id\": \"f9dcb2dddaa98525eefe00248ffd00f078bf1713\",\n \"content_id\": \"c052e0b135463c823de82fbb5db503ca88eccea3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 438,\n \"license_type\": \"no_license\",\n \"max_line_length\": 198,\n \"num_lines\": 13,\n \"path\": \"/Hints/159_Longest Substring with At Most Two Distinct Characters.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\nGiven a string, find the longest substring that contains only two unique characters. For example, given \\\"abcbbbbcccbdddadacb\\\", the longest substring that contains 2 unique character is \\\"bcbbbbcccb\\\".\\n\\nGiven a string S, find the length of the longest substring T that contains at most two distinct characters.\\nFor example,\\nGiven S = “eceba”,\\nT is “ece” which its length is 3.\\n\\n### Hints:\\n\\n#### References:\\n\\n#### Tags:\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.42252397537231445,\n \"alphanum_fraction\": 0.4480830729007721,\n \"avg_line_length\": 29.560976028442383,\n \"blob_id\": \"a050edbcd5a61e2ed7d9bbdff72443aca40ed526\",\n \"content_id\": \"c4d9c4574c17da011639a23e7af6d1fff16896d8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1252,\n \"license_type\": \"no_license\",\n \"max_line_length\": 74,\n \"num_lines\": 41,\n \"path\": \"/Solutions/188_Best Time to Buy and Sell Stock IV.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return an integer as the maximum profit \\n # def maxProfit(self, k, prices):\\n # if len(prices) < 1:\\n # return 0\\n # low = prices[0]\\n # high = prices[0]\\n # profit = []\\n # for i in range(len(prices)):\\n # if prices[i]>=high:\\n # high = prices[i]\\n # else:\\n # profit.append(high-low)\\n # low = prices[i]\\n # high = prices[i]\\n # profit.append(high-low)\\n # print profit\\n # profit = sorted(profit, reverse=True)\\n # return sum(profit[:k])\\n # \\n\\n def maxProfit(self, k, prices):\\n size = len(prices)\\n if k > size / 2:\\n return self.quickSolve(size, prices)\\n dp = [None] * (2 * k + 1)\\n dp[0] = 0\\n for i in range(size):\\n for j in range(min(2 * k, i + 1) , 0 , -1):\\n dp[j] = max(dp[j], dp[j - 1] + prices[i] * [1, -1][j % 2])\\n return max(dp)\\n\\n def quickSolve(self, size, prices):\\n sum = 0\\n for x in range(size - 1):\\n if prices[x + 1] > prices[x]:\\n sum += prices[x + 1] - prices[x]\\n return sum\\n\\ns = Solution()\\nprint s.maxProfit(2, [1,2,4,2,5,7,2,4,9,0])\"\n },\n {\n \"alpha_fraction\": 0.3533724248409271,\n \"alphanum_fraction\": 0.3631476163864136,\n \"avg_line_length\": 35.55356979370117,\n \"blob_id\": \"9f2c22777c8825a926c1de95baa79477016b1a47\",\n \"content_id\": \"3cd8b933506590b1eced2abf7837447c751faa4a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2046,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 56,\n \"path\": \"/Solutions/017_4Sum.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# class Solution:\\n # @return a list of lists of length 4, [[val1,val2,val3,val4]]\\n\\n # def fourSum(self, num, target):\\n # numLen, res, dict = len(num), set(), {}\\n # if numLen < 4:\\n # return []\\n # num.sort()\\n # for p in range(numLen):\\n # for q in range(p + 1, numLen):\\n # if num[p] + num[q] not in dict:\\n # dict[num[p] + num[q]] = [(p, q)]\\n # else:\\n # dict[num[p] + num[q]].append((p, q))\\n # print dict\\n # for i in range(numLen):\\n # for j in range(i + 1, numLen - 2):\\n # T = target - num[i] - num[j]\\n # if T in dict:\\n # for k in dict[T]:\\n # if k[0] > j:\\n # res.add((num[i], num[j], num[k[0]], num[k[1]]))\\n # return [list(i) for i in res]\\n\\n\\nclass Solution:\\n # @return a list of lists of length 4, [[val1,val2,val3,val4]]\\n\\n def fourSum(self, num, target):\\n numLen, dic = len(num), {}\\n res = []\\n if numLen < 4:\\n return []\\n # num.sort()\\n for p in range(numLen):\\n for q in range(p + 1, numLen):\\n if num[p] + num[q] not in dic:\\n dic[num[p] + num[q]] = [(p, q)]\\n else:\\n dic[num[p] + num[q]].append((p, q))\\n keyList = dic.keys()\\n for i in dic.keys():\\n if target-i in dic.keys():\\n for j in dic[i]:\\n for k in dic[target-i]:\\n if len(set(list(j) + list(k))) == 4:\\n s = list(j) + list(k)\\n # print s \\n l = [num[n] for n in s]\\n l.sort()\\n res.append(l)\\n result = []\\n for i in res:\\n if i not in result:\\n result.append(i)\\n return result\"\n },\n {\n \"alpha_fraction\": 0.5732683539390564,\n \"alphanum_fraction\": 0.5885833501815796,\n \"avg_line_length\": 34.92499923706055,\n \"blob_id\": \"c61d7aade9f2828b6ccffd3340344efb6907a6d5\",\n \"content_id\": \"b377b3c85eb0fbd213848a9d0ada5f899a1ab40e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2873,\n \"license_type\": \"no_license\",\n \"max_line_length\": 222,\n \"num_lines\": 80,\n \"path\": \"/Hints/001_Two Sum.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n> Given an array of integers, find two numbers such that they add up to a specific target number.\\n>\\n> The function twoSum should return indices of the two numbers such that they add up to the target, where index1 must be less than index2. Please note that your returned answers (both index1 and index2) are not zero-based.\\n>\\n> You may assume that each input would have exactly one solution.\\n>\\n> Input: numbers={2, 7, 11, 15}, target=9\\n>\\n> Output: index1=1, index2=2 \\n\\n### Hints:\\nAn naive way is pick one _number1_ and then find the number _target - number1_. This will cost O(n^2).\\n\\n```python\\nclass Solution:\\n # @return a tuple, (index1, index2)\\n\\n def twoSum(self, num, target):\\n for i in num:\\n if target - i in num:\\n return (num.index(i) + 1, num.index(target - i) + 1)\\n return (-1, -1)\\n```\\n__ISSUE__: Time Limit Exceeded, since the array will be large enough, and you cannot find from its beginning without have a proper order of that array.\\n\\nSo before compare the sum and the target, we need to sort the list first.\\n\\n```python\\nclass Solution:\\n # @return a tuple, (index1, index2)\\n\\n def twoSum(self, num, target):\\n for i in sorted(num, reverse=True):\\n if target - i in num:\\n return (num.index(i) + 1, num.index(target - i) + 1)\\n return (-1, -1)\\n```\\n\\nAnother way is to think about using two pointers, one is from beginning, and another one from the end of the SORTED list. And then return the index of its \\n\\n```python\\nclass Solution:\\n # @return a tuple, (index1, index2)\\n\\n def twoSum(self, num, target):\\n index = []\\n numtosort = num[:] \\t# deep copy\\n numtosort.sort()\\t# sort list\\n i = 0\\t\\t\\t\\t# pointer at beginning\\n j = len(numtosort) - 1 # pointer at end\\n while i < j:\\n if numtosort[i] + numtosort[j] == target:\\n for k in range(0, len(num)):\\n if num[k] == numtosort[i]:\\n index.append(k)\\n break\\n for k in range(len(num) - 1, -1, -1):\\n if num[k] == numtosort[j]:\\n index.append(k)\\n break\\n index.sort()\\n break\\n elif numtosort[i] + numtosort[j] < target:\\n i = i + 1\\n elif numtosort[i] + numtosort[j] > target:\\n j = j - 1\\n return (index[0] + 1, index[1] + 1)\\n```\\n\\n#### Note:\\n* the index should plus one.\\n* the possible range of target should between the first two sum of sorted list, and last two sum of sorted list.\\n* we could assume that the list number are all non-negatives.\\n* there may exist two or more number which are the same, be careful when handling the index. Especially do not use `list.index(x)`. \\n\\n#### References:\\n\\n#### Tags:\\n**list**, **sorted list**\"\n },\n {\n \"alpha_fraction\": 0.5729984045028687,\n \"alphanum_fraction\": 0.5839874148368835,\n \"avg_line_length\": 21.75,\n \"blob_id\": \"e9ec72b4f4ea57378a01e9410907a503fd7ed0f0\",\n \"content_id\": \"42181fd42558596520a575512787f4d87e32d468\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 637,\n \"license_type\": \"no_license\",\n \"max_line_length\": 108,\n \"num_lines\": 28,\n \"path\": \"/Hints/028_Divide Two Integers.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\nDivide two integers without using multiplication, division and mod operator.\\n\\nIf it is overflow, return MAX_INT.\\n\\n### Hints:\\n\\n1. like Eular's method\\n2. what if the divident or the divisor is negative?\\n3. consider time complexity\\n\\nInstead of minus divisor one by one, use a faster way to minus its polynomial powers. like x^1, x^2, x^4,...\\n'''\\n while a >= b:\\n summing = b\\n count = 1\\n while summing + summing <= a:\\n summing += summing\\n count += count\\n a -= summing\\n res += count\\n'''\\n#### References:\\n\\n\\n#### Tags:\\nMath, Binary Search\\n\"\n },\n {\n \"alpha_fraction\": 0.3583333194255829,\n \"alphanum_fraction\": 0.363095223903656,\n \"avg_line_length\": 25.28125,\n \"blob_id\": \"96e4b829d51b3f00515cba73b2244a574387dc46\",\n \"content_id\": \"6027046eebf15412823d0ecddda44b89fe05cf04\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 840,\n \"license_type\": \"no_license\",\n \"max_line_length\": 63,\n \"num_lines\": 32,\n \"path\": \"/Solutions/020_Valid Parentheses.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return a boolean\\n\\n def isValid(self, s):\\n lookup, stack = {\\\")\\\": \\\"(\\\", \\\"}\\\": \\\"{\\\", \\\"]\\\": \\\"[\\\"}, []\\n for i in s:\\n if i not in lookup:\\n stack.append(i)\\n else:\\n if len(stack) == 0 or stack.pop() != lookup[i]:\\n return False\\n return len(stack) == 0\\n\\n \\\"\\\"\\\"\\n naive way\\n \\\"\\\"\\\"\\n # def isValid(self, s):\\n # # match = [(\\\"(\\\",\\\")\\\"),(\\\"[\\\",\\\"]\\\"),(\\\"{\\\",\\\"}\\\")]\\n # match = {\\\")\\\": \\\"(\\\", \\\"]\\\": \\\"[\\\", \\\"}\\\": \\\"{\\\"}\\n # stack = []\\n # for c in s:\\n # if c not in match:\\n # stack.append(c)\\n # else:\\n # if len(stack) == 0:\\n # return False\\n # elif stack.pop() != match[c]:\\n # return False\\n # if len(stack) == 0:\\n # return True\\n # else:\\n # return False\"\n },\n {\n \"alpha_fraction\": 0.6992882490158081,\n \"alphanum_fraction\": 0.709964394569397,\n \"avg_line_length\": 25.714284896850586,\n \"blob_id\": \"098627b454154803e98b2efedf22883c1beccbf3\",\n \"content_id\": \"f7b3a8a439e6b4596d9dc3e68393f1de6fadd985\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 562,\n \"license_type\": \"no_license\",\n \"max_line_length\": 94,\n \"num_lines\": 21,\n \"path\": \"/Hints/170_Two Sum III.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\nDesign and implement a TwoSum class. It should support the following operations: add and find.\\n\\nadd - Add the number to an internal data structure.\\nfind - Find if there exists any pair of numbers which sum is equal to the value.\\n\\nFor example,\\nadd(1); add(3); add(5);\\nfind(4) -> true\\nfind(7) -> false\\n\\n### Hints:\\nInternal data strucuture? -> dict\\n1. use dict to store data\\n\\nIs there any duplicated values? \\nWhat is the maximum range of its size? \\n -> if it is too large, you need to calculate the range of matched sum\\n#### References:\\n\\n#### Tags:\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5127701163291931,\n \"alphanum_fraction\": 0.5474787354469299,\n \"avg_line_length\": 30.14285659790039,\n \"blob_id\": \"16af241a8e6a973d64f48efc7e70e197660cab91\",\n \"content_id\": \"616e113a91cc61f52c6d3380fcc34e0558e65663\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1527,\n \"license_type\": \"no_license\",\n \"max_line_length\": 209,\n \"num_lines\": 49,\n \"path\": \"/Hints/004_Add Two Numbers.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n>You are given two linked lists representing two non-negative numbers. The digits are stored in reverse order and each of their nodes contain a single digit. Add the two numbers and return it as a linked list.\\n>\\n>Input: (2 -> 4 -> 3) + (5 -> 6 -> 4)\\n>Output: 7 -> 0 -> 8\\n\\n### Hints:\\nJust operation of linked list.\\n\\n```python \\n# Definition for singly-linked list.\\n# class ListNode:\\n# def __init__(self, x):\\n# self.val = x\\n# self.next = None\\n\\nclass Solution:\\n # @return a ListNode\\n def addTwoNumbers(self, l1, l2):\\n if l1 == None: return l2\\n if l2 == None: return l1\\n flag = 0\\n dummy = ListNode(0); p = dummy\\n while l1 and l2:\\n p.next = ListNode((l1.val+l2.val+flag) % 10)\\n flag = (l1.val+l2.val+flag) / 10\\n l1 = l1.next; l2 = l2.next; p = p.next\\n if l2:\\n while l2:\\n p.next = ListNode((l2.val+flag) % 10)\\n flag = (l2.val+flag) / 10\\n l2 = l2.next; p = p.next\\n if l1:\\n while l1:\\n p.next = ListNode((l1.val+flag) % 10)\\n flag = (l1.val+flag) / 10\\n l1 = l1.next; p = p.next\\n if flag == 1: p.next = ListNode(1)\\n return dummy.next\\n```\\n#### Note:\\n* If one of the list is none, then return the other one as result.\\n\\n* If the size of them are not the same, calculate one by one and use flag to continue calculate until all the values are none.\\n\\n#### References:\\n\\n#### Tags:\\n**linked list**, \\n\"\n },\n {\n \"alpha_fraction\": 0.5074626803398132,\n \"alphanum_fraction\": 0.5223880410194397,\n \"avg_line_length\": 29.545454025268555,\n \"blob_id\": \"fd889c43095916142daf6cc0b12eda2332b25fda\",\n \"content_id\": \"ddbb282a61e66b93f9b74c7c4c02a0b70bfb2e86\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 335,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 11,\n \"path\": \"/Solutions/164_Maximum Gap.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param num, a list of integer\\n # @return an integer\\n\\n def maximumGap(self, num):\\n num = sorted(num)\\n if len(num) in [0, 1]:\\n return 0\\n new_num = [num[0]] + [num[i] for i in range(len(num) - 1)]\\n sub_num = [a-b for a, b in zip(num, new_num)]\\n return max(sub_num)\"\n },\n {\n \"alpha_fraction\": 0.7577937841415405,\n \"alphanum_fraction\": 0.7577937841415405,\n \"avg_line_length\": 51.125,\n \"blob_id\": \"a7ef052d017058b8ca778e81520f7225e0b6780c\",\n \"content_id\": \"7c5d139f1790bca4f6b4caf00f30a7149eaadbfc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"SQL\",\n \"length_bytes\": 417,\n \"license_type\": \"no_license\",\n \"max_line_length\": 109,\n \"num_lines\": 8,\n \"path\": \"/Solutions/183_Customers Who Never Order.sql\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Write your MySQL query statement below\\nSELECT Name FROM Customers c WHERE c.Id NOT IN (SELECT CustomerId FROM Orders o);\\n\\n-- # Write your MySQL query statement below\\n-- SELECT Name FROM Customers c WHERE NOT EXISTS (SELECT CustomerId FROM Orders o WHERE o.CustomerId = c.id);\\n\\n-- # Write your MySQL query statement below\\n-- SELECT Name FROM Customers c LEFT JOIN Orders o ON c.Id = o.CustomerId WHERE o.Id IS NULL;\\n\"\n },\n {\n \"alpha_fraction\": 0.4098939895629883,\n \"alphanum_fraction\": 0.45229682326316833,\n \"avg_line_length\": 27.299999237060547,\n \"blob_id\": \"324b7abde22672f804f5adba73a4d24d3fba2661\",\n \"content_id\": \"19cf318461d7b8039d1e119641a1636bcde30c93\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 283,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 10,\n \"path\": \"/Solutions/007_Reverse Integer.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return an integer\\n\\n def reverse(self, x):\\n tmp = str(x)\\n tmp = \\\"-\\\" + tmp[:0:-1] if tmp.startswith(\\\"-\\\") else tmp[::-1]\\n if int(tmp) > 2 ** 31 - 1 or int(tmp) < -2 ** 31 + 1:\\n return 0\\n else:\\n return int(tmp)\\n\"\n },\n {\n \"alpha_fraction\": 0.521327018737793,\n \"alphanum_fraction\": 0.521327018737793,\n \"avg_line_length\": 29.14285659790039,\n \"blob_id\": \"84ed24c37216915ad738bb5cf094e0c72403916b\",\n \"content_id\": \"df9527338574bd2547e86c2822cfe5b45c304865\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 844,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 28,\n \"path\": \"/Solutions/113_Path Sum II.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node\\n# class TreeNode:\\n# def __init__(self, x):\\n# self.val = x\\n# self.left = None\\n# self.right = None\\n\\n\\nclass Solution:\\n # @param root, a tree node\\n # @param sum, an integer\\n # @return a list of lists of integers\\n\\n def pathSum(self, root, sum):\\n def dfs(root, currsum, valuelist):\\n if root.left == None and root.right == None:\\n if currsum == sum:\\n res.append(valuelist)\\n if root.left:\\n dfs(root.left, currsum + root.left.val, valuelist + [root.left.val])\\n if root.right:\\n dfs(root.right, currsum + root.right.val, valuelist + [root.right.val])\\n\\n res = []\\n if root == None:\\n return []\\n dfs(root, root.val, [root.val])\\n return res\\n\"\n },\n {\n \"alpha_fraction\": 0.5057034492492676,\n \"alphanum_fraction\": 0.5285171270370483,\n \"avg_line_length\": 31.875,\n \"blob_id\": \"79035777b76af0704e88b59696919f74cb414eeb\",\n \"content_id\": \"e40748b9186e084f9f7560d17f65a7743a140819\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 263,\n \"license_type\": \"no_license\",\n \"max_line_length\": 68,\n \"num_lines\": 8,\n \"path\": \"/Solutions/001_Two Sum.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return a tuple, (index1, index2)\\n\\n def twoSum(self, num, target):\\n for i in sorted(num, reverse=True):\\n if target - i in num:\\n return (num.index(i) + 1, num.index(target - i) + 1)\\n return (-1, -1)\\n\"\n },\n {\n \"alpha_fraction\": 0.7414075136184692,\n \"alphanum_fraction\": 0.7528641819953918,\n \"avg_line_length\": 46,\n \"blob_id\": \"9cb3a2407f6a041b6c936d852065a247ca851a08\",\n \"content_id\": \"e11c7a57b6c10a815a2ec2b03585ffb6af144787\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1833,\n \"license_type\": \"no_license\",\n \"max_line_length\": 294,\n \"num_lines\": 39,\n \"path\": \"/Hints/008_String to Integer (atoi).md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\nImplement atoi to convert a string to an integer.\\n\\n**Hint**: Carefully consider all possible input cases. If you want a challenge, please do not see below and ask yourself what are the possible input cases.\\n\\n**Notes**: It is intended for this problem to be specified vaguely (ie, no given input specs). You are responsible to gather all the input requirements up front.\\n\\n**Requirements for atoi**:\\nThe function first discards as many whitespace characters as necessary until the first non-whitespace character is found. Then, starting from this character, takes an optional initial plus or minus sign followed by as many numerical digits as possible, and interprets them as a numerical value.\\n\\nThe string can contain additional characters after those that form the integral number, which are ignored and have no effect on the behavior of this function.\\n\\nIf the first sequence of non-whitespace characters in str is not a valid integral number, or if no such sequence exists because either str is empty or it contains only whitespace characters, no conversion is performed.\\n\\nIf no valid conversion could be performed, a zero value is returned. If the correct value is out of the range of representable values, INT_MAX (2147483647) or INT_MIN (-2147483648) is returned.\\n\\n### Hints:\\n\\n* remove whitespaces: `strip()`\\n* check first char of the left string: + or -\\n* check the following char one by one, until to the end or meet char is not a digit: `isdigit()`\\n* convert to int(), check the size of the number meet its ranges.\\n\\nANOTHER approach:\\nif we can use packages, then use `re` to search string, and convert it to int. \\n```python\\nimport re\\n\\n\\nclass Solution:\\n # @return an integer\\n\\n def atoi(self, str):\\n return int(re.search('[+-]?\\\\d+', str.strip()).group(0))\\n```\\n#### References:\\n\\n#### Tags:\\nMath, String\\n\"\n },\n {\n \"alpha_fraction\": 0.45679011940956116,\n \"alphanum_fraction\": 0.4722222089767456,\n \"avg_line_length\": 26,\n \"blob_id\": \"2664fade02441fd22158359c2851ebb41967f014\",\n \"content_id\": \"a0b37ad9cce4a8c7b321a84f1470a1cd3ff59019\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 324,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 12,\n \"path\": \"/Solutions/052_Maximum Subarray.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param A, a list of integers\\n # @return an integer\\n\\n def maxSubArray(self, A):\\n length = len(A)\\n max_dp = [0] * (length)\\n if A:\\n max_dp[0] = A[0]\\n for i in range(1, length):\\n max_dp[i] = max(max_dp[i - 1] + A[i], A[i])\\n return max(max_dp)\\n\"\n },\n {\n \"alpha_fraction\": 0.38823530077934265,\n \"alphanum_fraction\": 0.42352941632270813,\n \"avg_line_length\": 29.058822631835938,\n \"blob_id\": \"cae10500a79881f2e1ab5f9af12159ea5c4cfb4e\",\n \"content_id\": \"6323486af6e8a34c7dbf6b85a24205cc3a90a233\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 510,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 17,\n \"path\": \"/Solutions/167_Two Sum II.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n def twoSum(self, lst, target):\\n for i in range(len(lst)):\\n j = target - lst[i]\\n for ii in range(len(lst)-1, i, -1):\\n if lst[ii]<j:\\n break\\n if lst[ii] == j:\\n return [i+1, ii+1]\\n return [-1,-1]\\n # for i in lst:\\n # if target - i in lst:\\n # return [lst.index(i)+1, lst.index(target-i)+1]\\n # return [-1, -1]\\n\\ns = Solution()\\nprint s.twoSum([2,2,11,15], 26)\"\n },\n {\n \"alpha_fraction\": 0.6248612403869629,\n \"alphanum_fraction\": 0.6304106712341309,\n \"avg_line_length\": 41.9523811340332,\n \"blob_id\": \"abfc5275e9dc0188ecdd4e52f328f90fec3b48f4\",\n \"content_id\": \"a4fa2156fa5760b462de9d66b368a8032fcd1fbb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"SQL\",\n \"length_bytes\": 901,\n \"license_type\": \"no_license\",\n \"max_line_length\": 87,\n \"num_lines\": 21,\n \"path\": \"/Solutions/185_Department Top Three Salaries.sql\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"SELECT d.NAME AS Department, t.NAME AS Employee, Salary FROM (\\n SELECT DepartmentId,\\n NAME,\\n Salary, \\n @rank := IF(@prevDeptId != DepartmentId, 1, \\n IF(@prevSalary = Salary, @rank, @rank + 1) ) AS Rank,\\n @prevDeptId := DepartmentId AS prevDeptId,\\n @prevSalary := Salary AS prevSalary\\n FROM Employee e, (SELECT @rank := 0, @prevDeptId := NULL, @prevSalary := NULL) r\\n ORDER BY DepartmentId ASC, Salary DESC\\n) t INNER JOIN Department d ON t.DepartmentId = d.ID\\nWHERE t.rank <= 3\\n\\n\\n-- # Solution in Discuss\\n-- SELECT D.Name AS Department, E.Name AS Employee, E.Salary AS Salary \\n-- FROM Employee E, Department D\\n-- WHERE (SELECT COUNT(DISTINCT(Salary)) FROM Employee \\n-- WHERE DepartmentId = E.DepartmentId AND Salary > E.Salary) < 3\\n-- AND E.DepartmentId = D.Id \\n-- ORDER by E.DepartmentId, E.Salary DESC;\"\n },\n {\n \"alpha_fraction\": 0.32452574372291565,\n \"alphanum_fraction\": 0.3414634168148041,\n \"avg_line_length\": 33.32558059692383,\n \"blob_id\": \"010029f04a6ebd06448dc29ff394fceacb22b92c\",\n \"content_id\": \"f5717e52e12733086a79ee185fb4e91c628295b0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1476,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 43,\n \"path\": \"/Solutions/015_3Sum.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return a list of lists of length 3, [[val1,val2,val3]]\\n\\n def threeSum(self, num):\\n \\\"\\\"\\\" TLE version \\\"\\\"\\\"\\n # result_list = []\\n # length = len(num)\\n # if length < 3:\\n # return []\\n # origin = list(num)\\n # for i in range(length):\\n # for j in range(i + 1, length):\\n # m = num[i]\\n # n = num[j]\\n # seek = 0 - m - n\\n # num.pop(j)\\n # num.pop(i)\\n # if seek in num:\\n # result = sorted([m, n, seek])\\n # if result not in result_list:\\n # result_list.append(result)\\n # num = list(origin)\\n # return result_list\\n\\n num, result, i = sorted(num), [], 0\\n while i < len(num) - 2:\\n j, k = i + 1, len(num) - 1\\n while j < k:\\n if num[i] + num[j] + num[k] < 0:\\n j += 1\\n elif num[i] + num[j] + num[k] > 0:\\n k -= 1\\n else:\\n result.append([num[i], num[j], num[k]])\\n j, k = j + 1, k - 1\\n while j < k and num[j] == num[j - 1]:\\n j += 1\\n while j < k and num[k] == num[k + 1]:\\n k -= 1\\n i += 1\\n while i < len(num) - 2 and num[i] == num[i - 1]:\\n i += 1\\n return result\\n\"\n },\n {\n \"alpha_fraction\": 0.6251511573791504,\n \"alphanum_fraction\": 0.6590084433555603,\n \"avg_line_length\": 28.535715103149414,\n \"blob_id\": \"8a8ed2214381d6f2e64f877fb361ea05b1d46fc7\",\n \"content_id\": \"5e11e06fb51ddb1c5e766f78c4c955c9543753dc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 833,\n \"license_type\": \"no_license\",\n \"max_line_length\": 176,\n \"num_lines\": 28,\n \"path\": \"/Hints/017_4Sum.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\nGiven an array S of n integers, are there elements a, b, c, and d in S such that a + b + c + d = target? Find all unique quadruplets in the array which gives the sum of target.\\n\\nNote:\\nElements in a quadruplet (a,b,c,d) must be in non-descending order. (ie, a ≤ b ≤ c ≤ d)\\nThe solution set must not contain duplicate quadruplets.\\n For example, given array S = {1 0 -1 0 -2 2}, and target = 0.\\n\\n A solution set is:\\n (-1, 0, 0, 1)\\n (-2, -1, 1, 2)\\n (-2, 0, 0, 2)\\n\\n### Hints:\\n\\n\\nQ:\\n1. len(numbers) < 4\\n2. create a dic to store all possible sum of two\\n3. pick one from dic and find its remaining in dic as well \\n4. match if there two picked out tuple have no common values \\n5. return the unduplicate list (quadruplets)\\n\\n#### References:\\n3Sum, closet 3Sum, 2Sum\\n#### Tags:\\nArray, Hash Table, Two Pointer\\n\"\n },\n {\n \"alpha_fraction\": 0.6809045076370239,\n \"alphanum_fraction\": 0.6997487545013428,\n \"avg_line_length\": 29.615385055541992,\n \"blob_id\": \"0dffd3bb452bbc69ec12da3700ff15cbe1c8eab3\",\n \"content_id\": \"c664f04f4c3ad1f94bfb07399a65d4ded81c1e67\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 796,\n \"license_type\": \"no_license\",\n \"max_line_length\": 120,\n \"num_lines\": 26,\n \"path\": \"/Hints/019_Remove Nth Node From End of List.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\nGiven a linked list, remove the nth node from the end of list and return its head.\\n\\nFor example,\\n\\n Given linked list: 1->2->3->4->5, and n = 2.\\n\\n After removing the second node from the end, the linked list becomes 1->2->3->5.\\n\\nNote: \\nGiven n will always be valid. \\nTry to do this in one pass. \\n\\n### Hints:\\n1. use two pointers, one in front one in after. Both pointed to head; \\n2. first use the front one move to the nth node, and then let both of them move together; \\n3. until the first one move to the end of the linked list, then the later one is pointed to the nth node from the end; \\n4. need another pointer to point the previous node of the later one; \\n5. let the prev one pointer to the next node of later one.\\n\\n\\n#### References:\\n \\n#### Tags:\\npointer, linked-list\\n\"\n },\n {\n \"alpha_fraction\": 0.5165048837661743,\n \"alphanum_fraction\": 0.5339806079864502,\n \"avg_line_length\": 27.61111068725586,\n \"blob_id\": \"271fc196a469a26e5ab03ee42b5a9bbfc90f590c\",\n \"content_id\": \"3aa3d195244109e28d25d335a03c19870ed35d2d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 515,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 18,\n \"path\": \"/Solutions/187_Repeated DNA Sequences.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param s, a string\\n # @return a list of strings\\n\\n def findRepeatedDnaSequences(self, s):\\n seq = {}\\n res = []\\n for i in range(len(s)):\\n if s[i:i + 10] in seq:\\n if s[i:i + 10] not in res:\\n res.append(s[i:i + 10])\\n else:\\n seq[s[i:i + 10]] = 1\\n return res\\n\\ns = Solution()\\n# print s.findRepeatedDnaSequences(\\\"AAAAACCCCCAAAAACCCCCCAAAAAGGGTTT\\\")\\nprint s.findRepeatedDnaSequences(\\\"AAAAAAAAAAAA\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5747126340866089,\n \"alphanum_fraction\": 0.5785440802574158,\n \"avg_line_length\": 28,\n \"blob_id\": \"9ebd746612374894e2f9d02ef417349b5f0af3d9\",\n \"content_id\": \"881aa6dadb64f60b9ffd697d86555b46ee944f9c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 261,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 9,\n \"path\": \"/Solutions/067_Plus One.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param digits, a list of integer digits\\n # @return a list of integer digits\\n\\n def plusOne(self, digits):\\n \\tdigits = [str(i) for i in digits]\\n st = \\\"\\\".join(digits)\\n val = int(st) + 1\\n return map(int, str(val))\\n\"\n },\n {\n \"alpha_fraction\": 0.7151162624359131,\n \"alphanum_fraction\": 0.7238371968269348,\n \"avg_line_length\": 23.571428298950195,\n \"blob_id\": \"73692544f6111b192e06e1e580957f50665e7789\",\n \"content_id\": \"8460e9e8e147ec2c17a440af0f76bba95479c50a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 344,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 14,\n \"path\": \"/Hints/014_Longest Common Prefix.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\nWrite a function to find the longest common prefix string amongst an array of strings.\\n\\n### Hints:\\nAssumptions: \\n1. no spaces; \\n2. two functions like `min` and `max` of string list will retune the minimum and maximum string;\\n3. instead of comparing all strings, compare min and max instead;\\n\\n\\n#### References:\\n\\n#### Tags:\\nString\\n\"\n },\n {\n \"alpha_fraction\": 0.7280966639518738,\n \"alphanum_fraction\": 0.7507553100585938,\n \"avg_line_length\": 46.21428680419922,\n \"blob_id\": \"72e12b435c29856a42e989e7674c57732b04b376\",\n \"content_id\": \"5e80b4cdc70801f0371acc517c90ffb374d5162e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 662,\n \"license_type\": \"no_license\",\n \"max_line_length\": 222,\n \"num_lines\": 14,\n \"path\": \"/Hints/167_Two Sum II.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\nGiven an array of integers that is already sorted in ascending order, find two numbers such that they add up to a specific target number. \\nThe function twoSum should return indices of the two numbers such that they add up to the target, where index1 must be less than index2. Please note that your returned answers (both index1 and index2) are not zero-based. \\nYou may assume that each input would have exactly one solution.\\nInput: numbers={2, 7, 11, 15}, target=9\\nOutput: index1=1, index2=2\\n\\n### Hints:\\nIs there any duplicated values? If yes the index are consecutive. \\nHow to take the advantage of sorted list. \\n\\n#### References:\\n\\n#### Tags:\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6417391300201416,\n \"alphanum_fraction\": 0.6486956477165222,\n \"avg_line_length\": 34.9375,\n \"blob_id\": \"eb599fd38373cc110e311d3608934bd79d7e3abf\",\n \"content_id\": \"6df0c443c95011f2225b53ce063c319cf1ccc94e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 575,\n \"license_type\": \"no_license\",\n \"max_line_length\": 118,\n \"num_lines\": 16,\n \"path\": \"/Hints/020_Valid Parentheses.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\nGiven a string containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.\\n\\nThe brackets must close in the correct order, \\\"()\\\" and \\\"()[]{}\\\" are all valid but \\\"(]\\\" and \\\"([)]\\\" are not.\\n\\n### Hints:\\nStack to push in and pop out to match brackets.\\n1. define dict of brackets, one trick to define them like {\\\")\\\":\\\"(\\\"} \\n2. like a compiler push into stack if not in keys \\n3. pop out when it is a key, and match them together \\n4. check the size of stack at the end of processing. \\n\\n#### References:\\n\\n#### Tags:\\nStack String\\n\"\n },\n {\n \"alpha_fraction\": 0.4482758641242981,\n \"alphanum_fraction\": 0.4482758641242981,\n \"avg_line_length\": 5.333333492279053,\n \"blob_id\": \"c66cc93e5267222f838813433c1a7799d737daa1\",\n \"content_id\": \"48b3ef506f50a320894c617fd1ad9482748b31ea\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 58,\n \"license_type\": \"no_license\",\n \"max_line_length\": 16,\n \"num_lines\": 9,\n \"path\": \"/Hints/096_Unique Binary Search Trees II.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\n\\n\\n### Hints:\\n\\n#### References:\\n\\n#### Tags:\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7338709831237793,\n \"alphanum_fraction\": 0.7338709831237793,\n \"avg_line_length\": 23.733333587646484,\n \"blob_id\": \"499f0a24bf581407b5461761fa05968fa18f1d5b\",\n \"content_id\": \"516efdbb76392eccfd2a2b5e38d81ed269dab76e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 372,\n \"license_type\": \"no_license\",\n \"max_line_length\": 107,\n \"num_lines\": 15,\n \"path\": \"/Hints/100_Same Tree.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\nGiven two binary trees, write a function to check if they are equal or not.\\n\\nTwo binary trees are considered equal if they are structurally identical and the nodes have the same value.\\n\\n### Hints:\\n\\nRecursive way:\\nif both val to be none: True\\none is None while the other is not: False\\nBoth are not None: compare their value and \\n#### References:\\n\\n#### Tags:\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7086494565010071,\n \"alphanum_fraction\": 0.7374810576438904,\n \"avg_line_length\": 24.384614944458008,\n \"blob_id\": \"38584408886e73d3fd1e998d6765d81c4ed3a44a\",\n \"content_id\": \"507b707fac414749370417dbb96dec6cf2b7d2a8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 659,\n \"license_type\": \"no_license\",\n \"max_line_length\": 193,\n \"num_lines\": 26,\n \"path\": \"/Hints/187_Repeated DNA Sequences.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\nAll DNA is composed of a series of nucleotides abbreviated as A, C, G, and T, for example: \\\"ACGAATTCCG\\\". When studying DNA, it is sometimes useful to identify repeated sequences within the DNA.\\n\\nWrite a function to find all the 10-letter-long sequences (substrings) that occur more than once in a DNA molecule.\\n\\nFor example,\\n\\nGiven s = \\\"AAAAACCCCCAAAAACCCCCCAAAAAGGGTTT\\\",\\n\\nReturn:\\n[\\\"AAAAACCCCC\\\", \\\"CCCCCAAAAA\\\"].\\n\\n### Hints:\\nMy approach: \\nstore 10-letter-string to dict, and then check strings. \\n\\n1. check duplicated string in result.\\n\\nTime cost O(N), Memory too much\\n\\n#### References:\\n78 136 137 187 169 \\n\\n#### Tags:\\nDictionary, bit manuplate\"\n },\n {\n \"alpha_fraction\": 0.669767439365387,\n \"alphanum_fraction\": 0.6837209463119507,\n \"avg_line_length\": 21.63157844543457,\n \"blob_id\": \"009bb8304f7a8b2f5f64d14552ff8e0820c4cea8\",\n \"content_id\": \"01f0712f00c1131fce7df740435c6f3d582092fc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 430,\n \"license_type\": \"no_license\",\n \"max_line_length\": 150,\n \"num_lines\": 19,\n \"path\": \"/Hints/047_Rotate Image.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\nYou are given an n x n 2D matrix representing an image.\\n\\nRotate the image by 90 degrees (clockwise).\\n\\nFollow up:\\nCould you do this in-place?\\n\\n### Hints:\\n1. replace with [i][j] with [j][i] \\n2. reverse elements in each line \\n\\nQ:\\nThink about using a matrix multiplication way to do a rotation, what matrix to choose in that problem (sin \\\\theta cos \\\\theta ...) let \\\\theta == pi/2 \\n#### References:\\n\\n#### Tags:\\nArray\\n\"\n },\n {\n \"alpha_fraction\": 0.42450493574142456,\n \"alphanum_fraction\": 0.43440595269203186,\n \"avg_line_length\": 34.130435943603516,\n \"blob_id\": \"7507357f3999547478827baef556a154a6995e1d\",\n \"content_id\": \"5a1d1629a231effcc6891831621b95a993079932\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 808,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 23,\n \"path\": \"/Solutions/010_Regular Expression Matching.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return a boolean\\n\\n def isMatch(self, s, p):\\n previousRow = [True]\\n for i in range(0, len(p)):\\n if p[i] == '*':\\n previousRow.append(previousRow[i - 1])\\n else:\\n previousRow.append(False)\\n\\n for letter in range(0, len(s)):\\n actualRow = [False]\\n for i in range(0, len(p)):\\n if p[i] == '*':\\n temp = actualRow[i - 1] or (previousRow[i + 1] and (p[i - 1] == s[letter] or p[i - 1] == '.'))\\n elif p[i] == '.':\\n temp = previousRow[i]\\n else:\\n temp = previousRow[i] and p[i] == s[letter]\\n actualRow.append(temp)\\n previousRow = actualRow\\n return previousRow[len(p)]\\n\"\n },\n {\n \"alpha_fraction\": 0.44974446296691895,\n \"alphanum_fraction\": 0.4846678078174591,\n \"avg_line_length\": 28.350000381469727,\n \"blob_id\": \"ac93fe0c25517fd6c36d0a6fb83d920aeb7d41e8\",\n \"content_id\": \"51fae4737e9dc65a2af8a63845b3e9a4fcdc0833\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1174,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 40,\n \"path\": \"/Solutions/198_House Robber.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param num, a list of integer\\n # @return an integer\\n\\n def rob(self, num):\\n # Approach 1: Greedy algorithm: NOT CORRECT!!!\\n # length = len(num)\\n # if length == 0:\\n # return 0\\n # elif length == 1:\\n # return num[0]\\n\\n # max_num = max(num)\\n # max_index = num.index(max_num)\\n # if max_index == 0:\\n # return max_num + self.rob(num[2:])\\n # elif max_index == length - 1:\\n # return max_num + self.rob(num[:max_index-1])\\n # else:\\n # return max_num + self.rob(num[:max_index-1] + num[max_index+2:])\\n\\n # Approach 2: sum of odd and even number: NOT CORRECT!!!\\n # return max(sum(num[1::2]), sum(num[::2]))\\n\\n # Approach 3: Dynanmic programming\\n length = len(num)\\n dp = [0] * (length + 1)\\n if length:\\n dp[1] = num[0]\\n for i in range(2, length + 1):\\n dp[i] = max(dp[i - 1], dp[i - 2] + num[i - 1])\\n print dp\\n return dp[length]\\n\\ns = Solution()\\nprint s.rob([1, 1])\\nprint s.rob([2, 3, 2])\\nprint s.rob([2, 1, 1, 2])\\nprint s.rob([0, 0, 0])\\nprint s.rob([1, 7, 9, 4])\\n\"\n },\n {\n \"alpha_fraction\": 0.6908665299415588,\n \"alphanum_fraction\": 0.7400468587875366,\n \"avg_line_length\": 30.629629135131836,\n \"blob_id\": \"657612135124258c172841adc350baf5da36322a\",\n \"content_id\": \"a4c77d7a0467998b505d11c56db0246fed4c7a17\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 854,\n \"license_type\": \"no_license\",\n \"max_line_length\": 170,\n \"num_lines\": 27,\n \"path\": \"/Hints/007_Reverse Integer.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\nReverse digits of an integer.\\n\\nExample1: x = 123, return 321\\n\\nExample2: x = -123, return -321\\n\\nHere are some good questions to ask before coding. Bonus points for you if you have already thought through this!\\n\\nIf the integer's last digit is 0, what should the output be? ie, cases such as 10, 100.\\n\\nDid you notice that the reversed integer might overflow? Assume the input is a 32-bit integer, then the reverse of 1000000003 overflows. How should you handle such cases?\\n\\nFor the purpose of this problem, assume that your function returns 0 when the reversed integer overflows.\\n\\n### Hints:\\n* Thinking about negative numbers\\n* Thinking about overflow problems `-2**31+1 < x < 2**31-1`\\n* Thinking about begin with zeros\\n\\nTake advantage of python, convert int to str, convert str to int, reverse str `[::-1]`\\n\\n#### References:\\n\\n#### Tags:\\nMath\\n\"\n },\n {\n \"alpha_fraction\": 0.33253589272499084,\n \"alphanum_fraction\": 0.3564593195915222,\n \"avg_line_length\": 22.22222137451172,\n \"blob_id\": \"3aa4a49ae030d858602e7538c507414d05ecbb45\",\n \"content_id\": \"248b6b9d2306a1dd9acbff62f618cde9f612652f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 418,\n \"license_type\": \"no_license\",\n \"max_line_length\": 33,\n \"num_lines\": 18,\n \"path\": \"/Solutions/069_Sqrt(x).py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param x, an integer\\n # @return an integer\\n\\n def sqrt(self, x):\\n if x == 0:\\n return 0\\n i = 1\\n j = x / 2 + 1\\n while(i <= j):\\n center = (i + j) / 2\\n if center ** 2 == x:\\n return center\\n elif center ** 2 > x:\\n j = center - 1\\n else:\\n i = center + 1\\n return j\\n\"\n },\n {\n \"alpha_fraction\": 0.4157509207725525,\n \"alphanum_fraction\": 0.46886447072029114,\n \"avg_line_length\": 25,\n \"blob_id\": \"c521bff59c77b0e4100280833769c22f66695170\",\n \"content_id\": \"ec76c36313afda1d8fd6661e3e6c22b306443539\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 546,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 21,\n \"path\": \"/Solutions/165_Compare Version Numbers.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param version1, a string\\n # @param version2, a string\\n # @return an integer\\n\\n def compareVersion(self, version1, version2):\\n v1 = [int(x) for x in version1.split('.')]\\n v2 = [int(x) for x in version2.split('.')]\\n rv1, rv2 = 0, 0\\n for i in range(len(v1)):\\n rv1 += v1[i] * 0.1 ** i\\n\\n for j in range(len(v2)):\\n rv2 += v2[j] * 0.1 ** j\\n\\n if rv1 > rv2:\\n return 1\\n elif rv1 < rv2:\\n return -1\\n else:\\n return 0\\n\"\n },\n {\n \"alpha_fraction\": 0.3679775297641754,\n \"alphanum_fraction\": 0.40449437499046326,\n \"avg_line_length\": 24.428571701049805,\n \"blob_id\": \"235964fa6e4098ecd0b1d7177bb66d8de389c5bf\",\n \"content_id\": \"c8d46eec16dbd06464667665d4581cecd09b3213\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 356,\n \"license_type\": \"no_license\",\n \"max_line_length\": 47,\n \"num_lines\": 14,\n \"path\": \"/Solutions/168_Excel Sheet Column Title.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return a string\\n\\n def convertToTitle(self, num):\\n alpha = [chr(i) for i in range(65, 91)]\\n res = []\\n while num > 0:\\n t = num % 26\\n print t\\n res.append(alpha[t - 1])\\n num = (num / 26)\\n if t == 0:\\n num -= 1\\n return ''.join(res[::-1])\\n\"\n },\n {\n \"alpha_fraction\": 0.5932203531265259,\n \"alphanum_fraction\": 0.6033898591995239,\n \"avg_line_length\": 36,\n \"blob_id\": \"05a07276b6e040f9310c7b9272e607ca886bd777\",\n \"content_id\": \"96e660788c10155489d192244de22034b6e8efeb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"SQL\",\n \"length_bytes\": 295,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 8,\n \"path\": \"/Solutions/178_Rank Scores.sql\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Write your MySQL query statement below\\nSELECT Score, Rank FROM(\\n SELECT Score,\\n @curRank := @curRank + IF(@prevScore = Score, 0, 1) AS Rank,\\n @prevScore := Score\\n FROM Scores s, (SELECT @curRank := 0) r, (SELECT @prevScore := NULL) p\\n ORDER BY Score DESC\\n) t;\"\n },\n {\n \"alpha_fraction\": 0.35548171401023865,\n \"alphanum_fraction\": 0.3698781728744507,\n \"avg_line_length\": 31.25,\n \"blob_id\": \"7f2c9c3dcea82a5d5a9c97d54359640f54aaf472\",\n \"content_id\": \"347ff086639593c3e1e19dc5ad8ab411ca76db59\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 903,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 28,\n \"path\": \"/Solutions/035_Valid Sudoku.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param board, a 9x9 2D array\\n # @return a boolean\\n\\n def isValidSudoku(self, board):\\n def isValid(x, y, tmp):\\n for i in range(9):\\n if board[i][y] == tmp:\\n return False\\n for i in range(9):\\n if board[x][i] == tmp:\\n return False\\n for i in range(3):\\n for j in range(3):\\n if board[(x / 3) * 3 + i][(y / 3) * 3 + j] == tmp:\\n return False\\n return True\\n for i in range(9):\\n for j in range(9):\\n if board[i][j] == '.':\\n continue\\n tmp = board[i][j]\\n board[i][j] = 'D'\\n if isValid(i, j, tmp) == False:\\n return False\\n else:\\n board[i][j] = tmp\\n return True\\n\"\n },\n {\n \"alpha_fraction\": 0.3467000722885132,\n \"alphanum_fraction\": 0.3583959937095642,\n \"avg_line_length\": 30.5,\n \"blob_id\": \"bb96fc1b5ea77413d5c1f20df7f3f00b69dd66f9\",\n \"content_id\": \"c198503602a54f387c8f78264b6e383a6b6ff65a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1197,\n \"license_type\": \"no_license\",\n \"max_line_length\": 82,\n \"num_lines\": 38,\n \"path\": \"/Solutions/068_Text Justification.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param words, a list of strings\\n # @param L, an integer\\n # @return a list of strings\\n\\n def fullJustify(self, words, L):\\n res = []\\n i = 0\\n while i < len(words):\\n size = 0\\n begin = i\\n while i < len(words):\\n newsize = len(words[i]) if size == 0 else size + len(words[i]) + 1\\n if newsize <= L:\\n size = newsize\\n else:\\n break\\n i += 1\\n spaceCount = L - size\\n if i - begin - 1 > 0 and i < len(words):\\n everyCount = spaceCount / (i - begin - 1)\\n spaceCount %= i - begin - 1\\n else:\\n everyCount = 0\\n j = begin\\n while j < i:\\n if j == begin:\\n s = words[j]\\n else:\\n s += ' ' * (everyCount + 1)\\n if spaceCount > 0 and i < len(words):\\n s += ' '\\n spaceCount -= 1\\n s += words[j]\\n j += 1\\n s += ' ' * spaceCount\\n res.append(s)\\n return res\\n\"\n },\n {\n \"alpha_fraction\": 0.7432098984718323,\n \"alphanum_fraction\": 0.7432098984718323,\n \"avg_line_length\": 22.823530197143555,\n \"blob_id\": \"3c897faf251633ce5b2294844f1578ee5e9811ba\",\n \"content_id\": \"8abb4501e0a856584d1eb0f82c7f1d5313a912c9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 405,\n \"license_type\": \"no_license\",\n \"max_line_length\": 112,\n \"num_lines\": 17,\n \"path\": \"/Hints/188_Best Time to Buy and Sell Stock IV.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\nSay you have an array for which the ith element is the price of a given stock on day i.\\n\\nDesign an algorithm to find the maximum profit. You may complete at most k transactions.\\n\\nNote:\\nYou may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again).\\n\\n### Hints:\\n\\n#### References:\\n\\nOther three related questions\\n\\n#### Tags:\\nDynamic Programming\\n\"\n },\n {\n \"alpha_fraction\": 0.42509135603904724,\n \"alphanum_fraction\": 0.4409256875514984,\n \"avg_line_length\": 29.407407760620117,\n \"blob_id\": \"2d3e5fdba308090d93e9cbf6c626c7219d263bd7\",\n \"content_id\": \"c558254d0677292a086ae446f55ae3482d98576a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 821,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 27,\n \"path\": \"/Solutions/030_Next Permutation.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param num, a list of integer\\n # @return a list of integer\\n\\n def nextPermutation(self, num):\\n if len(num) <= 1:\\n return num\\n partition = -1\\n for i in range(len(num) - 2, -1, -1):\\n if num[i] < num[i + 1]:\\n partition = i\\n break\\n if partition == -1:\\n num.reverse()\\n return num\\n else:\\n for i in range(len(num) - 1, partition, -1):\\n if num[i] > num[partition]:\\n num[i], num[partition] = num[partition], num[i]\\n break\\n left = partition + 1\\n right = len(num) - 1\\n while left < right:\\n num[left], num[right] = num[right], num[left]\\n left += 1\\n right -= 1\\n return num\\n\"\n },\n {\n \"alpha_fraction\": 0.4509246051311493,\n \"alphanum_fraction\": 0.4694167971611023,\n \"avg_line_length\": 27.1200008392334,\n \"blob_id\": \"670f51b5740065e13976cf564528830d5f50d945\",\n \"content_id\": \"3e50f52f64e71e8d2f0dea7a5a78ffdf3c7ae7ab\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 703,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 25,\n \"path\": \"/Solutions/014_Longest Common Prefix.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n\\n def longestCommonPrefix(self, strs):\\n \\\"Given a list of pathnames, returns the longest common leading component\\\"\\n if not strs:\\n return ''\\n s1 = min(strs)\\n s2 = max(strs)\\n print s1, s2\\n for i, c in enumerate(s1):\\n if c != s2[i]:\\n return s1[:i]\\n return s1\\n\\n\\n # def longestCommonPrefix(self, strs):\\n # if len(strs) == 0:\\n # return \\\"\\\"\\n # longest = strs[0]\\n # for str in strs[1:]:\\n # i = 0\\n # while i < len(str) and i < len(longest) and str[i] == longest[i]:\\n # i += 1\\n # longest = longest[:i]\\n # return longest\\n\"\n },\n {\n \"alpha_fraction\": 0.5034013390541077,\n \"alphanum_fraction\": 0.5034013390541077,\n \"avg_line_length\": 22.210525512695312,\n \"blob_id\": \"8dd46e41ba8e8926cb46dcaf4f9b0ecdd91a5203\",\n \"content_id\": \"86059284aee6e13e0448d1b2e563fe2b7fc4c693\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 441,\n \"license_type\": \"no_license\",\n \"max_line_length\": 51,\n \"num_lines\": 19,\n \"path\": \"/Solutions/141_Linked List Cycle.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# class ListNode:\\n# def __init__(self, x):\\n# self.val = x\\n# self.next = None\\n\\n\\nclass Solution:\\n # @param head, a ListNode\\n # @return a boolean\\n\\n def hasCycle(self, head):\\n fastP = head\\n slowP = head\\n while fastP != None and fastP.next != None:\\n fastP = fastP.next.next\\n slowP = slowP.next\\n if fastP == slowP:\\n return True\\n return False\\n\"\n },\n {\n \"alpha_fraction\": 0.4438040256500244,\n \"alphanum_fraction\": 0.45437079668045044,\n \"avg_line_length\": 40.63999938964844,\n \"blob_id\": \"ebefa4c5423a690c1b86dc1c22e4d2fac657a281\",\n \"content_id\": \"c4f60fdd04e0920d0225a11f531bc6fb3fab13b8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1041,\n \"license_type\": \"no_license\",\n \"max_line_length\": 110,\n \"num_lines\": 25,\n \"path\": \"/Solutions/150_Evaluate Reverse Polish Notation.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param tokens, a list of string\\n # @return an integer\\n\\n # def evalRPN(self, tokens):\\n # while len(tokens) > 2:\\n # for i in tokens:\\n # if i in [\\\"+\\\", \\\"-\\\", \\\"*\\\", \\\"/\\\"]:\\n # index = tokens.index(i)\\n # res = eval(\\n # tokens[index - 2] + tokens[index] + tokens[index - 1])\\n # tokens.pop(index - 2)\\n # tokens.pop(index - 2)\\n # tokens.pop(index - 2)\\n # tokens.insert(index - 2, str(res))\\n # return tokens[0]\\n def evalRPN(self, tokens):\\n numerals, operators = [], {\\\"+\\\": operator.add, \\\"-\\\": operator.sub, \\\"*\\\": operator.mul, \\\"/\\\": operator.div}\\n for token in tokens:\\n if token.isdigit() or token[1:].isdigit():\\n numerals.append(int(token))\\n else:\\n x, y = numerals.pop(), numerals.pop()\\n numerals.append(int(operators[token](y * 1.0, x)))\\n return numerals.pop()\\n\"\n },\n {\n \"alpha_fraction\": 0.34840425848960876,\n \"alphanum_fraction\": 0.36968085169792175,\n \"avg_line_length\": 22.5,\n \"blob_id\": \"469913479df61626c2379796f3c422feb7654a2e\",\n \"content_id\": \"0148af4aff513baeb932a88b6f0c61b7f847815b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 376,\n \"license_type\": \"no_license\",\n \"max_line_length\": 36,\n \"num_lines\": 16,\n \"path\": \"/Solutions/154_Find Minimum in Rotated Sorted Array II.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param num, a list of integer\\n # @return an integer\\n\\n def findMin(self, num):\\n i = 0\\n length = len(num)\\n m = num[0]\\n while(i < length-1):\\n if num[i + 1] <= num[i]:\\n if num[i+1]<m:\\n m = num[i+1]\\n i += 1\\n else:\\n i += 1\\n return m\\n\"\n },\n {\n \"alpha_fraction\": 0.46000000834465027,\n \"alphanum_fraction\": 0.48235294222831726,\n \"avg_line_length\": 35.956520080566406,\n \"blob_id\": \"66940edcc9e516d761bdc19d256db66a44a5e4ba\",\n \"content_id\": \"17a522e557d2a67edc574e207695ca4172f07e46\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 850,\n \"license_type\": \"no_license\",\n \"max_line_length\": 77,\n \"num_lines\": 23,\n \"path\": \"/Solutions/012_Integer to Roman.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# wiki: Roman Number: http://en.wikipedia.org/wiki/Roman_numerals\\nclass Solution:\\n # @return a string\\n\\n def intToRoman(self, num):\\n result = \\\"\\\"\\n transTable = {\\n 1: \\\"I\\\", 5: \\\"V\\\", 10: \\\"X\\\", 50: \\\"L\\\", 100: \\\"C\\\", 500: \\\"D\\\", 1000: \\\"M\\\"}\\n for trans in sorted(transTable.keys())[::-1]:\\n while num != 0 and num >= trans:\\n num -= trans\\n result += transTable[trans]\\n if num == 0:\\n break\\n\\n simplifyTable = {\\\"DCCCC\\\": \\\"CM\\\", \\\"CCCC\\\": \\\"CD\\\", \\\"LXXXX\\\":\\n \\\"XC\\\", \\\"XXXX\\\": \\\"XL\\\", \\\"VIIII\\\": \\\"IX\\\", \\\"IIII\\\": \\\"IV\\\"}\\n simplifyContent = [\\\"DCCCC\\\", \\\"CCCC\\\", \\\"LXXXX\\\", \\\"XXXX\\\", \\\"VIIII\\\", \\\"IIII\\\"]\\n for item in simplifyContent:\\n if item in result:\\n result = result.replace(item, simplifyTable[item])\\n\\n return result\\n\"\n },\n {\n \"alpha_fraction\": 0.6651480793952942,\n \"alphanum_fraction\": 0.7061503529548645,\n \"avg_line_length\": 23.38888931274414,\n \"blob_id\": \"0c13fd1ac63617f3040e261f326d02c3b6377953\",\n \"content_id\": \"77038a33589f8cea9437d45b416877cb3df5a901\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 439,\n \"license_type\": \"no_license\",\n \"max_line_length\": 150,\n \"num_lines\": 18,\n \"path\": \"/Hints/041_Trapping Rain Water.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\nGiven n non-negative integers representing an elevation map where the width of each bar is 1, compute how much water it is able to trap after raining.\\n\\nFor example, \\nGiven [0,1,0,2,1,0,1,3,2,1,2,1], return 6.\\n\\n### Hints:\\n\\n1. scan from right to left to find max\\n2. scan from left to right to find max\\n3. compare the min of the list to the exact value\\n4. sum all gaps up\\n\\n#### References:\\n\\n#### Tags:\\nArray, Stack, Two Pointer\\n\"\n },\n {\n \"alpha_fraction\": 0.5243902206420898,\n \"alphanum_fraction\": 0.5383275151252747,\n \"avg_line_length\": 27.75,\n \"blob_id\": \"4109bc26614a8f357346ed63fa4378f5bec2e8df\",\n \"content_id\": \"abcd1ae07b2153dbe4aaa850836de2a1c448af83\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 574,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 20,\n \"path\": \"/Solutions/136_Single Number.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import operator\\nclass Solution:\\n # @param A, a list of integer\\n # @return an integer\\n\\n def singleNumber(self, A):\\n ## This is a dic implementation\\n # dic = {}\\n # for i in A:\\n # dic[i]=dic.get(i, 0) + 1\\n # for k in dic:\\n # if dic[k] == 1:\\n # return k\\n ## This is the implementation in lambda\\n # return reduce(lambda x, y: operator.xor(x,y), A)\\n ## This is the most efficient way using reduce\\n return reduce(operator.xor, A)\\n\\ns = Solution()\\nprint s.singleNumber([1,2,2,3,3])\"\n },\n {\n \"alpha_fraction\": 0.4881889820098877,\n \"alphanum_fraction\": 0.5059055089950562,\n \"avg_line_length\": 35.28571319580078,\n \"blob_id\": \"75e4770be9c7fa3e5ee9209165e136e5b8068b99\",\n \"content_id\": \"0b1973563ab3ed48d66ddb37c61d2ea6cc5fcf9f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 508,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 14,\n \"path\": \"/Solutions/162_Find Peak Element.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param num, a list of integer\\n # @return an integer\\n\\n def findPeakElement(self, num):\\n pre_num = [num[i] for i in range(1, len(num))] + [float('-inf')]\\n suf_num = [float('-inf')] + [num[i] for i in range(len(num) - 1)]\\n\\n sub_num_1 = [n - p for n, p in zip(num, pre_num)]\\n sub_num_2 = [n - s for n, s in zip(num, suf_num)]\\n for i in range(len(num)):\\n if sub_num_1[i] > 0 and sub_num_2[i] > 0:\\n return i\\n return 0\\n\"\n },\n {\n \"alpha_fraction\": 0.5196078419685364,\n \"alphanum_fraction\": 0.5220588445663452,\n \"avg_line_length\": 23.058822631835938,\n \"blob_id\": \"9e79ff6de6528df7052af35c31b4ec6baadbd2b4\",\n \"content_id\": \"c2962d00b0b655a1e16aeb5b19af1a057e12b671\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 408,\n \"license_type\": \"no_license\",\n \"max_line_length\": 57,\n \"num_lines\": 17,\n \"path\": \"/Solutions/151_Reverse Words in a String.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Note: Done\\nclass Solution:\\n # @param s, a string\\n # @return a string\\n def reverseWords(self, s):\\n if s.isspace():\\n return \\\"\\\"\\n else:\\n sList = s.split()\\n re = sList[::-1]\\n return \\\" \\\".join(re).strip\\n \\n# Given an input string, reverse the string word by word.\\n# \\n# For example,\\n# Given s = \\\"the sky is blue\\\",\\n# return \\\"blue is sky the\\\".\"\n },\n {\n \"alpha_fraction\": 0.38808372616767883,\n \"alphanum_fraction\": 0.4138486385345459,\n \"avg_line_length\": 23.84000015258789,\n \"blob_id\": \"8c5402ac2189dd6aeb6f5c78f5bfc91cd16bba31\",\n \"content_id\": \"b3032728c89847b9dcfbdbde35c3faaa23011943\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 621,\n \"license_type\": \"no_license\",\n \"max_line_length\": 47,\n \"num_lines\": 25,\n \"path\": \"/Solutions/009_Palindrome Number.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return a boolean\\n\\n def isPalindrome(self, x):\\n if x < 0:\\n return False\\n divider = 1\\n while(x / divider >= 10):\\n divider *= 10\\n while(x != 0):\\n left = x / divider\\n right = x % 10\\n if left != right:\\n return False\\n x %= divider\\n x /= 10\\n divider /= 100\\n return True\\n\\n # def isPalindrome(self, x):\\n # if str(x).startswith(\\\"-\\\"):\\n # return False\\n # y = str(x)[::-1]\\n # res = True if y == y[::-1] else False\\n # return res\\n\"\n },\n {\n \"alpha_fraction\": 0.4513692259788513,\n \"alphanum_fraction\": 0.47119924426078796,\n \"avg_line_length\": 30.909090042114258,\n \"blob_id\": \"078642458ab4e993d34d3b4416863c07e10ef1dd\",\n \"content_id\": \"9eccb8f98035b511cda2ea05f0963852f26f1161\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1059,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 33,\n \"path\": \"/Solutions/095_Unique Binary Search Trees.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return an integer\\n\\n def numTrees(self, n):\\n ## Recursive way\\n # TIME LIMITATION ERROR\\n # if n == 0:\\n # return 1\\n # if n == 1:\\n # return 1\\n # if n == 2:\\n # return 2\\n # count = 0\\n # for i in range(n):\\n # count += self.numTrees(i)*self.numTrees(n-i-1)\\n # return count\\n \\n # numOfTrees[i] == j means for n is i, there are j unique\\n # binary search trees for it.\\n ## Iterative way\\n numOfTrees = [1, 1, 2]\\n nextTrying = len(numOfTrees)\\n while nextTrying <= n:\\n # Comptute how many unique binary search trees are there for\\n # n == nextTrying.\\n # f(3) = f(0)*f(2) + f(1)*f(1) + f(2)*f(0)\\n count = 0\\n for center in xrange(nextTrying):\\n count += numOfTrees[center] * \\\\\\n numOfTrees[nextTrying - center - 1]\\n numOfTrees.append(count)\\n nextTrying += 1\\n return numOfTrees[n]\\n \\n\\n\"\n },\n {\n \"alpha_fraction\": 0.581818163394928,\n \"alphanum_fraction\": 0.585454523563385,\n \"avg_line_length\": 20.153846740722656,\n \"blob_id\": \"e881d9369010ffc0c5400ca196c299447b3e275b\",\n \"content_id\": \"99843dfea08df26d38895b3312836405ca3095b9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 275,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 13,\n \"path\": \"/Hints/021_Generate Parentheses.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\nGiven n pairs of parentheses, write a function to generate all combinations of well-formed parentheses.\\n\\nFor example, given n = 3, a solution set is:\\n\\n\\\"((()))\\\", \\\"(()())\\\", \\\"(())()\\\", \\\"()(())\\\", \\\"()()()\\\"\\n\\n### Hints:\\n\\n#### References:\\n\\n#### Tags:\\nBacktracking String\\n\"\n },\n {\n \"alpha_fraction\": 0.7119155526161194,\n \"alphanum_fraction\": 0.7315233945846558,\n \"avg_line_length\": 27.782608032226562,\n \"blob_id\": \"eec55d3feaf8334c290365d6870a7d90ab9ec8a5\",\n \"content_id\": \"65011bccc785e3afb32d8dc806e9215702d9643c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 663,\n \"license_type\": \"no_license\",\n \"max_line_length\": 170,\n \"num_lines\": 23,\n \"path\": \"/Hints/054_Jump Game.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\nGiven an array of non-negative integers, you are initially positioned at the first index of the array.\\n\\nEach element in the array represents your maximum jump length at that position.\\n\\nDetermine if you are able to reach the last index.\\n\\nFor example:\\nA = [2,3,1,1,4], return true.\\n\\nA = [3,2,1,0,4], return false.\\n\\n### Hints:\\n\\n1. what if the value is 0?\\n2. define a variable named reachable, which is the maximum index you can reach at this moment\\n\\nfrom its first index, make the reachable to be the max of reachable and index + value. If the next index is less than reachable, then reture false, otherwise return true.\\n\\n#### References:\\n\\n#### Tags:\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.3695652186870575,\n \"alphanum_fraction\": 0.37826088070869446,\n \"avg_line_length\": 27.75,\n \"blob_id\": \"9c2c13a1d491914dab088e5a6289f3c9f7dc44a3\",\n \"content_id\": \"a1cb0ea6f1acd30a9ee33770a4de83f8e1746659\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 920,\n \"license_type\": \"no_license\",\n \"max_line_length\": 67,\n \"num_lines\": 32,\n \"path\": \"/Solutions/029_Substring with Concatenation of All Words.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param S, a string\\n # @param L, a list of string\\n # @return a list of integer\\n\\n def findSubstring(self, S, L):\\n words = {}\\n wordNum = len(L)\\n for i in L:\\n if i not in words:\\n words[i] = 1\\n else:\\n words[i] += 1\\n wordLen = len(L[0])\\n res = []\\n for i in range(len(S) + 1 - wordLen * wordNum):\\n curr = {}\\n j = 0\\n while j < wordNum:\\n word = S[i + j * wordLen:i + j * wordLen + wordLen]\\n if word not in words:\\n break\\n if word not in curr:\\n curr[word] = 1\\n else:\\n curr[word] += 1\\n if curr[word] > words[word]:\\n break\\n j += 1\\n if j == wordNum:\\n res.append(i)\\n return res\\n\"\n },\n {\n \"alpha_fraction\": 0.4684014916419983,\n \"alphanum_fraction\": 0.49814125895500183,\n \"avg_line_length\": 28.88888931274414,\n \"blob_id\": \"adb7985672b79c1c1ee1bc549fafac4afaedd5ab\",\n \"content_id\": \"31f85b4d003b62287785cdd402a625b79ccf8936\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 269,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 9,\n \"path\": \"/Solutions/002_Median of Two Sorted Arrays.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return a float\\n\\n def findMedianSortedArrays(self, A, B):\\n temp = sorted(A + B)\\n if len(temp) % 2 == 0:\\n return (temp[len(temp) / 2] + temp[len(temp) / 2 - 1]) / 2.0\\n else:\\n return temp[len(temp) / 2]\\n\"\n },\n {\n \"alpha_fraction\": 0.5043478012084961,\n \"alphanum_fraction\": 0.5093167424201965,\n \"avg_line_length\": 27.75,\n \"blob_id\": \"c96f8ef720889b3429630bff37b5a06af46de114\",\n \"content_id\": \"ff4aa4cf3ef0fbf608352564b33dd3fb46c2ba50\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 805,\n \"license_type\": \"no_license\",\n \"max_line_length\": 56,\n \"num_lines\": 28,\n \"path\": \"/Solutions/096_Unique Binary Search Trees II.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node\\n# class TreeNode:\\n# def __init__(self, x):\\n# self.val = x\\n# self.left = None\\n# self.right = None\\n\\n\\nclass Solution:\\n # @return a list of tree node\\n\\n def generateTrees(self, n):\\n return self.generateTreesRecur(1, n)\\n\\n def generateTreesRecur(self, low, high):\\n result = []\\n if low > high:\\n result.append(None)\\n for i in range(low, high + 1):\\n left = self.generateTreesRecur(low, i - 1)\\n right = self.generateTreesRecur(i + 1, high)\\n for j in left:\\n for k in right:\\n current = TreeNode(i)\\n current.left = j\\n current.right = k\\n result.append(current)\\n return result\\n\"\n },\n {\n \"alpha_fraction\": 0.5966348052024841,\n \"alphanum_fraction\": 0.6066393852233887,\n \"avg_line_length\": 41.28845977783203,\n \"blob_id\": \"3052e4de7bc8cc2e5064a7231ea49ef58dfc338d\",\n \"content_id\": \"c9c48385f46a0ed673f4a62c8270034fd40e5d51\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2199,\n \"license_type\": \"no_license\",\n \"max_line_length\": 535,\n \"num_lines\": 52,\n \"path\": \"/Hints/003_Longest Substring Without Repeating Characters.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n> Given a string, find the length of the longest substring without repeating characters. For example, the longest substring without repeating letters for \\\"abcabcbb\\\" is \\\"abc\\\", which the length is 3. For \\\"bbbbb\\\" the longest substring is \\\"b\\\", with the length of 1.\\n\\n### Hints:\\nFirst find a char, if it has been visited before, then compare the substring from its last appearance to the position it visited with the substring from this position to the same length substrings. For example, \\\"dabcabcd\\\", visited from the beginning, and then at the 5th position, it finds out \\\"a\\\" has been explored before, then get the substring from \\\"a\\\" to \\\"a\\\" which is \\\"abc\\\", now get the same length string from the second \\\"a\\\", which is \\\"abc\\\", then compare them whether they are the same or not. Update the maximum length variable. \\n\\nUse hashtable to indicate whether a char has been visited or not.\\n\\nUse a `ord` build-in function to find out its Unicode value.\\n\\n```python\\nclass Solution:\\n # @return an integer\\n def lengthOfLongestSubstring(self, s):\\n start = 0\\n maxlen = 0\\n hashtable = [-1 for i in range(256)]\\n for i in range(len(s)):\\n if hashtable[ord(s[i])] != -1:\\n while start <= hashtable[ord(s[i])]:\\n hashtable[ord(s[start])] = -1\\n start += 1\\n if i - start + 1 > maxlen: maxlen = i - start + 1\\n hashtable[ord(s[i])] = i\\n return maxlen\\n```\\n```python\\nclass Solution:\\n # @return an integer\\n def lengthOfLongestSubstring(self, s):\\n start = 0\\n maxlen = 0\\n dict = {}\\n for i in range(len(s)):\\n dict[s[i]] = -1\\n for i in range(len(s)):\\n if dict[s[i]] != -1:\\n while start <= dict[s[i]]:\\n dict[s[start]] = -1\\n start += 1\\n if i - start + 1 > maxlen: maxlen = i - start + 1\\n dict[s[i]] = i\\n return maxlen\\n```\\n#### Note:\\n* What if a char appears in several places, and how can you compare them? For example: \\\"abcbaabcba\\\", the maximum substring is \\\"abcba\\\".\\n\\n* How to update the hashtable?\\n#### References:\\n\\n#### Tags:\\n**hashtable**, **string**\\n\"\n },\n {\n \"alpha_fraction\": 0.3715847134590149,\n \"alphanum_fraction\": 0.3825136721134186,\n \"avg_line_length\": 31.294116973876953,\n \"blob_id\": \"0d72948493936805fb84bc8cc515be0fd7f426c9\",\n \"content_id\": \"6612e3ad02569abe31768cf06ac2929b022c7e28\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 549,\n \"license_type\": \"no_license\",\n \"max_line_length\": 109,\n \"num_lines\": 17,\n \"path\": \"/Solutions/125_Valid Palindrome.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param s, a string\\n # @return a boolean\\n\\n def isPalindrome(self, s):\\n if s == '':\\n return True\\n else:\\n sTmp = ''\\n for i in range(0, len(s)):\\n if s[i] >= 'a' and s[i] <= 'z' or s[i] >= '0' and s[i] <= '9' or s[i] >= 'A' and s[i] <= 'Z':\\n sTmp += s[i]\\n sTmp = sTmp.lower()\\n for i in range(0, len(sTmp) / 2):\\n if sTmp[i] != sTmp[len(sTmp) - 1 - i]:\\n return False\\n return True\\n\"\n },\n {\n \"alpha_fraction\": 0.4508928656578064,\n \"alphanum_fraction\": 0.4776785671710968,\n \"avg_line_length\": 24,\n \"blob_id\": \"bf141d37ff04fa4c71427d90bd6f78bf498ec350\",\n \"content_id\": \"fe729f7557c811cbed28d9256f692995edf2869f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 224,\n \"license_type\": \"no_license\",\n \"max_line_length\": 45,\n \"num_lines\": 9,\n \"path\": \"/Solutions/171_Excel Sheet Column Number.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param s, a string\\n # @return an integer\\n def titleToNumber(self, s):\\n res = 0\\n l = len(s)\\n for i in range(l):\\n res += 26**i*(ord(s[l-i-1]) - 64)\\n return res\"\n },\n {\n \"alpha_fraction\": 0.479353666305542,\n \"alphanum_fraction\": 0.48833033442497253,\n \"avg_line_length\": 26.850000381469727,\n \"blob_id\": \"d1ae552a026196118bfef63f1fefb04d0f18dec4\",\n \"content_id\": \"cb708f9201f40e0933fc92ad6f68145551a205f4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 557,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 20,\n \"path\": \"/Solutions/054_Jump Game.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param A, a list of integers\\n # @return a boolean\\n\\n # def canJump(self, A):\\n # reachable = 0\\n # while reachable < len(A) - 1 and A[reachable] != 0:\\n # reachable = reachable + A[reachable]\\n # if reachable == len(A) - 1:\\n # return True\\n # else:\\n # return False\\n\\n def canJump(self, A):\\n reachable = 0\\n for i in range(len(A)):\\n if i > reachable:\\n return False\\n reachable = max(reachable, i + A[i])\\n return True\\n\"\n },\n {\n \"alpha_fraction\": 0.683582067489624,\n \"alphanum_fraction\": 0.6895522475242615,\n \"avg_line_length\": 19.875,\n \"blob_id\": \"85f1cdd6d6b6593bf9cfff4d383f8b86c8d28b9f\",\n \"content_id\": \"a34336717b72d3250ba8e8868a9725ad901da7bc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 335,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 16,\n \"path\": \"/Hints/186_Reverse Words in a String II.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\nGiven an input string, reverse the string word by word.\\nFor example,\\nGiven s = \\\"the sky is blue\\\",\\nreturn \\\"blue is sky the\\\".\\n\\n### Hints:\\n1. split string to list \\n2. join the reversed list to a new string.\\n\\nOption:\\nwhat if there is a period sign? \\nwhat if the first letter should be captial?\\n#### References:\\n\\n#### Tags:\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.570762038230896,\n \"alphanum_fraction\": 0.5816485285758972,\n \"avg_line_length\": 28.227272033691406,\n \"blob_id\": \"02400f795d051a631c43a371985035bcb092b599\",\n \"content_id\": \"6b1b3ea2480deb1f2eb465d95ea7b302b443c28c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 643,\n \"license_type\": \"no_license\",\n \"max_line_length\": 92,\n \"num_lines\": 22,\n \"path\": \"/Solutions/110_Balanced Binary Tree.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node\\n# class TreeNode:\\n# def __init__(self, x):\\n# self.val = x\\n# self.left = None\\n# self.right = None\\n\\n\\nclass Solution:\\n # @param root, a tree node\\n # @return a boolean\\n\\n def isBalanced(self, root):\\n return self.getHeight(root) != -1\\n\\n def getHeight(self, root):\\n if root == None:\\n return 0\\n left_height, right_height = self.getHeight(root.left), self.getHeight(root.right)\\n if left_height < 0 or right_height < 0 or math.fabs(left_height - right_height) > 1:\\n return -1\\n return max(left_height, right_height) + 1\\n\"\n },\n {\n \"alpha_fraction\": 0.33595284819602966,\n \"alphanum_fraction\": 0.34774067997932434,\n \"avg_line_length\": 21.130434036254883,\n \"blob_id\": \"85057256c44ae768f71df7dc7c4dbe01150b8db2\",\n \"content_id\": \"7a4991c69aa6580d08cc68027c6072a3eefaa42e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 509,\n \"license_type\": \"no_license\",\n \"max_line_length\": 42,\n \"num_lines\": 23,\n \"path\": \"/Solutions/037_Count and Say.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return a string\\n\\n def count(self, s):\\n t = ''\\n count = 0\\n curr = '#'\\n for i in s:\\n if i != curr:\\n if curr != '#':\\n t += str(count) + curr\\n curr = i\\n count = 1\\n else:\\n count += 1\\n t += str(count) + curr\\n return t\\n\\n def countAndSay(self, n):\\n s = '1'\\n for i in range(2, n + 1):\\n s = self.count(s)\\n return s\\n\"\n },\n {\n \"alpha_fraction\": 0.4523507058620453,\n \"alphanum_fraction\": 0.4866581857204437,\n \"avg_line_length\": 42.72222137451172,\n \"blob_id\": \"367bd35685d5bd4cc547de142a31bc7e2a3e9864\",\n \"content_id\": \"5cec0fe32700e0cf4330986b28048c42d6c3e3ff\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 787,\n \"license_type\": \"no_license\",\n \"max_line_length\": 83,\n \"num_lines\": 18,\n \"path\": \"/Solutions/174_Dungeon Game.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param dungeon, a list of lists of integers\\n # @return a integer\\n\\n def calculateMinimumHP(self, dungeon):\\n m = len(dungeon)\\n n = len(dungeon[0])\\n dp = [[0 for index in range(n)] for index in range(m)]\\n dp[m - 1][n - 1] = max(1, 1 - dungeon[m - 1][n - 1])\\n for row in reversed(range(m - 1)):\\n dp[row][n - 1] = max(1, dp[row + 1][n - 1] - dungeon[row][n - 1])\\n for col in reversed(range(n - 1)):\\n dp[m - 1][col] = max(1, dp[m - 1][col + 1] - dungeon[m - 1][col])\\n for row in reversed(range(m - 1)):\\n for col in reversed(range(n - 1)):\\n dp[row][col] = max(\\n 1, min(dp[row + 1][col], dp[row][col + 1]) - dungeon[row][col])\\n return dp[0][0]\\n\"\n },\n {\n \"alpha_fraction\": 0.7068965435028076,\n \"alphanum_fraction\": 0.7210031151771545,\n \"avg_line_length\": 30.899999618530273,\n \"blob_id\": \"0499064e6fc272d69b81a416efeefaa75dce7e50\",\n \"content_id\": \"1c1d0c165afdaa4c33d2201c7aba01f64ca9205f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 638,\n \"license_type\": \"no_license\",\n \"max_line_length\": 281,\n \"num_lines\": 20,\n \"path\": \"/Hints/122_Best Time to Buy and Sell Stock II.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n\\nSay you have an array for which the ith element is the price of a given stock on day i.\\n\\nDesign an algorithm to find the maximum profit. You may complete as many transactions as you like (ie, buy one and sell one share of the stock multiple times). However, you may not engage in multiple transactions at the same time (ie, you must sell the stock before you buy again).\\n\\n### Hints:\\n\\nThis problem you can buy stock several times, so the maximum profit is to buy it at lower price and sell it every higher price.\\n\\n1,2,3,4,5,2,3,4,5\\n^, , , ,|,^, , ,|\\n\\n\\n#### References:\\n\\nOther stockpricing problems:\\n\\n#### Tags:\\nArray, Greedy\\n\"\n },\n {\n \"alpha_fraction\": 0.6883116960525513,\n \"alphanum_fraction\": 0.6935064792633057,\n \"avg_line_length\": 18.25,\n \"blob_id\": \"74f4a9e3f626b438f4ed81462205cde39ba440a0\",\n \"content_id\": \"ca28cb938efd4369a989454b8f6776a2213271a0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 385,\n \"license_type\": \"no_license\",\n \"max_line_length\": 114,\n \"num_lines\": 20,\n \"path\": \"/Hints/104_Maximum Depth of Binary Tree.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n Given a binary tree, find its maximum depth.\\n\\nThe maximum depth is the number of nodes along the longest path from the root node down to the farthest leaf node.\\n\\n### Hints:\\n\\nDo it in a recursive way:\\nD_{node} = max{D_{node.left}, D_{node.right}} + 1\\n\\nwhere 1 is the node itself\\n\\n\\nQ:\\nHow to implement it in a iterative way.\\n\\n#### References:\\n\\n#### Tags:\\nDepth-fisrt, Tree\\n\"\n },\n {\n \"alpha_fraction\": 0.7055016160011292,\n \"alphanum_fraction\": 0.7135922312736511,\n \"avg_line_length\": 43.07143020629883,\n \"blob_id\": \"d7c021eb69b8bd85415eaa6906869589e69ac421\",\n \"content_id\": \"509b48e6f1242d005b28eb7faeffcbfbc8993ebe\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 626,\n \"license_type\": \"no_license\",\n \"max_line_length\": 177,\n \"num_lines\": 14,\n \"path\": \"/Hints/161_One Edit Distance.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\nGiven two strings S and T, determine if they are both one edit distance apart.\\n\\nHint:\\n1. If | n – m | is greater than 1, we know immediately both are not one-edit distance apart.\\n2. It might help if you consider these cases separately, m == n and m ≠ n.\\n3. Assume that m is always ≤ n, which greatly simplifies the conditional statements. If m > n, we could just simply swap S and T.\\n4. If m == n, it becomes finding if there is exactly one modified operation. If m ≠ n, you do not have to consider the delete operation. Just consider the insert operation in T.\\n\\n### Hints:\\n\\n#### References:\\n\\n#### Tags:\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7757575511932373,\n \"alphanum_fraction\": 0.7757575511932373,\n \"avg_line_length\": 40.375,\n \"blob_id\": \"0e029464a39cca248e1853f9054d229fe879d1f7\",\n \"content_id\": \"07ab8ab4a81c6739088fc88f8b039c76d93194cd\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"SQL\",\n \"length_bytes\": 330,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 8,\n \"path\": \"/Solutions/175_Combine Two Tables.sql\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Write your MySQL query statement below\\nSELECT p.FirstName, p.LastName, a.City, a.State\\nFROM Person p LEFT OUTER JOIN Address a USING (PersonId);\\n\\n# Write your MySQL query statement below\\n-- SELECT Person.FirstName, Person.LastName, Address.City, Address.State\\n-- FROM Person, Address\\n-- WHERE Person.PersonId = Address.PersonId;\"\n },\n {\n \"alpha_fraction\": 0.44788485765457153,\n \"alphanum_fraction\": 0.6990841627120972,\n \"avg_line_length\": 62.72222137451172,\n \"blob_id\": \"486fc45c10dbc726d3243057274925f860b9d688\",\n \"content_id\": \"de95e2579561d809d81ca9a8db823f1c57e0eecc\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2301,\n \"license_type\": \"no_license\",\n \"max_line_length\": 453,\n \"num_lines\": 36,\n \"path\": \"/README.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Leetcode_Python\\n* This is a repository for solutions to [Leetcode Online Judge Problems](https://oj.leetcode.com/). It helps you to prepare the interview of tech companies.\\n\\n* Folder [Solutions](https://github.com/jinhw1989/Leetcode_Python/tree/master/Solutions) contains all the Python approaches to the problems, and [Hints](https://github.com/jinhw1989/Leetcode_Python/tree/master/Hints) contains explainations to the problems respectively. \\n\\n* All the problems are ordered by the created time, not by the difficulties of them. Some problems are still under consideration, and may not be the best approaches.\\n\\n\\t\\n## TODO List:\\n* Not implemented problems(Time Limit Exceeded):\\n\\t005, 036, 132, 148\\n\\n* Add hint to each problem.\\n\\n* ~~PEP8 Format~~.\\n\\n\\n## Resources:\\n* [Cracking the Coding Interview: 150 Programming Questions and Solutions](http://www.amazon.com/Cracking-Coding-Interview-Programming-Questions/dp/098478280X)\\n\\n* [The Google Resume: How to Prepare for a Career and Land a Job at Apple, Microsoft, Google, or any Top Tech Company](http://www.amazon.com/The-Google-Resume-Prepare-Microsoft/dp/0470927623)\\n\\n* [CareerCup](http://www.careercup.com/)\\n\\nYou can also look into [jw2013/Leetcode](https://github.com/jw2013/Leetcode) for references. And it also refers to parts of work of [南郭子綦](http://www.cnblogs.com/zuoyuan/tag/leetcode/). Thanks to both of them.\\n\\n## Difficulty\\n\\n### Easy: \\n006, 007, 008, 009, 013, 014, 019, 020, 021, 026, 027, 028, 036, 038, 058, 066, 067, 070, 083, 088, 100, 101, 102, 104, 107, 110, 111, 112, 118, 119, 125, 155, 157, 160, 165, 168, 169, 170, 171, 172\\n\\n### Medium:\\n001, 002, 003, 005, 011, 012, 015, 016, 017, 018, 022, 024, 029, 031, 034, 035, 039, 040, 043, 046, 048, 049, 050, 053, 054, 055, 059, 060, 061, 062, 063, 064, 069, 071, 073, 074, 075, 077, 078, 079, 080, 081, 082, 086, 089, 090, 091, 092, 093, 094, 095, 096, 098, 103, 105, 106, 108, 109, 113, 114, 116, 120, 121, 122, 127, 129, 130, 131, 133, 134, 136, 137, 139, 141, 142, 143, 144, 147, 148, 150, 151, 152, 153, 156, 161, 162, 163, 166, 167, 173, 179\\n\\n### Hard:\\n004, 010, 023, 025, 030, 032, 033, 037, 041, 042, 044, 045, 047, 051, 052, 056, 057, 065, 068, 072, 076, 084, 085, 087, 097, 099, 115, 117, 123, 124, 126, 128, 132, 135, 138, 140, 145, 146, 149, 154, 158, 159, 164, 174\"\n },\n {\n \"alpha_fraction\": 0.49569496512413025,\n \"alphanum_fraction\": 0.5018450021743774,\n \"avg_line_length\": 31.520000457763672,\n \"blob_id\": \"6d3206de88f96adc1731fd84df200ebe6ab47834\",\n \"content_id\": \"e290ea5fc8511c710bc226e3327b85f5170f76ff\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 813,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 25,\n \"path\": \"/Solutions/041_Trapping Rain Water.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param A, a list of integers\\n # @return an integer\\n\\n def trap(self, A):\\n # from right to left\\n leftmosthigh = [0 for i in range(len(A))]\\n leftmax = 0\\n for i in range(len(A)):\\n if A[i] > leftmax:\\n leftmax = A[i]\\n leftmosthigh[i] = leftmax\\n # from left to right \\n rightmosthigh = [0 for i in range(len(A))]\\n rightmax = 0\\n for i in reversed(range(len(A))):\\n if A[i] > rightmax:\\n rightmax = A[i]\\n rightmosthigh[i] = rightmax\\n # print rightmosthigh\\n total = 0\\n for i in range(len(A)):\\n if min(rightmosthigh[i], leftmosthigh[i]) > A[i]:\\n total += min(rightmosthigh[i], leftmosthigh[i]) - A[i]\\n return total\\n\"\n },\n {\n \"alpha_fraction\": 0.57505863904953,\n \"alphanum_fraction\": 0.6032056212425232,\n \"avg_line_length\": 43.10344696044922,\n \"blob_id\": \"4646f8ebb2be6dfaf200dead85f7e1dc794ed9f5\",\n \"content_id\": \"95575f440538fc2d2bc13ec26f4f760c5326977b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2558,\n \"license_type\": \"no_license\",\n \"max_line_length\": 526,\n \"num_lines\": 58,\n \"path\": \"/Hints/002_Median of Two Sorted Arrays.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\n > There are two sorted arrays A and B of size m and n respectively. Find the median of the two sorted arrays. The overall run time complexity should be O(log (m+n)).\\n\\n### Hints:\\nFirst we need to know what is median. \\n> The median of a finite list of numbers can be found by arranging all the observations from lowest value to highest value and picking the middle one (e.g., the median of {3, 3, 5, 9, 11} is 5). If there is an even number of observations, then there is no single middle value; the median is then usually defined to be the mean of the two middle values (the median of {3, 5, 7, 9} is (5 + 7) / 2 = 6), which corresponds to interpreting the median as the fully trimmed mid-range. [wikipedia](http://en.wikipedia.org/wiki/Median)\\n\\nThen a simple implementation should be like this:\\n```python\\nclass Solution:\\n # @return a float\\n\\n def findMedianSortedArrays(self, A, B):\\n temp = sorted(A + B)\\n if len(temp) % 2 == 0:\\n return (temp[len(temp) / 2] + temp[len(temp) / 2 - 1]) / 2.0\\n else:\\n return temp[len(temp) / 2]\\n```\\nHowever we should think about the complexity of this implementation, it seems OK, but the `sorted()` function cost O(n log (n)), which is not the required operation cost.\\n\\nThink about recursive way to solve this problem\\nFor example, if we want to find the 7th number of two list A = [1,3,4,5]; B = [2,4,6,8,9,10]. Let x be the 7th number, then choose the third number of each list, and then compare them. 4<6, it means 1,3,4 are not the 7th number, then compare A=[5], B = [2,4,6,8,9,10], and find 4(7-3)th number. And do the recursive way.\\n\\n```python\\nclass Solution:\\n # @return a float\\n\\n def getKth(self, A, B, k):\\n lenA = len(A)\\n lenB = len(B)\\n if lenA > lenB:\\n return self.getKth(B, A, k)\\n if lenA == 0:\\n return B[k - 1]\\n if k == 1:\\n return min(A[0], B[0])\\n pa = min(k / 2, lenA)\\n pb = k - pa\\n if A[pa - 1] <= B[pb - 1]:\\n return self.getKth(A[pa:], B, pb)\\n else:\\n return self.getKth(A, B[pb:], pa)\\n\\n def findMedianSortedArrays(self, A, B):\\n lenA = len(A)\\n lenB = len(B)\\n if (lenA + lenB) % 2 == 1:\\n return self.getKth(A, B, (lenA + lenB) / 2 + 1)\\n else:\\n # must multiply 0.5 for return a float else it will return an int\\n return (self.getKth(A, B, (lenA + lenB) / 2) + self.getKth(A, B, (lenA + lenB) / 2 + 1)) * 0.5\\n```\\n\\n#### References:\\n\\n#### Tags:\\n**list**, **recursion**\\n\"\n },\n {\n \"alpha_fraction\": 0.47999998927116394,\n \"alphanum_fraction\": 0.4959999918937683,\n \"avg_line_length\": 17.75,\n \"blob_id\": \"bb8ff811194e760a4708eb80717a615deda035e6\",\n \"content_id\": \"c841efa87f0a2bca124fdd82addb0ac635ff93c4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 375,\n \"license_type\": \"no_license\",\n \"max_line_length\": 48,\n \"num_lines\": 20,\n \"path\": \"/Solutions/170_Two Sum III.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class TwoSum:\\n\\n def __init__(self):\\n self.table = dict()\\n\\n def add(self, n):\\n self.table[n] = self.table.get(n, 0) + 1\\n\\n def find(self, value):\\n for i in self.table:\\n c = value - i\\n if c in self.table or c == i:\\n return True\\n return False\\n\\nts = TwoSum()\\nts.add(1)\\nts.add(2)\\nts.add(4)\\nprint ts.find(7)\\n\"\n },\n {\n \"alpha_fraction\": 0.43431636691093445,\n \"alphanum_fraction\": 0.4611259996891022,\n \"avg_line_length\": 22.3125,\n \"blob_id\": \"68597bcaf6a097bc861f7d51fc011d8ebae191d9\",\n \"content_id\": \"25f8be7244c345435b6fa47c7539d4ac3592e657\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 373,\n \"license_type\": \"no_license\",\n \"max_line_length\": 43,\n \"num_lines\": 16,\n \"path\": \"/Solutions/201_Happy Number.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param {integer} n\\n # @return {boolean}\\n\\n def isHappy(self, n):\\n l = [i for i in str(n)]\\n s = 0\\n count = 0\\n while s != 1 and count < 10:\\n s = sum([int(i)**2 for i in l])\\n l = [i for i in str(s)]\\n count += 1\\n return True if s == 1 else False\\n\\ns = Solution()\\nprint s.isHappy(19)\\n\"\n },\n {\n \"alpha_fraction\": 0.5570032596588135,\n \"alphanum_fraction\": 0.6753528714179993,\n \"avg_line_length\": 33.074073791503906,\n \"blob_id\": \"72e4d8f8517c9e5532f5aa6354780faa1c6f0e08\",\n \"content_id\": \"d46dbde5b3807693b375f245c670836d250caa2f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 921,\n \"license_type\": \"no_license\",\n \"max_line_length\": 120,\n \"num_lines\": 27,\n \"path\": \"/Hints/013_Roman to Integer.md\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"### Problem:\\nGiven a roman numeral, convert it to an integer.\\n\\nInput is guaranteed to be within the range from 1 to 3999.\\n\\n### Hints:\\nRead the wikipedia first: [Roman Numerals](http://en.wikipedia.org/wiki/Roman_numerals)\\n\\nI: 1, V: 5, X: 10, L: 50, C: 100, D: 500, M: 1000.\\nFor example: \\n2015 = 1000 + 1000 + 10 + 5 = MMXV \\n2014 = 1000 + 1000 + 10 + 5 - 1 = MMXIV \\n2013 = 1000 + 1000 + 10 + 1 + 1 + 1 = MMXIII\\n\\nNotice that 4 = IV = V-I \\nGenerally, Roman Numerals are represent by descending order. If a pair of the two letters are in ascending order, \\nthe result is the substraction the smaller one from the larger one.\\n\\nFirst make some assumptions:\\n1. The number is an integer; \\n2. The range of the integers is from 1 to 3999, which is I to 3999 = 1000 + 1000 + 1000 + 1000 - 100 + 100 - 10 + 10 - 1\\n= MMMCMXCIX \\n3. Question: what's the maximum number Roman numerals can represent? \\n\\n#### References:\\n\\n#### Tags:\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.526392936706543,\n \"alphanum_fraction\": 0.526392936706543,\n \"avg_line_length\": 24.259260177612305,\n \"blob_id\": \"8243502986d72ae887bc4aabb9073a8c4f7d0e05\",\n \"content_id\": \"f30e28143161688e87521a5a48ea6e6bafb9b4b4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 682,\n \"license_type\": \"no_license\",\n \"max_line_length\": 49,\n \"num_lines\": 27,\n \"path\": \"/Solutions/099_Recover Binary Search Tree.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Definition for a binary tree node\\n# class TreeNode:\\n# def __init__(self, x):\\n# self.val = x\\n# self.left = None\\n# self.right = None\\n\\n\\nclass Solution:\\n # @param root, a tree node\\n # @return a tree node\\n\\n def inorder(self, root, list, listp):\\n if root:\\n self.inorder(root.left, list, listp)\\n list.append(root.val)\\n listp.append(root)\\n self.inorder(root.right, list, listp)\\n\\n def recoverTree(self, root):\\n list = []\\n listp = []\\n self.inorder(root, list, listp)\\n list.sort()\\n for i in range(len(list)):\\n listp[i].val = list[i]\\n return root\\n\"\n },\n {\n \"alpha_fraction\": 0.406815767288208,\n \"alphanum_fraction\": 0.42492014169692993,\n \"avg_line_length\": 29.29032325744629,\n \"blob_id\": \"8ce1c341b4c3e3bfae9d7c408a80e022e91cc2b7\",\n \"content_id\": \"4f28984d5641deedcf4fe1e5900c4ddf36dc79b9\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 939,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 31,\n \"path\": \"/Solutions/059_Permutation Sequence.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return a string\\n # def dfs(self, n, k, string, stringlist):\\n # if len(stringlist) == n:\\n # Solution.count += 1\\n # if Solution.count == k:\\n # print stringlist\\n # return\\n # for i in range(len(string)):\\n # self.dfs(n, k, string[:i]+string[i+1:], stringlist+string[i])\\n\\n # def getPermutation(self, n, k):\\n # string = ''\\n # for i in range(n): string += str(i+1)\\n # Solution.count = 0\\n # self.dfs(n, k, string, '')\\n def getPermutation(self, n, k):\\n res = ''\\n k -= 1\\n fac = 1\\n for i in range(1, n):\\n fac *= i\\n num = [1, 2, 3, 4, 5, 6, 7, 8, 9]\\n for i in reversed(range(n)):\\n curr = num[k / fac]\\n res += str(curr)\\n num.remove(curr)\\n if i != 0:\\n k %= fac\\n fac /= i\\n return res\\n\"\n },\n {\n \"alpha_fraction\": 0.3598130941390991,\n \"alphanum_fraction\": 0.3995327055454254,\n \"avg_line_length\": 20.9743595123291,\n \"blob_id\": \"62dc7253804489de58224be5c07b5860af6a6c59\",\n \"content_id\": \"78d03c8a132ef4f7a5bf25c020ceb888dccdbe45\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 856,\n \"license_type\": \"no_license\",\n \"max_line_length\": 65,\n \"num_lines\": 39,\n \"path\": \"/Solutions/008_String to Integer (atoi).py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @return an integer\\n\\n def atoi(self, str):\\n s = str.strip()\\n if len(s) == 0:\\n return 0\\n INT_MAX, INT_MIN = 2147483647, -2147483648\\n sign = 1\\n if s[0] in '+-':\\n if s[0] == '-':\\n sign = -1\\n s = s[1:]\\n if s.isdigit():\\n res = int(s)\\n else:\\n i = 0\\n while s[i].isdigit():\\n i += 1\\n if i != 0:\\n s = s[0:i]\\n res = int(s)\\n else:\\n return 0\\n if res > INT_MAX:\\n return INT_MIN if sign == -1 else INT_MAX\\n return sign * res\\n\\n\\n\\n# Option, if can use packages\\n# import re\\n\\n\\n# class Solution:\\n# # @return an integer\\n\\n# def atoi(self, str):\\n# return int(re.search('[+-]?\\\\d+', str.strip()).group(0))\"\n },\n {\n \"alpha_fraction\": 0.5227272510528564,\n \"alphanum_fraction\": 0.5397727489471436,\n \"avg_line_length\": 22.46666717529297,\n \"blob_id\": \"4a77a2e874de2136170ac3e0d3d9e41c7f8bc145\",\n \"content_id\": \"5e1b25ec653fe37346f3484f8dec40b2f5cb4c41\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 352,\n \"license_type\": \"no_license\",\n \"max_line_length\": 73,\n \"num_lines\": 15,\n \"path\": \"/Solutions/191_Numbers of 1 Bits.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n # @param n, an integer\\n # @return an integer\\n\\n def hammingWeight(self, n):\\n # return bin(n)[2:].count('1')\\n # Bit Manuplation, the performance is worse than string operation\\n ans = 0\\n while n:\\n ans += n & 1\\n n >>= 1\\n return ans\\n\\ns = Solution()\\nprint s.hammingWeight(3)\\n\"\n },\n {\n \"alpha_fraction\": 0.7291666865348816,\n \"alphanum_fraction\": 0.7291666865348816,\n \"avg_line_length\": 15.166666984558105,\n \"blob_id\": \"182bf660d4cc6bfd643492e8f86a9d931bc458dc\",\n \"content_id\": \"c42452856ad14a05143904eb055820ef93b00344\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 96,\n \"license_type\": \"no_license\",\n \"max_line_length\": 35,\n \"num_lines\": 6,\n \"path\": \"/Solutions/161_One Edit Distance.py\",\n \"repo_name\": \"jinhw1989/Leetcode_Python\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"class Solution:\\n\\tdef isOneEditDistance(self, s, t):\\n\\t\\tpass\\n\\ns = Solution()\\ns.isOneEditDistance()\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":79,"string":"79"}}},{"rowIdx":2979185,"cells":{"repo_name":{"kind":"string","value":"MrTrustworthy/typeutils"},"repo_url":{"kind":"string","value":"https://github.com/MrTrustworthy/typeutils"},"snapshot_id":{"kind":"string","value":"9e75d429fc3d9e8320a0f8bfd22a105bbbae4570"},"revision_id":{"kind":"string","value":"8c65987eae3a396081063e9c6dc0e1f7f6ada8a2"},"directory_id":{"kind":"string","value":"506cfb6eda98c81b7fd44ba36949925933f5b843"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-03-12T09:24:56.471693","string":"2020-03-12T09:24:56.471693"},"revision_date":{"kind":"timestamp","value":"2018-04-22T09:18:59","string":"2018-04-22T09:18:59"},"committer_date":{"kind":"timestamp","value":"2018-04-22T09:18:59","string":"2018-04-22T09:18:59"},"github_id":{"kind":"number","value":130552246,"string":"130,552,246"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.875,"alphanum_fraction":0.875,"avg_line_length":48,"blob_id":"524d08b351336da9cd1cf581603d7385f9e5796e","content_id":"4f47385a5e0354b8482382263abbf7ddca3c9f25","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":48,"license_type":"permissive","max_line_length":48,"num_lines":1,"path":"/typeutils/__init__.py","repo_name":"MrTrustworthy/typeutils","src_encoding":"UTF-8","text":"from .dynamic_typecheck import dynamic_typecheck"},{"alpha_fraction":0.5809869766235352,"alphanum_fraction":0.6036936044692993,"avg_line_length":25.85365867614746,"blob_id":"10417502e734370676467e4aab994c0ee88e1291","content_id":"7da81bc26e9df02a14d0e6f55f692332982ca403","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3303,"license_type":"permissive","max_line_length":116,"num_lines":123,"path":"/tests/test_dynamic_typecheck.py","repo_name":"MrTrustworthy/typeutils","src_encoding":"UTF-8","text":"from pytest import raises\n\nfrom typeutils import dynamic_typecheck\nfrom typing import List, Dict, Tuple, Any\n\n\nclass TestClassObject:\n pass\n\n\ndef test_dynamic_typecheck_with_simple_types():\n @dynamic_typecheck()\n def test_args_basic(i: int, s: str, o: TestClassObject) -> None:\n pass\n\n obj = TestClassObject()\n\n test_args_basic(1, \"2\", obj)\n test_args_basic(1, s=\"2\", o=obj)\n test_args_basic(1, \"2\", o=obj)\n test_args_basic(i=1, s=\"2\", o=obj)\n test_args_basic(s=\"2\", o=obj, i=1)\n test_args_basic(i=1, o=obj, s=\"2\")\n\n with raises(ValueError):\n test_args_basic(1, \"2\", \"3\")\n\n with raises(ValueError):\n test_args_basic(1, \"2\", \"3\")\n\n with raises(ValueError):\n test_args_basic(1, 2, obj)\n\n with raises(ValueError):\n test_args_basic(\"1\", \"2\", obj)\n\n with raises(ValueError):\n test_args_basic(1, s=2, o=obj)\n\n with raises(ValueError):\n test_args_basic(i=\"1\", o=obj, s=\"2\")\n\n with raises(ValueError):\n test_args_basic(i=1, o=22, s=\"2\")\n\n\ndef test_dynamic_typecheck_with_string_types():\n @dynamic_typecheck()\n def test_args_with_strings(i: \"int\", s: \"str\", o: \"TestClassObject\") -> None:\n pass\n\n obj = TestClassObject()\n\n test_args_with_strings(1, \"2\", obj)\n test_args_with_strings(1, s=\"2\", o=obj)\n test_args_with_strings(1, \"2\", o=obj)\n test_args_with_strings(i=1, s=\"2\", o=obj)\n test_args_with_strings(s=\"2\", o=obj, i=1)\n test_args_with_strings(i=1, o=obj, s=\"2\")\n\n with raises(ValueError):\n test_args_with_strings(1, \"2\", \"3\")\n\n with raises(ValueError):\n test_args_with_strings(1, \"2\", \"3\")\n\n with raises(ValueError):\n test_args_with_strings(1, 2, obj)\n\n with raises(ValueError):\n test_args_with_strings(\"1\", \"2\", obj)\n\n with raises(ValueError):\n test_args_with_strings(1, s=2, o=obj)\n\n with raises(ValueError):\n test_args_with_strings(i=\"1\", o=obj, s=\"2\")\n\n with raises(ValueError):\n test_args_with_strings(i=1, o=22, s=\"2\")\n\n\ndef test_dynamic_typecheck_with_composite_types():\n @dynamic_typecheck()\n def test_composite(a: List[str], b: Dict[str, str], c: Tuple[TestClassObject, int], d: List[List[int]]) -> None:\n pass\n\n obj = TestClassObject()\n\n correct_a = [\"This\", \"is\", \"ok\"]\n wrong_a = [\"This\", \"is\", 45]\n correct_b = {\"hello\": \"you\"}\n wrong_b = {\"No\": [\"wrong\", \"type\"]}\n correct_c = (obj, 23)\n wrong_c = (\"Bananas\", 23)\n correct_d = [[2, 3], [4, 5]]\n wrong_d = [[2, 3], [4, [5, 6]]]\n\n test_composite(correct_a, correct_b, correct_c, correct_d)\n test_composite(correct_a, correct_b, correct_c, d=correct_d)\n test_composite(correct_a, correct_b, c=correct_c, d=correct_d)\n test_composite(b=correct_b, c=correct_c, d=correct_d, a=correct_a)\n\n with raises(ValueError):\n test_composite(wrong_a, correct_b, correct_c, correct_d)\n with raises(ValueError):\n test_composite(correct_a, wrong_b, correct_c, d=correct_d)\n with raises(ValueError):\n test_composite(correct_a, correct_b, c=wrong_c, d=correct_d)\n with raises(ValueError):\n test_composite(b=correct_b, c=correct_c, d=wrong_d, a=correct_a)\n\n\ndef test_return_values():\n pass\n\n\ndef test_mixed_kwargs():\n pass\n\n\ndef test_benchmark():\n pass\n"},{"alpha_fraction":0.672900378704071,"alphanum_fraction":0.6739204525947571,"avg_line_length":36.70512771606445,"blob_id":"a66688f82b9c2246451ca703954b9cbecfef62cb","content_id":"dbacdae793006c89a99065c82aa66943be364437","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2941,"license_type":"permissive","max_line_length":113,"num_lines":78,"path":"/typeutils/dynamic_typecheck.py","repo_name":"MrTrustworthy/typeutils","src_encoding":"UTF-8","text":"from functools import wraps\nfrom inspect import signature\nfrom typing import Callable, List, Dict, Any, Tuple\n\nAnyList = List[Any]\nAnyDict = Dict[Any, Any]\nAnyTuple = Tuple[Any, ...]\nAnyCallable = Callable[..., Any]\n\n_follow_wrapped = True\n\n\ndef _failure_handler(msg: str) -> None:\n raise ValueError(msg)\n\n\ndef configure_module():\n pass\n\n\ndef dynamic_typecheck(*, follow_wrapped: bool = _follow_wrapped):\n def _dynamic_typecheck_wrap(func: AnyCallable) -> AnyCallable:\n sig_args, sig_kwargs, sig_return = _get_signature(func, follow_wrapped)\n\n @wraps(func)\n def _typechecked_function_call(*args: AnyTuple, **kwargs: AnyDict) -> Any:\n _validate_all_args(sig_args, sig_kwargs, args, kwargs)\n val = func(*args, **kwargs)\n _check_return(sig_return, val)\n return val\n\n return _typechecked_function_call\n\n return _dynamic_typecheck_wrap\n\n\ndef _get_signature(func: AnyCallable, follow_wrapped: bool) -> Tuple[List[Any], Dict[str, Any], Any]:\n sig = signature(func, follow_wrapped=follow_wrapped)\n sig_args = [v for k, v in sig.parameters.items() if v.kind in (v.POSITIONAL_ONLY, v.POSITIONAL_OR_KEYWORD)]\n sig_kwargs = {k: v for k, v in sig.parameters.items() if v.kind in (v.KEYWORD_ONLY, v.POSITIONAL_OR_KEYWORD)}\n sig_return = sig.return_annotation\n return sig_args, sig_kwargs, sig_return\n\n\ndef _validate_all_args(sig_args: AnyList, sig_kwargs: AnyDict, args: AnyTuple, kwargs: AnyDict) -> None:\n for arg, sig_arg in zip(args, sig_args):\n _validate_type(arg, sig_arg.annotation)\n\n for key, kwarg in kwargs.items():\n sig_kwarg = sig_kwargs[key]\n _validate_type(kwarg, sig_kwarg.annotation)\n\n\ndef _check_return(sig_return: Any, value: Any) -> None:\n _validate_type(value, sig_return)\n\n\ndef _validate_type(value: Any, value_type: Any) -> None:\n # None is not a type, so we have to compare it to None directly instead of a type\n if value_type is None and value is None:\n return\n\n # Checks where value_type is of a primitive type like \"int\" or \"str\" - this behaves covariantly!\n if isinstance(value_type, type) and isinstance(value, value_type):\n return\n\n # Types can also be declared as strings in order to \"forward-declare\" them,\n # most often used for not-yet-imported custom classes\n if isinstance(value_type, str) and True:\n # The easiest check: Does the class name match up?\n # However, this approach always behaves invariantly (matches up with PEP-484 though)\n if type(value).__name__ == value_type:\n return\n # An alternative approach would be to parse the full class name, import it\n # and then compare the value to the imported class\n # Checking sys.modules does not help since the module might not be imported at that time\n\n _failure_handler(f\"Return value {value} should be of type {value_type} but is {type(value)}\")\n"},{"alpha_fraction":0.7978141903877258,"alphanum_fraction":0.7978141903877258,"avg_line_length":60,"blob_id":"c249e97116ea77d7df979846eb317467598a6ba1","content_id":"1d48e7c9e854fb599373c559077963de8e76f72f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":183,"license_type":"permissive","max_line_length":169,"num_lines":3,"path":"/README.md","repo_name":"MrTrustworthy/typeutils","src_encoding":"UTF-8","text":"# typeutils\n\nA Python decorator that allows you to compare the parameters that a function gets at runtime against the type hints of a function, throwing an error if it doesn't match.\n"}],"string":"[\n {\n \"alpha_fraction\": 0.875,\n \"alphanum_fraction\": 0.875,\n \"avg_line_length\": 48,\n \"blob_id\": \"524d08b351336da9cd1cf581603d7385f9e5796e\",\n \"content_id\": \"4f47385a5e0354b8482382263abbf7ddca3c9f25\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 48,\n \"license_type\": \"permissive\",\n \"max_line_length\": 48,\n \"num_lines\": 1,\n \"path\": \"/typeutils/__init__.py\",\n \"repo_name\": \"MrTrustworthy/typeutils\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from .dynamic_typecheck import dynamic_typecheck\"\n },\n {\n \"alpha_fraction\": 0.5809869766235352,\n \"alphanum_fraction\": 0.6036936044692993,\n \"avg_line_length\": 25.85365867614746,\n \"blob_id\": \"10417502e734370676467e4aab994c0ee88e1291\",\n \"content_id\": \"7da81bc26e9df02a14d0e6f55f692332982ca403\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3303,\n \"license_type\": \"permissive\",\n \"max_line_length\": 116,\n \"num_lines\": 123,\n \"path\": \"/tests/test_dynamic_typecheck.py\",\n \"repo_name\": \"MrTrustworthy/typeutils\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from pytest import raises\\n\\nfrom typeutils import dynamic_typecheck\\nfrom typing import List, Dict, Tuple, Any\\n\\n\\nclass TestClassObject:\\n pass\\n\\n\\ndef test_dynamic_typecheck_with_simple_types():\\n @dynamic_typecheck()\\n def test_args_basic(i: int, s: str, o: TestClassObject) -> None:\\n pass\\n\\n obj = TestClassObject()\\n\\n test_args_basic(1, \\\"2\\\", obj)\\n test_args_basic(1, s=\\\"2\\\", o=obj)\\n test_args_basic(1, \\\"2\\\", o=obj)\\n test_args_basic(i=1, s=\\\"2\\\", o=obj)\\n test_args_basic(s=\\\"2\\\", o=obj, i=1)\\n test_args_basic(i=1, o=obj, s=\\\"2\\\")\\n\\n with raises(ValueError):\\n test_args_basic(1, \\\"2\\\", \\\"3\\\")\\n\\n with raises(ValueError):\\n test_args_basic(1, \\\"2\\\", \\\"3\\\")\\n\\n with raises(ValueError):\\n test_args_basic(1, 2, obj)\\n\\n with raises(ValueError):\\n test_args_basic(\\\"1\\\", \\\"2\\\", obj)\\n\\n with raises(ValueError):\\n test_args_basic(1, s=2, o=obj)\\n\\n with raises(ValueError):\\n test_args_basic(i=\\\"1\\\", o=obj, s=\\\"2\\\")\\n\\n with raises(ValueError):\\n test_args_basic(i=1, o=22, s=\\\"2\\\")\\n\\n\\ndef test_dynamic_typecheck_with_string_types():\\n @dynamic_typecheck()\\n def test_args_with_strings(i: \\\"int\\\", s: \\\"str\\\", o: \\\"TestClassObject\\\") -> None:\\n pass\\n\\n obj = TestClassObject()\\n\\n test_args_with_strings(1, \\\"2\\\", obj)\\n test_args_with_strings(1, s=\\\"2\\\", o=obj)\\n test_args_with_strings(1, \\\"2\\\", o=obj)\\n test_args_with_strings(i=1, s=\\\"2\\\", o=obj)\\n test_args_with_strings(s=\\\"2\\\", o=obj, i=1)\\n test_args_with_strings(i=1, o=obj, s=\\\"2\\\")\\n\\n with raises(ValueError):\\n test_args_with_strings(1, \\\"2\\\", \\\"3\\\")\\n\\n with raises(ValueError):\\n test_args_with_strings(1, \\\"2\\\", \\\"3\\\")\\n\\n with raises(ValueError):\\n test_args_with_strings(1, 2, obj)\\n\\n with raises(ValueError):\\n test_args_with_strings(\\\"1\\\", \\\"2\\\", obj)\\n\\n with raises(ValueError):\\n test_args_with_strings(1, s=2, o=obj)\\n\\n with raises(ValueError):\\n test_args_with_strings(i=\\\"1\\\", o=obj, s=\\\"2\\\")\\n\\n with raises(ValueError):\\n test_args_with_strings(i=1, o=22, s=\\\"2\\\")\\n\\n\\ndef test_dynamic_typecheck_with_composite_types():\\n @dynamic_typecheck()\\n def test_composite(a: List[str], b: Dict[str, str], c: Tuple[TestClassObject, int], d: List[List[int]]) -> None:\\n pass\\n\\n obj = TestClassObject()\\n\\n correct_a = [\\\"This\\\", \\\"is\\\", \\\"ok\\\"]\\n wrong_a = [\\\"This\\\", \\\"is\\\", 45]\\n correct_b = {\\\"hello\\\": \\\"you\\\"}\\n wrong_b = {\\\"No\\\": [\\\"wrong\\\", \\\"type\\\"]}\\n correct_c = (obj, 23)\\n wrong_c = (\\\"Bananas\\\", 23)\\n correct_d = [[2, 3], [4, 5]]\\n wrong_d = [[2, 3], [4, [5, 6]]]\\n\\n test_composite(correct_a, correct_b, correct_c, correct_d)\\n test_composite(correct_a, correct_b, correct_c, d=correct_d)\\n test_composite(correct_a, correct_b, c=correct_c, d=correct_d)\\n test_composite(b=correct_b, c=correct_c, d=correct_d, a=correct_a)\\n\\n with raises(ValueError):\\n test_composite(wrong_a, correct_b, correct_c, correct_d)\\n with raises(ValueError):\\n test_composite(correct_a, wrong_b, correct_c, d=correct_d)\\n with raises(ValueError):\\n test_composite(correct_a, correct_b, c=wrong_c, d=correct_d)\\n with raises(ValueError):\\n test_composite(b=correct_b, c=correct_c, d=wrong_d, a=correct_a)\\n\\n\\ndef test_return_values():\\n pass\\n\\n\\ndef test_mixed_kwargs():\\n pass\\n\\n\\ndef test_benchmark():\\n pass\\n\"\n },\n {\n \"alpha_fraction\": 0.672900378704071,\n \"alphanum_fraction\": 0.6739204525947571,\n \"avg_line_length\": 36.70512771606445,\n \"blob_id\": \"a66688f82b9c2246451ca703954b9cbecfef62cb\",\n \"content_id\": \"dbacdae793006c89a99065c82aa66943be364437\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2941,\n \"license_type\": \"permissive\",\n \"max_line_length\": 113,\n \"num_lines\": 78,\n \"path\": \"/typeutils/dynamic_typecheck.py\",\n \"repo_name\": \"MrTrustworthy/typeutils\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from functools import wraps\\nfrom inspect import signature\\nfrom typing import Callable, List, Dict, Any, Tuple\\n\\nAnyList = List[Any]\\nAnyDict = Dict[Any, Any]\\nAnyTuple = Tuple[Any, ...]\\nAnyCallable = Callable[..., Any]\\n\\n_follow_wrapped = True\\n\\n\\ndef _failure_handler(msg: str) -> None:\\n raise ValueError(msg)\\n\\n\\ndef configure_module():\\n pass\\n\\n\\ndef dynamic_typecheck(*, follow_wrapped: bool = _follow_wrapped):\\n def _dynamic_typecheck_wrap(func: AnyCallable) -> AnyCallable:\\n sig_args, sig_kwargs, sig_return = _get_signature(func, follow_wrapped)\\n\\n @wraps(func)\\n def _typechecked_function_call(*args: AnyTuple, **kwargs: AnyDict) -> Any:\\n _validate_all_args(sig_args, sig_kwargs, args, kwargs)\\n val = func(*args, **kwargs)\\n _check_return(sig_return, val)\\n return val\\n\\n return _typechecked_function_call\\n\\n return _dynamic_typecheck_wrap\\n\\n\\ndef _get_signature(func: AnyCallable, follow_wrapped: bool) -> Tuple[List[Any], Dict[str, Any], Any]:\\n sig = signature(func, follow_wrapped=follow_wrapped)\\n sig_args = [v for k, v in sig.parameters.items() if v.kind in (v.POSITIONAL_ONLY, v.POSITIONAL_OR_KEYWORD)]\\n sig_kwargs = {k: v for k, v in sig.parameters.items() if v.kind in (v.KEYWORD_ONLY, v.POSITIONAL_OR_KEYWORD)}\\n sig_return = sig.return_annotation\\n return sig_args, sig_kwargs, sig_return\\n\\n\\ndef _validate_all_args(sig_args: AnyList, sig_kwargs: AnyDict, args: AnyTuple, kwargs: AnyDict) -> None:\\n for arg, sig_arg in zip(args, sig_args):\\n _validate_type(arg, sig_arg.annotation)\\n\\n for key, kwarg in kwargs.items():\\n sig_kwarg = sig_kwargs[key]\\n _validate_type(kwarg, sig_kwarg.annotation)\\n\\n\\ndef _check_return(sig_return: Any, value: Any) -> None:\\n _validate_type(value, sig_return)\\n\\n\\ndef _validate_type(value: Any, value_type: Any) -> None:\\n # None is not a type, so we have to compare it to None directly instead of a type\\n if value_type is None and value is None:\\n return\\n\\n # Checks where value_type is of a primitive type like \\\"int\\\" or \\\"str\\\" - this behaves covariantly!\\n if isinstance(value_type, type) and isinstance(value, value_type):\\n return\\n\\n # Types can also be declared as strings in order to \\\"forward-declare\\\" them,\\n # most often used for not-yet-imported custom classes\\n if isinstance(value_type, str) and True:\\n # The easiest check: Does the class name match up?\\n # However, this approach always behaves invariantly (matches up with PEP-484 though)\\n if type(value).__name__ == value_type:\\n return\\n # An alternative approach would be to parse the full class name, import it\\n # and then compare the value to the imported class\\n # Checking sys.modules does not help since the module might not be imported at that time\\n\\n _failure_handler(f\\\"Return value {value} should be of type {value_type} but is {type(value)}\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.7978141903877258,\n \"alphanum_fraction\": 0.7978141903877258,\n \"avg_line_length\": 60,\n \"blob_id\": \"c249e97116ea77d7df979846eb317467598a6ba1\",\n \"content_id\": \"1d48e7c9e854fb599373c559077963de8e76f72f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 183,\n \"license_type\": \"permissive\",\n \"max_line_length\": 169,\n \"num_lines\": 3,\n \"path\": \"/README.md\",\n \"repo_name\": \"MrTrustworthy/typeutils\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# typeutils\\n\\nA Python decorator that allows you to compare the parameters that a function gets at runtime against the type hints of a function, throwing an error if it doesn't match.\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2979186,"cells":{"repo_name":{"kind":"string","value":"praneetwalvekar33/Calculator_GUI"},"repo_url":{"kind":"string","value":"https://github.com/praneetwalvekar33/Calculator_GUI"},"snapshot_id":{"kind":"string","value":"06261228daa9e33f4dfb1a1e578501c74c833f03"},"revision_id":{"kind":"string","value":"e9d9c9d679bca810c2d730713847b96da6ea6a57"},"directory_id":{"kind":"string","value":"e757d9706cab19cb6eac44d149d129000c0eed89"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-08-04T01:54:22.035467","string":"2023-08-04T01:54:22.035467"},"revision_date":{"kind":"timestamp","value":"2021-09-21T15:08:50","string":"2021-09-21T15:08:50"},"committer_date":{"kind":"timestamp","value":"2021-09-21T15:08:50","string":"2021-09-21T15:08:50"},"github_id":{"kind":"number","value":356921803,"string":"356,921,803"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5781188607215881,"alphanum_fraction":0.6251475811004639,"avg_line_length":23.795122146606445,"blob_id":"5e9632eaa3f15578ff7c697e6ea845fea8801256","content_id":"7c940740189cf359edacfa0ca5a7d1fadae92ae2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":5082,"license_type":"no_license","max_line_length":93,"num_lines":205,"path":"/calculator.py","repo_name":"praneetwalvekar33/Calculator_GUI","src_encoding":"UTF-8","text":"from tkinter import *\nfrom math import *\n\nroot = Tk()\nroot.title(\"Calculator\")\n\ne = Entry(root, width = 50 , borderwidth = 5)\ne.grid(row = 0, column = 0, columnspan = 4, padx = 10, pady = 10)\n\n#Variable\ncount = 0 #To check if the number is a float value \nmom = 0 #for checking if operator is used after 1st calculation\nflag=0 #To clear the display after the calculation\nhe=0 #To check how many times equalto is used in a calculation \n\nopera=0\nf_num=0\n#Functions\ndef click(n):\n\tglobal flag\n\tif flag==1:\n\t\te.delete(0,END)\n\t\tflag=0\n\t\tglobal mom\n\t\tmom=0\n\tcurrent = e.get()\n\te.delete(0,END)\n\ta = str(current) + str(n)\n\tif n == \".\":\n\t\tglobal count\n\t\tcount = 1\n\te.insert(0,a)\n\ndef clear_func():\n\tglobal mom\n\tmom = 0\n\tglobal flag\n\tflag=0\n\tglobal count\n\tcount=0\n\tglobal he\n\the=0\n\te.delete(0 , END)\n\ndef add_func(n):\n\tglobal mom\n\tmom = mom + 1\n\tif mom > 1:\n\t\tglobal he\n\t\the=1\n\t\tfunc()\n\tglobal first_number\n\tfirst_number = e.get()\t\n\tif(first_number==\"\"):\n\t\te.insert(0,\"Syntax Error\")\n\t\treturn\n\tglobal f_num\n\tif count == 1: \n\t\tf_num = float(first_number)\n\telse:\n\t\tf_num = int(first_number)\n\te.delete(0 , END)\n\tglobal opera\n\topera = n\n\tglobal flag\n\tflag=0\n\ndef func():\n\tglobal flag\n\tglobal f_num\n\tglobal opera\n\tif he==0:\n\t\tmom=0\n\tsecond_number = e.get()\n\tglobal count\n\tfor i in second_number:\n\t\tif(i==\".\"):\n\t\t\tcount=1\n\tif(second_number==\"\" or second_number==\"Syntax Error\"):\n\t\te.delete(0,END)\n\t\te.insert(0, \"Syntax Error\")\n\t\tflag=1\n\t\treturn\n\tif f_num==0:\n\t\te.delete(0, END)\n\t\te.insert(0, second_number)\n\t\tflag=1\n\t\treturn\n\te.delete(0,END)\n\tif count == 1:\n\t\ts_num = float(second_number)\n\telse:\n\t\ts_num = int(second_number)\n\tif opera==0:\n\t\te.insert(0, \"Syntax Error\")\n\t\treturn\n\tif opera == \"+\":\n\t\tresult = f_num + s_num\n\telif opera == \"-\":\n\t\tresult = f_num - s_num\n\telif opera == \"*\":\n\t\tresult = f_num * s_num\n\telse:\n\t\tresult = f_num / s_num\n\te.insert(0, result)\n\tflag=1\n\tf_num=s_num\n\ndef power():\n\tglobal flag\n\tflag=1\n\tnumber = e.get()\n\tglobal count\n\tfor i in number:\n\t\tif(i==\".\"):\n\t\t\tcount=1\n\tif count == 1:\n\t\tnum = float(number)\n\telse:\n\t\tnum = int(number)\n\tresult = num ** 2\n\te.delete(0, END)\n\te.insert(0, result)\n\ndef square():\n\tglobal flag\n\tflag=1\n\tnumber = e.get()\n\tglobal count\n\tfor i in number:\n\t\tif(i==\".\"):\n\t\t\tcount=1\n\tif count == 1:\n\t\tnum = float(number)\n\telse:\n\t\tnum = int(number)\n\tresult = sqrt(num)\n\te.delete(0, END)\n\te.insert(0, result)\n\ndef reciporcal():\n\tglobal flag\n\tflag=1\n\tnumber = e.get()\n\tglobal count\n\tfor i in number:\n\t\tif(i==\".\"):\n\t\t\tcount=1\n\tif count == 1:\n\t\tnum = float(number)\n\telse:\n\t\tnum = int(number)\n\tresult = 1 / num\n\te.delete(0, END)\n\te.insert(0, result)\n\n#Define Buttons\nbutton_1 = Button(root, text = \"1\", padx = 35, pady = 20, command = lambda:click(1))\nbutton_2 = Button(root, text = \"2\", padx = 35, pady = 20, command = lambda:click(2))\nbutton_3 = Button(root, text = \"3\", padx = 35, pady = 20, command = lambda:click(3))\nbutton_4 = Button(root, text = \"4\", padx = 35, pady = 20, command = lambda:click(4))\nbutton_5 = Button(root, text = \"5\", padx = 35, pady = 20, command = lambda:click(5))\nbutton_6 = Button(root, text = \"6\", padx = 35, pady = 20, command = lambda:click(6))\nbutton_7 = Button(root, text = \"7\", padx = 35, pady = 20, command = lambda:click(7))\nbutton_8 = Button(root, text = \"8\", padx = 35, pady = 20, command = lambda:click(8))\nbutton_9 = Button(root, text = \"9\", padx = 35, pady = 20, command = lambda:click(9))\nbutton_0 = Button(root, text = \"0\", padx = 35, pady = 20, command = lambda:click(0))\nbutton_01 = Button(root, text = \".\", padx = 35, pady = 20, command = lambda:click(\".\"))\nbutton_02 = Button(root, text = \"Clear\", padx = 25, pady = 20, command = clear_func)\nbutton_add = Button(root, text = \"+\", padx = 35, pady = 20, command = lambda:add_func(\"+\"))\nbutton_sub = Button(root, text = \"-\", padx = 35, pady = 20, command = lambda:add_func(\"-\"))\nbutton_multi = Button(root, text = \"*\", padx = 35, pady = 20, command = lambda:add_func(\"*\"))\nbutton_div = Button(root, text = \"/\", padx = 35, pady = 20, command = lambda:add_func(\"/\"))\nbutton_equ = Button(root, text = \"=\", padx = 35, pady = 20, command = func)\nbutton_reci = Button(root, text = \"1/x\", padx = 25, pady = 20, command = reciporcal)\nbutton_squ = Button(root, text = \"x^2\", padx = 25, pady = 20, command = power)\nbutton_squrt = Button(root, text = \"sqrt x\", padx = 25, pady = 20, command = square)\n\n#Display buttons\nbutton_reci.grid(row = 1, column = 0)\nbutton_squ.grid(row = 1, column = 1)\nbutton_squrt.grid(row = 1, column = 2)\nbutton_div.grid(row = 1, column = 3)\n\nbutton_7.grid(row = 2, column = 0)\nbutton_8.grid(row = 2, column = 1)\nbutton_9.grid(row = 2, column = 2)\nbutton_multi.grid(row = 2, column = 3)\n\nbutton_4.grid(row = 3, column = 0)\nbutton_5.grid(row = 3, column = 1)\nbutton_6.grid(row = 3, column = 2)\nbutton_sub.grid(row = 3, column = 3)\n\nbutton_3.grid(row = 4, column = 2)\nbutton_2.grid(row = 4, column = 1)\nbutton_1.grid(row = 4, column = 0)\nbutton_add.grid(row = 4, column = 3)\n\nbutton_02.grid(row = 5, column = 0)\nbutton_0.grid(row = 5, column = 1)\nbutton_01.grid(row = 5, column = 2)\nbutton_equ.grid(row = 5, column = 3)\n\nroot.mainloop()"}],"string":"[\n {\n \"alpha_fraction\": 0.5781188607215881,\n \"alphanum_fraction\": 0.6251475811004639,\n \"avg_line_length\": 23.795122146606445,\n \"blob_id\": \"5e9632eaa3f15578ff7c697e6ea845fea8801256\",\n \"content_id\": \"7c940740189cf359edacfa0ca5a7d1fadae92ae2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 5082,\n \"license_type\": \"no_license\",\n \"max_line_length\": 93,\n \"num_lines\": 205,\n \"path\": \"/calculator.py\",\n \"repo_name\": \"praneetwalvekar33/Calculator_GUI\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from tkinter import *\\nfrom math import *\\n\\nroot = Tk()\\nroot.title(\\\"Calculator\\\")\\n\\ne = Entry(root, width = 50 , borderwidth = 5)\\ne.grid(row = 0, column = 0, columnspan = 4, padx = 10, pady = 10)\\n\\n#Variable\\ncount = 0 #To check if the number is a float value \\nmom = 0 #for checking if operator is used after 1st calculation\\nflag=0 #To clear the display after the calculation\\nhe=0 #To check how many times equalto is used in a calculation \\n\\nopera=0\\nf_num=0\\n#Functions\\ndef click(n):\\n\\tglobal flag\\n\\tif flag==1:\\n\\t\\te.delete(0,END)\\n\\t\\tflag=0\\n\\t\\tglobal mom\\n\\t\\tmom=0\\n\\tcurrent = e.get()\\n\\te.delete(0,END)\\n\\ta = str(current) + str(n)\\n\\tif n == \\\".\\\":\\n\\t\\tglobal count\\n\\t\\tcount = 1\\n\\te.insert(0,a)\\n\\ndef clear_func():\\n\\tglobal mom\\n\\tmom = 0\\n\\tglobal flag\\n\\tflag=0\\n\\tglobal count\\n\\tcount=0\\n\\tglobal he\\n\\the=0\\n\\te.delete(0 , END)\\n\\ndef add_func(n):\\n\\tglobal mom\\n\\tmom = mom + 1\\n\\tif mom > 1:\\n\\t\\tglobal he\\n\\t\\the=1\\n\\t\\tfunc()\\n\\tglobal first_number\\n\\tfirst_number = e.get()\\t\\n\\tif(first_number==\\\"\\\"):\\n\\t\\te.insert(0,\\\"Syntax Error\\\")\\n\\t\\treturn\\n\\tglobal f_num\\n\\tif count == 1: \\n\\t\\tf_num = float(first_number)\\n\\telse:\\n\\t\\tf_num = int(first_number)\\n\\te.delete(0 , END)\\n\\tglobal opera\\n\\topera = n\\n\\tglobal flag\\n\\tflag=0\\n\\ndef func():\\n\\tglobal flag\\n\\tglobal f_num\\n\\tglobal opera\\n\\tif he==0:\\n\\t\\tmom=0\\n\\tsecond_number = e.get()\\n\\tglobal count\\n\\tfor i in second_number:\\n\\t\\tif(i==\\\".\\\"):\\n\\t\\t\\tcount=1\\n\\tif(second_number==\\\"\\\" or second_number==\\\"Syntax Error\\\"):\\n\\t\\te.delete(0,END)\\n\\t\\te.insert(0, \\\"Syntax Error\\\")\\n\\t\\tflag=1\\n\\t\\treturn\\n\\tif f_num==0:\\n\\t\\te.delete(0, END)\\n\\t\\te.insert(0, second_number)\\n\\t\\tflag=1\\n\\t\\treturn\\n\\te.delete(0,END)\\n\\tif count == 1:\\n\\t\\ts_num = float(second_number)\\n\\telse:\\n\\t\\ts_num = int(second_number)\\n\\tif opera==0:\\n\\t\\te.insert(0, \\\"Syntax Error\\\")\\n\\t\\treturn\\n\\tif opera == \\\"+\\\":\\n\\t\\tresult = f_num + s_num\\n\\telif opera == \\\"-\\\":\\n\\t\\tresult = f_num - s_num\\n\\telif opera == \\\"*\\\":\\n\\t\\tresult = f_num * s_num\\n\\telse:\\n\\t\\tresult = f_num / s_num\\n\\te.insert(0, result)\\n\\tflag=1\\n\\tf_num=s_num\\n\\ndef power():\\n\\tglobal flag\\n\\tflag=1\\n\\tnumber = e.get()\\n\\tglobal count\\n\\tfor i in number:\\n\\t\\tif(i==\\\".\\\"):\\n\\t\\t\\tcount=1\\n\\tif count == 1:\\n\\t\\tnum = float(number)\\n\\telse:\\n\\t\\tnum = int(number)\\n\\tresult = num ** 2\\n\\te.delete(0, END)\\n\\te.insert(0, result)\\n\\ndef square():\\n\\tglobal flag\\n\\tflag=1\\n\\tnumber = e.get()\\n\\tglobal count\\n\\tfor i in number:\\n\\t\\tif(i==\\\".\\\"):\\n\\t\\t\\tcount=1\\n\\tif count == 1:\\n\\t\\tnum = float(number)\\n\\telse:\\n\\t\\tnum = int(number)\\n\\tresult = sqrt(num)\\n\\te.delete(0, END)\\n\\te.insert(0, result)\\n\\ndef reciporcal():\\n\\tglobal flag\\n\\tflag=1\\n\\tnumber = e.get()\\n\\tglobal count\\n\\tfor i in number:\\n\\t\\tif(i==\\\".\\\"):\\n\\t\\t\\tcount=1\\n\\tif count == 1:\\n\\t\\tnum = float(number)\\n\\telse:\\n\\t\\tnum = int(number)\\n\\tresult = 1 / num\\n\\te.delete(0, END)\\n\\te.insert(0, result)\\n\\n#Define Buttons\\nbutton_1 = Button(root, text = \\\"1\\\", padx = 35, pady = 20, command = lambda:click(1))\\nbutton_2 = Button(root, text = \\\"2\\\", padx = 35, pady = 20, command = lambda:click(2))\\nbutton_3 = Button(root, text = \\\"3\\\", padx = 35, pady = 20, command = lambda:click(3))\\nbutton_4 = Button(root, text = \\\"4\\\", padx = 35, pady = 20, command = lambda:click(4))\\nbutton_5 = Button(root, text = \\\"5\\\", padx = 35, pady = 20, command = lambda:click(5))\\nbutton_6 = Button(root, text = \\\"6\\\", padx = 35, pady = 20, command = lambda:click(6))\\nbutton_7 = Button(root, text = \\\"7\\\", padx = 35, pady = 20, command = lambda:click(7))\\nbutton_8 = Button(root, text = \\\"8\\\", padx = 35, pady = 20, command = lambda:click(8))\\nbutton_9 = Button(root, text = \\\"9\\\", padx = 35, pady = 20, command = lambda:click(9))\\nbutton_0 = Button(root, text = \\\"0\\\", padx = 35, pady = 20, command = lambda:click(0))\\nbutton_01 = Button(root, text = \\\".\\\", padx = 35, pady = 20, command = lambda:click(\\\".\\\"))\\nbutton_02 = Button(root, text = \\\"Clear\\\", padx = 25, pady = 20, command = clear_func)\\nbutton_add = Button(root, text = \\\"+\\\", padx = 35, pady = 20, command = lambda:add_func(\\\"+\\\"))\\nbutton_sub = Button(root, text = \\\"-\\\", padx = 35, pady = 20, command = lambda:add_func(\\\"-\\\"))\\nbutton_multi = Button(root, text = \\\"*\\\", padx = 35, pady = 20, command = lambda:add_func(\\\"*\\\"))\\nbutton_div = Button(root, text = \\\"/\\\", padx = 35, pady = 20, command = lambda:add_func(\\\"/\\\"))\\nbutton_equ = Button(root, text = \\\"=\\\", padx = 35, pady = 20, command = func)\\nbutton_reci = Button(root, text = \\\"1/x\\\", padx = 25, pady = 20, command = reciporcal)\\nbutton_squ = Button(root, text = \\\"x^2\\\", padx = 25, pady = 20, command = power)\\nbutton_squrt = Button(root, text = \\\"sqrt x\\\", padx = 25, pady = 20, command = square)\\n\\n#Display buttons\\nbutton_reci.grid(row = 1, column = 0)\\nbutton_squ.grid(row = 1, column = 1)\\nbutton_squrt.grid(row = 1, column = 2)\\nbutton_div.grid(row = 1, column = 3)\\n\\nbutton_7.grid(row = 2, column = 0)\\nbutton_8.grid(row = 2, column = 1)\\nbutton_9.grid(row = 2, column = 2)\\nbutton_multi.grid(row = 2, column = 3)\\n\\nbutton_4.grid(row = 3, column = 0)\\nbutton_5.grid(row = 3, column = 1)\\nbutton_6.grid(row = 3, column = 2)\\nbutton_sub.grid(row = 3, column = 3)\\n\\nbutton_3.grid(row = 4, column = 2)\\nbutton_2.grid(row = 4, column = 1)\\nbutton_1.grid(row = 4, column = 0)\\nbutton_add.grid(row = 4, column = 3)\\n\\nbutton_02.grid(row = 5, column = 0)\\nbutton_0.grid(row = 5, column = 1)\\nbutton_01.grid(row = 5, column = 2)\\nbutton_equ.grid(row = 5, column = 3)\\n\\nroot.mainloop()\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979187,"cells":{"repo_name":{"kind":"string","value":"Ahmed-Elbagoury/ML578"},"repo_url":{"kind":"string","value":"https://github.com/Ahmed-Elbagoury/ML578"},"snapshot_id":{"kind":"string","value":"a423f9a53014280781fafe0b3d5e52794574f702"},"revision_id":{"kind":"string","value":"62d9cc008321cbda1f09ba590903b9b596a5987d"},"directory_id":{"kind":"string","value":"e89b082a04ce57d2a3876cb2a82cf1c85844385c"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-08-23T13:37:56.210409","string":"2021-08-23T13:37:56.210409"},"revision_date":{"kind":"timestamp","value":"2017-12-05T02:58:33","string":"2017-12-05T02:58:33"},"committer_date":{"kind":"timestamp","value":"2017-12-05T02:58:33","string":"2017-12-05T02:58:33"},"github_id":{"kind":"number","value":109450535,"string":"109,450,535"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5680789351463318,"alphanum_fraction":0.6082167625427246,"avg_line_length":55.48473358154297,"blob_id":"8a2d6df793b8af07a23c873f170299809c62e3c1","content_id":"a1d976750bbd678cdf9be2ba69c9e0c556609aef","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":14799,"license_type":"no_license","max_line_length":122,"num_lines":262,"path":"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/run.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"from preprocess_transcation_table import preprocess_transcation_table\nfrom preprocess_user_logs_table import preprocess_user_logs_table\nfrom join_tables import join_tables\nfrom split_data_random import split_data_random\nfrom generate_histograms import generate_histograms\nfrom kfold_cross_validation import kfold_cross_validation\nfrom select_c_for_SVM_using_kfold_CV import select_c_for_SVM_using_kfold_CV\nfrom classify_test_users import classify_test_users\nfrom classify_test_users import test_different_number_of_samples\nfrom select_parameters_for_MLP_using_kfold_CV import select_parameters_for_MLP_using_kfold_CV\nimport os.path as path\nimport os\ndef run (step_number):\n if step_number == 1:\n # Step 1: Preprocess transcation data.\n transcations_input_filename = 'transactions.csv'\n transcations_output_filename = 'transactions_preprocessed.csv'\n preprocess_transcation_table(transcations_input_filename,\n transcations_output_filename)\n elif step_number == 2:\n # Step 2: Preprocess user_logs data\n user_logs_input_filename = 'user_logs.csv'\n user_logs_output_filename = 'user_logs_preprocessed.csv'\n preprocess_user_logs_table(user_logs_input_filename,\n user_logs_output_filename)\n elif step_number == 3:\n # Step 3: Join all tables based on user_id\n transcations_output_filename = 'transactions_preprocessed.csv'\n user_logs_output_filename = 'user_logs_preprocessed.csv'\n train_input_filename = 'train.csv'\n member_input_filename = 'members.csv'\n joined_table_output_filename = 'joined_train_data.csv'\n join_tables(transcations_output_filename, user_logs_output_filename,\n train_input_filename, member_input_filename,\n joined_table_output_filename)\n elif step_number == 4:\n # Step 4: Split joined table file into 10 subsets randomly.\n split_number = 10\n train_data_joined = 'joined_train_data.csv'\n output_folder = 'train_subsets'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n train_ratio = 0.8\n split_data_random(train_data_joined, split_number, output_folder,\n train_ratio)\n elif step_number == 5:\n # Step 5: Generate features histogram.\n train_file = path.join('train_subsets', '0_train.csv')\n output_folder = 'histograms'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n k = 10\n kfold_cross_validation(k, train_file, ['features_histogram'], output_folder, [])\n elif step_number == 6:\n # Step 6: Run PCA.\n train_file = path.join('train_subsets', '0_train.csv')\n output_folder = 'pca'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n k = 10\n kfold_cross_validation(k, train_file, ['pca'], output_folder, [])\n elif step_number == 7:\n # Step 7: Run univariate_fea_selection fearure selection.\n train_file = path.join('train_subsets', '0_train.csv')\n output_folder = 'univariate_fea_selection'\n k = 10\n C = 10\n number_of_features_to_select = 7\n stats_vals = kfold_cross_validation(k, train_file, ['univariate_fea_selection'],\n output_folder, [number_of_features_to_select, C])\n elif step_number == 8:\n # Step 8: Run linear_SVC fearure selection.\n train_file = path.join('train_subsets', '0_train.csv')\n output_folder = 'linear_SVC'\n k = 10\n sparsity_param = 0.002\n C = 10\n stats_vals = kfold_cross_validation(k, train_file, ['linear_SVC'],\n output_folder, [sparsity_param, C])\n elif step_number == 9:\n # Step 9: Run 10 fold cross validation to choose C for linear SVM with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'linear_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 'linear'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 10:\n # Step 10: Run 10 fold cross validation to choose C for linear SVM with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'linear_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 'linear'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n elif step_number == 11:\n # Step 11: Run 10 fold cross validation to choose C for kernel SVM with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'kernel_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 'rbf'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 12:\n # Step 12: Run 10 fold cross validation to choose C for kernel SVM with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'kernel_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 'rbf'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n elif step_number == 13:\n # Step 13: Run 10 fold cross validation to choose C for one class linear SVM with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'one_class_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 'linear'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 14:\n # Step 14: Run 10 fold cross validation to choose C for one class linear SVM with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'one_class_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 'linear'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n elif step_number == 15:\n # Step 15: Run 10 fold cross validation to choose C for one class kernel SVM with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'one_class_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 'rbf'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 16:\n # Step 16: Run 10 fold cross validation to choose C for one class kernel SVM with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'one_class_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 'rbf'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n elif step_number == 17:\n # Step 17: For submitting on Kaggle, join the Kaggle test data with the other tables.\n transcations_output_filename = 'transactions_preprocessed.csv'\n user_logs_output_filename = 'user_logs_preprocessed.csv'\n train_input_filename = 'sample_submission.csv'\n member_input_filename = 'members.csv'\n joined_table_output_filename = 'joined_test_data.csv'\n join_tables(transcations_output_filename, user_logs_output_filename,\n train_input_filename, member_input_filename,\n joined_table_output_filename)\n elif step_number == 18:\n # Step 18: Classify Kaggle test user, using SVM with C learned form 10 fold cross validation.\n train_file = 'joined_train_data.csv'\n test_file = 'joined_test_data.csv'\n test_file_to_get_users = 'sample_submission.csv'\n methods_to_run = ['univariate_fea_selection', 'preceptron']\n number_of_features_to_select = 14\n C = 10\n kernel = 'linear'\n write_prediction = 1\n prediction_file = 'prediction_naive_MLP_all_10_200.csv'\n options = [number_of_features_to_select, C, kernel, write_prediction, prediction_file]\n classify_test_users(train_file, test_file, methods_to_run, test_file_to_get_users, options)\n elif step_number == 19:\n # Step 19: 10 fold CV for MLP using univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n params_list = [(10,10,10), (10,10,10,10,10), (10,10,10,10,10,10,10,10,10,10),\n (50,50,50), (50,50,50,50,50), (50,50,50,50,50,50,50,50,50,50),\n (100,100,100), (100,100,100,100,100), (100,100,100,100,100,100,100,100,100,100)]\n classification_alg = 'preceptron'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = ''\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\n fea_selection_alg, params_list, k,\n number_of_features_to_select)\n elif step_number == 20:\n # Step 20: 10 fold CV for MLP using linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n params_list = [(10,10,10), (10,10,10,10,10), (10,10,10,10,10,10,10,10,10,10),\n (50,50,50), (50,50,50,50,50), (50,50,50,50,50,50,50,50,50,50),\n (100,100,100), (100,100,100,100,100), (100,100,100,100,100,100,100,100,100,100)]\n classification_alg = 'preceptron'\n fea_selection_alg = 'linear_SVC'\n kernel = ''\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\n fea_selection_alg, params_list, k,\n sparsity_param)\n elif step_number == 21:\n # Step 21: Test different number of samples for different classification algorithms.\n # Parameters are selected based on CV best in precision and recall.\n train_file = path.join('train_subsets', '0_train.csv')\n test_file = path.join('train_subsets', '0_test.csv')\n test_file_to_get_users = 'sample_submission.csv'\n methods_to_run_list = [['univariate_fea_selection', 'linear_svm'], ['univariate_fea_selection', 'kernel_svm'],\n ['univariate_fea_selection', 'naive_bayes'], ['univariate_fea_selection', 'preceptron']]\n write_prediction = 0\n prediction_file = ''\n number_of_features_to_select = 7\n linear_SVM_options = [number_of_features_to_select, 1, 'linear', write_prediction, prediction_file]\n kernel_SVM_options = [number_of_features_to_select, 1, 'rbf', write_prediction, prediction_file]\n naive_bayes_options = [number_of_features_to_select, 0, '', write_prediction, prediction_file]\n preceptron_options = [number_of_features_to_select, (10, 10, 10), '', write_prediction, prediction_file]\n options_list = [linear_SVM_options, kernel_SVM_options, naive_bayes_options, preceptron_options]\n different_number_of_samples = range(2000, 22000, 2000)\n output_folder = 'different_number_of_samples_experiment'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n test_different_number_of_samples(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list,\n different_number_of_samples, output_folder)\n elif step_number == 22:\n # Step 22: Test different number of samples for different classification algorithms.\n # Parameters are selected based on CV best in specificty.\n train_file = path.join('train_subsets', '0_train.csv')\n test_file = path.join('train_subsets', '0_test.csv')\n test_file_to_get_users = 'sample_submission.csv'\n methods_to_run_list = [['univariate_fea_selection', 'linear_svm'], ['univariate_fea_selection', 'kernel_svm'],\n ['univariate_fea_selection', 'naive_bayes'], ['univariate_fea_selection', 'preceptron']]\n write_prediction = 0\n prediction_file = ''\n number_of_features_to_select = 7\n linear_SVM_options = [number_of_features_to_select, 10, 'linear', write_prediction, prediction_file]\n kernel_SVM_options = [number_of_features_to_select, 85, 'rbf', write_prediction, prediction_file]\n naive_bayes_options = [number_of_features_to_select, 0, '', write_prediction, prediction_file]\n preceptron_options = [number_of_features_to_select, (10, 10, 10), '', write_prediction, prediction_file]\n options_list = [linear_SVM_options, kernel_SVM_options, naive_bayes_options, preceptron_options]\n different_number_of_samples = range(2000, 22000, 2000)\n output_folder = 'different_number_of_samples_experiment'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n test_different_number_of_samples(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list,\n different_number_of_samples, output_folder)\n"},{"alpha_fraction":0.6732240319252014,"alphanum_fraction":0.6874316930770874,"avg_line_length":26.727272033691406,"blob_id":"fb66966d8c91262764c0273fbbd85547408d8e9e","content_id":"5c349cec29149905e1a7a037a2b480fed0781e51","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":915,"license_type":"no_license","max_line_length":84,"num_lines":33,"path":"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/svm.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import numpy as np\nfrom sklearn import svm\ndef train_kernel_svm (data, labels, c):\n\tclf = svm.SVC(C = c, random_state=0)\n\tclf.fit(data, labels.ravel())\n\treturn clf\n\ndef train_linear_svm (data, labels, c):\n\tlin_clf = svm.LinearSVC(C = c, random_state=0)\n\tlin_clf.fit(data, labels.ravel())\n\treturn lin_clf\n\ndef train_one_class_svm(data, kernel):\n clf = svm.OneClassSVM(kernel=kernel, random_state=0)\n\tclf.fit(data)\n\treturn clf\ndef classify_one_class_svm(clf, test_data):\n\tpred_confidence = clf.predict(test_data)\n\t\"\"\"\n\tleave label is 1\n\tstay label 0\n\t\"\"\"\n\t\"\"\"\n predicted value\n -1 will leave\n\t1 will stay\n\t\"\"\"\n\tpred = [1 if val == 1 else 0 for val in pred_confidence]\n\treturn pred, pred_confidence\ndef classify (clf, test_data):\n\tpred_confidence = clf.decision_function(test_data)\n\tpred = [clf.classes_[1] if val > 0 else clf.classes_[0] for val in pred_confidence]\n\treturn pred, pred_confidence\n"},{"alpha_fraction":0.6785714030265808,"alphanum_fraction":0.75,"avg_line_length":13,"blob_id":"e7228010dbf60a3effe504b3a32dd3086b90c3fa","content_id":"68982394a75e8a67a5f76c34a1b7edb35cac8a96","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":28,"license_type":"no_license","max_line_length":19,"num_lines":2,"path":"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/run_all.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"from run import run\nrun(18)\n"},{"alpha_fraction":0.7561349868774414,"alphanum_fraction":0.7607361674308777,"avg_line_length":35.27777862548828,"blob_id":"7075d4d24198424d1dbf2965c9d9d8b897887ea2","content_id":"f7d9c7343322d9d0274ecb47c7dd75fd3f455360","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":652,"license_type":"no_license","max_line_length":79,"num_lines":18,"path":"/fea_selection.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"from sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import chi2\nfrom sklearn.svm import LinearSVC\nfrom sklearn.feature_selection import SelectFromModel\n\ndef univariate_fea_selection(X, y, k):\n\tselector = SelectKBest(chi2, k=k)\n\tselector.fit(X, y)\n\tindices = selector.get_support(indices=True)\n\treturn indices\n\ndef my_SelectFromModel(X, y, sparsity_param):\n\tlsvc = LinearSVC(C=sparsity_param, penalty=\"l1\", dual=False).fit(X, y.ravel())\n\tmodel = SelectFromModel(lsvc, prefit=True)\n\t# X_new = model.transform(X)\n\tindex_flag = model.get_support()\n\tindices = [i for i in range(len(index_flag)) if index_flag[i]]\n\treturn indices"},{"alpha_fraction":0.6450524926185608,"alphanum_fraction":0.6611694097518921,"avg_line_length":45,"blob_id":"3b97a2fb02c71cb5f5c7178396af5ae87ab8144a","content_id":"cc61e27966cab67aff1410ae7eff00ae63164bbe","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2668,"license_type":"no_license","max_line_length":140,"num_lines":58,"path":"/runner_wrapper.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import os.path as path\nimport os\nfrom kfold_cross_validation import kfold_cross_validation\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef run(c_val, training_file_path, num_feas, k):\n\t# classification_alg_list = ['linear_svm', 'kernel_svm']\n\tclassification_alg_list = ['one_class_svm']\n\tfea_selection_alg_list = ['univariate_fea_selection', 'linear_SVC']\n\tkernel = 'rbf'\n\tfor i in range(len(classification_alg_list)):\n\t\tfor j in range(len(fea_selection_alg_list)):\n\t\t\tclassification_alg = classification_alg_list[i]\n\t\t\tfea_selection_alg = fea_selection_alg_list[j]\n\t\t\tprint '%s, %s' % (classification_alg, fea_selection_alg)\n\t\t\toutput_folder = '%s/%s/%s_%s' %(classification_alg, kernel,classification_alg, fea_selection_alg)\n\t\t\tif not os.path.exists(output_folder):\n\t\t\t\t\tos.makedirs(output_folder)\n\t\t\toutput_file = open('%s/stats.txt' %(output_folder), 'a')\n\t\t\tlabels = ['Avg Error', 'Var Error', 'Avg Recall', 'Var recall', 'Avg Precision', 'Var Precision', 'Avg Specificity', 'Var Specificity']\n\t\t\toutput_file.write('C\\t')\n\t\t\tfor label in labels:\n\t\t\t\toutput_file.write('%s\\t' %(label))\n\t\t\toutput_file.write('\\n')\n\t\t\t# output_file.write('C\\tAvg Error\\tVar Error\\tAvg Recall\\tVar recall\\tAvg Precision\\tVar Precision\\tAvg Specificity\\tVar Specificity\\n')\n\t\t\tvals_to_plot = []\n\t\t\tfor c in c_val:\n\t\t\t\tprint '\\tC = %f' %(c)\n\t\t\t\tmean, var = kfold_cross_validation(k, training_file_path, [fea_selection_alg, classification_alg], output_folder, [num_feas, c, kernel])\n\t\t\t\toutput_file = open('%s/stats.txt' %(output_folder), 'a')\n\t\t\t\toutput_file.write('%f\\t' %(c))\n\t\t\t\tvals_to_plot.append([])\n\t\t\t\tfor k in range(mean.shape[0]):\n\t\t\t\t\toutput_file.write('%f\\t%f\\t' %(mean[k], var[k]))\n\t\t\t\t\tvals_to_plot[-1].extend([mean[k], var[k]])\n\t\t\t\toutput_file.write('\\n')\n\t\t\t\toutput_file.close()\n\t\t\tplot_lists(c_val, labels, vals_to_plot, '%s/stats.png' %(output_folder), range(len(vals_to_plot)), False)\n\ndef plot_lists(n_vals, labels, vals, output_file, columns_to_plot, set_yticks):\n\t\tmax_y = 0\n\t\tfor column_index in range(len(vals[0])):\n\t\t\tplt.plot(n_vals, list(np.array(vals)[:, column_index]), label=labels[column_index])\n\t\tplt.xlabel('C')\n\t\tplt.legend([labels[i] for i in columns_to_plot], loc=\"center right\")\n\t\tplt.xlim(0, n_vals[-1])\n\t\tif set_yticks:\n\t\t\tplt.yticks(list(np.linspace(0, 5, 21)))\n\t\t\tplt.ylim(0, 5)\n\t\t# plt.title('Trained using %s dataset, with batch size = 10, %d nodes'%(dataset_name, num_nodes)) \n\t\tplt.savefig(output_file)\t\n\t\tplt.clf()\nif __name__ == '__main__':\n\t# c_val = [0.1, 1, 10, 100, 1000]\n\tc_val = [1, 10, 25, 40, 55, 70, 85]\n\ttraining_file_path = path.join('train_subsets', '0_train.csv')\n\trun(c_val, training_file_path, num_feas = 5, k = 10)\n"},{"alpha_fraction":0.4867839515209198,"alphanum_fraction":0.502841055393219,"avg_line_length":70.34684753417969,"blob_id":"29f5ae185adf389ded795e4d9a45778c9ae3e93b","content_id":"c87e108d73eb9fa024d70d85bc7a27859c80c2de","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":47518,"license_type":"no_license","max_line_length":274,"num_lines":666,"path":"/classify_test_users.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import numpy as np\nimport csv\nfrom kfold_cross_validation import run_feature_selection_and_classification\nfrom matplotlib import pyplot\nfrom os import path\nimport os\nimport matplotlib.pyplot as plt\nimport random\n\ndef get_data(data_file):\n csvfile = open(data_file,'rb')\n lines = csv.reader(csvfile, delimiter = ',')\n lines = list(lines)\n header = lines[0]\n # Remove headers from the data.\n del lines[0]\n data = np.array([[float(elem) for elem in line[2:]] for line in lines])\n labels = np.array([[int(elem) for elem in line[1]] for line in lines])\n user_ids = np.array([[elem for elem in line[0]] for line in lines])\n return data, header, labels, user_ids\n\ndef classify_test_users(train_file, test_file, methods_to_run, test_file_to_get_users, options, class_1_weight = 1):\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\n labels = np.concatenate((train_labels, test_labels))\n train_size = len(train_labels)\n test_size = len(test_labels)\n train_index = range(train_size)\n test_index = range(train_size, (train_size + test_size))\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data, test_data, labels,\n train_index, test_index, test_user_ids,\n options, test_header, test_file_to_get_users, class_1_weight = class_1_weight)\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(error, recall, precision, specificity)\n\ndef test_different_thresholds(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list, threshold_list, output_folder, class_1_weight = 1):\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\n train_size = len(train_labels)\n test_size = len(test_labels)\n train_index = range(train_size)\n test_index = range(train_size, train_size + test_size)\n labels = np.concatenate((train_labels, test_labels))\n option_index = 0\n for method_to_run in methods_to_run_list:\n print(method_to_run)\n options = options_list[option_index]\n option_index = option_index + 1\n precisions = np.zeros(len(threshold_list))\n recalls = np.zeros(len(threshold_list))\n specificities = np.zeros(len(threshold_list))\n index = 0\n if len(options) < 6:\n error, recall, precision, specificity = run_feature_selection_and_classification(method_to_run, train_data, test_data, labels, train_index, test_index, test_user_ids, options, test_header, test_file_to_get_users, threshold_list = threshold_list)\n else:\n error, recall, precision, specificity = run_feature_selection_and_classification(method_to_run, train_data_subset, test_data, labels, train_index, test_index, test_user_ids, options, test_header, test_file_to_get_users, options[5], threshold_list = threshold_list)\n precisions[index] = precision\n recalls[index] = recall\n specificities[index] = specificity\n index = index + 1\ndef test_different_number_of_samples(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list,\n different_number_of_samples, output_folder):\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\n option_index = 0\n for methods_to_run in methods_to_run_list:\n print(methods_to_run)\n options = options_list[option_index]\n option_index = option_index + 1\n precisions = np.zeros(len(different_number_of_samples))\n recalls = np.zeros(len(different_number_of_samples))\n specificities = np.zeros(len(different_number_of_samples))\n index = 0\n for samples_number in different_number_of_samples:\n print(samples_number)\n train_data_subset = train_data[:samples_number,]\n train_labels_subset = train_labels[:samples_number,]\n labels = np.concatenate((train_labels_subset, test_labels))\n train_size = len(train_labels_subset)\n test_size = len(test_labels)\n train_index = range(train_size)\n test_index = range(train_size, (train_size + test_size))\n if len(options) < 6:\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data_subset,\n test_data, labels, train_index,\n test_index, test_user_ids, options,\n test_header, test_file_to_get_users)\n else:\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data_subset,\n test_data, labels, train_index,\n test_index, test_user_ids, options,\n test_header, test_file_to_get_users,\n class_1_weight = options[5])\n precisions[index] = precision\n recalls[index] = recall\n specificities[index] = specificity\n index = index + 1\n pyplot.figure()\n pyplot.ylim((-0.01, 1.1))\n pyplot.xlabel('Number of Samples')\n pyplot.plot(different_number_of_samples, precisions, color = 'r')\n pyplot.plot(different_number_of_samples, recalls, color = 'b')\n pyplot.plot(different_number_of_samples, specificities, color = 'g')\n if len(options) < 6:\n pyplot.legend([methods_to_run[1] + ' ' + options[2] + ' Precision',\n methods_to_run[1] + ' ' + options[2]+ ' Recall',\n methods_to_run[1] + ' ' + options[2] + ' Specificity'])\n pyplot.savefig(path.join(output_folder, 'different_samples_'+options[2]+'_'+methods_to_run[1]+'.png'))\n else:\n pyplot.legend([methods_to_run[1] + ' ' + options[2] + ' With Class Weight Precision',\n methods_to_run[1] + ' ' + options[2]+ ' With Class Weight Recall',\n methods_to_run[1] + ' ' + options[2] + ' With Class Weight Specificity'])\n pyplot.savefig(path.join(output_folder, 'different_samples_'+options[2]+'_'+methods_to_run[1]+'_with_class_weights.png')) \n pyplot.close()\n\n\ndef test_different_subsets(train_folder, test_file, methods_to_run_list, test_file_to_get_users,\n options_list, output_folder, train_files_count):\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\n option_index = 0\n for methods_to_run in methods_to_run_list:\n print(methods_to_run)\n options = options_list[option_index]\n option_index = option_index + 1\n precisions = np.zeros(train_files_count)\n recalls = np.zeros(train_files_count)\n specificities = np.zeros(train_files_count)\n index = 0\n for file_in_train_folder in os.listdir(train_folder):\n if 'train' in file_in_train_folder: # As the folder contain train and test files.\n print(file_in_train_folder)\n train_data, train_header, train_labels, train_user_ids = get_data(path.join(train_folder,\n file_in_train_folder))\n labels = np.concatenate((train_labels, test_labels))\n train_size = len(train_labels)\n test_size = len(test_labels)\n train_index = range(train_size)\n test_index = range(train_size, (train_size + test_size))\n if len(options) < 6:\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data,\n test_data, labels, train_index,\n test_index, test_user_ids, options,\n test_header, test_file_to_get_users)\n else:\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data,\n test_data, labels, train_index,\n test_index, test_user_ids, options,\n test_header, test_file_to_get_users,\n class_1_weight = options[5])\n precisions[index] = precision\n recalls[index] = recall\n specificities[index] = specificity\n index = index + 1\n pyplot.figure()\n pyplot.ylim((-0.01, 1.1))\n pyplot.xlabel('Train Subset Number')\n index = np.arange(train_files_count)\n bar_width = 0.2\n pyplot.bar(index, precisions, bar_width, color = 'r')\n pyplot.bar(index + bar_width, recalls, bar_width, color = 'b')\n pyplot.bar(index - bar_width, specificities, bar_width, color = 'g')\n pyplot.xticks(index + bar_width, tuple([str(i) for i in range(train_files_count)]))\n\n if len(options) < 6:\n pyplot.legend([methods_to_run[1] + ' ' + options[2] + ' Precision',\n methods_to_run[1] + ' ' + options[2]+ ' Recall',\n methods_to_run[1] + ' ' + options[2] + ' Specificity'])\n pyplot.savefig(path.join(output_folder, 'different_subsets_'+options[2]+'_'+methods_to_run[1]+'.png'))\n else:\n pyplot.legend([methods_to_run[1] + ' ' + options[2] + ' With Class Weight Precision',\n methods_to_run[1] + ' ' + options[2]+ ' With Class Weight Recall',\n methods_to_run[1] + ' ' + options[2] + ' With Class Weight Specificity'])\n pyplot.savefig(path.join(output_folder, 'different_subsets_'+options[2]+'_'+methods_to_run[1]+'_with_class_weights.png')) \n pyplot.close()\n\ndef plot_roc(fea_selection, classification, file_name, specificity, sensitivity):\n specificity_index = sorted(enumerate(specificity), key = lambda tup : tup[1])\n specificity = [tup[1] for tup in specificity_index]\n sensitivity = [sensitivity[tup[0]] for tup in specificity_index]\n auc = np.trapz(sensitivity, x=specificity)\n plt.xlabel('Specificity')\n plt.ylabel('Sensitivity')\n plt.plot(specificity,sensitivity)\n return auc\n\ndef generate_ROC(train_file, test_file, threshold_list = -1):\n random.seed(1)\n roc_prefix = 'ROC'\n if not os.path.exists(roc_prefix):\n os.makedirs(roc_prefix)\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\n train_test_labels = np.concatenate((train_labels, test_labels))\n train_train_labels = np.concatenate((train_labels, train_labels))\n train_size = len(train_labels)\n test_size = len(test_labels)\n train_index = range(train_size)\n test_index = range(train_size, (train_size + test_size))\n train_test_index = range(train_size, (train_size + train_size))\n \n legend = []\n\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'preceptron'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, tuple([50] * 10), '', 0],\n test_header, '', threshold_list = threshold_list)\n fea_selection_method = 'L$_1$-based fea selection'\n classification_method = 'Perceptron'\n legend += ['%s' %(classification_method)]\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\n print '>>linear_SVC, preceptron', auc\n # print '\\t', 'test_error', test_error\n # print '\\t', 'recall', recall\n # print '\\t', 'precision', precision\n # print '\\t', 'specificity', specificity\n\n c = 1\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'one_class_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, c, 'linear', 0, ''],\n test_header, '', threshold_list = threshold_list)\n fea_selection_method = 'L$_1$-based fea selection'\n classification_method = 'One-Class Linear SVM'\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\n print '>>linear_SVC, one class linear_svm, %d' %(c), auc\n # print '\\t', 'test_error', test_error\n # print '\\t', 'recall', recall\n # print '\\t', 'precision', precision\n # print '\\t', 'specificity', specificity\n legend += ['%s' %(classification_method)]\n c = 85\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'one_class_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, c, 'rbf', 0, ''],\n test_header, '', threshold_list = threshold_list)\n fea_selection_method = 'L$_1$-based fea selection'\n classification_method = 'One-Class RBF SVM'\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\n\n print '>>linear_SVC, one class rbf_svm, %d' %(c),auc\n # print '\\t', 'test_error', test_error\n # print '\\t', 'recall', recall\n # print '\\t', 'precision', precision\n # print '\\t', 'specificity', specificity\n legend += ['%s' %(classification_method)]\n#-----------------------\n class_1_weight = 1\n c = 55\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, c, 'linear', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n fea_selection_method = 'L$_1$-based fea selection'\n classification_method = 'Linear SVM'\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c, class_1_weight), auc\n # print '\\t', 'test_error', test_error\n # print '\\t', 'recall', recall\n # print '\\t', 'precision', precision\n # print '\\t', 'specificity', specificity\n legend += ['%s' %(classification_method)]\n\n c = 70\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, c, 'rbf', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n fea_selection_method = 'Chi-squared fea selection'\n classification_method = 'RBF SVM'\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\n\n print '>>univariate_fea_selection, RBF_svm, %d. class_1_weight = %d' %(c, class_1_weight), auc\n # print '\\t', 'test_error', test_error\n # print '\\t', 'recall', recall\n # print '\\t', 'precision', precision\n # print '\\t', 'specificity', specificity\n legend += ['%s' %(classification_method)]\n\n c = 85\n class_1_weight = 10\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, c, 'linear', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n\n fea_selection_method = 'L$_1$-based fea selection'\n classification_method = 'Linear SVM, class weights'\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s_%d_class_1_weight.png' %(roc_prefix, fea_selection_method, classification_method, class_1_weight), specificity, recall)\n\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c, class_1_weight), auc\n # print '\\t', 'test_error', test_error\n # print '\\t', 'recall', recall\n # print '\\t', 'precision', precision\n # print '\\t', 'specificity', specificity\n legend += ['%s' %(classification_method)]\n\n c = 1\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, c, 'rbf', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n \n fea_selection_method = 'Chi-squared fea selection'\n classification_method = 'RBF SVM, class weights'\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s_%d_class_1_weight.png' %(roc_prefix, fea_selection_method, classification_method, class_1_weight), specificity, recall)\n\n print '>>univariate_fea_selection, RBF_svm, %d. class_1_weight = %d' %(c, class_1_weight), auc\n # print '\\t', 'test_error', test_error\n # print '\\t', 'recall', recall\n # print '\\t', 'precision', precision\n # print '\\t', 'specificity', specificity\n legend += ['%s' %(classification_method)]\n\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'naive_bayes'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, 0, '', 0, ''],\n test_header, '', threshold_list = threshold_list)\n \n fea_selection_method = 'Chi-squared fea selection'\n classification_method = 'Naive Bayes'\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\n\n print '>>univariate_fea_selection, Naive Bayes', auc\n # print '\\t', 'test_error', test_error\n # print '\\t', 'recall', recall\n # print '\\t', 'precision', precision\n # print '\\t', 'specificity', specificity\n legend += ['%s' %(classification_method)]\n\n\n plt.plot([0, 1],[1, 0], '--')\n legend += ['Random']\n plt.legend(legend)\n plt.savefig('%s/roc.png' % (roc_prefix))\n\ndef generate_test_error_for_kernels(train_file, test_file, threshold_list = -1):\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\n train_test_labels = np.concatenate((train_labels, test_labels))\n train_train_labels = np.concatenate((train_labels, train_labels))\n train_size = len(train_labels)\n test_size = len(test_labels)\n train_index = range(train_size)\n test_index = range(train_size, (train_size + test_size))\n train_test_index = range(train_size, (train_size + train_size))\n \n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'preceptron'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, tuple([50] * 10), '', 0],\n test_header, '', threshold_list = threshold_list)\n print '>>linear_SVC, preceptron'\n print '\\t', 'test_error', test_error\n\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, tuple([50] * 5), '', 0],\n test_header, '', threshold_list = threshold_list)\n print 'univariate_fea_selection, preceptron'\n print '\\t', 'test_error', test_error\n\n c_list = [(55, 55), (1, 1)]\n for c in c_list:\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'one_class_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, c[0], 'linear', 0, ''],\n test_header, '', threshold_list = threshold_list)\n print '>>univariate_fea_selection, one class linear_svm, %d' %(c[0])\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n \n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'one_class_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, c[1], 'linear', 0, ''],\n test_header, '', threshold_list = threshold_list)\n print '>>linear_SVC, one class linear_svm, %d' %(c[1])\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n \n c_list = [(70, 70), (85, 85)]\n for c in c_list:\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'one_class_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, c[0], 'rbf', 0, ''],\n test_header, '', threshold_list = threshold_list)\n print '>>univariate_fea_selection, one class rbf_svm, %d' %(c[0])\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n \n\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'one_class_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, c[1], 'rbf', 0, ''],\n test_header, '', threshold_list = threshold_list)\n print '>>linear_SVC, one class rbf_svm, %d' %(c[1])\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n\n#-----------------------\n\n c_list = [(25, 85), (10, 55)]\n class_1_weight = 1\n for c in c_list:\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'linear_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, c[0], 'linear', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n print '>>univariate_fea_selection, linear_svm, %d. class_1_weight = %d' %(c[0], class_1_weight)\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n\n\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, c[1], 'linear', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c[1], class_1_weight)\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n\n\n c_list = [(1, 1), (70, 55)]\n for c in c_list:\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, c[0], 'rbf', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n print '>>univariate_fea_selection, linear_svm, %d. class_1_weight = %d' %(c[0], class_1_weight)\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'kernel_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, c[1], 'rbf', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c[1], class_1_weight)\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n\n c_list = [(10, 25), (10, 85)]\n class_1_weight = 10\n for c in c_list:\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'linear_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, c[0], 'linear', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n print '>>univariate_fea_selection, linear_svm, %d. class_1_weight = %d' %(c[0], class_1_weight)\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, c[1], 'linear', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c[1], class_1_weight)\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n\n\n c_list = [(40, 55), (1, 1)]\n for c in c_list:\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, c[0], 'rbf', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n print '>>univariate_fea_selection, linear_svm, %d. class_1_weight = %d' %(c[0], class_1_weight)\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'kernel_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, c[1], 'rbf', 0, ''],\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c[1], class_1_weight)\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'naive_bayes'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, 0, '', 0, ''],\n test_header, '', threshold_list = threshold_list)\n\n print '>>univariate_fea_selection, naive_bayes'\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'naive_bayes'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, 0, '', 0, ''],\n test_header, '', threshold_list = threshold_list)\n\n print '>>linear_SVC, naive_bayes'\n print 'Error: %0.3f, Recall: %0.3f\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\n\ndef generate_train_test_error_for_different_kernels(train_file, test_file, output_folder):\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\n train_test_labels = np.concatenate((train_labels, test_labels))\n train_train_labels = np.concatenate((train_labels, train_labels))\n train_size = len(train_labels)\n test_size = len(test_labels)\n train_index = range(train_size)\n test_index = range(train_size, (train_size + test_size))\n train_test_index = range(train_size, (train_size + train_size))\n # Run linear SVM.\n train_error_1inear = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [0.002, 55, 'linear', 0, ''], train_header, '')[0]\n test_error_linear = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, 55, 'linear', 0, ''],\n test_header, '')[0]\n\n # Run poly SVM with degree 2.\n train_error_poly_2 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [0.002, 40, 2, 0, ''], train_header, '')[0]\n test_error_poly_2 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, 40, 2, 0, ''],\n test_header, '')[0]\n # Run poly SVM with degree 3.\n train_error_poly_3 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [0.002, 70, 3, 0, ''], train_header, '')[0]\n test_error_poly_3 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, 70, 3, 0, ''],\n test_header, '')[0]\n # Run poly SVM with degree 4.\n train_error_poly_4 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [0.002, 55, 4, 0, ''], train_header, '')[0]\n test_error_poly_4 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, 55, 4, 0, ''],\n test_header, '')[0]\n\n # Run poly SVM with degree 10.\n train_error_poly_10 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [0.002, 1, 10, 0, ''], train_header, '')[0]\n test_error_poly_10 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, 1, 10, 0, ''],\n test_header, '')[0]\n \n # Run RBF Kernel SVM.\n train_error_rbf_kernel = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [7, 70, 'rbf', 0, ''], train_header, '')[0]\n test_error_rbf_kernel = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, 70, 'rbf', 0, ''],\n test_header, '')[0]\n pyplot.figure()\n pyplot.xlabel('Different Kernels')\n index = [1, 2, 3, 4, 5, 6]\n pyplot.xticks(index, ['Linear', 'Poly Degree 2', 'Poly Degree 3',\n 'Poly Degree 4', 'Poly Degree 10', 'RBF'], fontsize=7)\n pyplot.plot(index, [train_error_1inear, train_error_poly_2, train_error_poly_3,\n train_error_poly_4, train_error_poly_10, train_error_rbf_kernel], color = 'r')\n pyplot.plot(index, [test_error_linear, test_error_poly_2, test_error_poly_3,\n test_error_poly_4, test_error_poly_10, test_error_rbf_kernel], color = 'b')\n pyplot.legend(['Training Error', 'Testing Error'])\n pyplot.savefig(path.join(output_folder, 'SVM_with_different_kernels.png')) \n pyplot.close()\n\ndef generate_train_test_error_for_different_MLP(train_file, test_file, output_folder):\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\n train_test_labels = np.concatenate((train_labels, test_labels))\n train_train_labels = np.concatenate((train_labels, train_labels))\n train_size = len(train_labels)\n test_size = len(test_labels)\n train_index = range(train_size)\n test_index = range(train_size, (train_size + test_size))\n train_test_index = range(train_size, (train_size + train_size))\n # Run MLP with 3 layer.\n train_error_3 = run_feature_selection_and_classification(['linear_SVC', 'preceptron'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [0.002, tuple([50] * 3), '', 0], train_header, '')[0]\n test_error_3 = run_feature_selection_and_classification(['linear_SVC', 'preceptron'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [0.002, tuple([50] * 3), '', 0],\n test_header, '')[0]\n\n # Run MLP with 5 layer.\n train_error_5 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [7, tuple([50] * 5), '', 0], train_header, '')[0]\n test_error_5 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, tuple([50] * 5), '', 0],\n test_header, '')[0]\n # Run MLP with 10 layer.\n train_error_10 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [7, tuple([50] * 10), '', 0], train_header, '')[0]\n test_error_10 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, tuple([50] * 10), '', 0],\n test_header, '')[0]\n # Run MLP with 20 layer.\n train_error_13 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [7, tuple([50] * 13), '', 0], train_header, '')[0]\n test_error_13 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, tuple([50] * 13), '', 0],\n test_header, '')[0]\n\n # Run MLP with 50 layer.\n train_error_15 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [7, tuple([50] * 15), '', 0], train_header, '')[0]\n test_error_15 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, tuple([50] * 15), '', 0],\n test_header, '')[0]\n \n # Run MLP with 100 layer.\n train_error_20 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\n train_data, train_data, train_train_labels, train_index,\n train_test_index, train_user_ids,\n [7, tuple([50] * 20), '', 0], train_header, '')[0]\n test_error_20 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\n train_data, test_data, train_test_labels, train_index,\n test_index, test_user_ids, [7, tuple([50] * 20), '', 0],\n test_header, '')[0]\n pyplot.figure()\n pyplot.xlabel('Different Number of Layers')\n index = [1, 2, 3, 4, 5, 6]\n pyplot.xticks(index, ['3', '5', '10', '13', '15', '20'], fontsize=7)\n pyplot.plot(index, [train_error_3, train_error_5, train_error_10,\n train_error_13, train_error_15, train_error_20], color = 'r')\n pyplot.plot(index, [test_error_3, test_error_5, test_error_10,\n test_error_13, test_error_15, test_error_20], color = 'b')\n pyplot.legend(['Training Error', 'Testing Error'])\n pyplot.savefig(path.join(output_folder, 'MLP_with_different_layers.png')) \n pyplot.close()\n\n \n \n\nif __name__ == '__main__':\n # train_file = 'train_subsets/0_train.csv'\n # test_file = 'test_subsets/0_test.csv'\n \n # number_of_features_to_select = 7\n \n # # methods_to_run = ['linear_SVC', 'kernel_svm']\n # # C = 40\n # # class_1_weight = 10\n # # kernel = 'rbf'\n\n # # write_prediction = 0\n # # prediction_file = ''\n # # sparsity_param = 0.002\n # # options = [sparsity_param, C, kernel, write_prediction, prediction_file]\n\n # write_prediction = 0\n # prediction_file = ''\n # methods_to_run = ['linear_SVC', 'preceptron']\n # options = [number_of_features_to_select, [100] * 10, '', write_prediction, prediction_file]\n # classify_test_users(train_file, test_file, methods_to_run, '', options)\n\n train_file = path.join('train_subsets', '0_train.csv')\n test_file = path.join('train_subsets', '0_test.csv')\n generate_test_error_for_kernels(train_file, test_file)\n\n"},{"alpha_fraction":0.6206896305084229,"alphanum_fraction":0.6448276042938232,"avg_line_length":40.39285659790039,"blob_id":"b1595fe0a89712403a23a9558f4c8c2bc2ca9e45","content_id":"58f08d018dcd540780d273b9a6379d20f2a401eb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1160,"license_type":"no_license","max_line_length":71,"num_lines":28,"path":"/generate_histograms.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import csv\nimport numpy as np\nfrom matplotlib import pyplot\nimport os.path as path\ndef generate_histograms(train_file, output_folder):\n train_csvfile = open(train_file,'rb')\n lines = csv.reader(train_csvfile, delimiter = ',')\n lines = list(lines)\n # Remove headers.\n del lines[0]\n data = np.array(lines)\n # Split data into positive class and neg class.\n data_class1 = data[data[:,1] == '0']\n data_class2 = data[data[:,1] == '1']\n features_number = len(data[0])\n for feature in range(features_number):\n if feature == 0 or feature == 1:\n # Skip user id and class label.\n continue\n feature_column_class1 = data_class1[:,feature]\n feature_column_class2 = data_class2[:,feature]\n feature_column_class1 = feature_column_class1.astype(np.float)\n feature_column_class2 = feature_column_class2.astype(np.float)\n pyplot.figure()\n pyplot.hist(feature_column_class1, 100, label='Class 0')\n pyplot.hist(feature_column_class2, 100, label='Class 1')\n pyplot.legend(loc='upper right')\n pyplot.savefig(path.join(output_folder, str(feature) + '.png'))\n\n"},{"alpha_fraction":0.580386757850647,"alphanum_fraction":0.5872007608413696,"avg_line_length":54.12690353393555,"blob_id":"8e4d11bfae4b4bc4b5bdeb74cce5d5bfd74e0c69","content_id":"a0782e81fcd1137a6e274ec98508059e400fe2ec","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":10860,"license_type":"no_license","max_line_length":128,"num_lines":197,"path":"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/kfold_cross_validation.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import math\nimport csv\nfrom my_PCA import MyPCA\nimport numpy as np\nfrom matplotlib import pyplot\nimport os.path as path\nfrom sklearn.naive_bayes import GaussianNB\nfrom calculate_performance_measures import calculate_performance_measures\nfrom sklearn import mixture\nfrom svm import *\nfrom fea_selection import *\nimport math\nfrom sklearn.neural_network import MLPClassifier\n\n\ndef write_predictions(validation_users_id, predicted_labels, options, test_file_to_get_users):\n write_prediction = options[3]\n if write_prediction == 1:\n test_csvfile = open(test_file_to_get_users,'rb')\n lines = csv.reader(test_csvfile, delimiter = ',')\n test_users = []\n for line in lines:\n test_users.append(line[0])\n prediction_filename = options[4]\n users_number = len(validation_users_id)\n validation_users_dict = {}\n index = 0\n for user in range(users_number):\n user_id = validation_users_id[user]\n validation_users_dict[''.join(user_id)] = index\n index = index + 1\n prediction_file = open(prediction_filename, 'w')\n prediction_file.write('msno,is_churn\\n')\n for user in test_users:\n if user in validation_users_dict:\n #normalized_prediction = 1.0/(1.0+ math.e **( -1 * predicted_labels[validation_users_dict[user]]))\n prediction_file.write(user+','+str(predicted_labels[validation_users_dict[user]][1])+'\\n')\n\t else:\n prediction_file.write(user+',0.03\\n')\n prediction_file.close()\n\ndef run_feature_selection_and_classification(methods_to_run, train_data, validation_data, labels,\n train_index, validation_index, validation_users_id,\n options, header, test_file_to_get_users):\n\tif 'univariate_fea_selection' in methods_to_run:\n\t\t# Run feature selection.\n\t\tnumber_of_features_to_select = options[0]\n\t\tselected_features_indices = univariate_fea_selection(train_data, labels[train_index],\n number_of_features_to_select)\n\t\ttrain_data = train_data[:, selected_features_indices]\n\t\tvalidation_data = validation_data[:, selected_features_indices]\n\t\tselected_featuees = np.array(header)[np.array(selected_features_indices)+2]\n\t\tfor selected_feature in range(len(selected_featuees)):\n\t\t\tprint(selected_featuees[selected_feature])\n\tif 'linear_SVC' in methods_to_run:\n\t\t# Run feature selection.\n\t\tsparsity_param = options[0]\n\t\tselected_features_indices = my_SelectFromModel(train_data, labels[train_index],\n sparsity_param)\n\t\ttrain_data = train_data[:, selected_features_indices]\n\t\tvalidation_data = validation_data[:, selected_features_indices]\n\t\tselected_featuees = np.array(header)[np.array(selected_features_indices)+2]\n\t\tfor selected_feature in range(len(selected_featuees)):\n\t\t\tprint(selected_featuees[selected_feature])\n\tif 'linear_svm' in methods_to_run: # Run linear SVM.\n\t\tC = options[1]\n\t\tlinear_svm_model = train_linear_svm(train_data, labels[train_index], C)\n\t\tpredicted_labels, predicted_values = classify(linear_svm_model, validation_data)\n\t\twrite_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n\t\terror, recall, precision, specificity = calculate_performance_measures(predicted_labels,\n labels[validation_index])\n return error, recall, precision, specificity\n\tif 'kernel_svm' in methods_to_run: # Run Kernel SVM.\n\t\tC = options[1]\n\t\tkernel_svm_model = train_kernel_svm(train_data, labels[train_index], C)\n\t\tpredicted_labels, predicted_values = classify(kernel_svm_model, validation_data)\n\t\twrite_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n\t\terror, recall, precision, specificity = calculate_performance_measures(predicted_labels,\n labels[validation_index])\n return error, recall, precision, specificity\n\tif 'one_class_svm' in methods_to_run:\n\t\ttemp = np.array([label[0] for label in labels[train_index]]) == 0\n\t\ttrain_data = train_data[temp, :]\n\t\tone_class_svm_model = train_one_class_svm(train_data, options[2])\n\t\tpredicted_labels, predicted_values = classify_one_class_svm(one_class_svm_model, train_data)\n\t\terror, recall, precision, specificity = calculate_performance_measures(predicted_labels,\n labels[validation_index])\n\t\twrite_predictions(validation_users_id, predicted_values, options, test_file_to_get_users) \n return error, recall, precision, specificity\n\tif 'preceptron' in methods_to_run: # Run multilayer preceptron.\n\t\tclf = MLPClassifier(hidden_layer_sizes = options[1], verbose=True)\n\t\tclf.fit(train_data, labels[train_index])\n\t\tpredicted_labels = clf.predict(validation_data)\n\t\terror, recall, precision, specificity = calculate_performance_measures(predicted_labels,\n labels[validation_index])\n predicted_values = clf.predict_proba(validation_data)\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n return error, recall, precision, specificity \n\tif 'naive_bayes' in methods_to_run:\n clf = GaussianNB()\n clf.fit(train_data, labels[train_index])\n predicted_labels = clf.predict(validation_data)\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels,\n labels[validation_index])\n predicted_values = clf.predict_proba(validation_data)\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n return error, recall, precision, specificity\n if 'guassian' in methods_to_run:\n clf = mixture.GaussianMixture(n_components=2,\n covariance_type='full')\n clf.fit(train_data, labels[train_index])\n predicted_labels = clf.predict(validation_data)\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels,\n labels[validation_index])\n predicted_values = clf.predict_proba(validation_data)\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n return error, recall, precision, specificity\n\treturn 0, 0, 0, 0\n\nfrom scipy import stats\nimport scipy as sp\ndef kfold_cross_validation(k, train_file, methods_to_run,\n output_folder, options):\n train_csvfile = open(train_file,'rb')\n lines = csv.reader(train_csvfile, delimiter = ',')\n lines = list(lines)\n header = lines[0]\n # Remove headers from the data.\n del lines[0]\n samples_number = len(lines)\n # Transform it to matrix.\n # Skip the user id and the label columns.\n data = np.array([[float(elem) for elem in line[2:]] for line in lines])\n labels = np.array([[int(elem) for elem in line[1]] for line in lines])\n user_ids = np.array([[elem for elem in line[0]] for line in lines])\n features_number = len(data[0])\n measures = np.zeros([4, k])\n for i in range(k):\n start_i = int(math.floor(samples_number * i / k))\n end_i = int(math.floor(samples_number * (i + 1) / k))\n validation_index = range(start_i, end_i)\n points_index = range(samples_number)\n train_index = list(set(points_index).difference(set(validation_index)));\n train_labels = labels[train_index, :]\n zero_indices = [j for j in range(len(train_labels)) if train_labels[j] == 0]\n one_indices = [j for j in range(len(train_labels)) if train_labels[j] == 1]\n train_data = data[train_index, :]\n validation_data = data[validation_index, :]\n if 'features_histogram' in methods_to_run:\n # Caclulate features histogram for each fold training data.\n for feature in range(features_number):\n train_data = data[train_index, feature]\n feature_column_class0 = train_data[zero_indices]\n feature_column_class1 = train_data[one_indices]\n pyplot.figure()\n pyplot.hist(feature_column_class0, 100, label='Class 0')\n pyplot.hist(feature_column_class1, 100, label='Class 1')\n pyplot.legend(loc='upper right')\n pyplot.savefig(path.join(output_folder, 'hist_fold=' + str(i) +\n '_' + header[feature+2] + '.png'))\n pyplot.close()\n if 'pca' in methods_to_run:\n # Run PCA.\n myPca = MyPCA()\n myPca.fit(train_data, n_components = 2)\n # Visualize the training data.\n projected_data = myPca.transform(train_data)\n pyplot.figure()\n pyplot.plot(projected_data[zero_indices, 0], projected_data[zero_indices, 1],\n 'o', color = 'r')\n pyplot.plot(projected_data[one_indices, 0], projected_data[one_indices, 1],\n 'x', color = 'b')\n pyplot.savefig(path.join(output_folder, 'pca_fold=' +\n str(i) + '.png'))\n pyplot.close()\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run,\n train_data, validation_data,\n labels, train_index, validation_index,\n user_ids[validation_index, :], options,\n header, '')\n measures[0, i] = error\n\tmeasures[1, i] = recall\n\tmeasures[2, i] = precision\n\tmeasures[3, i] = specificity\n stats_vals = []\n alpha_val = 0.95\n for i in range(4):\n mean, error = mean_confidence_interval(measures[i, :], alpha_val)\n stats_vals.append([mean, error])\n return stats_vals\n\ndef mean_confidence_interval(data, confidence=0.95):\n a = 1.0*np.array(data)\n n = len(a)\n m, se = np.mean(a), sp.stats.sem(a)\n h = se * sp.stats.t._ppf((1+confidence)/2., n-1)\n return m, h\n"},{"alpha_fraction":0.5353260636329651,"alphanum_fraction":0.55027174949646,"avg_line_length":35.849998474121094,"blob_id":"c65d587e2a399fe058298c28b08b16c5c407106d","content_id":"9bdd5f9031732c5287869d9508b1fad8cb0874f0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":736,"license_type":"no_license","max_line_length":84,"num_lines":20,"path":"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/plot_cross_validation_SVM.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef plot_cross_validation_SVM(k, n_vals, labels, mean_vals, error_vals, output_file,\n columns_to_plot, set_yticks):\n max_y = 0\n plt.clf()\n for i in range(mean_vals.shape[1]):\n plt.clf()\n plt.errorbar(n_vals, mean_vals[:, i], yerr = error_vals[:, i])\n plt.xlabel('C')\n # plt.legend([labels[i] for i in range(mean_vals.shape[1])])\n plt.ylabel(labels[i])\n plt.title(labels[i])\n # plt.legend(labels[i])\n # plt.xlim(n_vals[0], n_vals[-1])\n plt.savefig('%s_%s.png' %(output_file, labels[i]))\n if set_yticks:\n plt.yticks(list(np.linspace(0, 5, 21)))\n plt.ylim(0, 5)"},{"alpha_fraction":0.6140651702880859,"alphanum_fraction":0.6317895650863647,"avg_line_length":37.0217399597168,"blob_id":"ee81aed61df01760c9e65eed617d8e5ff2a354ee","content_id":"a957802604f98e13cfd70bdb0d5e7c2dd5728523","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1749,"license_type":"no_license","max_line_length":133,"num_lines":46,"path":"/svm.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import numpy as np\nfrom sklearn import svm\nfrom helper import sigmoid\n\ndef train_kernel_svm (data, labels, c, class_1_weight = 1):\n clf = svm.SVC(C = c, class_weight = {1: class_1_weight}, random_state=0)\n clf.fit(data, labels.ravel())\n return clf\n\ndef train_poly_kernel_svm (data, degree, labels, c, class_1_weight = 1):\n print('in poly svm, training data = '+str(data.shape))\n clf = svm.SVC(C = c, kernel = 'poly', degree = degree, random_state=0, verbose = 2)\n clf.fit(data, labels.ravel())\n return clf\n\ndef train_linear_svm (data, labels, c, class_1_weight = 1):\n lin_clf = svm.LinearSVC(C = c, class_weight = {1: class_1_weight}, random_state=0)\n lin_clf.fit(data, labels.ravel())\n return lin_clf\n\ndef train_one_class_svm(data, kernel):\n clf = svm.OneClassSVM(kernel=kernel, random_state=0)\n clf.fit(data)\n return clf\ndef classify_one_class_svm(clf, test_data, threshold = -1):\n if threshold == -1:\n pred_confidence = clf.predict(test_data)\n \"\"\"\n leave label is 1\n stay label 0\n \"\"\"\n \"\"\"\n predicted value\n -1 will leave\n 1 will stay\n \"\"\"\n pred = [1 if val == 1 else 0 for val in pred_confidence]\n return pred, pred_confidence\n else:\n pred_confidence = clf.decision_function(test_data)\n pred = [1 if (val if threshold == 0 else sigmoid(val)) > threshold else 0 for val in pred_confidence]\n return pred, pred_confidence\ndef classify (clf, test_data, threshold = 0):\n pred_confidence = clf.decision_function(test_data)\n pred = [clf.classes_[1] if (val if threshold == 0 else sigmoid(val)) > threshold else clf.classes_[0] for val in pred_confidence]\n return pred, pred_confidence\n"},{"alpha_fraction":0.7257142663002014,"alphanum_fraction":0.7295238375663757,"avg_line_length":28.22222137451172,"blob_id":"a320645ca3053dcf9c6bad6d242341e2d7a865d7","content_id":"9b799c47fcbdee64f2b010674cae31c00c677f09","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":525,"license_type":"no_license","max_line_length":79,"num_lines":18,"path":"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/my_PCA.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"from sklearn.decomposition import PCA, KernelPCA\nimport numpy as np\nclass MyPCA(object):\n\tdef fit(self, data, n_components):\n\t\tself.pca = PCA(n_components=n_components)\n\t\tself.pca.fit(data)\n\n\tdef transform(self, data):\n\t\treturn self.pca.transform(data)\n\n\nclass MyKernelPCA(object):\n\tdef fit(self, data, n_components):\n\t\t#self.pca = PCA(n_components=n_components)\n\t\tself.pca = KernelPCA(n_components = n_components, kernel = 'rbf', gamma = 10)\n\t\tself.pca.fit(data)\n\tdef transform(self, data):\n\t\treturn self.pca.transform(data)"},{"alpha_fraction":0.6037047505378723,"alphanum_fraction":0.6070727109909058,"avg_line_length":52.90909194946289,"blob_id":"ad0455d12c91881175b84bfb440df609cc84dced","content_id":"96cf053812d46959ae0605b8b6b298fc0e49e989","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3563,"license_type":"no_license","max_line_length":146,"num_lines":66,"path":"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/classify_test_users.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import numpy as np\nimport csv\nfrom kfold_cross_validation import run_feature_selection_and_classification\nfrom matplotlib import pyplot\nfrom os import path\n\ndef get_data(data_file):\n csvfile = open(data_file,'rb')\n lines = csv.reader(csvfile, delimiter = ',')\n lines = list(lines)\n header = lines[0]\n # Remove headers from the data.\n del lines[0]\n data = np.array([[float(elem) for elem in line[2:]] for line in lines])\n labels = np.array([[int(elem) for elem in line[1]] for line in lines])\n user_ids = np.array([[elem for elem in line[0]] for line in lines])\n return data, header, labels, user_ids\n\ndef classify_test_users(train_file, test_file, methods_to_run, test_file_to_get_users, options):\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\n labels = np.concatenate((train_labels, test_labels))\n train_size = len(train_labels)\n test_size = len(test_labels)\n train_index = range(train_size)\n test_index = range(train_size, (train_size + test_size))\n run_feature_selection_and_classification(methods_to_run, train_data, test_data, labels,\n train_index, test_index, test_user_ids,\n options, test_header, test_file_to_get_users)\n\ndef test_different_number_of_samples(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list,\n different_number_of_samples, output_folder):\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\n option_index = 0\n for methods_to_run in methods_to_run_list:\n print(methods_to_run)\n options = options_list[option_index]\n option_index = option_index + 1\n precisions = np.zeros(len(different_number_of_samples))\n recalls = np.zeros(len(different_number_of_samples))\n index = 0\n for samples_number in different_number_of_samples:\n print(samples_number)\n train_data_subset = train_data[:samples_number,]\n train_labels_subset = train_labels[:samples_number,]\n labels = np.concatenate((train_labels_subset, test_labels))\n train_size = len(train_labels_subset)\n test_size = len(test_labels)\n train_index = range(train_size)\n test_index = range(train_size, (train_size + test_size))\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data_subset, test_data, labels,\n train_index, test_index, test_user_ids,\n options, test_header, test_file_to_get_users)\n precisions[index] = precision\n recalls[index] = recall\n index = index + 1\n print(precision)\n print(recall)\n pyplot.figure()\n pyplot.xlabel('Number of Samples')\n pyplot.plot(different_number_of_samples, precisions, color = 'r')\n pyplot.plot(different_number_of_samples, recalls, color = 'b')\n pyplot.legend([methods_to_run[1] + ' Precision', methods_to_run[1] + ' Recall'])\n pyplot.savefig(path.join(output_folder, 'different_samples_'+methods_to_run[1]+'.png'))\n pyplot.close()\n \n"},{"alpha_fraction":0.5068492889404297,"alphanum_fraction":0.6164383292198181,"avg_line_length":13.600000381469727,"blob_id":"c4a32c2d33a11a990ff8102394148b72e53ca760","content_id":"88c09ca7d1f2bcaf838316a6a578cfbff474e802","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":73,"license_type":"no_license","max_line_length":24,"num_lines":5,"path":"/run_all.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"from run import run\n#for i in range(30, 36):\n# run(i)\nrun(39)\nrun(40)\n"},{"alpha_fraction":0.6417526006698608,"alphanum_fraction":0.6546391844749451,"avg_line_length":54.42856979370117,"blob_id":"53610a058e8f8b6407b305fa30af554cf24b21de","content_id":"8d1276834d00fd3b4123f9cb2879904a6211ecdb","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":776,"license_type":"no_license","max_line_length":81,"num_lines":14,"path":"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/calculate_performance_measures.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import numpy as np\ndef calculate_performance_measures(predicted_labels, actual_labels):\n # Positive class is class 0, which means user will renew.\n # Negative class is class 1, which means user won't renew.\n predicted_labels = np.array(predicted_labels)\n TP = float(np.sum(np.logical_and(predicted_labels == 0, actual_labels == 0)))\n FP = float(np.sum(np.logical_and(predicted_labels == 0, actual_labels == 1)))\n FN = float(np.sum(np.logical_and(predicted_labels == 1, actual_labels == 0)))\n TN = float(np.sum(np.logical_and(predicted_labels == 1, actual_labels == 1)))\n error = (FP + FN) / (TP + FP + FN + TN)\n recall = TP / (TP + FN)\n precision = TP / (TP + FP)\n specificity = TN / (TN + FP)\n return error, recall, precision, specificity\n"},{"alpha_fraction":0.5625,"alphanum_fraction":0.5743007063865662,"avg_line_length":41.24074172973633,"blob_id":"d5799b45c308b339b43da42808a26c9e041b0316","content_id":"56931843ab8b6ee234b38dc9c941aac2d0bafa00","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2288,"license_type":"no_license","max_line_length":107,"num_lines":54,"path":"/preprocess_transcation_table.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import csv\ndef preprocess_transcation_table(input_filename, output_filename):\n users_dict = {}; \n csvfile = open(input_filename,'rb')\n is_header = True\n row_count = 0\n for row in csv.reader(csvfile, delimiter = ','):\n if is_header: # Skip header line.\n is_header = False\n continue\n if row_count % 1000000 == 0:\n print(row_count)\n row_count = row_count + 1\n user_id = row[0]\n # Skip payment_method_id field as it is a categorical data.\n payment_plan_days = float(row[2])\n plan_list_price = float(row[3])\n actual_amount_paid = float(row[4])\n is_auto_renew = float(row[5])\n # Skip transaction_date and membership_expire_date as it is\n # categorical data.\n is_cancel = float(row[8])\n user_record_count = 1\n user_data = []\n if user_id in users_dict:\n user_data = users_dict[user_id]\n user_data[0] += payment_plan_days\n user_data[1] += plan_list_price\n user_data[2] += actual_amount_paid\n user_data[3] += is_auto_renew\n user_data[4] += is_cancel\n user_data[5] += user_record_count \n else: # First time to encouter this user.\n user_data = [payment_plan_days, plan_list_price, actual_amount_paid,\n is_auto_renew, is_cancel, user_record_count] \n # Add user to the dictionary.\n users_dict[user_id] = user_data\n # Average user records and then write it to the output file.\n output_file = open(output_filename, \"w\")\n output_file.write('msno,payment_plan_days,plan_list_price,actual_amount_paid,is_auto_renew,is_cancel\\n')\n for user_id in users_dict:\n user_data = users_dict[user_id]\n user_records_number = user_data[5]\n output_line = user_id\n fields_count = 0\n for user_attribute in user_data:\n user_attribute = user_attribute / user_records_number\n output_line = output_line + ',' + str(user_attribute)\n fields_count = fields_count + 1\n if fields_count == 5:\n # Fifth field is the count field, don't include.\n break\n output_file.write(output_line + '\\n')\n output_file.close()\n \n \n"},{"alpha_fraction":0.5264804363250732,"alphanum_fraction":0.5488268136978149,"avg_line_length":36.923728942871094,"blob_id":"2d8dde636be919befdfa9688864640e381c9ae57","content_id":"d9ef50dfc64608f15f999ed9d9f45c030a982d2b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4475,"license_type":"no_license","max_line_length":79,"num_lines":118,"path":"/join_tables.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import csv\ndef join_tables(preprocessed_transcation_file, preprocessed_user_logs_file,\n train_file, members_file, output_filename):\n users_dict = {};\n # Load users from the train file.\n train_csvfile = open(train_file,'rb')\n is_header = True\n print('Load Train Data\\n');\n row_count = 0\n for row in csv.reader(train_csvfile, delimiter = ','):\n if is_header: # Skip header line.\n is_header = False\n continue\n row_count = row_count+1\n if row_count % 1000000 == 0:\n print('In load train, row count = ' + str(row_count) + '\\n')\n user_id = row[0]\n is_churn = row[1]\n users_dict[user_id] = [is_churn]\n # Load users attributes from members file.\n members_csvfile = open(members_file,'rb')\n is_header = True\n print('Load Members Data\\n');\n row_count = 0\n for row in csv.reader(members_csvfile, delimiter = ','):\n if is_header: # Skip header line.\n is_header = False\n continue\n row_count = row_count+1\n if row_count % 1000000 == 0:\n print('In load member, row count = ' + str(row_count) + '\\n')\n user_id = row[0]\n age = row[2]\n gender = row[3]\n if gender == 'female':\n gender = 0\n elif gender == 'male':\n gender = 1\n else: # Missing field, skip.\n continue\n if int(age) < 0: # Invalid age value.\n continue\n if user_id in users_dict:\n user_data = users_dict[user_id]\n user_data.append(age)\n user_data.append(gender)\n users_dict[user_id] = user_data\n # Load users attributed from transcation file.\n transcation_csvfile = open(preprocessed_transcation_file,'rb')\n is_header = True\n print('Load Transcation Data\\n');\n row_count = 0\n for row in csv.reader(transcation_csvfile, delimiter = ','):\n if is_header: # Skip header line.\n is_header = False\n continue\n row_count = row_count+1\n if row_count % 1000000 == 0:\n print('In load transcations, row count = ' + str(row_count) + '\\n')\n user_id = row[0]\n payment_plan_days = row[1]\n plan_list_price = row[2]\n actual_amount_paid = row[3]\n is_auto_renew = row[4]\n is_cancel = row[5]\n if user_id in users_dict:\n user_data = users_dict[user_id]\n user_data.append(payment_plan_days)\n user_data.append(plan_list_price)\n user_data.append(actual_amount_paid)\n user_data.append(is_auto_renew)\n user_data.append(is_cancel)\n users_dict[user_id] = user_data\n # Load users attributed from users log file.\n user_logs_csvfile = open(preprocessed_user_logs_file,'rb')\n is_header = True\n print('Load User Logs Data\\n');\n row_count = 0\n for row in csv.reader(user_logs_csvfile, delimiter = ','):\n if is_header: # Skip header line.\n is_header = False\n continue\n row_count = row_count+1\n if row_count % 1000000 == 0:\n print('In load user logs, row count = ' + str(row_count) + '\\n')\n user_id = row[0]\n num_25 = row[1]\n num_50 = row[2]\n num_75 = row[3]\n num_985 = row[4]\n num_100 = row[5]\n num_unq = row[6]\n total_secs = row[7]\n if user_id in users_dict:\n user_data = users_dict[user_id]\n user_data.append(num_25)\n user_data.append(num_50)\n user_data.append(num_75)\n user_data.append(num_985)\n user_data.append(num_100)\n user_data.append(num_unq)\n user_data.append(total_secs)\n users_dict[user_id] = user_data\n # Write output files.\n output_file = open(output_filename, \"w\")\n output_file.write('msno,is_churn,age,gender,payment_plan_days,'\n +'plan_list_price,actual_amount_paid,is_auto_renew,'\n +'is_cancel,num_25,num_50,num_75,num_985,num_100,'\n + 'num_unq,total_hrs\\n')\n for user_id in users_dict:\n user_data = users_dict[user_id]\n output_line = user_id\n if len(user_data) < 15: # contains missing atrributes.\n continue\n for user_attribute in user_data:\n output_line = output_line + ',' + str(user_attribute)\n output_file.write(output_line + '\\n')\n output_file.close()\n"},{"alpha_fraction":0.5818583965301514,"alphanum_fraction":0.5862832069396973,"avg_line_length":39.75757598876953,"blob_id":"078b1e97fb99dac20b35628bf8450416e478eab6","content_id":"b3f67da0df5111cf2cde3b142ed5707d79597a91","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1356,"license_type":"no_license","max_line_length":86,"num_lines":33,"path":"/Documents/Purdue/Fall 2017/ml/Project/split_data_random.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import random\nimport os.path as path\nimport csv\nimport math\ndef split_data_random(file_name, split_number, output_folder, train_ratio):\n csvfile = open(file_name,'r')\n print('Load Data\\n');\n lines = csvfile.readlines()\n header = lines[0]\n del lines[0]\n random.seed(0)\n random.shuffle(lines)\n line_start = 0\n lines_per_split_number = math.floor(len(lines) / split_number)\n for split in range(split_number):\n # Write training data of the split.\n output_train_file = open(path.join(output_folder,\n str(split) + '_train.csv'), 'w')\n output_train_file.write(header)\n line_end = lines_per_split_number + line_start\n line_end_train = math.floor(train_ratio * lines_per_split_number) + line_start\n while line_start < line_end_train:\n output_train_file.write(lines[line_start])\n line_start = line_start + 1\n output_train_file.close()\n # Write testing data of the split.\n output_test_file = open(path.join(output_folder,\n str(split) + '_test.csv'), 'w')\n output_test_file.write(header)\n while line_start < line_end:\n output_test_file.write(lines[line_start])\n line_start = line_start + 1\n output_test_file.close()\n\n \n\n"},{"alpha_fraction":0.6844181418418884,"alphanum_fraction":0.692307710647583,"avg_line_length":23.190475463867188,"blob_id":"0a93c63806ff812f735f3bbfbc26bd7c7d99d5ea","content_id":"146619cd7b30eda0a853c4e33009b316536250ad","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":507,"license_type":"no_license","max_line_length":61,"num_lines":21,"path":"/helper.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import csv\nimport numpy as np\nimport math\nfrom decimal import Decimal\nimport warnings\nfrom scipy.stats import logistic\n\nsigmoid = lambda x : logistic.cdf(x)\n\n# def sigmoid(x):\n# \tlogistic.cdf(x)\n\ndef load_data(input_path):\n\trows = []\n\twith open(input_path, 'r') as csvfile:\n\t\treader = csv.reader(csvfile, delimiter=',')\n\t\tfor row in reader:\n\t\t\trows.append(row)\n\tlabels = [int(row[1]) for row in rows[1:]]\n\trows = [[float(num) for num in row[2:]] for row in rows[1:]]\n\treturn np.array(rows), np.array(labels)"},{"alpha_fraction":0.5020458102226257,"alphanum_fraction":0.5421440005302429,"avg_line_length":40.305084228515625,"blob_id":"d2cdc660cb140574ad6969a2cdaf8912379ce9f6","content_id":"d05ed7a6fcb8fe22e8953ff4685d45d1fff60a42","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2444,"license_type":"no_license","max_line_length":154,"num_lines":59,"path":"/Documents/Purdue/Fall 2017/ml/Project/preprocess_user_logs_table.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import csv\ndef preprocess_user_logs_table(input_filename, output_filename):\n users_dict = {}; \n csvfile = open(input_filename,'rb')\n is_header = True\n row_count = 0\n for row in csv.reader(csvfile, delimiter = ','):\n if is_header: # Skip header line.\n is_header = False\n continue\n if row_count % 1000000 == 0:\n print(row_count)\n row_count = row_count + 1\n user_id = row[0]\n # Skip date field as it is a categorical data.\n num_25 = float(row[2])\n num_50 = float(row[3])\n num_75 = float(row[4])\n num_985 = float(row[5])\n num_100 = float(row[6])\n num_unq = float(row[7])\n total_hrs = float(row[8]) / 3600.0\n # Skip corrupted logs as these features can't be neg.\n if float(row[2]) < 0 or float(row[3]) < 0 or float(row[4])< 0 or float(row[5]) < 0 or float(row[6]) < 0 or float(row[7]) < 0 or float(row[8]) < 0:\n continue\n user_record_count = 1\n user_data = []\n if user_id in users_dict:\n user_data = users_dict[user_id]\n user_data[0] += num_25\n user_data[1] += num_50\n user_data[2] += num_75\n user_data[3] += num_985\n user_data[4] += num_100\n user_data[5] += num_unq\n user_data[6] += total_hrs\n user_data[7] += user_record_count \n else: # First time to encouter this user.\n user_data = [num_25, num_50, num_75, num_985,\n num_100, num_unq, total_hrs, user_record_count] \n # Add user to the dictionary.\n users_dict[user_id] = user_data\n # Average user records and then write it to the output file.\n output_file = open(output_filename, \"w\")\n output_file.write('msno,num_25,num_50,num_75,num_985,num_100,num_unq,total_hrs\\n')\n for user_id in users_dict:\n user_data = users_dict[user_id]\n user_records_number = user_data[7]\n output_line = user_id\n fields_count = 0\n for user_attribute in user_data:\n user_attribute = user_attribute / user_records_number\n output_line = output_line + ',' + str(user_attribute)\n fields_count = fields_count + 1\n if fields_count == 7:\n # Seventh field is the count field, don't include.\n break\n output_file.write(output_line + '\\n')\n output_file.close()\n \n \n"},{"alpha_fraction":0.4883720874786377,"alphanum_fraction":0.6976743936538696,"avg_line_length":16.200000762939453,"blob_id":"f04e0329db7b539378abdbaaa934645392639e3f","content_id":"0785e52d2f1695167c06ee40dec90c39b965ff28","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":86,"license_type":"no_license","max_line_length":20,"num_lines":5,"path":"/requirements.txt","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"statsmodels==0.8.0\nmatplotlib==2.0.2\nnumpy==1.13.1\nscipy==0.19.1\nscikit_learn==0.19.1\n"},{"alpha_fraction":0.5625683665275574,"alphanum_fraction":0.600817084312439,"avg_line_length":55.72810363769531,"blob_id":"dae42710073eda8c6f58059438c9e2c4858be3b3","content_id":"fbe79eaecef708583b2564069ac326fe81e12c84","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":31086,"license_type":"no_license","max_line_length":168,"num_lines":548,"path":"/run.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"from preprocess_transcation_table import preprocess_transcation_table\nfrom preprocess_user_logs_table import preprocess_user_logs_table\nfrom join_tables import join_tables\nfrom split_data_random import split_data_random\nfrom generate_histograms import generate_histograms\nfrom kfold_cross_validation import kfold_cross_validation\nfrom select_c_for_SVM_using_kfold_CV import select_c_for_SVM_using_kfold_CV\nfrom classify_test_users import classify_test_users\nfrom classify_test_users import test_different_number_of_samples\nfrom classify_test_users import test_different_thresholds\nfrom select_parameters_for_MLP_using_kfold_CV import select_parameters_for_MLP_using_kfold_CV\nfrom select_feature_selection_Naive_Bayes_using_kfold_CV import select_feature_selection_Naive_Bayes_using_kfold_CV\nfrom classify_test_users import test_different_subsets\nfrom classify_test_users import generate_train_test_error_for_different_kernels\nfrom classify_test_users import generate_train_test_error_for_different_MLP\nfrom classify_test_users import generate_test_error_for_kernels\nfrom classify_test_users import generate_ROC\nimport os.path as path\nimport os\nimport numpy as np\nfrom hypothesis_testing import do_hypothesis_testing\nimport sys\n\ndef run (step_number):\n if step_number == 1:\n # Step 1: Preprocess transcation data.\n transcations_input_filename = 'transactions.csv'\n transcations_output_filename = 'transactions_preprocessed.csv'\n preprocess_transcation_table(transcations_input_filename,\n transcations_output_filename)\n elif step_number == 2:\n # Step 2: Preprocess user_logs data\n user_logs_input_filename = 'user_logs.csv'\n user_logs_output_filename = 'user_logs_preprocessed.csv'\n preprocess_user_logs_table(user_logs_input_filename,\n user_logs_output_filename)\n elif step_number == 3:\n # Step 3: Join all tables based on user_id\n transcations_output_filename = 'transactions_preprocessed.csv'\n user_logs_output_filename = 'user_logs_preprocessed.csv'\n train_input_filename = 'train.csv'\n member_input_filename = 'members.csv'\n joined_table_output_filename = 'joined_train_data.csv'\n join_tables(transcations_output_filename, user_logs_output_filename,\n train_input_filename, member_input_filename,\n joined_table_output_filename)\n elif step_number == 4:\n # Step 4: Split joined table file into 10 subsets randomly.\n split_number = 10\n train_data_joined = 'joined_train_data.csv'\n output_folder = 'train_subsets'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n train_ratio = 0.8\n split_data_random(train_data_joined, split_number, output_folder,\n train_ratio)\n elif step_number == 5:\n # Step 5: Generate features histogram.\n train_file = path.join('train_subsets', '0_train.csv')\n output_folder = 'histograms'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n k = 10\n kfold_cross_validation(k, train_file, ['features_histogram'], output_folder, [])\n elif step_number == 6:\n # Step 6: Run PCA.\n train_file = path.join('train_subsets', '0_train.csv')\n output_folder = 'pca'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n k = 10\n kfold_cross_validation(k, train_file, ['pca'], output_folder, [])\n elif step_number == 7:\n # Step 7: Run univariate_fea_selection fearure selection.\n train_file = path.join('train_subsets', '0_train.csv')\n output_folder = 'univariate_fea_selection'\n k = 10\n C = 10\n number_of_features_to_select = 7\n stats_vals = kfold_cross_validation(k, train_file, ['univariate_fea_selection'],\n output_folder, [number_of_features_to_select, C])\n elif step_number == 8:\n # Step 8: Run linear_SVC fearure selection.\n train_file = path.join('train_subsets', '0_train.csv')\n output_folder = 'linear_SVC'\n k = 10\n sparsity_param = 0.002\n C = 10\n stats_vals = kfold_cross_validation(k, train_file, ['linear_SVC'],\n output_folder, [sparsity_param, C])\n elif step_number == 9:\n # Step 9: Run 10 fold cross validation to choose C for linear SVM with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'linear_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 'linear'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 10:\n # Step 10: Run 10 fold cross validation to choose C for linear SVM with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'linear_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 'linear'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n elif step_number == 11:\n # Step 11: Run 10 fold cross validation to choose C for kernel SVM with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'kernel_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 'rbf'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 12:\n # Step 12: Run 10 fold cross validation to choose C for kernel SVM with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'kernel_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 'rbf'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n elif step_number == 13:\n # Step 13: Run 10 fold cross validation to choose C for one class linear SVM with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'one_class_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 'linear'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 14:\n # Step 14: Run 10 fold cross validation to choose C for one class linear SVM with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'one_class_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 'linear'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n elif step_number == 15:\n # Step 15: Run 10 fold cross validation to choose C for one class kernel SVM with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'one_class_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 'rbf'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 16:\n # Step 16: Run 10 fold cross validation to choose C for one class kernel SVM with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'one_class_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 'rbf'\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n elif step_number == 17:\n # Step 17: For submitting on Kaggle, join the Kaggle test data with the other tables.\n transcations_output_filename = 'transactions_preprocessed.csv'\n user_logs_output_filename = 'user_logs_preprocessed.csv'\n train_input_filename = 'sample_submission.csv'\n member_input_filename = 'members.csv'\n joined_table_output_filename = 'joined_test_data.csv'\n join_tables(transcations_output_filename, user_logs_output_filename,\n train_input_filename, member_input_filename,\n joined_table_output_filename)\n elif step_number == 18:\n # Step 18: Classify Kaggle test user, using SVM with C learned form 10 fold cross validation.\n train_file = 'joined_train_data.csv'\n test_file = 'joined_test_data.csv'\n test_file_to_get_users = 'sample_submission.csv'\n methods_to_run = ['univariate_fea_selection', 'preceptron']\n number_of_features_to_select = 14\n C = 10\n kernel = 'linear'\n write_prediction = 1\n prediction_file = 'prediction_naive_MLP_all_10_200.csv'\n options = [number_of_features_to_select, C, kernel, write_prediction, prediction_file]\n classify_test_users(train_file, test_file, methods_to_run, test_file_to_get_users, options)\n elif step_number == 19:\n # Step 19: Run 10 fold cross validation to choose C for linear SVM with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'linear_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 'linear'\n class_1_weight = 10\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select, class_1_weight)\n elif step_number == 20:\n # Step 20: Run 10 fold cross validation to choose C for linear SVM with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'linear_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 'linear'\n class_1_weight = 10\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param, class_1_weight)\n elif step_number == 21:\n # Step 21: Run 10 fold cross validation to choose C for kernel SVM with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'kernel_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 'rbf'\n class_1_weight = 10\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select, class_1_weight)\n elif step_number == 22:\n # Step 22: Run 10 fold cross validation to choose C for kernel SVM with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'kernel_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 'rbf'\n class_1_weight = 10\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param, class_1_weight)\n elif step_number == 23:\n # Step 23: 10 fold CV for MLP using univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n params_list = [(10,10,10), (10,10,10,10,10), (10,10,10,10,10,10,10,10,10,10),\n (50,50,50), (50,50,50,50,50), (50,50,50,50,50,50,50,50,50,50),\n (100,100,100), (100,100,100,100,100), (100,100,100,100,100,100,100,100,100,100)]\n classification_alg = 'preceptron'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = ''\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\n fea_selection_alg, params_list, k,\n number_of_features_to_select)\n elif step_number == 24:\n # Step 24: 10 fold CV for MLP using linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n params_list = [(10,10,10), (10,10,10,10,10), (10,10,10,10,10,10,10,10,10,10),\n (50,50,50), (50,50,50,50,50), (50,50,50,50,50,50,50,50,50,50),\n (100,100,100), (100,100,100,100,100), (100,100,100,100,100,100,100,100,100,100)]\n classification_alg = 'preceptron'\n fea_selection_alg = 'linear_SVC'\n kernel = ''\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\n fea_selection_alg, params_list, k,\n sparsity_param)\n elif step_number == 25:\n # Step 25: 10 fold CV for Naive Bayes using univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n classification_alg = 'naive_bayes'\n fea_selection_alg = 'univariate_fea_selection'\n select_feature_selection_Naive_Bayes_using_kfold_CV(train_file, classification_alg, fea_selection_alg,\n k, number_of_features_to_select)\n elif step_number == 26:\n # Step 26: 10 fold CV for Naive Bayes using linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n classification_alg = 'naive_bayes'\n fea_selection_alg = 'linear_SVC'\n select_feature_selection_Naive_Bayes_using_kfold_CV(train_file, classification_alg, fea_selection_alg,\n k, sparsity_param)\n \n elif step_number == 27:\n # Step 27: Test different number of samples for different classification algorithms.\n # Parameters and feature selection methods are selected based on CV best in specificty.\n train_file = path.join('train_subsets', '0_train.csv')\n test_file = path.join('train_subsets', '0_test.csv')\n test_file_to_get_users = 'sample_submission.csv'\n methods_to_run_list = [['linear_SVC', 'linear_svm'], ['univariate_fea_selection', 'kernel_svm'],\n ['linear_SVC', 'one_class_svm'], ['linear_SVC', 'one_class_svm'],\n ['linear_SVC', 'linear_svm'],['univariate_fea_selection', 'kernel_svm'],\n ['univariate_fea_selection', 'naive_bayes'], ['linear_SVC', 'preceptron']]\n write_prediction = 0\n prediction_file = ''\n number_of_features_to_select = 7\n sparsity_param = 0.002\n linear_SVM_options = [sparsity_param, 55, 'linear', write_prediction, prediction_file]\n kernel_SVM_options = [number_of_features_to_select, 70, 'rbf', write_prediction, prediction_file]\n one_class_linear_SVM_options = [sparsity_param, 1, 'linear', write_prediction, prediction_file]\n one_class_kernel_SVM_options = [sparsity_param, 85, 'rbf', write_prediction, prediction_file]\n class_weight_linear_SVM_options = [sparsity_param, 85, 'linear', write_prediction, prediction_file, 10]\n class_weight_kernel_SVM_options = [number_of_features_to_select, 1, 'rbf', write_prediction, prediction_file, 10]\n naive_bayes_options = [number_of_features_to_select, 0, '', write_prediction, prediction_file]\n preceptron_options = [sparsity_param, (50, 50, 50, 50, 50, 50, 50, 50, 50, 50), '', write_prediction, prediction_file]\n options_list = [linear_SVM_options, kernel_SVM_options, one_class_linear_SVM_options, one_class_kernel_SVM_options,\n class_weight_linear_SVM_options, class_weight_kernel_SVM_options, naive_bayes_options,\n preceptron_options]\n different_number_of_samples = range(2000, 22000, 2000)\n output_folder = 'different_number_of_samples_experiment'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n test_different_number_of_samples(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list,\n different_number_of_samples, output_folder)\n elif step_number == 28:\n # Step 28: Test different training subsets for different classification algorithms.\n # Parameters and feature selection methods are selected based on CV best in specificty.\n train_folder = 'train_subsets'\n test_file = path.join('train_subsets', '0_test.csv')\n test_file_to_get_users = 'sample_submission.csv'\n methods_to_run_list = [['linear_SVC', 'linear_svm'], ['univariate_fea_selection', 'kernel_svm'],\n ['linear_SVC', 'one_class_svm'], ['linear_SVC', 'one_class_svm'],\n ['linear_SVC', 'linear_svm'],['univariate_fea_selection', 'kernel_svm'],\n ['univariate_fea_selection', 'naive_bayes'], ['linear_SVC', 'preceptron']]\n write_prediction = 0\n prediction_file = ''\n number_of_features_to_select = 7\n sparsity_param = 0.002\n linear_SVM_options = [sparsity_param, 55, 'linear', write_prediction, prediction_file]\n kernel_SVM_options = [number_of_features_to_select, 70, 'rbf', write_prediction, prediction_file]\n one_class_linear_SVM_options = [sparsity_param, 1, 'linear', write_prediction, prediction_file]\n one_class_kernel_SVM_options = [sparsity_param, 85, 'rbf', write_prediction, prediction_file]\n class_weight_linear_SVM_options = [sparsity_param, 85, 'linear', write_prediction, prediction_file, 10]\n class_weight_kernel_SVM_options = [number_of_features_to_select, 1, 'rbf', write_prediction, prediction_file, 10]\n naive_bayes_options = [number_of_features_to_select, 0, '', write_prediction, prediction_file]\n preceptron_options = [sparsity_param, (50, 50, 50, 50, 50, 50, 50, 50, 50, 50), '', write_prediction, prediction_file]\n options_list = [linear_SVM_options, kernel_SVM_options, one_class_linear_SVM_options, one_class_kernel_SVM_options,\n class_weight_linear_SVM_options, class_weight_kernel_SVM_options, naive_bayes_options,\n preceptron_options]\n different_number_of_samples = range(2000, 22000, 2000)\n output_folder = 'different_subsets_experiment'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n train_files_count = 10\n test_different_subsets(train_folder, test_file, methods_to_run_list, test_file_to_get_users,\n options_list, output_folder, train_files_count)\n # elif step_number == 29:\n # # Step 29: Test different thresholds and plot the ROC curve for different classification algorithms.\n # # Parameters and feature selection methods are selected based on CV best in specificty.\n # threshold_list = [0.25, 0.5, 0.75, 1]\n # train_file = path.join('train_subsets', '0_train.csv')\n # test_file = path.join('train_subsets', '0_test.csv')\n # test_file_to_get_users = 'sample_submission.csv'\n # methods_to_run_list = [['linear_SVC', 'linear_svm'], ['univariate_fea_selection', 'kernel_svm'],\n # ['linear_SVC', 'one_class_svm'], ['linear_SVC', 'one_class_svm'],\n # ['linear_SVC', 'linear_svm'],['univariate_fea_selection', 'kernel_svm'],\n # ['univariate_fea_selection', 'naive_bayes'], ['linear_SVC', 'preceptron']]\n # write_prediction = 0\n # prediction_file = ''\n # number_of_features_to_select = 7\n # sparsity_param = 0.002\n # linear_SVM_options = [sparsity_param, 55, 'linear', write_prediction, prediction_file]\n # kernel_SVM_options = [number_of_features_to_select, 70, 'rbf', write_prediction, prediction_file]\n # one_class_linear_SVM_options = [sparsity_param, 1, 'linear', write_prediction, prediction_file]\n # one_class_kernel_SVM_options = [sparsity_param, 85, 'rbf', write_prediction, prediction_file]\n # class_weight_linear_SVM_options = [sparsity_param, 85, 'linear', write_prediction, prediction_file, 10]\n # class_weight_kernel_SVM_options = [number_of_features_to_select, 1, 'rbf', write_prediction, prediction_file, 10]\n # naive_bayes_options = [number_of_features_to_select, 0, '', write_prediction, prediction_file]\n # preceptron_options = [sparsity_param, (50, 50, 50, 50, 50, 50, 50, 50, 50, 50), '', write_prediction, prediction_file]\n # options_list = [linear_SVM_options, kernel_SVM_options, one_class_linear_SVM_options, one_class_kernel_SVM_options,\n # class_weight_linear_SVM_options, class_weight_kernel_SVM_options, naive_bayes_options,\n # preceptron_options]\n # different_number_of_samples = range(2000, 22000, 2000)\n # output_folder = 'different_thresholds_experiment'\n # if not path.exists(output_folder):\n # os.makedirs(output_folder)\n # test_different_thresholds(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list, threshold_list, output_folder, class_1_weight = 1)\n elif step_number == 30:\n # Step 30: Run 10 fold cross validation to choose C for poly SVM degree 2 with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'poly_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 2\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 31:\n # Step 31: Run 10 fold cross validation to choose C for poly SVM degree 2 with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'poly_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 2\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n elif step_number == 32:\n # Step 32: Run 10 fold cross validation to choose C for poly SVM degree 3 with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'poly_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 3\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 33:\n # Step 33: Run 10 fold cross validation to choose C for poly SVM degree 3 with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'poly_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 3\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n elif step_number == 34:\n # Step 34: Run 10 fold cross validation to choose C for poly SVM degree 4 with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'poly_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 4\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 35:\n # Step 35: Run 10 fold cross validation to choose C for poly SVM degree 4 with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'poly_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 4\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n\n elif step_number == 36:\n # Step 36: Run 10 fold cross validation to choose C for poly SVM degree 4 with univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'poly_svm'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = 10\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, number_of_features_to_select)\n elif step_number == 37:\n # Step 37: Run 10 fold cross validation to choose C for poly SVM degree 4 with linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n c_vals = [1, 10, 25, 40, 55, 70, 85]\n classification_alg = 'poly_svm'\n fea_selection_alg = 'linear_SVC'\n kernel = 10\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\n c_vals, k, sparsity_param)\n elif step_number == 38:\n # Step 38: Report training and testing errors for different SVM kernels.\n train_file = path.join('train_subsets', '0_train.csv')\n test_file = path.join('train_subsets', '0_test.csv')\n output_folder = 'different_SVM_kernels'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n generate_train_test_error_for_different_kernels(train_file, test_file, output_folder)\n\n elif step_number == 39:\n # Step 39: 10 fold CV for MLP using univariate_fea_selection.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n number_of_features_to_select = 7\n params_list = [tuple([50]*3), tuple([50]*5), tuple([50]*10),\n tuple([50]*13), tuple([50]*15), tuple([50]*20)]\n classification_alg = 'preceptron'\n fea_selection_alg = 'univariate_fea_selection'\n kernel = ''\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\n fea_selection_alg, params_list, k,\n number_of_features_to_select)\n elif step_number == 40:\n # Step 40: 10 fold CV for MLP using linear_SVC.\n train_file = path.join('train_subsets', '0_train.csv')\n k = 10\n sparsity_param = 0.002\n params_list = [tuple([50]*3), tuple([50]*5), tuple([50]*10),\n tuple([50]*13), tuple([50]*15), tuple([50]*20)]\n classification_alg = 'preceptron'\n fea_selection_alg = 'linear_SVC'\n kernel = ''\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\n fea_selection_alg, params_list, k,\n sparsity_param)\n elif step_number == 41:\n # Step 41: Report training and testing errors for different MLP layers.\n train_file = path.join('train_subsets', '0_train.csv')\n test_file = path.join('train_subsets', '0_test.csv')\n output_folder = 'different_MLP_kernels'\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n generate_train_test_error_for_different_MLP(train_file, test_file, output_folder)\n elif step_number == 42:\n #Step 42: Generate test error\n train_file = path.join('train_subsets', '0_train.csv')\n test_file = path.join('train_subsets', '0_test.csv')\n generate_test_error_for_kernels(train_file, test_file)\n elif step_number == 43:\n #Step 43: Plot ROC and compute AUC\n print 'In step_number', step_number\n train_file = path.join('train_subsets', '0_train.csv')\n test_file = path.join('train_subsets', '0_test.csv')\n # threshold_list = [0.25, 0.5, 0.75]\n threshold_list = list(np.arange(0.0, 1, 0.1))\n generate_ROC(train_file, test_file, threshold_list = threshold_list)\n elif step_number == 44:\n #Step 44: Perform Hypothesis testing\n k = 10\n train_file = path.join('train_subsets', '0_train.csv')\n do_hypothesis_testing(train_file, k)\n\nif __name__ == '__main__':\n step_number = 0\n try:\n step_number = int(sys.argv[1])\n except ValueError:\n print '\\'%s\\' is not a valid int. Please provide an integer' %(sys.argv[1]) \n run(step_number)"},{"alpha_fraction":0.5937321782112122,"alphanum_fraction":0.5982906222343445,"avg_line_length":53.84375,"blob_id":"73e64380474163e717513a63dbdd5ff18f33bce9","content_id":"5374d1e4ae66e319a34e00413c301a0b2a3f619f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1755,"license_type":"no_license","max_line_length":163,"num_lines":32,"path":"/select_feature_selection_Naive_Bayes_using_kfold_CV.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import os.path as path\nimport os\nfrom kfold_cross_validation import kfold_cross_validation\nfrom plot_cross_validation_SVM import plot_cross_validation_SVM\nimport random\nimport numpy as np\ndef select_feature_selection_Naive_Bayes_using_kfold_CV(train_file, classification_alg, fea_selection_alg,\n k, number_of_features_to_select):\n random.seed(0)\n output_folder = '%s/%s_%s' %(classification_alg, classification_alg, fea_selection_alg)\n if not path.exists(output_folder):\n os.makedirs(output_folder)\n output_file = open('%s/stats.txt' %(output_folder), 'a')\n labels = ['Avg Error', 'Var Error', 'Avg Recall', 'Var recall', 'Avg Precision', 'Var Precision', 'Avg Specificity', 'Var Specificity']\n output_file.write('C\\t')\n for label in labels:\n output_file.write('%s\\t' %(label))\n output_file.write('\\n')\n mean_vals = []\n error_vals = []\n stats_results = kfold_cross_validation(k, train_file, [fea_selection_alg, classification_alg], output_folder,\n [number_of_features_to_select,'' , '', 0])\n output_file = open('%s/stats.txt' %(output_folder), 'a')\n mean_vals.append([])\n error_vals.append([])\n for stat_record in stats_results:\n mean_vals[-1].append(stat_record[0])\n error_vals[-1].append(stat_record[1])\n output_file.write('%f\\t%f\\t' %(stat_record[0], stat_record[1]))\n output_file.write('\\n')\n output_file.close()\n #plot_cross_validation_SVM(k, parameter_list, labels, np.array(mean_vals), np.array(error_vals), '%s/stats' %(output_folder), range(len(mean_vals)), False)\n"},{"alpha_fraction":0.5813356041908264,"alphanum_fraction":0.6001712083816528,"avg_line_length":39.27586364746094,"blob_id":"272f93da42ecb7b572c5c75cc7d8fb69438bdc0e","content_id":"f65c8267e29e13999906751b20c68b11dc702992","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1168,"license_type":"no_license","max_line_length":79,"num_lines":29,"path":"/calculate_performance_measures.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import numpy as np\ndef calculate_performance_measures(predicted_labels, actual_labels):\n # Positive class is class 0, which means user will renew.\n # Negative class is class 1, which means user won't renew.\n predicted_labels = np.array(predicted_labels)\n actual_labels = np.array([cur_list[0] for cur_list in list(actual_labels)])\n TP = np.sum(np.logical_and(predicted_labels == 0, actual_labels == 0))\n FP = np.sum(np.logical_and(predicted_labels == 0, actual_labels == 1))\n FN = np.sum(np.logical_and(predicted_labels == 1, actual_labels == 0))\n TN = np.sum(np.logical_and(predicted_labels == 1, actual_labels == 1))\n if (TP + FP + FN + TN) != 0:\n\terror = float(FP + FN) / float(TP + FP + FN + TN)\n else:\n\terror = 0.0\n if (TP + FN) != 0:\n\trecall = TP / float(TP + FN)\n else:\n\trecall = 0.0\n if (TP + FP) != 0:\n\tprecision = TP / float(TP + FP)\n else:\n\tprecision = 0.0\n if (TN + FP) != 0:\n\tspecificity = TN / float(TN + FP)\n else:\n\tspecificity = 0\n #print 'TP = ', TP, 'FP = ', FP, 'FN = ', FN, 'TN = ', TN\n #print '%f/%f+%f = %f' %(TP, TP, FP, TP / float(TP + FP))\n return error, recall, precision, specificity\n"},{"alpha_fraction":0.5681818127632141,"alphanum_fraction":0.7045454382896423,"avg_line_length":10,"blob_id":"5138b504469af5b49e862c4e5c8c0cdd5346876b","content_id":"06b908f50be56dcee0b17b686bc9a3c31c8c7bb8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":44,"license_type":"no_license","max_line_length":19,"num_lines":4,"path":"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/run_linear_SVC.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"from run import run\nrun(12)\nrun(14)\nrun(16)\n"},{"alpha_fraction":0.6118788719177246,"alphanum_fraction":0.6184217929840088,"avg_line_length":57.900333404541016,"blob_id":"afae66790c76020cf7ebdf08f6aec0b4273d9d2b","content_id":"f304c34d4b4c3c30bff3927b8325d780fadd8f41","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":17729,"license_type":"no_license","max_line_length":146,"num_lines":301,"path":"/kfold_cross_validation.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import math\nimport csv\nfrom my_PCA import MyPCA\nimport numpy as np\nfrom matplotlib import pyplot\nimport os.path as path\nfrom sklearn.naive_bayes import GaussianNB\nfrom calculate_performance_measures import calculate_performance_measures\nfrom sklearn import mixture\nfrom svm import *\nfrom fea_selection import *\nimport math\nfrom sklearn.neural_network import MLPClassifier\nfrom helper import sigmoid\nimport math\nfrom scipy import stats\nimport scipy as sp\n\ndef write_predictions(validation_users_id, predicted_labels, options, test_file_to_get_users):\n write_prediction = options[3]\n if write_prediction == 1:\n test_csvfile = open(test_file_to_get_users,'rb')\n lines = csv.reader(test_csvfile, delimiter = ',')\n test_users = []\n for line in lines:\n test_users.append(line[0])\n prediction_filename = options[4]\n users_number = len(validation_users_id)\n validation_users_dict = {}\n index = 0\n for user in range(users_number):\n user_id = validation_users_id[user]\n validation_users_dict[''.join(user_id)] = index\n index = index + 1\n prediction_file = open(prediction_filename, 'w')\n prediction_file.write('msno,is_churn\\n')\n for user in test_users:\n if user in validation_users_dict:\n #normalized_prediction = 1.0/(1.0+ math.e **( -1 * predicted_labels[validation_users_dict[user]]))\n prediction_file.write(user+','+str(predicted_labels[validation_users_dict[user]][1])+'\\n')\n else:\n prediction_file.write(user+',0.03\\n')\n prediction_file.close()\n\ndef min_max_normalize(train_data):\n col_min = np.min(train_data, axis = 0)\n train_data = (train_data - col_min) / (np.max(train_data, axis = 0) - col_min)\n return train_data\n\ndef run_feature_selection_and_classification(methods_to_run, train_data, validation_data, labels,\n train_index, validation_index, validation_users_id,\n options, header, test_file_to_get_users,\n threshold_list = -1, class_1_weight = 1):\n if 'univariate_fea_selection' in methods_to_run:\n # Run feature selection.\n number_of_features_to_select = options[0]\n selected_features_indices = univariate_fea_selection(train_data, labels[train_index],\n number_of_features_to_select)\n train_data = train_data[:, selected_features_indices]\n validation_data = validation_data[:, selected_features_indices]\n selected_featuees = np.array(header)[np.array(selected_features_indices)+2]\n # for selected_feature in range(len(selected_featuees)):\n # print(selected_featuees[selected_feature])\n if 'linear_SVC' in methods_to_run:\n # Run feature selection.\n sparsity_param = options[0]\n selected_features_indices = my_SelectFromModel(train_data, labels[train_index],\n sparsity_param)\n train_data = train_data[:, selected_features_indices]\n validation_data = validation_data[:, selected_features_indices]\n selected_featuees = np.array(header)[np.array(selected_features_indices)+2]\n # for selected_feature in range(len(selected_featuees)):\n # print(selected_featuees[selected_feature])\n if 'linear_svm' in methods_to_run: # Run linear SVM.\n C = options[1]\n linear_svm_model = train_linear_svm(train_data, labels[train_index], C, class_1_weight)\n if threshold_list == -1:\n predicted_labels, predicted_values = classify(linear_svm_model, validation_data)\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n return error, recall, precision, specificity\n else:\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\n predicted_labels, predicted_values = classify(linear_svm_model, validation_data)\n threshold_list = get_threshold_list(predicted_values)\n for threshold in threshold_list:\n predicted_labels = [linear_svm_model.classes_[1] if val > threshold else linear_svm_model.classes_[0] for val in predicted_values]\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n error_list.append(error)\n recall_list.append(recall)\n precision_list.append(precision)\n specificity_list.append(specificity)\n return error_list, recall_list, precision_list, specificity_list\n if 'kernel_svm' in methods_to_run: # Run Kernel SVM.\n C = options[1]\n kernel_svm_model = train_kernel_svm(train_data, labels[train_index], C, class_1_weight)\n if threshold_list == -1:\n predicted_labels, predicted_values = classify(kernel_svm_model, validation_data)\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n return error, recall, precision, specificity\n else:\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\n predicted_labels, predicted_values = classify(kernel_svm_model, validation_data)\n threshold_list = get_threshold_list(predicted_values)\n for threshold in threshold_list:\n predicted_labels = [kernel_svm_model.classes_[1] if val > threshold else kernel_svm_model.classes_[0] for val in predicted_values]\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n error_list.append(error)\n recall_list.append(recall)\n precision_list.append(precision)\n specificity_list.append(specificity)\n return error_list, recall_list, precision_list, specificity_list\n if 'poly_svm' in methods_to_run:\n C = options[1]\n degree = options[2]\n train_data = train_data.astype(float)\n kernel_svm_model = train_poly_kernel_svm (min_max_normalize(train_data), degree, labels[train_index], C, class_1_weight)\n if threshold_list == -1:\n predicted_labels, predicted_values = classify(kernel_svm_model, min_max_normalize(validation_data))\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n print(validation_data.shape)\n print(len(labels[validation_index]))\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n return error, recall, precision, specificity\n else:\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\n predicted_labels, predicted_values = classify(kernel_svm_model, min_max_normalize(validation_data), threshold = threshold_list)\n threshold_list = get_threshold_list(predicted_values)\n for threshold in threshold_list:\n predicted_labels = [kernel_svm_model.classes_[1] if val > threshold else kernel_svm_model.classes_[0] for val in predicted_values]\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n error_list.append(error)\n recall_list.append(recall)\n precision_list.append(precision)\n specificity_list.append(specificity)\n return error_list, recall_list, precision_list, specificity_list\n if 'one_class_svm' in methods_to_run:\n temp = np.array([label[0] for label in labels[train_index]]) == 0\n train_data = train_data[temp, :]\n one_class_svm_model = train_one_class_svm(train_data, options[2])\n if threshold_list == -1:\n predicted_labels, predicted_values = classify_one_class_svm(one_class_svm_model, validation_data)\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users) \n return error, recall, precision, specificity\n else:\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\n predicted_labels, predicted_values = classify_one_class_svm(one_class_svm_model, validation_data, threshold = 1)\n threshold_list = get_threshold_list(predicted_values)\n for threshold in threshold_list:\n predicted_labels = [1 if val > threshold else 0 for val in predicted_values]\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users) \n error_list.append(error)\n recall_list.append(recall)\n precision_list.append(precision)\n specificity_list.append(specificity)\n return error_list, recall_list, precision_list, specificity_list\n if 'preceptron' in methods_to_run: # Run multilayer preceptron.\n clf = MLPClassifier(hidden_layer_sizes = options[1], verbose=False, random_state = 1\n)\n clf.fit(train_data, labels[train_index].ravel())\n if threshold_list == -1:\n predicted_labels = clf.predict(validation_data)\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n predicted_values = clf.predict_proba(validation_data)\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n return error, recall, precision, specificity \n else:\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\n predicted_values = clf.predict_proba(validation_data)\n threshold_list = get_threshold_list([val[1] for val in predicted_values])\n for threshold in threshold_list:\n predicted_labels = [1 if val[1] > threshold else 0 for val in predicted_values]\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n error_list.append(error)\n recall_list.append(recall)\n precision_list.append(precision)\n specificity_list.append(specificity)\n return error_list, recall_list, precision_list, specificity_list\n if 'naive_bayes' in methods_to_run:\n clf = GaussianNB()\n clf.fit(train_data, labels[train_index].ravel())\n if threshold_list == -1:\n predicted_labels = clf.predict(validation_data)\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n predicted_values = clf.predict_proba(validation_data)\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n return error, recall, precision, specificity\n else:\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\n predicted_values = clf.predict_proba(validation_data)\n threshold_list = get_threshold_list([val[1] for val in predicted_values])\n for threshold in threshold_list:\n predicted_labels = [1 if val[1] > threshold else 0 for val in predicted_values]\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n error_list.append(error)\n recall_list.append(recall)\n precision_list.append(precision)\n specificity_list.append(specificity)\n return error_list, recall_list, precision_list, specificity_list\n if 'guassian' in methods_to_run:\n clf = mixture.GaussianMixture(n_components=2, covariance_type='full')\n clf.fit(train_data, labels[train_index])\n predicted_labels = clf.predict(validation_data)\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\n predicted_values = clf.predict_proba(validation_data)\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\n return error, recall, precision, specificity\n return 0, 0, 0, 0\n\ndef get_threshold_list(x):\n x = sorted(x)\n num_steps = 20\n return [x[len(x)/num_steps * i] for i in range(num_steps)] + [max(x) + 1]\n\ndef kfold_cross_validation(k, train_file, methods_to_run,\n output_folder, options, class_1_weight = 1, return_measures = False):\n train_csvfile = open(train_file,'rb')\n lines = csv.reader(train_csvfile, delimiter = ',')\n lines = list(lines)\n header = lines[0]\n # Remove headers from the data.\n del lines[0]\n samples_number = len(lines)\n # Transform it to matrix.\n # Skip the user id and the label columns.\n data = np.array([[float(elem) for elem in line[2:]] for line in lines])\n labels = np.array([[int(elem) for elem in line[1]] for line in lines])\n user_ids = np.array([[elem for elem in line[0]] for line in lines])\n features_number = len(data[0])\n measures = np.zeros([4, k])\n for i in range(k):\n print '\\t', i\n start_i = int(math.floor(samples_number * i / k))\n end_i = int(math.floor(samples_number * (i + 1) / k))\n validation_index = range(start_i, end_i)\n points_index = range(samples_number)\n train_index = list(set(points_index).difference(set(validation_index)));\n train_labels = labels[train_index, :]\n zero_indices = [j for j in range(len(train_labels)) if train_labels[j] == 0]\n one_indices = [j for j in range(len(train_labels)) if train_labels[j] == 1]\n train_data = data[train_index, :]\n validation_data = data[validation_index, :]\n if 'features_histogram' in methods_to_run:\n # Caclulate features histogram for each fold training data.\n for feature in range(features_number):\n train_data = data[train_index, feature]\n feature_column_class0 = train_data[zero_indices]\n feature_column_class1 = train_data[one_indices]\n pyplot.figure()\n pyplot.hist(feature_column_class0, 100, label='Class 0')\n pyplot.hist(feature_column_class1, 100, label='Class 1')\n pyplot.legend(loc='upper right')\n pyplot.savefig(path.join(output_folder, 'hist_fold=' + str(i) +\n '_' + header[feature+2] + '.png'))\n pyplot.close()\n if 'pca' in methods_to_run:\n # Run PCA.\n myPca = MyPCA()\n myPca.fit(train_data, n_components = 2)\n # Visualize the training data.\n projected_data = myPca.transform(train_data)\n pyplot.figure()\n pyplot.plot(projected_data[zero_indices, 0], projected_data[zero_indices, 1],\n 'o', color = 'r')\n pyplot.plot(projected_data[one_indices, 0], projected_data[one_indices, 1],\n 'x', color = 'b')\n pyplot.savefig(path.join(output_folder, 'pca_fold=' +\n str(i) + '.png'))\n pyplot.close()\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run,\n train_data, validation_data,\n labels, train_index, validation_index,\n user_ids[validation_index, :], options,\n header, '', class_1_weight = class_1_weight)\n measures[0, i] = error\n measures[1, i] = recall\n measures[2, i] = precision\n measures[3, i] = specificity\n if return_measures:\n return measures[0]\n stats_vals = []\n alpha_val = 0.95\n for i in range(4):\n mean, error = mean_confidence_interval(measures[i, :], alpha_val)\n stats_vals.append([mean, error])\n return stats_vals\n\ndef mean_confidence_interval(data, confidence=0.95):\n a = 1.0*np.array(data)\n n = len(a)\n m, se = np.mean(a), sp.stats.sem(a)\n h = se * sp.stats.t._ppf((1+confidence)/2., n-1)\n return m, h\n"},{"alpha_fraction":0.6510263681411743,"alphanum_fraction":0.662756621837616,"avg_line_length":30.090909957885742,"blob_id":"cb13640e09331f027d535926d41d4aa436f24a79","content_id":"148a8388d4cf3e83e84f492fbb41819b492178e5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":341,"license_type":"no_license","max_line_length":61,"num_lines":11,"path":"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/helper.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import csv\nimport numpy as np\ndef load_data(input_path):\n\trows = []\n\twith open(input_path, 'r') as csvfile:\n\t\treader = csv.reader(csvfile, delimiter=',')\n\t\tfor row in reader:\n\t\t\trows.append(row)\n\tlabels = [int(row[1]) for row in rows[1:]]\n\trows = [[float(num) for num in row[2:]] for row in rows[1:]]\n\treturn np.array(rows), np.array(labels)"},{"alpha_fraction":0.6361031532287598,"alphanum_fraction":0.6459271311759949,"avg_line_length":47.86000061035156,"blob_id":"5ff5c016cad4b417baa10139d87724d185a3d44d","content_id":"7ac419bfdeeb522996ae5720c0d52a906abe272f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2443,"license_type":"no_license","max_line_length":88,"num_lines":50,"path":"/Documents/Purdue/Fall 2017/ml/Project/run.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"from preprocess_transcation_table import preprocess_transcation_table\nfrom preprocess_user_logs_table import preprocess_user_logs_table\nfrom join_tables import join_tables\nfrom split_data_random import split_data_random\nfrom generate_histograms import generate_histograms\nfrom kfold_cross_validation import kfold_cross_validation\nimport os.path as path\ndef run (step_number):\n if step_number == 1:\n # Step 1: Preprocess transcation data.\n transcations_input_filename = 'transactions.csv'\n transcations_output_filename = 'transactions_preprocessed.csv'\n preprocess_transcation_table(transcations_input_filename,\n transcations_output_filename)\n elif step_number == 2:\n # Step 2: Preprocess user_logs data\n user_logs_input_filename = 'user_logs.csv'\n user_logs_output_filename = 'user_logs_preprocessed.csv'\n preprocess_user_logs_table(user_logs_input_filename,\n user_logs_output_filename)\n elif step_number == 3:\n # Step 3: Join all tables based on user_id\n transcations_output_filename = 'transactions_preprocessed.csv'\n user_logs_output_filename = 'user_logs_preprocessed.csv'\n train_input_filename = 'train.csv'\n member_input_filename = 'members.csv'\n joined_table_output_filename = 'joined_train_data.csv'\n join_tables(transcations_output_filename, user_logs_output_filename,\n train_input_filename, member_input_filename,\n joined_table_output_filename)\n elif step_number == 4:\n # Step 4: Split joined table file into 10 subsets randomly.\n split_number = 10\n train_data_joined = 'joined_train_data.csv'\n output_folder = 'train_subsets'\n train_ratio = 0.8\n split_data_random(train_data_joined, split_number, output_folder,\n train_ratio)\n elif step_number == 5:\n # Step 5: Generate features histogram.\n train_file = path.join('train_subsets', '0_train.csv')\n output_folder = 'histograms'\n k = 10\n kfold_cross_validation(k, train_file, ['features_histogram'], output_folder, [])\n elif step_number == 6:\n # Step 6: Run PCA.\n train_file = path.join('train_subsets', '0_train.csv')\n output_folder = 'pca'\n k = 10\n kfold_cross_validation(k, train_file, ['pca'], output_folder, [])\n"},{"alpha_fraction":0.546486496925354,"alphanum_fraction":0.565675675868988,"avg_line_length":49,"blob_id":"4ca5a21a9da97c0c7c2b508b0f8de20c946cc2c6","content_id":"4b54b843ba623439666275eaf1270d588bbe8a1e","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3700,"license_type":"no_license","max_line_length":89,"num_lines":74,"path":"/Documents/Purdue/Fall 2017/ml/Project/kfold_cross_validation.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import math\nimport csv\nfrom my_PCA import MyPCA\nimport numpy as np\nfrom matplotlib import pyplot\nimport os.path as path\ndef kfold_cross_validation(k, train_file, methods_to_run,\n output_folder, options):\n train_csvfile = open(train_file,'rb')\n lines = csv.reader(train_csvfile, delimiter = ',')\n lines = list(lines)\n header = lines[0]\n # Remove headers from the data.\n del lines[0]\n samples_number = len(lines)\n # Transform it to matrix.\n # Skip the user id and the label columns.\n data = np.array([[float(elem) for elem in line[2:]] for line in lines])\n labels = np.array([[int(elem) for elem in line[1]] for line in lines])\n features_number = len(data[0])\n for i in range(k):\n start_i = int(math.floor(samples_number * i / k))\n end_i = int(math.floor(samples_number * (i + 1) / k))\n validation_index = range(start_i, end_i)\n points_index = range(samples_number)\n train_index = list(set(points_index).difference(set(validation_index)));\n train_labels = labels[train_index, :]\n zero_indices = [j for j in range(len(train_labels)) if train_labels[j] == 0]\n one_indices = [j for j in range(len(train_labels)) if train_labels[j] == 1]\n if 'features_histogram' in methods_to_run:\n # Caclulate features histogram for each fold training data.\n for feature in range(features_number):\n train_data = data[train_index, feature]\n feature_column_class0 = train_data[zero_indices]\n feature_column_class1 = train_data[one_indices]\n pyplot.figure()\n pyplot.hist(feature_column_class0, 100, label='Class 0')\n pyplot.hist(feature_column_class1, 100, label='Class 1')\n pyplot.legend(loc='upper right')\n pyplot.savefig(path.join(output_folder, 'hist_fold=' + str(i) +\n '_' + header[feature+2] + '.png'))\n pyplot.close()\n if 'pca' in methods_to_run:\n # Run PCA.\n myPca = MyPCA()\n train_data = data[train_index, :]\n #print(train_data[5566, :])\n #print(train_data[8660, :])\n #train_data = np.delete(train_data, [2695,5789,24610], 0)\n #train_labels = np.delete(train_labels, [2695,5789,24610], 0)\n zero_indices = [j for j in range(len(train_labels)) if train_labels[j] == 0]\n one_indices = [j for j in range(len(train_labels)) if train_labels[j] == 1]\n myPca.fit(train_data, n_components = 2)\n # Visualize the training data.\n projected_data = myPca.transform(train_data)\n pyplot.figure()\n #projected_data_np = np.array(projected_data[zero_indices, 0])\n #points_index_np = np.array(range(len(projected_data_np)))\n #print(points_index_np[projected_data_np >= 3e9])\n pyplot.plot(projected_data[zero_indices, 0], projected_data[zero_indices, 1],\n 'o', color = 'r')\n pyplot.plot(projected_data[one_indices, 0], projected_data[one_indices, 1],\n 'x', color = 'b')\n pyplot.savefig(path.join(output_folder, 'pca_fold=' +\n str(i) + '.png'))\n pyplot.close()\n if 'f1' in methods_to_run: # Run feature selection\n print('f1')\n if 'f2' in methods_to_run: # Run feature selection\n print('f2')\n if 'svm' in methods_to_run: # Run SVM.\n print('svm')\n if 'preceptron' in methods_to_run: # Run multilayer preceptron.\n print('preceptron')\n"},{"alpha_fraction":0.5974349975585938,"alphanum_fraction":0.6241539120674133,"avg_line_length":33.66666793823242,"blob_id":"aba415de8b4211f5866f111363d3f4047fc52220","content_id":"47ac1beb9dae48cb6b4fe0bec48f23a3da58a78b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2807,"license_type":"no_license","max_line_length":188,"num_lines":81,"path":"/hypothesis_testing.py","repo_name":"Ahmed-Elbagoury/ML578","src_encoding":"UTF-8","text":"import os.path as path\nimport os\nfrom kfold_cross_validation import kfold_cross_validation\nfrom plot_cross_validation_SVM import plot_cross_validation_SVM\nimport random\nimport numpy as np\nimport scipy\nimport statsmodels.sandbox.stats.multicomp\n\ndef do_hypothesis_testing(train_file, k):\n\tfea_selection_list = ['linear_SVC', 'linear_SVC', 'linear_SVC', 'linear_SVC', 'univariate_fea_selection', 'linear_SVC', 'univariate_fea_selection', 'univariate_fea_selection']\n\tclassification_list = ['preceptron', 'one_class_svm', 'one_class_svm', 'linear_svm', 'kernel_svm', 'linear_svm', 'kernel_svm', 'naive_bayes']\n\toptions_list = [\n\t[0.002, tuple([50] * 10), '', 0],\n\t[0.002, 1, 'linear', 0, ''],\n\t[0.002, 85, 'rbf', 0, ''],\n\t[0.002, 55, 'linear', 0, ''],\n\t[7, 70, 'rbf', 0, ''],\n\t[0.002, 85, 'linear', 0, ''],\n\t[7, 1, 'rbf', 0, ''],\n\t[7, 0, '', 0, '']\n\t]\n\tclass_weight_list = [1, 1, 1, 1, 1, 10, 10, 1]\n\n\tstats_results_list = []\n\tfor i in range(len(options_list)):\n\t\tprint 'i', i\n\t\tstats_results = kfold_cross_validation(k, train_file, [fea_selection_list[i], classification_list[i]], '', options_list[i], class_1_weight = class_weight_list[i], return_measures = True)\n\t\tstats_results_list += [stats_results]\n\t\n\t#doing hypothesis testing\n\tstats_results_list = np.array(stats_results_list)\n\tm, n = stats_results_list.shape\n\tprint 'm', m, 'n', n\n\tx_g = np.sum(stats_results_list) / float(n * m)\n\tx_bar_list = np.mean(stats_results_list, axis = 1)\n\tms_within = 0\n\t\n\tfor i in range(m):\n\t\tx_bar = x_bar_list[i]\n\t\tfor j in range(n):\n\t\t\tms_within += (stats_results_list[i][j] - x_bar) ** 2 /(float(m * (n-1)))\n\tdf_within = m * (n-1)\n\tms_between = 0\n\tfor i in range(m):\n\t\tms_between += n * (x_bar_list[i] - x_g)**2/ float(m-1)\n\tdf_between = m-1\n\tf_stat = ms_between / ms_within\t\n\tf = scipy.stats.f.ppf(0.95, df_between, df_within)\n\t# for row in stats_results_list:\n\t# \tfor num in row:\n\t# \t\tprint num,\n\t# \tprint ''\n\tprint 'F from table', f\n\tprint 'f_stat', f_stat\n\tif f_stat > f:\n\t\tprint 'Reject null hypothesis'\n\t\t\"\"\"\n\t\tIn this case we have to make Post Hoc Test\n\t\t\"\"\"\n\t\tcount = 0\n\t\tfor i in range(m):\n\t\t\tfor j in range(m):\n\t\t\t\t# if i == j:\n\t\t\t\t# \tcontinue\n\t\t\t\tdiff = abs(x_bar_list[i] - x_bar_list[j])/ (float(ms_within)/n)\n\t\t\t\tdiff_stat = statsmodels.sandbox.stats.multicomp.get_tukeyQcrit(n, df_within, 0.05)\n\t\t\t\tif diff > diff_stat:\n\t\t\t\t\t# print fea_selection_list[i], classification_list[i], options_list[i], 'class_1_weight', class_weight_list[i]\n\t\t\t\t\t# print 'Is statistically significant than'\n\t\t\t\t\t# print fea_selection_list[j], classification_list[j], options_list[j], 'class_1_weight', class_weight_list[j]\n\t\t\t\t\t# print '----------------'\n\t\t\t\t\tif x_bar_list[i] <= x_bar_list[j]:\n\t\t\t\t\t\tprint 'B\\t',\n\t\t\t\t\telse:\n\t\t\t\t\t\tprint 'W\\t',\n\t\t\t\telse:\n\t\t\t\t\tprint 'Not\\t',\n\t\t\tprint ''\n\telse:\n\t\tprint 'Accept null hypothesis'"}],"string":"[\n {\n \"alpha_fraction\": 0.5680789351463318,\n \"alphanum_fraction\": 0.6082167625427246,\n \"avg_line_length\": 55.48473358154297,\n \"blob_id\": \"8a2d6df793b8af07a23c873f170299809c62e3c1\",\n \"content_id\": \"a1d976750bbd678cdf9be2ba69c9e0c556609aef\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 14799,\n \"license_type\": \"no_license\",\n \"max_line_length\": 122,\n \"num_lines\": 262,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/run.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from preprocess_transcation_table import preprocess_transcation_table\\nfrom preprocess_user_logs_table import preprocess_user_logs_table\\nfrom join_tables import join_tables\\nfrom split_data_random import split_data_random\\nfrom generate_histograms import generate_histograms\\nfrom kfold_cross_validation import kfold_cross_validation\\nfrom select_c_for_SVM_using_kfold_CV import select_c_for_SVM_using_kfold_CV\\nfrom classify_test_users import classify_test_users\\nfrom classify_test_users import test_different_number_of_samples\\nfrom select_parameters_for_MLP_using_kfold_CV import select_parameters_for_MLP_using_kfold_CV\\nimport os.path as path\\nimport os\\ndef run (step_number):\\n if step_number == 1:\\n # Step 1: Preprocess transcation data.\\n transcations_input_filename = 'transactions.csv'\\n transcations_output_filename = 'transactions_preprocessed.csv'\\n preprocess_transcation_table(transcations_input_filename,\\n transcations_output_filename)\\n elif step_number == 2:\\n # Step 2: Preprocess user_logs data\\n user_logs_input_filename = 'user_logs.csv'\\n user_logs_output_filename = 'user_logs_preprocessed.csv'\\n preprocess_user_logs_table(user_logs_input_filename,\\n user_logs_output_filename)\\n elif step_number == 3:\\n # Step 3: Join all tables based on user_id\\n transcations_output_filename = 'transactions_preprocessed.csv'\\n user_logs_output_filename = 'user_logs_preprocessed.csv'\\n train_input_filename = 'train.csv'\\n member_input_filename = 'members.csv'\\n joined_table_output_filename = 'joined_train_data.csv'\\n join_tables(transcations_output_filename, user_logs_output_filename,\\n train_input_filename, member_input_filename,\\n joined_table_output_filename)\\n elif step_number == 4:\\n # Step 4: Split joined table file into 10 subsets randomly.\\n split_number = 10\\n train_data_joined = 'joined_train_data.csv'\\n output_folder = 'train_subsets'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n train_ratio = 0.8\\n split_data_random(train_data_joined, split_number, output_folder,\\n train_ratio)\\n elif step_number == 5:\\n # Step 5: Generate features histogram.\\n train_file = path.join('train_subsets', '0_train.csv')\\n output_folder = 'histograms'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n k = 10\\n kfold_cross_validation(k, train_file, ['features_histogram'], output_folder, [])\\n elif step_number == 6:\\n # Step 6: Run PCA.\\n train_file = path.join('train_subsets', '0_train.csv')\\n output_folder = 'pca'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n k = 10\\n kfold_cross_validation(k, train_file, ['pca'], output_folder, [])\\n elif step_number == 7:\\n # Step 7: Run univariate_fea_selection fearure selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n output_folder = 'univariate_fea_selection'\\n k = 10\\n C = 10\\n number_of_features_to_select = 7\\n stats_vals = kfold_cross_validation(k, train_file, ['univariate_fea_selection'],\\n output_folder, [number_of_features_to_select, C])\\n elif step_number == 8:\\n # Step 8: Run linear_SVC fearure selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n output_folder = 'linear_SVC'\\n k = 10\\n sparsity_param = 0.002\\n C = 10\\n stats_vals = kfold_cross_validation(k, train_file, ['linear_SVC'],\\n output_folder, [sparsity_param, C])\\n elif step_number == 9:\\n # Step 9: Run 10 fold cross validation to choose C for linear SVM with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'linear_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 'linear'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 10:\\n # Step 10: Run 10 fold cross validation to choose C for linear SVM with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'linear_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 'linear'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n elif step_number == 11:\\n # Step 11: Run 10 fold cross validation to choose C for kernel SVM with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'kernel_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 'rbf'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 12:\\n # Step 12: Run 10 fold cross validation to choose C for kernel SVM with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'kernel_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 'rbf'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n elif step_number == 13:\\n # Step 13: Run 10 fold cross validation to choose C for one class linear SVM with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'one_class_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 'linear'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 14:\\n # Step 14: Run 10 fold cross validation to choose C for one class linear SVM with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'one_class_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 'linear'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n elif step_number == 15:\\n # Step 15: Run 10 fold cross validation to choose C for one class kernel SVM with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'one_class_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 'rbf'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 16:\\n # Step 16: Run 10 fold cross validation to choose C for one class kernel SVM with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'one_class_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 'rbf'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n elif step_number == 17:\\n # Step 17: For submitting on Kaggle, join the Kaggle test data with the other tables.\\n transcations_output_filename = 'transactions_preprocessed.csv'\\n user_logs_output_filename = 'user_logs_preprocessed.csv'\\n train_input_filename = 'sample_submission.csv'\\n member_input_filename = 'members.csv'\\n joined_table_output_filename = 'joined_test_data.csv'\\n join_tables(transcations_output_filename, user_logs_output_filename,\\n train_input_filename, member_input_filename,\\n joined_table_output_filename)\\n elif step_number == 18:\\n # Step 18: Classify Kaggle test user, using SVM with C learned form 10 fold cross validation.\\n train_file = 'joined_train_data.csv'\\n test_file = 'joined_test_data.csv'\\n test_file_to_get_users = 'sample_submission.csv'\\n methods_to_run = ['univariate_fea_selection', 'preceptron']\\n number_of_features_to_select = 14\\n C = 10\\n kernel = 'linear'\\n write_prediction = 1\\n prediction_file = 'prediction_naive_MLP_all_10_200.csv'\\n options = [number_of_features_to_select, C, kernel, write_prediction, prediction_file]\\n classify_test_users(train_file, test_file, methods_to_run, test_file_to_get_users, options)\\n elif step_number == 19:\\n # Step 19: 10 fold CV for MLP using univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n params_list = [(10,10,10), (10,10,10,10,10), (10,10,10,10,10,10,10,10,10,10),\\n (50,50,50), (50,50,50,50,50), (50,50,50,50,50,50,50,50,50,50),\\n (100,100,100), (100,100,100,100,100), (100,100,100,100,100,100,100,100,100,100)]\\n classification_alg = 'preceptron'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = ''\\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\\n fea_selection_alg, params_list, k,\\n number_of_features_to_select)\\n elif step_number == 20:\\n # Step 20: 10 fold CV for MLP using linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n params_list = [(10,10,10), (10,10,10,10,10), (10,10,10,10,10,10,10,10,10,10),\\n (50,50,50), (50,50,50,50,50), (50,50,50,50,50,50,50,50,50,50),\\n (100,100,100), (100,100,100,100,100), (100,100,100,100,100,100,100,100,100,100)]\\n classification_alg = 'preceptron'\\n fea_selection_alg = 'linear_SVC'\\n kernel = ''\\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\\n fea_selection_alg, params_list, k,\\n sparsity_param)\\n elif step_number == 21:\\n # Step 21: Test different number of samples for different classification algorithms.\\n # Parameters are selected based on CV best in precision and recall.\\n train_file = path.join('train_subsets', '0_train.csv')\\n test_file = path.join('train_subsets', '0_test.csv')\\n test_file_to_get_users = 'sample_submission.csv'\\n methods_to_run_list = [['univariate_fea_selection', 'linear_svm'], ['univariate_fea_selection', 'kernel_svm'],\\n ['univariate_fea_selection', 'naive_bayes'], ['univariate_fea_selection', 'preceptron']]\\n write_prediction = 0\\n prediction_file = ''\\n number_of_features_to_select = 7\\n linear_SVM_options = [number_of_features_to_select, 1, 'linear', write_prediction, prediction_file]\\n kernel_SVM_options = [number_of_features_to_select, 1, 'rbf', write_prediction, prediction_file]\\n naive_bayes_options = [number_of_features_to_select, 0, '', write_prediction, prediction_file]\\n preceptron_options = [number_of_features_to_select, (10, 10, 10), '', write_prediction, prediction_file]\\n options_list = [linear_SVM_options, kernel_SVM_options, naive_bayes_options, preceptron_options]\\n different_number_of_samples = range(2000, 22000, 2000)\\n output_folder = 'different_number_of_samples_experiment'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n test_different_number_of_samples(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list,\\n different_number_of_samples, output_folder)\\n elif step_number == 22:\\n # Step 22: Test different number of samples for different classification algorithms.\\n # Parameters are selected based on CV best in specificty.\\n train_file = path.join('train_subsets', '0_train.csv')\\n test_file = path.join('train_subsets', '0_test.csv')\\n test_file_to_get_users = 'sample_submission.csv'\\n methods_to_run_list = [['univariate_fea_selection', 'linear_svm'], ['univariate_fea_selection', 'kernel_svm'],\\n ['univariate_fea_selection', 'naive_bayes'], ['univariate_fea_selection', 'preceptron']]\\n write_prediction = 0\\n prediction_file = ''\\n number_of_features_to_select = 7\\n linear_SVM_options = [number_of_features_to_select, 10, 'linear', write_prediction, prediction_file]\\n kernel_SVM_options = [number_of_features_to_select, 85, 'rbf', write_prediction, prediction_file]\\n naive_bayes_options = [number_of_features_to_select, 0, '', write_prediction, prediction_file]\\n preceptron_options = [number_of_features_to_select, (10, 10, 10), '', write_prediction, prediction_file]\\n options_list = [linear_SVM_options, kernel_SVM_options, naive_bayes_options, preceptron_options]\\n different_number_of_samples = range(2000, 22000, 2000)\\n output_folder = 'different_number_of_samples_experiment'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n test_different_number_of_samples(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list,\\n different_number_of_samples, output_folder)\\n\"\n },\n {\n \"alpha_fraction\": 0.6732240319252014,\n \"alphanum_fraction\": 0.6874316930770874,\n \"avg_line_length\": 26.727272033691406,\n \"blob_id\": \"fb66966d8c91262764c0273fbbd85547408d8e9e\",\n \"content_id\": \"5c349cec29149905e1a7a037a2b480fed0781e51\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 915,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 33,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/svm.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nfrom sklearn import svm\\ndef train_kernel_svm (data, labels, c):\\n\\tclf = svm.SVC(C = c, random_state=0)\\n\\tclf.fit(data, labels.ravel())\\n\\treturn clf\\n\\ndef train_linear_svm (data, labels, c):\\n\\tlin_clf = svm.LinearSVC(C = c, random_state=0)\\n\\tlin_clf.fit(data, labels.ravel())\\n\\treturn lin_clf\\n\\ndef train_one_class_svm(data, kernel):\\n clf = svm.OneClassSVM(kernel=kernel, random_state=0)\\n\\tclf.fit(data)\\n\\treturn clf\\ndef classify_one_class_svm(clf, test_data):\\n\\tpred_confidence = clf.predict(test_data)\\n\\t\\\"\\\"\\\"\\n\\tleave label is 1\\n\\tstay label 0\\n\\t\\\"\\\"\\\"\\n\\t\\\"\\\"\\\"\\n predicted value\\n -1 will leave\\n\\t1 will stay\\n\\t\\\"\\\"\\\"\\n\\tpred = [1 if val == 1 else 0 for val in pred_confidence]\\n\\treturn pred, pred_confidence\\ndef classify (clf, test_data):\\n\\tpred_confidence = clf.decision_function(test_data)\\n\\tpred = [clf.classes_[1] if val > 0 else clf.classes_[0] for val in pred_confidence]\\n\\treturn pred, pred_confidence\\n\"\n },\n {\n \"alpha_fraction\": 0.6785714030265808,\n \"alphanum_fraction\": 0.75,\n \"avg_line_length\": 13,\n \"blob_id\": \"e7228010dbf60a3effe504b3a32dd3086b90c3fa\",\n \"content_id\": \"68982394a75e8a67a5f76c34a1b7edb35cac8a96\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 28,\n \"license_type\": \"no_license\",\n \"max_line_length\": 19,\n \"num_lines\": 2,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/run_all.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from run import run\\nrun(18)\\n\"\n },\n {\n \"alpha_fraction\": 0.7561349868774414,\n \"alphanum_fraction\": 0.7607361674308777,\n \"avg_line_length\": 35.27777862548828,\n \"blob_id\": \"7075d4d24198424d1dbf2965c9d9d8b897887ea2\",\n \"content_id\": \"f7d9c7343322d9d0274ecb47c7dd75fd3f455360\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 652,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 18,\n \"path\": \"/fea_selection.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from sklearn.feature_selection import SelectKBest\\nfrom sklearn.feature_selection import chi2\\nfrom sklearn.svm import LinearSVC\\nfrom sklearn.feature_selection import SelectFromModel\\n\\ndef univariate_fea_selection(X, y, k):\\n\\tselector = SelectKBest(chi2, k=k)\\n\\tselector.fit(X, y)\\n\\tindices = selector.get_support(indices=True)\\n\\treturn indices\\n\\ndef my_SelectFromModel(X, y, sparsity_param):\\n\\tlsvc = LinearSVC(C=sparsity_param, penalty=\\\"l1\\\", dual=False).fit(X, y.ravel())\\n\\tmodel = SelectFromModel(lsvc, prefit=True)\\n\\t# X_new = model.transform(X)\\n\\tindex_flag = model.get_support()\\n\\tindices = [i for i in range(len(index_flag)) if index_flag[i]]\\n\\treturn indices\"\n },\n {\n \"alpha_fraction\": 0.6450524926185608,\n \"alphanum_fraction\": 0.6611694097518921,\n \"avg_line_length\": 45,\n \"blob_id\": \"3b97a2fb02c71cb5f5c7178396af5ae87ab8144a\",\n \"content_id\": \"cc61e27966cab67aff1410ae7eff00ae63164bbe\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2668,\n \"license_type\": \"no_license\",\n \"max_line_length\": 140,\n \"num_lines\": 58,\n \"path\": \"/runner_wrapper.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os.path as path\\nimport os\\nfrom kfold_cross_validation import kfold_cross_validation\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\n\\ndef run(c_val, training_file_path, num_feas, k):\\n\\t# classification_alg_list = ['linear_svm', 'kernel_svm']\\n\\tclassification_alg_list = ['one_class_svm']\\n\\tfea_selection_alg_list = ['univariate_fea_selection', 'linear_SVC']\\n\\tkernel = 'rbf'\\n\\tfor i in range(len(classification_alg_list)):\\n\\t\\tfor j in range(len(fea_selection_alg_list)):\\n\\t\\t\\tclassification_alg = classification_alg_list[i]\\n\\t\\t\\tfea_selection_alg = fea_selection_alg_list[j]\\n\\t\\t\\tprint '%s, %s' % (classification_alg, fea_selection_alg)\\n\\t\\t\\toutput_folder = '%s/%s/%s_%s' %(classification_alg, kernel,classification_alg, fea_selection_alg)\\n\\t\\t\\tif not os.path.exists(output_folder):\\n\\t\\t\\t\\t\\tos.makedirs(output_folder)\\n\\t\\t\\toutput_file = open('%s/stats.txt' %(output_folder), 'a')\\n\\t\\t\\tlabels = ['Avg Error', 'Var Error', 'Avg Recall', 'Var recall', 'Avg Precision', 'Var Precision', 'Avg Specificity', 'Var Specificity']\\n\\t\\t\\toutput_file.write('C\\\\t')\\n\\t\\t\\tfor label in labels:\\n\\t\\t\\t\\toutput_file.write('%s\\\\t' %(label))\\n\\t\\t\\toutput_file.write('\\\\n')\\n\\t\\t\\t# output_file.write('C\\\\tAvg Error\\\\tVar Error\\\\tAvg Recall\\\\tVar recall\\\\tAvg Precision\\\\tVar Precision\\\\tAvg Specificity\\\\tVar Specificity\\\\n')\\n\\t\\t\\tvals_to_plot = []\\n\\t\\t\\tfor c in c_val:\\n\\t\\t\\t\\tprint '\\\\tC = %f' %(c)\\n\\t\\t\\t\\tmean, var = kfold_cross_validation(k, training_file_path, [fea_selection_alg, classification_alg], output_folder, [num_feas, c, kernel])\\n\\t\\t\\t\\toutput_file = open('%s/stats.txt' %(output_folder), 'a')\\n\\t\\t\\t\\toutput_file.write('%f\\\\t' %(c))\\n\\t\\t\\t\\tvals_to_plot.append([])\\n\\t\\t\\t\\tfor k in range(mean.shape[0]):\\n\\t\\t\\t\\t\\toutput_file.write('%f\\\\t%f\\\\t' %(mean[k], var[k]))\\n\\t\\t\\t\\t\\tvals_to_plot[-1].extend([mean[k], var[k]])\\n\\t\\t\\t\\toutput_file.write('\\\\n')\\n\\t\\t\\t\\toutput_file.close()\\n\\t\\t\\tplot_lists(c_val, labels, vals_to_plot, '%s/stats.png' %(output_folder), range(len(vals_to_plot)), False)\\n\\ndef plot_lists(n_vals, labels, vals, output_file, columns_to_plot, set_yticks):\\n\\t\\tmax_y = 0\\n\\t\\tfor column_index in range(len(vals[0])):\\n\\t\\t\\tplt.plot(n_vals, list(np.array(vals)[:, column_index]), label=labels[column_index])\\n\\t\\tplt.xlabel('C')\\n\\t\\tplt.legend([labels[i] for i in columns_to_plot], loc=\\\"center right\\\")\\n\\t\\tplt.xlim(0, n_vals[-1])\\n\\t\\tif set_yticks:\\n\\t\\t\\tplt.yticks(list(np.linspace(0, 5, 21)))\\n\\t\\t\\tplt.ylim(0, 5)\\n\\t\\t# plt.title('Trained using %s dataset, with batch size = 10, %d nodes'%(dataset_name, num_nodes)) \\n\\t\\tplt.savefig(output_file)\\t\\n\\t\\tplt.clf()\\nif __name__ == '__main__':\\n\\t# c_val = [0.1, 1, 10, 100, 1000]\\n\\tc_val = [1, 10, 25, 40, 55, 70, 85]\\n\\ttraining_file_path = path.join('train_subsets', '0_train.csv')\\n\\trun(c_val, training_file_path, num_feas = 5, k = 10)\\n\"\n },\n {\n \"alpha_fraction\": 0.4867839515209198,\n \"alphanum_fraction\": 0.502841055393219,\n \"avg_line_length\": 70.34684753417969,\n \"blob_id\": \"29f5ae185adf389ded795e4d9a45778c9ae3e93b\",\n \"content_id\": \"c87e108d73eb9fa024d70d85bc7a27859c80c2de\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 47518,\n \"license_type\": \"no_license\",\n \"max_line_length\": 274,\n \"num_lines\": 666,\n \"path\": \"/classify_test_users.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport csv\\nfrom kfold_cross_validation import run_feature_selection_and_classification\\nfrom matplotlib import pyplot\\nfrom os import path\\nimport os\\nimport matplotlib.pyplot as plt\\nimport random\\n\\ndef get_data(data_file):\\n csvfile = open(data_file,'rb')\\n lines = csv.reader(csvfile, delimiter = ',')\\n lines = list(lines)\\n header = lines[0]\\n # Remove headers from the data.\\n del lines[0]\\n data = np.array([[float(elem) for elem in line[2:]] for line in lines])\\n labels = np.array([[int(elem) for elem in line[1]] for line in lines])\\n user_ids = np.array([[elem for elem in line[0]] for line in lines])\\n return data, header, labels, user_ids\\n\\ndef classify_test_users(train_file, test_file, methods_to_run, test_file_to_get_users, options, class_1_weight = 1):\\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\\n labels = np.concatenate((train_labels, test_labels))\\n train_size = len(train_labels)\\n test_size = len(test_labels)\\n train_index = range(train_size)\\n test_index = range(train_size, (train_size + test_size))\\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data, test_data, labels,\\n train_index, test_index, test_user_ids,\\n options, test_header, test_file_to_get_users, class_1_weight = class_1_weight)\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(error, recall, precision, specificity)\\n\\ndef test_different_thresholds(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list, threshold_list, output_folder, class_1_weight = 1):\\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\\n train_size = len(train_labels)\\n test_size = len(test_labels)\\n train_index = range(train_size)\\n test_index = range(train_size, train_size + test_size)\\n labels = np.concatenate((train_labels, test_labels))\\n option_index = 0\\n for method_to_run in methods_to_run_list:\\n print(method_to_run)\\n options = options_list[option_index]\\n option_index = option_index + 1\\n precisions = np.zeros(len(threshold_list))\\n recalls = np.zeros(len(threshold_list))\\n specificities = np.zeros(len(threshold_list))\\n index = 0\\n if len(options) < 6:\\n error, recall, precision, specificity = run_feature_selection_and_classification(method_to_run, train_data, test_data, labels, train_index, test_index, test_user_ids, options, test_header, test_file_to_get_users, threshold_list = threshold_list)\\n else:\\n error, recall, precision, specificity = run_feature_selection_and_classification(method_to_run, train_data_subset, test_data, labels, train_index, test_index, test_user_ids, options, test_header, test_file_to_get_users, options[5], threshold_list = threshold_list)\\n precisions[index] = precision\\n recalls[index] = recall\\n specificities[index] = specificity\\n index = index + 1\\ndef test_different_number_of_samples(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list,\\n different_number_of_samples, output_folder):\\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\\n option_index = 0\\n for methods_to_run in methods_to_run_list:\\n print(methods_to_run)\\n options = options_list[option_index]\\n option_index = option_index + 1\\n precisions = np.zeros(len(different_number_of_samples))\\n recalls = np.zeros(len(different_number_of_samples))\\n specificities = np.zeros(len(different_number_of_samples))\\n index = 0\\n for samples_number in different_number_of_samples:\\n print(samples_number)\\n train_data_subset = train_data[:samples_number,]\\n train_labels_subset = train_labels[:samples_number,]\\n labels = np.concatenate((train_labels_subset, test_labels))\\n train_size = len(train_labels_subset)\\n test_size = len(test_labels)\\n train_index = range(train_size)\\n test_index = range(train_size, (train_size + test_size))\\n if len(options) < 6:\\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data_subset,\\n test_data, labels, train_index,\\n test_index, test_user_ids, options,\\n test_header, test_file_to_get_users)\\n else:\\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data_subset,\\n test_data, labels, train_index,\\n test_index, test_user_ids, options,\\n test_header, test_file_to_get_users,\\n class_1_weight = options[5])\\n precisions[index] = precision\\n recalls[index] = recall\\n specificities[index] = specificity\\n index = index + 1\\n pyplot.figure()\\n pyplot.ylim((-0.01, 1.1))\\n pyplot.xlabel('Number of Samples')\\n pyplot.plot(different_number_of_samples, precisions, color = 'r')\\n pyplot.plot(different_number_of_samples, recalls, color = 'b')\\n pyplot.plot(different_number_of_samples, specificities, color = 'g')\\n if len(options) < 6:\\n pyplot.legend([methods_to_run[1] + ' ' + options[2] + ' Precision',\\n methods_to_run[1] + ' ' + options[2]+ ' Recall',\\n methods_to_run[1] + ' ' + options[2] + ' Specificity'])\\n pyplot.savefig(path.join(output_folder, 'different_samples_'+options[2]+'_'+methods_to_run[1]+'.png'))\\n else:\\n pyplot.legend([methods_to_run[1] + ' ' + options[2] + ' With Class Weight Precision',\\n methods_to_run[1] + ' ' + options[2]+ ' With Class Weight Recall',\\n methods_to_run[1] + ' ' + options[2] + ' With Class Weight Specificity'])\\n pyplot.savefig(path.join(output_folder, 'different_samples_'+options[2]+'_'+methods_to_run[1]+'_with_class_weights.png')) \\n pyplot.close()\\n\\n\\ndef test_different_subsets(train_folder, test_file, methods_to_run_list, test_file_to_get_users,\\n options_list, output_folder, train_files_count):\\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\\n option_index = 0\\n for methods_to_run in methods_to_run_list:\\n print(methods_to_run)\\n options = options_list[option_index]\\n option_index = option_index + 1\\n precisions = np.zeros(train_files_count)\\n recalls = np.zeros(train_files_count)\\n specificities = np.zeros(train_files_count)\\n index = 0\\n for file_in_train_folder in os.listdir(train_folder):\\n if 'train' in file_in_train_folder: # As the folder contain train and test files.\\n print(file_in_train_folder)\\n train_data, train_header, train_labels, train_user_ids = get_data(path.join(train_folder,\\n file_in_train_folder))\\n labels = np.concatenate((train_labels, test_labels))\\n train_size = len(train_labels)\\n test_size = len(test_labels)\\n train_index = range(train_size)\\n test_index = range(train_size, (train_size + test_size))\\n if len(options) < 6:\\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data,\\n test_data, labels, train_index,\\n test_index, test_user_ids, options,\\n test_header, test_file_to_get_users)\\n else:\\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data,\\n test_data, labels, train_index,\\n test_index, test_user_ids, options,\\n test_header, test_file_to_get_users,\\n class_1_weight = options[5])\\n precisions[index] = precision\\n recalls[index] = recall\\n specificities[index] = specificity\\n index = index + 1\\n pyplot.figure()\\n pyplot.ylim((-0.01, 1.1))\\n pyplot.xlabel('Train Subset Number')\\n index = np.arange(train_files_count)\\n bar_width = 0.2\\n pyplot.bar(index, precisions, bar_width, color = 'r')\\n pyplot.bar(index + bar_width, recalls, bar_width, color = 'b')\\n pyplot.bar(index - bar_width, specificities, bar_width, color = 'g')\\n pyplot.xticks(index + bar_width, tuple([str(i) for i in range(train_files_count)]))\\n\\n if len(options) < 6:\\n pyplot.legend([methods_to_run[1] + ' ' + options[2] + ' Precision',\\n methods_to_run[1] + ' ' + options[2]+ ' Recall',\\n methods_to_run[1] + ' ' + options[2] + ' Specificity'])\\n pyplot.savefig(path.join(output_folder, 'different_subsets_'+options[2]+'_'+methods_to_run[1]+'.png'))\\n else:\\n pyplot.legend([methods_to_run[1] + ' ' + options[2] + ' With Class Weight Precision',\\n methods_to_run[1] + ' ' + options[2]+ ' With Class Weight Recall',\\n methods_to_run[1] + ' ' + options[2] + ' With Class Weight Specificity'])\\n pyplot.savefig(path.join(output_folder, 'different_subsets_'+options[2]+'_'+methods_to_run[1]+'_with_class_weights.png')) \\n pyplot.close()\\n\\ndef plot_roc(fea_selection, classification, file_name, specificity, sensitivity):\\n specificity_index = sorted(enumerate(specificity), key = lambda tup : tup[1])\\n specificity = [tup[1] for tup in specificity_index]\\n sensitivity = [sensitivity[tup[0]] for tup in specificity_index]\\n auc = np.trapz(sensitivity, x=specificity)\\n plt.xlabel('Specificity')\\n plt.ylabel('Sensitivity')\\n plt.plot(specificity,sensitivity)\\n return auc\\n\\ndef generate_ROC(train_file, test_file, threshold_list = -1):\\n random.seed(1)\\n roc_prefix = 'ROC'\\n if not os.path.exists(roc_prefix):\\n os.makedirs(roc_prefix)\\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\\n train_test_labels = np.concatenate((train_labels, test_labels))\\n train_train_labels = np.concatenate((train_labels, train_labels))\\n train_size = len(train_labels)\\n test_size = len(test_labels)\\n train_index = range(train_size)\\n test_index = range(train_size, (train_size + test_size))\\n train_test_index = range(train_size, (train_size + train_size))\\n \\n legend = []\\n\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'preceptron'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, tuple([50] * 10), '', 0],\\n test_header, '', threshold_list = threshold_list)\\n fea_selection_method = 'L$_1$-based fea selection'\\n classification_method = 'Perceptron'\\n legend += ['%s' %(classification_method)]\\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\\n print '>>linear_SVC, preceptron', auc\\n # print '\\\\t', 'test_error', test_error\\n # print '\\\\t', 'recall', recall\\n # print '\\\\t', 'precision', precision\\n # print '\\\\t', 'specificity', specificity\\n\\n c = 1\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'one_class_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, c, 'linear', 0, ''],\\n test_header, '', threshold_list = threshold_list)\\n fea_selection_method = 'L$_1$-based fea selection'\\n classification_method = 'One-Class Linear SVM'\\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\\n print '>>linear_SVC, one class linear_svm, %d' %(c), auc\\n # print '\\\\t', 'test_error', test_error\\n # print '\\\\t', 'recall', recall\\n # print '\\\\t', 'precision', precision\\n # print '\\\\t', 'specificity', specificity\\n legend += ['%s' %(classification_method)]\\n c = 85\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'one_class_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, c, 'rbf', 0, ''],\\n test_header, '', threshold_list = threshold_list)\\n fea_selection_method = 'L$_1$-based fea selection'\\n classification_method = 'One-Class RBF SVM'\\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\\n\\n print '>>linear_SVC, one class rbf_svm, %d' %(c),auc\\n # print '\\\\t', 'test_error', test_error\\n # print '\\\\t', 'recall', recall\\n # print '\\\\t', 'precision', precision\\n # print '\\\\t', 'specificity', specificity\\n legend += ['%s' %(classification_method)]\\n#-----------------------\\n class_1_weight = 1\\n c = 55\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, c, 'linear', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n fea_selection_method = 'L$_1$-based fea selection'\\n classification_method = 'Linear SVM'\\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c, class_1_weight), auc\\n # print '\\\\t', 'test_error', test_error\\n # print '\\\\t', 'recall', recall\\n # print '\\\\t', 'precision', precision\\n # print '\\\\t', 'specificity', specificity\\n legend += ['%s' %(classification_method)]\\n\\n c = 70\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, c, 'rbf', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n fea_selection_method = 'Chi-squared fea selection'\\n classification_method = 'RBF SVM'\\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\\n\\n print '>>univariate_fea_selection, RBF_svm, %d. class_1_weight = %d' %(c, class_1_weight), auc\\n # print '\\\\t', 'test_error', test_error\\n # print '\\\\t', 'recall', recall\\n # print '\\\\t', 'precision', precision\\n # print '\\\\t', 'specificity', specificity\\n legend += ['%s' %(classification_method)]\\n\\n c = 85\\n class_1_weight = 10\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, c, 'linear', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n\\n fea_selection_method = 'L$_1$-based fea selection'\\n classification_method = 'Linear SVM, class weights'\\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s_%d_class_1_weight.png' %(roc_prefix, fea_selection_method, classification_method, class_1_weight), specificity, recall)\\n\\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c, class_1_weight), auc\\n # print '\\\\t', 'test_error', test_error\\n # print '\\\\t', 'recall', recall\\n # print '\\\\t', 'precision', precision\\n # print '\\\\t', 'specificity', specificity\\n legend += ['%s' %(classification_method)]\\n\\n c = 1\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, c, 'rbf', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n \\n fea_selection_method = 'Chi-squared fea selection'\\n classification_method = 'RBF SVM, class weights'\\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s_%d_class_1_weight.png' %(roc_prefix, fea_selection_method, classification_method, class_1_weight), specificity, recall)\\n\\n print '>>univariate_fea_selection, RBF_svm, %d. class_1_weight = %d' %(c, class_1_weight), auc\\n # print '\\\\t', 'test_error', test_error\\n # print '\\\\t', 'recall', recall\\n # print '\\\\t', 'precision', precision\\n # print '\\\\t', 'specificity', specificity\\n legend += ['%s' %(classification_method)]\\n\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'naive_bayes'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, 0, '', 0, ''],\\n test_header, '', threshold_list = threshold_list)\\n \\n fea_selection_method = 'Chi-squared fea selection'\\n classification_method = 'Naive Bayes'\\n auc = plot_roc(fea_selection_method, classification_method, '%s/%s_%s.png' %(roc_prefix, fea_selection_method, classification_method), specificity, recall)\\n\\n print '>>univariate_fea_selection, Naive Bayes', auc\\n # print '\\\\t', 'test_error', test_error\\n # print '\\\\t', 'recall', recall\\n # print '\\\\t', 'precision', precision\\n # print '\\\\t', 'specificity', specificity\\n legend += ['%s' %(classification_method)]\\n\\n\\n plt.plot([0, 1],[1, 0], '--')\\n legend += ['Random']\\n plt.legend(legend)\\n plt.savefig('%s/roc.png' % (roc_prefix))\\n\\ndef generate_test_error_for_kernels(train_file, test_file, threshold_list = -1):\\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\\n train_test_labels = np.concatenate((train_labels, test_labels))\\n train_train_labels = np.concatenate((train_labels, train_labels))\\n train_size = len(train_labels)\\n test_size = len(test_labels)\\n train_index = range(train_size)\\n test_index = range(train_size, (train_size + test_size))\\n train_test_index = range(train_size, (train_size + train_size))\\n \\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'preceptron'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, tuple([50] * 10), '', 0],\\n test_header, '', threshold_list = threshold_list)\\n print '>>linear_SVC, preceptron'\\n print '\\\\t', 'test_error', test_error\\n\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, tuple([50] * 5), '', 0],\\n test_header, '', threshold_list = threshold_list)\\n print 'univariate_fea_selection, preceptron'\\n print '\\\\t', 'test_error', test_error\\n\\n c_list = [(55, 55), (1, 1)]\\n for c in c_list:\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'one_class_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, c[0], 'linear', 0, ''],\\n test_header, '', threshold_list = threshold_list)\\n print '>>univariate_fea_selection, one class linear_svm, %d' %(c[0])\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n \\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'one_class_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, c[1], 'linear', 0, ''],\\n test_header, '', threshold_list = threshold_list)\\n print '>>linear_SVC, one class linear_svm, %d' %(c[1])\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n \\n c_list = [(70, 70), (85, 85)]\\n for c in c_list:\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'one_class_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, c[0], 'rbf', 0, ''],\\n test_header, '', threshold_list = threshold_list)\\n print '>>univariate_fea_selection, one class rbf_svm, %d' %(c[0])\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n \\n\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'one_class_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, c[1], 'rbf', 0, ''],\\n test_header, '', threshold_list = threshold_list)\\n print '>>linear_SVC, one class rbf_svm, %d' %(c[1])\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n\\n#-----------------------\\n\\n c_list = [(25, 85), (10, 55)]\\n class_1_weight = 1\\n for c in c_list:\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'linear_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, c[0], 'linear', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n print '>>univariate_fea_selection, linear_svm, %d. class_1_weight = %d' %(c[0], class_1_weight)\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n\\n\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, c[1], 'linear', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c[1], class_1_weight)\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n\\n\\n c_list = [(1, 1), (70, 55)]\\n for c in c_list:\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, c[0], 'rbf', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n print '>>univariate_fea_selection, linear_svm, %d. class_1_weight = %d' %(c[0], class_1_weight)\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'kernel_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, c[1], 'rbf', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c[1], class_1_weight)\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n\\n c_list = [(10, 25), (10, 85)]\\n class_1_weight = 10\\n for c in c_list:\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'linear_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, c[0], 'linear', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n print '>>univariate_fea_selection, linear_svm, %d. class_1_weight = %d' %(c[0], class_1_weight)\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, c[1], 'linear', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c[1], class_1_weight)\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n\\n\\n c_list = [(40, 55), (1, 1)]\\n for c in c_list:\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, c[0], 'rbf', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n print '>>univariate_fea_selection, linear_svm, %d. class_1_weight = %d' %(c[0], class_1_weight)\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'kernel_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, c[1], 'rbf', 0, ''],\\n test_header, '', class_1_weight = class_1_weight, threshold_list = threshold_list)\\n print '>>linear_SVC, linear_svm, %d. class_1_weight = %d' %(c[1], class_1_weight)\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['univariate_fea_selection', 'naive_bayes'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, 0, '', 0, ''],\\n test_header, '', threshold_list = threshold_list)\\n\\n print '>>univariate_fea_selection, naive_bayes'\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n\\n test_error,recall, precision, specificity = run_feature_selection_and_classification(['linear_SVC', 'naive_bayes'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, 0, '', 0, ''],\\n test_header, '', threshold_list = threshold_list)\\n\\n print '>>linear_SVC, naive_bayes'\\n print 'Error: %0.3f, Recall: %0.3f\\\\nPrecision: %0.3f, Specificity: %0.7f' %(test_error, recall, precision, specificity)\\n\\ndef generate_train_test_error_for_different_kernels(train_file, test_file, output_folder):\\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\\n train_test_labels = np.concatenate((train_labels, test_labels))\\n train_train_labels = np.concatenate((train_labels, train_labels))\\n train_size = len(train_labels)\\n test_size = len(test_labels)\\n train_index = range(train_size)\\n test_index = range(train_size, (train_size + test_size))\\n train_test_index = range(train_size, (train_size + train_size))\\n # Run linear SVM.\\n train_error_1inear = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [0.002, 55, 'linear', 0, ''], train_header, '')[0]\\n test_error_linear = run_feature_selection_and_classification(['linear_SVC', 'linear_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, 55, 'linear', 0, ''],\\n test_header, '')[0]\\n\\n # Run poly SVM with degree 2.\\n train_error_poly_2 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [0.002, 40, 2, 0, ''], train_header, '')[0]\\n test_error_poly_2 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, 40, 2, 0, ''],\\n test_header, '')[0]\\n # Run poly SVM with degree 3.\\n train_error_poly_3 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [0.002, 70, 3, 0, ''], train_header, '')[0]\\n test_error_poly_3 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, 70, 3, 0, ''],\\n test_header, '')[0]\\n # Run poly SVM with degree 4.\\n train_error_poly_4 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [0.002, 55, 4, 0, ''], train_header, '')[0]\\n test_error_poly_4 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, 55, 4, 0, ''],\\n test_header, '')[0]\\n\\n # Run poly SVM with degree 10.\\n train_error_poly_10 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [0.002, 1, 10, 0, ''], train_header, '')[0]\\n test_error_poly_10 = run_feature_selection_and_classification(['linear_SVC', 'poly_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, 1, 10, 0, ''],\\n test_header, '')[0]\\n \\n # Run RBF Kernel SVM.\\n train_error_rbf_kernel = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [7, 70, 'rbf', 0, ''], train_header, '')[0]\\n test_error_rbf_kernel = run_feature_selection_and_classification(['univariate_fea_selection', 'kernel_svm'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, 70, 'rbf', 0, ''],\\n test_header, '')[0]\\n pyplot.figure()\\n pyplot.xlabel('Different Kernels')\\n index = [1, 2, 3, 4, 5, 6]\\n pyplot.xticks(index, ['Linear', 'Poly Degree 2', 'Poly Degree 3',\\n 'Poly Degree 4', 'Poly Degree 10', 'RBF'], fontsize=7)\\n pyplot.plot(index, [train_error_1inear, train_error_poly_2, train_error_poly_3,\\n train_error_poly_4, train_error_poly_10, train_error_rbf_kernel], color = 'r')\\n pyplot.plot(index, [test_error_linear, test_error_poly_2, test_error_poly_3,\\n test_error_poly_4, test_error_poly_10, test_error_rbf_kernel], color = 'b')\\n pyplot.legend(['Training Error', 'Testing Error'])\\n pyplot.savefig(path.join(output_folder, 'SVM_with_different_kernels.png')) \\n pyplot.close()\\n\\ndef generate_train_test_error_for_different_MLP(train_file, test_file, output_folder):\\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\\n train_test_labels = np.concatenate((train_labels, test_labels))\\n train_train_labels = np.concatenate((train_labels, train_labels))\\n train_size = len(train_labels)\\n test_size = len(test_labels)\\n train_index = range(train_size)\\n test_index = range(train_size, (train_size + test_size))\\n train_test_index = range(train_size, (train_size + train_size))\\n # Run MLP with 3 layer.\\n train_error_3 = run_feature_selection_and_classification(['linear_SVC', 'preceptron'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [0.002, tuple([50] * 3), '', 0], train_header, '')[0]\\n test_error_3 = run_feature_selection_and_classification(['linear_SVC', 'preceptron'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [0.002, tuple([50] * 3), '', 0],\\n test_header, '')[0]\\n\\n # Run MLP with 5 layer.\\n train_error_5 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [7, tuple([50] * 5), '', 0], train_header, '')[0]\\n test_error_5 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, tuple([50] * 5), '', 0],\\n test_header, '')[0]\\n # Run MLP with 10 layer.\\n train_error_10 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [7, tuple([50] * 10), '', 0], train_header, '')[0]\\n test_error_10 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, tuple([50] * 10), '', 0],\\n test_header, '')[0]\\n # Run MLP with 20 layer.\\n train_error_13 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [7, tuple([50] * 13), '', 0], train_header, '')[0]\\n test_error_13 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, tuple([50] * 13), '', 0],\\n test_header, '')[0]\\n\\n # Run MLP with 50 layer.\\n train_error_15 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [7, tuple([50] * 15), '', 0], train_header, '')[0]\\n test_error_15 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, tuple([50] * 15), '', 0],\\n test_header, '')[0]\\n \\n # Run MLP with 100 layer.\\n train_error_20 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\\n train_data, train_data, train_train_labels, train_index,\\n train_test_index, train_user_ids,\\n [7, tuple([50] * 20), '', 0], train_header, '')[0]\\n test_error_20 = run_feature_selection_and_classification(['univariate_fea_selection', 'preceptron'],\\n train_data, test_data, train_test_labels, train_index,\\n test_index, test_user_ids, [7, tuple([50] * 20), '', 0],\\n test_header, '')[0]\\n pyplot.figure()\\n pyplot.xlabel('Different Number of Layers')\\n index = [1, 2, 3, 4, 5, 6]\\n pyplot.xticks(index, ['3', '5', '10', '13', '15', '20'], fontsize=7)\\n pyplot.plot(index, [train_error_3, train_error_5, train_error_10,\\n train_error_13, train_error_15, train_error_20], color = 'r')\\n pyplot.plot(index, [test_error_3, test_error_5, test_error_10,\\n test_error_13, test_error_15, test_error_20], color = 'b')\\n pyplot.legend(['Training Error', 'Testing Error'])\\n pyplot.savefig(path.join(output_folder, 'MLP_with_different_layers.png')) \\n pyplot.close()\\n\\n \\n \\n\\nif __name__ == '__main__':\\n # train_file = 'train_subsets/0_train.csv'\\n # test_file = 'test_subsets/0_test.csv'\\n \\n # number_of_features_to_select = 7\\n \\n # # methods_to_run = ['linear_SVC', 'kernel_svm']\\n # # C = 40\\n # # class_1_weight = 10\\n # # kernel = 'rbf'\\n\\n # # write_prediction = 0\\n # # prediction_file = ''\\n # # sparsity_param = 0.002\\n # # options = [sparsity_param, C, kernel, write_prediction, prediction_file]\\n\\n # write_prediction = 0\\n # prediction_file = ''\\n # methods_to_run = ['linear_SVC', 'preceptron']\\n # options = [number_of_features_to_select, [100] * 10, '', write_prediction, prediction_file]\\n # classify_test_users(train_file, test_file, methods_to_run, '', options)\\n\\n train_file = path.join('train_subsets', '0_train.csv')\\n test_file = path.join('train_subsets', '0_test.csv')\\n generate_test_error_for_kernels(train_file, test_file)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6206896305084229,\n \"alphanum_fraction\": 0.6448276042938232,\n \"avg_line_length\": 40.39285659790039,\n \"blob_id\": \"b1595fe0a89712403a23a9558f4c8c2bc2ca9e45\",\n \"content_id\": \"58f08d018dcd540780d273b9a6379d20f2a401eb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1160,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 28,\n \"path\": \"/generate_histograms.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import csv\\nimport numpy as np\\nfrom matplotlib import pyplot\\nimport os.path as path\\ndef generate_histograms(train_file, output_folder):\\n train_csvfile = open(train_file,'rb')\\n lines = csv.reader(train_csvfile, delimiter = ',')\\n lines = list(lines)\\n # Remove headers.\\n del lines[0]\\n data = np.array(lines)\\n # Split data into positive class and neg class.\\n data_class1 = data[data[:,1] == '0']\\n data_class2 = data[data[:,1] == '1']\\n features_number = len(data[0])\\n for feature in range(features_number):\\n if feature == 0 or feature == 1:\\n # Skip user id and class label.\\n continue\\n feature_column_class1 = data_class1[:,feature]\\n feature_column_class2 = data_class2[:,feature]\\n feature_column_class1 = feature_column_class1.astype(np.float)\\n feature_column_class2 = feature_column_class2.astype(np.float)\\n pyplot.figure()\\n pyplot.hist(feature_column_class1, 100, label='Class 0')\\n pyplot.hist(feature_column_class2, 100, label='Class 1')\\n pyplot.legend(loc='upper right')\\n pyplot.savefig(path.join(output_folder, str(feature) + '.png'))\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.580386757850647,\n \"alphanum_fraction\": 0.5872007608413696,\n \"avg_line_length\": 54.12690353393555,\n \"blob_id\": \"8e4d11bfae4b4bc4b5bdeb74cce5d5bfd74e0c69\",\n \"content_id\": \"a0782e81fcd1137a6e274ec98508059e400fe2ec\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 10860,\n \"license_type\": \"no_license\",\n \"max_line_length\": 128,\n \"num_lines\": 197,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/kfold_cross_validation.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import math\\nimport csv\\nfrom my_PCA import MyPCA\\nimport numpy as np\\nfrom matplotlib import pyplot\\nimport os.path as path\\nfrom sklearn.naive_bayes import GaussianNB\\nfrom calculate_performance_measures import calculate_performance_measures\\nfrom sklearn import mixture\\nfrom svm import *\\nfrom fea_selection import *\\nimport math\\nfrom sklearn.neural_network import MLPClassifier\\n\\n\\ndef write_predictions(validation_users_id, predicted_labels, options, test_file_to_get_users):\\n write_prediction = options[3]\\n if write_prediction == 1:\\n test_csvfile = open(test_file_to_get_users,'rb')\\n lines = csv.reader(test_csvfile, delimiter = ',')\\n test_users = []\\n for line in lines:\\n test_users.append(line[0])\\n prediction_filename = options[4]\\n users_number = len(validation_users_id)\\n validation_users_dict = {}\\n index = 0\\n for user in range(users_number):\\n user_id = validation_users_id[user]\\n validation_users_dict[''.join(user_id)] = index\\n index = index + 1\\n prediction_file = open(prediction_filename, 'w')\\n prediction_file.write('msno,is_churn\\\\n')\\n for user in test_users:\\n if user in validation_users_dict:\\n #normalized_prediction = 1.0/(1.0+ math.e **( -1 * predicted_labels[validation_users_dict[user]]))\\n prediction_file.write(user+','+str(predicted_labels[validation_users_dict[user]][1])+'\\\\n')\\n\\t else:\\n prediction_file.write(user+',0.03\\\\n')\\n prediction_file.close()\\n\\ndef run_feature_selection_and_classification(methods_to_run, train_data, validation_data, labels,\\n train_index, validation_index, validation_users_id,\\n options, header, test_file_to_get_users):\\n\\tif 'univariate_fea_selection' in methods_to_run:\\n\\t\\t# Run feature selection.\\n\\t\\tnumber_of_features_to_select = options[0]\\n\\t\\tselected_features_indices = univariate_fea_selection(train_data, labels[train_index],\\n number_of_features_to_select)\\n\\t\\ttrain_data = train_data[:, selected_features_indices]\\n\\t\\tvalidation_data = validation_data[:, selected_features_indices]\\n\\t\\tselected_featuees = np.array(header)[np.array(selected_features_indices)+2]\\n\\t\\tfor selected_feature in range(len(selected_featuees)):\\n\\t\\t\\tprint(selected_featuees[selected_feature])\\n\\tif 'linear_SVC' in methods_to_run:\\n\\t\\t# Run feature selection.\\n\\t\\tsparsity_param = options[0]\\n\\t\\tselected_features_indices = my_SelectFromModel(train_data, labels[train_index],\\n sparsity_param)\\n\\t\\ttrain_data = train_data[:, selected_features_indices]\\n\\t\\tvalidation_data = validation_data[:, selected_features_indices]\\n\\t\\tselected_featuees = np.array(header)[np.array(selected_features_indices)+2]\\n\\t\\tfor selected_feature in range(len(selected_featuees)):\\n\\t\\t\\tprint(selected_featuees[selected_feature])\\n\\tif 'linear_svm' in methods_to_run: # Run linear SVM.\\n\\t\\tC = options[1]\\n\\t\\tlinear_svm_model = train_linear_svm(train_data, labels[train_index], C)\\n\\t\\tpredicted_labels, predicted_values = classify(linear_svm_model, validation_data)\\n\\t\\twrite_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n\\t\\terror, recall, precision, specificity = calculate_performance_measures(predicted_labels,\\n labels[validation_index])\\n return error, recall, precision, specificity\\n\\tif 'kernel_svm' in methods_to_run: # Run Kernel SVM.\\n\\t\\tC = options[1]\\n\\t\\tkernel_svm_model = train_kernel_svm(train_data, labels[train_index], C)\\n\\t\\tpredicted_labels, predicted_values = classify(kernel_svm_model, validation_data)\\n\\t\\twrite_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n\\t\\terror, recall, precision, specificity = calculate_performance_measures(predicted_labels,\\n labels[validation_index])\\n return error, recall, precision, specificity\\n\\tif 'one_class_svm' in methods_to_run:\\n\\t\\ttemp = np.array([label[0] for label in labels[train_index]]) == 0\\n\\t\\ttrain_data = train_data[temp, :]\\n\\t\\tone_class_svm_model = train_one_class_svm(train_data, options[2])\\n\\t\\tpredicted_labels, predicted_values = classify_one_class_svm(one_class_svm_model, train_data)\\n\\t\\terror, recall, precision, specificity = calculate_performance_measures(predicted_labels,\\n labels[validation_index])\\n\\t\\twrite_predictions(validation_users_id, predicted_values, options, test_file_to_get_users) \\n return error, recall, precision, specificity\\n\\tif 'preceptron' in methods_to_run: # Run multilayer preceptron.\\n\\t\\tclf = MLPClassifier(hidden_layer_sizes = options[1], verbose=True)\\n\\t\\tclf.fit(train_data, labels[train_index])\\n\\t\\tpredicted_labels = clf.predict(validation_data)\\n\\t\\terror, recall, precision, specificity = calculate_performance_measures(predicted_labels,\\n labels[validation_index])\\n predicted_values = clf.predict_proba(validation_data)\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n return error, recall, precision, specificity \\n\\tif 'naive_bayes' in methods_to_run:\\n clf = GaussianNB()\\n clf.fit(train_data, labels[train_index])\\n predicted_labels = clf.predict(validation_data)\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels,\\n labels[validation_index])\\n predicted_values = clf.predict_proba(validation_data)\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n return error, recall, precision, specificity\\n if 'guassian' in methods_to_run:\\n clf = mixture.GaussianMixture(n_components=2,\\n covariance_type='full')\\n clf.fit(train_data, labels[train_index])\\n predicted_labels = clf.predict(validation_data)\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels,\\n labels[validation_index])\\n predicted_values = clf.predict_proba(validation_data)\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n return error, recall, precision, specificity\\n\\treturn 0, 0, 0, 0\\n\\nfrom scipy import stats\\nimport scipy as sp\\ndef kfold_cross_validation(k, train_file, methods_to_run,\\n output_folder, options):\\n train_csvfile = open(train_file,'rb')\\n lines = csv.reader(train_csvfile, delimiter = ',')\\n lines = list(lines)\\n header = lines[0]\\n # Remove headers from the data.\\n del lines[0]\\n samples_number = len(lines)\\n # Transform it to matrix.\\n # Skip the user id and the label columns.\\n data = np.array([[float(elem) for elem in line[2:]] for line in lines])\\n labels = np.array([[int(elem) for elem in line[1]] for line in lines])\\n user_ids = np.array([[elem for elem in line[0]] for line in lines])\\n features_number = len(data[0])\\n measures = np.zeros([4, k])\\n for i in range(k):\\n start_i = int(math.floor(samples_number * i / k))\\n end_i = int(math.floor(samples_number * (i + 1) / k))\\n validation_index = range(start_i, end_i)\\n points_index = range(samples_number)\\n train_index = list(set(points_index).difference(set(validation_index)));\\n train_labels = labels[train_index, :]\\n zero_indices = [j for j in range(len(train_labels)) if train_labels[j] == 0]\\n one_indices = [j for j in range(len(train_labels)) if train_labels[j] == 1]\\n train_data = data[train_index, :]\\n validation_data = data[validation_index, :]\\n if 'features_histogram' in methods_to_run:\\n # Caclulate features histogram for each fold training data.\\n for feature in range(features_number):\\n train_data = data[train_index, feature]\\n feature_column_class0 = train_data[zero_indices]\\n feature_column_class1 = train_data[one_indices]\\n pyplot.figure()\\n pyplot.hist(feature_column_class0, 100, label='Class 0')\\n pyplot.hist(feature_column_class1, 100, label='Class 1')\\n pyplot.legend(loc='upper right')\\n pyplot.savefig(path.join(output_folder, 'hist_fold=' + str(i) +\\n '_' + header[feature+2] + '.png'))\\n pyplot.close()\\n if 'pca' in methods_to_run:\\n # Run PCA.\\n myPca = MyPCA()\\n myPca.fit(train_data, n_components = 2)\\n # Visualize the training data.\\n projected_data = myPca.transform(train_data)\\n pyplot.figure()\\n pyplot.plot(projected_data[zero_indices, 0], projected_data[zero_indices, 1],\\n 'o', color = 'r')\\n pyplot.plot(projected_data[one_indices, 0], projected_data[one_indices, 1],\\n 'x', color = 'b')\\n pyplot.savefig(path.join(output_folder, 'pca_fold=' +\\n str(i) + '.png'))\\n pyplot.close()\\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run,\\n train_data, validation_data,\\n labels, train_index, validation_index,\\n user_ids[validation_index, :], options,\\n header, '')\\n measures[0, i] = error\\n\\tmeasures[1, i] = recall\\n\\tmeasures[2, i] = precision\\n\\tmeasures[3, i] = specificity\\n stats_vals = []\\n alpha_val = 0.95\\n for i in range(4):\\n mean, error = mean_confidence_interval(measures[i, :], alpha_val)\\n stats_vals.append([mean, error])\\n return stats_vals\\n\\ndef mean_confidence_interval(data, confidence=0.95):\\n a = 1.0*np.array(data)\\n n = len(a)\\n m, se = np.mean(a), sp.stats.sem(a)\\n h = se * sp.stats.t._ppf((1+confidence)/2., n-1)\\n return m, h\\n\"\n },\n {\n \"alpha_fraction\": 0.5353260636329651,\n \"alphanum_fraction\": 0.55027174949646,\n \"avg_line_length\": 35.849998474121094,\n \"blob_id\": \"c65d587e2a399fe058298c28b08b16c5c407106d\",\n \"content_id\": \"9bdd5f9031732c5287869d9508b1fad8cb0874f0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 736,\n \"license_type\": \"no_license\",\n \"max_line_length\": 84,\n \"num_lines\": 20,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/plot_cross_validation_SVM.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport matplotlib.pyplot as plt\\n\\ndef plot_cross_validation_SVM(k, n_vals, labels, mean_vals, error_vals, output_file,\\n columns_to_plot, set_yticks):\\n max_y = 0\\n plt.clf()\\n for i in range(mean_vals.shape[1]):\\n plt.clf()\\n plt.errorbar(n_vals, mean_vals[:, i], yerr = error_vals[:, i])\\n plt.xlabel('C')\\n # plt.legend([labels[i] for i in range(mean_vals.shape[1])])\\n plt.ylabel(labels[i])\\n plt.title(labels[i])\\n # plt.legend(labels[i])\\n # plt.xlim(n_vals[0], n_vals[-1])\\n plt.savefig('%s_%s.png' %(output_file, labels[i]))\\n if set_yticks:\\n plt.yticks(list(np.linspace(0, 5, 21)))\\n plt.ylim(0, 5)\"\n },\n {\n \"alpha_fraction\": 0.6140651702880859,\n \"alphanum_fraction\": 0.6317895650863647,\n \"avg_line_length\": 37.0217399597168,\n \"blob_id\": \"ee81aed61df01760c9e65eed617d8e5ff2a354ee\",\n \"content_id\": \"a957802604f98e13cfd70bdb0d5e7c2dd5728523\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1749,\n \"license_type\": \"no_license\",\n \"max_line_length\": 133,\n \"num_lines\": 46,\n \"path\": \"/svm.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nfrom sklearn import svm\\nfrom helper import sigmoid\\n\\ndef train_kernel_svm (data, labels, c, class_1_weight = 1):\\n clf = svm.SVC(C = c, class_weight = {1: class_1_weight}, random_state=0)\\n clf.fit(data, labels.ravel())\\n return clf\\n\\ndef train_poly_kernel_svm (data, degree, labels, c, class_1_weight = 1):\\n print('in poly svm, training data = '+str(data.shape))\\n clf = svm.SVC(C = c, kernel = 'poly', degree = degree, random_state=0, verbose = 2)\\n clf.fit(data, labels.ravel())\\n return clf\\n\\ndef train_linear_svm (data, labels, c, class_1_weight = 1):\\n lin_clf = svm.LinearSVC(C = c, class_weight = {1: class_1_weight}, random_state=0)\\n lin_clf.fit(data, labels.ravel())\\n return lin_clf\\n\\ndef train_one_class_svm(data, kernel):\\n clf = svm.OneClassSVM(kernel=kernel, random_state=0)\\n clf.fit(data)\\n return clf\\ndef classify_one_class_svm(clf, test_data, threshold = -1):\\n if threshold == -1:\\n pred_confidence = clf.predict(test_data)\\n \\\"\\\"\\\"\\n leave label is 1\\n stay label 0\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n predicted value\\n -1 will leave\\n 1 will stay\\n \\\"\\\"\\\"\\n pred = [1 if val == 1 else 0 for val in pred_confidence]\\n return pred, pred_confidence\\n else:\\n pred_confidence = clf.decision_function(test_data)\\n pred = [1 if (val if threshold == 0 else sigmoid(val)) > threshold else 0 for val in pred_confidence]\\n return pred, pred_confidence\\ndef classify (clf, test_data, threshold = 0):\\n pred_confidence = clf.decision_function(test_data)\\n pred = [clf.classes_[1] if (val if threshold == 0 else sigmoid(val)) > threshold else clf.classes_[0] for val in pred_confidence]\\n return pred, pred_confidence\\n\"\n },\n {\n \"alpha_fraction\": 0.7257142663002014,\n \"alphanum_fraction\": 0.7295238375663757,\n \"avg_line_length\": 28.22222137451172,\n \"blob_id\": \"a320645ca3053dcf9c6bad6d242341e2d7a865d7\",\n \"content_id\": \"9b799c47fcbdee64f2b010674cae31c00c677f09\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 525,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 18,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/my_PCA.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from sklearn.decomposition import PCA, KernelPCA\\nimport numpy as np\\nclass MyPCA(object):\\n\\tdef fit(self, data, n_components):\\n\\t\\tself.pca = PCA(n_components=n_components)\\n\\t\\tself.pca.fit(data)\\n\\n\\tdef transform(self, data):\\n\\t\\treturn self.pca.transform(data)\\n\\n\\nclass MyKernelPCA(object):\\n\\tdef fit(self, data, n_components):\\n\\t\\t#self.pca = PCA(n_components=n_components)\\n\\t\\tself.pca = KernelPCA(n_components = n_components, kernel = 'rbf', gamma = 10)\\n\\t\\tself.pca.fit(data)\\n\\tdef transform(self, data):\\n\\t\\treturn self.pca.transform(data)\"\n },\n {\n \"alpha_fraction\": 0.6037047505378723,\n \"alphanum_fraction\": 0.6070727109909058,\n \"avg_line_length\": 52.90909194946289,\n \"blob_id\": \"ad0455d12c91881175b84bfb440df609cc84dced\",\n \"content_id\": \"96cf053812d46959ae0605b8b6b298fc0e49e989\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3563,\n \"license_type\": \"no_license\",\n \"max_line_length\": 146,\n \"num_lines\": 66,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/classify_test_users.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\nimport csv\\nfrom kfold_cross_validation import run_feature_selection_and_classification\\nfrom matplotlib import pyplot\\nfrom os import path\\n\\ndef get_data(data_file):\\n csvfile = open(data_file,'rb')\\n lines = csv.reader(csvfile, delimiter = ',')\\n lines = list(lines)\\n header = lines[0]\\n # Remove headers from the data.\\n del lines[0]\\n data = np.array([[float(elem) for elem in line[2:]] for line in lines])\\n labels = np.array([[int(elem) for elem in line[1]] for line in lines])\\n user_ids = np.array([[elem for elem in line[0]] for line in lines])\\n return data, header, labels, user_ids\\n\\ndef classify_test_users(train_file, test_file, methods_to_run, test_file_to_get_users, options):\\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\\n labels = np.concatenate((train_labels, test_labels))\\n train_size = len(train_labels)\\n test_size = len(test_labels)\\n train_index = range(train_size)\\n test_index = range(train_size, (train_size + test_size))\\n run_feature_selection_and_classification(methods_to_run, train_data, test_data, labels,\\n train_index, test_index, test_user_ids,\\n options, test_header, test_file_to_get_users)\\n\\ndef test_different_number_of_samples(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list,\\n different_number_of_samples, output_folder):\\n train_data, train_header, train_labels, train_user_ids = get_data(train_file)\\n test_data, test_header, test_labels, test_user_ids = get_data(test_file)\\n option_index = 0\\n for methods_to_run in methods_to_run_list:\\n print(methods_to_run)\\n options = options_list[option_index]\\n option_index = option_index + 1\\n precisions = np.zeros(len(different_number_of_samples))\\n recalls = np.zeros(len(different_number_of_samples))\\n index = 0\\n for samples_number in different_number_of_samples:\\n print(samples_number)\\n train_data_subset = train_data[:samples_number,]\\n train_labels_subset = train_labels[:samples_number,]\\n labels = np.concatenate((train_labels_subset, test_labels))\\n train_size = len(train_labels_subset)\\n test_size = len(test_labels)\\n train_index = range(train_size)\\n test_index = range(train_size, (train_size + test_size))\\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run, train_data_subset, test_data, labels,\\n train_index, test_index, test_user_ids,\\n options, test_header, test_file_to_get_users)\\n precisions[index] = precision\\n recalls[index] = recall\\n index = index + 1\\n print(precision)\\n print(recall)\\n pyplot.figure()\\n pyplot.xlabel('Number of Samples')\\n pyplot.plot(different_number_of_samples, precisions, color = 'r')\\n pyplot.plot(different_number_of_samples, recalls, color = 'b')\\n pyplot.legend([methods_to_run[1] + ' Precision', methods_to_run[1] + ' Recall'])\\n pyplot.savefig(path.join(output_folder, 'different_samples_'+methods_to_run[1]+'.png'))\\n pyplot.close()\\n \\n\"\n },\n {\n \"alpha_fraction\": 0.5068492889404297,\n \"alphanum_fraction\": 0.6164383292198181,\n \"avg_line_length\": 13.600000381469727,\n \"blob_id\": \"c4a32c2d33a11a990ff8102394148b72e53ca760\",\n \"content_id\": \"88c09ca7d1f2bcaf838316a6a578cfbff474e802\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 73,\n \"license_type\": \"no_license\",\n \"max_line_length\": 24,\n \"num_lines\": 5,\n \"path\": \"/run_all.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from run import run\\n#for i in range(30, 36):\\n# run(i)\\nrun(39)\\nrun(40)\\n\"\n },\n {\n \"alpha_fraction\": 0.6417526006698608,\n \"alphanum_fraction\": 0.6546391844749451,\n \"avg_line_length\": 54.42856979370117,\n \"blob_id\": \"53610a058e8f8b6407b305fa30af554cf24b21de\",\n \"content_id\": \"8d1276834d00fd3b4123f9cb2879904a6211ecdb\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 776,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 14,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/calculate_performance_measures.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\ndef calculate_performance_measures(predicted_labels, actual_labels):\\n # Positive class is class 0, which means user will renew.\\n # Negative class is class 1, which means user won't renew.\\n predicted_labels = np.array(predicted_labels)\\n TP = float(np.sum(np.logical_and(predicted_labels == 0, actual_labels == 0)))\\n FP = float(np.sum(np.logical_and(predicted_labels == 0, actual_labels == 1)))\\n FN = float(np.sum(np.logical_and(predicted_labels == 1, actual_labels == 0)))\\n TN = float(np.sum(np.logical_and(predicted_labels == 1, actual_labels == 1)))\\n error = (FP + FN) / (TP + FP + FN + TN)\\n recall = TP / (TP + FN)\\n precision = TP / (TP + FP)\\n specificity = TN / (TN + FP)\\n return error, recall, precision, specificity\\n\"\n },\n {\n \"alpha_fraction\": 0.5625,\n \"alphanum_fraction\": 0.5743007063865662,\n \"avg_line_length\": 41.24074172973633,\n \"blob_id\": \"d5799b45c308b339b43da42808a26c9e041b0316\",\n \"content_id\": \"56931843ab8b6ee234b38dc9c941aac2d0bafa00\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2288,\n \"license_type\": \"no_license\",\n \"max_line_length\": 107,\n \"num_lines\": 54,\n \"path\": \"/preprocess_transcation_table.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import csv\\ndef preprocess_transcation_table(input_filename, output_filename):\\n users_dict = {}; \\n csvfile = open(input_filename,'rb')\\n is_header = True\\n row_count = 0\\n for row in csv.reader(csvfile, delimiter = ','):\\n if is_header: # Skip header line.\\n is_header = False\\n continue\\n if row_count % 1000000 == 0:\\n print(row_count)\\n row_count = row_count + 1\\n user_id = row[0]\\n # Skip payment_method_id field as it is a categorical data.\\n payment_plan_days = float(row[2])\\n plan_list_price = float(row[3])\\n actual_amount_paid = float(row[4])\\n is_auto_renew = float(row[5])\\n # Skip transaction_date and membership_expire_date as it is\\n # categorical data.\\n is_cancel = float(row[8])\\n user_record_count = 1\\n user_data = []\\n if user_id in users_dict:\\n user_data = users_dict[user_id]\\n user_data[0] += payment_plan_days\\n user_data[1] += plan_list_price\\n user_data[2] += actual_amount_paid\\n user_data[3] += is_auto_renew\\n user_data[4] += is_cancel\\n user_data[5] += user_record_count \\n else: # First time to encouter this user.\\n user_data = [payment_plan_days, plan_list_price, actual_amount_paid,\\n is_auto_renew, is_cancel, user_record_count] \\n # Add user to the dictionary.\\n users_dict[user_id] = user_data\\n # Average user records and then write it to the output file.\\n output_file = open(output_filename, \\\"w\\\")\\n output_file.write('msno,payment_plan_days,plan_list_price,actual_amount_paid,is_auto_renew,is_cancel\\\\n')\\n for user_id in users_dict:\\n user_data = users_dict[user_id]\\n user_records_number = user_data[5]\\n output_line = user_id\\n fields_count = 0\\n for user_attribute in user_data:\\n user_attribute = user_attribute / user_records_number\\n output_line = output_line + ',' + str(user_attribute)\\n fields_count = fields_count + 1\\n if fields_count == 5:\\n # Fifth field is the count field, don't include.\\n break\\n output_file.write(output_line + '\\\\n')\\n output_file.close()\\n \\n \\n\"\n },\n {\n \"alpha_fraction\": 0.5264804363250732,\n \"alphanum_fraction\": 0.5488268136978149,\n \"avg_line_length\": 36.923728942871094,\n \"blob_id\": \"2d8dde636be919befdfa9688864640e381c9ae57\",\n \"content_id\": \"d9ef50dfc64608f15f999ed9d9f45c030a982d2b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4475,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 118,\n \"path\": \"/join_tables.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import csv\\ndef join_tables(preprocessed_transcation_file, preprocessed_user_logs_file,\\n train_file, members_file, output_filename):\\n users_dict = {};\\n # Load users from the train file.\\n train_csvfile = open(train_file,'rb')\\n is_header = True\\n print('Load Train Data\\\\n');\\n row_count = 0\\n for row in csv.reader(train_csvfile, delimiter = ','):\\n if is_header: # Skip header line.\\n is_header = False\\n continue\\n row_count = row_count+1\\n if row_count % 1000000 == 0:\\n print('In load train, row count = ' + str(row_count) + '\\\\n')\\n user_id = row[0]\\n is_churn = row[1]\\n users_dict[user_id] = [is_churn]\\n # Load users attributes from members file.\\n members_csvfile = open(members_file,'rb')\\n is_header = True\\n print('Load Members Data\\\\n');\\n row_count = 0\\n for row in csv.reader(members_csvfile, delimiter = ','):\\n if is_header: # Skip header line.\\n is_header = False\\n continue\\n row_count = row_count+1\\n if row_count % 1000000 == 0:\\n print('In load member, row count = ' + str(row_count) + '\\\\n')\\n user_id = row[0]\\n age = row[2]\\n gender = row[3]\\n if gender == 'female':\\n gender = 0\\n elif gender == 'male':\\n gender = 1\\n else: # Missing field, skip.\\n continue\\n if int(age) < 0: # Invalid age value.\\n continue\\n if user_id in users_dict:\\n user_data = users_dict[user_id]\\n user_data.append(age)\\n user_data.append(gender)\\n users_dict[user_id] = user_data\\n # Load users attributed from transcation file.\\n transcation_csvfile = open(preprocessed_transcation_file,'rb')\\n is_header = True\\n print('Load Transcation Data\\\\n');\\n row_count = 0\\n for row in csv.reader(transcation_csvfile, delimiter = ','):\\n if is_header: # Skip header line.\\n is_header = False\\n continue\\n row_count = row_count+1\\n if row_count % 1000000 == 0:\\n print('In load transcations, row count = ' + str(row_count) + '\\\\n')\\n user_id = row[0]\\n payment_plan_days = row[1]\\n plan_list_price = row[2]\\n actual_amount_paid = row[3]\\n is_auto_renew = row[4]\\n is_cancel = row[5]\\n if user_id in users_dict:\\n user_data = users_dict[user_id]\\n user_data.append(payment_plan_days)\\n user_data.append(plan_list_price)\\n user_data.append(actual_amount_paid)\\n user_data.append(is_auto_renew)\\n user_data.append(is_cancel)\\n users_dict[user_id] = user_data\\n # Load users attributed from users log file.\\n user_logs_csvfile = open(preprocessed_user_logs_file,'rb')\\n is_header = True\\n print('Load User Logs Data\\\\n');\\n row_count = 0\\n for row in csv.reader(user_logs_csvfile, delimiter = ','):\\n if is_header: # Skip header line.\\n is_header = False\\n continue\\n row_count = row_count+1\\n if row_count % 1000000 == 0:\\n print('In load user logs, row count = ' + str(row_count) + '\\\\n')\\n user_id = row[0]\\n num_25 = row[1]\\n num_50 = row[2]\\n num_75 = row[3]\\n num_985 = row[4]\\n num_100 = row[5]\\n num_unq = row[6]\\n total_secs = row[7]\\n if user_id in users_dict:\\n user_data = users_dict[user_id]\\n user_data.append(num_25)\\n user_data.append(num_50)\\n user_data.append(num_75)\\n user_data.append(num_985)\\n user_data.append(num_100)\\n user_data.append(num_unq)\\n user_data.append(total_secs)\\n users_dict[user_id] = user_data\\n # Write output files.\\n output_file = open(output_filename, \\\"w\\\")\\n output_file.write('msno,is_churn,age,gender,payment_plan_days,'\\n +'plan_list_price,actual_amount_paid,is_auto_renew,'\\n +'is_cancel,num_25,num_50,num_75,num_985,num_100,'\\n + 'num_unq,total_hrs\\\\n')\\n for user_id in users_dict:\\n user_data = users_dict[user_id]\\n output_line = user_id\\n if len(user_data) < 15: # contains missing atrributes.\\n continue\\n for user_attribute in user_data:\\n output_line = output_line + ',' + str(user_attribute)\\n output_file.write(output_line + '\\\\n')\\n output_file.close()\\n\"\n },\n {\n \"alpha_fraction\": 0.5818583965301514,\n \"alphanum_fraction\": 0.5862832069396973,\n \"avg_line_length\": 39.75757598876953,\n \"blob_id\": \"078b1e97fb99dac20b35628bf8450416e478eab6\",\n \"content_id\": \"b3f67da0df5111cf2cde3b142ed5707d79597a91\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1356,\n \"license_type\": \"no_license\",\n \"max_line_length\": 86,\n \"num_lines\": 33,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Project/split_data_random.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import random\\nimport os.path as path\\nimport csv\\nimport math\\ndef split_data_random(file_name, split_number, output_folder, train_ratio):\\n csvfile = open(file_name,'r')\\n print('Load Data\\\\n');\\n lines = csvfile.readlines()\\n header = lines[0]\\n del lines[0]\\n random.seed(0)\\n random.shuffle(lines)\\n line_start = 0\\n lines_per_split_number = math.floor(len(lines) / split_number)\\n for split in range(split_number):\\n # Write training data of the split.\\n output_train_file = open(path.join(output_folder,\\n str(split) + '_train.csv'), 'w')\\n output_train_file.write(header)\\n line_end = lines_per_split_number + line_start\\n line_end_train = math.floor(train_ratio * lines_per_split_number) + line_start\\n while line_start < line_end_train:\\n output_train_file.write(lines[line_start])\\n line_start = line_start + 1\\n output_train_file.close()\\n # Write testing data of the split.\\n output_test_file = open(path.join(output_folder,\\n str(split) + '_test.csv'), 'w')\\n output_test_file.write(header)\\n while line_start < line_end:\\n output_test_file.write(lines[line_start])\\n line_start = line_start + 1\\n output_test_file.close()\\n\\n \\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6844181418418884,\n \"alphanum_fraction\": 0.692307710647583,\n \"avg_line_length\": 23.190475463867188,\n \"blob_id\": \"0a93c63806ff812f735f3bbfbc26bd7c7d99d5ea\",\n \"content_id\": \"146619cd7b30eda0a853c4e33009b316536250ad\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 507,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 21,\n \"path\": \"/helper.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import csv\\nimport numpy as np\\nimport math\\nfrom decimal import Decimal\\nimport warnings\\nfrom scipy.stats import logistic\\n\\nsigmoid = lambda x : logistic.cdf(x)\\n\\n# def sigmoid(x):\\n# \\tlogistic.cdf(x)\\n\\ndef load_data(input_path):\\n\\trows = []\\n\\twith open(input_path, 'r') as csvfile:\\n\\t\\treader = csv.reader(csvfile, delimiter=',')\\n\\t\\tfor row in reader:\\n\\t\\t\\trows.append(row)\\n\\tlabels = [int(row[1]) for row in rows[1:]]\\n\\trows = [[float(num) for num in row[2:]] for row in rows[1:]]\\n\\treturn np.array(rows), np.array(labels)\"\n },\n {\n \"alpha_fraction\": 0.5020458102226257,\n \"alphanum_fraction\": 0.5421440005302429,\n \"avg_line_length\": 40.305084228515625,\n \"blob_id\": \"d2cdc660cb140574ad6969a2cdaf8912379ce9f6\",\n \"content_id\": \"d05ed7a6fcb8fe22e8953ff4685d45d1fff60a42\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2444,\n \"license_type\": \"no_license\",\n \"max_line_length\": 154,\n \"num_lines\": 59,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Project/preprocess_user_logs_table.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import csv\\ndef preprocess_user_logs_table(input_filename, output_filename):\\n users_dict = {}; \\n csvfile = open(input_filename,'rb')\\n is_header = True\\n row_count = 0\\n for row in csv.reader(csvfile, delimiter = ','):\\n if is_header: # Skip header line.\\n is_header = False\\n continue\\n if row_count % 1000000 == 0:\\n print(row_count)\\n row_count = row_count + 1\\n user_id = row[0]\\n # Skip date field as it is a categorical data.\\n num_25 = float(row[2])\\n num_50 = float(row[3])\\n num_75 = float(row[4])\\n num_985 = float(row[5])\\n num_100 = float(row[6])\\n num_unq = float(row[7])\\n total_hrs = float(row[8]) / 3600.0\\n # Skip corrupted logs as these features can't be neg.\\n if float(row[2]) < 0 or float(row[3]) < 0 or float(row[4])< 0 or float(row[5]) < 0 or float(row[6]) < 0 or float(row[7]) < 0 or float(row[8]) < 0:\\n continue\\n user_record_count = 1\\n user_data = []\\n if user_id in users_dict:\\n user_data = users_dict[user_id]\\n user_data[0] += num_25\\n user_data[1] += num_50\\n user_data[2] += num_75\\n user_data[3] += num_985\\n user_data[4] += num_100\\n user_data[5] += num_unq\\n user_data[6] += total_hrs\\n user_data[7] += user_record_count \\n else: # First time to encouter this user.\\n user_data = [num_25, num_50, num_75, num_985,\\n num_100, num_unq, total_hrs, user_record_count] \\n # Add user to the dictionary.\\n users_dict[user_id] = user_data\\n # Average user records and then write it to the output file.\\n output_file = open(output_filename, \\\"w\\\")\\n output_file.write('msno,num_25,num_50,num_75,num_985,num_100,num_unq,total_hrs\\\\n')\\n for user_id in users_dict:\\n user_data = users_dict[user_id]\\n user_records_number = user_data[7]\\n output_line = user_id\\n fields_count = 0\\n for user_attribute in user_data:\\n user_attribute = user_attribute / user_records_number\\n output_line = output_line + ',' + str(user_attribute)\\n fields_count = fields_count + 1\\n if fields_count == 7:\\n # Seventh field is the count field, don't include.\\n break\\n output_file.write(output_line + '\\\\n')\\n output_file.close()\\n \\n \\n\"\n },\n {\n \"alpha_fraction\": 0.4883720874786377,\n \"alphanum_fraction\": 0.6976743936538696,\n \"avg_line_length\": 16.200000762939453,\n \"blob_id\": \"f04e0329db7b539378abdbaaa934645392639e3f\",\n \"content_id\": \"0785e52d2f1695167c06ee40dec90c39b965ff28\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 86,\n \"license_type\": \"no_license\",\n \"max_line_length\": 20,\n \"num_lines\": 5,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"statsmodels==0.8.0\\nmatplotlib==2.0.2\\nnumpy==1.13.1\\nscipy==0.19.1\\nscikit_learn==0.19.1\\n\"\n },\n {\n \"alpha_fraction\": 0.5625683665275574,\n \"alphanum_fraction\": 0.600817084312439,\n \"avg_line_length\": 55.72810363769531,\n \"blob_id\": \"dae42710073eda8c6f58059438c9e2c4858be3b3\",\n \"content_id\": \"fbe79eaecef708583b2564069ac326fe81e12c84\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 31086,\n \"license_type\": \"no_license\",\n \"max_line_length\": 168,\n \"num_lines\": 548,\n \"path\": \"/run.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from preprocess_transcation_table import preprocess_transcation_table\\nfrom preprocess_user_logs_table import preprocess_user_logs_table\\nfrom join_tables import join_tables\\nfrom split_data_random import split_data_random\\nfrom generate_histograms import generate_histograms\\nfrom kfold_cross_validation import kfold_cross_validation\\nfrom select_c_for_SVM_using_kfold_CV import select_c_for_SVM_using_kfold_CV\\nfrom classify_test_users import classify_test_users\\nfrom classify_test_users import test_different_number_of_samples\\nfrom classify_test_users import test_different_thresholds\\nfrom select_parameters_for_MLP_using_kfold_CV import select_parameters_for_MLP_using_kfold_CV\\nfrom select_feature_selection_Naive_Bayes_using_kfold_CV import select_feature_selection_Naive_Bayes_using_kfold_CV\\nfrom classify_test_users import test_different_subsets\\nfrom classify_test_users import generate_train_test_error_for_different_kernels\\nfrom classify_test_users import generate_train_test_error_for_different_MLP\\nfrom classify_test_users import generate_test_error_for_kernels\\nfrom classify_test_users import generate_ROC\\nimport os.path as path\\nimport os\\nimport numpy as np\\nfrom hypothesis_testing import do_hypothesis_testing\\nimport sys\\n\\ndef run (step_number):\\n if step_number == 1:\\n # Step 1: Preprocess transcation data.\\n transcations_input_filename = 'transactions.csv'\\n transcations_output_filename = 'transactions_preprocessed.csv'\\n preprocess_transcation_table(transcations_input_filename,\\n transcations_output_filename)\\n elif step_number == 2:\\n # Step 2: Preprocess user_logs data\\n user_logs_input_filename = 'user_logs.csv'\\n user_logs_output_filename = 'user_logs_preprocessed.csv'\\n preprocess_user_logs_table(user_logs_input_filename,\\n user_logs_output_filename)\\n elif step_number == 3:\\n # Step 3: Join all tables based on user_id\\n transcations_output_filename = 'transactions_preprocessed.csv'\\n user_logs_output_filename = 'user_logs_preprocessed.csv'\\n train_input_filename = 'train.csv'\\n member_input_filename = 'members.csv'\\n joined_table_output_filename = 'joined_train_data.csv'\\n join_tables(transcations_output_filename, user_logs_output_filename,\\n train_input_filename, member_input_filename,\\n joined_table_output_filename)\\n elif step_number == 4:\\n # Step 4: Split joined table file into 10 subsets randomly.\\n split_number = 10\\n train_data_joined = 'joined_train_data.csv'\\n output_folder = 'train_subsets'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n train_ratio = 0.8\\n split_data_random(train_data_joined, split_number, output_folder,\\n train_ratio)\\n elif step_number == 5:\\n # Step 5: Generate features histogram.\\n train_file = path.join('train_subsets', '0_train.csv')\\n output_folder = 'histograms'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n k = 10\\n kfold_cross_validation(k, train_file, ['features_histogram'], output_folder, [])\\n elif step_number == 6:\\n # Step 6: Run PCA.\\n train_file = path.join('train_subsets', '0_train.csv')\\n output_folder = 'pca'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n k = 10\\n kfold_cross_validation(k, train_file, ['pca'], output_folder, [])\\n elif step_number == 7:\\n # Step 7: Run univariate_fea_selection fearure selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n output_folder = 'univariate_fea_selection'\\n k = 10\\n C = 10\\n number_of_features_to_select = 7\\n stats_vals = kfold_cross_validation(k, train_file, ['univariate_fea_selection'],\\n output_folder, [number_of_features_to_select, C])\\n elif step_number == 8:\\n # Step 8: Run linear_SVC fearure selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n output_folder = 'linear_SVC'\\n k = 10\\n sparsity_param = 0.002\\n C = 10\\n stats_vals = kfold_cross_validation(k, train_file, ['linear_SVC'],\\n output_folder, [sparsity_param, C])\\n elif step_number == 9:\\n # Step 9: Run 10 fold cross validation to choose C for linear SVM with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'linear_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 'linear'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 10:\\n # Step 10: Run 10 fold cross validation to choose C for linear SVM with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'linear_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 'linear'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n elif step_number == 11:\\n # Step 11: Run 10 fold cross validation to choose C for kernel SVM with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'kernel_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 'rbf'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 12:\\n # Step 12: Run 10 fold cross validation to choose C for kernel SVM with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'kernel_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 'rbf'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n elif step_number == 13:\\n # Step 13: Run 10 fold cross validation to choose C for one class linear SVM with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'one_class_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 'linear'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 14:\\n # Step 14: Run 10 fold cross validation to choose C for one class linear SVM with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'one_class_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 'linear'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n elif step_number == 15:\\n # Step 15: Run 10 fold cross validation to choose C for one class kernel SVM with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'one_class_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 'rbf'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 16:\\n # Step 16: Run 10 fold cross validation to choose C for one class kernel SVM with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'one_class_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 'rbf'\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n elif step_number == 17:\\n # Step 17: For submitting on Kaggle, join the Kaggle test data with the other tables.\\n transcations_output_filename = 'transactions_preprocessed.csv'\\n user_logs_output_filename = 'user_logs_preprocessed.csv'\\n train_input_filename = 'sample_submission.csv'\\n member_input_filename = 'members.csv'\\n joined_table_output_filename = 'joined_test_data.csv'\\n join_tables(transcations_output_filename, user_logs_output_filename,\\n train_input_filename, member_input_filename,\\n joined_table_output_filename)\\n elif step_number == 18:\\n # Step 18: Classify Kaggle test user, using SVM with C learned form 10 fold cross validation.\\n train_file = 'joined_train_data.csv'\\n test_file = 'joined_test_data.csv'\\n test_file_to_get_users = 'sample_submission.csv'\\n methods_to_run = ['univariate_fea_selection', 'preceptron']\\n number_of_features_to_select = 14\\n C = 10\\n kernel = 'linear'\\n write_prediction = 1\\n prediction_file = 'prediction_naive_MLP_all_10_200.csv'\\n options = [number_of_features_to_select, C, kernel, write_prediction, prediction_file]\\n classify_test_users(train_file, test_file, methods_to_run, test_file_to_get_users, options)\\n elif step_number == 19:\\n # Step 19: Run 10 fold cross validation to choose C for linear SVM with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'linear_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 'linear'\\n class_1_weight = 10\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select, class_1_weight)\\n elif step_number == 20:\\n # Step 20: Run 10 fold cross validation to choose C for linear SVM with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'linear_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 'linear'\\n class_1_weight = 10\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param, class_1_weight)\\n elif step_number == 21:\\n # Step 21: Run 10 fold cross validation to choose C for kernel SVM with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'kernel_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 'rbf'\\n class_1_weight = 10\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select, class_1_weight)\\n elif step_number == 22:\\n # Step 22: Run 10 fold cross validation to choose C for kernel SVM with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'kernel_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 'rbf'\\n class_1_weight = 10\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param, class_1_weight)\\n elif step_number == 23:\\n # Step 23: 10 fold CV for MLP using univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n params_list = [(10,10,10), (10,10,10,10,10), (10,10,10,10,10,10,10,10,10,10),\\n (50,50,50), (50,50,50,50,50), (50,50,50,50,50,50,50,50,50,50),\\n (100,100,100), (100,100,100,100,100), (100,100,100,100,100,100,100,100,100,100)]\\n classification_alg = 'preceptron'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = ''\\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\\n fea_selection_alg, params_list, k,\\n number_of_features_to_select)\\n elif step_number == 24:\\n # Step 24: 10 fold CV for MLP using linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n params_list = [(10,10,10), (10,10,10,10,10), (10,10,10,10,10,10,10,10,10,10),\\n (50,50,50), (50,50,50,50,50), (50,50,50,50,50,50,50,50,50,50),\\n (100,100,100), (100,100,100,100,100), (100,100,100,100,100,100,100,100,100,100)]\\n classification_alg = 'preceptron'\\n fea_selection_alg = 'linear_SVC'\\n kernel = ''\\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\\n fea_selection_alg, params_list, k,\\n sparsity_param)\\n elif step_number == 25:\\n # Step 25: 10 fold CV for Naive Bayes using univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n classification_alg = 'naive_bayes'\\n fea_selection_alg = 'univariate_fea_selection'\\n select_feature_selection_Naive_Bayes_using_kfold_CV(train_file, classification_alg, fea_selection_alg,\\n k, number_of_features_to_select)\\n elif step_number == 26:\\n # Step 26: 10 fold CV for Naive Bayes using linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n classification_alg = 'naive_bayes'\\n fea_selection_alg = 'linear_SVC'\\n select_feature_selection_Naive_Bayes_using_kfold_CV(train_file, classification_alg, fea_selection_alg,\\n k, sparsity_param)\\n \\n elif step_number == 27:\\n # Step 27: Test different number of samples for different classification algorithms.\\n # Parameters and feature selection methods are selected based on CV best in specificty.\\n train_file = path.join('train_subsets', '0_train.csv')\\n test_file = path.join('train_subsets', '0_test.csv')\\n test_file_to_get_users = 'sample_submission.csv'\\n methods_to_run_list = [['linear_SVC', 'linear_svm'], ['univariate_fea_selection', 'kernel_svm'],\\n ['linear_SVC', 'one_class_svm'], ['linear_SVC', 'one_class_svm'],\\n ['linear_SVC', 'linear_svm'],['univariate_fea_selection', 'kernel_svm'],\\n ['univariate_fea_selection', 'naive_bayes'], ['linear_SVC', 'preceptron']]\\n write_prediction = 0\\n prediction_file = ''\\n number_of_features_to_select = 7\\n sparsity_param = 0.002\\n linear_SVM_options = [sparsity_param, 55, 'linear', write_prediction, prediction_file]\\n kernel_SVM_options = [number_of_features_to_select, 70, 'rbf', write_prediction, prediction_file]\\n one_class_linear_SVM_options = [sparsity_param, 1, 'linear', write_prediction, prediction_file]\\n one_class_kernel_SVM_options = [sparsity_param, 85, 'rbf', write_prediction, prediction_file]\\n class_weight_linear_SVM_options = [sparsity_param, 85, 'linear', write_prediction, prediction_file, 10]\\n class_weight_kernel_SVM_options = [number_of_features_to_select, 1, 'rbf', write_prediction, prediction_file, 10]\\n naive_bayes_options = [number_of_features_to_select, 0, '', write_prediction, prediction_file]\\n preceptron_options = [sparsity_param, (50, 50, 50, 50, 50, 50, 50, 50, 50, 50), '', write_prediction, prediction_file]\\n options_list = [linear_SVM_options, kernel_SVM_options, one_class_linear_SVM_options, one_class_kernel_SVM_options,\\n class_weight_linear_SVM_options, class_weight_kernel_SVM_options, naive_bayes_options,\\n preceptron_options]\\n different_number_of_samples = range(2000, 22000, 2000)\\n output_folder = 'different_number_of_samples_experiment'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n test_different_number_of_samples(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list,\\n different_number_of_samples, output_folder)\\n elif step_number == 28:\\n # Step 28: Test different training subsets for different classification algorithms.\\n # Parameters and feature selection methods are selected based on CV best in specificty.\\n train_folder = 'train_subsets'\\n test_file = path.join('train_subsets', '0_test.csv')\\n test_file_to_get_users = 'sample_submission.csv'\\n methods_to_run_list = [['linear_SVC', 'linear_svm'], ['univariate_fea_selection', 'kernel_svm'],\\n ['linear_SVC', 'one_class_svm'], ['linear_SVC', 'one_class_svm'],\\n ['linear_SVC', 'linear_svm'],['univariate_fea_selection', 'kernel_svm'],\\n ['univariate_fea_selection', 'naive_bayes'], ['linear_SVC', 'preceptron']]\\n write_prediction = 0\\n prediction_file = ''\\n number_of_features_to_select = 7\\n sparsity_param = 0.002\\n linear_SVM_options = [sparsity_param, 55, 'linear', write_prediction, prediction_file]\\n kernel_SVM_options = [number_of_features_to_select, 70, 'rbf', write_prediction, prediction_file]\\n one_class_linear_SVM_options = [sparsity_param, 1, 'linear', write_prediction, prediction_file]\\n one_class_kernel_SVM_options = [sparsity_param, 85, 'rbf', write_prediction, prediction_file]\\n class_weight_linear_SVM_options = [sparsity_param, 85, 'linear', write_prediction, prediction_file, 10]\\n class_weight_kernel_SVM_options = [number_of_features_to_select, 1, 'rbf', write_prediction, prediction_file, 10]\\n naive_bayes_options = [number_of_features_to_select, 0, '', write_prediction, prediction_file]\\n preceptron_options = [sparsity_param, (50, 50, 50, 50, 50, 50, 50, 50, 50, 50), '', write_prediction, prediction_file]\\n options_list = [linear_SVM_options, kernel_SVM_options, one_class_linear_SVM_options, one_class_kernel_SVM_options,\\n class_weight_linear_SVM_options, class_weight_kernel_SVM_options, naive_bayes_options,\\n preceptron_options]\\n different_number_of_samples = range(2000, 22000, 2000)\\n output_folder = 'different_subsets_experiment'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n train_files_count = 10\\n test_different_subsets(train_folder, test_file, methods_to_run_list, test_file_to_get_users,\\n options_list, output_folder, train_files_count)\\n # elif step_number == 29:\\n # # Step 29: Test different thresholds and plot the ROC curve for different classification algorithms.\\n # # Parameters and feature selection methods are selected based on CV best in specificty.\\n # threshold_list = [0.25, 0.5, 0.75, 1]\\n # train_file = path.join('train_subsets', '0_train.csv')\\n # test_file = path.join('train_subsets', '0_test.csv')\\n # test_file_to_get_users = 'sample_submission.csv'\\n # methods_to_run_list = [['linear_SVC', 'linear_svm'], ['univariate_fea_selection', 'kernel_svm'],\\n # ['linear_SVC', 'one_class_svm'], ['linear_SVC', 'one_class_svm'],\\n # ['linear_SVC', 'linear_svm'],['univariate_fea_selection', 'kernel_svm'],\\n # ['univariate_fea_selection', 'naive_bayes'], ['linear_SVC', 'preceptron']]\\n # write_prediction = 0\\n # prediction_file = ''\\n # number_of_features_to_select = 7\\n # sparsity_param = 0.002\\n # linear_SVM_options = [sparsity_param, 55, 'linear', write_prediction, prediction_file]\\n # kernel_SVM_options = [number_of_features_to_select, 70, 'rbf', write_prediction, prediction_file]\\n # one_class_linear_SVM_options = [sparsity_param, 1, 'linear', write_prediction, prediction_file]\\n # one_class_kernel_SVM_options = [sparsity_param, 85, 'rbf', write_prediction, prediction_file]\\n # class_weight_linear_SVM_options = [sparsity_param, 85, 'linear', write_prediction, prediction_file, 10]\\n # class_weight_kernel_SVM_options = [number_of_features_to_select, 1, 'rbf', write_prediction, prediction_file, 10]\\n # naive_bayes_options = [number_of_features_to_select, 0, '', write_prediction, prediction_file]\\n # preceptron_options = [sparsity_param, (50, 50, 50, 50, 50, 50, 50, 50, 50, 50), '', write_prediction, prediction_file]\\n # options_list = [linear_SVM_options, kernel_SVM_options, one_class_linear_SVM_options, one_class_kernel_SVM_options,\\n # class_weight_linear_SVM_options, class_weight_kernel_SVM_options, naive_bayes_options,\\n # preceptron_options]\\n # different_number_of_samples = range(2000, 22000, 2000)\\n # output_folder = 'different_thresholds_experiment'\\n # if not path.exists(output_folder):\\n # os.makedirs(output_folder)\\n # test_different_thresholds(train_file, test_file, methods_to_run_list, test_file_to_get_users, options_list, threshold_list, output_folder, class_1_weight = 1)\\n elif step_number == 30:\\n # Step 30: Run 10 fold cross validation to choose C for poly SVM degree 2 with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'poly_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 2\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 31:\\n # Step 31: Run 10 fold cross validation to choose C for poly SVM degree 2 with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'poly_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 2\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n elif step_number == 32:\\n # Step 32: Run 10 fold cross validation to choose C for poly SVM degree 3 with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'poly_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 3\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 33:\\n # Step 33: Run 10 fold cross validation to choose C for poly SVM degree 3 with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'poly_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 3\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n elif step_number == 34:\\n # Step 34: Run 10 fold cross validation to choose C for poly SVM degree 4 with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'poly_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 4\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 35:\\n # Step 35: Run 10 fold cross validation to choose C for poly SVM degree 4 with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'poly_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 4\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n\\n elif step_number == 36:\\n # Step 36: Run 10 fold cross validation to choose C for poly SVM degree 4 with univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'poly_svm'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = 10\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, number_of_features_to_select)\\n elif step_number == 37:\\n # Step 37: Run 10 fold cross validation to choose C for poly SVM degree 4 with linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n c_vals = [1, 10, 25, 40, 55, 70, 85]\\n classification_alg = 'poly_svm'\\n fea_selection_alg = 'linear_SVC'\\n kernel = 10\\n select_c_for_SVM_using_kfold_CV(train_file, kernel, classification_alg, fea_selection_alg,\\n c_vals, k, sparsity_param)\\n elif step_number == 38:\\n # Step 38: Report training and testing errors for different SVM kernels.\\n train_file = path.join('train_subsets', '0_train.csv')\\n test_file = path.join('train_subsets', '0_test.csv')\\n output_folder = 'different_SVM_kernels'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n generate_train_test_error_for_different_kernels(train_file, test_file, output_folder)\\n\\n elif step_number == 39:\\n # Step 39: 10 fold CV for MLP using univariate_fea_selection.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n number_of_features_to_select = 7\\n params_list = [tuple([50]*3), tuple([50]*5), tuple([50]*10),\\n tuple([50]*13), tuple([50]*15), tuple([50]*20)]\\n classification_alg = 'preceptron'\\n fea_selection_alg = 'univariate_fea_selection'\\n kernel = ''\\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\\n fea_selection_alg, params_list, k,\\n number_of_features_to_select)\\n elif step_number == 40:\\n # Step 40: 10 fold CV for MLP using linear_SVC.\\n train_file = path.join('train_subsets', '0_train.csv')\\n k = 10\\n sparsity_param = 0.002\\n params_list = [tuple([50]*3), tuple([50]*5), tuple([50]*10),\\n tuple([50]*13), tuple([50]*15), tuple([50]*20)]\\n classification_alg = 'preceptron'\\n fea_selection_alg = 'linear_SVC'\\n kernel = ''\\n select_parameters_for_MLP_using_kfold_CV(train_file, kernel, classification_alg,\\n fea_selection_alg, params_list, k,\\n sparsity_param)\\n elif step_number == 41:\\n # Step 41: Report training and testing errors for different MLP layers.\\n train_file = path.join('train_subsets', '0_train.csv')\\n test_file = path.join('train_subsets', '0_test.csv')\\n output_folder = 'different_MLP_kernels'\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n generate_train_test_error_for_different_MLP(train_file, test_file, output_folder)\\n elif step_number == 42:\\n #Step 42: Generate test error\\n train_file = path.join('train_subsets', '0_train.csv')\\n test_file = path.join('train_subsets', '0_test.csv')\\n generate_test_error_for_kernels(train_file, test_file)\\n elif step_number == 43:\\n #Step 43: Plot ROC and compute AUC\\n print 'In step_number', step_number\\n train_file = path.join('train_subsets', '0_train.csv')\\n test_file = path.join('train_subsets', '0_test.csv')\\n # threshold_list = [0.25, 0.5, 0.75]\\n threshold_list = list(np.arange(0.0, 1, 0.1))\\n generate_ROC(train_file, test_file, threshold_list = threshold_list)\\n elif step_number == 44:\\n #Step 44: Perform Hypothesis testing\\n k = 10\\n train_file = path.join('train_subsets', '0_train.csv')\\n do_hypothesis_testing(train_file, k)\\n\\nif __name__ == '__main__':\\n step_number = 0\\n try:\\n step_number = int(sys.argv[1])\\n except ValueError:\\n print '\\\\'%s\\\\' is not a valid int. Please provide an integer' %(sys.argv[1]) \\n run(step_number)\"\n },\n {\n \"alpha_fraction\": 0.5937321782112122,\n \"alphanum_fraction\": 0.5982906222343445,\n \"avg_line_length\": 53.84375,\n \"blob_id\": \"73e64380474163e717513a63dbdd5ff18f33bce9\",\n \"content_id\": \"5374d1e4ae66e319a34e00413c301a0b2a3f619f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1755,\n \"license_type\": \"no_license\",\n \"max_line_length\": 163,\n \"num_lines\": 32,\n \"path\": \"/select_feature_selection_Naive_Bayes_using_kfold_CV.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os.path as path\\nimport os\\nfrom kfold_cross_validation import kfold_cross_validation\\nfrom plot_cross_validation_SVM import plot_cross_validation_SVM\\nimport random\\nimport numpy as np\\ndef select_feature_selection_Naive_Bayes_using_kfold_CV(train_file, classification_alg, fea_selection_alg,\\n k, number_of_features_to_select):\\n random.seed(0)\\n output_folder = '%s/%s_%s' %(classification_alg, classification_alg, fea_selection_alg)\\n if not path.exists(output_folder):\\n os.makedirs(output_folder)\\n output_file = open('%s/stats.txt' %(output_folder), 'a')\\n labels = ['Avg Error', 'Var Error', 'Avg Recall', 'Var recall', 'Avg Precision', 'Var Precision', 'Avg Specificity', 'Var Specificity']\\n output_file.write('C\\\\t')\\n for label in labels:\\n output_file.write('%s\\\\t' %(label))\\n output_file.write('\\\\n')\\n mean_vals = []\\n error_vals = []\\n stats_results = kfold_cross_validation(k, train_file, [fea_selection_alg, classification_alg], output_folder,\\n [number_of_features_to_select,'' , '', 0])\\n output_file = open('%s/stats.txt' %(output_folder), 'a')\\n mean_vals.append([])\\n error_vals.append([])\\n for stat_record in stats_results:\\n mean_vals[-1].append(stat_record[0])\\n error_vals[-1].append(stat_record[1])\\n output_file.write('%f\\\\t%f\\\\t' %(stat_record[0], stat_record[1]))\\n output_file.write('\\\\n')\\n output_file.close()\\n #plot_cross_validation_SVM(k, parameter_list, labels, np.array(mean_vals), np.array(error_vals), '%s/stats' %(output_folder), range(len(mean_vals)), False)\\n\"\n },\n {\n \"alpha_fraction\": 0.5813356041908264,\n \"alphanum_fraction\": 0.6001712083816528,\n \"avg_line_length\": 39.27586364746094,\n \"blob_id\": \"272f93da42ecb7b572c5c75cc7d8fb69438bdc0e\",\n \"content_id\": \"f65c8267e29e13999906751b20c68b11dc702992\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1168,\n \"license_type\": \"no_license\",\n \"max_line_length\": 79,\n \"num_lines\": 29,\n \"path\": \"/calculate_performance_measures.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import numpy as np\\ndef calculate_performance_measures(predicted_labels, actual_labels):\\n # Positive class is class 0, which means user will renew.\\n # Negative class is class 1, which means user won't renew.\\n predicted_labels = np.array(predicted_labels)\\n actual_labels = np.array([cur_list[0] for cur_list in list(actual_labels)])\\n TP = np.sum(np.logical_and(predicted_labels == 0, actual_labels == 0))\\n FP = np.sum(np.logical_and(predicted_labels == 0, actual_labels == 1))\\n FN = np.sum(np.logical_and(predicted_labels == 1, actual_labels == 0))\\n TN = np.sum(np.logical_and(predicted_labels == 1, actual_labels == 1))\\n if (TP + FP + FN + TN) != 0:\\n\\terror = float(FP + FN) / float(TP + FP + FN + TN)\\n else:\\n\\terror = 0.0\\n if (TP + FN) != 0:\\n\\trecall = TP / float(TP + FN)\\n else:\\n\\trecall = 0.0\\n if (TP + FP) != 0:\\n\\tprecision = TP / float(TP + FP)\\n else:\\n\\tprecision = 0.0\\n if (TN + FP) != 0:\\n\\tspecificity = TN / float(TN + FP)\\n else:\\n\\tspecificity = 0\\n #print 'TP = ', TP, 'FP = ', FP, 'FN = ', FN, 'TN = ', TN\\n #print '%f/%f+%f = %f' %(TP, TP, FP, TP / float(TP + FP))\\n return error, recall, precision, specificity\\n\"\n },\n {\n \"alpha_fraction\": 0.5681818127632141,\n \"alphanum_fraction\": 0.7045454382896423,\n \"avg_line_length\": 10,\n \"blob_id\": \"5138b504469af5b49e862c4e5c8c0cdd5346876b\",\n \"content_id\": \"06b908f50be56dcee0b17b686bc9a3c31c8c7bb8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 44,\n \"license_type\": \"no_license\",\n \"max_line_length\": 19,\n \"num_lines\": 4,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/run_linear_SVC.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from run import run\\nrun(12)\\nrun(14)\\nrun(16)\\n\"\n },\n {\n \"alpha_fraction\": 0.6118788719177246,\n \"alphanum_fraction\": 0.6184217929840088,\n \"avg_line_length\": 57.900333404541016,\n \"blob_id\": \"afae66790c76020cf7ebdf08f6aec0b4273d9d2b\",\n \"content_id\": \"f304c34d4b4c3c30bff3927b8325d780fadd8f41\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 17729,\n \"license_type\": \"no_license\",\n \"max_line_length\": 146,\n \"num_lines\": 301,\n \"path\": \"/kfold_cross_validation.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import math\\nimport csv\\nfrom my_PCA import MyPCA\\nimport numpy as np\\nfrom matplotlib import pyplot\\nimport os.path as path\\nfrom sklearn.naive_bayes import GaussianNB\\nfrom calculate_performance_measures import calculate_performance_measures\\nfrom sklearn import mixture\\nfrom svm import *\\nfrom fea_selection import *\\nimport math\\nfrom sklearn.neural_network import MLPClassifier\\nfrom helper import sigmoid\\nimport math\\nfrom scipy import stats\\nimport scipy as sp\\n\\ndef write_predictions(validation_users_id, predicted_labels, options, test_file_to_get_users):\\n write_prediction = options[3]\\n if write_prediction == 1:\\n test_csvfile = open(test_file_to_get_users,'rb')\\n lines = csv.reader(test_csvfile, delimiter = ',')\\n test_users = []\\n for line in lines:\\n test_users.append(line[0])\\n prediction_filename = options[4]\\n users_number = len(validation_users_id)\\n validation_users_dict = {}\\n index = 0\\n for user in range(users_number):\\n user_id = validation_users_id[user]\\n validation_users_dict[''.join(user_id)] = index\\n index = index + 1\\n prediction_file = open(prediction_filename, 'w')\\n prediction_file.write('msno,is_churn\\\\n')\\n for user in test_users:\\n if user in validation_users_dict:\\n #normalized_prediction = 1.0/(1.0+ math.e **( -1 * predicted_labels[validation_users_dict[user]]))\\n prediction_file.write(user+','+str(predicted_labels[validation_users_dict[user]][1])+'\\\\n')\\n else:\\n prediction_file.write(user+',0.03\\\\n')\\n prediction_file.close()\\n\\ndef min_max_normalize(train_data):\\n col_min = np.min(train_data, axis = 0)\\n train_data = (train_data - col_min) / (np.max(train_data, axis = 0) - col_min)\\n return train_data\\n\\ndef run_feature_selection_and_classification(methods_to_run, train_data, validation_data, labels,\\n train_index, validation_index, validation_users_id,\\n options, header, test_file_to_get_users,\\n threshold_list = -1, class_1_weight = 1):\\n if 'univariate_fea_selection' in methods_to_run:\\n # Run feature selection.\\n number_of_features_to_select = options[0]\\n selected_features_indices = univariate_fea_selection(train_data, labels[train_index],\\n number_of_features_to_select)\\n train_data = train_data[:, selected_features_indices]\\n validation_data = validation_data[:, selected_features_indices]\\n selected_featuees = np.array(header)[np.array(selected_features_indices)+2]\\n # for selected_feature in range(len(selected_featuees)):\\n # print(selected_featuees[selected_feature])\\n if 'linear_SVC' in methods_to_run:\\n # Run feature selection.\\n sparsity_param = options[0]\\n selected_features_indices = my_SelectFromModel(train_data, labels[train_index],\\n sparsity_param)\\n train_data = train_data[:, selected_features_indices]\\n validation_data = validation_data[:, selected_features_indices]\\n selected_featuees = np.array(header)[np.array(selected_features_indices)+2]\\n # for selected_feature in range(len(selected_featuees)):\\n # print(selected_featuees[selected_feature])\\n if 'linear_svm' in methods_to_run: # Run linear SVM.\\n C = options[1]\\n linear_svm_model = train_linear_svm(train_data, labels[train_index], C, class_1_weight)\\n if threshold_list == -1:\\n predicted_labels, predicted_values = classify(linear_svm_model, validation_data)\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n return error, recall, precision, specificity\\n else:\\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\\n predicted_labels, predicted_values = classify(linear_svm_model, validation_data)\\n threshold_list = get_threshold_list(predicted_values)\\n for threshold in threshold_list:\\n predicted_labels = [linear_svm_model.classes_[1] if val > threshold else linear_svm_model.classes_[0] for val in predicted_values]\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n error_list.append(error)\\n recall_list.append(recall)\\n precision_list.append(precision)\\n specificity_list.append(specificity)\\n return error_list, recall_list, precision_list, specificity_list\\n if 'kernel_svm' in methods_to_run: # Run Kernel SVM.\\n C = options[1]\\n kernel_svm_model = train_kernel_svm(train_data, labels[train_index], C, class_1_weight)\\n if threshold_list == -1:\\n predicted_labels, predicted_values = classify(kernel_svm_model, validation_data)\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n return error, recall, precision, specificity\\n else:\\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\\n predicted_labels, predicted_values = classify(kernel_svm_model, validation_data)\\n threshold_list = get_threshold_list(predicted_values)\\n for threshold in threshold_list:\\n predicted_labels = [kernel_svm_model.classes_[1] if val > threshold else kernel_svm_model.classes_[0] for val in predicted_values]\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n error_list.append(error)\\n recall_list.append(recall)\\n precision_list.append(precision)\\n specificity_list.append(specificity)\\n return error_list, recall_list, precision_list, specificity_list\\n if 'poly_svm' in methods_to_run:\\n C = options[1]\\n degree = options[2]\\n train_data = train_data.astype(float)\\n kernel_svm_model = train_poly_kernel_svm (min_max_normalize(train_data), degree, labels[train_index], C, class_1_weight)\\n if threshold_list == -1:\\n predicted_labels, predicted_values = classify(kernel_svm_model, min_max_normalize(validation_data))\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n print(validation_data.shape)\\n print(len(labels[validation_index]))\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n return error, recall, precision, specificity\\n else:\\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\\n predicted_labels, predicted_values = classify(kernel_svm_model, min_max_normalize(validation_data), threshold = threshold_list)\\n threshold_list = get_threshold_list(predicted_values)\\n for threshold in threshold_list:\\n predicted_labels = [kernel_svm_model.classes_[1] if val > threshold else kernel_svm_model.classes_[0] for val in predicted_values]\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n error_list.append(error)\\n recall_list.append(recall)\\n precision_list.append(precision)\\n specificity_list.append(specificity)\\n return error_list, recall_list, precision_list, specificity_list\\n if 'one_class_svm' in methods_to_run:\\n temp = np.array([label[0] for label in labels[train_index]]) == 0\\n train_data = train_data[temp, :]\\n one_class_svm_model = train_one_class_svm(train_data, options[2])\\n if threshold_list == -1:\\n predicted_labels, predicted_values = classify_one_class_svm(one_class_svm_model, validation_data)\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users) \\n return error, recall, precision, specificity\\n else:\\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\\n predicted_labels, predicted_values = classify_one_class_svm(one_class_svm_model, validation_data, threshold = 1)\\n threshold_list = get_threshold_list(predicted_values)\\n for threshold in threshold_list:\\n predicted_labels = [1 if val > threshold else 0 for val in predicted_values]\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users) \\n error_list.append(error)\\n recall_list.append(recall)\\n precision_list.append(precision)\\n specificity_list.append(specificity)\\n return error_list, recall_list, precision_list, specificity_list\\n if 'preceptron' in methods_to_run: # Run multilayer preceptron.\\n clf = MLPClassifier(hidden_layer_sizes = options[1], verbose=False, random_state = 1\\n)\\n clf.fit(train_data, labels[train_index].ravel())\\n if threshold_list == -1:\\n predicted_labels = clf.predict(validation_data)\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n predicted_values = clf.predict_proba(validation_data)\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n return error, recall, precision, specificity \\n else:\\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\\n predicted_values = clf.predict_proba(validation_data)\\n threshold_list = get_threshold_list([val[1] for val in predicted_values])\\n for threshold in threshold_list:\\n predicted_labels = [1 if val[1] > threshold else 0 for val in predicted_values]\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n error_list.append(error)\\n recall_list.append(recall)\\n precision_list.append(precision)\\n specificity_list.append(specificity)\\n return error_list, recall_list, precision_list, specificity_list\\n if 'naive_bayes' in methods_to_run:\\n clf = GaussianNB()\\n clf.fit(train_data, labels[train_index].ravel())\\n if threshold_list == -1:\\n predicted_labels = clf.predict(validation_data)\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n predicted_values = clf.predict_proba(validation_data)\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n return error, recall, precision, specificity\\n else:\\n error_list, recall_list, precision_list, specificity_list = [],[], [], []\\n predicted_values = clf.predict_proba(validation_data)\\n threshold_list = get_threshold_list([val[1] for val in predicted_values])\\n for threshold in threshold_list:\\n predicted_labels = [1 if val[1] > threshold else 0 for val in predicted_values]\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n error_list.append(error)\\n recall_list.append(recall)\\n precision_list.append(precision)\\n specificity_list.append(specificity)\\n return error_list, recall_list, precision_list, specificity_list\\n if 'guassian' in methods_to_run:\\n clf = mixture.GaussianMixture(n_components=2, covariance_type='full')\\n clf.fit(train_data, labels[train_index])\\n predicted_labels = clf.predict(validation_data)\\n error, recall, precision, specificity = calculate_performance_measures(predicted_labels, labels[validation_index])\\n predicted_values = clf.predict_proba(validation_data)\\n write_predictions(validation_users_id, predicted_values, options, test_file_to_get_users)\\n return error, recall, precision, specificity\\n return 0, 0, 0, 0\\n\\ndef get_threshold_list(x):\\n x = sorted(x)\\n num_steps = 20\\n return [x[len(x)/num_steps * i] for i in range(num_steps)] + [max(x) + 1]\\n\\ndef kfold_cross_validation(k, train_file, methods_to_run,\\n output_folder, options, class_1_weight = 1, return_measures = False):\\n train_csvfile = open(train_file,'rb')\\n lines = csv.reader(train_csvfile, delimiter = ',')\\n lines = list(lines)\\n header = lines[0]\\n # Remove headers from the data.\\n del lines[0]\\n samples_number = len(lines)\\n # Transform it to matrix.\\n # Skip the user id and the label columns.\\n data = np.array([[float(elem) for elem in line[2:]] for line in lines])\\n labels = np.array([[int(elem) for elem in line[1]] for line in lines])\\n user_ids = np.array([[elem for elem in line[0]] for line in lines])\\n features_number = len(data[0])\\n measures = np.zeros([4, k])\\n for i in range(k):\\n print '\\\\t', i\\n start_i = int(math.floor(samples_number * i / k))\\n end_i = int(math.floor(samples_number * (i + 1) / k))\\n validation_index = range(start_i, end_i)\\n points_index = range(samples_number)\\n train_index = list(set(points_index).difference(set(validation_index)));\\n train_labels = labels[train_index, :]\\n zero_indices = [j for j in range(len(train_labels)) if train_labels[j] == 0]\\n one_indices = [j for j in range(len(train_labels)) if train_labels[j] == 1]\\n train_data = data[train_index, :]\\n validation_data = data[validation_index, :]\\n if 'features_histogram' in methods_to_run:\\n # Caclulate features histogram for each fold training data.\\n for feature in range(features_number):\\n train_data = data[train_index, feature]\\n feature_column_class0 = train_data[zero_indices]\\n feature_column_class1 = train_data[one_indices]\\n pyplot.figure()\\n pyplot.hist(feature_column_class0, 100, label='Class 0')\\n pyplot.hist(feature_column_class1, 100, label='Class 1')\\n pyplot.legend(loc='upper right')\\n pyplot.savefig(path.join(output_folder, 'hist_fold=' + str(i) +\\n '_' + header[feature+2] + '.png'))\\n pyplot.close()\\n if 'pca' in methods_to_run:\\n # Run PCA.\\n myPca = MyPCA()\\n myPca.fit(train_data, n_components = 2)\\n # Visualize the training data.\\n projected_data = myPca.transform(train_data)\\n pyplot.figure()\\n pyplot.plot(projected_data[zero_indices, 0], projected_data[zero_indices, 1],\\n 'o', color = 'r')\\n pyplot.plot(projected_data[one_indices, 0], projected_data[one_indices, 1],\\n 'x', color = 'b')\\n pyplot.savefig(path.join(output_folder, 'pca_fold=' +\\n str(i) + '.png'))\\n pyplot.close()\\n error, recall, precision, specificity = run_feature_selection_and_classification(methods_to_run,\\n train_data, validation_data,\\n labels, train_index, validation_index,\\n user_ids[validation_index, :], options,\\n header, '', class_1_weight = class_1_weight)\\n measures[0, i] = error\\n measures[1, i] = recall\\n measures[2, i] = precision\\n measures[3, i] = specificity\\n if return_measures:\\n return measures[0]\\n stats_vals = []\\n alpha_val = 0.95\\n for i in range(4):\\n mean, error = mean_confidence_interval(measures[i, :], alpha_val)\\n stats_vals.append([mean, error])\\n return stats_vals\\n\\ndef mean_confidence_interval(data, confidence=0.95):\\n a = 1.0*np.array(data)\\n n = len(a)\\n m, se = np.mean(a), sp.stats.sem(a)\\n h = se * sp.stats.t._ppf((1+confidence)/2., n-1)\\n return m, h\\n\"\n },\n {\n \"alpha_fraction\": 0.6510263681411743,\n \"alphanum_fraction\": 0.662756621837616,\n \"avg_line_length\": 30.090909957885742,\n \"blob_id\": \"cb13640e09331f027d535926d41d4aa436f24a79\",\n \"content_id\": \"148a8388d4cf3e83e84f492fbb41819b492178e5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 341,\n \"license_type\": \"no_license\",\n \"max_line_length\": 61,\n \"num_lines\": 11,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Kaggle Submission/helper.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import csv\\nimport numpy as np\\ndef load_data(input_path):\\n\\trows = []\\n\\twith open(input_path, 'r') as csvfile:\\n\\t\\treader = csv.reader(csvfile, delimiter=',')\\n\\t\\tfor row in reader:\\n\\t\\t\\trows.append(row)\\n\\tlabels = [int(row[1]) for row in rows[1:]]\\n\\trows = [[float(num) for num in row[2:]] for row in rows[1:]]\\n\\treturn np.array(rows), np.array(labels)\"\n },\n {\n \"alpha_fraction\": 0.6361031532287598,\n \"alphanum_fraction\": 0.6459271311759949,\n \"avg_line_length\": 47.86000061035156,\n \"blob_id\": \"5ff5c016cad4b417baa10139d87724d185a3d44d\",\n \"content_id\": \"7ac419bfdeeb522996ae5720c0d52a906abe272f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2443,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 50,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Project/run.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from preprocess_transcation_table import preprocess_transcation_table\\nfrom preprocess_user_logs_table import preprocess_user_logs_table\\nfrom join_tables import join_tables\\nfrom split_data_random import split_data_random\\nfrom generate_histograms import generate_histograms\\nfrom kfold_cross_validation import kfold_cross_validation\\nimport os.path as path\\ndef run (step_number):\\n if step_number == 1:\\n # Step 1: Preprocess transcation data.\\n transcations_input_filename = 'transactions.csv'\\n transcations_output_filename = 'transactions_preprocessed.csv'\\n preprocess_transcation_table(transcations_input_filename,\\n transcations_output_filename)\\n elif step_number == 2:\\n # Step 2: Preprocess user_logs data\\n user_logs_input_filename = 'user_logs.csv'\\n user_logs_output_filename = 'user_logs_preprocessed.csv'\\n preprocess_user_logs_table(user_logs_input_filename,\\n user_logs_output_filename)\\n elif step_number == 3:\\n # Step 3: Join all tables based on user_id\\n transcations_output_filename = 'transactions_preprocessed.csv'\\n user_logs_output_filename = 'user_logs_preprocessed.csv'\\n train_input_filename = 'train.csv'\\n member_input_filename = 'members.csv'\\n joined_table_output_filename = 'joined_train_data.csv'\\n join_tables(transcations_output_filename, user_logs_output_filename,\\n train_input_filename, member_input_filename,\\n joined_table_output_filename)\\n elif step_number == 4:\\n # Step 4: Split joined table file into 10 subsets randomly.\\n split_number = 10\\n train_data_joined = 'joined_train_data.csv'\\n output_folder = 'train_subsets'\\n train_ratio = 0.8\\n split_data_random(train_data_joined, split_number, output_folder,\\n train_ratio)\\n elif step_number == 5:\\n # Step 5: Generate features histogram.\\n train_file = path.join('train_subsets', '0_train.csv')\\n output_folder = 'histograms'\\n k = 10\\n kfold_cross_validation(k, train_file, ['features_histogram'], output_folder, [])\\n elif step_number == 6:\\n # Step 6: Run PCA.\\n train_file = path.join('train_subsets', '0_train.csv')\\n output_folder = 'pca'\\n k = 10\\n kfold_cross_validation(k, train_file, ['pca'], output_folder, [])\\n\"\n },\n {\n \"alpha_fraction\": 0.546486496925354,\n \"alphanum_fraction\": 0.565675675868988,\n \"avg_line_length\": 49,\n \"blob_id\": \"4ca5a21a9da97c0c7c2b508b0f8de20c946cc2c6\",\n \"content_id\": \"4b54b843ba623439666275eaf1270d588bbe8a1e\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3700,\n \"license_type\": \"no_license\",\n \"max_line_length\": 89,\n \"num_lines\": 74,\n \"path\": \"/Documents/Purdue/Fall 2017/ml/Project/kfold_cross_validation.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import math\\nimport csv\\nfrom my_PCA import MyPCA\\nimport numpy as np\\nfrom matplotlib import pyplot\\nimport os.path as path\\ndef kfold_cross_validation(k, train_file, methods_to_run,\\n output_folder, options):\\n train_csvfile = open(train_file,'rb')\\n lines = csv.reader(train_csvfile, delimiter = ',')\\n lines = list(lines)\\n header = lines[0]\\n # Remove headers from the data.\\n del lines[0]\\n samples_number = len(lines)\\n # Transform it to matrix.\\n # Skip the user id and the label columns.\\n data = np.array([[float(elem) for elem in line[2:]] for line in lines])\\n labels = np.array([[int(elem) for elem in line[1]] for line in lines])\\n features_number = len(data[0])\\n for i in range(k):\\n start_i = int(math.floor(samples_number * i / k))\\n end_i = int(math.floor(samples_number * (i + 1) / k))\\n validation_index = range(start_i, end_i)\\n points_index = range(samples_number)\\n train_index = list(set(points_index).difference(set(validation_index)));\\n train_labels = labels[train_index, :]\\n zero_indices = [j for j in range(len(train_labels)) if train_labels[j] == 0]\\n one_indices = [j for j in range(len(train_labels)) if train_labels[j] == 1]\\n if 'features_histogram' in methods_to_run:\\n # Caclulate features histogram for each fold training data.\\n for feature in range(features_number):\\n train_data = data[train_index, feature]\\n feature_column_class0 = train_data[zero_indices]\\n feature_column_class1 = train_data[one_indices]\\n pyplot.figure()\\n pyplot.hist(feature_column_class0, 100, label='Class 0')\\n pyplot.hist(feature_column_class1, 100, label='Class 1')\\n pyplot.legend(loc='upper right')\\n pyplot.savefig(path.join(output_folder, 'hist_fold=' + str(i) +\\n '_' + header[feature+2] + '.png'))\\n pyplot.close()\\n if 'pca' in methods_to_run:\\n # Run PCA.\\n myPca = MyPCA()\\n train_data = data[train_index, :]\\n #print(train_data[5566, :])\\n #print(train_data[8660, :])\\n #train_data = np.delete(train_data, [2695,5789,24610], 0)\\n #train_labels = np.delete(train_labels, [2695,5789,24610], 0)\\n zero_indices = [j for j in range(len(train_labels)) if train_labels[j] == 0]\\n one_indices = [j for j in range(len(train_labels)) if train_labels[j] == 1]\\n myPca.fit(train_data, n_components = 2)\\n # Visualize the training data.\\n projected_data = myPca.transform(train_data)\\n pyplot.figure()\\n #projected_data_np = np.array(projected_data[zero_indices, 0])\\n #points_index_np = np.array(range(len(projected_data_np)))\\n #print(points_index_np[projected_data_np >= 3e9])\\n pyplot.plot(projected_data[zero_indices, 0], projected_data[zero_indices, 1],\\n 'o', color = 'r')\\n pyplot.plot(projected_data[one_indices, 0], projected_data[one_indices, 1],\\n 'x', color = 'b')\\n pyplot.savefig(path.join(output_folder, 'pca_fold=' +\\n str(i) + '.png'))\\n pyplot.close()\\n if 'f1' in methods_to_run: # Run feature selection\\n print('f1')\\n if 'f2' in methods_to_run: # Run feature selection\\n print('f2')\\n if 'svm' in methods_to_run: # Run SVM.\\n print('svm')\\n if 'preceptron' in methods_to_run: # Run multilayer preceptron.\\n print('preceptron')\\n\"\n },\n {\n \"alpha_fraction\": 0.5974349975585938,\n \"alphanum_fraction\": 0.6241539120674133,\n \"avg_line_length\": 33.66666793823242,\n \"blob_id\": \"aba415de8b4211f5866f111363d3f4047fc52220\",\n \"content_id\": \"47ac1beb9dae48cb6b4fe0bec48f23a3da58a78b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2807,\n \"license_type\": \"no_license\",\n \"max_line_length\": 188,\n \"num_lines\": 81,\n \"path\": \"/hypothesis_testing.py\",\n \"repo_name\": \"Ahmed-Elbagoury/ML578\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os.path as path\\nimport os\\nfrom kfold_cross_validation import kfold_cross_validation\\nfrom plot_cross_validation_SVM import plot_cross_validation_SVM\\nimport random\\nimport numpy as np\\nimport scipy\\nimport statsmodels.sandbox.stats.multicomp\\n\\ndef do_hypothesis_testing(train_file, k):\\n\\tfea_selection_list = ['linear_SVC', 'linear_SVC', 'linear_SVC', 'linear_SVC', 'univariate_fea_selection', 'linear_SVC', 'univariate_fea_selection', 'univariate_fea_selection']\\n\\tclassification_list = ['preceptron', 'one_class_svm', 'one_class_svm', 'linear_svm', 'kernel_svm', 'linear_svm', 'kernel_svm', 'naive_bayes']\\n\\toptions_list = [\\n\\t[0.002, tuple([50] * 10), '', 0],\\n\\t[0.002, 1, 'linear', 0, ''],\\n\\t[0.002, 85, 'rbf', 0, ''],\\n\\t[0.002, 55, 'linear', 0, ''],\\n\\t[7, 70, 'rbf', 0, ''],\\n\\t[0.002, 85, 'linear', 0, ''],\\n\\t[7, 1, 'rbf', 0, ''],\\n\\t[7, 0, '', 0, '']\\n\\t]\\n\\tclass_weight_list = [1, 1, 1, 1, 1, 10, 10, 1]\\n\\n\\tstats_results_list = []\\n\\tfor i in range(len(options_list)):\\n\\t\\tprint 'i', i\\n\\t\\tstats_results = kfold_cross_validation(k, train_file, [fea_selection_list[i], classification_list[i]], '', options_list[i], class_1_weight = class_weight_list[i], return_measures = True)\\n\\t\\tstats_results_list += [stats_results]\\n\\t\\n\\t#doing hypothesis testing\\n\\tstats_results_list = np.array(stats_results_list)\\n\\tm, n = stats_results_list.shape\\n\\tprint 'm', m, 'n', n\\n\\tx_g = np.sum(stats_results_list) / float(n * m)\\n\\tx_bar_list = np.mean(stats_results_list, axis = 1)\\n\\tms_within = 0\\n\\t\\n\\tfor i in range(m):\\n\\t\\tx_bar = x_bar_list[i]\\n\\t\\tfor j in range(n):\\n\\t\\t\\tms_within += (stats_results_list[i][j] - x_bar) ** 2 /(float(m * (n-1)))\\n\\tdf_within = m * (n-1)\\n\\tms_between = 0\\n\\tfor i in range(m):\\n\\t\\tms_between += n * (x_bar_list[i] - x_g)**2/ float(m-1)\\n\\tdf_between = m-1\\n\\tf_stat = ms_between / ms_within\\t\\n\\tf = scipy.stats.f.ppf(0.95, df_between, df_within)\\n\\t# for row in stats_results_list:\\n\\t# \\tfor num in row:\\n\\t# \\t\\tprint num,\\n\\t# \\tprint ''\\n\\tprint 'F from table', f\\n\\tprint 'f_stat', f_stat\\n\\tif f_stat > f:\\n\\t\\tprint 'Reject null hypothesis'\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tIn this case we have to make Post Hoc Test\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tcount = 0\\n\\t\\tfor i in range(m):\\n\\t\\t\\tfor j in range(m):\\n\\t\\t\\t\\t# if i == j:\\n\\t\\t\\t\\t# \\tcontinue\\n\\t\\t\\t\\tdiff = abs(x_bar_list[i] - x_bar_list[j])/ (float(ms_within)/n)\\n\\t\\t\\t\\tdiff_stat = statsmodels.sandbox.stats.multicomp.get_tukeyQcrit(n, df_within, 0.05)\\n\\t\\t\\t\\tif diff > diff_stat:\\n\\t\\t\\t\\t\\t# print fea_selection_list[i], classification_list[i], options_list[i], 'class_1_weight', class_weight_list[i]\\n\\t\\t\\t\\t\\t# print 'Is statistically significant than'\\n\\t\\t\\t\\t\\t# print fea_selection_list[j], classification_list[j], options_list[j], 'class_1_weight', class_weight_list[j]\\n\\t\\t\\t\\t\\t# print '----------------'\\n\\t\\t\\t\\t\\tif x_bar_list[i] <= x_bar_list[j]:\\n\\t\\t\\t\\t\\t\\tprint 'B\\\\t',\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tprint 'W\\\\t',\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tprint 'Not\\\\t',\\n\\t\\t\\tprint ''\\n\\telse:\\n\\t\\tprint 'Accept null hypothesis'\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":29,"string":"29"}}},{"rowIdx":2979188,"cells":{"repo_name":{"kind":"string","value":"Raichain/Raichain"},"repo_url":{"kind":"string","value":"https://github.com/Raichain/Raichain"},"snapshot_id":{"kind":"string","value":"24c187503f79478c7d622538f8f88ffcb0d831a7"},"revision_id":{"kind":"string","value":"9232276b9a9f52d0d1311074249f4a5882f51f71"},"directory_id":{"kind":"string","value":"e834f5c0fd3555c32b448e206fe2f3a2b5e2d70b"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-05-06T06:53:07.934444","string":"2021-05-06T06:53:07.934444"},"revision_date":{"kind":"timestamp","value":"2017-12-19T23:00:46","string":"2017-12-19T23:00:46"},"committer_date":{"kind":"timestamp","value":"2017-12-19T23:00:46","string":"2017-12-19T23:00:46"},"github_id":{"kind":"number","value":113925197,"string":"113,925,197"},"star_events_count":{"kind":"number","value":6,"string":"6"},"fork_events_count":{"kind":"number","value":4,"string":"4"},"gha_license_id":{"kind":"string","value":"Unlicense"},"gha_created_at":{"kind":"timestamp","value":"2017-12-12T00:52:08","string":"2017-12-12T00:52:08"},"gha_updated_at":{"kind":"timestamp","value":"2017-12-14T20:16:32","string":"2017-12-14T20:16:32"},"gha_pushed_at":{"kind":"timestamp","value":"2017-12-19T00:57:20","string":"2017-12-19T00:57:20"},"gha_language":{"kind":"string","value":"HTML"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6842877864837646,"alphanum_fraction":0.688693106174469,"avg_line_length":29.977272033691406,"blob_id":"d426a047d2156b8c2a1de916600fe3290c748ce1","content_id":"c85435ccdd784f77fdb11c8d115a0673ab302b41","detected_licenses":["Unlicense"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1362,"license_type":"permissive","max_line_length":192,"num_lines":44,"path":"/chain/views.py","repo_name":"Raichain/Raichain","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nfrom chain.model.wallet_info import *\nfrom chain.model.block_info import *\nfrom chain.model.peers import *\nfrom django.shortcuts import render\nimport json\n\ndef index(request):\n\treturn render(request, 'index.html')\n\ndef wallet(request):\n\taddress = request.GET.get('w')\n\twallet = Wallet(address)\n\tif wallet.error:\n\t\treturn render(request, 'wallet.html', {'error':wallet.error})\t\n\telse:\n\t\ttransactions = wallet.transactions()\n\t\treturn render(request, 'wallet.html', {'address':address, 'balance':wallet.balance, 'blockcount':wallet.blockCount, 'transactions':transactions, 'representative':wallet.representativeBlock})\n\ndef block(request):\n\tblock_hash = request.GET.get('b')\n\tblock = Block(block_hash)\n\tb = json.loads(block.get_info()['contents'])\n\taccount = block.account\n\tblock2 = ''\n\tb2 = ''\n\tisReceivedOrSent = ''\n\tif b[\"type\"] == \"receive\" or b[\"type\"] == \"open\":\n\t\tblocks = Block(b['source'])\n\t\tb2 = json.loads(blocks.get_info()['contents'])\n\t\taccount\t= blocks.account\n\n\treturn render(request, 'block.html', {'binfo':b, 'block_hash':block_hash, 'account':account, 'block2':b2})\n\n\n\ndef peers(request):\n\tips = get_peers()['peers']\n\tpeers = []\n\tfor ip in ips:\n\t\tip = ip.replace('[::ffff:', '').replace(']', '')\n\t\tpeers.append(withoutPort(ip)) \n\treturn render(request, 'peers.html', {'peers':peers})"},{"alpha_fraction":0.7018970251083374,"alphanum_fraction":0.7018970251083374,"avg_line_length":34,"blob_id":"81a2b60bb84cc42596bdb7b810bea5938b73c2f9","content_id":"201710b999ba97d8fefaebf3a1d3809ce5db461c","detected_licenses":["Unlicense"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":738,"license_type":"permissive","max_line_length":107,"num_lines":21,"path":"/chain/model/wallet_info.py","repo_name":"Raichain/Raichain","src_encoding":"UTF-8","text":"from chain.model.chain import SearchChain \nfrom chain.model.rai_raw import raw_rai\n\nclass Wallet:\n\n\tdef __init__(self, address):\n\t\tcommand = '{\"action\": \"account_info\", \"account\": \"%s\"}' %address\n\t\n\t\tsearch = SearchChain(command)\n\t\tself.address = address\n\t\tself.error = False;\n\t\tself.openBlock = search.info['open_block']\n\t\tself.frontier = search.info['frontier']\n\t\tself.representativeBlock = search.info['representative_block']\n\t\tself.balance = raw_rai(search.info['balance'])\n\t\tself.blockCount = search.info['block_count']\n\n\tdef transactions(self):\n\t\tcommand = '{ \"action\": \"account_history\", \"account\": \"%s\", \"count\": %s}' %(self.address, self.blockCount)\n\t\ttransactions = SearchChain(command)\n\t\treturn transactions.info['history']\n\n\t\n"},{"alpha_fraction":0.6073298454284668,"alphanum_fraction":0.6596858501434326,"avg_line_length":16.18181800842285,"blob_id":"4eac87144f357275c58020c7778e77da64f901c8","content_id":"b05c9a0a081cc40eebd8da169e0395e33cfd7f9e","detected_licenses":["Unlicense"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":191,"license_type":"permissive","max_line_length":58,"num_lines":11,"path":"/chain/model/chain.py","repo_name":"Raichain/Raichain","src_encoding":"UTF-8","text":"import json\nimport requests\n\nclass SearchChain:\n\n\tdef __init__(self, command):\n\t\t\n\t\tr = requests.post('http://127.0.0.1:7076', data=command)\n\t\tinfo = json.loads(r.text)\n\n\t\tself.info = info \n\n"},{"alpha_fraction":0.6426734924316406,"alphanum_fraction":0.6529563069343567,"avg_line_length":19.473684310913086,"blob_id":"602a91f55f0005a2f7cb909c6baf4065e185d4df","content_id":"a5db80d26621138042d78195d58207280293ec13","detected_licenses":["Unlicense"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":389,"license_type":"permissive","max_line_length":43,"num_lines":19,"path":"/chain/model/peers.py","repo_name":"Raichain/Raichain","src_encoding":"UTF-8","text":"from chain.model.chain import SearchChain \nimport requests\nimport json\n\ndef get_peers():\n\tcommand = '{ \"action\": \"peers\" }'\n\tsearch = SearchChain(command)\n\treturn search.info\n\ndef withoutPort(ip):\n\twithout_port = []\n\tip = ip.split('.')\n\tfor i in range(4):\n\t\tif i < 3:\n\t\t\twithout_port.append(ip[i])\n\t\telif i == 3:\n\t\t\twithout_port.append(ip[i].split(':')[0])\n\n\treturn '.'.join(without_port)\n"},{"alpha_fraction":0.7669396996498108,"alphanum_fraction":0.7684288620948792,"avg_line_length":48.703704833984375,"blob_id":"0197f813b99533dbe0df22dd361830c0ca19565e","content_id":"767ab012fb74993a6fe86d9e2f44b3dd14776902","detected_licenses":["Unlicense"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1343,"license_type":"permissive","max_line_length":213,"num_lines":27,"path":"/CONTRIBUTING.md","repo_name":"Raichain/Raichain","src_encoding":"UTF-8","text":"# How can you contribute with the project? \n\n It's very simple: make a Fork of the project and make your change. After that, make a pull request and we you avaliate your code.\n \n## Ideas to contribute\n\nWe have by now much things to implement, so, if you want to help us, these are some ideas to what you can contribute for:\n\n### Javascript:\n\n. [typeInput.js](https://github.com/Raichain/Raichain/blob/master/chain/static/js/typeInput.js) -> Make the regular expressions better in the block/accounts searcher to detect if the search is a block ou a wallet.\n Every block must have 64 characters and these characters must be letters in upper case or numbers.\n \n### Style:\n\n[Templates Folder](https://github.com/Raichain/Raichain/tree/master/chain/templates)|[Static Folder](https://github.com/Raichain/Raichain/tree/master/chain/static)\n\n.If you like design and UX you can contribute to the design and UX of all pages.\n\n### Python:\n\n.We use Python with django in Backend. So you can contribute to database management, improve connections, etc\n\n. You can implement more functions for the Legder information. We use the Requests module to make the local requests to the RPC.\nYou can read the RPC documentation [here](https://github.com/clemahieu/raiblocks/wiki/RPC-protocol).\n\n.Help to develop Rest APIs. We intend to use Django Rest framework. \n"},{"alpha_fraction":0.6779661178588867,"alphanum_fraction":0.6779661178588867,"avg_line_length":24.428571701049805,"blob_id":"c752416d808ee2aa4bc2ab1739fe8568d6918692","content_id":"5773eb26608634a036137fdcafb18558a2e55b70","detected_licenses":["Unlicense"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":177,"license_type":"permissive","max_line_length":62,"num_lines":7,"path":"/chain/model/rai_raw.py","repo_name":"Raichain/Raichain","src_encoding":"UTF-8","text":"from chain.model.chain import *\n\ndef raw_rai(value):\n\tcommand = '{ \"action\": \"rai_from_raw\", \"amount\": %s }' %value\n\tresult = SearchChain(command)\n\n\treturn result.info['amount']"},{"alpha_fraction":0.6486023664474487,"alphanum_fraction":0.6799771785736084,"avg_line_length":30.872726440429688,"blob_id":"0ad7662ce4645ae712ef06ebb9138fb4eb2a51d0","content_id":"0ad50168121f9b29ae818c5774b0ec15614c0aad","detected_licenses":["Unlicense"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1753,"license_type":"permissive","max_line_length":244,"num_lines":55,"path":"/README.md","repo_name":"Raichain/Raichain","src_encoding":"UTF-8","text":"# Raichain\n\n## Introduction:\nRaiChain is a blockchain searcher for the cryptocurrency Raiblocks (XRB).\n## Requirements:\n\n### 1. Download the Project:\n```bash\n$ git clone https://github.com/Raichain/Raichain.git\n ```\n### 2. Install python modules requirements:\n```bash\n# pip3 install -r requirements.txt\n```\n### 3. Download RaiBlocks Wallet and Configure RPC:\nDownload the Raiblocks Wallet for your currently Operational System [here](https://raiblocks.net/#getwallets).\n\n#### Wait for the wallet synchronizes with the entire blockchain\n**OBS:** for a faster download, you can download the blockchain for [x64](https://yadi.sk/d/fcZgyES73Jzj5T) and for [x86](https://yadi.sk/d/fa1oDDsT3JznEY) systems directly from a server. Then, copy and paste \"data.ldb\" to the Raiblocks folder.\n\nThen, open the config.json file at Raiblocks folder and configure the RPC like this:\n```json\n\"rpc\": {\n \"address\": \"::ffff:127.0.0.1\",\n \"port\": \"7076\",\n \"enable_control\": \"true\",\n \"frontier_request_limit\": \"16384\",\n \"chain_request_limit\": \"16384\"\n },\n \"rpc_enable\": \"true\",\n \"opencl_enable\": \"false\",\n \"opencl\": {\n \"platform\": \"0\",\n \"device\": \"0\",\n \"threads\": \"1048576\"\n }\n```\n#### Restart your wallet.\n\n**Raiblocks Folder:**\n```\nWindows: C:\\Users\\<user\\AppData\\Local\\RaiBlocks\\\nOSX: /Users/<user>/Library/RaiBlocks/\nLinux: /home/<user>/RaiBlocks/\n```\n\n### 4. Start Python Server:\nyou should make sure that your wallet is synchronized and installed on your computer for a better experience with the project interface, as it depends on the portfolio\n```bash\n# python3 manage.py runserver\n```\n### 5. Acess Page Project\n```https\nhttp://127.0.0.1:8000\n```\n"},{"alpha_fraction":0.676886796951294,"alphanum_fraction":0.676886796951294,"avg_line_length":27.266666412353516,"blob_id":"6ce5546b5512add5a9dd1e0290dde3e450d7912d","content_id":"76e476185df86398eda44de18e7075179fc807de","detected_licenses":["Unlicense"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":424,"license_type":"permissive","max_line_length":80,"num_lines":15,"path":"/chain/model/block_info.py","repo_name":"Raichain/Raichain","src_encoding":"UTF-8","text":"from chain.model.chain import SearchChain \nfrom chain.model.rai_raw import raw_rai\nimport json\n\nclass Block:\n\n\tdef __init__(self, block_hash):\n\t\taccount = SearchChain('{\"action\": \"block_account\", \"hash\": \"%s\"}' %block_hash)\n\t\tself.account = account.info['account']\n\t\tself.hash = block_hash\n\n\tdef get_info(self):\n\t\tcommand = '{ \"action\": \"block\", \"hash\": \"%s\" }' %self.hash\n\t\tblock = SearchChain(command)\n\t\treturn block.info\n"},{"alpha_fraction":0.6264591217041016,"alphanum_fraction":0.6264591217041016,"avg_line_length":22.363636016845703,"blob_id":"0b195d33e0342f33273f31b54f5f9a70b0aef2fd","content_id":"3130df8a6c3c4c2c07df28d8a2a270f35c11121f","detected_licenses":["Unlicense"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":257,"license_type":"permissive","max_line_length":37,"num_lines":11,"path":"/raiblockschain/urls.py","repo_name":"Raichain/Raichain","src_encoding":"UTF-8","text":"from django.conf.urls import url\nfrom django.contrib import admin\nfrom chain.views import *\n\nurlpatterns = [\n url(r'^admin/', admin.site.urls),\n url(r'^$', index),\n url(r'^wallet$', wallet),\n url(r'^block$', block),\n url(r'^peers$', peers)\n]\n"},{"alpha_fraction":0.5797373652458191,"alphanum_fraction":0.5909943580627441,"avg_line_length":22.173913955688477,"blob_id":"238be50dc6e8b8a95a5ffddece2870cccfb8cafb","content_id":"39d444e05c4d4ebf8bed2c67a6a58a68be8d156c","detected_licenses":["Unlicense"],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":533,"license_type":"permissive","max_line_length":71,"num_lines":23,"path":"/chain/static/js/typeInput.js","repo_name":"Raichain/Raichain","src_encoding":"UTF-8","text":"\nfunction typeInput(){\n\n\tlet input = getId('searcher-input');\n\n\tlet wallet_default = /^xrb_/;\n\tlet block_default = /\\b[0-9A-F]{64}\\b/;\n\n\tif(wallet_default.test(input.value)){\n\t\t$('.mdl-button').disabled = false;\n\t\t$('.searcher').action = \"/wallet\";\n\t\tinput.name = \"w\";\n\n\t}else if(block_default.test(input.value) && input.value.length == 64){\n\t\t$('.mdl-button').disabled = false;\n\t\t$('.searcher').action = \"/block\";\n\t\tinput.name = \"b\";\n\n\t}else{\n\t\t$('.mdl-button').disabled = true;\n\t\t$('.searcher').action = \"\";\n\t\tinput.name = \"\";\n\t}\n}"}],"string":"[\n {\n \"alpha_fraction\": 0.6842877864837646,\n \"alphanum_fraction\": 0.688693106174469,\n \"avg_line_length\": 29.977272033691406,\n \"blob_id\": \"d426a047d2156b8c2a1de916600fe3290c748ce1\",\n \"content_id\": \"c85435ccdd784f77fdb11c8d115a0673ab302b41\",\n \"detected_licenses\": [\n \"Unlicense\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1362,\n \"license_type\": \"permissive\",\n \"max_line_length\": 192,\n \"num_lines\": 44,\n \"path\": \"/chain/views.py\",\n \"repo_name\": \"Raichain/Raichain\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\nfrom __future__ import unicode_literals\\nfrom chain.model.wallet_info import *\\nfrom chain.model.block_info import *\\nfrom chain.model.peers import *\\nfrom django.shortcuts import render\\nimport json\\n\\ndef index(request):\\n\\treturn render(request, 'index.html')\\n\\ndef wallet(request):\\n\\taddress = request.GET.get('w')\\n\\twallet = Wallet(address)\\n\\tif wallet.error:\\n\\t\\treturn render(request, 'wallet.html', {'error':wallet.error})\\t\\n\\telse:\\n\\t\\ttransactions = wallet.transactions()\\n\\t\\treturn render(request, 'wallet.html', {'address':address, 'balance':wallet.balance, 'blockcount':wallet.blockCount, 'transactions':transactions, 'representative':wallet.representativeBlock})\\n\\ndef block(request):\\n\\tblock_hash = request.GET.get('b')\\n\\tblock = Block(block_hash)\\n\\tb = json.loads(block.get_info()['contents'])\\n\\taccount = block.account\\n\\tblock2 = ''\\n\\tb2 = ''\\n\\tisReceivedOrSent = ''\\n\\tif b[\\\"type\\\"] == \\\"receive\\\" or b[\\\"type\\\"] == \\\"open\\\":\\n\\t\\tblocks = Block(b['source'])\\n\\t\\tb2 = json.loads(blocks.get_info()['contents'])\\n\\t\\taccount\\t= blocks.account\\n\\n\\treturn render(request, 'block.html', {'binfo':b, 'block_hash':block_hash, 'account':account, 'block2':b2})\\n\\n\\n\\ndef peers(request):\\n\\tips = get_peers()['peers']\\n\\tpeers = []\\n\\tfor ip in ips:\\n\\t\\tip = ip.replace('[::ffff:', '').replace(']', '')\\n\\t\\tpeers.append(withoutPort(ip)) \\n\\treturn render(request, 'peers.html', {'peers':peers})\"\n },\n {\n \"alpha_fraction\": 0.7018970251083374,\n \"alphanum_fraction\": 0.7018970251083374,\n \"avg_line_length\": 34,\n \"blob_id\": \"81a2b60bb84cc42596bdb7b810bea5938b73c2f9\",\n \"content_id\": \"201710b999ba97d8fefaebf3a1d3809ce5db461c\",\n \"detected_licenses\": [\n \"Unlicense\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 738,\n \"license_type\": \"permissive\",\n \"max_line_length\": 107,\n \"num_lines\": 21,\n \"path\": \"/chain/model/wallet_info.py\",\n \"repo_name\": \"Raichain/Raichain\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from chain.model.chain import SearchChain \\nfrom chain.model.rai_raw import raw_rai\\n\\nclass Wallet:\\n\\n\\tdef __init__(self, address):\\n\\t\\tcommand = '{\\\"action\\\": \\\"account_info\\\", \\\"account\\\": \\\"%s\\\"}' %address\\n\\t\\n\\t\\tsearch = SearchChain(command)\\n\\t\\tself.address = address\\n\\t\\tself.error = False;\\n\\t\\tself.openBlock = search.info['open_block']\\n\\t\\tself.frontier = search.info['frontier']\\n\\t\\tself.representativeBlock = search.info['representative_block']\\n\\t\\tself.balance = raw_rai(search.info['balance'])\\n\\t\\tself.blockCount = search.info['block_count']\\n\\n\\tdef transactions(self):\\n\\t\\tcommand = '{ \\\"action\\\": \\\"account_history\\\", \\\"account\\\": \\\"%s\\\", \\\"count\\\": %s}' %(self.address, self.blockCount)\\n\\t\\ttransactions = SearchChain(command)\\n\\t\\treturn transactions.info['history']\\n\\n\\t\\n\"\n },\n {\n \"alpha_fraction\": 0.6073298454284668,\n \"alphanum_fraction\": 0.6596858501434326,\n \"avg_line_length\": 16.18181800842285,\n \"blob_id\": \"4eac87144f357275c58020c7778e77da64f901c8\",\n \"content_id\": \"b05c9a0a081cc40eebd8da169e0395e33cfd7f9e\",\n \"detected_licenses\": [\n \"Unlicense\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 191,\n \"license_type\": \"permissive\",\n \"max_line_length\": 58,\n \"num_lines\": 11,\n \"path\": \"/chain/model/chain.py\",\n \"repo_name\": \"Raichain/Raichain\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import json\\nimport requests\\n\\nclass SearchChain:\\n\\n\\tdef __init__(self, command):\\n\\t\\t\\n\\t\\tr = requests.post('http://127.0.0.1:7076', data=command)\\n\\t\\tinfo = json.loads(r.text)\\n\\n\\t\\tself.info = info \\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6426734924316406,\n \"alphanum_fraction\": 0.6529563069343567,\n \"avg_line_length\": 19.473684310913086,\n \"blob_id\": \"602a91f55f0005a2f7cb909c6baf4065e185d4df\",\n \"content_id\": \"a5db80d26621138042d78195d58207280293ec13\",\n \"detected_licenses\": [\n \"Unlicense\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 389,\n \"license_type\": \"permissive\",\n \"max_line_length\": 43,\n \"num_lines\": 19,\n \"path\": \"/chain/model/peers.py\",\n \"repo_name\": \"Raichain/Raichain\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from chain.model.chain import SearchChain \\nimport requests\\nimport json\\n\\ndef get_peers():\\n\\tcommand = '{ \\\"action\\\": \\\"peers\\\" }'\\n\\tsearch = SearchChain(command)\\n\\treturn search.info\\n\\ndef withoutPort(ip):\\n\\twithout_port = []\\n\\tip = ip.split('.')\\n\\tfor i in range(4):\\n\\t\\tif i < 3:\\n\\t\\t\\twithout_port.append(ip[i])\\n\\t\\telif i == 3:\\n\\t\\t\\twithout_port.append(ip[i].split(':')[0])\\n\\n\\treturn '.'.join(without_port)\\n\"\n },\n {\n \"alpha_fraction\": 0.7669396996498108,\n \"alphanum_fraction\": 0.7684288620948792,\n \"avg_line_length\": 48.703704833984375,\n \"blob_id\": \"0197f813b99533dbe0df22dd361830c0ca19565e\",\n \"content_id\": \"767ab012fb74993a6fe86d9e2f44b3dd14776902\",\n \"detected_licenses\": [\n \"Unlicense\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1343,\n \"license_type\": \"permissive\",\n \"max_line_length\": 213,\n \"num_lines\": 27,\n \"path\": \"/CONTRIBUTING.md\",\n \"repo_name\": \"Raichain/Raichain\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# How can you contribute with the project? \\n\\n It's very simple: make a Fork of the project and make your change. After that, make a pull request and we you avaliate your code.\\n \\n## Ideas to contribute\\n\\nWe have by now much things to implement, so, if you want to help us, these are some ideas to what you can contribute for:\\n\\n### Javascript:\\n\\n. [typeInput.js](https://github.com/Raichain/Raichain/blob/master/chain/static/js/typeInput.js) -> Make the regular expressions better in the block/accounts searcher to detect if the search is a block ou a wallet.\\n Every block must have 64 characters and these characters must be letters in upper case or numbers.\\n \\n### Style:\\n\\n[Templates Folder](https://github.com/Raichain/Raichain/tree/master/chain/templates)|[Static Folder](https://github.com/Raichain/Raichain/tree/master/chain/static)\\n\\n.If you like design and UX you can contribute to the design and UX of all pages.\\n\\n### Python:\\n\\n.We use Python with django in Backend. So you can contribute to database management, improve connections, etc\\n\\n. You can implement more functions for the Legder information. We use the Requests module to make the local requests to the RPC.\\nYou can read the RPC documentation [here](https://github.com/clemahieu/raiblocks/wiki/RPC-protocol).\\n\\n.Help to develop Rest APIs. We intend to use Django Rest framework. \\n\"\n },\n {\n \"alpha_fraction\": 0.6779661178588867,\n \"alphanum_fraction\": 0.6779661178588867,\n \"avg_line_length\": 24.428571701049805,\n \"blob_id\": \"c752416d808ee2aa4bc2ab1739fe8568d6918692\",\n \"content_id\": \"5773eb26608634a036137fdcafb18558a2e55b70\",\n \"detected_licenses\": [\n \"Unlicense\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 177,\n \"license_type\": \"permissive\",\n \"max_line_length\": 62,\n \"num_lines\": 7,\n \"path\": \"/chain/model/rai_raw.py\",\n \"repo_name\": \"Raichain/Raichain\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from chain.model.chain import *\\n\\ndef raw_rai(value):\\n\\tcommand = '{ \\\"action\\\": \\\"rai_from_raw\\\", \\\"amount\\\": %s }' %value\\n\\tresult = SearchChain(command)\\n\\n\\treturn result.info['amount']\"\n },\n {\n \"alpha_fraction\": 0.6486023664474487,\n \"alphanum_fraction\": 0.6799771785736084,\n \"avg_line_length\": 30.872726440429688,\n \"blob_id\": \"0ad7662ce4645ae712ef06ebb9138fb4eb2a51d0\",\n \"content_id\": \"0ad50168121f9b29ae818c5774b0ec15614c0aad\",\n \"detected_licenses\": [\n \"Unlicense\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1753,\n \"license_type\": \"permissive\",\n \"max_line_length\": 244,\n \"num_lines\": 55,\n \"path\": \"/README.md\",\n \"repo_name\": \"Raichain/Raichain\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Raichain\\n\\n## Introduction:\\nRaiChain is a blockchain searcher for the cryptocurrency Raiblocks (XRB).\\n## Requirements:\\n\\n### 1. Download the Project:\\n```bash\\n$ git clone https://github.com/Raichain/Raichain.git\\n ```\\n### 2. Install python modules requirements:\\n```bash\\n# pip3 install -r requirements.txt\\n```\\n### 3. Download RaiBlocks Wallet and Configure RPC:\\nDownload the Raiblocks Wallet for your currently Operational System [here](https://raiblocks.net/#getwallets).\\n\\n#### Wait for the wallet synchronizes with the entire blockchain\\n**OBS:** for a faster download, you can download the blockchain for [x64](https://yadi.sk/d/fcZgyES73Jzj5T) and for [x86](https://yadi.sk/d/fa1oDDsT3JznEY) systems directly from a server. Then, copy and paste \\\"data.ldb\\\" to the Raiblocks folder.\\n\\nThen, open the config.json file at Raiblocks folder and configure the RPC like this:\\n```json\\n\\\"rpc\\\": {\\n \\\"address\\\": \\\"::ffff:127.0.0.1\\\",\\n \\\"port\\\": \\\"7076\\\",\\n \\\"enable_control\\\": \\\"true\\\",\\n \\\"frontier_request_limit\\\": \\\"16384\\\",\\n \\\"chain_request_limit\\\": \\\"16384\\\"\\n },\\n \\\"rpc_enable\\\": \\\"true\\\",\\n \\\"opencl_enable\\\": \\\"false\\\",\\n \\\"opencl\\\": {\\n \\\"platform\\\": \\\"0\\\",\\n \\\"device\\\": \\\"0\\\",\\n \\\"threads\\\": \\\"1048576\\\"\\n }\\n```\\n#### Restart your wallet.\\n\\n**Raiblocks Folder:**\\n```\\nWindows: C:\\\\Users\\\\<user\\\\AppData\\\\Local\\\\RaiBlocks\\\\\\nOSX: /Users/<user>/Library/RaiBlocks/\\nLinux: /home/<user>/RaiBlocks/\\n```\\n\\n### 4. Start Python Server:\\nyou should make sure that your wallet is synchronized and installed on your computer for a better experience with the project interface, as it depends on the portfolio\\n```bash\\n# python3 manage.py runserver\\n```\\n### 5. Acess Page Project\\n```https\\nhttp://127.0.0.1:8000\\n```\\n\"\n },\n {\n \"alpha_fraction\": 0.676886796951294,\n \"alphanum_fraction\": 0.676886796951294,\n \"avg_line_length\": 27.266666412353516,\n \"blob_id\": \"6ce5546b5512add5a9dd1e0290dde3e450d7912d\",\n \"content_id\": \"76e476185df86398eda44de18e7075179fc807de\",\n \"detected_licenses\": [\n \"Unlicense\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 424,\n \"license_type\": \"permissive\",\n \"max_line_length\": 80,\n \"num_lines\": 15,\n \"path\": \"/chain/model/block_info.py\",\n \"repo_name\": \"Raichain/Raichain\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from chain.model.chain import SearchChain \\nfrom chain.model.rai_raw import raw_rai\\nimport json\\n\\nclass Block:\\n\\n\\tdef __init__(self, block_hash):\\n\\t\\taccount = SearchChain('{\\\"action\\\": \\\"block_account\\\", \\\"hash\\\": \\\"%s\\\"}' %block_hash)\\n\\t\\tself.account = account.info['account']\\n\\t\\tself.hash = block_hash\\n\\n\\tdef get_info(self):\\n\\t\\tcommand = '{ \\\"action\\\": \\\"block\\\", \\\"hash\\\": \\\"%s\\\" }' %self.hash\\n\\t\\tblock = SearchChain(command)\\n\\t\\treturn block.info\\n\"\n },\n {\n \"alpha_fraction\": 0.6264591217041016,\n \"alphanum_fraction\": 0.6264591217041016,\n \"avg_line_length\": 22.363636016845703,\n \"blob_id\": \"0b195d33e0342f33273f31b54f5f9a70b0aef2fd\",\n \"content_id\": \"3130df8a6c3c4c2c07df28d8a2a270f35c11121f\",\n \"detected_licenses\": [\n \"Unlicense\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 257,\n \"license_type\": \"permissive\",\n \"max_line_length\": 37,\n \"num_lines\": 11,\n \"path\": \"/raiblockschain/urls.py\",\n \"repo_name\": \"Raichain/Raichain\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.conf.urls import url\\nfrom django.contrib import admin\\nfrom chain.views import *\\n\\nurlpatterns = [\\n url(r'^admin/', admin.site.urls),\\n url(r'^$', index),\\n url(r'^wallet$', wallet),\\n url(r'^block$', block),\\n url(r'^peers$', peers)\\n]\\n\"\n },\n {\n \"alpha_fraction\": 0.5797373652458191,\n \"alphanum_fraction\": 0.5909943580627441,\n \"avg_line_length\": 22.173913955688477,\n \"blob_id\": \"238be50dc6e8b8a95a5ffddece2870cccfb8cafb\",\n \"content_id\": \"39d444e05c4d4ebf8bed2c67a6a58a68be8d156c\",\n \"detected_licenses\": [\n \"Unlicense\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 533,\n \"license_type\": \"permissive\",\n \"max_line_length\": 71,\n \"num_lines\": 23,\n \"path\": \"/chain/static/js/typeInput.js\",\n \"repo_name\": \"Raichain/Raichain\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nfunction typeInput(){\\n\\n\\tlet input = getId('searcher-input');\\n\\n\\tlet wallet_default = /^xrb_/;\\n\\tlet block_default = /\\\\b[0-9A-F]{64}\\\\b/;\\n\\n\\tif(wallet_default.test(input.value)){\\n\\t\\t$('.mdl-button').disabled = false;\\n\\t\\t$('.searcher').action = \\\"/wallet\\\";\\n\\t\\tinput.name = \\\"w\\\";\\n\\n\\t}else if(block_default.test(input.value) && input.value.length == 64){\\n\\t\\t$('.mdl-button').disabled = false;\\n\\t\\t$('.searcher').action = \\\"/block\\\";\\n\\t\\tinput.name = \\\"b\\\";\\n\\n\\t}else{\\n\\t\\t$('.mdl-button').disabled = true;\\n\\t\\t$('.searcher').action = \\\"\\\";\\n\\t\\tinput.name = \\\"\\\";\\n\\t}\\n}\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":10,"string":"10"}}},{"rowIdx":2979189,"cells":{"repo_name":{"kind":"string","value":"mehranhussain/dnChatNetwork"},"repo_url":{"kind":"string","value":"https://github.com/mehranhussain/dnChatNetwork"},"snapshot_id":{"kind":"string","value":"45b0dd33cec5292b7b284192ced9d8ce89768b82"},"revision_id":{"kind":"string","value":"30af64086420618d5e6b0a9a8e5edddf5a2c7780"},"directory_id":{"kind":"string","value":"66035dcebdbffe79e84a78ac1c82f6460476b671"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-07-14T00:12:12.300313","string":"2021-07-14T00:12:12.300313"},"revision_date":{"kind":"timestamp","value":"2017-10-13T18:09:15","string":"2017-10-13T18:09:15"},"committer_date":{"kind":"timestamp","value":"2017-10-13T18:09:15","string":"2017-10-13T18:09:15"},"github_id":{"kind":"number","value":104764433,"string":"104,764,433"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5157977938652039,"alphanum_fraction":0.5285940170288086,"avg_line_length":34.35195541381836,"blob_id":"421cedc621591e2c397de774ca9a867f2cb1e4d9","content_id":"48852ff1cfaba3a16a04ac8a2cbb5d35ebf80062","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6330,"license_type":"no_license","max_line_length":285,"num_lines":179,"path":"/chatClient.py","repo_name":"mehranhussain/dnChatNetwork","src_encoding":"UTF-8","text":"# dnClient.py\n# Example Usage: python chatClient.py localhost 42015\n\n# importing required modules\nimport socket\nimport select\nimport sys\nfrom random import randint\n\n# Declaring socket\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\nuserName = \"\"\n\n# Main function\ndef main():\n\n # Usage of the chatClient.py\n if len(sys.argv) < 3:\n print 'Usage : python chatClient.py hostname port'\n sys.exit()\n\n host = sys.argv[1]\n port = int(sys.argv[2])\n\n # State of the connection\n CONNECTED = False\n AUTHENTICATED = False\n \n s.settimeout(2)\n\n # Connect to chatServer\n try:\n s.connect((host, port))\n except:\n print 'Unable to connect to chatServer.'\n sys.exit()\n\n CONNECTED = True\n print 'Connected to chatServer.'\n\n # Protocol implementation\n\n commands = ['AUTH', 'SEND', 'ACKN', 'OKAY', 'FAIL', 'INVD', 'ARRV', 'LEFT', 'SRVR']\n\n authFlag = True\n\n # Authentication Stage\n while 1:\n if not authFlag:\n break\n if CONNECTED == True:\n # Random number generated for chatClient Reference, ask for name and password\n chatClientReference = randint(1, 100)\n chatClientName = raw_input(\"Enter name: \")\n chatClientPassword = raw_input(\"Enter password: \")\n\n authMessage = commands[0]\n authMessage += \" \"\n authMessage += str(chatClientReference)\n authMessage += \" \\r\\n\"\n authMessage += chatClientName\n userName = chatClientName\n authMessage += \"\\r\\n\"\n authMessage += chatClientPassword\n\n s.send(authMessage)\n\n socket_list = [sys.stdin, s]\n\n # Get the list sockets which are readable\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\n\n for sock in read_sockets:\n # Incoming message from chatServer\n if sock == s:\n data = sock.recv(4096)\n authResponse = data.split()\n print authResponse\n\n # If OKAY\n if data == commands[3] + \" \" + str(chatClientReference):\n print data\n authFlag = False\n AUTHENTICATED = True\n\n break\n\n # If FAIL\n elif authResponse[0] == commands[4]:\n if authResponse[2] == \"PASSWORD\":\n print authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[2] + \": The password is not acceptable for authentication on this server.\"\n elif authResponse[2] == \"NAME\":\n print authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[2] + \": The specified name is already in use by another client.\"\n elif authResponse[2] == \"NUMBER\":\n print authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[2] + \": A number is not valid, either because it has already been used for another entity in case it was newly generated by the client, or there is no relevant entity that the number refers to.\"\n\n # When client is connected and authenticated\n while 1:\n socket_list = [sys.stdin, s]\n # Get the list sockets which are readable\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\n\n # Random number generated for chatMesssageReference\n chatMessageReference = randint(1, 10000)\n\n for sock in read_sockets:\n\n # Incoming message from chatServer\n\n if sock == s:\n data = sock.recv(4096)\n authResponse = data.split()\n\n # If OKAY\n if data == commands[3] + \" \" + str(chatMessageReference):\n sys.stdout.write(data)\n\n # If SEND\n elif authResponse[0] == commands[1]:\n print data\n idclt = randint(100, 50000)\n s.send(\"ACKN \" + authResponse[1] + \"\\r\\n\" + authResponse[2] + \"\\r\\n\" + str(s.getsockname()[1]))\n\n # If ARRV\n elif authResponse[0] == commands[6]:\n print \"The following user is connected now.\\r\\nUser Reference: \" + authResponse[1] + \"\\r\\nUsername: \" + authResponse[2] + \"\\r\\nIpAddress: \" + authResponse[3]\n\n # If LEFT\n elif authResponse[0] == commands[7]:\n print \"The following user has left.\\r\\nUser Reference: \" + authResponse[1]\n\n # If FAIL\n elif authResponse[0] == commands[4]:\n if authResponse[2] == \"LENGHT\":\n print authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[2] + \": The chat message text is too long.\"\n elif authResponse[2] == commands[5]:\n print authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[2] + \": A malformed message or a message that is not valid in the current state of the client.\"\n \n s.close()\n print \"Connection to chatServer closed\"\n \n elif authResponse[0] == \"CLTS\":\n\n for i in range(1,len(authResponse)):\n print authResponse[i]\n\n else:\n print data\n\n\n else:\n if AUTHENTICATED == True:\n chatMessageInput = sys.stdin.readline()\n\n if \"list\" in chatMessageInput:\n s.send(\"CLTS \" + str(userName))\n break\n\t\t \n chatMessageRcvr = raw_input(\"Enter * for broadcasting or Reference Number of specific client: \")\n\n chatMessage = commands[1]\n chatMessage += \" \"\n chatMessage += str(chatMessageReference)\n chatMessage += \" \\r\\n\"\n chatMessage += chatMessageRcvr\n chatMessage += \"\\r\\n\"\n chatMessage += chatMessageInput\n\n s.send(chatMessage)\n\n# Run the main method, If user presses ctrl+c close the socket connection\nif __name__ == \"__main__\":\n\n try:\n main()\n except KeyboardInterrupt:\n s.send(\"CLOSED\")\n s.close()\n\n\n"},{"alpha_fraction":0.7998196482658386,"alphanum_fraction":0.7998196482658386,"avg_line_length":57.3684196472168,"blob_id":"3436aa678b43b9fb9910d0fbea4780bbaea5a659","content_id":"5837be98400e01d8d5ab42a33beb45f65af1afe8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1109,"license_type":"no_license","max_line_length":443,"num_lines":19,"path":"/README.md","repo_name":"mehranhussain/dnChatNetwork","src_encoding":"UTF-8","text":"## dnChatNetwork\n\nOur project consists of multi-clients and multi servers connecting with each other in acyclcic topology as well in cyclic. It still has a few bugs. The recent addition we did was displaying the clients that were already connected when a new user connected with a server. We handled the deletetion of users from the lists that disconnects from the server. And handled the acknowledgements while broadcasting as well as sending to specific user.\n\n### Functionality\n\nThe dnChatNetwork can send broadcast messages to all the clients connected in a server. Not only a single server but also in a network of servers the implementation works fine. We can send a message to a specific user too. We handle the client or server leaving or arriving in the network and successfully notifying all.\n\nWe are also handling a few conditions other than this that are quite useful if implemented on a bigger scale like checking for a malformed message, checking for duplicate clients etc.\n\n#### Usage\n\n#### For chatClient:\n\npython chatClient.py ip_address port\n\n#### For chatServer\n\npython chatServer.py port\n"},{"alpha_fraction":0.4911535680294037,"alphanum_fraction":0.5025556087493896,"avg_line_length":40.691802978515625,"blob_id":"5465b15bfb5d5fbd780f099691eedfadd6bdcaf6","content_id":"24b01b8b9e4e28adda7ff98815d3be0fb509d3f3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":12717,"license_type":"no_license","max_line_length":227,"num_lines":305,"path":"/Main-GUi.py","repo_name":"mehranhussain/dnChatNetwork","src_encoding":"UTF-8","text":"# dnClient.py\n# Example Usage: python chatClient.py localhost 42015\n\n# importing required modules\nimport socket\nimport select\nimport sys\nfrom random import randint\nimport threading\ntry:\n import Tkinter, ttk\nexcept:\n import tkinter as Tkinter\n import tkinter.ttk as ttk\n\nif len(sys.argv) < 3:\n print 'Usage : python chatClient.py hostname port'\n sys.exit()\n\nhost = sys.argv[1]\nport = int(sys.argv[2])\n\n# State of the connection\nCONNECTED = False\nAUTHENTICATED = False\n\n# Protocol implementation\n\ncommands = ['AUTH', 'SEND', 'ACKN', 'OKAY', 'FAIL', 'INVD', 'ARRV', 'LEFT', 'SRVR']\n\nclass SOCKETS:\n def __init__(self):\n # Declaring socket\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n def load(self, ip_address, port, text,status,server_info):\n self.ip_address = ip_address\n self.port = port\n self.history = text\n self.status = status\n self.server_info = server_info\n return\n\n def bind(self):\n while True:\n try:\n s.connect((host, port))\n s.settimeout(2)\n\n # Connect to chatServer\n try:\n s.connect((host, port))\n except:\n print 'Unable to connect to chatServer.'\n sys.exit()\n\n CONNECTED = True\n print 'Connected to chatServer.'\n\n threading.Thread(target=self.recv).start()\n break\n\n except:\n pass\n\n def recv(self):\n authFlag = True\n\n # Authentication Stage\n while 1:\n if not authFlag:\n break\n if CONNECTED == True:\n # Random number generated for chatClient Reference, ask for name and password\n chatClientReference = randint(1, 100)\n\n chatClientName = \"dsdsdds\"\n chatClientPassword = \"dnServer\"\n\n authMessage = commands[0]\n authMessage += \" \"\n authMessage += str(chatClientReference)\n authMessage += \" \\r\\n\"\n authMessage += chatClientName\n authMessage += \"\\r\\n\"\n authMessage += chatClientPassword\n\n s.send(authMessage)\n\n socket_list = [sys.stdin, s]\n\n # Get the list sockets which are readable\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\n\n for sock in read_sockets:\n # Incoming message from chatServer\n if sock == s:\n data = sock.recv(4096)\n authResponse = data.split()\n print authResponse\n\n # If OKAY\n if data == commands[3] + \" \" + str(chatClientReference):\n authFlag = False\n AUTHENTICATED = True\n break\n\n # If FAIL\n elif authResponse[0] == commands[4]:\n if authResponse[2] == \"PASSWORD\":\n self.printData(authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[\n 2] + \": The password is not acceptable for authentication on this server.\")\n elif authResponse[2] == \"NAME\":\n self.printData(authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[\n 2] + \": The specified name is already in use by another client.\")\n elif authResponse[2] == \"NUMBER\":\n self.printData(authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[\n 2] + \": A number is not valid, either because it has already been used for another entity in case it was newly generated by the client, or there is no relevant entity that the number refers to.\")\n\n # When client is connected and authenticated\n while 1:\n socket_list = [sys.stdin, s]\n # Get the list sockets which are readable\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\n\n # Random number generated for chatMesssageReference\n chatMessageReference = randint(1, 10000)\n\n for sock in read_sockets:\n\n # Incoming message from chatServer\n\n if sock == s:\n data = sock.recv(4096)\n authResponse = data.split()\n\n # If OKAY\n if data == commands[3] + \" \" + str(chatMessageReference):\n self.printData(data)\n\n # If SEND\n elif authResponse[0] == commands[1]:\n self.printData(data)\n s.send(\"ACKN \" + authResponse[1] + \"\\r\\n\" + authResponse[2])\n\n # If ARRV\n elif authResponse[0] == commands[6]:\n self.printData(\"The following user is connected now.\\r\\nUser Reference: \" + authResponse[\n 1] + \"\\r\\nUsername: \" + authResponse[2] + \"\\r\\nIpAddress: \" + authResponse[3])\n\n # If LEFT\n elif authResponse[0] == commands[7]:\n self.printData(\"The following user has left.\\r\\nUser Reference: \" + authResponse[1])\n\n # If FAIL\n elif authResponse[0] == commands[4]:\n if authResponse[2] == \"LENGHT\":\n self.printData(authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[\n 2] + \": The chat message text is too long.\")\n elif authResponse[2] == commands[5]:\n self.printData(authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[\n 2] + \": A malformed message or a message that is not valid in the current state of the client.\")\n s.close()\n self.printData(\"Connection to chatServer closed.\")\n else:\n self.printData(data)\n\n else:\n if AUTHENTICATED == True:\n chatMessageInput = sys.stdin.readline()\n chatMessageRcvr = raw_input(\"Enter * for broadcasting or Reference Number of specific client: \")\n\n chatMessage = commands[1]\n chatMessage += \" \"\n chatMessage += str(chatMessageReference)\n chatMessage += \" \\r\\n\"\n chatMessage += chatMessageRcvr\n chatMessage += \"\\r\\n\"\n chatMessage += chatMessageInput\n\n s.send(chatMessage)\n\n def printData(self,data):\n data = 'Other : ' + data + '\\n'\n start = self.history.index('end') + \"-1l\"\n self.history.insert(\"end\", data)\n end = self.history.index('end') + \"-1l\"\n self.history.tag_add(\"Received\", start, end)\n self.history.tag_config(\"Received\", foreground='green')\n\nclass ClientMode(Tkinter.Tk):\n def __init__(self, *args, **kwargs):\n Tkinter.Tk.__init__(self, *args, **kwargs)\n self.resizable(0, 0)\n self.create_additional_widgets()\n\n def socket_connections_start(self):\n if len(host.split('.')) == 4:\n print(\"Thread Started\")\n threading.Thread(target=socket_connections).start()\n\n def socket_connections(self):\n print(\"[=] Bindings\")\n s.bind()\n\n def create_additional_widgets(self):\n self.create_panel_for_widget()\n self.create_panel_for_connections_info()\n self.create_panel_for_chat_history()\n self.create_panel_for_sending_text()\n self.create_panel_for_authentication_text()\n\n\n def send_text_message(self):\n if CONNECTED == True:\n input_data = self.Sending_data.get('1.0', 'end')\n if len(input_data) != 1:\n s.send(input_data)\n input_data = 'me: ' + input_data\n start = self.history.index('end') + \"-1l\"\n self.history.insert(\"end\", input_data)\n end = self.history.index('end') + \"-1l\"\n self.history.tag_add(\"SENT\", start, end)\n self.Sending_data.delete('1.0', 'end')\n self.history.tag_config(\"SENT\", foreground='Blue')\n\n pass\n else:\n print(\"Enter Correct information\")\n\n else:\n print(\"No Connection\")\n\n def create_panel_for_authentication_text(self):\n # Here Creating Authentication Panel\n self.username = Tkinter.Text(self.Authenticaion_panel, font=('arial 12 italic'), width=18, height=1)\n self.username.pack(side='left')\n self.password = Tkinter.Text(self.Authenticaion_panel, font=('arial 12 italic'), width=18, height=1)\n self.password.pack(side='left')\n self.authentication_Trigger = Tkinter.Button(self.Authenticaion_panel, text='Authenticate', width=15, height=1,\n command=self.get_data)\n self.authentication_Trigger.pack(side='left')\n return\n\n def get_data(self):\n username = self.username.get('1.0', 'end')\n password = self.password.get('1.0', 'end')\n self.username.config(state='disable')\n self.password.config(state='disable')\n self.authentication_Trigger(state='disable')\n\n\n\n def create_panel_for_sending_text(self):\n # Here Creating Sending Panel\n self.Sending_data = Tkinter.Text(self.Sending_panel, font=('arial 12 italic'), width=37, height=2)\n self.Sending_data.pack(side='left')\n self.Sending_Trigger = Tkinter.Button(self.Sending_panel, text='Send', width=15, height=2,\n command=self.send_text_message)\n self.Sending_Trigger.pack(side='left')\n return\n\n def create_panel_for_chat_history(self):\n # Here Creating Chat History\n self.history = Tkinter.Text(self.history_frame, font=('arial 12 bold italic'), width=50, height=15)\n self.history.pack()\n return\n\n def create_panel_for_widget(self):\n self.Authenticaion_panel = Tkinter.LabelFrame(self, text='Authentication')\n self.Authenticaion_panel.pack(side='top')\n # First For Connection Information\n self.Connection_info = Tkinter.LabelFrame(self, text='Server')\n self.Connection_info.pack(side='top', expand='yes', fill='both')\n # Creating Second For Chatting History\n self.history_frame = Tkinter.LabelFrame(self, text='Chat History')\n self.history_frame.pack(side='top')\n # Creating Third For Sending Text Message\n self.Sending_panel = Tkinter.LabelFrame(self, text='Send Message')\n self.Sending_panel.pack(side='top')\n return\n\n def create_panel_for_connections_info(self):\n self.frame = ttk.Frame(self.Connection_info)\n self.frame.pack(side='top', padx=10, pady=10)\n # Creating Main Information Panel\n ttk.Label(self.frame, text='Host: ', relief=\"groove\", anchor='center', width=25).grid(row=1,\n column=1,\n ipadx=10,\n ipady=5)\n ttk.Label(self.frame, text=host, relief='sunken', anchor='center', width=25).grid(row=1,\n column=2,\n ipadx=10,\n ipady=5)\n ttk.Label(self.frame, text='Port: ', relief=\"groove\", anchor='center', width=25).grid(\n row=2, column=1, ipadx=10, ipady=5)\n ttk.Label(self.frame, text=port, relief=\"sunken\", anchor=\"center\", width=25).grid(row=2, column=2,\n ipadx=10,\n ipady=5)\n\n return\n\n\nif __name__ == '__main__':\n ClientMode(className='chatClient').mainloop()\n\n"},{"alpha_fraction":0.4587346017360687,"alphanum_fraction":0.4681559205055237,"avg_line_length":36.08462905883789,"blob_id":"73349f00c009bf5c19472b0c4d70bc00f8e7fe44","content_id":"298b666e9b1091ee09926eb5d7bd8ac952861c3b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":47764,"license_type":"no_license","max_line_length":291,"num_lines":1288,"path":"/gui.py","repo_name":"mehranhussain/dnChatNetwork","src_encoding":"UTF-8","text":"import sys\nif not sys.hexversion > 0x03000000:\n version = 2\nelse:\n version = 3\nif len(sys.argv) > 1 and sys.argv[1] == \"-cli\":\n print(\"Starting command line chat\")\n isCLI = True\nelse:\n isCLI = False\n\n\nif version == 2:\n from Tkinter import *\n from tkFileDialog import asksaveasfilename\nif version == 3:\n from tkinter import *\n from tkinter.filedialog import asksaveasfilename\nimport threading\nimport socket\nimport random\nimport math\nimport select\nfrom enum import Enum\nimport sys\nimport fcntl\nimport struct\nfrom random import randint\nfrom collections import defaultdict\nimport traceback\nimport copy\n\n\ndef get_ip_address(ifname):\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n return socket.inet_ntoa(fcntl.ioctl(\n s.fileno(),\n 0x8915, # SIOCGIFADDR\n struct.pack('256s', ifname[:15])\n )[20:24])\n\n\n\nclass state(Enum):\n CONN = 0\n AUTH = 1\n SEND = 2\n ACKN = 3\n ARRV = 4\n LEFT = 5\n\n\n\n# Function to broadcast chat messages to all connected clients\ndef broadcast_data(sock, message):\n # Do not send the message to the client who has send us the message\n for sck in CONNECTION_LIST:\n if sck != server_socket and sck != sock:\n try:\n sck.send(message)\n except:\n # If chatClient pressed ctrl+c for example\n sck.close()\n CONNECTION_LIST.remove(sck)\n\n\n\n\n# GLOBALS\nconn_array = [] # stores open sockets\nsecret_array = dict() # key: the open sockets in conn_array,\n # value: integers for encryption\nusername_array = dict() # key: the open sockets in conn_array,\n # value: usernames for the connection\ncontact_array = dict() # key: ip address as a string, value: [port, username]\n\nusername = \"Self\"\n\nlocation = 0\nport = 0\ntop = \"\"\n\nmain_body_text = 0\n#-GLOBALS-\n\n# So,\n # x_encode your message with the key, then pass that to\n # refract to get a string out of it.\n # To decrypt, pass the message back to x_encode, and then back to refract\n\ndef binWord(word):\n \"\"\"Converts the string into binary.\"\"\"\n master = \"\"\n for letter in word:\n temp = bin(ord(letter))[2:]\n while len(temp) < 7:\n temp = '0' + temp\n master = master + temp\n return master\n\ndef xcrypt(message, key):\n \"\"\"Encrypts the binary message by the binary key.\"\"\"\n count = 0\n master = \"\"\n for letter in message:\n if count == len(key):\n count = 0\n master += str(int(letter) ^ int(key[count]))\n count += 1\n return master\n\ndef x_encode(string, number):\n \"\"\"Encrypts the string by the number.\"\"\"\n return xcrypt(binWord(string), bin(number)[2:])\n\ndef refract(binary):\n \"\"\"Returns the string representation of the binary.\n Has trouble with spaces.\n\n \"\"\"\n master = \"\"\n for x in range(0, int(len(binary) / 7)):\n master += chr(int(binary[x * 7: (x + 1) * 7], 2) + 0)\n return master\n\n\ndef formatNumber(number):\n \"\"\"Ensures that number is at least length 4 by\n adding extra 0s to the front.\n\n \"\"\"\n temp = str(number)\n while len(temp) < 4:\n temp = '0' + temp\n return temp\n\ndef netThrow(conn, secret, message):\n \"\"\"Sends message through the open socket conn with the encryption key\n secret. Sends the length of the incoming message, then sends the actual\n message.\n\n \"\"\"\n try:\n conn.send(formatNumber(len(x_encode(message, secret))).encode())\n conn.send(x_encode(message, secret).encode())\n except socket.error:\n if len(conn_array) != 0:\n writeToScreen(\n \"Connection issue. Sending message failed.\", \"System\")\n processFlag(\"-001\")\n\ndef netCatch(conn, secret):\n \"\"\"Receive and return the message through open socket conn, decrypting\n using key secret. If the message length begins with - instead of a number,\n process as a flag and return 1.\n\n \"\"\"\n try:\n data = conn.recv(4)\n if data.decode()[0] == '-':\n processFlag(data.decode(), conn)\n return 1\n data = conn.recv(int(data.decode()))\n return refract(xcrypt(data.decode(), bin(secret)[2:]))\n except socket.error:\n if len(conn_array) != 0:\n writeToScreen(\n \"Connection issue. Receiving message failed.\", \"System\")\n processFlag(\"-001\")\n\ndef isPrime(number):\n \"\"\"Checks to see if a number is prime.\"\"\"\n x = 1\n if number == 2 or number == 3:\n return True\n while x < math.sqrt(number):\n x += 1\n if number % x == 0:\n return False\n return True\n\ndef processFlag(number, conn=None):\n \"\"\"Process the flag corresponding to number, using open socket conn\n if necessary.\n\n \"\"\"\n global statusConnect\n global conn_array\n global secret_array\n global username_array\n global contact_array\n global isCLI\n t = int(number[1:])\n if t == 1: # disconnect\n # in the event of single connection being left or if we're just a\n # client\n if len(conn_array) == 1:\n writeToScreen(\"Connection closed.\", \"System\")\n dump = secret_array.pop(conn_array[0])\n dump = conn_array.pop()\n try:\n dump.close()\n except socket.error:\n print(\"Issue with someone being bad about disconnecting\")\n if not isCLI:\n statusConnect.set(\"Connect\")\n connecter.config(state=NORMAL)\n return\n\n if conn != None:\n writeToScreen(\"Connect to \" + conn.getsockname()\n [0] + \" closed.\", \"System\")\n dump = secret_array.pop(conn)\n conn_array.remove(conn)\n conn.close()\n\n if t == 2: # username change\n name = netCatch(conn, secret_array[conn])\n if(isUsernameFree(name)):\n writeToScreen(\n \"User \" + username_array[conn] + \" has changed their username to \" + name, \"System\")\n username_array[conn] = name\n contact_array[\n conn.getpeername()[0]] = [conn.getpeername()[1], name]\n\n # passing a friend who this should connect to (I am assuming it will be\n # running on the same port as the other session)\n if t == 4:\n data = conn.recv(4)\n data = conn.recv(int(data.decode()))\n Client(data.decode(),\n int(contact_array[conn.getpeername()[0]][0])).start()\n\ndef processUserCommands(command, param):\n \"\"\"Processes commands passed in via the / text input.\"\"\"\n global conn_array\n global secret_array\n global username\n\n if command == \"nick\": # change nickname\n for letter in param[0]:\n if letter == \" \" or letter == \"\\n\":\n if isCLI:\n error_window(0, \"Invalid username. No spaces allowed.\")\n else:\n error_window(root, \"Invalid username. No spaces allowed.\")\n return\n if isUsernameFree(param[0]):\n writeToScreen(\"Username is being changed to \" + param[0], \"System\")\n for conn in conn_array:\n conn.send(\"-002\".encode())\n netThrow(conn, secret_array[conn], param[0])\n username = param[0]\n else:\n writeToScreen(param[0] +\n \" is already taken as a username\", \"System\")\n if command == \"disconnect\": # disconnects from current connection\n for conn in conn_array:\n conn.send(\"-001\".encode())\n processFlag(\"-001\")\n if command == \"connect\": # connects to passed in host port\n if(options_sanitation(param[1], param[0])):\n Client(param[0], int(param[1])).start()\n if command == \"host\": # starts server on passed in port\n if(options_sanitation(param[0])):\n Server(int(param[0])).start()\n\ndef isUsernameFree(name):\n \"\"\"Checks to see if the username name is free for use.\"\"\"\n global username_array\n global username\n for conn in username_array:\n if name == username_array[conn] or name == username:\n return False\n return True\n\ndef passFriends(conn):\n \"\"\"Sends conn all of the people currently in conn_array so they can connect\n to them.\n\n \"\"\"\n global conn_array\n for connection in conn_array:\n if conn != connection:\n conn.send(\"-004\".encode())\n conn.send(\n formatNumber(len(connection.getpeername()[0])).encode()) # pass the ip address\n conn.send(connection.getpeername()[0].encode())\n # conn.send(formatNumber(len(connection.getpeername()[1])).encode()) #pass the port number\n # conn.send(connection.getpeername()[1].encode())\n\n#--------------------------------------------------------------------------\n\ndef client_options_window(master):\n \"\"\"Launches client options window for getting destination hostname\n and port.\n\n \"\"\"\n top = Toplevel(master)\n top.title(\"Connection options\")\n top.protocol(\"WM_DELETE_WINDOW\", lambda: optionDelete(top))\n top.grab_set()\n Label(top, text=\"Server IP:\").grid(row=0)\n location = Entry(top)\n location.grid(row=0, column=1)\n location.focus_set()\n Label(top, text=\"Port:\").grid(row=1)\n port = Entry(top)\n port.grid(row=1, column=1)\n go = Button(top, text=\"Connect\", command=lambda:\n client_options_go(location.get(), port.get(), top))\n go.grid(row=2, column=1)\n\ndef client_options_go(dest, port, window):\n \"Processes the options entered by the user in the client options window.\"\"\"\n if options_sanitation(port, dest):\n if not isCLI:\n window.destroy()\n Client(dest, int(port)).start()\n elif isCLI:\n sys.exit(1)\n\ndef options_sanitation(por, loc=\"\"):\n \"\"\"Checks to make sure the port and destination ip are both valid.\n Launches error windows if there are any issues.\n\n \"\"\"\n global root\n if version == 2:\n por = unicode(por)\n if isCLI:\n root = 0\n if not por.isdigit():\n error_window(root, \"Please input a port number.\")\n return False\n if int(por) < 0 or 65555 < int(por):\n error_window(root, \"Please input a port number between 0 and 65555\")\n return False\n if loc != \"\":\n if not ip_process(loc.split(\".\")):\n error_window(root, \"Please input a valid ip address.\")\n return False\n return True\n\ndef ip_process(ipArray):\n \"\"\"Checks to make sure every section of the ip is a valid number.\"\"\"\n if len(ipArray) != 4:\n return False\n for ip in ipArray:\n if version == 2:\n ip = unicode(ip)\n if not ip.isdigit():\n return False\n t = int(ip)\n if t < 0 or 255 < t:\n return False\n return True\n\n#------------------------------------------------------------------------------\n\ndef server_options_window(master):\n \"\"\"Launches server options window for getting port.\"\"\"\n top = Toplevel(master)\n top.title(\"Connection options\")\n top.grab_set()\n top.protocol(\"WM_DELETE_WINDOW\", lambda: optionDelete(top))\n Label(top, text=\"Port:\").grid(row=0)\n port = Entry(top)\n port.grid(row=0, column=1)\n port.focus_set()\n go = Button(top, text=\"Launch\", command=lambda:\n server_options_go(port.get(), top))\n go.grid(row=1, column=1)\n\ndef server_options_go(port, window):\n \"\"\"Processes the options entered by the user in the\n server options window.\n\n \"\"\"\n if options_sanitation(port):\n if not isCLI:\n window.destroy()\n Server(int(port)).start()\n elif isCLI:\n sys.exit(1)\n\n#-------------------------------------------------------------------------\n\ndef username_options_window(master):\n \"\"\"Launches username options window for setting username.\"\"\"\n top = Toplevel(master)\n top.title(\"Username options\")\n top.grab_set()\n Label(top, text=\"Username:\").grid(row=0)\n name = Entry(top)\n name.focus_set()\n name.grid(row=0, column=1)\n go = Button(top, text=\"Change\", command=lambda:\n username_options_go(name.get(), top))\n go.grid(row=1, column=1)\n\n\ndef username_options_go(name, window):\n \"\"\"Processes the options entered by the user in the\n server options window.\n\n \"\"\"\n processUserCommands(\"nick\", [name])\n window.destroy()\n\n#-------------------------------------------------------------------------\n\ndef error_window(master, texty):\n \"\"\"Launches a new window to display the message texty.\"\"\"\n global isCLI\n if isCLI:\n writeToScreen(texty, \"System\")\n else:\n window = Toplevel(master)\n window.title(\"ERROR\")\n window.grab_set()\n Label(window, text=texty).pack()\n go = Button(window, text=\"OK\", command=window.destroy)\n go.pack()\n go.focus_set()\n\ndef optionDelete(window):\n connecter.config(state=NORMAL)\n window.destroy()\n\n#-----------------------------------------------------------------------------\n# Contacts window\n\ndef contacts_window(master):\n \"\"\"Displays the contacts window, allowing the user to select a recent\n connection to reuse.\n\n \"\"\"\n global contact_array\n cWindow = Toplevel(master)\n cWindow.title(\"Contacts\")\n cWindow.grab_set()\n scrollbar = Scrollbar(cWindow, orient=VERTICAL)\n listbox = Listbox(cWindow, yscrollcommand=scrollbar.set)\n scrollbar.config(command=listbox.yview)\n scrollbar.pack(side=RIGHT, fill=Y)\n buttons = Frame(cWindow)\n cBut = Button(buttons, text=\"Connect\",\n command=lambda: contacts_connect(\n listbox.get(ACTIVE).split(\" \")))\n cBut.pack(side=LEFT)\n dBut = Button(buttons, text=\"Remove\",\n command=lambda: contacts_remove(\n listbox.get(ACTIVE).split(\" \"), listbox))\n dBut.pack(side=LEFT)\n aBut = Button(buttons, text=\"Add\",\n command=lambda: contacts_add(listbox, cWindow))\n aBut.pack(side=LEFT)\n buttons.pack(side=BOTTOM)\n\n for person in contact_array:\n listbox.insert(END, contact_array[person][1] + \" \" +\n person + \" \" + contact_array[person][0])\n listbox.pack(side=LEFT, fill=BOTH, expand=1)\n\ndef contacts_connect(item):\n \"\"\"Establish a connection between two contacts.\"\"\"\n Client(item[1], int(item[2])).start()\n\ndef contacts_remove(item, listbox):\n \"\"\"Remove a contact.\"\"\"\n if listbox.size() != 0:\n listbox.delete(ACTIVE)\n global contact_array\n h = contact_array.pop(item[1])\n\n\ndef contacts_add(listbox, master):\n \"\"\"Add a contact.\"\"\"\n aWindow = Toplevel(master)\n aWindow.title(\"Contact add\")\n Label(aWindow, text=\"Username:\").grid(row=0)\n name = Entry(aWindow)\n name.focus_set()\n name.grid(row=0, column=1)\n Label(aWindow, text=\"IP:\").grid(row=1)\n ip = Entry(aWindow)\n ip.grid(row=1, column=1)\n Label(aWindow, text=\"Port:\").grid(row=2)\n port = Entry(aWindow)\n port.grid(row=2, column=1)\n go = Button(aWindow, text=\"Add\", command=lambda:\n contacts_add_helper(name.get(), ip.get(), port.get(),\n aWindow, listbox))\n go.grid(row=3, column=1)\n\n\ndef contacts_add_helper(username, ip, port, window, listbox):\n \"\"\"Contact adding helper function. Recognizes invalid usernames and\n adds contact to listbox and contact_array.\n\n \"\"\"\n for letter in username:\n if letter == \" \" or letter == \"\\n\":\n error_window(root, \"Invalid username. No spaces allowed.\")\n return\n if options_sanitation(port, ip):\n listbox.insert(END, username + \" \" + ip + \" \" + port)\n contact_array[ip] = [port, username]\n window.destroy()\n return\n\ndef load_contacts():\n \"\"\"Loads the recent chats out of the persistent file contacts.dat.\"\"\"\n global contact_array\n try:\n filehandle = open(\"data\\\\contacts.dat\", \"r\")\n except IOError:\n return\n line = filehandle.readline()\n while len(line) != 0:\n temp = (line.rstrip('\\n')).split(\" \") # format: ip, port, name\n contact_array[temp[0]] = temp[1:]\n line = filehandle.readline()\n filehandle.close()\n\ndef dump_contacts():\n \"\"\"Saves the recent chats to the persistent file contacts.dat.\"\"\"\n global contact_array\n try:\n filehandle = open(\"data\\\\contacts.dat\", \"w\")\n except IOError:\n print(\"Can't dump contacts.\")\n return\n for contact in contact_array:\n filehandle.write(\n contact + \" \" + str(contact_array[contact][0]) + \" \" +\n contact_array[contact][1] + \"\\n\")\n filehandle.close()\n\n#-----------------------------------------------------------------------------\n\n# places the text from the text bar on to the screen and sends it to\n# everyone this program is connected to\ndef placeText(text):\n \"\"\"Places the text from the text bar on to the screen and sends it to\n everyone this program is connected to.\n\n \"\"\"\n global conn_array\n global secret_array\n global username\n writeToScreen(text, username)\n for person in conn_array:\n netThrow(person, secret_array[person], text)\n\ndef writeToScreen(text, username=\"\"):\n \"\"\"Places text to main text body in format \"username: text\".\"\"\"\n global main_body_text\n global isCLI\n if isCLI:\n if username:\n print(username + \": \" + text)\n else:\n print(text)\n else:\n main_body_text.config(state=NORMAL)\n main_body_text.insert(END, '\\n')\n if username:\n main_body_text.insert(END, username + \": \")\n main_body_text.insert(END, text)\n main_body_text.yview(END)\n main_body_text.config(state=DISABLED)\n\ndef processUserText(event):\n \"\"\"Takes text from text bar input and calls processUserCommands if it\n begins with '/'.\n\n \"\"\"\n data = text_input.get()\n if data[0] != \"/\": # is not a command\n placeText(data)\n else:\n if data.find(\" \") == -1:\n command = data[1:]\n else:\n command = data[1:data.find(\" \")]\n params = data[data.find(\" \") + 1:].split(\" \")\n processUserCommands(command, params)\n text_input.delete(0, END)\n\n\ndef processUserInput(text):\n \"\"\"ClI version of processUserText.\"\"\"\n if text[0] != \"/\":\n placeText(text)\n else:\n if text.find(\" \") == -1:\n command = text[1:]\n else:\n command = text[1:text.find(\" \")]\n params = text[text.find(\" \") + 1:].split(\" \")\n processUserCommands(command, params)\n\n\n#-------------------------------------------------------------------------\n\nclass Server (threading.Thread):\n \"A class for a Server instance.\"\"\"\n def __init__(self, port):\n threading.Thread.__init__(self)\n self.port = port\n\n def run(self):\n\n port = self.port\n\n # List to keep track of socket descriptors`\n CONNECTION_LIST = []\n AUTH_LIST = []\n SRVR_LIST = []\n SRVR_LIST_ADDR = []\n RECV_BUFFER = 4029\n PORT = port\n backlog = 10\n HOST = \"\"#get_ip_address('wlp2s0') #\"10.9.24.36\"\n\n server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n server_address = (HOST, PORT)\n server_socket.bind(server_address)\n server_socket.listen(backlog)\n\n # Add server socket to the list of readable connections\n CONNECTION_LIST.append(server_socket)\n\n print (\"chatServer started on port\" + str(PORT))\n\n clientRefNo = {}\n clientSocket = {}\n serverRefNo = {}\n serverSocket = {}\n serverSocketList = {}\n serverNetwork = {}\n userName = {}\n userPassword = {}\n userDescription = {}\n sendLoopRef = {}\n ackLoopRef = {}\n arvLoopRef = {}\n lftLoopRef = {}\n\n cur_state = -1\n\n CONNECTION_LIST.append(sys.stdin)\n failure = False\n\n while True:\n \n # Get the list sockets which are ready to be read through select\n read_sockets, write_sockets, error_sockets = select.select(CONNECTION_LIST, [], [])\n\n for sock in read_sockets:\n # New connection\n if sock == server_socket:\n # Handle the case in which there is a new connection recieved through server_socket\n sockfd, addr = server_socket.accept()\n\n userDescription[sockfd] = addr \n # srv_addr = \"\" \n # for v in SRVR_LIST_ADDR: \n # srv_addr += \" \" + str(v) \n # #sockfd.send(\"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr) \n # for srvr in SRVR_LIST: \n # print \"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr \n # srvr.send(\"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr) \n #sockfd.send(\"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr) \n \n\n CONNECTION_LIST.append(sockfd)\n cur_state = state.CONN\n print (\"(%s, %s) connected\" % addr)\n\n #-----------------------------\n\n # Some incoming message from a client \n elif sock == sys.stdin: \n srv_in = sys.stdin.readline().split() \n if srv_in[0] == \"connect\": \n srv_in_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) \n # Connect to chatServer \n try: \n srv_in_socket.connect((srv_in[1], int(srv_in[2])))\n except socket.error as msg: \n print( \"Socket Error: %s\", msg)\n # Random number generated for chatClient Reference, ask for name and password \n rfn = randint(1, 10000) \n ok = False \n while not ok: \n try: \n while serverRefNo[rfn]: \n rfn = randint(1, 10000) \n except KeyError: \n ok = True \n\n serverRefNo[rfn] = srv_in_socket \n SRVR_LIST.append(srv_in_socket) \n SRVR_LIST_ADDR.append(str(srv_in_socket.getsockname()[1])) \n serverSocket[srv_in_socket] = True \n CONNECTION_LIST.append(srv_in_socket) \n\n srv_in_socket.send(\"SRVR \" + str(rfn)) \n\n # srv_addr = \"\" \n # for v in SRVR_LIST_ADDR: \n # srv_addr += \" \" + str(v) \n\n # for srvr in SRVR_LIST: \n # print \"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr \n # srvr.send(\"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr) \n # for svd in SRVR_LIST_ADDR: \n # serverSocketList[svd] = [str(server_socket.getsockname()[1])] \n \n \n elif srv_in[0] == \"exit\":\n server_socket.close()\n sys.exit()\n else:\n print( \"Enter the correct command e.g. connect 133.0.9.3 42015\")\n\n\n #------------------------------\n\n #broadcast_data(sockfd, \"[%s:%s] entered room\\n\" % addr)\n\n else:\n # Data recieved from client, process it\n try:\n # In Windows, sometimes when a TCP program closes abruptly,\n # a \"Connection reset by peer\" exception will be thrown\n data = sock.recv(RECV_BUFFER)\n if data == \"CLOSED\":\n AUTH_LIST.remove(sock)\n ref = clientSocket[sock]\n\n for sck in AUTH_LIST:\n sck.send(\"LEFT \" + clientSocket[sock])\n\n for srvr in SRVR_LIST:\n srvr.send(\"LEFT \" + clientSocket[sock])\n\n del clientSocket[sock]\n del clientRefNo[ref]\n \n\n # command = com_str[0]\n # ref_no = com_str[1]\n # line2 = com_str[2]\n # line3 = com_str[3]\n\n com_str =[]\n for cmd in data.split():\n com_str.append(cmd)\n\n #----------------\n\n try: \n if serverSocket[sock]: \n this_is_server = True \n except: \n this_is_server = False \n #----------------\n \n try:\n if com_str[0] == \"AUTH\":\n try:\n if clientRefNo[com_str[1]]:\n sock.send(\"FAIL \"+com_str[1]+\"\\r\\nNUMBER\")\n except:\n\n for s, name in userName.iteritems():\n if name == com_str[2]:\n sock.send(\"FAIL \"+com_str[2]+\"\\r\\nNAME\")\n failure = True\n break\n\n\n if not com_str[3].isalnum():\n sock.send(\"FAIL \"+com_str[1]+\"\\r\\nPASSWORD\")\n failure = True\n\n if failure:\n failure = False\n break\n\n\n clientRefNo[com_str[1]] = sock\n clientSocket[sock] = com_str[1]\n userName[sock] = com_str[2]\n userPassword[sock] = com_str[3]\n\n if com_str[3] == \"dnServer\":\n\n cur_state = state.AUTH\n AUTH_LIST.append(sock)\n\n sock.send(\"OKAY \"+com_str[1])\n\n for sck in AUTH_LIST:\n if sck != sock:\n sck.send(\"ARRV \" + clientSocket[sock] + \"\\r\\n\" + userName[sock] + \"\\r\\n\" + \"description...\")\n\n for srvr in SRVR_LIST:\n srvr.send(\"ARRV \" + com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + \"description...\" + \"\\r\\n\" + \"1\")\n\n else:\n sock.send(\"FAIL \"+com_str[1]+\"\\r\\nPASSWORD\")\n\n elif com_str[0] == \"SEND\" and cur_state == state.AUTH and not this_is_server:\n\n if com_str[1] in sendLoopRef:\n pass\n else:\n sendLoopRef[com_str[1]] = True\n sock.send(\"OKAY \"+com_str[1])\n\n #SRVR_LIST = list(set(CONNECTION_LIST) - set(set([sys.stdin, server_socket]) | set(AUTH_LIST)))\n\n msg = \"\"\n for m in com_str[3:]:\n msg += m\n\n if len(msg) > 4096:\n sock.send(\"FAIL \"+com_str[1]+\"\\r\\nLENGHT\")\n break\n\n elif \"*\" in msg:\n sock.send(\"INVD 0\")\n break\n\n if com_str[2] == \"*\":\n \n #Broadcast to clients\n for sck in AUTH_LIST:\n if sck != sock:\n sck.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + clientSocket[sock] + \"\\r\\n\" + msg)\n\n #Broadcast to servers \n for srvr in SRVR_LIST: \n srvr.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + clientSocket[sock] + \"\\r\\n\" + msg) \n #broadcast_data(sock, \"SEND \"+ com_str[1] + \"\\r\\n\" + clientSocket[sock] + \"\\r\\n\" + msg)\n \n else:\n if com_str[2] in clientRefNo:\n clientRefNo[com_str[2]].send(\"SEND \" + com_str[1] + \"\\r\\n\" + clientSocket[sock] + \"\\r\\n\" + msg)\n else: \n for srvr in SRVR_LIST: \n srvr.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + clientSocket[sock] + \"\\r\\n\" + msg)\n\n elif com_str[0] == \"ACKN\" and cur_state == state.AUTH:\n if com_str[1] in ackLoopRef:\n pass\n else:\n ackLoopRef[com_str[1]] = True\n try:\n if clientSocket[sock]:\n try:\n if clientRefNo[com_str[2]]:\n clientRefNo[com_str[2]].send(\"ACKN \"+ com_str[1])\n except:\n for srvr in SRVR_LIST:\n srvr.send(\"ACKN \"+ com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + clientSocket[sock])\n \n except:\n try:\n if clientRefNo[com_str[2]]:\n clientRefNo[com_str[2]].send(\"ACKN \"+ com_str[1])\n except:\n for srvr in SRVR_LIST:\n srvr.send(\"ACKN \"+ com_str[1] + \"\\r\\n\" + com_str[2])\n \n \n elif com_str[0] == \"SRVR\": \n\n serverSocket[sock] = com_str[1] \n SRVR_LIST.append(sock) \n SRVR_LIST_ADDR.append(str(sock.getsockname()[1])) \n print (\"The communication is from server\")\n\n\n elif com_str[0] == \"ARRV\":\n if com_str[1] in arvLoopRef:\n pass\n else:\n arvLoopRef[com_str[1]] = True\n uReferenceNumber = com_str[1]\n uName = com_str[2]\n uDescription = com_str[3]\n uhops = com_str[4]\n\n for sck in AUTH_LIST:\n sck.send(\"ARRV \" + uReferenceNumber + \"\\r\\n\" + uName + \"\\r\\n\" + uDescription)\n\n for srvr in SRVR_LIST: \n if srvr != sock:\n srvr.send(\"ARRV \" + uReferenceNumber + \"\\r\\n\" + uName + \"\\r\\n\" + uDescription + \"\\r\\n\" + \"1\") \n\n \n\n elif com_str[0] == \"LEFT\":\n if com_str[1] in lftLoopRef:\n pass\n else:\n lftLoopRef[com_str[1]] = True\n ref = com_str[1]\n\n for sck in AUTH_LIST:\n sck.send(\"LEFT \" + ref)\n\n for srvr in SRVR_LIST:\n srvr.send(\"LEFT \" + ref)\n\n sk = clientRefNo[ref]\n del clientRefNo[ref]\n del clientSocket[sk]\n\n print (\"The following user is not reachable\" + com_str[1])\n\n elif com_str[0] == \"SEND\" and this_is_server: \n\n if com_str[1] in sendLoopRef:\n pass\n else:\n sendLoopRef[com_str[1]] = True\n print (\"send and this is server\")\n\n msg = \"\" \n for m in com_str[4:]: \n msg += m\n\n if com_str[2] != \"*\": \n if com_str[2] in clientRefNo:\n clientRefNo[com_str[2]].send(\"SEND \" + com_str[1] + \"\\r\\n\" + com_str[3] + \"\\r\\n\" + msg)\n else: \n for srvr in SRVR_LIST:\n if srvr != sock: \n srvr.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + com_str[3] + \"\\r\\n\" + msg) \n else: \n #Broadcast to clients \n for sck in AUTH_LIST: \n sck.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + com_str[3] + \"\\r\\n\" + msg) \n \n #Broadcast to servers \n for srvr in SRVR_LIST:\n if srvr != sock: \n srvr.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + com_str[3] + \"\\r\\n\" + msg) \n \n\n\n elif com_str[0] == \"INVD\" and com_str[1] == 0:\n print (\"server close connection\")\n sock.close()\n\n # elif com_str[0] == \"SARRV\": \n # print \"sr arrival\" \n # srv_list = \"\" \n # for s in com_str[2:]: \n # srv_list += \" \" + s \n # srv_list = srv_list.split() \n # serverSocketList[str(com_str[1])] = str(srv_list) \n # print \"recv\" \n # for srvr in SRVR_LIST: \n # if srvr != sock: \n # srvr.send(\"SARRV \" + com_str[1] + \" \"+ srv_list)\n\n\n except:\n # If chatClient pressed ctrl+c for example\n sock.close()\n CONNECTION_LIST.remove(sock)\n\n except:\n #broadcast_data(sock, \"Client (%s, %s) is offline\" % addr)\n print (\"Client (%s, %s) is offline\", addr)\n sock.close()\n CONNECTION_LIST.remove(sock)\n continue\n\n server_socket.close()\n\n\n passFriends(CONNECTION_LIST)\n threading.Thread(target=Runner, args=(conn, secret)).start()\n Server(self.port).start()\n\n\nclass Client (threading.Thread):\n \"\"\"A class for a Client instance.\"\"\"\n def __init__(self, host, port):\n threading.Thread.__init__(self)\n self.port = port\n self.host = host\n\n def run(self):\n # Declaring socket\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n host = self.host\n port = self.port\n\n # State of the connection\n CONNECTED = False\n AUTHENTICATED = False\n \n s.settimeout(2)\n\n # Connect to chatServer\n try:\n s.connect((host, port))\n except:\n print ('Unable to connect to chatServer.')\n sys.exit()\n\n CONNECTED = True\n print ('Connected to chatServer.')\n\n # Protocol implementation\n\n commands = ['AUTH', 'SEND', 'ACKN', 'OKAY', 'FAIL', 'INVD', 'ARRV', 'LEFT', 'SRVR']\n\n authFlag = True\n\n # Authentication Stage\n while 1:\n if not authFlag:\n break\n if CONNECTED == True:\n # Random number generated for chatClient Reference, ask for name and password\n chatClientReference = randint(1, 100)\n chatClientName = input(\"Enter name: \")\n chatClientPassword = input(\"Enter password: \")\n\n authMessage = commands[0]\n authMessage += \" \"\n authMessage += str(chatClientReference)\n authMessage += \" \\r\\n\"\n authMessage += chatClientName\n authMessage += \"\\r\\n\"\n authMessage += chatClientPassword\n\n s.send(authMessage)\n\n socket_list = [sys.stdin, s]\n\n # Get the list sockets which are readable\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\n\n for sock in read_sockets:\n # Incoming message from chatServer\n if sock == s:\n data = sock.recv(4096)\n authResponse = data.split()\n print( authResponse)\n\n # If OKAY\n if data == commands[3] + \" \" + str(chatClientReference):\n \n authFlag = False\n AUTHENTICATED = True\n break\n\n # If FAIL\n elif authResponse[0] == commands[4]:\n if authResponse[2] == \"PASSWORD\":\n print (authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[2] + \": The password is not acceptable for authentication on this server.\")\n elif authResponse[2] == \"NAME\":\n print (authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[2] + \": The specified name is already in use by another client.\")\n elif authResponse[2] == \"NUMBER\":\n print (authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[2] + \": A number is not valid, either because it has already been used for another entity in case it was newly generated by the client, or there is no relevant entity that the number refers to.\")\n\n # When client is connected and authenticated\n while 1:\n socket_list = [sys.stdin, s]\n # Get the list sockets which are readable\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\n\n # Random number generated for chatMesssageReference\n chatMessageReference = randint(1, 10000)\n\n for sock in read_sockets:\n\n # Incoming message from chatServer\n\n if sock == s:\n data = sock.recv(4096)\n authResponse = data.split()\n\n # If OKAY\n if data == commands[3] + \" \" + str(chatMessageReference):\n sys.stdout.write(data)\n\n # If SEND\n elif authResponse[0] == commands[1]:\n \n s.send(\"ACKN \" + authResponse[1] + \"\\r\\n\" + authResponse[2])\n\n # If ARRV\n elif authResponse[0] == commands[6]:\n print (\"The following user is connected now.\\r\\nUser Reference: \" + authResponse[1] + \"\\r\\nUsername: \" + authResponse[2] + \"\\r\\nIpAddress: \" + authResponse[3])\n\n # If LEFT\n elif authResponse[0] == commands[7]:\n print (\"The following user has left.\\r\\nUser Reference: \" + authResponse[1])\n\n # If FAIL\n elif authResponse[0] == commands[4]:\n if authResponse[2] == \"LENGHT\":\n print (authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[2] + \": The chat message text is too long.\")\n elif authResponse[2] == commands[5]:\n print (authResponse[0] + \" \" + authResponse[1] + \" \" + authResponse[2] + \": A malformed message or a message that is not valid in the current state of the client.\")\n s.close()\n print (\"Connection to chatServer closed.\")\n else:\n print (data)\n\n else:\n if AUTHENTICATED == True:\n chatMessageInput = sys.stdin.readline()\n chatMessageRcvr = input(\"Enter * for broadcasting or Reference Number of specific client: \")\n\n chatMessage = commands[1]\n chatMessage += \" \"\n chatMessage += str(chatMessageReference)\n chatMessage += \" \\r\\n\"\n chatMessage += chatMessageRcvr\n chatMessage += \"\\r\\n\"\n chatMessage += chatMessageInput\n\n s.send(chatMessage)\n\n threading.Thread(target=Runner, args=(conn, secret)).start()\n # Server(self.port).start()\n # ##########################################################################THIS\n # IS GOOD, BUT I CAN'T TEST ON ONE MACHINE\n\ndef Runner(conn, secret):\n global username_array\n while 1:\n data = netCatch(conn, secret)\n if data != 1:\n writeToScreen(data, username_array[conn])\n\n#-------------------------------------------------------------------------\n# Menu helpers\n\ndef QuickClient():\n \"\"\"Menu window for connection options.\"\"\"\n window = Toplevel(root)\n window.title(\"Connection options\")\n window.grab_set()\n Label(window, text=\"Server IP:\").grid(row=0)\n destination = Entry(window)\n destination.grid(row=0, column=1)\n go = Button(window, text=\"Connect\", command=lambda:\n client_options_go(destination.get(), \"9999\", window))\n go.grid(row=1, column=1)\n\n\ndef QuickServer():\n \"\"\"Quickstarts a server.\"\"\"\n Server(9999).start()\n\ndef saveHistory():\n \"\"\"Saves history with Tkinter's asksaveasfilename dialog.\"\"\"\n global main_body_text\n file_name = asksaveasfilename(\n title=\"Choose save location\",\n filetypes=[('Plain text', '*.txt'), ('Any File', '*.*')])\n try:\n filehandle = open(file_name + \".txt\", \"w\")\n except IOError:\n print(\"Can't save history.\")\n return\n contents = main_body_text.get(1.0, END)\n for line in contents:\n filehandle.write(line)\n filehandle.close()\n\n\ndef connects(clientType):\n global conn_array\n connecter.config(state=DISABLED)\n if len(conn_array) == 0:\n if clientType == 0:\n client_options_window(root)\n if clientType == 1:\n server_options_window(root)\n else:\n # connecter.config(state=NORMAL)\n for connection in conn_array:\n connection.send(\"-001\".encode())\n processFlag(\"-001\")\n\n\ndef toOne():\n global clientType\n clientType = 0\n\n\ndef toTwo():\n global clientType\n clientType = 1\n\n\n#-------------------------------------------------------------------------\n\n\nif len(sys.argv) > 1 and sys.argv[1] == \"-cli\":\n print(\"Starting command line chat\")\n\nelse:\n root = Tk()\n root.title(\"Chat\")\n\n menubar = Menu(root)\n\n file_menu = Menu(menubar, tearoff=0)\n file_menu.add_command(label=\"Save chat\", command=lambda: saveHistory())\n file_menu.add_command(label=\"Change username\",\n command=lambda: username_options_window(root))\n file_menu.add_command(label=\"Exit\", command=lambda: root.destroy())\n menubar.add_cascade(label=\"File\", menu=file_menu)\n\n connection_menu = Menu(menubar, tearoff=0)\n connection_menu.add_command(label=\"Quick Connect\", command=QuickClient)\n connection_menu.add_command(\n label=\"Connect on port\", command=lambda: client_options_window(root))\n connection_menu.add_command(\n label=\"Disconnect\", command=lambda: processFlag(\"-001\"))\n menubar.add_cascade(label=\"Connect\", menu=connection_menu)\n\n server_menu = Menu(menubar, tearoff=0)\n server_menu.add_command(label=\"Launch server\", command=QuickServer)\n server_menu.add_command(label=\"Listen on port\",\n command=lambda: server_options_window(root))\n menubar.add_cascade(label=\"Server\", menu=server_menu)\n\n menubar.add_command(label=\"Contacts\", command=lambda:\n contacts_window(root))\n\n root.config(menu=menubar)\n\n main_body = Frame(root, height=20, width=50)\n\n main_body_text = Text(main_body)\n body_text_scroll = Scrollbar(main_body)\n main_body_text.focus_set()\n body_text_scroll.pack(side=RIGHT, fill=Y)\n main_body_text.pack(side=LEFT, fill=Y)\n body_text_scroll.config(command=main_body_text.yview)\n main_body_text.config(yscrollcommand=body_text_scroll.set)\n main_body.pack()\n\n main_body_text.insert(END, \"Welcome to the chat program!\")\n main_body_text.config(state=DISABLED)\n\n text_input = Entry(root, width=60)\n text_input.bind(\"<Return>\", processUserText)\n text_input.pack()\n\n statusConnect = StringVar()\n statusConnect.set(\"Connect\")\n clientType = 1\n Radiobutton(root, text=\"Client\", variable=clientType,\n value=0, command=toOne).pack(anchor=E)\n Radiobutton(root, text=\"Server\", variable=clientType,\n value=1, command=toTwo).pack(anchor=E)\n connecter = Button(root, textvariable=statusConnect,\n command=lambda: connects(clientType))\n connecter.pack()\n\n load_contacts()\n\n#------------------------------------------------------------#\n\n root.mainloop()\n\n dump_contacts()"},{"alpha_fraction":0.3422677218914032,"alphanum_fraction":0.3525563180446625,"avg_line_length":40.20217514038086,"blob_id":"f99a0bb46933a4501f58aa682c562d397a366b57","content_id":"9e4697281e2c4bba95ac54796ea5854fd5da6585","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":18953,"license_type":"no_license","max_line_length":144,"num_lines":460,"path":"/chatServer.py","repo_name":"mehranhussain/dnChatNetwork","src_encoding":"UTF-8","text":"# chatServer.py\n# Usage: python chatServer.py\n\nimport socket\nimport select\nfrom enum import Enum\nimport sys\nimport fcntl\nimport struct\nfrom random import randint\nfrom collections import defaultdict\nimport traceback\nimport copy\n\ndef get_ip_address(ifname):\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n return socket.inet_ntoa(fcntl.ioctl(\n s.fileno(),\n 0x8915, # SIOCGIFADDR\n struct.pack('256s', ifname[:15])\n )[20:24])\n\n\n\nclass state(Enum):\n CONN = 0\n AUTH = 1\n SEND = 2\n ACKN = 3\n ARRV = 4\n LEFT = 5\n\n\n\n# Function to broadcast chat messages to all connected clients\ndef broadcast_data(sock, message):\n # Do not send the message to the client who has send us the message\n for sck in CONNECTION_LIST:\n print \"in loop\"\n if sck != server_socket and sck != sock:\n try:\n sck.send(message)\n print \"sent\"\n except:\n # If chatClient pressed ctrl+c for example\n sck.close()\n CONNECTION_LIST.remove(sck)\n\n\nif __name__ == \"__main__\":\n\n # Usage of the chatClient.py\n if len(sys.argv) < 1:\n print 'Usage : python chatServer.py port(optional)'\n sys.exit()\n elif len(sys.argv) < 2:\n port = 42015\n else:\n port = int(sys.argv[1])\n \n\n # List to keep track of socket descriptors`\n CONNECTION_LIST = []\n AUTH_LIST = []\n SRVR_LIST = []\n SRVR_LIST_ADDR = []\n RECV_BUFFER = 4029\n PORT = port\n backlog = 10\n HOST = \"127.0.0.1\"#get_ip_address('wlp2s0') #\"10.9.24.36\"\n\n server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n server_address = (HOST, PORT)\n server_socket.bind(server_address)\n server_socket.listen(backlog)\n\n # Add server socket to the list of readable connections\n CONNECTION_LIST.append(server_socket)\n\n print \"chatServer started on port \" + str(PORT)\n\n clientRefNo = {}\n clientSocket = {}\n serverRefNo = {}\n serverSocket = {}\n serverSocketList = {}\n serverNetwork = {}\n userName = {}\n userPassword = {}\n userDescription = {}\n sendLoopRef = {}\n ackLoopRef = {}\n arvLoopRef = {}\n lftLoopRef = {}\n\n cur_state = -1\n\n CONNECTION_LIST.append(sys.stdin)\n failure = False\n\n while True:\n \n # Get the list sockets which are ready to be read through select\n read_sockets, write_sockets, error_sockets = select.select(CONNECTION_LIST, [], [])\n\n for sock in read_sockets:\n # New connection\n if sock == server_socket:\n # Handle the case in which there is a new connection recieved through server_socket\n sockfd, addr = server_socket.accept()\n\n userDescription[sockfd] = addr \n # srv_addr = \"\" \n # for v in SRVR_LIST_ADDR: \n # srv_addr += \" \" + str(v) \n # #sockfd.send(\"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr) \n # for srvr in SRVR_LIST: \n # print \"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr \n # srvr.send(\"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr) \n #sockfd.send(\"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr) \n \n\n CONNECTION_LIST.append(sockfd)\n cur_state = state.CONN\n print \"(%s, %s) connected\" % addr\n\n #-----------------------------\n\n # Some incoming message from a client \n elif sock == sys.stdin: \n srv_in = sys.stdin.readline().split() \n \n if len(srv_in) > 2:\n port = srv_in[2]\n else:\n port = 42015\n \n if srv_in[0] == \"connect\": \n srv_in_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) \n # Connect to chatServer \n try: \n srv_in_socket.connect((srv_in[1], int(port)))\n \n except socket.error as msg: \n print \"Socket Error: %s\" % msg \n # Random number generated for chatClient Reference, ask for name and password \n rfn = randint(1, 10000) \n ok = False\n while not ok: \n try: \n while serverRefNo[rfn]:\n rfn = randint(1, 10000)\n except KeyError: \n ok = True \n\n serverRefNo[rfn] = srv_in_socket \n SRVR_LIST.append(srv_in_socket) \n SRVR_LIST_ADDR.append(str(srv_in_socket.getsockname()[1])) \n serverSocket[srv_in_socket] = True \n CONNECTION_LIST.append(srv_in_socket) \n\n srv_in_socket.send(\"SRVR \" + str(rfn)) \n\n # srv_addr = \"\" \n # for v in SRVR_LIST_ADDR: \n # srv_addr += \" \" + str(v) \n\n # for srvr in SRVR_LIST: \n # print \"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr \n # srvr.send(\"SARRV \" + str(server_socket.getsockname()[1]) + \" \" + srv_addr) \n # for svd in SRVR_LIST_ADDR: \n # serverSocketList[svd] = [str(server_socket.getsockname()[1])] \n \n \n elif srv_in[0] == \"exit\":\n server_socket.close()\n sys.exit()\n else:\n print \"Enter the correct command e.g. connect 133.0.9.3 42015\"\n\n\n #------------------------------\n\n #broadcast_data(sockfd, \"[%s:%s] entered room\\n\" % addr)\n\n else:\n # Data recieved from client, process it\n try:\n # In Windows, sometimes when a TCP program closes abruptly,\n # a \"Connection reset by peer\" exception will be thrown\n data = sock.recv(RECV_BUFFER)\n if data == \"CLOSED\":\n AUTH_LIST.remove(sock)\n ref = clientSocket[sock]\n\n print \"closed\"\n\n for sck in AUTH_LIST:\n sck.send(\"LEFT \" + clientSocket[sock])\n\n for srvr in SRVR_LIST:\n srvr.send(\"LEFT \" + clientSocket[sock])\n print \"srvr left sent\"\n\n del userName[sock]\n del clientSocket[sock]\n del clientRefNo[ref]\n\n \n\n # command = com_str[0]\n # ref_no = com_str[1]\n # line2 = com_str[2]\n # line3 = com_str[3]\n\n com_str =[]\n for cmd in data.split():\n com_str.append(cmd)\n\n #----------------\n\n try: \n if serverSocket[sock]: \n this_is_server = True \n except: \n this_is_server = False \n #----------------\n \n try:\n if com_str[0] == \"AUTH\":\n try:\n if clientRefNo[com_str[1]]:\n sock.send(\"FAIL \"+com_str[1]+\"\\r\\nNUMBER\")\n except:\n\n for s, name in userName.iteritems():\n if name == com_str[2]:\n sock.send(\"FAIL \"+com_str[2]+\"\\r\\nNAME\")\n failure = True\n break\n\n\n if not com_str[3].isalnum():\n sock.send(\"FAIL \"+com_str[1]+\"\\r\\nPASSWORD\")\n failure = True\n\n if failure:\n failure = False\n break\n\n\n clientRefNo[com_str[1]] = sock\n clientSocket[sock] = com_str[1]\n userName[sock] = com_str[2]\n userPassword[sock] = com_str[3]\n\n if com_str[3] == \"dnServer\":\n\n cur_state = state.AUTH\n AUTH_LIST.append(sock)\n\n sock.send(\"OKAY \"+com_str[1])\n\n for sck in AUTH_LIST:\n if sck != sock:\n sck.send(\"ARRV \" + clientSocket[sock] + \"\\r\\n\" + userName[sock] + \"\\r\\n\" + \"description...\")\n\n for srvr in SRVR_LIST:\n srvr.send(\"ARRV \" + com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + \"description...\" + \"\\r\\n\" + \"1\")\n\n else:\n sock.send(\"FAIL \"+com_str[1]+\"\\r\\nPASSWORD\")\n\n elif com_str[0] == \"SEND\" and cur_state == state.AUTH and not this_is_server:\n\n if com_str[1] in sendLoopRef:\n pass\n else:\n sendLoopRef[com_str[1]] = True\n sock.send(\"OKAY \"+com_str[1])\n\n #SRVR_LIST = list(set(CONNECTION_LIST) - set(set([sys.stdin, server_socket]) | set(AUTH_LIST)))\n\n msg = \"\"\n for m in com_str[3:]:\n msg += m + \" \"\n\n\n if len(msg) > 4096:\n sock.send(\"FAIL \"+com_str[1]+\"\\r\\nLENGHT\")\n break\n\n elif \"*\" in msg:\n sock.send(\"INVD 0\")\n break\n\n if com_str[2] == \"*\":\n \n #Broadcast to clients\n for sck in AUTH_LIST:\n if sck != sock:\n sck.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + clientSocket[sock] + \"\\r\\n\" + msg)\n\n #Broadcast to servers \n for srvr in SRVR_LIST: \n srvr.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + clientSocket[sock] + \"\\r\\n\" + msg) \n #broadcast_data(sock, \"SEND \"+ com_str[1] + \"\\r\\n\" + clientSocket[sock] + \"\\r\\n\" + msg)\n \n else:\n if com_str[2] in clientRefNo:\n clientRefNo[com_str[2]].send(\"SEND \" + com_str[1] + \"\\r\\n\" + clientSocket[sock] + \"\\r\\n\" + msg)\n else: \n for srvr in SRVR_LIST: \n srvr.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + clientSocket[sock] + \"\\r\\n\" + msg)\n\n elif com_str[0] == \"ACKN\" and cur_state == state.AUTH:\n\n if com_str[3] in ackLoopRef:\n pass\n else:\n ackLoopRef[com_str[3]] = True\n try:\n if clientSocket[sock]:\n try:\n if clientRefNo[com_str[2]]:\n clientRefNo[com_str[2]].send(\"ACKN \"+ com_str[1])\n except:\n for srvr in SRVR_LIST:\n srvr.send(\"ACKN \"+ com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + com_str[3])\n\n except:\n try:\n if clientRefNo[com_str[2]]:\n clientRefNo[com_str[2]].send(\"ACKN \"+ com_str[1])\n except:\n for srvr in SRVR_LIST:\n srvr.send(\"ACKN \"+ com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + com_str[3])\n \n \n elif com_str[0] == \"SRVR\": \n\n serverSocket[sock] = com_str[1] \n SRVR_LIST.append(sock) \n SRVR_LIST_ADDR.append(str(sock.getsockname()[1])) \n print \"The communication is from server\"\n\n\n elif com_str[0] == \"ARRV\":\n if com_str[1] in arvLoopRef:\n pass\n else:\n arvLoopRef[com_str[1]] = True\n uReferenceNumber = com_str[1]\n uName = com_str[2]\n uDescription = com_str[3]\n uhops = com_str[4]\n\n for sck in AUTH_LIST:\n sck.send(\"ARRV \" + uReferenceNumber + \"\\r\\n\" + uName + \"\\r\\n\" + uDescription)\n\n for srvr in SRVR_LIST: \n if srvr != sock:\n srvr.send(\"ARRV \" + uReferenceNumber + \"\\r\\n\" + uName + \"\\r\\n\" + uDescription + \"\\r\\n\" + \"1\") \n\n \n\n elif com_str[0] == \"LEFT\":\n if com_str[1] in lftLoopRef:\n pass\n else:\n lftLoopRef[com_str[1]] = True\n ref = com_str[1]\n\n for sck in AUTH_LIST:\n sck.send(\"LEFT \" + ref)\n\n for srvr in SRVR_LIST:\n srvr.send(\"LEFT \" + ref)\n\n sk = clientRefNo[ref]\n del userName[sk]\n del clientRefNo[ref]\n del clientSocket[sk]\n\n print \"The following user is not reachable\" + com_str[1]\n\n elif com_str[0] == \"SEND\" and this_is_server: \n\n if com_str[1] in sendLoopRef:\n pass\n else:\n sendLoopRef[com_str[1]] = True\n print com_str \n print \"send and this is server\" \n\n msg = \"\" \n for m in com_str[4:]: \n msg += m + \" \"\n\n if com_str[2] != \"*\": \n if com_str[2] in clientRefNo:\n clientRefNo[com_str[2]].send(\"SEND \" + com_str[1] + \"\\r\\n\" + com_str[3] + \"\\r\\n\" + msg)\n else: \n for srvr in SRVR_LIST:\n if srvr != sock: \n srvr.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + com_str[3] + \"\\r\\n\" + msg) \n else: \n #Broadcast to clients \n for sck in AUTH_LIST: \n print \"sent client\" \n sck.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + com_str[3] + \"\\r\\n\" + msg) \n \n #Broadcast to servers \n for srvr in SRVR_LIST:\n if srvr != sock: \n srvr.send(\"SEND \"+ com_str[1] + \"\\r\\n\" + com_str[2] + \"\\r\\n\" + com_str[3] + \"\\r\\n\" + msg) \n \n print \"SEND\"\n\n\n elif com_str[0] == \"INVD\" and com_str[1] == 0:\n print \"server close connection\"\n sock.close()\n elif com_str[0] == \"CLTS\":\n\n clts = \"\"\n for s, name in userName.iteritems():\n clts += \"\\r\\n\" + name\n\n sock.send(\"CLTS\" + clts)\n\n\n\n # elif com_str[0] == \"SARRV\": \n # print \"sr arrival\" \n # srv_list = \"\" \n # for s in com_str[2:]: \n # srv_list += \" \" + s \n # srv_list = srv_list.split() \n # serverSocketList[str(com_str[1])] = str(srv_list) \n # print \"recv\" \n # for srvr in SRVR_LIST: \n # if srvr != sock: \n # srvr.send(\"SARRV \" + com_str[1] + \" \"+ srv_list)\n\n\n except:\n # If chatClient pressed ctrl+c for example\n sock.close()\n CONNECTION_LIST.remove(sock)\n\n except:\n #broadcast_data(sock, \"Client (%s, %s) is offline\" % addr)\n print \"Client (%s, %s) is offline\" % addr\n sock.close()\n CONNECTION_LIST.remove(sock)\n continue\n\n server_socket.close()\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5157977938652039,\n \"alphanum_fraction\": 0.5285940170288086,\n \"avg_line_length\": 34.35195541381836,\n \"blob_id\": \"421cedc621591e2c397de774ca9a867f2cb1e4d9\",\n \"content_id\": \"48852ff1cfaba3a16a04ac8a2cbb5d35ebf80062\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6330,\n \"license_type\": \"no_license\",\n \"max_line_length\": 285,\n \"num_lines\": 179,\n \"path\": \"/chatClient.py\",\n \"repo_name\": \"mehranhussain/dnChatNetwork\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# dnClient.py\\n# Example Usage: python chatClient.py localhost 42015\\n\\n# importing required modules\\nimport socket\\nimport select\\nimport sys\\nfrom random import randint\\n\\n# Declaring socket\\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n\\nuserName = \\\"\\\"\\n\\n# Main function\\ndef main():\\n\\n # Usage of the chatClient.py\\n if len(sys.argv) < 3:\\n print 'Usage : python chatClient.py hostname port'\\n sys.exit()\\n\\n host = sys.argv[1]\\n port = int(sys.argv[2])\\n\\n # State of the connection\\n CONNECTED = False\\n AUTHENTICATED = False\\n \\n s.settimeout(2)\\n\\n # Connect to chatServer\\n try:\\n s.connect((host, port))\\n except:\\n print 'Unable to connect to chatServer.'\\n sys.exit()\\n\\n CONNECTED = True\\n print 'Connected to chatServer.'\\n\\n # Protocol implementation\\n\\n commands = ['AUTH', 'SEND', 'ACKN', 'OKAY', 'FAIL', 'INVD', 'ARRV', 'LEFT', 'SRVR']\\n\\n authFlag = True\\n\\n # Authentication Stage\\n while 1:\\n if not authFlag:\\n break\\n if CONNECTED == True:\\n # Random number generated for chatClient Reference, ask for name and password\\n chatClientReference = randint(1, 100)\\n chatClientName = raw_input(\\\"Enter name: \\\")\\n chatClientPassword = raw_input(\\\"Enter password: \\\")\\n\\n authMessage = commands[0]\\n authMessage += \\\" \\\"\\n authMessage += str(chatClientReference)\\n authMessage += \\\" \\\\r\\\\n\\\"\\n authMessage += chatClientName\\n userName = chatClientName\\n authMessage += \\\"\\\\r\\\\n\\\"\\n authMessage += chatClientPassword\\n\\n s.send(authMessage)\\n\\n socket_list = [sys.stdin, s]\\n\\n # Get the list sockets which are readable\\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\\n\\n for sock in read_sockets:\\n # Incoming message from chatServer\\n if sock == s:\\n data = sock.recv(4096)\\n authResponse = data.split()\\n print authResponse\\n\\n # If OKAY\\n if data == commands[3] + \\\" \\\" + str(chatClientReference):\\n print data\\n authFlag = False\\n AUTHENTICATED = True\\n\\n break\\n\\n # If FAIL\\n elif authResponse[0] == commands[4]:\\n if authResponse[2] == \\\"PASSWORD\\\":\\n print authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[2] + \\\": The password is not acceptable for authentication on this server.\\\"\\n elif authResponse[2] == \\\"NAME\\\":\\n print authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[2] + \\\": The specified name is already in use by another client.\\\"\\n elif authResponse[2] == \\\"NUMBER\\\":\\n print authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[2] + \\\": A number is not valid, either because it has already been used for another entity in case it was newly generated by the client, or there is no relevant entity that the number refers to.\\\"\\n\\n # When client is connected and authenticated\\n while 1:\\n socket_list = [sys.stdin, s]\\n # Get the list sockets which are readable\\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\\n\\n # Random number generated for chatMesssageReference\\n chatMessageReference = randint(1, 10000)\\n\\n for sock in read_sockets:\\n\\n # Incoming message from chatServer\\n\\n if sock == s:\\n data = sock.recv(4096)\\n authResponse = data.split()\\n\\n # If OKAY\\n if data == commands[3] + \\\" \\\" + str(chatMessageReference):\\n sys.stdout.write(data)\\n\\n # If SEND\\n elif authResponse[0] == commands[1]:\\n print data\\n idclt = randint(100, 50000)\\n s.send(\\\"ACKN \\\" + authResponse[1] + \\\"\\\\r\\\\n\\\" + authResponse[2] + \\\"\\\\r\\\\n\\\" + str(s.getsockname()[1]))\\n\\n # If ARRV\\n elif authResponse[0] == commands[6]:\\n print \\\"The following user is connected now.\\\\r\\\\nUser Reference: \\\" + authResponse[1] + \\\"\\\\r\\\\nUsername: \\\" + authResponse[2] + \\\"\\\\r\\\\nIpAddress: \\\" + authResponse[3]\\n\\n # If LEFT\\n elif authResponse[0] == commands[7]:\\n print \\\"The following user has left.\\\\r\\\\nUser Reference: \\\" + authResponse[1]\\n\\n # If FAIL\\n elif authResponse[0] == commands[4]:\\n if authResponse[2] == \\\"LENGHT\\\":\\n print authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[2] + \\\": The chat message text is too long.\\\"\\n elif authResponse[2] == commands[5]:\\n print authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[2] + \\\": A malformed message or a message that is not valid in the current state of the client.\\\"\\n \\n s.close()\\n print \\\"Connection to chatServer closed\\\"\\n \\n elif authResponse[0] == \\\"CLTS\\\":\\n\\n for i in range(1,len(authResponse)):\\n print authResponse[i]\\n\\n else:\\n print data\\n\\n\\n else:\\n if AUTHENTICATED == True:\\n chatMessageInput = sys.stdin.readline()\\n\\n if \\\"list\\\" in chatMessageInput:\\n s.send(\\\"CLTS \\\" + str(userName))\\n break\\n\\t\\t \\n chatMessageRcvr = raw_input(\\\"Enter * for broadcasting or Reference Number of specific client: \\\")\\n\\n chatMessage = commands[1]\\n chatMessage += \\\" \\\"\\n chatMessage += str(chatMessageReference)\\n chatMessage += \\\" \\\\r\\\\n\\\"\\n chatMessage += chatMessageRcvr\\n chatMessage += \\\"\\\\r\\\\n\\\"\\n chatMessage += chatMessageInput\\n\\n s.send(chatMessage)\\n\\n# Run the main method, If user presses ctrl+c close the socket connection\\nif __name__ == \\\"__main__\\\":\\n\\n try:\\n main()\\n except KeyboardInterrupt:\\n s.send(\\\"CLOSED\\\")\\n s.close()\\n\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7998196482658386,\n \"alphanum_fraction\": 0.7998196482658386,\n \"avg_line_length\": 57.3684196472168,\n \"blob_id\": \"3436aa678b43b9fb9910d0fbea4780bbaea5a659\",\n \"content_id\": \"5837be98400e01d8d5ab42a33beb45f65af1afe8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1109,\n \"license_type\": \"no_license\",\n \"max_line_length\": 443,\n \"num_lines\": 19,\n \"path\": \"/README.md\",\n \"repo_name\": \"mehranhussain/dnChatNetwork\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"## dnChatNetwork\\n\\nOur project consists of multi-clients and multi servers connecting with each other in acyclcic topology as well in cyclic. It still has a few bugs. The recent addition we did was displaying the clients that were already connected when a new user connected with a server. We handled the deletetion of users from the lists that disconnects from the server. And handled the acknowledgements while broadcasting as well as sending to specific user.\\n\\n### Functionality\\n\\nThe dnChatNetwork can send broadcast messages to all the clients connected in a server. Not only a single server but also in a network of servers the implementation works fine. We can send a message to a specific user too. We handle the client or server leaving or arriving in the network and successfully notifying all.\\n\\nWe are also handling a few conditions other than this that are quite useful if implemented on a bigger scale like checking for a malformed message, checking for duplicate clients etc.\\n\\n#### Usage\\n\\n#### For chatClient:\\n\\npython chatClient.py ip_address port\\n\\n#### For chatServer\\n\\npython chatServer.py port\\n\"\n },\n {\n \"alpha_fraction\": 0.4911535680294037,\n \"alphanum_fraction\": 0.5025556087493896,\n \"avg_line_length\": 40.691802978515625,\n \"blob_id\": \"5465b15bfb5d5fbd780f099691eedfadd6bdcaf6\",\n \"content_id\": \"24b01b8b9e4e28adda7ff98815d3be0fb509d3f3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 12717,\n \"license_type\": \"no_license\",\n \"max_line_length\": 227,\n \"num_lines\": 305,\n \"path\": \"/Main-GUi.py\",\n \"repo_name\": \"mehranhussain/dnChatNetwork\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# dnClient.py\\n# Example Usage: python chatClient.py localhost 42015\\n\\n# importing required modules\\nimport socket\\nimport select\\nimport sys\\nfrom random import randint\\nimport threading\\ntry:\\n import Tkinter, ttk\\nexcept:\\n import tkinter as Tkinter\\n import tkinter.ttk as ttk\\n\\nif len(sys.argv) < 3:\\n print 'Usage : python chatClient.py hostname port'\\n sys.exit()\\n\\nhost = sys.argv[1]\\nport = int(sys.argv[2])\\n\\n# State of the connection\\nCONNECTED = False\\nAUTHENTICATED = False\\n\\n# Protocol implementation\\n\\ncommands = ['AUTH', 'SEND', 'ACKN', 'OKAY', 'FAIL', 'INVD', 'ARRV', 'LEFT', 'SRVR']\\n\\nclass SOCKETS:\\n def __init__(self):\\n # Declaring socket\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n\\n def load(self, ip_address, port, text,status,server_info):\\n self.ip_address = ip_address\\n self.port = port\\n self.history = text\\n self.status = status\\n self.server_info = server_info\\n return\\n\\n def bind(self):\\n while True:\\n try:\\n s.connect((host, port))\\n s.settimeout(2)\\n\\n # Connect to chatServer\\n try:\\n s.connect((host, port))\\n except:\\n print 'Unable to connect to chatServer.'\\n sys.exit()\\n\\n CONNECTED = True\\n print 'Connected to chatServer.'\\n\\n threading.Thread(target=self.recv).start()\\n break\\n\\n except:\\n pass\\n\\n def recv(self):\\n authFlag = True\\n\\n # Authentication Stage\\n while 1:\\n if not authFlag:\\n break\\n if CONNECTED == True:\\n # Random number generated for chatClient Reference, ask for name and password\\n chatClientReference = randint(1, 100)\\n\\n chatClientName = \\\"dsdsdds\\\"\\n chatClientPassword = \\\"dnServer\\\"\\n\\n authMessage = commands[0]\\n authMessage += \\\" \\\"\\n authMessage += str(chatClientReference)\\n authMessage += \\\" \\\\r\\\\n\\\"\\n authMessage += chatClientName\\n authMessage += \\\"\\\\r\\\\n\\\"\\n authMessage += chatClientPassword\\n\\n s.send(authMessage)\\n\\n socket_list = [sys.stdin, s]\\n\\n # Get the list sockets which are readable\\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\\n\\n for sock in read_sockets:\\n # Incoming message from chatServer\\n if sock == s:\\n data = sock.recv(4096)\\n authResponse = data.split()\\n print authResponse\\n\\n # If OKAY\\n if data == commands[3] + \\\" \\\" + str(chatClientReference):\\n authFlag = False\\n AUTHENTICATED = True\\n break\\n\\n # If FAIL\\n elif authResponse[0] == commands[4]:\\n if authResponse[2] == \\\"PASSWORD\\\":\\n self.printData(authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[\\n 2] + \\\": The password is not acceptable for authentication on this server.\\\")\\n elif authResponse[2] == \\\"NAME\\\":\\n self.printData(authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[\\n 2] + \\\": The specified name is already in use by another client.\\\")\\n elif authResponse[2] == \\\"NUMBER\\\":\\n self.printData(authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[\\n 2] + \\\": A number is not valid, either because it has already been used for another entity in case it was newly generated by the client, or there is no relevant entity that the number refers to.\\\")\\n\\n # When client is connected and authenticated\\n while 1:\\n socket_list = [sys.stdin, s]\\n # Get the list sockets which are readable\\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\\n\\n # Random number generated for chatMesssageReference\\n chatMessageReference = randint(1, 10000)\\n\\n for sock in read_sockets:\\n\\n # Incoming message from chatServer\\n\\n if sock == s:\\n data = sock.recv(4096)\\n authResponse = data.split()\\n\\n # If OKAY\\n if data == commands[3] + \\\" \\\" + str(chatMessageReference):\\n self.printData(data)\\n\\n # If SEND\\n elif authResponse[0] == commands[1]:\\n self.printData(data)\\n s.send(\\\"ACKN \\\" + authResponse[1] + \\\"\\\\r\\\\n\\\" + authResponse[2])\\n\\n # If ARRV\\n elif authResponse[0] == commands[6]:\\n self.printData(\\\"The following user is connected now.\\\\r\\\\nUser Reference: \\\" + authResponse[\\n 1] + \\\"\\\\r\\\\nUsername: \\\" + authResponse[2] + \\\"\\\\r\\\\nIpAddress: \\\" + authResponse[3])\\n\\n # If LEFT\\n elif authResponse[0] == commands[7]:\\n self.printData(\\\"The following user has left.\\\\r\\\\nUser Reference: \\\" + authResponse[1])\\n\\n # If FAIL\\n elif authResponse[0] == commands[4]:\\n if authResponse[2] == \\\"LENGHT\\\":\\n self.printData(authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[\\n 2] + \\\": The chat message text is too long.\\\")\\n elif authResponse[2] == commands[5]:\\n self.printData(authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[\\n 2] + \\\": A malformed message or a message that is not valid in the current state of the client.\\\")\\n s.close()\\n self.printData(\\\"Connection to chatServer closed.\\\")\\n else:\\n self.printData(data)\\n\\n else:\\n if AUTHENTICATED == True:\\n chatMessageInput = sys.stdin.readline()\\n chatMessageRcvr = raw_input(\\\"Enter * for broadcasting or Reference Number of specific client: \\\")\\n\\n chatMessage = commands[1]\\n chatMessage += \\\" \\\"\\n chatMessage += str(chatMessageReference)\\n chatMessage += \\\" \\\\r\\\\n\\\"\\n chatMessage += chatMessageRcvr\\n chatMessage += \\\"\\\\r\\\\n\\\"\\n chatMessage += chatMessageInput\\n\\n s.send(chatMessage)\\n\\n def printData(self,data):\\n data = 'Other : ' + data + '\\\\n'\\n start = self.history.index('end') + \\\"-1l\\\"\\n self.history.insert(\\\"end\\\", data)\\n end = self.history.index('end') + \\\"-1l\\\"\\n self.history.tag_add(\\\"Received\\\", start, end)\\n self.history.tag_config(\\\"Received\\\", foreground='green')\\n\\nclass ClientMode(Tkinter.Tk):\\n def __init__(self, *args, **kwargs):\\n Tkinter.Tk.__init__(self, *args, **kwargs)\\n self.resizable(0, 0)\\n self.create_additional_widgets()\\n\\n def socket_connections_start(self):\\n if len(host.split('.')) == 4:\\n print(\\\"Thread Started\\\")\\n threading.Thread(target=socket_connections).start()\\n\\n def socket_connections(self):\\n print(\\\"[=] Bindings\\\")\\n s.bind()\\n\\n def create_additional_widgets(self):\\n self.create_panel_for_widget()\\n self.create_panel_for_connections_info()\\n self.create_panel_for_chat_history()\\n self.create_panel_for_sending_text()\\n self.create_panel_for_authentication_text()\\n\\n\\n def send_text_message(self):\\n if CONNECTED == True:\\n input_data = self.Sending_data.get('1.0', 'end')\\n if len(input_data) != 1:\\n s.send(input_data)\\n input_data = 'me: ' + input_data\\n start = self.history.index('end') + \\\"-1l\\\"\\n self.history.insert(\\\"end\\\", input_data)\\n end = self.history.index('end') + \\\"-1l\\\"\\n self.history.tag_add(\\\"SENT\\\", start, end)\\n self.Sending_data.delete('1.0', 'end')\\n self.history.tag_config(\\\"SENT\\\", foreground='Blue')\\n\\n pass\\n else:\\n print(\\\"Enter Correct information\\\")\\n\\n else:\\n print(\\\"No Connection\\\")\\n\\n def create_panel_for_authentication_text(self):\\n # Here Creating Authentication Panel\\n self.username = Tkinter.Text(self.Authenticaion_panel, font=('arial 12 italic'), width=18, height=1)\\n self.username.pack(side='left')\\n self.password = Tkinter.Text(self.Authenticaion_panel, font=('arial 12 italic'), width=18, height=1)\\n self.password.pack(side='left')\\n self.authentication_Trigger = Tkinter.Button(self.Authenticaion_panel, text='Authenticate', width=15, height=1,\\n command=self.get_data)\\n self.authentication_Trigger.pack(side='left')\\n return\\n\\n def get_data(self):\\n username = self.username.get('1.0', 'end')\\n password = self.password.get('1.0', 'end')\\n self.username.config(state='disable')\\n self.password.config(state='disable')\\n self.authentication_Trigger(state='disable')\\n\\n\\n\\n def create_panel_for_sending_text(self):\\n # Here Creating Sending Panel\\n self.Sending_data = Tkinter.Text(self.Sending_panel, font=('arial 12 italic'), width=37, height=2)\\n self.Sending_data.pack(side='left')\\n self.Sending_Trigger = Tkinter.Button(self.Sending_panel, text='Send', width=15, height=2,\\n command=self.send_text_message)\\n self.Sending_Trigger.pack(side='left')\\n return\\n\\n def create_panel_for_chat_history(self):\\n # Here Creating Chat History\\n self.history = Tkinter.Text(self.history_frame, font=('arial 12 bold italic'), width=50, height=15)\\n self.history.pack()\\n return\\n\\n def create_panel_for_widget(self):\\n self.Authenticaion_panel = Tkinter.LabelFrame(self, text='Authentication')\\n self.Authenticaion_panel.pack(side='top')\\n # First For Connection Information\\n self.Connection_info = Tkinter.LabelFrame(self, text='Server')\\n self.Connection_info.pack(side='top', expand='yes', fill='both')\\n # Creating Second For Chatting History\\n self.history_frame = Tkinter.LabelFrame(self, text='Chat History')\\n self.history_frame.pack(side='top')\\n # Creating Third For Sending Text Message\\n self.Sending_panel = Tkinter.LabelFrame(self, text='Send Message')\\n self.Sending_panel.pack(side='top')\\n return\\n\\n def create_panel_for_connections_info(self):\\n self.frame = ttk.Frame(self.Connection_info)\\n self.frame.pack(side='top', padx=10, pady=10)\\n # Creating Main Information Panel\\n ttk.Label(self.frame, text='Host: ', relief=\\\"groove\\\", anchor='center', width=25).grid(row=1,\\n column=1,\\n ipadx=10,\\n ipady=5)\\n ttk.Label(self.frame, text=host, relief='sunken', anchor='center', width=25).grid(row=1,\\n column=2,\\n ipadx=10,\\n ipady=5)\\n ttk.Label(self.frame, text='Port: ', relief=\\\"groove\\\", anchor='center', width=25).grid(\\n row=2, column=1, ipadx=10, ipady=5)\\n ttk.Label(self.frame, text=port, relief=\\\"sunken\\\", anchor=\\\"center\\\", width=25).grid(row=2, column=2,\\n ipadx=10,\\n ipady=5)\\n\\n return\\n\\n\\nif __name__ == '__main__':\\n ClientMode(className='chatClient').mainloop()\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.4587346017360687,\n \"alphanum_fraction\": 0.4681559205055237,\n \"avg_line_length\": 36.08462905883789,\n \"blob_id\": \"73349f00c009bf5c19472b0c4d70bc00f8e7fe44\",\n \"content_id\": \"298b666e9b1091ee09926eb5d7bd8ac952861c3b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 47764,\n \"license_type\": \"no_license\",\n \"max_line_length\": 291,\n \"num_lines\": 1288,\n \"path\": \"/gui.py\",\n \"repo_name\": \"mehranhussain/dnChatNetwork\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import sys\\nif not sys.hexversion > 0x03000000:\\n version = 2\\nelse:\\n version = 3\\nif len(sys.argv) > 1 and sys.argv[1] == \\\"-cli\\\":\\n print(\\\"Starting command line chat\\\")\\n isCLI = True\\nelse:\\n isCLI = False\\n\\n\\nif version == 2:\\n from Tkinter import *\\n from tkFileDialog import asksaveasfilename\\nif version == 3:\\n from tkinter import *\\n from tkinter.filedialog import asksaveasfilename\\nimport threading\\nimport socket\\nimport random\\nimport math\\nimport select\\nfrom enum import Enum\\nimport sys\\nimport fcntl\\nimport struct\\nfrom random import randint\\nfrom collections import defaultdict\\nimport traceback\\nimport copy\\n\\n\\ndef get_ip_address(ifname):\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n return socket.inet_ntoa(fcntl.ioctl(\\n s.fileno(),\\n 0x8915, # SIOCGIFADDR\\n struct.pack('256s', ifname[:15])\\n )[20:24])\\n\\n\\n\\nclass state(Enum):\\n CONN = 0\\n AUTH = 1\\n SEND = 2\\n ACKN = 3\\n ARRV = 4\\n LEFT = 5\\n\\n\\n\\n# Function to broadcast chat messages to all connected clients\\ndef broadcast_data(sock, message):\\n # Do not send the message to the client who has send us the message\\n for sck in CONNECTION_LIST:\\n if sck != server_socket and sck != sock:\\n try:\\n sck.send(message)\\n except:\\n # If chatClient pressed ctrl+c for example\\n sck.close()\\n CONNECTION_LIST.remove(sck)\\n\\n\\n\\n\\n# GLOBALS\\nconn_array = [] # stores open sockets\\nsecret_array = dict() # key: the open sockets in conn_array,\\n # value: integers for encryption\\nusername_array = dict() # key: the open sockets in conn_array,\\n # value: usernames for the connection\\ncontact_array = dict() # key: ip address as a string, value: [port, username]\\n\\nusername = \\\"Self\\\"\\n\\nlocation = 0\\nport = 0\\ntop = \\\"\\\"\\n\\nmain_body_text = 0\\n#-GLOBALS-\\n\\n# So,\\n # x_encode your message with the key, then pass that to\\n # refract to get a string out of it.\\n # To decrypt, pass the message back to x_encode, and then back to refract\\n\\ndef binWord(word):\\n \\\"\\\"\\\"Converts the string into binary.\\\"\\\"\\\"\\n master = \\\"\\\"\\n for letter in word:\\n temp = bin(ord(letter))[2:]\\n while len(temp) < 7:\\n temp = '0' + temp\\n master = master + temp\\n return master\\n\\ndef xcrypt(message, key):\\n \\\"\\\"\\\"Encrypts the binary message by the binary key.\\\"\\\"\\\"\\n count = 0\\n master = \\\"\\\"\\n for letter in message:\\n if count == len(key):\\n count = 0\\n master += str(int(letter) ^ int(key[count]))\\n count += 1\\n return master\\n\\ndef x_encode(string, number):\\n \\\"\\\"\\\"Encrypts the string by the number.\\\"\\\"\\\"\\n return xcrypt(binWord(string), bin(number)[2:])\\n\\ndef refract(binary):\\n \\\"\\\"\\\"Returns the string representation of the binary.\\n Has trouble with spaces.\\n\\n \\\"\\\"\\\"\\n master = \\\"\\\"\\n for x in range(0, int(len(binary) / 7)):\\n master += chr(int(binary[x * 7: (x + 1) * 7], 2) + 0)\\n return master\\n\\n\\ndef formatNumber(number):\\n \\\"\\\"\\\"Ensures that number is at least length 4 by\\n adding extra 0s to the front.\\n\\n \\\"\\\"\\\"\\n temp = str(number)\\n while len(temp) < 4:\\n temp = '0' + temp\\n return temp\\n\\ndef netThrow(conn, secret, message):\\n \\\"\\\"\\\"Sends message through the open socket conn with the encryption key\\n secret. Sends the length of the incoming message, then sends the actual\\n message.\\n\\n \\\"\\\"\\\"\\n try:\\n conn.send(formatNumber(len(x_encode(message, secret))).encode())\\n conn.send(x_encode(message, secret).encode())\\n except socket.error:\\n if len(conn_array) != 0:\\n writeToScreen(\\n \\\"Connection issue. Sending message failed.\\\", \\\"System\\\")\\n processFlag(\\\"-001\\\")\\n\\ndef netCatch(conn, secret):\\n \\\"\\\"\\\"Receive and return the message through open socket conn, decrypting\\n using key secret. If the message length begins with - instead of a number,\\n process as a flag and return 1.\\n\\n \\\"\\\"\\\"\\n try:\\n data = conn.recv(4)\\n if data.decode()[0] == '-':\\n processFlag(data.decode(), conn)\\n return 1\\n data = conn.recv(int(data.decode()))\\n return refract(xcrypt(data.decode(), bin(secret)[2:]))\\n except socket.error:\\n if len(conn_array) != 0:\\n writeToScreen(\\n \\\"Connection issue. Receiving message failed.\\\", \\\"System\\\")\\n processFlag(\\\"-001\\\")\\n\\ndef isPrime(number):\\n \\\"\\\"\\\"Checks to see if a number is prime.\\\"\\\"\\\"\\n x = 1\\n if number == 2 or number == 3:\\n return True\\n while x < math.sqrt(number):\\n x += 1\\n if number % x == 0:\\n return False\\n return True\\n\\ndef processFlag(number, conn=None):\\n \\\"\\\"\\\"Process the flag corresponding to number, using open socket conn\\n if necessary.\\n\\n \\\"\\\"\\\"\\n global statusConnect\\n global conn_array\\n global secret_array\\n global username_array\\n global contact_array\\n global isCLI\\n t = int(number[1:])\\n if t == 1: # disconnect\\n # in the event of single connection being left or if we're just a\\n # client\\n if len(conn_array) == 1:\\n writeToScreen(\\\"Connection closed.\\\", \\\"System\\\")\\n dump = secret_array.pop(conn_array[0])\\n dump = conn_array.pop()\\n try:\\n dump.close()\\n except socket.error:\\n print(\\\"Issue with someone being bad about disconnecting\\\")\\n if not isCLI:\\n statusConnect.set(\\\"Connect\\\")\\n connecter.config(state=NORMAL)\\n return\\n\\n if conn != None:\\n writeToScreen(\\\"Connect to \\\" + conn.getsockname()\\n [0] + \\\" closed.\\\", \\\"System\\\")\\n dump = secret_array.pop(conn)\\n conn_array.remove(conn)\\n conn.close()\\n\\n if t == 2: # username change\\n name = netCatch(conn, secret_array[conn])\\n if(isUsernameFree(name)):\\n writeToScreen(\\n \\\"User \\\" + username_array[conn] + \\\" has changed their username to \\\" + name, \\\"System\\\")\\n username_array[conn] = name\\n contact_array[\\n conn.getpeername()[0]] = [conn.getpeername()[1], name]\\n\\n # passing a friend who this should connect to (I am assuming it will be\\n # running on the same port as the other session)\\n if t == 4:\\n data = conn.recv(4)\\n data = conn.recv(int(data.decode()))\\n Client(data.decode(),\\n int(contact_array[conn.getpeername()[0]][0])).start()\\n\\ndef processUserCommands(command, param):\\n \\\"\\\"\\\"Processes commands passed in via the / text input.\\\"\\\"\\\"\\n global conn_array\\n global secret_array\\n global username\\n\\n if command == \\\"nick\\\": # change nickname\\n for letter in param[0]:\\n if letter == \\\" \\\" or letter == \\\"\\\\n\\\":\\n if isCLI:\\n error_window(0, \\\"Invalid username. No spaces allowed.\\\")\\n else:\\n error_window(root, \\\"Invalid username. No spaces allowed.\\\")\\n return\\n if isUsernameFree(param[0]):\\n writeToScreen(\\\"Username is being changed to \\\" + param[0], \\\"System\\\")\\n for conn in conn_array:\\n conn.send(\\\"-002\\\".encode())\\n netThrow(conn, secret_array[conn], param[0])\\n username = param[0]\\n else:\\n writeToScreen(param[0] +\\n \\\" is already taken as a username\\\", \\\"System\\\")\\n if command == \\\"disconnect\\\": # disconnects from current connection\\n for conn in conn_array:\\n conn.send(\\\"-001\\\".encode())\\n processFlag(\\\"-001\\\")\\n if command == \\\"connect\\\": # connects to passed in host port\\n if(options_sanitation(param[1], param[0])):\\n Client(param[0], int(param[1])).start()\\n if command == \\\"host\\\": # starts server on passed in port\\n if(options_sanitation(param[0])):\\n Server(int(param[0])).start()\\n\\ndef isUsernameFree(name):\\n \\\"\\\"\\\"Checks to see if the username name is free for use.\\\"\\\"\\\"\\n global username_array\\n global username\\n for conn in username_array:\\n if name == username_array[conn] or name == username:\\n return False\\n return True\\n\\ndef passFriends(conn):\\n \\\"\\\"\\\"Sends conn all of the people currently in conn_array so they can connect\\n to them.\\n\\n \\\"\\\"\\\"\\n global conn_array\\n for connection in conn_array:\\n if conn != connection:\\n conn.send(\\\"-004\\\".encode())\\n conn.send(\\n formatNumber(len(connection.getpeername()[0])).encode()) # pass the ip address\\n conn.send(connection.getpeername()[0].encode())\\n # conn.send(formatNumber(len(connection.getpeername()[1])).encode()) #pass the port number\\n # conn.send(connection.getpeername()[1].encode())\\n\\n#--------------------------------------------------------------------------\\n\\ndef client_options_window(master):\\n \\\"\\\"\\\"Launches client options window for getting destination hostname\\n and port.\\n\\n \\\"\\\"\\\"\\n top = Toplevel(master)\\n top.title(\\\"Connection options\\\")\\n top.protocol(\\\"WM_DELETE_WINDOW\\\", lambda: optionDelete(top))\\n top.grab_set()\\n Label(top, text=\\\"Server IP:\\\").grid(row=0)\\n location = Entry(top)\\n location.grid(row=0, column=1)\\n location.focus_set()\\n Label(top, text=\\\"Port:\\\").grid(row=1)\\n port = Entry(top)\\n port.grid(row=1, column=1)\\n go = Button(top, text=\\\"Connect\\\", command=lambda:\\n client_options_go(location.get(), port.get(), top))\\n go.grid(row=2, column=1)\\n\\ndef client_options_go(dest, port, window):\\n \\\"Processes the options entered by the user in the client options window.\\\"\\\"\\\"\\n if options_sanitation(port, dest):\\n if not isCLI:\\n window.destroy()\\n Client(dest, int(port)).start()\\n elif isCLI:\\n sys.exit(1)\\n\\ndef options_sanitation(por, loc=\\\"\\\"):\\n \\\"\\\"\\\"Checks to make sure the port and destination ip are both valid.\\n Launches error windows if there are any issues.\\n\\n \\\"\\\"\\\"\\n global root\\n if version == 2:\\n por = unicode(por)\\n if isCLI:\\n root = 0\\n if not por.isdigit():\\n error_window(root, \\\"Please input a port number.\\\")\\n return False\\n if int(por) < 0 or 65555 < int(por):\\n error_window(root, \\\"Please input a port number between 0 and 65555\\\")\\n return False\\n if loc != \\\"\\\":\\n if not ip_process(loc.split(\\\".\\\")):\\n error_window(root, \\\"Please input a valid ip address.\\\")\\n return False\\n return True\\n\\ndef ip_process(ipArray):\\n \\\"\\\"\\\"Checks to make sure every section of the ip is a valid number.\\\"\\\"\\\"\\n if len(ipArray) != 4:\\n return False\\n for ip in ipArray:\\n if version == 2:\\n ip = unicode(ip)\\n if not ip.isdigit():\\n return False\\n t = int(ip)\\n if t < 0 or 255 < t:\\n return False\\n return True\\n\\n#------------------------------------------------------------------------------\\n\\ndef server_options_window(master):\\n \\\"\\\"\\\"Launches server options window for getting port.\\\"\\\"\\\"\\n top = Toplevel(master)\\n top.title(\\\"Connection options\\\")\\n top.grab_set()\\n top.protocol(\\\"WM_DELETE_WINDOW\\\", lambda: optionDelete(top))\\n Label(top, text=\\\"Port:\\\").grid(row=0)\\n port = Entry(top)\\n port.grid(row=0, column=1)\\n port.focus_set()\\n go = Button(top, text=\\\"Launch\\\", command=lambda:\\n server_options_go(port.get(), top))\\n go.grid(row=1, column=1)\\n\\ndef server_options_go(port, window):\\n \\\"\\\"\\\"Processes the options entered by the user in the\\n server options window.\\n\\n \\\"\\\"\\\"\\n if options_sanitation(port):\\n if not isCLI:\\n window.destroy()\\n Server(int(port)).start()\\n elif isCLI:\\n sys.exit(1)\\n\\n#-------------------------------------------------------------------------\\n\\ndef username_options_window(master):\\n \\\"\\\"\\\"Launches username options window for setting username.\\\"\\\"\\\"\\n top = Toplevel(master)\\n top.title(\\\"Username options\\\")\\n top.grab_set()\\n Label(top, text=\\\"Username:\\\").grid(row=0)\\n name = Entry(top)\\n name.focus_set()\\n name.grid(row=0, column=1)\\n go = Button(top, text=\\\"Change\\\", command=lambda:\\n username_options_go(name.get(), top))\\n go.grid(row=1, column=1)\\n\\n\\ndef username_options_go(name, window):\\n \\\"\\\"\\\"Processes the options entered by the user in the\\n server options window.\\n\\n \\\"\\\"\\\"\\n processUserCommands(\\\"nick\\\", [name])\\n window.destroy()\\n\\n#-------------------------------------------------------------------------\\n\\ndef error_window(master, texty):\\n \\\"\\\"\\\"Launches a new window to display the message texty.\\\"\\\"\\\"\\n global isCLI\\n if isCLI:\\n writeToScreen(texty, \\\"System\\\")\\n else:\\n window = Toplevel(master)\\n window.title(\\\"ERROR\\\")\\n window.grab_set()\\n Label(window, text=texty).pack()\\n go = Button(window, text=\\\"OK\\\", command=window.destroy)\\n go.pack()\\n go.focus_set()\\n\\ndef optionDelete(window):\\n connecter.config(state=NORMAL)\\n window.destroy()\\n\\n#-----------------------------------------------------------------------------\\n# Contacts window\\n\\ndef contacts_window(master):\\n \\\"\\\"\\\"Displays the contacts window, allowing the user to select a recent\\n connection to reuse.\\n\\n \\\"\\\"\\\"\\n global contact_array\\n cWindow = Toplevel(master)\\n cWindow.title(\\\"Contacts\\\")\\n cWindow.grab_set()\\n scrollbar = Scrollbar(cWindow, orient=VERTICAL)\\n listbox = Listbox(cWindow, yscrollcommand=scrollbar.set)\\n scrollbar.config(command=listbox.yview)\\n scrollbar.pack(side=RIGHT, fill=Y)\\n buttons = Frame(cWindow)\\n cBut = Button(buttons, text=\\\"Connect\\\",\\n command=lambda: contacts_connect(\\n listbox.get(ACTIVE).split(\\\" \\\")))\\n cBut.pack(side=LEFT)\\n dBut = Button(buttons, text=\\\"Remove\\\",\\n command=lambda: contacts_remove(\\n listbox.get(ACTIVE).split(\\\" \\\"), listbox))\\n dBut.pack(side=LEFT)\\n aBut = Button(buttons, text=\\\"Add\\\",\\n command=lambda: contacts_add(listbox, cWindow))\\n aBut.pack(side=LEFT)\\n buttons.pack(side=BOTTOM)\\n\\n for person in contact_array:\\n listbox.insert(END, contact_array[person][1] + \\\" \\\" +\\n person + \\\" \\\" + contact_array[person][0])\\n listbox.pack(side=LEFT, fill=BOTH, expand=1)\\n\\ndef contacts_connect(item):\\n \\\"\\\"\\\"Establish a connection between two contacts.\\\"\\\"\\\"\\n Client(item[1], int(item[2])).start()\\n\\ndef contacts_remove(item, listbox):\\n \\\"\\\"\\\"Remove a contact.\\\"\\\"\\\"\\n if listbox.size() != 0:\\n listbox.delete(ACTIVE)\\n global contact_array\\n h = contact_array.pop(item[1])\\n\\n\\ndef contacts_add(listbox, master):\\n \\\"\\\"\\\"Add a contact.\\\"\\\"\\\"\\n aWindow = Toplevel(master)\\n aWindow.title(\\\"Contact add\\\")\\n Label(aWindow, text=\\\"Username:\\\").grid(row=0)\\n name = Entry(aWindow)\\n name.focus_set()\\n name.grid(row=0, column=1)\\n Label(aWindow, text=\\\"IP:\\\").grid(row=1)\\n ip = Entry(aWindow)\\n ip.grid(row=1, column=1)\\n Label(aWindow, text=\\\"Port:\\\").grid(row=2)\\n port = Entry(aWindow)\\n port.grid(row=2, column=1)\\n go = Button(aWindow, text=\\\"Add\\\", command=lambda:\\n contacts_add_helper(name.get(), ip.get(), port.get(),\\n aWindow, listbox))\\n go.grid(row=3, column=1)\\n\\n\\ndef contacts_add_helper(username, ip, port, window, listbox):\\n \\\"\\\"\\\"Contact adding helper function. Recognizes invalid usernames and\\n adds contact to listbox and contact_array.\\n\\n \\\"\\\"\\\"\\n for letter in username:\\n if letter == \\\" \\\" or letter == \\\"\\\\n\\\":\\n error_window(root, \\\"Invalid username. No spaces allowed.\\\")\\n return\\n if options_sanitation(port, ip):\\n listbox.insert(END, username + \\\" \\\" + ip + \\\" \\\" + port)\\n contact_array[ip] = [port, username]\\n window.destroy()\\n return\\n\\ndef load_contacts():\\n \\\"\\\"\\\"Loads the recent chats out of the persistent file contacts.dat.\\\"\\\"\\\"\\n global contact_array\\n try:\\n filehandle = open(\\\"data\\\\\\\\contacts.dat\\\", \\\"r\\\")\\n except IOError:\\n return\\n line = filehandle.readline()\\n while len(line) != 0:\\n temp = (line.rstrip('\\\\n')).split(\\\" \\\") # format: ip, port, name\\n contact_array[temp[0]] = temp[1:]\\n line = filehandle.readline()\\n filehandle.close()\\n\\ndef dump_contacts():\\n \\\"\\\"\\\"Saves the recent chats to the persistent file contacts.dat.\\\"\\\"\\\"\\n global contact_array\\n try:\\n filehandle = open(\\\"data\\\\\\\\contacts.dat\\\", \\\"w\\\")\\n except IOError:\\n print(\\\"Can't dump contacts.\\\")\\n return\\n for contact in contact_array:\\n filehandle.write(\\n contact + \\\" \\\" + str(contact_array[contact][0]) + \\\" \\\" +\\n contact_array[contact][1] + \\\"\\\\n\\\")\\n filehandle.close()\\n\\n#-----------------------------------------------------------------------------\\n\\n# places the text from the text bar on to the screen and sends it to\\n# everyone this program is connected to\\ndef placeText(text):\\n \\\"\\\"\\\"Places the text from the text bar on to the screen and sends it to\\n everyone this program is connected to.\\n\\n \\\"\\\"\\\"\\n global conn_array\\n global secret_array\\n global username\\n writeToScreen(text, username)\\n for person in conn_array:\\n netThrow(person, secret_array[person], text)\\n\\ndef writeToScreen(text, username=\\\"\\\"):\\n \\\"\\\"\\\"Places text to main text body in format \\\"username: text\\\".\\\"\\\"\\\"\\n global main_body_text\\n global isCLI\\n if isCLI:\\n if username:\\n print(username + \\\": \\\" + text)\\n else:\\n print(text)\\n else:\\n main_body_text.config(state=NORMAL)\\n main_body_text.insert(END, '\\\\n')\\n if username:\\n main_body_text.insert(END, username + \\\": \\\")\\n main_body_text.insert(END, text)\\n main_body_text.yview(END)\\n main_body_text.config(state=DISABLED)\\n\\ndef processUserText(event):\\n \\\"\\\"\\\"Takes text from text bar input and calls processUserCommands if it\\n begins with '/'.\\n\\n \\\"\\\"\\\"\\n data = text_input.get()\\n if data[0] != \\\"/\\\": # is not a command\\n placeText(data)\\n else:\\n if data.find(\\\" \\\") == -1:\\n command = data[1:]\\n else:\\n command = data[1:data.find(\\\" \\\")]\\n params = data[data.find(\\\" \\\") + 1:].split(\\\" \\\")\\n processUserCommands(command, params)\\n text_input.delete(0, END)\\n\\n\\ndef processUserInput(text):\\n \\\"\\\"\\\"ClI version of processUserText.\\\"\\\"\\\"\\n if text[0] != \\\"/\\\":\\n placeText(text)\\n else:\\n if text.find(\\\" \\\") == -1:\\n command = text[1:]\\n else:\\n command = text[1:text.find(\\\" \\\")]\\n params = text[text.find(\\\" \\\") + 1:].split(\\\" \\\")\\n processUserCommands(command, params)\\n\\n\\n#-------------------------------------------------------------------------\\n\\nclass Server (threading.Thread):\\n \\\"A class for a Server instance.\\\"\\\"\\\"\\n def __init__(self, port):\\n threading.Thread.__init__(self)\\n self.port = port\\n\\n def run(self):\\n\\n port = self.port\\n\\n # List to keep track of socket descriptors`\\n CONNECTION_LIST = []\\n AUTH_LIST = []\\n SRVR_LIST = []\\n SRVR_LIST_ADDR = []\\n RECV_BUFFER = 4029\\n PORT = port\\n backlog = 10\\n HOST = \\\"\\\"#get_ip_address('wlp2s0') #\\\"10.9.24.36\\\"\\n\\n server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\\n server_address = (HOST, PORT)\\n server_socket.bind(server_address)\\n server_socket.listen(backlog)\\n\\n # Add server socket to the list of readable connections\\n CONNECTION_LIST.append(server_socket)\\n\\n print (\\\"chatServer started on port\\\" + str(PORT))\\n\\n clientRefNo = {}\\n clientSocket = {}\\n serverRefNo = {}\\n serverSocket = {}\\n serverSocketList = {}\\n serverNetwork = {}\\n userName = {}\\n userPassword = {}\\n userDescription = {}\\n sendLoopRef = {}\\n ackLoopRef = {}\\n arvLoopRef = {}\\n lftLoopRef = {}\\n\\n cur_state = -1\\n\\n CONNECTION_LIST.append(sys.stdin)\\n failure = False\\n\\n while True:\\n \\n # Get the list sockets which are ready to be read through select\\n read_sockets, write_sockets, error_sockets = select.select(CONNECTION_LIST, [], [])\\n\\n for sock in read_sockets:\\n # New connection\\n if sock == server_socket:\\n # Handle the case in which there is a new connection recieved through server_socket\\n sockfd, addr = server_socket.accept()\\n\\n userDescription[sockfd] = addr \\n # srv_addr = \\\"\\\" \\n # for v in SRVR_LIST_ADDR: \\n # srv_addr += \\\" \\\" + str(v) \\n # #sockfd.send(\\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr) \\n # for srvr in SRVR_LIST: \\n # print \\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr \\n # srvr.send(\\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr) \\n #sockfd.send(\\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr) \\n \\n\\n CONNECTION_LIST.append(sockfd)\\n cur_state = state.CONN\\n print (\\\"(%s, %s) connected\\\" % addr)\\n\\n #-----------------------------\\n\\n # Some incoming message from a client \\n elif sock == sys.stdin: \\n srv_in = sys.stdin.readline().split() \\n if srv_in[0] == \\\"connect\\\": \\n srv_in_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) \\n # Connect to chatServer \\n try: \\n srv_in_socket.connect((srv_in[1], int(srv_in[2])))\\n except socket.error as msg: \\n print( \\\"Socket Error: %s\\\", msg)\\n # Random number generated for chatClient Reference, ask for name and password \\n rfn = randint(1, 10000) \\n ok = False \\n while not ok: \\n try: \\n while serverRefNo[rfn]: \\n rfn = randint(1, 10000) \\n except KeyError: \\n ok = True \\n\\n serverRefNo[rfn] = srv_in_socket \\n SRVR_LIST.append(srv_in_socket) \\n SRVR_LIST_ADDR.append(str(srv_in_socket.getsockname()[1])) \\n serverSocket[srv_in_socket] = True \\n CONNECTION_LIST.append(srv_in_socket) \\n\\n srv_in_socket.send(\\\"SRVR \\\" + str(rfn)) \\n\\n # srv_addr = \\\"\\\" \\n # for v in SRVR_LIST_ADDR: \\n # srv_addr += \\\" \\\" + str(v) \\n\\n # for srvr in SRVR_LIST: \\n # print \\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr \\n # srvr.send(\\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr) \\n # for svd in SRVR_LIST_ADDR: \\n # serverSocketList[svd] = [str(server_socket.getsockname()[1])] \\n \\n \\n elif srv_in[0] == \\\"exit\\\":\\n server_socket.close()\\n sys.exit()\\n else:\\n print( \\\"Enter the correct command e.g. connect 133.0.9.3 42015\\\")\\n\\n\\n #------------------------------\\n\\n #broadcast_data(sockfd, \\\"[%s:%s] entered room\\\\n\\\" % addr)\\n\\n else:\\n # Data recieved from client, process it\\n try:\\n # In Windows, sometimes when a TCP program closes abruptly,\\n # a \\\"Connection reset by peer\\\" exception will be thrown\\n data = sock.recv(RECV_BUFFER)\\n if data == \\\"CLOSED\\\":\\n AUTH_LIST.remove(sock)\\n ref = clientSocket[sock]\\n\\n for sck in AUTH_LIST:\\n sck.send(\\\"LEFT \\\" + clientSocket[sock])\\n\\n for srvr in SRVR_LIST:\\n srvr.send(\\\"LEFT \\\" + clientSocket[sock])\\n\\n del clientSocket[sock]\\n del clientRefNo[ref]\\n \\n\\n # command = com_str[0]\\n # ref_no = com_str[1]\\n # line2 = com_str[2]\\n # line3 = com_str[3]\\n\\n com_str =[]\\n for cmd in data.split():\\n com_str.append(cmd)\\n\\n #----------------\\n\\n try: \\n if serverSocket[sock]: \\n this_is_server = True \\n except: \\n this_is_server = False \\n #----------------\\n \\n try:\\n if com_str[0] == \\\"AUTH\\\":\\n try:\\n if clientRefNo[com_str[1]]:\\n sock.send(\\\"FAIL \\\"+com_str[1]+\\\"\\\\r\\\\nNUMBER\\\")\\n except:\\n\\n for s, name in userName.iteritems():\\n if name == com_str[2]:\\n sock.send(\\\"FAIL \\\"+com_str[2]+\\\"\\\\r\\\\nNAME\\\")\\n failure = True\\n break\\n\\n\\n if not com_str[3].isalnum():\\n sock.send(\\\"FAIL \\\"+com_str[1]+\\\"\\\\r\\\\nPASSWORD\\\")\\n failure = True\\n\\n if failure:\\n failure = False\\n break\\n\\n\\n clientRefNo[com_str[1]] = sock\\n clientSocket[sock] = com_str[1]\\n userName[sock] = com_str[2]\\n userPassword[sock] = com_str[3]\\n\\n if com_str[3] == \\\"dnServer\\\":\\n\\n cur_state = state.AUTH\\n AUTH_LIST.append(sock)\\n\\n sock.send(\\\"OKAY \\\"+com_str[1])\\n\\n for sck in AUTH_LIST:\\n if sck != sock:\\n sck.send(\\\"ARRV \\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + userName[sock] + \\\"\\\\r\\\\n\\\" + \\\"description...\\\")\\n\\n for srvr in SRVR_LIST:\\n srvr.send(\\\"ARRV \\\" + com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + \\\"description...\\\" + \\\"\\\\r\\\\n\\\" + \\\"1\\\")\\n\\n else:\\n sock.send(\\\"FAIL \\\"+com_str[1]+\\\"\\\\r\\\\nPASSWORD\\\")\\n\\n elif com_str[0] == \\\"SEND\\\" and cur_state == state.AUTH and not this_is_server:\\n\\n if com_str[1] in sendLoopRef:\\n pass\\n else:\\n sendLoopRef[com_str[1]] = True\\n sock.send(\\\"OKAY \\\"+com_str[1])\\n\\n #SRVR_LIST = list(set(CONNECTION_LIST) - set(set([sys.stdin, server_socket]) | set(AUTH_LIST)))\\n\\n msg = \\\"\\\"\\n for m in com_str[3:]:\\n msg += m\\n\\n if len(msg) > 4096:\\n sock.send(\\\"FAIL \\\"+com_str[1]+\\\"\\\\r\\\\nLENGHT\\\")\\n break\\n\\n elif \\\"*\\\" in msg:\\n sock.send(\\\"INVD 0\\\")\\n break\\n\\n if com_str[2] == \\\"*\\\":\\n \\n #Broadcast to clients\\n for sck in AUTH_LIST:\\n if sck != sock:\\n sck.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + msg)\\n\\n #Broadcast to servers \\n for srvr in SRVR_LIST: \\n srvr.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + msg) \\n #broadcast_data(sock, \\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + msg)\\n \\n else:\\n if com_str[2] in clientRefNo:\\n clientRefNo[com_str[2]].send(\\\"SEND \\\" + com_str[1] + \\\"\\\\r\\\\n\\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + msg)\\n else: \\n for srvr in SRVR_LIST: \\n srvr.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + msg)\\n\\n elif com_str[0] == \\\"ACKN\\\" and cur_state == state.AUTH:\\n if com_str[1] in ackLoopRef:\\n pass\\n else:\\n ackLoopRef[com_str[1]] = True\\n try:\\n if clientSocket[sock]:\\n try:\\n if clientRefNo[com_str[2]]:\\n clientRefNo[com_str[2]].send(\\\"ACKN \\\"+ com_str[1])\\n except:\\n for srvr in SRVR_LIST:\\n srvr.send(\\\"ACKN \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + clientSocket[sock])\\n \\n except:\\n try:\\n if clientRefNo[com_str[2]]:\\n clientRefNo[com_str[2]].send(\\\"ACKN \\\"+ com_str[1])\\n except:\\n for srvr in SRVR_LIST:\\n srvr.send(\\\"ACKN \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2])\\n \\n \\n elif com_str[0] == \\\"SRVR\\\": \\n\\n serverSocket[sock] = com_str[1] \\n SRVR_LIST.append(sock) \\n SRVR_LIST_ADDR.append(str(sock.getsockname()[1])) \\n print (\\\"The communication is from server\\\")\\n\\n\\n elif com_str[0] == \\\"ARRV\\\":\\n if com_str[1] in arvLoopRef:\\n pass\\n else:\\n arvLoopRef[com_str[1]] = True\\n uReferenceNumber = com_str[1]\\n uName = com_str[2]\\n uDescription = com_str[3]\\n uhops = com_str[4]\\n\\n for sck in AUTH_LIST:\\n sck.send(\\\"ARRV \\\" + uReferenceNumber + \\\"\\\\r\\\\n\\\" + uName + \\\"\\\\r\\\\n\\\" + uDescription)\\n\\n for srvr in SRVR_LIST: \\n if srvr != sock:\\n srvr.send(\\\"ARRV \\\" + uReferenceNumber + \\\"\\\\r\\\\n\\\" + uName + \\\"\\\\r\\\\n\\\" + uDescription + \\\"\\\\r\\\\n\\\" + \\\"1\\\") \\n\\n \\n\\n elif com_str[0] == \\\"LEFT\\\":\\n if com_str[1] in lftLoopRef:\\n pass\\n else:\\n lftLoopRef[com_str[1]] = True\\n ref = com_str[1]\\n\\n for sck in AUTH_LIST:\\n sck.send(\\\"LEFT \\\" + ref)\\n\\n for srvr in SRVR_LIST:\\n srvr.send(\\\"LEFT \\\" + ref)\\n\\n sk = clientRefNo[ref]\\n del clientRefNo[ref]\\n del clientSocket[sk]\\n\\n print (\\\"The following user is not reachable\\\" + com_str[1])\\n\\n elif com_str[0] == \\\"SEND\\\" and this_is_server: \\n\\n if com_str[1] in sendLoopRef:\\n pass\\n else:\\n sendLoopRef[com_str[1]] = True\\n print (\\\"send and this is server\\\")\\n\\n msg = \\\"\\\" \\n for m in com_str[4:]: \\n msg += m\\n\\n if com_str[2] != \\\"*\\\": \\n if com_str[2] in clientRefNo:\\n clientRefNo[com_str[2]].send(\\\"SEND \\\" + com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[3] + \\\"\\\\r\\\\n\\\" + msg)\\n else: \\n for srvr in SRVR_LIST:\\n if srvr != sock: \\n srvr.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + com_str[3] + \\\"\\\\r\\\\n\\\" + msg) \\n else: \\n #Broadcast to clients \\n for sck in AUTH_LIST: \\n sck.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[3] + \\\"\\\\r\\\\n\\\" + msg) \\n \\n #Broadcast to servers \\n for srvr in SRVR_LIST:\\n if srvr != sock: \\n srvr.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + com_str[3] + \\\"\\\\r\\\\n\\\" + msg) \\n \\n\\n\\n elif com_str[0] == \\\"INVD\\\" and com_str[1] == 0:\\n print (\\\"server close connection\\\")\\n sock.close()\\n\\n # elif com_str[0] == \\\"SARRV\\\": \\n # print \\\"sr arrival\\\" \\n # srv_list = \\\"\\\" \\n # for s in com_str[2:]: \\n # srv_list += \\\" \\\" + s \\n # srv_list = srv_list.split() \\n # serverSocketList[str(com_str[1])] = str(srv_list) \\n # print \\\"recv\\\" \\n # for srvr in SRVR_LIST: \\n # if srvr != sock: \\n # srvr.send(\\\"SARRV \\\" + com_str[1] + \\\" \\\"+ srv_list)\\n\\n\\n except:\\n # If chatClient pressed ctrl+c for example\\n sock.close()\\n CONNECTION_LIST.remove(sock)\\n\\n except:\\n #broadcast_data(sock, \\\"Client (%s, %s) is offline\\\" % addr)\\n print (\\\"Client (%s, %s) is offline\\\", addr)\\n sock.close()\\n CONNECTION_LIST.remove(sock)\\n continue\\n\\n server_socket.close()\\n\\n\\n passFriends(CONNECTION_LIST)\\n threading.Thread(target=Runner, args=(conn, secret)).start()\\n Server(self.port).start()\\n\\n\\nclass Client (threading.Thread):\\n \\\"\\\"\\\"A class for a Client instance.\\\"\\\"\\\"\\n def __init__(self, host, port):\\n threading.Thread.__init__(self)\\n self.port = port\\n self.host = host\\n\\n def run(self):\\n # Declaring socket\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n\\n host = self.host\\n port = self.port\\n\\n # State of the connection\\n CONNECTED = False\\n AUTHENTICATED = False\\n \\n s.settimeout(2)\\n\\n # Connect to chatServer\\n try:\\n s.connect((host, port))\\n except:\\n print ('Unable to connect to chatServer.')\\n sys.exit()\\n\\n CONNECTED = True\\n print ('Connected to chatServer.')\\n\\n # Protocol implementation\\n\\n commands = ['AUTH', 'SEND', 'ACKN', 'OKAY', 'FAIL', 'INVD', 'ARRV', 'LEFT', 'SRVR']\\n\\n authFlag = True\\n\\n # Authentication Stage\\n while 1:\\n if not authFlag:\\n break\\n if CONNECTED == True:\\n # Random number generated for chatClient Reference, ask for name and password\\n chatClientReference = randint(1, 100)\\n chatClientName = input(\\\"Enter name: \\\")\\n chatClientPassword = input(\\\"Enter password: \\\")\\n\\n authMessage = commands[0]\\n authMessage += \\\" \\\"\\n authMessage += str(chatClientReference)\\n authMessage += \\\" \\\\r\\\\n\\\"\\n authMessage += chatClientName\\n authMessage += \\\"\\\\r\\\\n\\\"\\n authMessage += chatClientPassword\\n\\n s.send(authMessage)\\n\\n socket_list = [sys.stdin, s]\\n\\n # Get the list sockets which are readable\\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\\n\\n for sock in read_sockets:\\n # Incoming message from chatServer\\n if sock == s:\\n data = sock.recv(4096)\\n authResponse = data.split()\\n print( authResponse)\\n\\n # If OKAY\\n if data == commands[3] + \\\" \\\" + str(chatClientReference):\\n \\n authFlag = False\\n AUTHENTICATED = True\\n break\\n\\n # If FAIL\\n elif authResponse[0] == commands[4]:\\n if authResponse[2] == \\\"PASSWORD\\\":\\n print (authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[2] + \\\": The password is not acceptable for authentication on this server.\\\")\\n elif authResponse[2] == \\\"NAME\\\":\\n print (authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[2] + \\\": The specified name is already in use by another client.\\\")\\n elif authResponse[2] == \\\"NUMBER\\\":\\n print (authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[2] + \\\": A number is not valid, either because it has already been used for another entity in case it was newly generated by the client, or there is no relevant entity that the number refers to.\\\")\\n\\n # When client is connected and authenticated\\n while 1:\\n socket_list = [sys.stdin, s]\\n # Get the list sockets which are readable\\n read_sockets, write_sockets, error_sockets = select.select(socket_list, [], [])\\n\\n # Random number generated for chatMesssageReference\\n chatMessageReference = randint(1, 10000)\\n\\n for sock in read_sockets:\\n\\n # Incoming message from chatServer\\n\\n if sock == s:\\n data = sock.recv(4096)\\n authResponse = data.split()\\n\\n # If OKAY\\n if data == commands[3] + \\\" \\\" + str(chatMessageReference):\\n sys.stdout.write(data)\\n\\n # If SEND\\n elif authResponse[0] == commands[1]:\\n \\n s.send(\\\"ACKN \\\" + authResponse[1] + \\\"\\\\r\\\\n\\\" + authResponse[2])\\n\\n # If ARRV\\n elif authResponse[0] == commands[6]:\\n print (\\\"The following user is connected now.\\\\r\\\\nUser Reference: \\\" + authResponse[1] + \\\"\\\\r\\\\nUsername: \\\" + authResponse[2] + \\\"\\\\r\\\\nIpAddress: \\\" + authResponse[3])\\n\\n # If LEFT\\n elif authResponse[0] == commands[7]:\\n print (\\\"The following user has left.\\\\r\\\\nUser Reference: \\\" + authResponse[1])\\n\\n # If FAIL\\n elif authResponse[0] == commands[4]:\\n if authResponse[2] == \\\"LENGHT\\\":\\n print (authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[2] + \\\": The chat message text is too long.\\\")\\n elif authResponse[2] == commands[5]:\\n print (authResponse[0] + \\\" \\\" + authResponse[1] + \\\" \\\" + authResponse[2] + \\\": A malformed message or a message that is not valid in the current state of the client.\\\")\\n s.close()\\n print (\\\"Connection to chatServer closed.\\\")\\n else:\\n print (data)\\n\\n else:\\n if AUTHENTICATED == True:\\n chatMessageInput = sys.stdin.readline()\\n chatMessageRcvr = input(\\\"Enter * for broadcasting or Reference Number of specific client: \\\")\\n\\n chatMessage = commands[1]\\n chatMessage += \\\" \\\"\\n chatMessage += str(chatMessageReference)\\n chatMessage += \\\" \\\\r\\\\n\\\"\\n chatMessage += chatMessageRcvr\\n chatMessage += \\\"\\\\r\\\\n\\\"\\n chatMessage += chatMessageInput\\n\\n s.send(chatMessage)\\n\\n threading.Thread(target=Runner, args=(conn, secret)).start()\\n # Server(self.port).start()\\n # ##########################################################################THIS\\n # IS GOOD, BUT I CAN'T TEST ON ONE MACHINE\\n\\ndef Runner(conn, secret):\\n global username_array\\n while 1:\\n data = netCatch(conn, secret)\\n if data != 1:\\n writeToScreen(data, username_array[conn])\\n\\n#-------------------------------------------------------------------------\\n# Menu helpers\\n\\ndef QuickClient():\\n \\\"\\\"\\\"Menu window for connection options.\\\"\\\"\\\"\\n window = Toplevel(root)\\n window.title(\\\"Connection options\\\")\\n window.grab_set()\\n Label(window, text=\\\"Server IP:\\\").grid(row=0)\\n destination = Entry(window)\\n destination.grid(row=0, column=1)\\n go = Button(window, text=\\\"Connect\\\", command=lambda:\\n client_options_go(destination.get(), \\\"9999\\\", window))\\n go.grid(row=1, column=1)\\n\\n\\ndef QuickServer():\\n \\\"\\\"\\\"Quickstarts a server.\\\"\\\"\\\"\\n Server(9999).start()\\n\\ndef saveHistory():\\n \\\"\\\"\\\"Saves history with Tkinter's asksaveasfilename dialog.\\\"\\\"\\\"\\n global main_body_text\\n file_name = asksaveasfilename(\\n title=\\\"Choose save location\\\",\\n filetypes=[('Plain text', '*.txt'), ('Any File', '*.*')])\\n try:\\n filehandle = open(file_name + \\\".txt\\\", \\\"w\\\")\\n except IOError:\\n print(\\\"Can't save history.\\\")\\n return\\n contents = main_body_text.get(1.0, END)\\n for line in contents:\\n filehandle.write(line)\\n filehandle.close()\\n\\n\\ndef connects(clientType):\\n global conn_array\\n connecter.config(state=DISABLED)\\n if len(conn_array) == 0:\\n if clientType == 0:\\n client_options_window(root)\\n if clientType == 1:\\n server_options_window(root)\\n else:\\n # connecter.config(state=NORMAL)\\n for connection in conn_array:\\n connection.send(\\\"-001\\\".encode())\\n processFlag(\\\"-001\\\")\\n\\n\\ndef toOne():\\n global clientType\\n clientType = 0\\n\\n\\ndef toTwo():\\n global clientType\\n clientType = 1\\n\\n\\n#-------------------------------------------------------------------------\\n\\n\\nif len(sys.argv) > 1 and sys.argv[1] == \\\"-cli\\\":\\n print(\\\"Starting command line chat\\\")\\n\\nelse:\\n root = Tk()\\n root.title(\\\"Chat\\\")\\n\\n menubar = Menu(root)\\n\\n file_menu = Menu(menubar, tearoff=0)\\n file_menu.add_command(label=\\\"Save chat\\\", command=lambda: saveHistory())\\n file_menu.add_command(label=\\\"Change username\\\",\\n command=lambda: username_options_window(root))\\n file_menu.add_command(label=\\\"Exit\\\", command=lambda: root.destroy())\\n menubar.add_cascade(label=\\\"File\\\", menu=file_menu)\\n\\n connection_menu = Menu(menubar, tearoff=0)\\n connection_menu.add_command(label=\\\"Quick Connect\\\", command=QuickClient)\\n connection_menu.add_command(\\n label=\\\"Connect on port\\\", command=lambda: client_options_window(root))\\n connection_menu.add_command(\\n label=\\\"Disconnect\\\", command=lambda: processFlag(\\\"-001\\\"))\\n menubar.add_cascade(label=\\\"Connect\\\", menu=connection_menu)\\n\\n server_menu = Menu(menubar, tearoff=0)\\n server_menu.add_command(label=\\\"Launch server\\\", command=QuickServer)\\n server_menu.add_command(label=\\\"Listen on port\\\",\\n command=lambda: server_options_window(root))\\n menubar.add_cascade(label=\\\"Server\\\", menu=server_menu)\\n\\n menubar.add_command(label=\\\"Contacts\\\", command=lambda:\\n contacts_window(root))\\n\\n root.config(menu=menubar)\\n\\n main_body = Frame(root, height=20, width=50)\\n\\n main_body_text = Text(main_body)\\n body_text_scroll = Scrollbar(main_body)\\n main_body_text.focus_set()\\n body_text_scroll.pack(side=RIGHT, fill=Y)\\n main_body_text.pack(side=LEFT, fill=Y)\\n body_text_scroll.config(command=main_body_text.yview)\\n main_body_text.config(yscrollcommand=body_text_scroll.set)\\n main_body.pack()\\n\\n main_body_text.insert(END, \\\"Welcome to the chat program!\\\")\\n main_body_text.config(state=DISABLED)\\n\\n text_input = Entry(root, width=60)\\n text_input.bind(\\\"<Return>\\\", processUserText)\\n text_input.pack()\\n\\n statusConnect = StringVar()\\n statusConnect.set(\\\"Connect\\\")\\n clientType = 1\\n Radiobutton(root, text=\\\"Client\\\", variable=clientType,\\n value=0, command=toOne).pack(anchor=E)\\n Radiobutton(root, text=\\\"Server\\\", variable=clientType,\\n value=1, command=toTwo).pack(anchor=E)\\n connecter = Button(root, textvariable=statusConnect,\\n command=lambda: connects(clientType))\\n connecter.pack()\\n\\n load_contacts()\\n\\n#------------------------------------------------------------#\\n\\n root.mainloop()\\n\\n dump_contacts()\"\n },\n {\n \"alpha_fraction\": 0.3422677218914032,\n \"alphanum_fraction\": 0.3525563180446625,\n \"avg_line_length\": 40.20217514038086,\n \"blob_id\": \"f99a0bb46933a4501f58aa682c562d397a366b57\",\n \"content_id\": \"9e4697281e2c4bba95ac54796ea5854fd5da6585\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 18953,\n \"license_type\": \"no_license\",\n \"max_line_length\": 144,\n \"num_lines\": 460,\n \"path\": \"/chatServer.py\",\n \"repo_name\": \"mehranhussain/dnChatNetwork\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# chatServer.py\\n# Usage: python chatServer.py\\n\\nimport socket\\nimport select\\nfrom enum import Enum\\nimport sys\\nimport fcntl\\nimport struct\\nfrom random import randint\\nfrom collections import defaultdict\\nimport traceback\\nimport copy\\n\\ndef get_ip_address(ifname):\\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\\n return socket.inet_ntoa(fcntl.ioctl(\\n s.fileno(),\\n 0x8915, # SIOCGIFADDR\\n struct.pack('256s', ifname[:15])\\n )[20:24])\\n\\n\\n\\nclass state(Enum):\\n CONN = 0\\n AUTH = 1\\n SEND = 2\\n ACKN = 3\\n ARRV = 4\\n LEFT = 5\\n\\n\\n\\n# Function to broadcast chat messages to all connected clients\\ndef broadcast_data(sock, message):\\n # Do not send the message to the client who has send us the message\\n for sck in CONNECTION_LIST:\\n print \\\"in loop\\\"\\n if sck != server_socket and sck != sock:\\n try:\\n sck.send(message)\\n print \\\"sent\\\"\\n except:\\n # If chatClient pressed ctrl+c for example\\n sck.close()\\n CONNECTION_LIST.remove(sck)\\n\\n\\nif __name__ == \\\"__main__\\\":\\n\\n # Usage of the chatClient.py\\n if len(sys.argv) < 1:\\n print 'Usage : python chatServer.py port(optional)'\\n sys.exit()\\n elif len(sys.argv) < 2:\\n port = 42015\\n else:\\n port = int(sys.argv[1])\\n \\n\\n # List to keep track of socket descriptors`\\n CONNECTION_LIST = []\\n AUTH_LIST = []\\n SRVR_LIST = []\\n SRVR_LIST_ADDR = []\\n RECV_BUFFER = 4029\\n PORT = port\\n backlog = 10\\n HOST = \\\"127.0.0.1\\\"#get_ip_address('wlp2s0') #\\\"10.9.24.36\\\"\\n\\n server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\\n server_address = (HOST, PORT)\\n server_socket.bind(server_address)\\n server_socket.listen(backlog)\\n\\n # Add server socket to the list of readable connections\\n CONNECTION_LIST.append(server_socket)\\n\\n print \\\"chatServer started on port \\\" + str(PORT)\\n\\n clientRefNo = {}\\n clientSocket = {}\\n serverRefNo = {}\\n serverSocket = {}\\n serverSocketList = {}\\n serverNetwork = {}\\n userName = {}\\n userPassword = {}\\n userDescription = {}\\n sendLoopRef = {}\\n ackLoopRef = {}\\n arvLoopRef = {}\\n lftLoopRef = {}\\n\\n cur_state = -1\\n\\n CONNECTION_LIST.append(sys.stdin)\\n failure = False\\n\\n while True:\\n \\n # Get the list sockets which are ready to be read through select\\n read_sockets, write_sockets, error_sockets = select.select(CONNECTION_LIST, [], [])\\n\\n for sock in read_sockets:\\n # New connection\\n if sock == server_socket:\\n # Handle the case in which there is a new connection recieved through server_socket\\n sockfd, addr = server_socket.accept()\\n\\n userDescription[sockfd] = addr \\n # srv_addr = \\\"\\\" \\n # for v in SRVR_LIST_ADDR: \\n # srv_addr += \\\" \\\" + str(v) \\n # #sockfd.send(\\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr) \\n # for srvr in SRVR_LIST: \\n # print \\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr \\n # srvr.send(\\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr) \\n #sockfd.send(\\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr) \\n \\n\\n CONNECTION_LIST.append(sockfd)\\n cur_state = state.CONN\\n print \\\"(%s, %s) connected\\\" % addr\\n\\n #-----------------------------\\n\\n # Some incoming message from a client \\n elif sock == sys.stdin: \\n srv_in = sys.stdin.readline().split() \\n \\n if len(srv_in) > 2:\\n port = srv_in[2]\\n else:\\n port = 42015\\n \\n if srv_in[0] == \\\"connect\\\": \\n srv_in_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) \\n # Connect to chatServer \\n try: \\n srv_in_socket.connect((srv_in[1], int(port)))\\n \\n except socket.error as msg: \\n print \\\"Socket Error: %s\\\" % msg \\n # Random number generated for chatClient Reference, ask for name and password \\n rfn = randint(1, 10000) \\n ok = False\\n while not ok: \\n try: \\n while serverRefNo[rfn]:\\n rfn = randint(1, 10000)\\n except KeyError: \\n ok = True \\n\\n serverRefNo[rfn] = srv_in_socket \\n SRVR_LIST.append(srv_in_socket) \\n SRVR_LIST_ADDR.append(str(srv_in_socket.getsockname()[1])) \\n serverSocket[srv_in_socket] = True \\n CONNECTION_LIST.append(srv_in_socket) \\n\\n srv_in_socket.send(\\\"SRVR \\\" + str(rfn)) \\n\\n # srv_addr = \\\"\\\" \\n # for v in SRVR_LIST_ADDR: \\n # srv_addr += \\\" \\\" + str(v) \\n\\n # for srvr in SRVR_LIST: \\n # print \\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr \\n # srvr.send(\\\"SARRV \\\" + str(server_socket.getsockname()[1]) + \\\" \\\" + srv_addr) \\n # for svd in SRVR_LIST_ADDR: \\n # serverSocketList[svd] = [str(server_socket.getsockname()[1])] \\n \\n \\n elif srv_in[0] == \\\"exit\\\":\\n server_socket.close()\\n sys.exit()\\n else:\\n print \\\"Enter the correct command e.g. connect 133.0.9.3 42015\\\"\\n\\n\\n #------------------------------\\n\\n #broadcast_data(sockfd, \\\"[%s:%s] entered room\\\\n\\\" % addr)\\n\\n else:\\n # Data recieved from client, process it\\n try:\\n # In Windows, sometimes when a TCP program closes abruptly,\\n # a \\\"Connection reset by peer\\\" exception will be thrown\\n data = sock.recv(RECV_BUFFER)\\n if data == \\\"CLOSED\\\":\\n AUTH_LIST.remove(sock)\\n ref = clientSocket[sock]\\n\\n print \\\"closed\\\"\\n\\n for sck in AUTH_LIST:\\n sck.send(\\\"LEFT \\\" + clientSocket[sock])\\n\\n for srvr in SRVR_LIST:\\n srvr.send(\\\"LEFT \\\" + clientSocket[sock])\\n print \\\"srvr left sent\\\"\\n\\n del userName[sock]\\n del clientSocket[sock]\\n del clientRefNo[ref]\\n\\n \\n\\n # command = com_str[0]\\n # ref_no = com_str[1]\\n # line2 = com_str[2]\\n # line3 = com_str[3]\\n\\n com_str =[]\\n for cmd in data.split():\\n com_str.append(cmd)\\n\\n #----------------\\n\\n try: \\n if serverSocket[sock]: \\n this_is_server = True \\n except: \\n this_is_server = False \\n #----------------\\n \\n try:\\n if com_str[0] == \\\"AUTH\\\":\\n try:\\n if clientRefNo[com_str[1]]:\\n sock.send(\\\"FAIL \\\"+com_str[1]+\\\"\\\\r\\\\nNUMBER\\\")\\n except:\\n\\n for s, name in userName.iteritems():\\n if name == com_str[2]:\\n sock.send(\\\"FAIL \\\"+com_str[2]+\\\"\\\\r\\\\nNAME\\\")\\n failure = True\\n break\\n\\n\\n if not com_str[3].isalnum():\\n sock.send(\\\"FAIL \\\"+com_str[1]+\\\"\\\\r\\\\nPASSWORD\\\")\\n failure = True\\n\\n if failure:\\n failure = False\\n break\\n\\n\\n clientRefNo[com_str[1]] = sock\\n clientSocket[sock] = com_str[1]\\n userName[sock] = com_str[2]\\n userPassword[sock] = com_str[3]\\n\\n if com_str[3] == \\\"dnServer\\\":\\n\\n cur_state = state.AUTH\\n AUTH_LIST.append(sock)\\n\\n sock.send(\\\"OKAY \\\"+com_str[1])\\n\\n for sck in AUTH_LIST:\\n if sck != sock:\\n sck.send(\\\"ARRV \\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + userName[sock] + \\\"\\\\r\\\\n\\\" + \\\"description...\\\")\\n\\n for srvr in SRVR_LIST:\\n srvr.send(\\\"ARRV \\\" + com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + \\\"description...\\\" + \\\"\\\\r\\\\n\\\" + \\\"1\\\")\\n\\n else:\\n sock.send(\\\"FAIL \\\"+com_str[1]+\\\"\\\\r\\\\nPASSWORD\\\")\\n\\n elif com_str[0] == \\\"SEND\\\" and cur_state == state.AUTH and not this_is_server:\\n\\n if com_str[1] in sendLoopRef:\\n pass\\n else:\\n sendLoopRef[com_str[1]] = True\\n sock.send(\\\"OKAY \\\"+com_str[1])\\n\\n #SRVR_LIST = list(set(CONNECTION_LIST) - set(set([sys.stdin, server_socket]) | set(AUTH_LIST)))\\n\\n msg = \\\"\\\"\\n for m in com_str[3:]:\\n msg += m + \\\" \\\"\\n\\n\\n if len(msg) > 4096:\\n sock.send(\\\"FAIL \\\"+com_str[1]+\\\"\\\\r\\\\nLENGHT\\\")\\n break\\n\\n elif \\\"*\\\" in msg:\\n sock.send(\\\"INVD 0\\\")\\n break\\n\\n if com_str[2] == \\\"*\\\":\\n \\n #Broadcast to clients\\n for sck in AUTH_LIST:\\n if sck != sock:\\n sck.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + msg)\\n\\n #Broadcast to servers \\n for srvr in SRVR_LIST: \\n srvr.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + msg) \\n #broadcast_data(sock, \\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + msg)\\n \\n else:\\n if com_str[2] in clientRefNo:\\n clientRefNo[com_str[2]].send(\\\"SEND \\\" + com_str[1] + \\\"\\\\r\\\\n\\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + msg)\\n else: \\n for srvr in SRVR_LIST: \\n srvr.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + clientSocket[sock] + \\\"\\\\r\\\\n\\\" + msg)\\n\\n elif com_str[0] == \\\"ACKN\\\" and cur_state == state.AUTH:\\n\\n if com_str[3] in ackLoopRef:\\n pass\\n else:\\n ackLoopRef[com_str[3]] = True\\n try:\\n if clientSocket[sock]:\\n try:\\n if clientRefNo[com_str[2]]:\\n clientRefNo[com_str[2]].send(\\\"ACKN \\\"+ com_str[1])\\n except:\\n for srvr in SRVR_LIST:\\n srvr.send(\\\"ACKN \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + com_str[3])\\n\\n except:\\n try:\\n if clientRefNo[com_str[2]]:\\n clientRefNo[com_str[2]].send(\\\"ACKN \\\"+ com_str[1])\\n except:\\n for srvr in SRVR_LIST:\\n srvr.send(\\\"ACKN \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + com_str[3])\\n \\n \\n elif com_str[0] == \\\"SRVR\\\": \\n\\n serverSocket[sock] = com_str[1] \\n SRVR_LIST.append(sock) \\n SRVR_LIST_ADDR.append(str(sock.getsockname()[1])) \\n print \\\"The communication is from server\\\"\\n\\n\\n elif com_str[0] == \\\"ARRV\\\":\\n if com_str[1] in arvLoopRef:\\n pass\\n else:\\n arvLoopRef[com_str[1]] = True\\n uReferenceNumber = com_str[1]\\n uName = com_str[2]\\n uDescription = com_str[3]\\n uhops = com_str[4]\\n\\n for sck in AUTH_LIST:\\n sck.send(\\\"ARRV \\\" + uReferenceNumber + \\\"\\\\r\\\\n\\\" + uName + \\\"\\\\r\\\\n\\\" + uDescription)\\n\\n for srvr in SRVR_LIST: \\n if srvr != sock:\\n srvr.send(\\\"ARRV \\\" + uReferenceNumber + \\\"\\\\r\\\\n\\\" + uName + \\\"\\\\r\\\\n\\\" + uDescription + \\\"\\\\r\\\\n\\\" + \\\"1\\\") \\n\\n \\n\\n elif com_str[0] == \\\"LEFT\\\":\\n if com_str[1] in lftLoopRef:\\n pass\\n else:\\n lftLoopRef[com_str[1]] = True\\n ref = com_str[1]\\n\\n for sck in AUTH_LIST:\\n sck.send(\\\"LEFT \\\" + ref)\\n\\n for srvr in SRVR_LIST:\\n srvr.send(\\\"LEFT \\\" + ref)\\n\\n sk = clientRefNo[ref]\\n del userName[sk]\\n del clientRefNo[ref]\\n del clientSocket[sk]\\n\\n print \\\"The following user is not reachable\\\" + com_str[1]\\n\\n elif com_str[0] == \\\"SEND\\\" and this_is_server: \\n\\n if com_str[1] in sendLoopRef:\\n pass\\n else:\\n sendLoopRef[com_str[1]] = True\\n print com_str \\n print \\\"send and this is server\\\" \\n\\n msg = \\\"\\\" \\n for m in com_str[4:]: \\n msg += m + \\\" \\\"\\n\\n if com_str[2] != \\\"*\\\": \\n if com_str[2] in clientRefNo:\\n clientRefNo[com_str[2]].send(\\\"SEND \\\" + com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[3] + \\\"\\\\r\\\\n\\\" + msg)\\n else: \\n for srvr in SRVR_LIST:\\n if srvr != sock: \\n srvr.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + com_str[3] + \\\"\\\\r\\\\n\\\" + msg) \\n else: \\n #Broadcast to clients \\n for sck in AUTH_LIST: \\n print \\\"sent client\\\" \\n sck.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[3] + \\\"\\\\r\\\\n\\\" + msg) \\n \\n #Broadcast to servers \\n for srvr in SRVR_LIST:\\n if srvr != sock: \\n srvr.send(\\\"SEND \\\"+ com_str[1] + \\\"\\\\r\\\\n\\\" + com_str[2] + \\\"\\\\r\\\\n\\\" + com_str[3] + \\\"\\\\r\\\\n\\\" + msg) \\n \\n print \\\"SEND\\\"\\n\\n\\n elif com_str[0] == \\\"INVD\\\" and com_str[1] == 0:\\n print \\\"server close connection\\\"\\n sock.close()\\n elif com_str[0] == \\\"CLTS\\\":\\n\\n clts = \\\"\\\"\\n for s, name in userName.iteritems():\\n clts += \\\"\\\\r\\\\n\\\" + name\\n\\n sock.send(\\\"CLTS\\\" + clts)\\n\\n\\n\\n # elif com_str[0] == \\\"SARRV\\\": \\n # print \\\"sr arrival\\\" \\n # srv_list = \\\"\\\" \\n # for s in com_str[2:]: \\n # srv_list += \\\" \\\" + s \\n # srv_list = srv_list.split() \\n # serverSocketList[str(com_str[1])] = str(srv_list) \\n # print \\\"recv\\\" \\n # for srvr in SRVR_LIST: \\n # if srvr != sock: \\n # srvr.send(\\\"SARRV \\\" + com_str[1] + \\\" \\\"+ srv_list)\\n\\n\\n except:\\n # If chatClient pressed ctrl+c for example\\n sock.close()\\n CONNECTION_LIST.remove(sock)\\n\\n except:\\n #broadcast_data(sock, \\\"Client (%s, %s) is offline\\\" % addr)\\n print \\\"Client (%s, %s) is offline\\\" % addr\\n sock.close()\\n CONNECTION_LIST.remove(sock)\\n continue\\n\\n server_socket.close()\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":5,"string":"5"}}},{"rowIdx":2979190,"cells":{"repo_name":{"kind":"string","value":"kandarpkakkad/Matlab-Python-Calculator"},"repo_url":{"kind":"string","value":"https://github.com/kandarpkakkad/Matlab-Python-Calculator"},"snapshot_id":{"kind":"string","value":"08df86b6bc5370ba9ec2ec95515bca565744678c"},"revision_id":{"kind":"string","value":"1dcd69d9d1449f8f29f71b7a17ec0afa87411108"},"directory_id":{"kind":"string","value":"8231f47e27f314735f900bf87a3b3b760e66569f"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-02-12T11:17:28.408877","string":"2023-02-12T11:17:28.408877"},"revision_date":{"kind":"timestamp","value":"2021-01-10T11:09:06","string":"2021-01-10T11:09:06"},"committer_date":{"kind":"timestamp","value":"2021-01-10T11:09:06","string":"2021-01-10T11:09:06"},"github_id":{"kind":"number","value":325247829,"string":"325,247,829"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5,"alphanum_fraction":0.699999988079071,"avg_line_length":14,"blob_id":"f8d66594b601cf8ff5f3fa195020085a8cd3d18d","content_id":"0fb03f2f6b9dbd4456f5935eb6844564fc3f28f3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":30,"license_type":"no_license","max_line_length":17,"num_lines":2,"path":"/requirements.txt","repo_name":"kandarpkakkad/Matlab-Python-Calculator","src_encoding":"UTF-8","text":"matlab~=0.1\nstreamlit~=0.73.1\n"},{"alpha_fraction":0.5848806500434875,"alphanum_fraction":0.6100795865058899,"avg_line_length":21.84848403930664,"blob_id":"e5e9a81ef2cdc79332b7a679ad917df53c011a4e","content_id":"e37d0567f15a2ee382b15230eeee871ca7ddafb7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":754,"license_type":"no_license","max_line_length":62,"num_lines":33,"path":"/calculator.py","repo_name":"kandarpkakkad/Matlab-Python-Calculator","src_encoding":"UTF-8","text":"import matlab.engine\nimport streamlit as st\n\neng = matlab.engine.start_matlab()\n\nst.set_page_config(\n page_title=\"Calculator\",\n initial_sidebar_state=\"expanded\",\n)\n\nst.title('Calculator')\nst.markdown(\"---\")\n\ninp1 = st.number_input(label=\"Enter Number 1\")\noption = st.selectbox('Select Operator', ('+', '-', 'x', '/'))\ninp2 = st.number_input(label=\"Enter Number 2\")\n\nans = 0\n\nif option == '+':\n ans = eng.add(inp1, inp2, nargout=1)\nelif option == '-':\n ans = eng.sub(inp1, inp2, nargout=1)\nelif option == 'x':\n ans = eng.mul(inp1, inp2, nargout=1)\nelif option == '/':\n if inp2 != 0:\n ans = eng.div(inp1, inp2, nargout=1)\n else:\n ans = \"Cannot Divide By Zero\"\n\nst.markdown(\"### Answer = \" + str(ans))\n# st.text(str(ans))\n"},{"alpha_fraction":0.7154597043991089,"alphanum_fraction":0.7216230034828186,"avg_line_length":30.918033599853516,"blob_id":"901df32eff6fce4cb80a5415349e1f2d40ebd825","content_id":"d3873bbb740bcb65a5c30b2440e2c79d6745a36c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1947,"license_type":"no_license","max_line_length":205,"num_lines":61,"path":"/README.md","repo_name":"kandarpkakkad/Matlab-Python-Calculator","src_encoding":"UTF-8","text":"# Matlab-Python-Calculator\nPython Streamlit Calculator with Matlab Backend. The main idea of the project is to understand how to merge python and matlab.\n\n## Setup Matlab and Python\n\n> Install Python3.6 or Python3.8 (dont forget to add the path to system variables)\n\n> Create Virtual Environment:\n>> Ubuntu / macOS\n>>> $ python3 -m venv /path/to/virtualenv\n\n>> Windows\n>>> $ python -m venv /path/to/virtualenv\n\n> Activate Virtual Environment and install dependencies:\n>> Ubuntu / macOS\n>>> $ source /path/to/virtualenv/bin/activate\n\n>>> $ pip3 install -r requirements.txt\n\n>> Windows\n>>> $ /path/to/virtualenv/Scripts/activate\n\n>>> $ pip3 install -r requirements.txt\n\n> Open MATLAB & go to its command line & run the commands:\n>> cd(matlabroot, 'extern', 'engines', 'python')\n\n>> Ubuntu\n>>> system(\"sudo python3 setup.py install build --build-base='/path/to/virtualenv' install --prefix='/path/to/virtualenv'\")\n\n>> Windows\n>>> system(\"python3 setup.py install build --build-base='/path/to/virtualenv' install --prefix='/path/to/virtualenv'\")\n\n> Open project directory in python IDE (PyCharm preferable)\n>> Set the virtual environment for the project from settings -> Project:ProjectName -> Python Interpreter -> Select if available else add from settings symbol.\n>> Now open the terminal and run the following command:\n>>> $ streamlit run gui.py\n\n>>> If Numpy error (In Windows):\n>>>> go to virtual environment directory -> Lib -> site-packages -> numpy -> __init__.py\n\n>>>> Now go to the function win_check and comment the code \"a = a % 17\"\n\n>>>> Run again... Et voila\n\n## Running The Project\n\n> streamlit run calculator.py\n\n## Output\n\n\n\n## Changes That Can Be Made\n\n> The evaluate.py file containes the function to solve the infix expression. So if you want to take infix expression as input and solve the output you can do that. But then you won't be able to use Matlab.\n\n## To-Do\n\n- [ ] Make Scientific Calculator\n"},{"alpha_fraction":0.3815445005893707,"alphanum_fraction":0.4005235731601715,"avg_line_length":25.34482765197754,"blob_id":"8208862a01c6df0c942ac0135c967e811bb81f50","content_id":"edadc90191c95d53bfbef0e1915dca3bc5f2ccf0","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1528,"license_type":"no_license","max_line_length":81,"num_lines":58,"path":"/evaluation.py","repo_name":"kandarpkakkad/Matlab-Python-Calculator","src_encoding":"UTF-8","text":"def precedence(op):\n if op == '+' or op == '-':\n return 1\n if op == '*' or op == '/':\n return 2\n return 0\n\n\ndef applyOp(a, b, op):\n if op == '+':\n return a + b\n if op == '-':\n return a - b\n if op == '*':\n return a * b\n if op == '/':\n return a // b\n\n\ndef evaluate(tokens):\n values = []\n ops = []\n i = 0\n while i < len(tokens):\n if tokens[i] == ' ':\n i += 1\n continue\n elif tokens[i] == '(':\n ops.append(tokens[i])\n elif tokens[i].isdigit():\n val = 0\n while (i < len(tokens) and\n tokens[i].isdigit()):\n val = (val * 10) + int(tokens[i])\n i += 1\n values.append(val)\n i -= 1\n elif tokens[i] == ')':\n while len(ops) != 0 and ops[-1] != '(':\n val2 = values.pop()\n val1 = values.pop()\n op = ops.pop()\n values.append(applyOp(val1, val2, op))\n ops.pop()\n else:\n while len(ops) != 0 and precedence(ops[-1]) >= precedence(tokens[i]):\n val2 = values.pop()\n val1 = values.pop()\n op = ops.pop()\n values.append(applyOp(val1, val2, op))\n ops.append(tokens[i])\n i += 1\n while len(ops) != 0:\n val2 = values.pop()\n val1 = values.pop()\n op = ops.pop()\n values.append(applyOp(val1, val2, op))\n return values[-1]\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5,\n \"alphanum_fraction\": 0.699999988079071,\n \"avg_line_length\": 14,\n \"blob_id\": \"f8d66594b601cf8ff5f3fa195020085a8cd3d18d\",\n \"content_id\": \"0fb03f2f6b9dbd4456f5935eb6844564fc3f28f3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 30,\n \"license_type\": \"no_license\",\n \"max_line_length\": 17,\n \"num_lines\": 2,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"kandarpkakkad/Matlab-Python-Calculator\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"matlab~=0.1\\nstreamlit~=0.73.1\\n\"\n },\n {\n \"alpha_fraction\": 0.5848806500434875,\n \"alphanum_fraction\": 0.6100795865058899,\n \"avg_line_length\": 21.84848403930664,\n \"blob_id\": \"e5e9a81ef2cdc79332b7a679ad917df53c011a4e\",\n \"content_id\": \"e37d0567f15a2ee382b15230eeee871ca7ddafb7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 754,\n \"license_type\": \"no_license\",\n \"max_line_length\": 62,\n \"num_lines\": 33,\n \"path\": \"/calculator.py\",\n \"repo_name\": \"kandarpkakkad/Matlab-Python-Calculator\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import matlab.engine\\nimport streamlit as st\\n\\neng = matlab.engine.start_matlab()\\n\\nst.set_page_config(\\n page_title=\\\"Calculator\\\",\\n initial_sidebar_state=\\\"expanded\\\",\\n)\\n\\nst.title('Calculator')\\nst.markdown(\\\"---\\\")\\n\\ninp1 = st.number_input(label=\\\"Enter Number 1\\\")\\noption = st.selectbox('Select Operator', ('+', '-', 'x', '/'))\\ninp2 = st.number_input(label=\\\"Enter Number 2\\\")\\n\\nans = 0\\n\\nif option == '+':\\n ans = eng.add(inp1, inp2, nargout=1)\\nelif option == '-':\\n ans = eng.sub(inp1, inp2, nargout=1)\\nelif option == 'x':\\n ans = eng.mul(inp1, inp2, nargout=1)\\nelif option == '/':\\n if inp2 != 0:\\n ans = eng.div(inp1, inp2, nargout=1)\\n else:\\n ans = \\\"Cannot Divide By Zero\\\"\\n\\nst.markdown(\\\"### Answer = \\\" + str(ans))\\n# st.text(str(ans))\\n\"\n },\n {\n \"alpha_fraction\": 0.7154597043991089,\n \"alphanum_fraction\": 0.7216230034828186,\n \"avg_line_length\": 30.918033599853516,\n \"blob_id\": \"901df32eff6fce4cb80a5415349e1f2d40ebd825\",\n \"content_id\": \"d3873bbb740bcb65a5c30b2440e2c79d6745a36c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1947,\n \"license_type\": \"no_license\",\n \"max_line_length\": 205,\n \"num_lines\": 61,\n \"path\": \"/README.md\",\n \"repo_name\": \"kandarpkakkad/Matlab-Python-Calculator\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Matlab-Python-Calculator\\nPython Streamlit Calculator with Matlab Backend. The main idea of the project is to understand how to merge python and matlab.\\n\\n## Setup Matlab and Python\\n\\n> Install Python3.6 or Python3.8 (dont forget to add the path to system variables)\\n\\n> Create Virtual Environment:\\n>> Ubuntu / macOS\\n>>> $ python3 -m venv /path/to/virtualenv\\n\\n>> Windows\\n>>> $ python -m venv /path/to/virtualenv\\n\\n> Activate Virtual Environment and install dependencies:\\n>> Ubuntu / macOS\\n>>> $ source /path/to/virtualenv/bin/activate\\n\\n>>> $ pip3 install -r requirements.txt\\n\\n>> Windows\\n>>> $ /path/to/virtualenv/Scripts/activate\\n\\n>>> $ pip3 install -r requirements.txt\\n\\n> Open MATLAB & go to its command line & run the commands:\\n>> cd(matlabroot, 'extern', 'engines', 'python')\\n\\n>> Ubuntu\\n>>> system(\\\"sudo python3 setup.py install build --build-base='/path/to/virtualenv' install --prefix='/path/to/virtualenv'\\\")\\n\\n>> Windows\\n>>> system(\\\"python3 setup.py install build --build-base='/path/to/virtualenv' install --prefix='/path/to/virtualenv'\\\")\\n\\n> Open project directory in python IDE (PyCharm preferable)\\n>> Set the virtual environment for the project from settings -> Project:ProjectName -> Python Interpreter -> Select if available else add from settings symbol.\\n>> Now open the terminal and run the following command:\\n>>> $ streamlit run gui.py\\n\\n>>> If Numpy error (In Windows):\\n>>>> go to virtual environment directory -> Lib -> site-packages -> numpy -> __init__.py\\n\\n>>>> Now go to the function win_check and comment the code \\\"a = a % 17\\\"\\n\\n>>>> Run again... Et voila\\n\\n## Running The Project\\n\\n> streamlit run calculator.py\\n\\n## Output\\n\\n\\n\\n## Changes That Can Be Made\\n\\n> The evaluate.py file containes the function to solve the infix expression. So if you want to take infix expression as input and solve the output you can do that. But then you won't be able to use Matlab.\\n\\n## To-Do\\n\\n- [ ] Make Scientific Calculator\\n\"\n },\n {\n \"alpha_fraction\": 0.3815445005893707,\n \"alphanum_fraction\": 0.4005235731601715,\n \"avg_line_length\": 25.34482765197754,\n \"blob_id\": \"8208862a01c6df0c942ac0135c967e811bb81f50\",\n \"content_id\": \"edadc90191c95d53bfbef0e1915dca3bc5f2ccf0\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1528,\n \"license_type\": \"no_license\",\n \"max_line_length\": 81,\n \"num_lines\": 58,\n \"path\": \"/evaluation.py\",\n \"repo_name\": \"kandarpkakkad/Matlab-Python-Calculator\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def precedence(op):\\n if op == '+' or op == '-':\\n return 1\\n if op == '*' or op == '/':\\n return 2\\n return 0\\n\\n\\ndef applyOp(a, b, op):\\n if op == '+':\\n return a + b\\n if op == '-':\\n return a - b\\n if op == '*':\\n return a * b\\n if op == '/':\\n return a // b\\n\\n\\ndef evaluate(tokens):\\n values = []\\n ops = []\\n i = 0\\n while i < len(tokens):\\n if tokens[i] == ' ':\\n i += 1\\n continue\\n elif tokens[i] == '(':\\n ops.append(tokens[i])\\n elif tokens[i].isdigit():\\n val = 0\\n while (i < len(tokens) and\\n tokens[i].isdigit()):\\n val = (val * 10) + int(tokens[i])\\n i += 1\\n values.append(val)\\n i -= 1\\n elif tokens[i] == ')':\\n while len(ops) != 0 and ops[-1] != '(':\\n val2 = values.pop()\\n val1 = values.pop()\\n op = ops.pop()\\n values.append(applyOp(val1, val2, op))\\n ops.pop()\\n else:\\n while len(ops) != 0 and precedence(ops[-1]) >= precedence(tokens[i]):\\n val2 = values.pop()\\n val1 = values.pop()\\n op = ops.pop()\\n values.append(applyOp(val1, val2, op))\\n ops.append(tokens[i])\\n i += 1\\n while len(ops) != 0:\\n val2 = values.pop()\\n val1 = values.pop()\\n op = ops.pop()\\n values.append(applyOp(val1, val2, op))\\n return values[-1]\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":4,"string":"4"}}},{"rowIdx":2979191,"cells":{"repo_name":{"kind":"string","value":"aquatiger/misc.-exercises"},"repo_url":{"kind":"string","value":"https://github.com/aquatiger/misc.-exercises"},"snapshot_id":{"kind":"string","value":"d7c86d896dd9429d14df8f465ee49ec79ab8757c"},"revision_id":{"kind":"string","value":"59233899902a548ea1f6788c26e183ad28eaa16c"},"directory_id":{"kind":"string","value":"af55eb550befddf83858158086c72e9ed717c51d"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-05-03T10:13:48.321452","string":"2021-05-03T10:13:48.321452"},"revision_date":{"kind":"timestamp","value":"2018-03-31T01:56:36","string":"2018-03-31T01:56:36"},"committer_date":{"kind":"timestamp","value":"2018-03-31T01:56:36","string":"2018-03-31T01:56:36"},"github_id":{"kind":"number","value":120531039,"string":"120,531,039"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5994694828987122,"alphanum_fraction":0.6180371642112732,"avg_line_length":21.5625,"blob_id":"b9df37b9851e49669c6aa43560b20ccf2a8535ac","content_id":"b7b1292fdd8f0efff68a2594ab757d3f83589ee5","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":377,"license_type":"no_license","max_line_length":54,"num_lines":16,"path":"/fortune cookie.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"import random\r\n\r\nfortune = random.randint(1, 5)\r\n\r\nif fortune == 1:\r\n print(\"Woohoo! you're the winner!\")\r\nelif fortune == 2:\r\n print(\"I'm sorry, your bladder will empty today.\")\r\nelif fortune == 3:\r\n print(\"Woohoo! You win a free colonoscopy!\")\r\nelif fortune == 4:\r\n print(\"Beware the pirates!\")\r\nelif fortune == 5:\r\n print(\"Agamas love you!\")\r\nelse:\r\n end\r\n"},{"alpha_fraction":0.4734848439693451,"alphanum_fraction":0.5227272510528564,"avg_line_length":12.666666984558105,"blob_id":"551dbe5a5c17c0d8f29db776d6533e8fe7f6dc91","content_id":"b6bce21096cf354aff6c9c818f6b15dce0b5dea1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":264,"license_type":"no_license","max_line_length":31,"num_lines":18,"path":"/test.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"import random\r\n\r\n# start off with empty list\r\nheads = 0\r\ntails = 0\r\ncount = 0\r\n\r\n\r\nfor i in range(100):\r\n count += 1\r\n toss = random.randint(1, 2)\r\n if toss == 1:\r\n heads += 1\r\n elif toss == 2:\r\n tails += 1\r\n\r\nprint(heads)\r\nprint(tails)\r\n"},{"alpha_fraction":0.65887850522995,"alphanum_fraction":0.672897219657898,"avg_line_length":21.77777862548828,"blob_id":"da74884c9cc6de96a24253b4c40a6ed2969a1db0","content_id":"9adc049793d87e9e87cc63f626ff3332ea746740","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":214,"license_type":"no_license","max_line_length":53,"num_lines":9,"path":"/launchcode ques 1.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"def accomplice(numberOfBars):\r\n numberOfBars = input(\"How many bars to remove? \")\r\n x = numberOfBars\r\n largestHole = ((2^x)/36)\r\n return largestHole\r\n\r\naccomplice(numberOfBars)\r\n\r\n#print(largestHole)\r\n"},{"alpha_fraction":0.6545454263687134,"alphanum_fraction":0.6545454263687134,"avg_line_length":25.5,"blob_id":"9cb75c2a639b456ae4a768f94c403f36edc43fd4","content_id":"13749a445bbffa691a639b44e1c65e05b9514ef6","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":55,"license_type":"no_license","max_line_length":33,"num_lines":2,"path":"/gameover.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"print(\"Game Over\")\r\ninput(\"\\n\\nPress enter to exit.\")\r\n"},{"alpha_fraction":0.658823549747467,"alphanum_fraction":0.6666666865348816,"avg_line_length":24.842105865478516,"blob_id":"d85c67d1f41112ad07ad7fcec31562f395992f7d","content_id":"44e89e15f60e6511fbcf962aea21359a016796d4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":510,"license_type":"no_license","max_line_length":75,"num_lines":19,"path":"/quotationmanipulation.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"#Quotation Manipulation\r\n#demonstrates string methods\r\n\r\n#quote from IBM Chairman, Thomas Watson, in 1943.\r\nquote = \"\\\"I think there is a world market for maybe five computers.\\\"\"\r\n\r\nprint(\"Original quote: \", quote)\r\n\r\nprint(\"\\nIn UPPERCASE: \", quote.upper())\r\n\r\nprint(\"\\nIn lowercase: \", quote.lower())\r\n\r\nprint (\"\\nIn Title: \", quote.title())\r\n\r\nprint(\"\\nWith a minor replacement: \", quote.replace(\"five\", \"millions of\"))\r\n\r\nprint(\"\\nOriginal quote is still: \", quote)\r\n\r\ninput(\"\\n\\nPress the enter key to exit.\")\r\n"},{"alpha_fraction":0.5764706134796143,"alphanum_fraction":0.6627451181411743,"avg_line_length":29.875,"blob_id":"24a5f43b03df9a17701eb696219059407c004572","content_id":"a8d7345f7d8d63f0bdf85299dc186b67f2ddd9a1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":255,"license_type":"no_license","max_line_length":55,"num_lines":8,"path":"/tipper.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"bill = float(input(\"What is the price of the bill?: \"))\r\ntip15 = bill*1.15\r\ntip20 = bill*1.20\r\n#used 29.99 for the bill to get the answer from before\r\n\r\n\r\nprint(\"Bill plus 15% gratuity is \" + str(tip15))\r\nprint(\"Bill plus 20% gratuity is \" + str(tip20))\r\n"},{"alpha_fraction":0.7267267107963562,"alphanum_fraction":0.7267267107963562,"avg_line_length":40.625,"blob_id":"0970cdf8bd3ac5f5d9f8be1911b2de99bac9f6e7","content_id":"6c337ad91f5e3417d1b1b0a50f0a679af35c0066","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":333,"license_type":"no_license","max_line_length":91,"num_lines":8,"path":"/count for user.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"# counts for the user, at beginning and ending points and by what interval the user desires\n\nlow = int(input(\"Please enter your beginning number to count: \"))\nhigh = int(input(\"Please enter your ending number to count: \"))\ninterval = int(input(\"Please enter counting interval: \"))\n\nfor i in range (low, high, interval):\n print(i)\n"},{"alpha_fraction":0.6468085050582886,"alphanum_fraction":0.651063859462738,"avg_line_length":22.979591369628906,"blob_id":"cec13d4024f52f100c8075e5f1b43408790377bb","content_id":"9fb4dac80b6cd3bf566b7381fc6a44a4ac7c563f","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1175,"license_type":"no_license","max_line_length":72,"num_lines":49,"path":"/random word game.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"# does not do what I want it to do; not sure why\n# user chooses random word from a tuple\n# computer prints out all words in jumbled form\n# user is told how many letters are in the chosen word\n# user gets only 5 guesses\n\n\nimport random\n\n# creates the tuple to choose from\nWORDS = (\"python\", \"jumble\", \"easy\", \"difficult\", \"answer\", \"xylophone\")\n\nword = random.choice(WORDS)\ncorrect = word\njumble = \"\"\ncount = 0\nscore = 5\n\n\"\"\"\nWelcom to Random Word Game\nFrom the list of scrambled words,\nChoose the one the computer chose.\n\"\"\"\nprint(\"The word is \", len(word), \"letters long.\")\n# print(word)\n\nfor word in WORDS:\n position = random.randrange(len(word))\n jumble += word[position]\n word = word[:position] + word[(position + 1):] * (len(word))\n\nprint(jumble)\n\nguess = input(\"\\nYour guess is: \")\nwhile guess != correct and guess != \"\":\n print(guess)\n\nif guess == correct:\n print(\"That's it! You guessed correcntly!\\n\")\n print(\"Thanks for playing!\")\n \ninput(\"\\nPress the enter key to exit.\")\n\n\"\"\"\nwhile word:\n position = random.randrange(len(word))\n jumble += word[position]\n word = word[:position] + word[(position + 1):]\n\"\"\"\n"},{"alpha_fraction":0.6430768966674805,"alphanum_fraction":0.6800000071525574,"avg_line_length":25.08333396911621,"blob_id":"3330c56a706c0de234839375eb3c86d6c9283cb8","content_id":"32024a3a575093eb66d9813e57a09349e50dd669","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":325,"license_type":"no_license","max_line_length":76,"num_lines":12,"path":"/randomnumb.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"#generates random numbers\r\n\r\n\r\nimport random\r\n\r\n#generate random numbers 1-6 to imitate dice and show off two different ways\r\ndie1 = random.randint(1,6)\r\ndie2 = random.randrange(6) + 1\r\n\r\ntotal = die1 + die2\r\nprint(\"you rolled a \", die1, \"and a \", die2, \"for a total of \", total)\r\ninput (\"\\n\\nPress the enter key to exit.\")\r\n"},{"alpha_fraction":0.6567164063453674,"alphanum_fraction":0.676616907119751,"avg_line_length":38.20000076293945,"blob_id":"bf746bf88a655bbc1de9d99c91054158e53e34dd","content_id":"99ef09b4e8ad6923e21b33aae238f7f890e88c0c","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":201,"license_type":"no_license","max_line_length":58,"num_lines":5,"path":"/favoritefood.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"food1 = input(\"What is your favorite food?: \")\r\nfood2 = input(\"What is your second most favorite food?: \")\r\n\r\nprint(\"Your favorite food is: \" + food1)\r\nprint(\"Your second favorite food is: \" + food2)\r\n"},{"alpha_fraction":0.6010555624961853,"alphanum_fraction":0.6088171601295471,"avg_line_length":32.54166793823242,"blob_id":"1bdf0507d3f934e9b9ac5b718f92c204d3072732","content_id":"10e555bb986008cdda099ab81386480c7383e3a8","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3221,"license_type":"no_license","max_line_length":103,"num_lines":96,"path":"/character creator.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"# Character craeator program for role playing\n\npool = 30\n\nattributes = {\"Strength\": 0, \"Health\": 0, \"Wisdom\": 0, \"Dexterity\": 0}\n\ndef menu():\n print(\"\\nYou have\", pool, \"points\")\n print(\"\"\"\n You can apply them to your Strength, Health, Wisdom, or Dexterity.\n \n Press:\n \n 0 to Quit\n 1 to apply points to Strength\n 2 to apply points to Health\n 3 to apply points to Wisdom\n 4 to apply points to Dexterity\n 5 to apply points from Strength to your pool\n 6 to apply points from Health to your pool\n 7 to apply points from Wisdom to your pool\n 8 to apply points from Dexterity to your pool\n \"\"\")\n\nmenu()\nchoice = input(\"Choice: \")\n\nif choice == \"0\":\n print(\"Good-bye.\")\n input(\"\\nPress enter to exit.\")\n\nif choice != \"0\":\n if choice == \"1\":\n strengthChoice = input(\"How many points would you like to transfer from pool to Strength?: \")\n print(strengthChoice)\n attributes[\"Strength\"] += int(strengthChoice)\n print(attributes.get(\"Strength\"))\n pool -= int(strengthChoice)\n print(pool)\n\n elif choice == \"2\":\n healthChoice = input(\"How many points would you like to transfer from pool to Health?: \")\n print(healthChoice)\n attributes[\"Health\"] += int(healthChoice)\n print(attributes.get(\"Health\"))\n pool -= int(healthChoice)\n print(pool)\n\n elif choice == \"3\":\n wisdomChoice = input(\"How many points would you like to transfer from pool to Wisdom?: \")\n print(wisdomChoice)\n attributes[\"Wisdom\"] += int(wisdomChoice)\n print(attributes.get(\"Wisdom\"))\n pool -= int(wisdomChoice)\n print(pool)\n\n elif choice == \"4\":\n dexterityChoice = input(\"How many points would you like to transfer from pool to Dexterity?: \")\n print(dexterityChoice)\n attributes[\"Dexterity\"] += int(dexterityChoice)\n print(attributes.get(\"Dexterity\"))\n pool -= int(dexterityChoice)\n print(pool)\n\n\n elif choice == \"5\":\n strengthPool = input(\"How many points would you like to transfer from Strength to Pool?: \")\n print(strengthPool)\n attributes[\"Strength\"] -= int(strengthPool)\n print(attributes.get(\"Strength\"))\n pool += int(strengthPool)\n print(pool)\n\n elif choice == \"6\":\n healthPool = input(\"How many points would you like to transfer from Healthto Pool?: \")\n print(healthPool)\n attributes[\"Health\"] -= int(healthPool)\n print(attributes.get(\"Health\"))\n pool += int(healthPool)\n print(pool)\n\n elif choice == \"7\":\n wisdomPool = input(\"How many points would you like to transfer from Wisdom to Pool?: \")\n print(wisdomPool)\n attributes[\"Wisdom\"] -= int(wisdomPool)\n print(attributes.get(\"Wisdom\"))\n pool += int(wisdomPool)\n print(pool)\n\n elif choice == \"8\":\n dexterityPool = input(\"How many points would you like to transfer from Dexterity to Pool?: \")\n print(dexterityPool)\n attributes[\"Dexterity\"] -= int(dexterityPool)\n print(attributes.get(\"Dexterity\"))\n pool += int(dexterityPool)\n print(pool)\n\n"},{"alpha_fraction":0.7350993156433105,"alphanum_fraction":0.748344361782074,"avg_line_length":24.16666603088379,"blob_id":"bb61640252d8f2b6e057169a8c2f7bdc1dacf235","content_id":"055b8d07e10444c05972aca65aa5df804ecee9f7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":151,"license_type":"no_license","max_line_length":70,"num_lines":6,"path":"/backwards print.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"# print user input backwards\n\nmessage = input(\"Please enter a message you want printed backwards: \")\n\nbackmessage = message[-1::-1]\nprint(backmessage)\n"},{"alpha_fraction":0.6041666865348816,"alphanum_fraction":0.609375,"avg_line_length":30.91428565979004,"blob_id":"de30ef3e97633f51a6e9ae90ea7bb41f9157a9a0","content_id":"2d21bc78a605685de3cd51381017a491bc106f49","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1152,"license_type":"no_license","max_line_length":80,"num_lines":35,"path":"/computer guesses number.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"#computer guesses number a human thought of\r\n\r\nimport random\r\n\r\ndef computer_guess():\r\n # lower limit not 11\r\n ll = 1\r\n # upper limit\r\n ul = 100\r\n\r\n guess = random.randint(ll, ul)\r\n print(guess)\r\n print(\"Is this guess higher or lower than the number you chose?\")\r\n print(\"\\nPress h for computer to guess a higher number.\")\r\n print(\"\\nPress l for computer to guess a lower number.\")\r\n print(\"\\nOr press c if the computer was correct.\")\r\n \r\nsuggestion = input()\r\ncomputer_guess()\r\n\r\nif suggestion != 'c':\r\n if suggestion == 'h':\r\n guess = ll # make the new lower limit the guess that was just made\r\n print(guess) # print guess and ll, to see if they match\r\n print(ll)\r\n computer_guess() # then run the function again to continue\r\n if suggestion == 'l':\r\n guess = ul # make the new upper limit the guess that was just made\r\n print(guess) # print guess and ul, to see if they match\r\n print(ul)\r\n computer_guess() # then run the funtion again to continue\r\n \r\n\r\nif suggestion == 'c':\r\n print(\"You are correct. The number is \", guess)\r\n"},{"alpha_fraction":0.6056854724884033,"alphanum_fraction":0.6121045351028442,"avg_line_length":30.157142639160156,"blob_id":"b259512223f54ea13d8193e7d903a94e2df8d919","content_id":"20701ed9ac95eb9cfdc691ba1ae3893592a00169","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2181,"license_type":"no_license","max_line_length":96,"num_lines":70,"path":"/son and father.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"# user enters a name\n# finds name of father from a dictionary\n\nchoice = input(\"\"\"\n Press:\n 0 to Quit\n 1 to add a son-father pair\n 2 to replace a son-father pair\n 3 to delete a son-father pair\n 4 to check a son-father pair\n 5 to check a son-grandfather pair\n \"\"\")\n\n# dictionary that has the son as the key and the father as the value\nsonFather = {\"Michael\": (\"Jordan\", \"Jenkins\"),\n \"Sid\": (\"Ceasar\", \"Brutus\"),\n \"Elizabeth\": (\"George\", \"George\"),\n \"George\": (\"Washington\", \"Carver\")}\n\nif choice == \"0\":\n print(\"\\nGoodbye.\")\n input(\"Press enter to exit.\")\n\n# add a son:father pair\nif choice == \"1\":\n addSon = input(\"Who is the son?: \")\n if addSon not in sonFather:\n addFather = input(\"Who is the father?: \")\n sonFather[addSon] = addFather\n else:\n print(\"\\nThis son already exists.\")\n print(sonFather)\n\n# replace a son:father pair\nif choice == \"2\":\n replaceSon = input(\"Who would you like to replace?: \")\n if replaceSon in sonFather:\n replaceFather = input(\"Who is the father?: \")\n sonFather[replaceSon] = replaceFather\n else:\n print(\"This son does not exist in our dictionary. Please try again, perhaps adding it.\")\n print(sonFather)\n\n# delete a son:father pair\nif choice == \"3\":\n deleteSon = input(\"Which son would you like to delete? : \")\n deleteFather = input(\"Are you sure you want to delete? : \")\n if deleteSon in sonFather and deleteFather == \"Y\":\n del sonFather[deleteSon]\n print(deleteSon + \"is deleted.\")\n print(sonFather)\n else:\n print(\"Goodbye.\")\n print(deleteSon, \"is not in our dictionary.\")\n\nif choice == \"4\":\n checkSon = input(\"Whose father would you like to see?: \")\n heritage = sonFather[checkSon]\n father = heritage[0]\n print(checkSon + \"\\'s father is \" + father)\nelse:\n print(\"Father is unknown\")\n\nif choice == \"5\":\n checkSon = input(\"Whose grandfather would you like to see?: \")\n heritage = sonFather[checkSon]\n father = heritage[1]\n print(checkSon + \"\\'s grandfather is \" + father)\nelse:\n print(\"Grandfather is unknown\")\n"},{"alpha_fraction":0.6052631735801697,"alphanum_fraction":0.6271929740905762,"avg_line_length":27.5,"blob_id":"62471b3bbb8bff0bfc86ab46531ded93dcad292c","content_id":"aa1b435001f04caba3f3cb40517614589189d8a4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":228,"license_type":"no_license","max_line_length":46,"num_lines":8,"path":"/askNumber.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"def ask_number(question, low, high):\n \"\"\"Ask for a number within a range.\"\"\"\n response = None\n while response not in range(low, high, 1):\n response = input(question)\n return response\n\nask_number(5, 1, 10)\n"},{"alpha_fraction":0.7567567825317383,"alphanum_fraction":0.7567567825317383,"avg_line_length":23.33333396911621,"blob_id":"a58cda2b72620b88a5c0aabad40da4d86850eb3f","content_id":"0279b6f92464e9a3246dcdbb6f4760f90c60a59d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":74,"license_type":"no_license","max_line_length":53,"num_lines":3,"path":"/README.md","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"# misc.-exercises\n\nThis is a repository of exercises from a Python book.\n\n"},{"alpha_fraction":0.6319499015808105,"alphanum_fraction":0.6374315023422241,"avg_line_length":23.55769157409668,"blob_id":"7709b91a1c358123fe192b0c023130097f465137","content_id":"f5bbb14a1796ef33cbd0f44cca06b9c72ebcb393","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1277,"license_type":"no_license","max_line_length":72,"num_lines":52,"path":"/word jumble.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"# Word Jumble, works as far as I know\n# copied skeleton from book, but added counting, hints, scoring system\n# computer picks random word from a tuple and then jumbles it up\n# player has to guess original word\n\n\nimport random\n\n# creates the tuple to choose from\nWORDS = (\"python\", \"jumble\", \"easy\", \"difficult\", \"answer\", \"xylophone\")\n\nword = random.choice(WORDS)\ncorrect = word\njumble = \"\"\ncount = 0\nscore = 5\n\nwhile word:\n position = random.randrange(len(word))\n jumble += word[position]\n word = word[:position] + word[(position + 1):]\n\nprint(\n\"\"\"\nWelcome to Word Jumble\n Unscramble the letters to make a word.\n (Press the enter key at the prompt to quit.)\n\"\"\"\n)\nprint(\"The jumble is: \", jumble)\n\nguess = input(\"\\nYour guess is: \")\nwhile guess != correct and guess != \"\":\n count += 1\n if count >= 5:\n break\n print(\"Sorry, that's not it.\")\n #do another loop for hinting\n hint = input(\"Would you like a hint? \")\n if hint == \"yes\":\n score -= 1\n print(\"The word begins with: \", correct[0])\n else:\n guess = input(\"\\nYour guess is: \")\n\n\nif guess == correct:\n print(\"That's it! You guessed correctly!\\n\")\n print(\"Your score is: \", score)\n print(\"Thanks for playing!\")\n\ninput(\"\\nPress the enter key to exit.\")\n"},{"alpha_fraction":0.5705329179763794,"alphanum_fraction":0.6144200563430786,"avg_line_length":20.785715103149414,"blob_id":"1dc9371a41bd926fcac96805be0dddded5cad677","content_id":"dec47ce0b84c36e361e5b5083c440baef6b30998","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":319,"license_type":"no_license","max_line_length":36,"num_lines":14,"path":"/launchcode ques 2.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"def permutate(string1, string2):\r\n newstring1 = string1.split()\r\n newstring2 = string2.split()\r\n newerstring1 = newstring1.sort()\r\n newerstring2 = newstring2.sort()\r\n \r\n if newerstring1 != newerstring2:\r\n return False\r\n else:\r\n return True\r\n \r\n \r\n\r\npermutate(string1, string2)\r\n"},{"alpha_fraction":0.6684492230415344,"alphanum_fraction":0.6684492230415344,"avg_line_length":22.375,"blob_id":"e1e98a9c0ccb7f5218c6f21e221843b719f6868f","content_id":"30cd0b06506836fc16e10404240d81a1e10a8af7","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":187,"license_type":"no_license","max_line_length":90,"num_lines":8,"path":"/print word.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"import random\n\nwords = [\"hemoglobin\", \"Aquarius\", \"jellyfish\", \"tanzanite\", \"loyalty\", \"Queen Elizabeth\"]\n\nfor word in words:\n word = random.choice(words)\n print(word)\n del word\n"},{"alpha_fraction":0.6933701634407043,"alphanum_fraction":0.6933701634407043,"avg_line_length":35.20000076293945,"blob_id":"c1aeb838a271d12bf6adce7c060841026dd4eb51","content_id":"e49045cfc99eaec85ee9d0b51631b0e919307547","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":362,"license_type":"no_license","max_line_length":79,"num_lines":10,"path":"/message length.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"# create length of message and see if the letter e is in the message\n\nmessage = input(\"Please enter a message: \")\nprint(\"Your message is \" + str(len(message)) + \" characters long\")\nif \"e\" in message:\n print(\"The most common letter of the English language is in your message.\")\nelse:\n print(\"There is no letter \\\"e\\\" here\")\n\ninput(\"\\nPress enter to exit.\")\n"},{"alpha_fraction":0.753731369972229,"alphanum_fraction":0.7574626803398132,"avg_line_length":25.799999237060547,"blob_id":"2a93eac2960045606f0a123454ab7e2342ae1a25","content_id":"4184a2b881771cfed08414ed1fada2a00885243d","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":268,"license_type":"no_license","max_line_length":66,"num_lines":10,"path":"/random indexing.py","repo_name":"aquatiger/misc.-exercises","src_encoding":"UTF-8","text":"# have the computer randomly index from a string the user's input.\n# print the index number and the character of the input.\n\nimport random\n\nmessage = input(\"Please type in message: \")\n\nindexed = random.randrange(0, len(message))\nprint(indexed)\nprint(message[indexed])\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5994694828987122,\n \"alphanum_fraction\": 0.6180371642112732,\n \"avg_line_length\": 21.5625,\n \"blob_id\": \"b9df37b9851e49669c6aa43560b20ccf2a8535ac\",\n \"content_id\": \"b7b1292fdd8f0efff68a2594ab757d3f83589ee5\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 377,\n \"license_type\": \"no_license\",\n \"max_line_length\": 54,\n \"num_lines\": 16,\n \"path\": \"/fortune cookie.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import random\\r\\n\\r\\nfortune = random.randint(1, 5)\\r\\n\\r\\nif fortune == 1:\\r\\n print(\\\"Woohoo! you're the winner!\\\")\\r\\nelif fortune == 2:\\r\\n print(\\\"I'm sorry, your bladder will empty today.\\\")\\r\\nelif fortune == 3:\\r\\n print(\\\"Woohoo! You win a free colonoscopy!\\\")\\r\\nelif fortune == 4:\\r\\n print(\\\"Beware the pirates!\\\")\\r\\nelif fortune == 5:\\r\\n print(\\\"Agamas love you!\\\")\\r\\nelse:\\r\\n end\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.4734848439693451,\n \"alphanum_fraction\": 0.5227272510528564,\n \"avg_line_length\": 12.666666984558105,\n \"blob_id\": \"551dbe5a5c17c0d8f29db776d6533e8fe7f6dc91\",\n \"content_id\": \"b6bce21096cf354aff6c9c818f6b15dce0b5dea1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 264,\n \"license_type\": \"no_license\",\n \"max_line_length\": 31,\n \"num_lines\": 18,\n \"path\": \"/test.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import random\\r\\n\\r\\n# start off with empty list\\r\\nheads = 0\\r\\ntails = 0\\r\\ncount = 0\\r\\n\\r\\n\\r\\nfor i in range(100):\\r\\n count += 1\\r\\n toss = random.randint(1, 2)\\r\\n if toss == 1:\\r\\n heads += 1\\r\\n elif toss == 2:\\r\\n tails += 1\\r\\n\\r\\nprint(heads)\\r\\nprint(tails)\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.65887850522995,\n \"alphanum_fraction\": 0.672897219657898,\n \"avg_line_length\": 21.77777862548828,\n \"blob_id\": \"da74884c9cc6de96a24253b4c40a6ed2969a1db0\",\n \"content_id\": \"9adc049793d87e9e87cc63f626ff3332ea746740\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 214,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 9,\n \"path\": \"/launchcode ques 1.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def accomplice(numberOfBars):\\r\\n numberOfBars = input(\\\"How many bars to remove? \\\")\\r\\n x = numberOfBars\\r\\n largestHole = ((2^x)/36)\\r\\n return largestHole\\r\\n\\r\\naccomplice(numberOfBars)\\r\\n\\r\\n#print(largestHole)\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6545454263687134,\n \"alphanum_fraction\": 0.6545454263687134,\n \"avg_line_length\": 25.5,\n \"blob_id\": \"9cb75c2a639b456ae4a768f94c403f36edc43fd4\",\n \"content_id\": \"13749a445bbffa691a639b44e1c65e05b9514ef6\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 55,\n \"license_type\": \"no_license\",\n \"max_line_length\": 33,\n \"num_lines\": 2,\n \"path\": \"/gameover.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"print(\\\"Game Over\\\")\\r\\ninput(\\\"\\\\n\\\\nPress enter to exit.\\\")\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.658823549747467,\n \"alphanum_fraction\": 0.6666666865348816,\n \"avg_line_length\": 24.842105865478516,\n \"blob_id\": \"d85c67d1f41112ad07ad7fcec31562f395992f7d\",\n \"content_id\": \"44e89e15f60e6511fbcf962aea21359a016796d4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 510,\n \"license_type\": \"no_license\",\n \"max_line_length\": 75,\n \"num_lines\": 19,\n \"path\": \"/quotationmanipulation.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#Quotation Manipulation\\r\\n#demonstrates string methods\\r\\n\\r\\n#quote from IBM Chairman, Thomas Watson, in 1943.\\r\\nquote = \\\"\\\\\\\"I think there is a world market for maybe five computers.\\\\\\\"\\\"\\r\\n\\r\\nprint(\\\"Original quote: \\\", quote)\\r\\n\\r\\nprint(\\\"\\\\nIn UPPERCASE: \\\", quote.upper())\\r\\n\\r\\nprint(\\\"\\\\nIn lowercase: \\\", quote.lower())\\r\\n\\r\\nprint (\\\"\\\\nIn Title: \\\", quote.title())\\r\\n\\r\\nprint(\\\"\\\\nWith a minor replacement: \\\", quote.replace(\\\"five\\\", \\\"millions of\\\"))\\r\\n\\r\\nprint(\\\"\\\\nOriginal quote is still: \\\", quote)\\r\\n\\r\\ninput(\\\"\\\\n\\\\nPress the enter key to exit.\\\")\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.5764706134796143,\n \"alphanum_fraction\": 0.6627451181411743,\n \"avg_line_length\": 29.875,\n \"blob_id\": \"24a5f43b03df9a17701eb696219059407c004572\",\n \"content_id\": \"a8d7345f7d8d63f0bdf85299dc186b67f2ddd9a1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 255,\n \"license_type\": \"no_license\",\n \"max_line_length\": 55,\n \"num_lines\": 8,\n \"path\": \"/tipper.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"bill = float(input(\\\"What is the price of the bill?: \\\"))\\r\\ntip15 = bill*1.15\\r\\ntip20 = bill*1.20\\r\\n#used 29.99 for the bill to get the answer from before\\r\\n\\r\\n\\r\\nprint(\\\"Bill plus 15% gratuity is \\\" + str(tip15))\\r\\nprint(\\\"Bill plus 20% gratuity is \\\" + str(tip20))\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.7267267107963562,\n \"alphanum_fraction\": 0.7267267107963562,\n \"avg_line_length\": 40.625,\n \"blob_id\": \"0970cdf8bd3ac5f5d9f8be1911b2de99bac9f6e7\",\n \"content_id\": \"6c337ad91f5e3417d1b1b0a50f0a679af35c0066\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 333,\n \"license_type\": \"no_license\",\n \"max_line_length\": 91,\n \"num_lines\": 8,\n \"path\": \"/count for user.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# counts for the user, at beginning and ending points and by what interval the user desires\\n\\nlow = int(input(\\\"Please enter your beginning number to count: \\\"))\\nhigh = int(input(\\\"Please enter your ending number to count: \\\"))\\ninterval = int(input(\\\"Please enter counting interval: \\\"))\\n\\nfor i in range (low, high, interval):\\n print(i)\\n\"\n },\n {\n \"alpha_fraction\": 0.6468085050582886,\n \"alphanum_fraction\": 0.651063859462738,\n \"avg_line_length\": 22.979591369628906,\n \"blob_id\": \"cec13d4024f52f100c8075e5f1b43408790377bb\",\n \"content_id\": \"9fb4dac80b6cd3bf566b7381fc6a44a4ac7c563f\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1175,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 49,\n \"path\": \"/random word game.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# does not do what I want it to do; not sure why\\n# user chooses random word from a tuple\\n# computer prints out all words in jumbled form\\n# user is told how many letters are in the chosen word\\n# user gets only 5 guesses\\n\\n\\nimport random\\n\\n# creates the tuple to choose from\\nWORDS = (\\\"python\\\", \\\"jumble\\\", \\\"easy\\\", \\\"difficult\\\", \\\"answer\\\", \\\"xylophone\\\")\\n\\nword = random.choice(WORDS)\\ncorrect = word\\njumble = \\\"\\\"\\ncount = 0\\nscore = 5\\n\\n\\\"\\\"\\\"\\nWelcom to Random Word Game\\nFrom the list of scrambled words,\\nChoose the one the computer chose.\\n\\\"\\\"\\\"\\nprint(\\\"The word is \\\", len(word), \\\"letters long.\\\")\\n# print(word)\\n\\nfor word in WORDS:\\n position = random.randrange(len(word))\\n jumble += word[position]\\n word = word[:position] + word[(position + 1):] * (len(word))\\n\\nprint(jumble)\\n\\nguess = input(\\\"\\\\nYour guess is: \\\")\\nwhile guess != correct and guess != \\\"\\\":\\n print(guess)\\n\\nif guess == correct:\\n print(\\\"That's it! You guessed correcntly!\\\\n\\\")\\n print(\\\"Thanks for playing!\\\")\\n \\ninput(\\\"\\\\nPress the enter key to exit.\\\")\\n\\n\\\"\\\"\\\"\\nwhile word:\\n position = random.randrange(len(word))\\n jumble += word[position]\\n word = word[:position] + word[(position + 1):]\\n\\\"\\\"\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.6430768966674805,\n \"alphanum_fraction\": 0.6800000071525574,\n \"avg_line_length\": 25.08333396911621,\n \"blob_id\": \"3330c56a706c0de234839375eb3c86d6c9283cb8\",\n \"content_id\": \"32024a3a575093eb66d9813e57a09349e50dd669\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 325,\n \"license_type\": \"no_license\",\n \"max_line_length\": 76,\n \"num_lines\": 12,\n \"path\": \"/randomnumb.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#generates random numbers\\r\\n\\r\\n\\r\\nimport random\\r\\n\\r\\n#generate random numbers 1-6 to imitate dice and show off two different ways\\r\\ndie1 = random.randint(1,6)\\r\\ndie2 = random.randrange(6) + 1\\r\\n\\r\\ntotal = die1 + die2\\r\\nprint(\\\"you rolled a \\\", die1, \\\"and a \\\", die2, \\\"for a total of \\\", total)\\r\\ninput (\\\"\\\\n\\\\nPress the enter key to exit.\\\")\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6567164063453674,\n \"alphanum_fraction\": 0.676616907119751,\n \"avg_line_length\": 38.20000076293945,\n \"blob_id\": \"bf746bf88a655bbc1de9d99c91054158e53e34dd\",\n \"content_id\": \"99ef09b4e8ad6923e21b33aae238f7f890e88c0c\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 201,\n \"license_type\": \"no_license\",\n \"max_line_length\": 58,\n \"num_lines\": 5,\n \"path\": \"/favoritefood.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"food1 = input(\\\"What is your favorite food?: \\\")\\r\\nfood2 = input(\\\"What is your second most favorite food?: \\\")\\r\\n\\r\\nprint(\\\"Your favorite food is: \\\" + food1)\\r\\nprint(\\\"Your second favorite food is: \\\" + food2)\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6010555624961853,\n \"alphanum_fraction\": 0.6088171601295471,\n \"avg_line_length\": 32.54166793823242,\n \"blob_id\": \"1bdf0507d3f934e9b9ac5b718f92c204d3072732\",\n \"content_id\": \"10e555bb986008cdda099ab81386480c7383e3a8\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3221,\n \"license_type\": \"no_license\",\n \"max_line_length\": 103,\n \"num_lines\": 96,\n \"path\": \"/character creator.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Character craeator program for role playing\\n\\npool = 30\\n\\nattributes = {\\\"Strength\\\": 0, \\\"Health\\\": 0, \\\"Wisdom\\\": 0, \\\"Dexterity\\\": 0}\\n\\ndef menu():\\n print(\\\"\\\\nYou have\\\", pool, \\\"points\\\")\\n print(\\\"\\\"\\\"\\n You can apply them to your Strength, Health, Wisdom, or Dexterity.\\n \\n Press:\\n \\n 0 to Quit\\n 1 to apply points to Strength\\n 2 to apply points to Health\\n 3 to apply points to Wisdom\\n 4 to apply points to Dexterity\\n 5 to apply points from Strength to your pool\\n 6 to apply points from Health to your pool\\n 7 to apply points from Wisdom to your pool\\n 8 to apply points from Dexterity to your pool\\n \\\"\\\"\\\")\\n\\nmenu()\\nchoice = input(\\\"Choice: \\\")\\n\\nif choice == \\\"0\\\":\\n print(\\\"Good-bye.\\\")\\n input(\\\"\\\\nPress enter to exit.\\\")\\n\\nif choice != \\\"0\\\":\\n if choice == \\\"1\\\":\\n strengthChoice = input(\\\"How many points would you like to transfer from pool to Strength?: \\\")\\n print(strengthChoice)\\n attributes[\\\"Strength\\\"] += int(strengthChoice)\\n print(attributes.get(\\\"Strength\\\"))\\n pool -= int(strengthChoice)\\n print(pool)\\n\\n elif choice == \\\"2\\\":\\n healthChoice = input(\\\"How many points would you like to transfer from pool to Health?: \\\")\\n print(healthChoice)\\n attributes[\\\"Health\\\"] += int(healthChoice)\\n print(attributes.get(\\\"Health\\\"))\\n pool -= int(healthChoice)\\n print(pool)\\n\\n elif choice == \\\"3\\\":\\n wisdomChoice = input(\\\"How many points would you like to transfer from pool to Wisdom?: \\\")\\n print(wisdomChoice)\\n attributes[\\\"Wisdom\\\"] += int(wisdomChoice)\\n print(attributes.get(\\\"Wisdom\\\"))\\n pool -= int(wisdomChoice)\\n print(pool)\\n\\n elif choice == \\\"4\\\":\\n dexterityChoice = input(\\\"How many points would you like to transfer from pool to Dexterity?: \\\")\\n print(dexterityChoice)\\n attributes[\\\"Dexterity\\\"] += int(dexterityChoice)\\n print(attributes.get(\\\"Dexterity\\\"))\\n pool -= int(dexterityChoice)\\n print(pool)\\n\\n\\n elif choice == \\\"5\\\":\\n strengthPool = input(\\\"How many points would you like to transfer from Strength to Pool?: \\\")\\n print(strengthPool)\\n attributes[\\\"Strength\\\"] -= int(strengthPool)\\n print(attributes.get(\\\"Strength\\\"))\\n pool += int(strengthPool)\\n print(pool)\\n\\n elif choice == \\\"6\\\":\\n healthPool = input(\\\"How many points would you like to transfer from Healthto Pool?: \\\")\\n print(healthPool)\\n attributes[\\\"Health\\\"] -= int(healthPool)\\n print(attributes.get(\\\"Health\\\"))\\n pool += int(healthPool)\\n print(pool)\\n\\n elif choice == \\\"7\\\":\\n wisdomPool = input(\\\"How many points would you like to transfer from Wisdom to Pool?: \\\")\\n print(wisdomPool)\\n attributes[\\\"Wisdom\\\"] -= int(wisdomPool)\\n print(attributes.get(\\\"Wisdom\\\"))\\n pool += int(wisdomPool)\\n print(pool)\\n\\n elif choice == \\\"8\\\":\\n dexterityPool = input(\\\"How many points would you like to transfer from Dexterity to Pool?: \\\")\\n print(dexterityPool)\\n attributes[\\\"Dexterity\\\"] -= int(dexterityPool)\\n print(attributes.get(\\\"Dexterity\\\"))\\n pool += int(dexterityPool)\\n print(pool)\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.7350993156433105,\n \"alphanum_fraction\": 0.748344361782074,\n \"avg_line_length\": 24.16666603088379,\n \"blob_id\": \"bb61640252d8f2b6e057169a8c2f7bdc1dacf235\",\n \"content_id\": \"055b8d07e10444c05972aca65aa5df804ecee9f7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 151,\n \"license_type\": \"no_license\",\n \"max_line_length\": 70,\n \"num_lines\": 6,\n \"path\": \"/backwards print.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# print user input backwards\\n\\nmessage = input(\\\"Please enter a message you want printed backwards: \\\")\\n\\nbackmessage = message[-1::-1]\\nprint(backmessage)\\n\"\n },\n {\n \"alpha_fraction\": 0.6041666865348816,\n \"alphanum_fraction\": 0.609375,\n \"avg_line_length\": 30.91428565979004,\n \"blob_id\": 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\\r\\nsuggestion = input()\\r\\ncomputer_guess()\\r\\n\\r\\nif suggestion != 'c':\\r\\n if suggestion == 'h':\\r\\n guess = ll # make the new lower limit the guess that was just made\\r\\n print(guess) # print guess and ll, to see if they match\\r\\n print(ll)\\r\\n computer_guess() # then run the function again to continue\\r\\n if suggestion == 'l':\\r\\n guess = ul # make the new upper limit the guess that was just made\\r\\n print(guess) # print guess and ul, to see if they match\\r\\n print(ul)\\r\\n computer_guess() # then run the funtion again to continue\\r\\n \\r\\n\\r\\nif suggestion == 'c':\\r\\n print(\\\"You are correct. The number is \\\", guess)\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6056854724884033,\n \"alphanum_fraction\": 0.6121045351028442,\n \"avg_line_length\": 30.157142639160156,\n \"blob_id\": \"b259512223f54ea13d8193e7d903a94e2df8d919\",\n \"content_id\": \"20701ed9ac95eb9cfdc691ba1ae3893592a00169\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2181,\n \"license_type\": \"no_license\",\n \"max_line_length\": 96,\n \"num_lines\": 70,\n \"path\": \"/son and father.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# user enters a name\\n# finds name of father from a dictionary\\n\\nchoice = input(\\\"\\\"\\\"\\n Press:\\n 0 to Quit\\n 1 to add a son-father pair\\n 2 to replace a son-father pair\\n 3 to delete a son-father pair\\n 4 to check a son-father pair\\n 5 to check a son-grandfather pair\\n \\\"\\\"\\\")\\n\\n# dictionary that has the son as the key and the father as the value\\nsonFather = {\\\"Michael\\\": (\\\"Jordan\\\", \\\"Jenkins\\\"),\\n \\\"Sid\\\": (\\\"Ceasar\\\", \\\"Brutus\\\"),\\n \\\"Elizabeth\\\": (\\\"George\\\", \\\"George\\\"),\\n \\\"George\\\": (\\\"Washington\\\", \\\"Carver\\\")}\\n\\nif choice == \\\"0\\\":\\n print(\\\"\\\\nGoodbye.\\\")\\n input(\\\"Press enter to exit.\\\")\\n\\n# add a son:father pair\\nif choice == \\\"1\\\":\\n addSon = input(\\\"Who is the son?: \\\")\\n if addSon not in sonFather:\\n addFather = input(\\\"Who is the father?: \\\")\\n sonFather[addSon] = addFather\\n else:\\n print(\\\"\\\\nThis son already exists.\\\")\\n print(sonFather)\\n\\n# replace a son:father pair\\nif choice == \\\"2\\\":\\n replaceSon = input(\\\"Who would you like to replace?: \\\")\\n if replaceSon in sonFather:\\n replaceFather = input(\\\"Who is the father?: \\\")\\n sonFather[replaceSon] = replaceFather\\n else:\\n print(\\\"This son does not exist in our dictionary. Please try again, perhaps adding it.\\\")\\n print(sonFather)\\n\\n# delete a son:father pair\\nif choice == \\\"3\\\":\\n deleteSon = input(\\\"Which son would you like to delete? : \\\")\\n deleteFather = input(\\\"Are you sure you want to delete? : \\\")\\n if deleteSon in sonFather and deleteFather == \\\"Y\\\":\\n del sonFather[deleteSon]\\n print(deleteSon + \\\"is deleted.\\\")\\n print(sonFather)\\n else:\\n print(\\\"Goodbye.\\\")\\n print(deleteSon, \\\"is not in our dictionary.\\\")\\n\\nif choice == \\\"4\\\":\\n checkSon = input(\\\"Whose father would you like to see?: \\\")\\n heritage = sonFather[checkSon]\\n father = heritage[0]\\n print(checkSon + \\\"\\\\'s father is \\\" + father)\\nelse:\\n print(\\\"Father is unknown\\\")\\n\\nif choice == \\\"5\\\":\\n checkSon = input(\\\"Whose grandfather would you like to see?: \\\")\\n heritage = sonFather[checkSon]\\n father = heritage[1]\\n print(checkSon + \\\"\\\\'s grandfather is \\\" + father)\\nelse:\\n print(\\\"Grandfather is unknown\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.6052631735801697,\n \"alphanum_fraction\": 0.6271929740905762,\n \"avg_line_length\": 27.5,\n \"blob_id\": \"62471b3bbb8bff0bfc86ab46531ded93dcad292c\",\n \"content_id\": \"aa1b435001f04caba3f3cb40517614589189d8a4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 228,\n \"license_type\": \"no_license\",\n \"max_line_length\": 46,\n \"num_lines\": 8,\n \"path\": \"/askNumber.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def ask_number(question, low, high):\\n \\\"\\\"\\\"Ask for a number within a range.\\\"\\\"\\\"\\n response = None\\n while response not in range(low, high, 1):\\n response = input(question)\\n return response\\n\\nask_number(5, 1, 10)\\n\"\n },\n {\n \"alpha_fraction\": 0.7567567825317383,\n \"alphanum_fraction\": 0.7567567825317383,\n \"avg_line_length\": 23.33333396911621,\n \"blob_id\": \"a58cda2b72620b88a5c0aabad40da4d86850eb3f\",\n \"content_id\": \"0279b6f92464e9a3246dcdbb6f4760f90c60a59d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 74,\n \"license_type\": \"no_license\",\n \"max_line_length\": 53,\n \"num_lines\": 3,\n \"path\": \"/README.md\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# misc.-exercises\\n\\nThis is a repository of exercises from a Python book.\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6319499015808105,\n \"alphanum_fraction\": 0.6374315023422241,\n \"avg_line_length\": 23.55769157409668,\n \"blob_id\": \"7709b91a1c358123fe192b0c023130097f465137\",\n \"content_id\": \"f5bbb14a1796ef33cbd0f44cca06b9c72ebcb393\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1277,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 52,\n \"path\": \"/word jumble.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Word Jumble, works as far as I know\\n# copied skeleton from book, but added counting, hints, scoring system\\n# computer picks random word from a tuple and then jumbles it up\\n# player has to guess original word\\n\\n\\nimport random\\n\\n# creates the tuple to choose from\\nWORDS = (\\\"python\\\", \\\"jumble\\\", \\\"easy\\\", \\\"difficult\\\", \\\"answer\\\", \\\"xylophone\\\")\\n\\nword = random.choice(WORDS)\\ncorrect = word\\njumble = \\\"\\\"\\ncount = 0\\nscore = 5\\n\\nwhile word:\\n position = random.randrange(len(word))\\n jumble += word[position]\\n word = word[:position] + word[(position + 1):]\\n\\nprint(\\n\\\"\\\"\\\"\\nWelcome to Word Jumble\\n Unscramble the letters to make a word.\\n (Press the enter key at the prompt to quit.)\\n\\\"\\\"\\\"\\n)\\nprint(\\\"The jumble is: \\\", jumble)\\n\\nguess = input(\\\"\\\\nYour guess is: \\\")\\nwhile guess != correct and guess != \\\"\\\":\\n count += 1\\n if count >= 5:\\n break\\n print(\\\"Sorry, that's not it.\\\")\\n #do another loop for hinting\\n hint = input(\\\"Would you like a hint? \\\")\\n if hint == \\\"yes\\\":\\n score -= 1\\n print(\\\"The word begins with: \\\", correct[0])\\n else:\\n guess = input(\\\"\\\\nYour guess is: \\\")\\n\\n\\nif guess == correct:\\n print(\\\"That's it! You guessed correctly!\\\\n\\\")\\n print(\\\"Your score is: \\\", score)\\n print(\\\"Thanks for playing!\\\")\\n\\ninput(\\\"\\\\nPress the enter key to exit.\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.5705329179763794,\n \"alphanum_fraction\": 0.6144200563430786,\n \"avg_line_length\": 20.785715103149414,\n \"blob_id\": \"1dc9371a41bd926fcac96805be0dddded5cad677\",\n \"content_id\": \"dec47ce0b84c36e361e5b5083c440baef6b30998\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 319,\n \"license_type\": \"no_license\",\n \"max_line_length\": 36,\n \"num_lines\": 14,\n \"path\": \"/launchcode ques 2.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"def permutate(string1, string2):\\r\\n newstring1 = string1.split()\\r\\n newstring2 = string2.split()\\r\\n newerstring1 = newstring1.sort()\\r\\n newerstring2 = newstring2.sort()\\r\\n \\r\\n if newerstring1 != newerstring2:\\r\\n return False\\r\\n else:\\r\\n return True\\r\\n \\r\\n \\r\\n\\r\\npermutate(string1, string2)\\r\\n\"\n },\n {\n \"alpha_fraction\": 0.6684492230415344,\n \"alphanum_fraction\": 0.6684492230415344,\n \"avg_line_length\": 22.375,\n \"blob_id\": \"e1e98a9c0ccb7f5218c6f21e221843b719f6868f\",\n \"content_id\": \"30cd0b06506836fc16e10404240d81a1e10a8af7\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 187,\n \"license_type\": \"no_license\",\n \"max_line_length\": 90,\n \"num_lines\": 8,\n \"path\": \"/print word.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import random\\n\\nwords = [\\\"hemoglobin\\\", \\\"Aquarius\\\", \\\"jellyfish\\\", \\\"tanzanite\\\", \\\"loyalty\\\", \\\"Queen Elizabeth\\\"]\\n\\nfor word in words:\\n word = random.choice(words)\\n print(word)\\n del word\\n\"\n },\n {\n \"alpha_fraction\": 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exit.\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.753731369972229,\n \"alphanum_fraction\": 0.7574626803398132,\n \"avg_line_length\": 25.799999237060547,\n \"blob_id\": \"2a93eac2960045606f0a123454ab7e2342ae1a25\",\n \"content_id\": \"4184a2b881771cfed08414ed1fada2a00885243d\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 268,\n \"license_type\": \"no_license\",\n \"max_line_length\": 66,\n \"num_lines\": 10,\n \"path\": \"/random indexing.py\",\n \"repo_name\": \"aquatiger/misc.-exercises\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# have the computer randomly index from a string the user's input.\\n# print the index number and the character of the input.\\n\\nimport random\\n\\nmessage = input(\\\"Please type in message: \\\")\\n\\nindexed = random.randrange(0, len(message))\\nprint(indexed)\\nprint(message[indexed])\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":21,"string":"21"}}},{"rowIdx":2979192,"cells":{"repo_name":{"kind":"string","value":"Kashif-E/Python-3-programming-specialization"},"repo_url":{"kind":"string","value":"https://github.com/Kashif-E/Python-3-programming-specialization"},"snapshot_id":{"kind":"string","value":"b878a68e89fe48aef8fcee3e46b1e387bce20f7d"},"revision_id":{"kind":"string","value":"0d3d54b6dd62776f6c1625d091f503d450332182"},"directory_id":{"kind":"string","value":"a524a2df42991f445797c1ae9b5090e7d1114414"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-10-01T14:08:50.535656","string":"2022-10-01T14:08:50.535656"},"revision_date":{"kind":"timestamp","value":"2020-06-09T06:57:39","string":"2020-06-09T06:57:39"},"committer_date":{"kind":"timestamp","value":"2020-06-09T06:57:39","string":"2020-06-09T06:57:39"},"github_id":{"kind":"number","value":264770492,"string":"264,770,492"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7068965435028076,"alphanum_fraction":0.7084639668464661,"avg_line_length":48.153846740722656,"blob_id":"bdda054f3f51789788c02f670140eac08e492881","content_id":"5bfa9b00de01788fd8a2a6006c4dc49041886f36","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":642,"license_type":"no_license","max_line_length":202,"num_lines":13,"path":"/Python Basics/Assesment_5.py","repo_name":"Kashif-E/Python-3-programming-specialization","src_encoding":"UTF-8","text":"#Write a program that extracts the last three items in the list sports and assigns it to the variable last. Make sure to write your code so that it works no matter how many items are in the list.\nsports = ['cricket', 'football', 'volleyball', 'baseball', 'softball', 'track and field', 'curling', 'ping pong', 'hockey']\n\nlast = sports[-3:]\n#\n\n#Write code that combines the following variables so that the sentence “You are doing a great job, keep it up!” is assigned to the variable message. Do not edit the values assigned to by, az, io, or qy.\nby = \"You are\"\naz = \"doing a great \"\nio = \"job\"\nqy = \"keep it up!\"\n\nmessage = by, az, io, qy"},{"alpha_fraction":0.8039215803146362,"alphanum_fraction":0.805374026298523,"avg_line_length":90.86666870117188,"blob_id":"c0454e69aa5158a6a74e72d66345bfacce2e28fc","content_id":"3bf316b13b997062a216bd0274666b8c9ca4e0a4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":1383,"license_type":"no_license","max_line_length":315,"num_lines":15,"path":"/README.md","repo_name":"Kashif-E/Python-3-programming-specialization","src_encoding":"UTF-8","text":"# PYTHON 3 PROGRAMMING SPECIALIZATION ON COURSERA #\n\n## OFFERED BY UNIVERSITY OF MICHIGAN ##\n\n### About this Specialization ###\n\nThis specialization teaches the fundamentals of programming in Python 3. We will begin at the beginning, with variables, conditionals, and loops, and get to some intermediate material like keyword parameters, list comprehensions, lambda expressions, and class inheritance.\n\nYou will have lots of opportunities to practice. You will also learn ways to reason about program execution, so that it is no longer mysterious and you are able to debug programs when they don’t work.\n\nBy the end of the specialization, you’ll be writing programs that query Internet APIs for data and extract useful information from them. And you’ll be able to learn to use new modules and APIs on your own by reading the documentation. That will give you a great launch toward being an independent Python programmer.\n\nThis specialization is a good next step for you if you have completed Python for Everybody but want a more in-depth treatment of Python fundamentals and more practice, so that you can proceed with confidence to specializations like Applied Data Science with Python.\n\nBut it is also appropriate as a first set of courses in Python if you are already familiar with some other programming language, or if you are up for the challenge of diving in head-first."},{"alpha_fraction":0.8055555820465088,"alphanum_fraction":0.8083058595657349,"avg_line_length":116.30644989013672,"blob_id":"add58a9a17821aac5cd900d7a55bc291c64be198","content_id":"7d5e8a003cca8b196a6f0502417a471d5a87f962","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":7286,"license_type":"no_license","max_line_length":1038,"num_lines":62,"path":"/Data Collection and Processing with Python/Syllabus.md","repo_name":"Kashif-E/Python-3-programming-specialization","src_encoding":"UTF-8","text":"##Welcome to Data Collection and Processing with Python##\n### Getting Started! ###\nIn order to be successful in this course you will need to have a willingness to put in the time and engage with the material. This course has been designed with non-programming learners in mind so there is no expectation that you will know how to write code in another language. That being said, it doesn’t hurt to have experience; learners who have taken the Python for Everybody specialization will already have a familiarity with some of the content that will be in the course, and others may find it useful to take that specialization before taking this course. This course is part of a specialization and the expectation is that you have completed the first course, Python Basics.\n\nWeek by week\nIn week one the video lectures and activities from the Runestone textbook will cover more complex data structures. By the end of this week, you will have learned how to process json formatted data, traverse nested data using nested iteration, and extract values from nested data.\n\nIn week two you will be learning more advanced forms of accumulation. By the end of the week, you will have learned how to use the map and filter functions in combination with functions to transform or filter out data and store the resulting data in a new object. You will have also learned how to accumulate data using a list comprehension.\n\nIn week three you will learn how to request data from the internet using Application Programming Interfaces (APIs). By the end of the week, you will have learned how to access data from a few APIs, cache data that you have requested, and also learned how to read and work with other APIs that were not touched on in the module.\n\nExercises\nThroughout the course, there will be opportunities for you to practice your skills and test your understanding of the material so far. These are ungraded opportunities, and you can try them as much as you would like without penalty for any incorrect answers. For each lecture video, there will be an accompanying page or pages in the interactive textbook which have questions at the bottom for you to use. The questions, listed under “Check Your Understanding”, are meant to help you tell if you’re getting the main points from the material. There will also be a page at the end of almost every chapter labeled “Exercises”. There will be questions here that you can practice with, which will be more similar to a graded quiz in both structure and difficulty. Finally, there will be a practice tool that you can access, which is hosted in the textbook. With this practice tool, you can select a topic, and you will then be prompted with a question to answer. You can move on to the next question regardless of if you complete the question.\n\nInteractive textbook - Check Your Understanding\nInteractive textbook - Exercises\nInteractive textbook - Practice Tool\nAt the beginning of each lesson, there is a link that does double duty: 1) it logs you into the textbook, enabling the rest of the textbook links; 2) it takes you the Practice Tool.\n\nQuizzes\nOnce per lesson, there will be a graded quiz - sometimes referred to as a chapter assessment. The quiz is meant to provide a formal assessment of your understanding, and will mostly be a mix of multiple choice, fill in the blank, and short coding questions. These graded quizzes will be hosted in the interactive textbook, and the results will be sent back to coursera automatically.\n\nInteractive textbook - Graded Quizzes\nThe quizzes have a due date very far in the future, just because all runestone assignments have due dates. You can ignore them. You’ll be done with this Specialization way before the due dates.\n\nGrading Formula for Data Collection and Processing with Python\nOur quizzes are all set up to give you automated feedback right away and to let you try as many times as you want until and to always grade your \"best\" answer (usually your last one). We've set a passing threshold of 100%, because mastery of the early material is critical to success on later material.\n\nFor the same reason, we strongly encourage you to use the practice tool every day. It's an easy way to review previous concepts and it's rewarding to see how topics that were once difficult become easy as you progress.\n\nPassing Threshold: 100%\n\nWeek One: 25%\n\nAssessment - Nested Data and Iteration (25%)\n\nWeek Two: 25%\n\nAssessment - Advanced Accumulation (25%)\n\nWeek Three: 50%\n\nProject - OMDB and TasteDive Mashup (50%)\n\nGetting Help\nIf you are having problems logging in to the interactive textbook for the course, try logging in again via the \"Log in to Textbook\" page for the course. This should restore your access to the interactive textbook.\n\nIf the problem is with the Coursera platform, please check out the Coursera Learner Support Forums.\n\nIf you have questions with the content of the course, or questions about programming in Python or with the toolkits described, you can contact your peers and the course instructors in the Coursera Discussion Forums. If you have more general questions about Python (i.e., not specific to operating the Runestone textbook), go to Stack Overflow.\n\nWhile learning from each other is very important we all recognize the difference between collaboration and simply copying and pasting work done by others. We strongly encourage you to work collaboratively using good judgment. Similarly, please respect all copyright rules of all materials, including books, articles, etc. While we ultimately cannot monitor everything, Coursera and my team will monitor the forums for any activity that violates such codes of conduct and basic civility to each other. Participation in the discussion forum is not required, though it is strongly encouraged.\n\nFor more information, please reach out to Coursera learner support via our online chat forums in the Learner Help Center.\n\nAccessibility\nWe are committed to developing accessible learning experiences for the widest possible audience. We recognize that learners with disabilities (including but not limited to visual impairments, hearing impairments, cognitive disabilities, or motor disabilities) might need more specific accessibility-related support to achieve learning goals in this course. If you experience any accessibility barriers, such as missing or inadequate alt-text, screen-reader inaccessible navigation, erroneous or incomprehensible captioning, please use this Accessibility Feedback Form to notify our team, and we will be more than happy to help.\n\nIf you encounter other types of issues such as broken links, missing course resources, specific course content errors, or questions about the platform in general, please post and request staff help in the Discussion Forums instead of using the Accessibility Feedback Form to ensure your request most efficiently reaches its intended destination.\n\nContact with the Instructor\nGiven the large number of students in this course the teaching assistants or instructor(s) should not be contacted directly with any questions. While we appreciate the time and effort you put into the course, responding to questions is virtually impossible. But teaching assistants will monitor the forums and will keep the instructor informed on a regular basis."},{"alpha_fraction":0.7004147171974182,"alphanum_fraction":0.7233471870422363,"avg_line_length":52.2337646484375,"blob_id":"f9707306923285be5eaa2293063ec8bf9c8248aa","content_id":"ed0cae14e407d82249c54614be67dede29e02ff3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":4099,"license_type":"no_license","max_line_length":656,"num_lines":77,"path":"/utf-8''assignment_1.py","repo_name":"Kashif-E/Python-3-programming-specialization","src_encoding":"UTF-8","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Assignment 1: Building a Better Contact Sheet\n# In the lectures for this week you were shown how to make a contact sheet for digital photographers, and how you can take one image and create nine different variants based on the brightness of that image. In this assignment you are going to change the colors of the image, creating variations based on a single photo. There are many complex ways to change a photograph using variations, such as changing a black and white image to either \"cool\" variants, which have light purple and blues in them, or \"warm\" variants, which have touches of yellow and may look sepia toned. In this assignment, you'll be just changing the image one color channel at a time\n# \n# Your assignment is to learn how to take the stub code provided in the lecture (cleaned up below), and generate the following output image:\n# \n# ![](./readonly/assignment1.png \"\")\n# \n# From the image you can see there are two parameters which are being varied for each sub-image. First, the rows are changed by color channel, where the top is the red channel, the middle is the green channel, and the bottom is the blue channel. Wait, why don't the colors look more red, green, and blue, in that order? Because the change you to be making is the ratio, or intensity, or that channel, in relationship to the other channels. We're going to use three different intensities, 0.1 (reduce the channel a lot), 0.5 (reduce the channel in half), and 0.9 (reduce the channel only a little bit).\n# \n# For instance, a pixel represented as (200, 100, 50) is a sort of burnt orange color. So the top row of changes would create three alternative pixels, varying the first channel (red). one at (20, 100, 50), one at (100, 100, 50), and one at (180, 100, 50). The next row would vary the second channel (blue), and would create pixels of color values (200, 10, 50), (200, 50, 50) and (200, 90, 50).\n# \n# Note: A font is included for your usage if you would like! It's located in the file `readonly/fanwood-webfont.ttf`\n# \n# Need some hints? Use them sparingly, see how much you can get done on your own first! The sample code given in the class has been cleaned up below, you might want to start from that.\n\n# In[12]:\n\n\nimport PIL\nfrom PIL import Image\nfrom PIL import ImageEnhance\nfrom PIL import ImageFont, ImageDraw\n# read image and convert to RGB\nimage=Image.open(\"readonly/msi_recruitment.gif\")\nimage=image.convert('RGB')\n# build a list of 9 images which have different color\nimages=[]\nlables=[]\nfor i in range(3):\n for j in (0.1,0.5,0.9):\n source = image.split()\n mid = source[i].point(lambda x:x*j)\n source[i].paste(mid)\n im = Image.merge(image.mode, source)\n lables.append('channel {} intensity {}'.format(i,j))\n images.append(im)\nfont = ImageFont.truetype(\"readonly/fanwood-webfont.ttf\",75)\n# create a contact sheet from different color\nfirst_image=images[0]\ncontact_sheet=PIL.Image.new(first_image.mode, (first_image.width*3,first_image.height*3+3*85))\nx=0\ny=0\ndraw = ImageDraw.Draw(contact_sheet)\nfor i,img in enumerate(images):\n # Lets paste the current image into the contact sheet\n contact_sheet.paste(img, (x, y) )\n draw.text((x,y+first_image.height+5), lables[i], font=font)\n # Now we update our X position. If it is going to be the width of the image, then we set it to 0\n # and update Y as well to point to the next \"line\" of the contact sheet.\n if x+first_image.width == contact_sheet.width:\n x=0\n y=y+first_image.height+85\n else:\n x=x+first_image.width\n\n# resize and display the contact sheet\ncontact_sheet = contact_sheet.resize((int(contact_sheet.width/2),int(contact_sheet.height/2) ))\ndisplay(contact_sheet)\n\n\n# ## HINT 1\n# \n# Check out the `PIL.ImageDraw module` for helpful functions\n\n# ## HINT 2\n# \n# Did you find the `text()` function of `PIL.ImageDraw`?\n\n# ## HINT 3\n# \n# Have you seen the `PIL.ImageFont` module? Try loading the font with a size of 75 or so.\n\n# ## HINT 4\n# These hints aren't really enough, we should probably generate some more.\n"},{"alpha_fraction":0.6496551632881165,"alphanum_fraction":0.6703448295593262,"avg_line_length":32.344825744628906,"blob_id":"10456225cb337a9b7bc16765dd2464039633919a","content_id":"a8f4243965c8e247e501dee10eed7f37ae132ec3","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2908,"license_type":"no_license","max_line_length":355,"num_lines":87,"path":"/Python Basics/Assesment_7.py","repo_name":"Kashif-E/Python-3-programming-specialization","src_encoding":"UTF-8","text":"#rainfall_mi is a string that contains the average number of inches of rainfall in Michigan for every month (in inches) with every month separated by a comma. Write code to compute the number of months that have more than 3 inches of rainfall. Store the result in the variable num_rainy_months. In other words, count the number of items with values > 3.0.\n#Hard-coded answers will receive no credit.rainfall_mi = \"1.65, 1.46, 2.05, 3.03, 3.35, 3.46, 2.83, 3.23, 3.5, 2.52, 2.8, 1.85\"\nnum_rainy_months = []\nmylist = rainfall_mi.split(\",\")\nnum_rainy_months = 0\nprint(mylist)\n\nfor num in mylist:\n if float(num) > 3.0 :\n \n num_rainy_months += 1\nprint(num_rainy_months)\n\n#The variable sentence stores a string. Write code to determine how many words in sentence start and end with the same letter, including one-letter words. Store the result in the variable same_letter_count.\n\nsentence = \"students flock to the arb for a variety of outdoor activities such as jogging and picnicking\"\nwords = sentence.split()\nprint(words)\nsame_letter_count = 0\nfor word in words:\n \n if word[0] == word[-1]:\n \n same_letter_count += 1\n \nprint(same_letter_count)\n\n\n#Write code to count the number of strings in list items that have the character w in it. Assign that number to the variable acc_num.\n\n#HINT 1: Use the accumulation pattern!\n\n#HINT 2: the in operator checks whether a substring is present in a string.\n\n#Hard-coded answers will receive no credit.#\n\nitems = [\"whirring\", \"wow!\", \"calendar\", \"wry\", \"glass\", \"\", \"llama\",\"tumultuous\",\"owing\"]\nacc_num = 0\nfor i in items:\n \n if \"w\" in i[0:]:\n \n acc_num += 1\n \nprint(acc_num) \n \n\n#Write code that counts the number of words in sentence that contain either an “a” or an “e”. Store the result in the variable num_a_or_e.\n\n# Note 1: be sure to not double-count words that contain both an a and an e.\n\n# HINT 1: Use the in operator.\n\n# HINT 2: You can either use or or elif.\n\n# Hard-coded answers will receive no credit.\n\n\n\nsentence = \"python is a high level general purpose programming language that can be applied to many different classes of problems.\"\nwords = sentence.split()\nnum_a_or_e = 0\nfor word in words:\n \n if \"a\" in word[0:]:\n print(\"mujju\")\n num_a_or_e += 1\n elif \"e\" in word[0:]:\n print(\"mujju\")\n num_a_or_e += 1\n \n \nprint(num_a_or_e)\n \n\n\n#Write code that will count the number of vowels in the sentence s and assign the result to the variable num_vowels. For this problem, vowels are only a, e, i, o, and u. Hint: use the in operator with vowels.\ns = \"singing in the rain and playing in the rain are two entirely different situations but both can be fun\"\nvowels = ['a','e','i','o','u']\nnum_vowels = 0\nfor char in s:\n \n if char in vowels:\n print(char)\n num_vowels += 1\n \nprint(num_vowels)"}],"string":"[\n {\n \"alpha_fraction\": 0.7068965435028076,\n \"alphanum_fraction\": 0.7084639668464661,\n \"avg_line_length\": 48.153846740722656,\n \"blob_id\": \"bdda054f3f51789788c02f670140eac08e492881\",\n \"content_id\": \"5bfa9b00de01788fd8a2a6006c4dc49041886f36\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 642,\n \"license_type\": \"no_license\",\n \"max_line_length\": 202,\n \"num_lines\": 13,\n \"path\": \"/Python Basics/Assesment_5.py\",\n \"repo_name\": \"Kashif-E/Python-3-programming-specialization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#Write a program that extracts the last three items in the list sports and assigns it to the variable last. Make sure to write your code so that it works no matter how many items are in the list.\\nsports = ['cricket', 'football', 'volleyball', 'baseball', 'softball', 'track and field', 'curling', 'ping pong', 'hockey']\\n\\nlast = sports[-3:]\\n#\\n\\n#Write code that combines the following variables so that the sentence “You are doing a great job, keep it up!” is assigned to the variable message. Do not edit the values assigned to by, az, io, or qy.\\nby = \\\"You are\\\"\\naz = \\\"doing a great \\\"\\nio = \\\"job\\\"\\nqy = \\\"keep it up!\\\"\\n\\nmessage = by, az, io, qy\"\n },\n {\n \"alpha_fraction\": 0.8039215803146362,\n \"alphanum_fraction\": 0.805374026298523,\n \"avg_line_length\": 90.86666870117188,\n \"blob_id\": \"c0454e69aa5158a6a74e72d66345bfacce2e28fc\",\n \"content_id\": \"3bf316b13b997062a216bd0274666b8c9ca4e0a4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 1383,\n \"license_type\": \"no_license\",\n \"max_line_length\": 315,\n \"num_lines\": 15,\n \"path\": \"/README.md\",\n \"repo_name\": \"Kashif-E/Python-3-programming-specialization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# PYTHON 3 PROGRAMMING SPECIALIZATION ON COURSERA #\\n\\n## OFFERED BY UNIVERSITY OF MICHIGAN ##\\n\\n### About this Specialization ###\\n\\nThis specialization teaches the fundamentals of programming in Python 3. We will begin at the beginning, with variables, conditionals, and loops, and get to some intermediate material like keyword parameters, list comprehensions, lambda expressions, and class inheritance.\\n\\nYou will have lots of opportunities to practice. You will also learn ways to reason about program execution, so that it is no longer mysterious and you are able to debug programs when they don’t work.\\n\\nBy the end of the specialization, you’ll be writing programs that query Internet APIs for data and extract useful information from them. And you’ll be able to learn to use new modules and APIs on your own by reading the documentation. That will give you a great launch toward being an independent Python programmer.\\n\\nThis specialization is a good next step for you if you have completed Python for Everybody but want a more in-depth treatment of Python fundamentals and more practice, so that you can proceed with confidence to specializations like Applied Data Science with Python.\\n\\nBut it is also appropriate as a first set of courses in Python if you are already familiar with some other programming language, or if you are up for the challenge of diving in head-first.\"\n },\n {\n \"alpha_fraction\": 0.8055555820465088,\n \"alphanum_fraction\": 0.8083058595657349,\n \"avg_line_length\": 116.30644989013672,\n \"blob_id\": \"add58a9a17821aac5cd900d7a55bc291c64be198\",\n \"content_id\": \"7d5e8a003cca8b196a6f0502417a471d5a87f962\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 7286,\n \"license_type\": \"no_license\",\n \"max_line_length\": 1038,\n \"num_lines\": 62,\n \"path\": \"/Data Collection and Processing with Python/Syllabus.md\",\n \"repo_name\": \"Kashif-E/Python-3-programming-specialization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"##Welcome to Data Collection and Processing with Python##\\n### Getting Started! ###\\nIn order to be successful in this course you will need to have a willingness to put in the time and engage with the material. This course has been designed with non-programming learners in mind so there is no expectation that you will know how to write code in another language. That being said, it doesn’t hurt to have experience; learners who have taken the Python for Everybody specialization will already have a familiarity with some of the content that will be in the course, and others may find it useful to take that specialization before taking this course. This course is part of a specialization and the expectation is that you have completed the first course, Python Basics.\\n\\nWeek by week\\nIn week one the video lectures and activities from the Runestone textbook will cover more complex data structures. By the end of this week, you will have learned how to process json formatted data, traverse nested data using nested iteration, and extract values from nested data.\\n\\nIn week two you will be learning more advanced forms of accumulation. By the end of the week, you will have learned how to use the map and filter functions in combination with functions to transform or filter out data and store the resulting data in a new object. You will have also learned how to accumulate data using a list comprehension.\\n\\nIn week three you will learn how to request data from the internet using Application Programming Interfaces (APIs). By the end of the week, you will have learned how to access data from a few APIs, cache data that you have requested, and also learned how to read and work with other APIs that were not touched on in the module.\\n\\nExercises\\nThroughout the course, there will be opportunities for you to practice your skills and test your understanding of the material so far. These are ungraded opportunities, and you can try them as much as you would like without penalty for any incorrect answers. For each lecture video, there will be an accompanying page or pages in the interactive textbook which have questions at the bottom for you to use. The questions, listed under “Check Your Understanding”, are meant to help you tell if you’re getting the main points from the material. There will also be a page at the end of almost every chapter labeled “Exercises”. There will be questions here that you can practice with, which will be more similar to a graded quiz in both structure and difficulty. Finally, there will be a practice tool that you can access, which is hosted in the textbook. With this practice tool, you can select a topic, and you will then be prompted with a question to answer. You can move on to the next question regardless of if you complete the question.\\n\\nInteractive textbook - Check Your Understanding\\nInteractive textbook - Exercises\\nInteractive textbook - Practice Tool\\nAt the beginning of each lesson, there is a link that does double duty: 1) it logs you into the textbook, enabling the rest of the textbook links; 2) it takes you the Practice Tool.\\n\\nQuizzes\\nOnce per lesson, there will be a graded quiz - sometimes referred to as a chapter assessment. The quiz is meant to provide a formal assessment of your understanding, and will mostly be a mix of multiple choice, fill in the blank, and short coding questions. These graded quizzes will be hosted in the interactive textbook, and the results will be sent back to coursera automatically.\\n\\nInteractive textbook - Graded Quizzes\\nThe quizzes have a due date very far in the future, just because all runestone assignments have due dates. You can ignore them. You’ll be done with this Specialization way before the due dates.\\n\\nGrading Formula for Data Collection and Processing with Python\\nOur quizzes are all set up to give you automated feedback right away and to let you try as many times as you want until and to always grade your \\\"best\\\" answer (usually your last one). We've set a passing threshold of 100%, because mastery of the early material is critical to success on later material.\\n\\nFor the same reason, we strongly encourage you to use the practice tool every day. It's an easy way to review previous concepts and it's rewarding to see how topics that were once difficult become easy as you progress.\\n\\nPassing Threshold: 100%\\n\\nWeek One: 25%\\n\\nAssessment - Nested Data and Iteration (25%)\\n\\nWeek Two: 25%\\n\\nAssessment - Advanced Accumulation (25%)\\n\\nWeek Three: 50%\\n\\nProject - OMDB and TasteDive Mashup (50%)\\n\\nGetting Help\\nIf you are having problems logging in to the interactive textbook for the course, try logging in again via the \\\"Log in to Textbook\\\" page for the course. This should restore your access to the interactive textbook.\\n\\nIf the problem is with the Coursera platform, please check out the Coursera Learner Support Forums.\\n\\nIf you have questions with the content of the course, or questions about programming in Python or with the toolkits described, you can contact your peers and the course instructors in the Coursera Discussion Forums. If you have more general questions about Python (i.e., not specific to operating the Runestone textbook), go to Stack Overflow.\\n\\nWhile learning from each other is very important we all recognize the difference between collaboration and simply copying and pasting work done by others. We strongly encourage you to work collaboratively using good judgment. Similarly, please respect all copyright rules of all materials, including books, articles, etc. While we ultimately cannot monitor everything, Coursera and my team will monitor the forums for any activity that violates such codes of conduct and basic civility to each other. Participation in the discussion forum is not required, though it is strongly encouraged.\\n\\nFor more information, please reach out to Coursera learner support via our online chat forums in the Learner Help Center.\\n\\nAccessibility\\nWe are committed to developing accessible learning experiences for the widest possible audience. We recognize that learners with disabilities (including but not limited to visual impairments, hearing impairments, cognitive disabilities, or motor disabilities) might need more specific accessibility-related support to achieve learning goals in this course. If you experience any accessibility barriers, such as missing or inadequate alt-text, screen-reader inaccessible navigation, erroneous or incomprehensible captioning, please use this Accessibility Feedback Form to notify our team, and we will be more than happy to help.\\n\\nIf you encounter other types of issues such as broken links, missing course resources, specific course content errors, or questions about the platform in general, please post and request staff help in the Discussion Forums instead of using the Accessibility Feedback Form to ensure your request most efficiently reaches its intended destination.\\n\\nContact with the Instructor\\nGiven the large number of students in this course the teaching assistants or instructor(s) should not be contacted directly with any questions. While we appreciate the time and effort you put into the course, responding to questions is virtually impossible. But teaching assistants will monitor the forums and will keep the instructor informed on a regular basis.\"\n },\n {\n \"alpha_fraction\": 0.7004147171974182,\n \"alphanum_fraction\": 0.7233471870422363,\n \"avg_line_length\": 52.2337646484375,\n \"blob_id\": \"f9707306923285be5eaa2293063ec8bf9c8248aa\",\n \"content_id\": \"ed0cae14e407d82249c54614be67dede29e02ff3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 4099,\n \"license_type\": \"no_license\",\n \"max_line_length\": 656,\n \"num_lines\": 77,\n \"path\": \"/utf-8''assignment_1.py\",\n \"repo_name\": \"Kashif-E/Python-3-programming-specialization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/env python\\n# coding: utf-8\\n\\n# # Assignment 1: Building a Better Contact Sheet\\n# In the lectures for this week you were shown how to make a contact sheet for digital photographers, and how you can take one image and create nine different variants based on the brightness of that image. In this assignment you are going to change the colors of the image, creating variations based on a single photo. There are many complex ways to change a photograph using variations, such as changing a black and white image to either \\\"cool\\\" variants, which have light purple and blues in them, or \\\"warm\\\" variants, which have touches of yellow and may look sepia toned. In this assignment, you'll be just changing the image one color channel at a time\\n# \\n# Your assignment is to learn how to take the stub code provided in the lecture (cleaned up below), and generate the following output image:\\n# \\n# ![](./readonly/assignment1.png \\\"\\\")\\n# \\n# From the image you can see there are two parameters which are being varied for each sub-image. First, the rows are changed by color channel, where the top is the red channel, the middle is the green channel, and the bottom is the blue channel. Wait, why don't the colors look more red, green, and blue, in that order? Because the change you to be making is the ratio, or intensity, or that channel, in relationship to the other channels. We're going to use three different intensities, 0.1 (reduce the channel a lot), 0.5 (reduce the channel in half), and 0.9 (reduce the channel only a little bit).\\n# \\n# For instance, a pixel represented as (200, 100, 50) is a sort of burnt orange color. So the top row of changes would create three alternative pixels, varying the first channel (red). one at (20, 100, 50), one at (100, 100, 50), and one at (180, 100, 50). The next row would vary the second channel (blue), and would create pixels of color values (200, 10, 50), (200, 50, 50) and (200, 90, 50).\\n# \\n# Note: A font is included for your usage if you would like! It's located in the file `readonly/fanwood-webfont.ttf`\\n# \\n# Need some hints? Use them sparingly, see how much you can get done on your own first! The sample code given in the class has been cleaned up below, you might want to start from that.\\n\\n# In[12]:\\n\\n\\nimport PIL\\nfrom PIL import Image\\nfrom PIL import ImageEnhance\\nfrom PIL import ImageFont, ImageDraw\\n# read image and convert to RGB\\nimage=Image.open(\\\"readonly/msi_recruitment.gif\\\")\\nimage=image.convert('RGB')\\n# build a list of 9 images which have different color\\nimages=[]\\nlables=[]\\nfor i in range(3):\\n for j in (0.1,0.5,0.9):\\n source = image.split()\\n mid = source[i].point(lambda x:x*j)\\n source[i].paste(mid)\\n im = Image.merge(image.mode, source)\\n lables.append('channel {} intensity {}'.format(i,j))\\n images.append(im)\\nfont = ImageFont.truetype(\\\"readonly/fanwood-webfont.ttf\\\",75)\\n# create a contact sheet from different color\\nfirst_image=images[0]\\ncontact_sheet=PIL.Image.new(first_image.mode, (first_image.width*3,first_image.height*3+3*85))\\nx=0\\ny=0\\ndraw = ImageDraw.Draw(contact_sheet)\\nfor i,img in enumerate(images):\\n # Lets paste the current image into the contact sheet\\n contact_sheet.paste(img, (x, y) )\\n draw.text((x,y+first_image.height+5), lables[i], font=font)\\n # Now we update our X position. If it is going to be the width of the image, then we set it to 0\\n # and update Y as well to point to the next \\\"line\\\" of the contact sheet.\\n if x+first_image.width == contact_sheet.width:\\n x=0\\n y=y+first_image.height+85\\n else:\\n x=x+first_image.width\\n\\n# resize and display the contact sheet\\ncontact_sheet = contact_sheet.resize((int(contact_sheet.width/2),int(contact_sheet.height/2) ))\\ndisplay(contact_sheet)\\n\\n\\n# ## HINT 1\\n# \\n# Check out the `PIL.ImageDraw module` for helpful functions\\n\\n# ## HINT 2\\n# \\n# Did you find the `text()` function of `PIL.ImageDraw`?\\n\\n# ## HINT 3\\n# \\n# Have you seen the `PIL.ImageFont` module? Try loading the font with a size of 75 or so.\\n\\n# ## HINT 4\\n# These hints aren't really enough, we should probably generate some more.\\n\"\n },\n {\n \"alpha_fraction\": 0.6496551632881165,\n \"alphanum_fraction\": 0.6703448295593262,\n \"avg_line_length\": 32.344825744628906,\n \"blob_id\": \"10456225cb337a9b7bc16765dd2464039633919a\",\n \"content_id\": \"a8f4243965c8e247e501dee10eed7f37ae132ec3\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2908,\n \"license_type\": \"no_license\",\n \"max_line_length\": 355,\n \"num_lines\": 87,\n \"path\": \"/Python Basics/Assesment_7.py\",\n \"repo_name\": \"Kashif-E/Python-3-programming-specialization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#rainfall_mi is a string that contains the average number of inches of rainfall in Michigan for every month (in inches) with every month separated by a comma. Write code to compute the number of months that have more than 3 inches of rainfall. Store the result in the variable num_rainy_months. In other words, count the number of items with values > 3.0.\\n#Hard-coded answers will receive no credit.rainfall_mi = \\\"1.65, 1.46, 2.05, 3.03, 3.35, 3.46, 2.83, 3.23, 3.5, 2.52, 2.8, 1.85\\\"\\nnum_rainy_months = []\\nmylist = rainfall_mi.split(\\\",\\\")\\nnum_rainy_months = 0\\nprint(mylist)\\n\\nfor num in mylist:\\n if float(num) > 3.0 :\\n \\n num_rainy_months += 1\\nprint(num_rainy_months)\\n\\n#The variable sentence stores a string. Write code to determine how many words in sentence start and end with the same letter, including one-letter words. Store the result in the variable same_letter_count.\\n\\nsentence = \\\"students flock to the arb for a variety of outdoor activities such as jogging and picnicking\\\"\\nwords = sentence.split()\\nprint(words)\\nsame_letter_count = 0\\nfor word in words:\\n \\n if word[0] == word[-1]:\\n \\n same_letter_count += 1\\n \\nprint(same_letter_count)\\n\\n\\n#Write code to count the number of strings in list items that have the character w in it. Assign that number to the variable acc_num.\\n\\n#HINT 1: Use the accumulation pattern!\\n\\n#HINT 2: the in operator checks whether a substring is present in a string.\\n\\n#Hard-coded answers will receive no credit.#\\n\\nitems = [\\\"whirring\\\", \\\"wow!\\\", \\\"calendar\\\", \\\"wry\\\", \\\"glass\\\", \\\"\\\", \\\"llama\\\",\\\"tumultuous\\\",\\\"owing\\\"]\\nacc_num = 0\\nfor i in items:\\n \\n if \\\"w\\\" in i[0:]:\\n \\n acc_num += 1\\n \\nprint(acc_num) \\n \\n\\n#Write code that counts the number of words in sentence that contain either an “a” or an “e”. Store the result in the variable num_a_or_e.\\n\\n# Note 1: be sure to not double-count words that contain both an a and an e.\\n\\n# HINT 1: Use the in operator.\\n\\n# HINT 2: You can either use or or elif.\\n\\n# Hard-coded answers will receive no credit.\\n\\n\\n\\nsentence = \\\"python is a high level general purpose programming language that can be applied to many different classes of problems.\\\"\\nwords = sentence.split()\\nnum_a_or_e = 0\\nfor word in words:\\n \\n if \\\"a\\\" in word[0:]:\\n print(\\\"mujju\\\")\\n num_a_or_e += 1\\n elif \\\"e\\\" in word[0:]:\\n print(\\\"mujju\\\")\\n num_a_or_e += 1\\n \\n \\nprint(num_a_or_e)\\n \\n\\n\\n#Write code that will count the number of vowels in the sentence s and assign the result to the variable num_vowels. For this problem, vowels are only a, e, i, o, and u. Hint: use the in operator with vowels.\\ns = \\\"singing in the rain and playing in the rain are two entirely different situations but both can be fun\\\"\\nvowels = ['a','e','i','o','u']\\nnum_vowels = 0\\nfor char in s:\\n \\n if char in vowels:\\n print(char)\\n num_vowels += 1\\n \\nprint(num_vowels)\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":5,"string":"5"}}},{"rowIdx":2979193,"cells":{"repo_name":{"kind":"string","value":"ez3r0sec/massmgmt"},"repo_url":{"kind":"string","value":"https://github.com/ez3r0sec/massmgmt"},"snapshot_id":{"kind":"string","value":"05701c91d83874ff7928e797227ece32d2ffe8a9"},"revision_id":{"kind":"string","value":"a6064239efcfc655474bd5fc3308edea9e1ee546"},"directory_id":{"kind":"string","value":"00260b4b21b241f8efae416ff0ba3eb9265b42bc"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-06-25T12:18:50.162982","string":"2021-06-25T12:18:50.162982"},"revision_date":{"kind":"timestamp","value":"2021-06-08T02:47:57","string":"2021-06-08T02:47:57"},"committer_date":{"kind":"timestamp","value":"2021-06-08T02:47:57","string":"2021-06-08T02:47:57"},"github_id":{"kind":"number","value":132782514,"string":"132,782,514"},"star_events_count":{"kind":"number","value":2,"string":"2"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"string","value":"MIT"},"gha_created_at":{"kind":"timestamp","value":"2018-05-09T16:11:49","string":"2018-05-09T16:11:49"},"gha_updated_at":{"kind":"timestamp","value":"2018-06-29T14:03:56","string":"2018-06-29T14:03:56"},"gha_pushed_at":{"kind":"timestamp","value":"2018-07-02T15:47:09","string":"2018-07-02T15:47:09"},"gha_language":{"kind":"string","value":"Shell"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7020906209945679,"alphanum_fraction":0.7055749297142029,"avg_line_length":43.128204345703125,"blob_id":"e4c8d88b06f479296ff36cd7a9c56d5040d794ab","content_id":"d5408a5bddc994cf2b5088f64f385d9c52be9d01","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1722,"license_type":"permissive","max_line_length":147,"num_lines":39,"path":"/enableARD","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/sh\n# enableARD (UNIX exec.)\n# enableARD arg\n# Last Edited: 6/25/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\n\nlineTemplate=\"com.apple.RemoteDesktop.agent\"\nARD=\"$(launchctl list | grep '^\\d.*RemoteDesktop.*')\"\nAgent=\"$(echo $ARD | awk '/com.apple.RemoteDesktop.agent/ {print $(NF) }')\"\nscriptEnabled=\"$(cat $configFile | awk '/EnableARD/ {print $2}')\"\n\n### SCRIPT\n# check if the script is enabled in the config file\nif [ \"$scriptEnabled\" == \"Disable\" ] ; then\n if [ \"$Agent\" == \"$lineTemplate\" ] ; then\n\techo \"$logStart ARD disabled but turned on, turning off\" >> \"$logFile\"\n\t/System/Library/CoreServices/RemoteManagement/ARDAgent.app/Contents/Resources/kickstart -deactivate -configure -access -off\n\techo \"$logStart ARD is now off\" >> \"$logFile\"\n else\n\techo \"$logStart ARD is Disabled and off\" >> \"$logFile\"\n fi\nelif [ \"$scriptEnabled\" == \"Enable\" ] ; then\n if [ \"$Agent\" == \"$lineTemplate\" ] ; then\n echo \"$logStart ARD already enabled, exiting\" >> \"$logFile\"\n else\n \tprivs=\"-DeleteFiles -ControlObserve -TextMessages -OpenQuitApps -GenerateReports -RestartShutDown -SendFiles -ChangeSettings\"\n \techo \"$logStart Privileges: $privs\" >> \"$logFile\"\n \techo \"$logStart Kickstarting ARD\" >> \"$logFile\"\n \t/System/Library/CoreServices/RemoteManagement/ARDAgent.app/Contents/Resources/kickstart -activate -configure -allowAccessFor -specifiedUsers\n \techo \"$logStart Giving $adminUser correct privileges\" >> \"$logFile\"\n \t/System/Library/CoreServices/RemoteManagement/ARDAgent.app/Contents/Resources/kickstart -configure -access -on -privs $privs -users $adminUser\n exit\n fi\nelse\n echo \"$logStart Invalid enableARD configuration\" >> \"$logFile\"\nfi\nexit\n\n"},{"alpha_fraction":0.6538051962852478,"alphanum_fraction":0.6628525853157043,"avg_line_length":45.974998474121094,"blob_id":"3afe9324336cd388ad3ebb2bf3d9333584d4c894","content_id":"0a64ed0e41906275a11ccdb707f265924a33e053","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":3758,"license_type":"permissive","max_line_length":214,"num_lines":80,"path":"/firefox","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/sh\n# Script to download or update Firefox\n# https://www.jamf.com/jamf-nation/third-party-products/files/764/firefox-install-update\n# Last Edited: 10/30/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\n\ndmgfile=\"FF.dmg\"\nscriptEnabled=\"$(cat $configFile | awk '/UpdateFirefox/ {print $2}')\"\ndeleteIfInstalled=\"$(cat $configFile | awk '/Firefox-DeleteIfInstalled/ {print $2}')\"\n\n### SCRIPT\nif [ \"$scriptEnabled\" == \"Disable\" ] ; then\n if [ \"$deleteIfInstalled\" == \"Yes\" ] ; then\n\techo \"$logStart Deleting Firefox\" >> \"$logFile\"\n\trm -rf /Applications/Firefox.app\n elif [ \"$deleteIfInstalled\" == \"No\" ] ; then\n\techo \"$logStart Firefox script is disabled but Firefox is allowed on this system\" >> \"$logFile\"\n else\n\techo \"$logStart Invalid configuration for DeleteIfInstalled key in the chrome section of the configuration file\" >> \"$logFile\"\n fi\n exit\nelif [ \"$scriptEnabled\" == \"Enable\" ] ; then\n # Get OS version and adjust for use with the URL string\n OSvers_URL=$( sw_vers -productVersion | sed 's/[.]/_/g' )\n # Set the User Agent string for use with curl\n userAgent=\"Mozilla/5.0 (Macintosh; Intel Mac OS X ${OSvers_URL}) AppleWebKit/535.6.2 (KHTML, like Gecko) Version/5.2 Safari/535.6.2\"\n # Get the latest version of Reader available from Firefox page.\n latestver=\"$(/usr/bin/curl -s -A \"$userAgent\" https://www.mozilla.org/en-US/firefox/new/ | grep 'data-latest-firefox' | sed -e 's/.* data-latest-firefox=\"\\(.*\\)\".*/\\1/' -e 's/\\\"//' | /usr/bin/awk '{print $1}')\"\n # if the latestver curl fails, exit\n if [ \"$latestver\" == \"\" ] ; then\n \techo \"$logStart latestver curl failed, exiting\" >> \"$logFile\"\n \texit\n else\n \techo \"$logStart Latest Version is: $latestver\" >> \"$logFile\"\n fi\n # Get the version number of the currently-installed FF, if any.\n if [ -e \"/Applications/Firefox.app\" ] ; then\n currentinstalledver=\"$(/usr/bin/defaults read /Applications/Firefox.app/Contents/Info CFBundleShortVersionString)\"\n \techo \"$logStart Current installed version is: $currentinstalledver\" >> \"$logFile\"\n \tif [ ${latestver} = ${currentinstalledver} ] ; then\n echo \"$logStart Firefox is current. Exiting\" >> \"$logFile\"\n exit\n \tfi\n else\n currentinstalledver=\"none\"\n \techo \"$logStart Firefox is not installed, installing\" >> \"$logFile\"\n fi\n\n url=\"https://download-installer.cdn.mozilla.net/pub/firefox/releases/${latestver}/mac/en-US/Firefox%20${latestver}.dmg\"\n echo \"$logStart Latest version of the URL is: $url\" >> \"$logFile\"\n\n # Compare the two versions, if they are different or Firefox is not present then download and install the new version.\n if [ \"${currentinstalledver}\" != \"${latestver}\" ] ; then\n echo \"$logStart Curling Firefox dmg\" >> \"$logFile\"\n \tcurl -s -o /tmp/${dmgfile} ${url}\n \thdiutil attach /tmp/${dmgfile} -nobrowse -quiet\n \tditto -rsrc \"/Volumes/Firefox/Firefox.app\" \"/Applications/Firefox.app\"\n \tsleep 10\n hdiutil detach $(/bin/df | /usr/bin/grep Firefox | awk '{print $1}') -quiet\n \tsleep 10\n \trm /tmp/${dmgfile}\n\n # double check to see if the new version got updated\n \tnewlyinstalledver=\"$(/usr/bin/defaults read /Applications/Firefox.app/Contents/Info CFBundleShortVersionString)\"\n \tif [ \"${latestver}\" = \"${newlyinstalledver}\" ] ; then\n echo \"$logStart Firefox has been updated\" >> \"$logFile\"\n \telse\n echo \"$logStart Firefox update unsuccessful.\" >> \"$logFile\"\n exit\n \tfi\n\t # If Firefox is up to date already, log it and exit.\n else\n \techo \"$logStart Firefox is already up to date, running ${currentinstalledver}.\" >> \"$logFile\"\n fi\n exit\nelse\n echo \"$logStart Invalid Firefox configuration\" >> \"$logFile\"\nfi\n"},{"alpha_fraction":0.6802045702934265,"alphanum_fraction":0.6946449875831604,"avg_line_length":42.16883087158203,"blob_id":"cae3ce083bd6d268af26a8c4271348ae5e5540eb","content_id":"abcbb2b6556d9a6794fe6d62f987dfcdac76c0fc","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":3324,"license_type":"permissive","max_line_length":145,"num_lines":77,"path":"/adobeReader","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/sh\n# adobeReader (UNIX. Exec.)\n# Script to download and install Adobe Reader DC\n# modified from: https://www.jamf.com/jamf-nation/third-party-products/files/761/\n# adobe-reader-installer-updater\n# Last Edited: 10/30/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\nscriptEnabled=\"$(cat $configFile | awk '/AdobeReader/ {print $2}')\"\n\n### FUNCTIONS\nfunction check_enabled {\n\tif [ \"$scriptEnabled\" == \"Enable\" ] ; then\n\t\techo \"$logStart adobeReader is enabled, comparing versions\" >> \"$logFile\"\n\t\tupdate_reader\n\telse\n\t\techo \"$logStart adobeReader is not enabled\" >> \"$logFile\"\n\tfi\n}\n\nfunction update_reader {\n\t## Get OS version and adjust for use with the URL string\n\tOSvers_URL=$( sw_vers -productVersion | sed 's/[.]/_/g' )\n\t## Set the User Agent string for use with curl\n\tuserAgent=\"Mozilla/5.0 (Macintosh; Intel Mac OS X ${OSvers_URL}) AppleWebKit/535.6.2 (KHTML, like Gecko) Version/5.2 Safari/535.6.2\"\n\t# Get the latest version of Reader available from Adobe's About Reader page.\n\tlatestver=\"$()\"\n\twhile [ -z \"$latestver\" ]\n\tdo\n\t\tlatestver=\"$(curl -s -L -A \"$userAgent\" https://get.adobe.com/reader/ | grep \"<strong>Version\" | sed -e 's/<[^>][^>]*>//g' | awk '{print $2}')\"\n\tdone\n\techo \"$logStart Latest Version is: $latestver\" >> \"$logFile\"\n\tlatestvernorm=\"$(echo ${latestver} | sed -e 's/20//')\"\n\t# Get the version number of the currently-installed Adobe Reader, if any.\n\tif [ -e \"/Applications/Adobe Acrobat Reader DC.app\" ]; then\n\t\tcurrentinstalledver=\"$(defaults read /Applications/Adobe\\ Acrobat\\ Reader\\ DC.app/Contents/Info CFBundleShortVersionString)\"\n\t\techo \"$logStart Current installed version is: $currentinstalledver\" >> \"$logFile\"\n\t\tif [ ${latestvernorm} = ${currentinstalledver} ]; then\n\t\t\techo \"$logStart Adobe Reader DC is current. Exiting\" >> \"$logFile\"\n\t\t\texit\n\t\tfi\n\telse\n\t\tcurrentinstalledver=\"none\"\n\t\techo \"$logStart Adobe Reader DC is not installed\" >> \"$logFile\"\n\tfi\n\tARCurrVersNormalized=$( echo $latestver | sed -e 's/[.]//g' -e 's/20//' )\n\turl=\"\"\n\turl1=\"http://ardownload.adobe.com/pub/adobe/reader/mac/AcrobatDC/${ARCurrVersNormalized}/AcroRdrDC_${ARCurrVersNormalized}_MUI.dmg\"\n\turl2=\"\"\n\t#Build URL\n\turl=\"$(echo \"${url1}${url2}\")\"\n\t# Compare the two versions, if they are different or Adobe Reader is not present then download and install the new version.\n\tif [ \"${currentinstalledver}\" != \"${latestvernorm}\" ]; then\n\t\tcurl -s -o /tmp/reader.dmg ${url} >> \"$logFile\" 2>&1\n\t\thdiutil attach /tmp/reader.dmg -nobrowse -quiet >> \"$logFile\" 2>&1\n\t\tinstaller -pkg /Volumes/AcroRdrDC_${ARCurrVersNormalized}_MUI/AcroRdrDC_${ARCurrVersNormalized}_MUI.pkg -target / >> \"$logFile\" 2>&1\n\t\tsleep 10\n\t\thdiutil detach $(df | grep Adobe\\ Acrobat\\ Reader\\ DC\\ Installer | awk '{print $1}') -quiet >> \"$logFile\" 2>&1\n\t\tsleep 10\n\t\trm /tmp/reader.dmg >> \"$logFile\" 2>&1\n\t\t#double check to see if the new version got updated\n\t\tnewlyinstalledver=\"$(defaults read /Applications/Adobe\\ Acrobat\\ Reader\\ DC.app/Contents/Info CFBundleShortVersionString)\"\n \t\tif [ \"${latestvernorm}\" = \"${newlyinstalledver}\" ] ; then\n\t\t\techo \"$logStart Adobe Reader is updated/installed\" >> \"$logFile\"\n\t\telse\n \t\techo \"$logStart ERROR: Adobe Reader update unsuccessful.\" >> \"$logFile\"\n\t\t\texit\n\t\tfi\n\telse\n\t\techo \"$logStart Adobe Reader is already up to date.\" >> \"$logFile\"\n\tfi\n}\n\n### SCRIPT\ncheck_enabled\nexit\n"},{"alpha_fraction":0.6288492679595947,"alphanum_fraction":0.6361426115036011,"avg_line_length":29.850000381469727,"blob_id":"cdc20405c7fa1fded0cd15bc490ae9459304f963","content_id":"5090023562ea4756e43713f5f9f8832f04a60005","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1234,"license_type":"permissive","max_line_length":112,"num_lines":40,"path":"/checkChrome","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/usr/bin/python\n# checkChrome (Python 2.7)\n# Check if chrome is installed/up to date and install if not installed or out of date\n# Last Edited: 6/25/18\n\n### IMPORTS\nimport json\nimport urllib2\nimport os.path\nimport plistlib\n\n### VARIABLES\nurl = 'http://omahaproxy.appspot.com/all.json'\nosPath = \"/Applications/Google Chrome.app\"\n\n### SCRIPT\nif os.path.exists(osPath) is True:\n resp = urllib2.urlopen(url)\n data = json.loads(resp.read())\n for each in data:\n if each.get(\"os\") == \"mac\":\n versions = each.get(\"versions\")\n for version in versions:\n if version.get(\"channel\") == \"stable\":\n latest = (version.get(\"current_version\")) \n # plist variables for comparison - if Chrome is not in /Applications, the script will create the update file\n plistloc = \"/Applications/Google Chrome.app/Contents/Info.plist\"\n pl = plistlib.readPlist(plistloc)\n pver = pl[\"CFBundleShortVersionString\"]\n if latest == pver:\n exit\n else:\n file = open(\"/tmp/updateChrome.txt\",\"w\") \n file.write(\"Please update chrome\") \n exit\nelse:\n file = open(\"/tmp/updateChrome.txt\",\"w\") \n file.write(\"Please update chrome\") \n exit\nexit\n"},{"alpha_fraction":0.6465781331062317,"alphanum_fraction":0.6547497510910034,"avg_line_length":31.633333206176758,"blob_id":"d4802bb699339ddcc0b64911d4c7904d62e9ee76","content_id":"e5757e1ae0b12e184319564c5a2a410a517b50d9","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":979,"license_type":"permissive","max_line_length":88,"num_lines":30,"path":"/setTimezone","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# setTimezone (UNIX exec.)\n# set timezone\n# Last Edited: 6/25/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\n\nscriptEnabled=\"$(cat $configFile | awk '/SetTimezone/ {print $2}')\"\ntimeZone=\"$(cat $configFile | awk '/TimeZone/ {print $2}')\"\ncurrentTimeZone=\"$(systemsetup -gettimezone | awk '/Time Zone/ {print $(3) }')\"\n\n### SCRIPT\nif [ \"$scriptEnabled\" == \"Disable\" ] ; then\n echo \"$logStart setTimezone disabled\" >> \"$logFile\"\n exit\nelif [ \"$scriptEnabled\" == \"Enable\" ] ; then\n if [ \"$currentTimeZone\" == \"$timeZone\" ] ; then\n echo \"$logStart System timezone already set to $timeZone, exiting\" >> \"$logFile\"\n exit\n else\n \techo \"Setting timezone to $timeZone\" >> \"$logFile\"\n \tsystemsetup -settimezone \"$timeZone\" >> \"$logFile\"\n \tkillall SystemUIServer\n \techo \"$logStart Timezone set to $timeZone, exiting\" >> \"$logFile\"\n fi\nelse\n echo \"$logStart Invalid configuration for setTimezone\" >> \"$logFile\"\nfi\nexit\n"},{"alpha_fraction":0.6451191902160645,"alphanum_fraction":0.650669276714325,"avg_line_length":37.75949478149414,"blob_id":"748390d50caba508055fc98b7f7397a389dec2f3","content_id":"ead56d028d203823d15e8df49e3822dac223e6e3","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":3063,"license_type":"permissive","max_line_length":82,"num_lines":79,"path":"/powerOptions","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# powerOptions (UNIX exec.)\n# set scheduled reboots and other power settings on macOS\n# Last Edited: 6/25/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\n\nscriptEnabled=\"$(cat $configFile | awk '/PowerOptions/ {print $2}')\"\n\n### SCRIPT\nif [ \"$scriptEnabled\" == \"Disable\" ] ; then\n echo \"$logStart powerOptions disabled\" >> \"$logFile\"\n exit\nelif [ \"$scriptEnabled\" == \"Enable\" ] ; then\n # display sleep time\n displaySleepTime=\"$(cat $configFile | awk '/DisplaySleepTime/ {print $2}')\"\n if [ \"$displaySleepTime\" != \"\" ] ; then\n\techo \"$logStart Setting display sleep to $displaySleepTime\" >> \"$logFile\"\n\tpmset -a displaysleep \"$displaySleepTime\"\n else\n\techo \"$logStart No display sleep time specified\" >> \"$logFile\"\n fi\n # disk sleep time\n diskSleepTime=\"$(cat $configFile | awk '/DiskSleepTime/ {print $2}')\"\n if [ \"$diskSleepTime\" != \"\" ] ; then\n\techo \"$logStart Setting disk sleep to $diskSleepTime\" >> \"$logFile\"\n\tpmset -a disksleep \"$diskSleepTime\"\n else\n\techo \"$logStart No disk sleep time specified\" >> \"$logFile\"\n fi\n # system sleep time\n sysSleepTime=\"$(cat $configFile | awk '/SystemSleepTime/ {print $2}')\"\n if [ \"$sysSleepTime\" != \"\" ] ; then\n\techo \"$logStart Setting disk sleep to $sysSleepTime\" >> \"$logFile\"\n\tpmset -a disksleep \"$sysSleepTime\"\n else\n\techo \"$logStart No system sleep time specified\" >> \"$logFile\"\n fi\n # scheduled reboots, shutdown, sleep, and wake\n # reboot setup --\n rebootDays=\"$(cat $configFile | awk '/RebootDays/ {print $2}')\"\n rebootTime=\"$(cat $configFile | awk '/RebootTime/ {print $2}')\"\n if [ \"$rebootDays\" != \"\" ] ; then\n\techo \"$logStart Setting restart to $rebootDays at $rebootTime\" >> \"$logFile\"\n\tpmset repeat restart \"$rebootDays\" \"$rebootTime\"\n else\n\techo \"$logStart No reboot options specified\" >> \"$logFile\"\n fi\n # shutdown setup --\n shutdownDays=\"$(cat $configFile | awk '/ShutdownDays/ {print $2}')\"\n shutdownTime=\"$(cat $configFile | awk '/ShutdownTime/ {print $2}')\"\n if [ \"$shutdownDays\" != \"\" ] ; then\n\techo \"$logStart Setting shutdown to $shutdownDays at $shutdownTime\" >> \"$logFile\"\n\tpmset repeat shutdown \"$shutdownDays\" \"$shutdownTime\"\n else\n\techo \"$logStart No shutdown options specified\" >> \"$logFile\"\n fi\n # sleep options --\n sleepDays=\"$(cat $configFile | awk '/SleepDays/ {print $2}')\"\n sleepTime=\"$(cat $configFile | awk '/SleepTime/ {print $2}')\"\n if [ \"$sleepDays\" != \"\" ] ; then\n\techo \"$logStart Setting wake to $wakeDays at $wakeTime\" >> \"$logFile\"\n\tpmset repeat sleep \"$sleepDays\" \"$sleepTime\"\n else\n\techo \"$logStart No sleep options specified\" >> \"$logFile\"\n fi\n # wake options --\n wakeDays=\"$(cat $configFile | awk '/WakeDays/ {print $2}')\"\n wakeTime=\"$(cat $configFile | awk '/WakeTime/ {print $2}')\"\n if [ \"$wakeDays\" != \"\" ] ; then\n\tpmset repeat wakeorpoweron \"$wakeDays\" \"$wakeTime\"\n else\n\techo \"$logStart No wake options specified\" >> \"$logFile\"\n fi\nelse\n echo \"$logStart Invalid powerOptions configuration\" >> \"$logFile\"\nfi\nexit\n\n"},{"alpha_fraction":0.674606204032898,"alphanum_fraction":0.6780637502670288,"avg_line_length":45.48214340209961,"blob_id":"9a1a387349060f689ff01e28add3ce6d4d222141","content_id":"7de0c609b35fb74f654220719fe56ead06282827","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":2603,"license_type":"permissive","max_line_length":128,"num_lines":56,"path":"/chrome","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# chrome (UNIX exec.)\n# update/install Google Chrome\n# Last Edited: 9/18/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\n\ndmgfile=\"googlechrome.dmg\"\nvolname=\"Google Chrome\"\nurl=\"https://dl.google.com/chrome/mac/stable/GGRO/googlechrome.dmg\"\nscriptEnabled=\"$(cat $configFile | awk '/UpdateChrome/ {print $2}')\"\ndeleteIfInstalled=\"$(cat $configFile | awk '/DeleteIfInstalled/ {print $2}')\"\nincognitoMode=\"$(cat $configFile | awk '/IncognitoMode/ {print $2}')\"\n\n### SCRIPT\n# check if the script is enabled in the config file\nif [ \"$scriptEnabled\" == \"Disable\" ] ; then\n if [ \"$deleteIfInstalled\" == \"Yes\" ] ; then\n\t\techo \"$logStart Removing Google Chrome.app\" >> \"$logFile\"\n\t\trm -rf /Applications/Googe\\ Chrome.app\n elif [ \"$deleteIfInstalled\" == \"No\" ] ; then\n\t\techo \"$logStart Chrome script disabled but Google Chrome allowed on this system\" >> \"$logFile\"\n else\n\t\techo \"$logStart Invalid configuration for DeleteIfInstalled key in the chrome section of the configuration file\" >> \"$logFile\"\n fi\n exit\nelif [ \"$scriptEnabled\" == \"Enable\" ] ; then\n # check if the .txt file created by checkChrome.py exists and if so, update chrome\n if [ -f \"/tmp/updateChrome.txt\" ] ; then\n \techo \"$logStart Installing/updating Google Chrome\" >> \"$logFile\"\n \tcurl -s -o /tmp/$dmgfile $url\n \thdiutil attach /tmp/$dmgfile -nobrowse -quiet\n \tditto -rsrc \"/Volumes/$volname/Google Chrome.app\" \"/Applications/Google Chrome.app\"\n \thdiutil detach $(/bin/df | /usr/bin/grep \"$volname\" | awk '{print $1}') -quiet\n \trm /tmp/\"$dmgfile\"\n # check if incognito mode is enabled or disabled\n if [ \"$incognitoMode\" == \"Enabled\" ] ; then\n \t echo \"$logStart Incognito mode is allowed on this system\" >> \"$logFile\"\n \t rm /Library/Preferences/com.google.Chrome.plist\n elif [ \"$incognitoMode\" == \"Disable\" ] ; then\n \t echo \"$logStart Moving com.google.Chrome.plist (disable incognito mode) into /Library/Preferences\" >> \"$logFile\"\n \t cp /usr/local/massmgmt/conf/com.google.Chrome.plist /Library/Preferences\n \t echo \"$logStart Google Chrome installed/updated and incognito mode disabled, exiting\" >> \"$logFile\"\n else\n \t echo \"$logStart Unknown Incognito Mode configuration option\" >> \"$logFile\"\n fi\n # close out log and clean out /tmp/updateChrome.txt file to avoid triggering the script again after updating\n rm /tmp/updateChrome.txt\n else\n echo \"$logStart Chrome already up to date\" >> \"$logFile\"\n fi\nelse\n echo \"$logStart Invalid Chrome configuration\" >> \"$logFile\"\nfi\nexit\n"},{"alpha_fraction":0.7799597382545471,"alphanum_fraction":0.7822256088256836,"avg_line_length":46.85542297363281,"blob_id":"c6e060ffdfc999f5a848d60926c561ae828e645f","content_id":"0a3b35ff7af9477302143877bb451526c2ee19f7","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":3976,"license_type":"permissive","max_line_length":113,"num_lines":83,"path":"/README.md","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"# massmgmt\nVersion 2.1.4 \nLast Edited: 06/07/21 \n\n# Purpose\nThe massmgmt suite came about as an attempt to automate configuration and patch management\non macOS laptops stored in shared-use carts and desktops in computer labs without requiring a \ncentral server or system to manage endpoints. Massmgmt can be used in combination with Munki\nto extend your management capabilities. The main advantage of massmgmt over other systems is\nthat it is extremely lightweight and if the desired configuration of endpoints does not\nfrequently change, endpoints can maintain their configuration without intervention, which \nallows technicians to more efficiently spend their time.\n\n# Description\nThe massmgmt suite uses a series of bash and python scripts to maintain a desired \nstate configuration on macOS endpoints. Massmgmt is configured to run on startup\nat a defined interval using a launch daemon (org.massmgmt.plist). Because the \nsuite is built on multiple scripts, it is modular and easy for system administrators to \nmaintain. The storage directory for the suite of scripts is /usr/local/massmgmt/bin.\nIn addition to these scripts there are plans for endpoint log analysis and security\nmodules.\n\nThe first step of each script checks if the setting is enabled and if the \nconfiguration matches the desired state defined in the configuration file\n/usr/local/massmgmt/massmgmt.config. Editing the configuration file and\nredistributing it to endpoints is the most simple means of altering the desired\nstate of managed machines.\n\nIf the desired state already exists no action must be taken and the script will \nexit and the next script will run. If the setting does not match the desired \nstate, the script will correct the discrepancy. Typically, any files that are \ngenerated by massmgmt will be placed in /tmp unless they are meant to to be used \nlater. Persistant and installable plist files will be stored within \n/usr/local/massmgmt/conf.\n\nThe massmgmt tool has logging built into the scripts that are included. The\nlog file is located by default at /usr/local/massmgmt/massmgmt.log. The log may \nalso be flushed by entering the command:\n\n<code>massmgmt flushLog</code>\n\nLogs may also be retrieved to the desktop directory of the current user with:\n\nmassmgmt pullLog</code>\n\nMassmgmt can also be called so that admins can interactively call individual scripts\nby entering the command:\n\n<code>massmgmt -im</code>\n\nThis command enters “interactive mode” and is logged as such for auditing interactions.\nMassmgmt may also be run in its entirety by an admin or root user by entering:\n\n<code>massmgmt</code>\n\nMassmgmt also supports installing and removing configuration profiles. Profiles should\nbe stored in /usr/local/massmgmt/mconfig so they could be installed whenever the desired\nstate is not in place. The administrator can interactively install profiles using the\nfollowing command:\n\n<code>massmgmt installProfiles</code>\n\nFor especially restrictive profiles, a profile removal command is available during\ntroubleshooting:\n\n<code>massmgmt rmProfiles</code>\n\n# Requirements\n\nAll that is required on the client devices for massmgmt before installation is a local account \nwith administrator privileges to run massmgmt scripts. No web servers are \nnecessary. For maximum flexibility, massmgmt can be paired with Munki so that configurations\ncan be changed at will or massmgmt can be updated.\n\n# Installation Directions\n\nThe easiest way to customize massmgmt for an environment is to download the zip file\nfrom the project page, edit the configuration file with a text editor, and package it\nwith Packages.app from Whitebox or PackageMaker.app from Apple using installmassmgmt.sh\nas a post-install script. The installmassmgmt.sh script relies on using /tmp/massmgmt\nas the target directory in the package.\n\nHappy Configuring!\n"},{"alpha_fraction":0.6810513734817505,"alphanum_fraction":0.6888976097106934,"avg_line_length":38.21538543701172,"blob_id":"bbf8d3041a4e581fd9cd0cecbc90e7bf5bab57d7","content_id":"d37483d99b7317beca80ca149033864ab978b70b","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":5098,"license_type":"permissive","max_line_length":104,"num_lines":130,"path":"/setFirewall","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# setFirewall (UNIX Exec.)\n# Control the ALF firewall\n# Last Edited: 11/15/18\n\n# VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\nscriptEnabled=\"$(cat $configFile | awk '/SetFirewall/ {print $2}')\"\n\n### FUNCTIONS\nfunction check_scriptEnabled {\n\tif [ \"$check_scriptEnabled\" == \"Disable\" ] ; then\n\t\techo \"$logStart setFirewall disabled\" >> \"$logFile\"\n\t\texit\n\telse\n\t\techo \"$logStart setFirewall is enabled\" >> \"$logFile\"\n\tfi\n}\n\nfunction check_on {\n\tglobalState=\"$(sudo defaults read /Library/Preferences/com.apple.alf.plist globalstate)\"\n\tif [ \"$globalState\" == \"1\" ] ; then\n\t\techo \"$logStart Firewall is already on\" >> \"$logFile\"\n\t\techo \"$logStart Turning off ALF to make changes\" >> \"$logFile\"\n\t\t/usr/libexec/ApplicationFirewall/socketfilterfw --setglobalstate off >> \"$logFile\" 2>&1\n\telse\n\t\techo \"$logStart Firewall is off\" >> \"$logFile\"\n\tfi\n}\n\nfunction check_logging {\n\tloggingSet=\"$(defaults read /Library/Preferences/com.apple.alf.plist loggingenabled)\"\n\tchkConfigLogging=\"$(cat $configFile | awk '/FirewallLogging/ {print $2}')\"\n\tchkConfigLevel=\"$(cat $configFile | awk '/LoggingLevel/ {print $2}')\"\n\tif [ \"$loggingSet\" == \"1\" ] ; then\n\t\techo \"$logStart Logging is already ON\" >> \"$logFile\"\n\t\t/usr/libexec/ApplicationFirewall/socketfilterfw --setloggingopt $chkConfigLevel >> \"$logFile\" 2>&1\n\telse\n\t\techo \"Logging is set to OFF\" >> \"$logFile\"\n\t\tif [ \"$chkConfigLogging\" == \"Enable\" ] ; then\n\t\t\techo \"$logStart Firewall logging is enabled in the config file, enabling\" >> \"$logFile\"\n\t\t\t/usr/libexec/ApplicationFirewall/socketfilterfw --setloggingmode on\n\t\t\techo \"$logStart Setting logging level to $chkConfigLevel\" >> \"$logFile\"\n\t\t\t/usr/libexec/ApplicationFirewall/socketfilterfw --setloggingopt $chkConfigLevel >> \"$logFile\" 2>&1\n\t\telse\n\t\t\techo \"$logStart Firewall logging is disabled in the config file\" >> \"$logFile\"\n\t\tfi\n\tfi\n}\n\nfunction check_signed {\n\tallowSigned1=\"$(defaults read /Library/Preferences/com.apple.alf.plist allowdownloadsignedenabled)\"\n\tallowSigned2=\"$(defaults read /Library/Preferences/com.apple.alf.plist allowsignedenabled)\"\n\tconfigAllow=\"$(cat $configFile | awk '/AllowSignedApps/ {print $2}')\"\n\tif [ \"$allowSigned1\" == \"1\" ] ; then\n\t\techo \"$logStart Allow download signed enabled\" >> \"$logFile\"\n\t\tif [ \"$configAllow\" == \"Enable\" ] ; then\n\t \t\techo \"$logStart Allow download signed enabled in config file\" >> \"$logFile\"\n\t \telse\n\t \techo \"$logStart Allow download signed in config file, disabling\" >> \"$logFile\"\n\t \t/usr/libexec/ApplicationFirewall/socketfilterfw --setallowsigned off >> \"$logFile\" 2>&1\n\t \tfi\n\telse\n\t\techo \"$logStart Allow download signed disabled\" >> \"$logFile\"\n\t\tif [ \"$configAllow\" == \"Enable\" ] ; then\n\t \t\techo \"$logStart Allow download signed enabled in config file, enabling\" >> \"$logFile\"\n\t \t\t/usr/libexec/ApplicationFirewall/socketfilterfw --setallowsigned on >> \"$logFile\" 2>&1\n\t \telse\n\t \techo \"$logStart Allow download signed disabled in config file\" >> \"$logFile\"\n\t \tfi\n\tfi\n\t#####\n\tif [ \"$allowSigned2\" == \"1\" ] ; then\n\t\techo \"$logStart Allow signed enabled\" >> \"$logFile\"\n\t\tif [ \"$configAllow\" == \"Enable\" ] ; then\n\t \t\techo \"$logStart Allow signed enabled in config file\" >> \"$logFile\"\n\t \telse\n\t \techo \"$logStart Allow signed disabled in config file, disabling\" >> \"$logFile\"\n\t \t/usr/libexec/ApplicationFirewall/socketfilterfw --setallowsigned off >> \"$logFile\" 2>&1\n\t \tfi\n\telse\n\t\techo \"$logStart Allow signed disabled\" >> \"$logFile\"\n\t\tif [ \"$configAllow\" == \"Enable\" ] ; then\n\t \t\techo \"$logStart Allow signed enabled in config file, enabling\" >> \"$logFile\"\n\t \t\t/usr/libexec/ApplicationFirewall/socketfilterfw --setallowsigned on >> \"$logFile\" 2>&1\n\t \telse\n\t \techo \"$logStart Allow download signed disabled in config file\" >> \"$logFile\"\n\t \tfi\n\tfi\n}\n\nfunction check_stealth {\n\tstealthModeState=\"$(defaults read /Library/Preferences/com.apple.alf.plist stealthenabled)\"\n\tconfigAllow=\"$(cat $configFile | awk '/StealthMode/ {print $2}')\"\n\tif [ \"$stealthModeState\" == \"1\" ] ; then\n\t\techo \"$logStart Stealth Mode enabled\" >> \"$logFile\"\n\t\tif [ \"$configAllow\" == \"Enable\" ] ; then\n\t\t\techo \"$logStart Stealth Mode enabled in config file\" >> \"$logFile\"\n\t\telse\n\t\t\techo \"$logStart Stealth Mode disabled in config file\" >> \"$logFile\"\n\t\t\tdefaults write /Library/Preferences/com.apple.alf.plist stealthenabled -bool False >> \"$logFile\" 2>&1\n\t\tfi\n\telse\n\t\techo \"$logStart Stealth Mode disabled\" >> \"$logFile\"\n\t\tif [ \"$configAllow\" == \"Enable\" ] ; then\n\t\t\techo \"$logStart Stealth Mode enabled in config file, enabling\" >> \"$logFile\"\n\t\t\tdefaults write /Library/Preferences/com.apple.alf.plist stealthenabled -bool True >> \"$logFile\" 2>&1\n\t\telse\n\t\t\techo \"$logStart Stealth Mode disabled in config file\" >> \"$logFile\"\n\t\tfi\n\tfi\n}\n\nfunction turn_on {\n\tif [ \"$check_scriptEnabled\" == \"Disable\" ] ; then\n\t\texit\n\telse\n\t\techo \"$logStart Turning firewall back on\" >> \"$logFile\"\n\t\t/usr/libexec/ApplicationFirewall/socketfilterfw --setglobalstate on >> \"$logFile\" 2>&1\n\tfi\n}\n\n### SCRIPT\ncheck_scriptEnabled\ncheck_on\ncheck_logging\ncheck_signed\ncheck_stealth\nturn_on\nexit\n"},{"alpha_fraction":0.7161458134651184,"alphanum_fraction":0.7256944179534912,"avg_line_length":40.14285659790039,"blob_id":"adb60ba2c514240bdde5a6d123ccaca019a51064","content_id":"d4f38b82a1d1fd50a1b26a0efec6c374954398a9","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1152,"license_type":"permissive","max_line_length":112,"num_lines":28,"path":"/sus","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# sus (Unix Exec.)\n# set or not set the software update server\n# Last Edited: 6/25/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\n\nscriptEnabled=\"$(cat $configFile | awk '/SUS/ {print $2}')\"\ncatalogURL=\"$(cat $configFile | awk '/ServerAddress/ {print $2}')\"\n\n### SCRIPT\nif [ \"$scriptEnabled\" == \"Enable\" ] ; then\n\t# set to sus determined in config file\n\tchkConfiguredSUS=\"$(defaults read /Library/Preferences/com.apple.SoftwareUpdate.plist CatalogURL)\"\n\tif [ \"$chkConfiguredSUS\" == \"$catalogURL\" ] ; then\n\t\techo \"$logStart Software Update Server already configured to $catalogURL\" >> \"$logFile\"\n\telse\n\t\techo \"$logStart Setting Software Update Server to $catalogURL\" >> \"$logFile\"\n\t\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate.plist CatalogURL \"$catalogURL\" >> \"$logFile\" 2>&1\n\tfi\nelif [ \"$scriptEnabled\" == \"Disable\" ] ; then\n\techo \"$logStart Software Update Server configuration is disabled in the configuration file\" >> \"$logFile\"\n\tdefaults delete /Library/Preferences/com.apple.SoftwareUpdate.plist CatalogURL >> \"$logFile\" 2>&1\nelse\n\techo \"$logStart SUS configuration is invalid\" >> \"$logFile\"\nfi\nexit\n"},{"alpha_fraction":0.6620553135871887,"alphanum_fraction":0.6798418760299683,"avg_line_length":24.299999237060547,"blob_id":"44febe0d3bb8d9a5293bbfeba41a7b8bf7f90525","content_id":"e34e023992df5898d7f4ab9ae0c1dea11c382e31","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":506,"license_type":"permissive","max_line_length":81,"num_lines":20,"path":"/massmgmtlib.sh","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# massmgmtlib.sh\n# bash library for massmgmt\n# Last Edited: 7/2/18\n\n### VARIABLES\n# files\nconfigFile=\"/usr/local/massmgmt/massmgmt.config\"\nlogFile=\"$(cat $configFile | awk '/LogFile/ {print $2}')\"\n\n# user information\nadminUserID=\"$(cat $configFile | awk '/AdminUserID/ {print $2}')\"\nadminUser=\"$(dscl . list /Users UniqueID | grep $adminUserID | awk '{print $1}')\"\n\n# system information\nstarDate=\"$(date +20%y-%m-%d_%H:%M:%S)\"\nhostName=\"$(hostname)\"\n\n# log entry\nlogStart=\"$starDate $hostName\"\n"},{"alpha_fraction":0.7381370663642883,"alphanum_fraction":0.764938473701477,"avg_line_length":21.097087860107422,"blob_id":"47b5f9f928424d86844d212863e83ea67e6ac35d","content_id":"7c4b93be8a203dc4b38a5c367aa4bcabab9e9d05","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":2276,"license_type":"permissive","max_line_length":71,"num_lines":103,"path":"/installmassmgmt.sh","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# installmassmgmt.sh\n# install the components of the massmgmt suite in the correct locations\n# use as a postinstall script when packaging in .pkg format\n# Last Edited: 11/13/18\n\nuserName=\"$(whoami)\"\n\n# check if we have the appropriate privileges\nif [ \"$userName\" != \"root\" ] ; then\n\techo \"$0 must be run using sudo\"\n\texit\nfi\n\n# remove previous installation\nif [ -e \"/usr/local/massmgmt\" ] ; then\n rm -rf /usr/local/massmgmt\n rm /usr/local/bin/massmgmt\n rm /Library/LaunchDaemons/org.massmgmt.plist\nfi\n\n# set up directories\nmkdir /usr/local/massmgmt\nmkdir /usr/local/massmgmt/bin\nmkdir /usr/local/massmgmt/conf\nmkdir /usr/local/massmgmt/mconfig\nmkdir /usr/local/massmgmt/lib\n\n# move components into place\ncd /tmp/massmgmt\n\ncp massmgmt /usr/local/massmgmt/\n\ncopyLoc=\"/usr/local/massmgmt/bin\"\n\ncp autoUpdate $copyLoc\ncp adobeReader $copyLoc\ncp checkChrome $copyLoc\ncp chrome $copyLoc\ncp firefox $copyLoc\ncp guestAccount $copyLoc\ncp enableSSH $copyLoc\ncp enableARD $copyLoc\ncp lockSysPrefs $copyLoc\ncp munki $copyLoc\ncp powerOptions $copyLoc\ncp setFirewall $copyLoc\ncp setHostname $copyLoc\ncp setTimezone $copyLoc\ncp sus $copyLoc\n\ncp com.google.Chrome.plist /usr/local/massmgmt/conf\ncp massmgmt.config /usr/local/massmgmt\n\ncp massmgmtlib.sh /usr/local/massmgmt/lib\n\ncp org.massmgmt.plist /Library/LaunchDaemons\n\nif [ -d /usr/local/bin ] ; then\n\techo \"/usr/local/bin exists!\"\nelse\n\tmkdir /usr/local/bin\nfi\n\nln -s '/usr/local/massmgmt/massmgmt' '/usr/local/bin/'\n\n# chown\n\nconfigFile=\"/usr/local/massmgmt/massmgmt.config\"\nadminUser=\"$(cat $configFile | awk '/AdminUserID/ {print $2}')\"\n\n# touch the massmgmt.log file so we can chown it\ntouch /usr/local/massmgmt/massmgmt.log\n\n# chown the entire directory\nchown -R $adminUser /usr/local/massmgmt\n\n# chmod\nchmod 755 /usr/local/massmgmt/massmgmt\ncd /usr/local/massmgmt/bin\nchmod 755 autoUpdate\nchmod 755 adobeReader\nchmod 755 chrome\nchmod 755 firefox\nchmod 755 guestAccount\nchmod 755 enableSSH\nchmod 755 enableARD\nchmod 755 lockSysPrefs\nchmod 755 munki\nchmod 755 powerOptions\nchmod 755 setFirewall\nchmod 755 setHostname\nchmod 755 setTimezone\nchmod 755 sus\nchmod 755 checkChrome\n\nchmod 755 /usr/local/massmgmt/lib/massmgmtlib.sh\n\nif [ -e \"/usr/local/massmgmt/massmgmt.config\" ] ; then\n exit 0\nelse\n exit 1\nfi\n"},{"alpha_fraction":0.7086931467056274,"alphanum_fraction":0.7202072739601135,"avg_line_length":37.599998474121094,"blob_id":"4aba57e62e9230bc80cd17222176d4167cfc3c7e","content_id":"859283a4174c1d5f69d30dc8d8da4b2e95cb776a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1737,"license_type":"permissive","max_line_length":106,"num_lines":45,"path":"/lockSysPrefs","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# lockSysPrefs (UNIX. Exec.)\n# Script to lock system-wide preferences from non-admin users\n# https://derflounder.wordpress.com/2014/02/16/managing-the-authorization-database-in-os-x-mavericks/\n# Last Edited: 6/25/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\n\nscriptEnabled=\"$(cat $configFile | awk '/LockSystemPreferences/ {print $2}')\"\n\n### FUNCTIONS\nfunction check_enabled {\n\t if [ \"$scriptEnabled\" == \"Enable\" ] ; then\n\t\t echo \"$logStart lockSysPrefs is enabled, locking\" >> \"$logFile\"\n\t\t lock_prefs\n\t elif [ \"$scriptEnabled\" == \"Disable\" ] ; then\n\t\t echo \"$logStart lockSysPrefs is disabled, unlocking\" >> \"$logFile\"\n\t\t unlock_prefs\n\t else\n\t\t echo \"$logStart lockSysPrefs has an invalid configuration in the config file, exiting\" >> \"$logFile\"\n\t\t exit\n\t fi\n}\n\nfunction lock_prefs {\n\t echo \"$logStart Locking system-wide preferences from non-admin users\" >> \"$logFile\"\n\t security authorizationdb read system.preferences > /tmp/system.preferences.plist 2>> \"$logFile\"\n\t /usr/libexec/PlistBuddy -c 'Set :shared false' /tmp/system.preferences.plist 2>> \"$logFile\"\n \t security authorizationdb write system.preferences < /tmp/system.preferences.plist 2>> \"$logFile\"\n\t rm /tmp/system.preferences.plist\n}\n\nfunction unlock_prefs {\n\t echo \"$logStart Unlocking system-wide preferences from non-admin users\" >> \"$logFile\"\n\t security authorizationdb read system.preferences > /tmp/system.preferences.plist 2>> \"$logFile\"\n\t /usr/libexec/PlistBuddy -c 'Set :shared true' /tmp/system.preferences.plist 2>> \"$logFile\"\n\t security authorizationdb write system.preferences < /tmp/system.preferences.plist 2>> \"$logFile\"\n\t rm /tmp/system.preferences.plist\n}\n\n\n### SCRIPT\ncheck_enabled\nexit\n"},{"alpha_fraction":0.7339877486228943,"alphanum_fraction":0.7384049296379089,"avg_line_length":38.95098114013672,"blob_id":"183f54ff407b052c09be625e76461143e0687800","content_id":"5ae963337b7d79b1e429aa41314235e9095a78e7","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":4075,"license_type":"permissive","max_line_length":140,"num_lines":102,"path":"/guestAccount","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# guestAccount\n# disable the macOS guest account\n# based on hxxps://github[.]com/sheagcraig/guestAccount/blob/master/guest_account by Shea Craig\n# Last Edited: 6/26/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\nguest=\"$(cat $configFile | awk '/GuestAccount/ {print $2}')\"\nenabled=\"$(defaults read /Library/Preferences/com.apple.loginwindow GuestEnabled)\"\nguestPassword=\"$(cat $configFile | awk '/GuestPassword/ {print $2}')\"\nfileServerAccess=\"$(cat $configFile | awk '/GuestFileServerAccess/ {print $2}')\"\n\n### FUNCTIONS\nfunction server_access {\n\t# allow or deny file server access\n\tif [ \"$fileServerAccess\" == \"Allow\" ] ; then\n\t\tdefaults write /Library/Preferences/com.apple.AppleFileServer guestAccess -bool YES\n\t\tdefaults write /Library/Preferences/SystemConfiguration/com.apple.smb.server AllowGuestAccess -bool YES\n\telif [ \"$fileServerAccess\" == \"Deny\" ] ; then\n\t\tdefaults write /Library/Preferences/com.apple.AppleFileServer guestAccess -bool NO\n\t\tdefaults write /Library/Preferences/SystemConfiguration/com.apple.smb.server AllowGuestAccess -bool NO\n\telse\n\t\techo \"$logStart Guest File Server Access Not Specified\" >> \"$logFile\"\n\tfi\n}\n\nfunction disable_guest {\n\tif [ -e \"/Users/Guest\" ] ; then\n\t\techo \"$logStart Deleting and Disabling the Guest User Account\" >> \"$logFile\"\n\t\tdscl . -delete /Users/Guest\n\t\t# write to the login window plist to disable Guest\n\t\tdefaults write /Library/Preferences/com.apple.loginwindow GuestEnabled -bool NO\n\t\techo \"$logStart Guest User Account Disabled\" >> \"$logFile\"\n\tfi\n}\n\nfunction enable_guest {\n\techo \"$logStart Creating and Enabling the Guest User Account\"\n\tdscl . -create /Users/Guest\n\tdefaults write /Library/Preferences/com.apple.loginwindow GuestEnabled -bool YES\n\t\n\tdscl . -create /Users/Guest dsAttrTypeNative:_defaultLanguage en\n\tdscl . -create /Users/Guest dsAttrTypeNative:_guest true\n\tdscl . -create /Users/Guest dsAttrTypeNative:_writers__defaultLanguage Guest\n\tdscl . -create /Users/Guest dsAttrTypeNative:_writers__LinkedIdentity Guest\n\tdscl . -create /Users/Guest dsAttrTypeNative:_writers__UserCertificate Guest\n\tdscl . -create /Users/Guest AuthenticationHint ''\n\tdscl . -create /Users/Guest NFSHomeDirectory /Users/Guest\n\t# sometimes it takes a second for everything to get settled\n\tsleep 2\n\tdscl . -passwd /Users/Guest \"$guestPassword\"\n\tsleep 2\n\t\n\tdscl . -create /Users/Guest Picture \"/System/Library/CoreServices/CoreTypes.bundle/Contents/Resources/UserIcon.icns\"\n\tdscl . -create /Users/Guest PrimaryGroupID 201\n\tdscl . -create /Users/Guest RealName \"Guest User\"\n\tdscl . -create /Users/Guest RecordName Guest\n\tdscl . -create /Users/Guest UniqueID 201\n\tdscl . -create /Users/Guest UserShell /bin/bash\n\t#security add-generic-password -a Guest -s com.apple.loginwindow.guest-account -D \"application password\" /Library/Keychains/System.keychain\n\t\n\techo \"$logStart Guest User Account Enabled\"\n}\n\n### SCRIPT\necho \"$logStart Checking current status of the Guest User Account\" >> \"$logFile\"\n\nif [ \"$enabled\" == \"1\" ] ; then\n\t# guest account is enabled on the host\n\techo \"$logStart Guest User Account is currently enabled\" >> \"$logFile\"\n\tif [ \"$guest\" == \"Enable\" ] ; then\n\t\techo \"$logStart Guest Account is already enabled\" >> \"$logFile\"\n\t\texit\n\telif [ \"$guest\" == \"Disable\" ] ; then\n\t\techo \"$logStart Guest Account is enabled but should be disabled\" >> \"$logFile\"\n\t\tdisable_guest\n\t\tserver_access\n\t\texit\n\telse\n\t\techo \"$logFile Invalid GuestAccount configuration\" >> \"$logFile\"\n\t\texit\n\tfi\nelif [ \"$enabled\" == \"0\" ] ; then\n\t# guest account is disabled on the host\n\techo \"$logStart Guest User Account is currently disabled\" >> \"$logFile\"\n\tif [ \"$guest\" == \"Enable\" ] ; then\n\t\techo \"$logStart Guest Account is currently disabled but should be enabled\" >> \"$logFile\"\n\t\tenable_guest\n\t\tserver_access\n\t\texit\n\telif [ \"$guest\" == \"Disable\" ] ; then\n\t\techo \"$logStart Guest Account is already disabled\" >> \"$logFile\"\n\t\texit\n\telse\n\t\techo \"$logFile Invalid GuestAccount configuration\" >> \"$logFile\"\n\t\texit\n\tfi\nelse\n\techo \"$logStart Could not determine the status of the Guest User Account\" >> \"$logFile\"\n\texit\nfi\n"},{"alpha_fraction":0.6900826692581177,"alphanum_fraction":0.6983470916748047,"avg_line_length":36.230770111083984,"blob_id":"2bae57fdd4f8b861b8449a14c589da39a96e93d0","content_id":"1d53b5271503a99c763f9846392406f27c7d6778","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":968,"license_type":"permissive","max_line_length":87,"num_lines":26,"path":"/munki","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# munki (UNIX exec.)\n# configure munki (if desired)\n# Last Edited: 6/16/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\n\nscriptEnabled=\"$(cat $configFile | awk '/Munki/ {print $2}')\"\nmunkiURL=\"$(cat $configFile | awk '/MunkiURL/ {print $2}')\"\nmunkiClient=\"$(cat $configFile | awk '/MunkiClient/ {print $2}')\"\n\n### SCRIPT\nif [ \"$scriptEnabled\" == \"Disable\" ] ; then\n echo \"$logStart Munki configuration is disabled\" >> \"$logFile\"\n exit\nelif [ \"$scriptEnabled\" == \"Enable\" ] ; then\n echo \"$logStart Configuring Munki URL: $munkiURL\" >> \"$logFile\"\n defaults write /Library/Preferences/ManagedInstalls SoftwareRepoURL \"$munkiURL\"\n echo \"$logStart Configuring Munki Client: $munkiClient\" >> \"$logFile\"\n defaults write /Library/Preferences/ManagedInstalls ClientIdentifier \"$munkiClient\"\n echo \"$logStart Munki configuration complete\" >> \"$logFile\"\nelse\n echo \"$logStart Munki configuration invalid\" >> \"$logFile\"\nfi\nexit\n"},{"alpha_fraction":0.6513761281967163,"alphanum_fraction":0.657214343547821,"avg_line_length":34.264705657958984,"blob_id":"1f93b894a37237dab555abe56f8d5a60cec18b16","content_id":"d88e125ec04c5d2b5753a228281a1bb327646151","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1199,"license_type":"permissive","max_line_length":115,"num_lines":34,"path":"/setHostname","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# setName (UNIX exec.)\n# set Hostname\n# Last Edited: 6/25/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\n\nscriptEnabled=\"$(cat $configFile | awk '/SetHostname/ {print $2}')\"\nhostnameScheme=\"$(cat $configFile | awk '/HostnameFormat/ {print $2}')\"\n\n### SCRIPT\nif [ \"$scriptEnabled\" == \"Disable\" ] ; then\n echo \"$logStart setHostname disabled\" >> \"$logFile\"\n exit\nelif [ \"$scriptEnabled\" == \"Enable\" ] ; then\n echo \"$logStart setHostname enabled\" >> \"$logFile\"\n if [ \"$hostnameScheme\" == \"Serial\" ] ; then\n\tserial=\"$(/usr/sbin/system_profiler SPHardwareDataType | /usr/bin/awk '/Serial\\ Number\\ \\(system\\)/ {print $NF}')\"\n\tif [ \"$hostName\" == \"$serial\" ] ; then\n \t echo \"$logStart The computer hostname already set to serial number, exiting\" >> \"$logFile\"\n\telse\n \t scutil --set ComputerName $serial\n \t scutil --set HostName $serial\n \t scutil --set LocalHostName $serial\n \t echo \"$logStart Computer hostname set to $serial, exiting\" >> \"$logFile\"\n\tfi\n else\n\techo \"$logStart non-serial hostname format. Implement this feature\" >> \"$logFile\"\n fi\nelse\n echo \"$logStart Invalid setHostname configuration\" >> \"$logFile\"\nfi\nexit\n"},{"alpha_fraction":0.69749516248703,"alphanum_fraction":0.7042388916015625,"avg_line_length":40.52000045776367,"blob_id":"38f7fdf805665e9955d7949873305273a678f4bd","content_id":"9620c9a7171381a3af2fac39ee6b61e692073f34","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":5190,"license_type":"permissive","max_line_length":127,"num_lines":125,"path":"/autoUpdate","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# autoUpdateCheck (UNIX Exec.)\n# Check to see if auto updates are set to the desired value\n# Last Edited: 10/30/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\n\n# check curent settings\nautoCheckEnabled=\"$(defaults read /Library/Preferences/com.apple.SoftwareUpdate | awk '/AutomaticCheckEnabled = / {print $3}')\"\ncommerceAutoUpdate=\"$(defaults read /Library/Preferences/com.apple.commerce | awk '/AutoUpdate = / {print $3}')\"\nautoMacUpdate=\"$(defaults read /Library/Preferences/com.apple.commerce | awk '/AutoUpdateRestartRequired = / {print $3}')\"\nautoMacDownload=\"$(defaults read /Library/Preferences/com.apple.SoftwareUpdate | awk '/AutomaticDownload = / {print $3}')\"\nsysDataFiles1=\"$(defaults read /Library/Preferences/com.apple.SoftwareUpdate | awk '/CriticalUpdateInstall = / {print $3}')\"\nsysDataFiles2=\"$(defaults read /Library/Preferences/com.apple.SoftwareUpdate | awk '/ConfigDataInstall = / {print $3}')\"\n\n# check config file for desired settings\nautoCheck=\"$(cat $configFile | awk '/AutomaticCheck/ {print $2}')\"\nappUpdate=\"$(cat $configFile | awk '/AppUpdate/ {print $2}')\"\nmacUpdate=\"$(cat $configFile | awk '/MacOSUpdate/ {print $2}')\"\nautoDownload=\"$(cat $configFile | awk '/AutomaticDownload/ {print $2}')\"\nsystemFiles=\"$(cat $configFile | awk '/SystemDataFile/ {print $2}')\"\n\n# assign value for plist based on config file --------------------------------\n# automatically check for updates\nif [ \"$autoCheck\" == \"Enable\" ] ; then\n\tautoCheck=\"1;\"\nelse\n\tautoCheck=\"0;\"\nfi\n\n# update macOS apps\nif [ \"$appUpdate\" == \"Enable\" ] ; then\n\tappUpdate=\"1;\"\nelse\n\tappUpdate=\"0;\"\nfi\n\n# update macOS automatically\nif [ \"$macUpdate\" == \"Enable\" ] ; then\n\tmacUpdate=\"1;\"\nelse\n\tmacUpdate=\"0;\"\nfi\n\n# download updates automatically for installation\nif [ \"$autoDownload\" == \"Enable\" ] ; then\n\tautoDownload=\"1;\"\nelse\n\tautoDownload=\"0;\"\nfi\n\n# install system files automatically\nif [ \"$systemFiles\" == \"Enable\" ] ; then\n\tsystemFiles=\"1;\"\nelse\n\tsystemFiles=\"0;\"\nfi\n\n##### set parameters\n# parameter that specifies scheduled checking for updates\nif [ \"$autoCheck\" == \"$autoCheckEnabled\" ] ; then\n\techo \"$logStart Desired state in place for automatic checking for updates\" >> \"$logFile\"\nelse\n\tif [ \"$autoCheck\" == \"0;\" ] ; then # if current value is false (off), turn on\n \techo \"$logStart Setting Software Update scheduled checks --> On\" >> \"$logFile\"\n \t\tsoftwareupdate --schedule on\n \telse # if current value is true (on), turn off\n\t\techo \"$logStart Setting Software Update scheduled checks --> Off\" >> \"$logFile\"\n\t\tsoftwareupdate --schedule off\n \tfi\nfi\n# parameter for automatic app updates\nif [ \"$appUpdate\" == \"$commerceAutoUpdate\" ] ; then\n\techo \"$logStart Desired state in place for app store automatic updates\" >> \"$logFile\"\nelse\n\tif [ \"$appUpdate\" == \"0;\" ] ; then\n \t\techo \"$logStart Setting automatic app updates --> On\" >> \"$logFile\"\n \t\tdefaults write /Library/Preferences/com.apple.commerce AutoUpdate -bool TRUE\n \telse\n \t\techo \"$logStart Setting automatic app updates --> Off\" >> \"$logFile\"\n \t\tdefaults write /Library/Preferences/com.apple.commerce AutoUpdate -bool FALSE\n \tfi\nfi\n# parameter for macOS updates\nif [ \"$macUpdate\" == \"$autoMacUpdate\" ] ; then\n\techo \"$logStart Desired state in place for automatic macOS updates\" >> \"$logFile\"\nelse\n\tif [ \"$macUpdate\" == \"0;\" ] ; then\n \techo \"$logStart Setting automatic macOS updates --> On\" >> \"$logFile\"\n \t\tdefaults write /Library/Preferences/com.apple.commerce AutoUpdateRestartRequired -bool TRUE\n \telse\n \t\techo \"$logStart Setting automatic macOS updates --> Off\" >> \"$logFile\"\n \t\tdefaults write /Library/Preferences/com.apple.commerce AutoUpdateRestartRequired -bool FALSE\n \tfi\nfi\n# parameter for automatic download of available updates\nif [ \"$autoDownload\" == \"$autoMacDownload\" ] ; then\n\techo \"$logStart Desired state in place for automatic downloads of macOS updates\" >> \"$logFile\"\nelse\n\tif [ \"$autoMacDownload\" == \"0;\" ] ; then\n \t\techo \"$logStart Setting automatic download --> On\" >> \"$logFile\"\n \t\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate AutomaticDownload -bool TRUE\n \telse\n \t\techo \"$logStart Setting automatic download --> Off\" >> \"$logFile\"\n \t\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate AutomaticDownload -bool FALSE\n \tfi\nfi\n# \"install system data files\"\nif [ \"$systemFiles\" == \"$sysDataFiles1\" ] ; then\n\techo \"$logStart Desired state in place for automatic macOS updates\" >> \"$logFile\"\nelse\n\tif [ \"$systemFiles\" == \"0;\" ] ; then\n \t\techo \"$logStart Setting install system data files --> On\" >> \"$logFile\"\n \t\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate CriticalUpdateInstall -bool TRUE\n \t\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate ConfigDataInstall -bool TRUE\n \telse\n \t\techo \"$logStart Setting install system data files --> Off ***Not Recommended\" >> \"$logFile\"\n \t\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate CriticalUpdateInstall -bool False\n \t\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate ConfigDataInstall -bool False\n \tfi\nfi\n\necho \"$logStart All parameters set, exiting\" >> \"$logFile\"\nexit\n"},{"alpha_fraction":0.6153255105018616,"alphanum_fraction":0.6187824010848999,"avg_line_length":27.927778244018555,"blob_id":"bcaf83c7a5de3cea6e921fab9c1074e46c49bb8d","content_id":"3a869a0f3ee9a85f5b16005a222665031b930b5c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":5207,"license_type":"permissive","max_line_length":96,"num_lines":180,"path":"/massmgmt","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# massmgmt\n# launchd-based macOS desired-state management\n# Last Edited: 6/25/18\n\n### VARIABLES\nconfigFile=\"/usr/local/massmgmt/massmgmt.config\"\n\n# define admin user\nadminUser=\"$(cat $configFile | awk '/AdminUserID/ {print $2}')\"\n\n# log file\nlogFile=\"$(cat $configFile | awk '/LogFile/ {print $2}')\"\nstarDate=\"$(date)\"\nhostName=\"$(hostname)\"\n\n# define location of help page\nhelpPage=\"$(cat $configFile | awk '/HelpPage/ {print $2}')\"\n\n# define bin location for scripts\nbinLoc=\"$(cat $configFile | awk '/ScriptLocation/ {print $2}')\"\n\n# define directory for configuration profiles\nprofLoc=\"$(cat $configFile | awk '/MobileConfigLocation/ {print $2}')\"\n\n### FUNCTIONS\nfunction massmgmtRun {\n $binLoc/autoUpdate\n $binLoc/adobeReader\n $binLoc/checkChrome\n $binLoc/chrome\n $binLoc/enableARD\n $binLoc/enableSSH\n $binLoc/firefox\n $binLoc/guestAccount\n $binLoc/lockSysPrefs\n $binLoc/munki\n $binLoc/powerOptions\n $binLoc/setFirewall\n $binLoc/setHostname\n $binLoc/setTimezone\n $binLoc/sus\n}\n\nfunction interactiveMode {\n echo\n ls -l \"$binLoc\" | awk '/-/ {print $9}'\n echo\n printf \"Run which script? Or enter 'q' to quit -> \"\n read A\n if [ \"$A\" == \"q\" ] ; then\n \texit\n elif [ \"$A\" == \"autoUpdate\" ] ; then\n\techo \"$logStart -- Running autoUpdate interactively\" | tee \"$logFile\"\n \t$binLoc/autoUpdate\n elif [ \"$A\" == \"adobeReader\" ] ; then\n \techo \"$logStart -- Running adobeReader interactively\" | tee \"$logFile\"\n\t$binLoc/adobeReader\n elif [ \"$A\" == \"chrome\" ] ; then\n\techo \"$logStart -- Running chrome interactively\" | tee \"$logFile\"\n \t$binLoc/checkChrome\n\t$binLoc/chrome\n elif [ \"$A\" == \"enableARD\" ] ; then\n\techo \"$logStart -- Running enableARD interactively\" | tee \"$logFile\"\n \t$binLoc/enableARD\n elif [ \"$A\" == \"enableSSH\" ] ; then\n\techo \"$logStart -- Running enableSSH interactively\" | tee \"$logFile\"\n \t$binLoc/enableSSH \n elif [ \"$A\" == \"firefox\" ] ; then\n \techo \"$logStart -- Running firefox interactively\" | tee \"$logFile\"\n \t$binLoc/firefox\n elif [ \"$A\" == \"guestAccount\" ] ; then\n \techo \"$logStart -- Running guestAccount interactively\" | tee \"$logFile\"\n \t$binLoc/guestAccount\n elif [ \"$A\" == \"munki\" ] ; then\n \techo \"$logStart -- Running munki interactively\" | tee \"$logFile\"\n \t$binLoc/munki\n elif [ \"$A\" == \"lockSysPrefs\" ] ; then\n \techo \"$logStart --Running lockSysPrefs interactively\" | tee \"$logFile\"\n \t$binLoc/lockSysPrefs\n elif [ \"$A\" == \"powerOptions\" ] ; then\n \techo \"$logStart -- Running powerOptions interactively\" | tee \"$logFile\"\n\t$binLoc/powerOptions\n elif [ \"$A\" == \"setFirewall\" ] ; then\n \techo \"$logStart -- Running setFirewall\" | tee \"$logFile\"\n \t$binLoc/setFirewall\n elif [ \"$A\" == \"setHostname\" ] ; then\n \techo \"$logStart -- Running setHostname interactively\" | tee \"$logFile\"\n \t$binLoc/setHostname\n elif [ \"$A\" == \"setTimeZone\" ] ; then\n \techo \"$logStart -- Running setTimezone interactively\" | tee \"$logFile\"\n \t$binLoc/setTimezone\n elif [ \"$A\" == \"sus\" ] ; then\n \techo \"$logStart -- Running sus interactively\" | tee \"$logFile\"\n \t$binLoc/sus\n else\n \tinteractiveMode\n fi\n}\n\nfunction flushLog {\n printf \"Are you sure you want to flush the log directory? (y/n) -> \"\n read A\n if [ \"$A\" == \"n\" ] ; then\n exit\n elif [ \"$A\" == \"y\" ] ; then\n echo \"Removing $logFile\"\n rm \"$logFile\"\n touch \"$logFile\"\n echo \"Log flushed\"\n exit\n else\n echo \"Invalid option\"\n fi\n}\n\nfunction pullLog {\n currentUser=\"$(id -un)\"\n echo \"Pulling log\"\n cp \"$logFile\" \"/Users/$currentUser/Desktop/\"\n if [ -e \"/Users/$currentUser/Desktop/massmgmt.log\" ] ; then\n \topen \"/Users/$currentUser/Desktop\"\n\topen \"/Users/$currentUser/Desktop/massmgmt.log\"\n \techo \"Process Complete\"\n else\n \texit\n fi\n}\n\nfunction rmProfiles {\n echo \"Removing all configuration profiles installed on this computer.\"\n profiles -D\n}\n\nfunction installProfiles {\n printf \"Install all configuration profiles stored in /usr/local/massmgmt/mconfig? (y/n) -> \"\n read B\n if [ \"$B\" == \"n\" ] ; then\n exit\n elif [ \"B\" == \"y\" ] ; then\n echo \"Installing all configuration profiles stored in $profLoc\"\n cd \"$profLoc\"\n ls -l | awk '/-rw/ {print $(9) }' >> \"$profLoc/profiles.txt\"\n cat \"$profLoc/profiles.txt\" | while read line\n do\n profiles -I \"$profLoc/$line\"\n echo \"Profiles installed.\"\n done\n else\n exit\n fi\n}\n\n### SCRIPT\n# check if we are running with root privs\nif [ \"$(whoami)\" != \"root\" ] ; then\n echo \"$logStart Attempt to run as non-root -> $(whoami)\" >> \"$logFile\"\n exit\nfi\n\n# check arguments\n# options that can be called using: $ massmgmt <option>\nif [ \"$1\" == \"flushLog\" ] ; then\n flushLog\nelif [ \"$1\" == \"pullLog\" ] ; then\n pullLog\nelif [ \"$1\" == \"rmProfiles\" ] ; then\n rmProfiles\nelif [ \"$1\" == \"installProfiles\" ] ; then\n installProfiles\nelif [ \"$1\" == \"help\" ] ; then\n cat $helpPage\nelif [ \"$1\" == \"-im\" ] ; then\n echo \"Entering interactive mode...\"\n interactiveMode\nelse\n # if no option is specified, $ massmgmt will run the tool\n massmgmtRun\nfi\nexit\n"},{"alpha_fraction":0.6886409521102905,"alphanum_fraction":0.6942190527915955,"avg_line_length":40.08333206176758,"blob_id":"49818240bf56f107d418e4995bd3f70ae31b2468","content_id":"ef63162742c14ffffcf3deba87fc6df49ded249e","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Shell","length_bytes":1972,"license_type":"permissive","max_line_length":114,"num_lines":48,"path":"/enableSSH","repo_name":"ez3r0sec/massmgmt","src_encoding":"UTF-8","text":"#!/bin/bash\n# enableSSH (UNIX exec.)\n# enables ssh for $adminUser\n# Last Edited: 6/25/18\n\n### VARIABLES\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\n\nsshCheck=\"$(systemsetup getremotelogin | awk '/Remote/ {print $(3) }')\"\nscriptEnabled=\"$(cat $configFile | awk '/EnableSSH/ {print $2}')\"\n\n### SCRIPT\n# check if the script is enabled in the config file\nif [ \"$scriptEnabled\" == \"Disable\" ] ; then\n\techo \"$logStart enableSSH is disabled in the configuration file\" >> \"$logFile\"\n\tif [ \"$sshCheck\" == \"On\" ] ; then\n \t\techo \"$logStart SSH disabled but is on, turning off SSH\" >> \"$logFile\"\n\t\tsystemsetup setremotelogin off\n\t\tsystemsetup setremotelogin off\n\telse\n\t\techo \"$logStart SSH is disabled\" >> \"$logFile\"\n\tfi\n\texit\nelif [ \"$scriptEnabled\" == \"Enable\" ] ; then\n\tif [ \"$sshCheck\" == \"On\" ] ; then\n\t\techo \"$logStart SSH was already set to On\" >> \"$logFile\"\n\telif [ \"$sshCheck\" == \"Off\" ] ; then\n\t\techo \"$logStart SSH set to Off\" >> \"$logFile\"\n\t\tuserID=\"$(/usr/bin/dscl localhost -read /Local/Default/Users/$adminUser | grep GeneratedUID | awk '{print $2}')\"\n\t\tif [ \"$userID\" != \"\" ] ; then\n\t\t\techo \"$logStart Granting SSH Access for $adminUser with GUID $userID\" >> \"$logFile\"\n\t\t\t/usr/bin/dscl localhost -create /Local/Default/Groups/com.apple.access_ssh\n\t\t\t/usr/bin/dscl localhost -append /Local/Default/Groups/com.apple.access_ssh GroupMembership \"$adminUser\"\n\t\t\t/usr/bin/dscl localhost -append /Local/Default/Groups/com.apple.access_ssh GroupMembers \"$userID\"\n\t\t\t/usr/bin/dscl localhost -append /Local/Default/Groups/com.apple.access_ssh RealName 'Remote Login Group'\n\t\t\t/usr/bin/dscl localhost -append /Local/Default/Groups/com.apple.access_ssh PrimaryGroupID 104\n\t\t\tsystemsetup setremotelogin on\n\t\t\techo \"$logStart SSH set to On\" >> \"$logFile\"\n\t\telse\n\t\t\techo \"$logStart No record was found for $adminUser\" >> \"$logFile\"\n\t\tfi\n\telse\n \techo \"$logStart Failed, exiting\" >> \"$logFile\"\n\tfi\nelse\n\techo \"$logStart Invalid enableSSH configuration\" >> \"$logFile\"\nfi\nexit\n"}],"string":"[\n {\n \"alpha_fraction\": 0.7020906209945679,\n \"alphanum_fraction\": 0.7055749297142029,\n \"avg_line_length\": 43.128204345703125,\n \"blob_id\": \"e4c8d88b06f479296ff36cd7a9c56d5040d794ab\",\n \"content_id\": \"d5408a5bddc994cf2b5088f64f385d9c52be9d01\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1722,\n \"license_type\": \"permissive\",\n \"max_line_length\": 147,\n \"num_lines\": 39,\n \"path\": \"/enableARD\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/sh\\n# enableARD (UNIX exec.)\\n# enableARD arg\\n# Last Edited: 6/25/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\nlineTemplate=\\\"com.apple.RemoteDesktop.agent\\\"\\nARD=\\\"$(launchctl list | grep '^\\\\d.*RemoteDesktop.*')\\\"\\nAgent=\\\"$(echo $ARD | awk '/com.apple.RemoteDesktop.agent/ {print $(NF) }')\\\"\\nscriptEnabled=\\\"$(cat $configFile | awk '/EnableARD/ {print $2}')\\\"\\n\\n### SCRIPT\\n# check if the script is enabled in the config file\\nif [ \\\"$scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n if [ \\\"$Agent\\\" == \\\"$lineTemplate\\\" ] ; then\\n\\techo \\\"$logStart ARD disabled but turned on, turning off\\\" >> \\\"$logFile\\\"\\n\\t/System/Library/CoreServices/RemoteManagement/ARDAgent.app/Contents/Resources/kickstart -deactivate -configure -access -off\\n\\techo \\\"$logStart ARD is now off\\\" >> \\\"$logFile\\\"\\n else\\n\\techo \\\"$logStart ARD is Disabled and off\\\" >> \\\"$logFile\\\"\\n fi\\nelif [ \\\"$scriptEnabled\\\" == \\\"Enable\\\" ] ; then\\n if [ \\\"$Agent\\\" == \\\"$lineTemplate\\\" ] ; then\\n echo \\\"$logStart ARD already enabled, exiting\\\" >> \\\"$logFile\\\"\\n else\\n \\tprivs=\\\"-DeleteFiles -ControlObserve -TextMessages -OpenQuitApps -GenerateReports -RestartShutDown -SendFiles -ChangeSettings\\\"\\n \\techo \\\"$logStart Privileges: $privs\\\" >> \\\"$logFile\\\"\\n \\techo \\\"$logStart Kickstarting ARD\\\" >> \\\"$logFile\\\"\\n \\t/System/Library/CoreServices/RemoteManagement/ARDAgent.app/Contents/Resources/kickstart -activate -configure -allowAccessFor -specifiedUsers\\n \\techo \\\"$logStart Giving $adminUser correct privileges\\\" >> \\\"$logFile\\\"\\n \\t/System/Library/CoreServices/RemoteManagement/ARDAgent.app/Contents/Resources/kickstart -configure -access -on -privs $privs -users $adminUser\\n exit\\n fi\\nelse\\n echo \\\"$logStart Invalid enableARD configuration\\\" >> \\\"$logFile\\\"\\nfi\\nexit\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6538051962852478,\n \"alphanum_fraction\": 0.6628525853157043,\n \"avg_line_length\": 45.974998474121094,\n \"blob_id\": \"3afe9324336cd388ad3ebb2bf3d9333584d4c894\",\n \"content_id\": \"0a64ed0e41906275a11ccdb707f265924a33e053\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 3758,\n \"license_type\": \"permissive\",\n \"max_line_length\": 214,\n \"num_lines\": 80,\n \"path\": \"/firefox\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/sh\\n# Script to download or update Firefox\\n# https://www.jamf.com/jamf-nation/third-party-products/files/764/firefox-install-update\\n# Last Edited: 10/30/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\ndmgfile=\\\"FF.dmg\\\"\\nscriptEnabled=\\\"$(cat $configFile | awk '/UpdateFirefox/ {print $2}')\\\"\\ndeleteIfInstalled=\\\"$(cat $configFile | awk '/Firefox-DeleteIfInstalled/ {print $2}')\\\"\\n\\n### SCRIPT\\nif [ \\\"$scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n if [ \\\"$deleteIfInstalled\\\" == \\\"Yes\\\" ] ; then\\n\\techo \\\"$logStart Deleting Firefox\\\" >> \\\"$logFile\\\"\\n\\trm -rf /Applications/Firefox.app\\n elif [ \\\"$deleteIfInstalled\\\" == \\\"No\\\" ] ; then\\n\\techo \\\"$logStart Firefox script is disabled but Firefox is allowed on this system\\\" >> \\\"$logFile\\\"\\n else\\n\\techo \\\"$logStart Invalid configuration for DeleteIfInstalled key in the chrome section of the configuration file\\\" >> \\\"$logFile\\\"\\n fi\\n exit\\nelif [ \\\"$scriptEnabled\\\" == \\\"Enable\\\" ] ; then\\n # Get OS version and adjust for use with the URL string\\n OSvers_URL=$( sw_vers -productVersion | sed 's/[.]/_/g' )\\n # Set the User Agent string for use with curl\\n userAgent=\\\"Mozilla/5.0 (Macintosh; Intel Mac OS X ${OSvers_URL}) AppleWebKit/535.6.2 (KHTML, like Gecko) Version/5.2 Safari/535.6.2\\\"\\n # Get the latest version of Reader available from Firefox page.\\n latestver=\\\"$(/usr/bin/curl -s -A \\\"$userAgent\\\" https://www.mozilla.org/en-US/firefox/new/ | grep 'data-latest-firefox' | sed -e 's/.* data-latest-firefox=\\\"\\\\(.*\\\\)\\\".*/\\\\1/' -e 's/\\\\\\\"//' | /usr/bin/awk '{print $1}')\\\"\\n # if the latestver curl fails, exit\\n if [ \\\"$latestver\\\" == \\\"\\\" ] ; then\\n \\techo \\\"$logStart latestver curl failed, exiting\\\" >> \\\"$logFile\\\"\\n \\texit\\n else\\n \\techo \\\"$logStart Latest Version is: $latestver\\\" >> \\\"$logFile\\\"\\n fi\\n # Get the version number of the currently-installed FF, if any.\\n if [ -e \\\"/Applications/Firefox.app\\\" ] ; then\\n currentinstalledver=\\\"$(/usr/bin/defaults read /Applications/Firefox.app/Contents/Info CFBundleShortVersionString)\\\"\\n \\techo \\\"$logStart Current installed version is: $currentinstalledver\\\" >> \\\"$logFile\\\"\\n \\tif [ ${latestver} = ${currentinstalledver} ] ; then\\n echo \\\"$logStart Firefox is current. Exiting\\\" >> \\\"$logFile\\\"\\n exit\\n \\tfi\\n else\\n currentinstalledver=\\\"none\\\"\\n \\techo \\\"$logStart Firefox is not installed, installing\\\" >> \\\"$logFile\\\"\\n fi\\n\\n url=\\\"https://download-installer.cdn.mozilla.net/pub/firefox/releases/${latestver}/mac/en-US/Firefox%20${latestver}.dmg\\\"\\n echo \\\"$logStart Latest version of the URL is: $url\\\" >> \\\"$logFile\\\"\\n\\n # Compare the two versions, if they are different or Firefox is not present then download and install the new version.\\n if [ \\\"${currentinstalledver}\\\" != \\\"${latestver}\\\" ] ; then\\n echo \\\"$logStart Curling Firefox dmg\\\" >> \\\"$logFile\\\"\\n \\tcurl -s -o /tmp/${dmgfile} ${url}\\n \\thdiutil attach /tmp/${dmgfile} -nobrowse -quiet\\n \\tditto -rsrc \\\"/Volumes/Firefox/Firefox.app\\\" \\\"/Applications/Firefox.app\\\"\\n \\tsleep 10\\n hdiutil detach $(/bin/df | /usr/bin/grep Firefox | awk '{print $1}') -quiet\\n \\tsleep 10\\n \\trm /tmp/${dmgfile}\\n\\n # double check to see if the new version got updated\\n \\tnewlyinstalledver=\\\"$(/usr/bin/defaults read /Applications/Firefox.app/Contents/Info CFBundleShortVersionString)\\\"\\n \\tif [ \\\"${latestver}\\\" = \\\"${newlyinstalledver}\\\" ] ; then\\n echo \\\"$logStart Firefox has been updated\\\" >> \\\"$logFile\\\"\\n \\telse\\n echo \\\"$logStart Firefox update unsuccessful.\\\" >> \\\"$logFile\\\"\\n exit\\n \\tfi\\n\\t # If Firefox is up to date already, log it and exit.\\n else\\n \\techo \\\"$logStart Firefox is already up to date, running ${currentinstalledver}.\\\" >> \\\"$logFile\\\"\\n fi\\n exit\\nelse\\n echo \\\"$logStart Invalid Firefox configuration\\\" >> \\\"$logFile\\\"\\nfi\\n\"\n },\n {\n \"alpha_fraction\": 0.6802045702934265,\n \"alphanum_fraction\": 0.6946449875831604,\n \"avg_line_length\": 42.16883087158203,\n \"blob_id\": \"cae3ce083bd6d268af26a8c4271348ae5e5540eb\",\n \"content_id\": \"abcbb2b6556d9a6794fe6d62f987dfcdac76c0fc\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 3324,\n \"license_type\": \"permissive\",\n \"max_line_length\": 145,\n \"num_lines\": 77,\n \"path\": \"/adobeReader\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/sh\\n# adobeReader (UNIX. Exec.)\\n# Script to download and install Adobe Reader DC\\n# modified from: https://www.jamf.com/jamf-nation/third-party-products/files/761/\\n# adobe-reader-installer-updater\\n# Last Edited: 10/30/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\nscriptEnabled=\\\"$(cat $configFile | awk '/AdobeReader/ {print $2}')\\\"\\n\\n### FUNCTIONS\\nfunction check_enabled {\\n\\tif [ \\\"$scriptEnabled\\\" == \\\"Enable\\\" ] ; then\\n\\t\\techo \\\"$logStart adobeReader is enabled, comparing versions\\\" >> \\\"$logFile\\\"\\n\\t\\tupdate_reader\\n\\telse\\n\\t\\techo \\\"$logStart adobeReader is not enabled\\\" >> \\\"$logFile\\\"\\n\\tfi\\n}\\n\\nfunction update_reader {\\n\\t## Get OS version and adjust for use with the URL string\\n\\tOSvers_URL=$( sw_vers -productVersion | sed 's/[.]/_/g' )\\n\\t## Set the User Agent string for use with curl\\n\\tuserAgent=\\\"Mozilla/5.0 (Macintosh; Intel Mac OS X ${OSvers_URL}) AppleWebKit/535.6.2 (KHTML, like Gecko) Version/5.2 Safari/535.6.2\\\"\\n\\t# Get the latest version of Reader available from Adobe's About Reader page.\\n\\tlatestver=\\\"$()\\\"\\n\\twhile [ -z \\\"$latestver\\\" ]\\n\\tdo\\n\\t\\tlatestver=\\\"$(curl -s -L -A \\\"$userAgent\\\" https://get.adobe.com/reader/ | grep \\\"<strong>Version\\\" | sed -e 's/<[^>][^>]*>//g' | awk '{print $2}')\\\"\\n\\tdone\\n\\techo \\\"$logStart Latest Version is: $latestver\\\" >> \\\"$logFile\\\"\\n\\tlatestvernorm=\\\"$(echo ${latestver} | sed -e 's/20//')\\\"\\n\\t# Get the version number of the currently-installed Adobe Reader, if any.\\n\\tif [ -e \\\"/Applications/Adobe Acrobat Reader DC.app\\\" ]; then\\n\\t\\tcurrentinstalledver=\\\"$(defaults read /Applications/Adobe\\\\ Acrobat\\\\ Reader\\\\ DC.app/Contents/Info CFBundleShortVersionString)\\\"\\n\\t\\techo \\\"$logStart Current installed version is: $currentinstalledver\\\" >> \\\"$logFile\\\"\\n\\t\\tif [ ${latestvernorm} = ${currentinstalledver} ]; then\\n\\t\\t\\techo \\\"$logStart Adobe Reader DC is current. Exiting\\\" >> \\\"$logFile\\\"\\n\\t\\t\\texit\\n\\t\\tfi\\n\\telse\\n\\t\\tcurrentinstalledver=\\\"none\\\"\\n\\t\\techo \\\"$logStart Adobe Reader DC is not installed\\\" >> \\\"$logFile\\\"\\n\\tfi\\n\\tARCurrVersNormalized=$( echo $latestver | sed -e 's/[.]//g' -e 's/20//' )\\n\\turl=\\\"\\\"\\n\\turl1=\\\"http://ardownload.adobe.com/pub/adobe/reader/mac/AcrobatDC/${ARCurrVersNormalized}/AcroRdrDC_${ARCurrVersNormalized}_MUI.dmg\\\"\\n\\turl2=\\\"\\\"\\n\\t#Build URL\\n\\turl=\\\"$(echo \\\"${url1}${url2}\\\")\\\"\\n\\t# Compare the two versions, if they are different or Adobe Reader is not present then download and install the new version.\\n\\tif [ \\\"${currentinstalledver}\\\" != \\\"${latestvernorm}\\\" ]; then\\n\\t\\tcurl -s -o /tmp/reader.dmg ${url} >> \\\"$logFile\\\" 2>&1\\n\\t\\thdiutil attach /tmp/reader.dmg -nobrowse -quiet >> \\\"$logFile\\\" 2>&1\\n\\t\\tinstaller -pkg /Volumes/AcroRdrDC_${ARCurrVersNormalized}_MUI/AcroRdrDC_${ARCurrVersNormalized}_MUI.pkg -target / >> \\\"$logFile\\\" 2>&1\\n\\t\\tsleep 10\\n\\t\\thdiutil detach $(df | grep Adobe\\\\ Acrobat\\\\ Reader\\\\ DC\\\\ Installer | awk '{print $1}') -quiet >> \\\"$logFile\\\" 2>&1\\n\\t\\tsleep 10\\n\\t\\trm /tmp/reader.dmg >> \\\"$logFile\\\" 2>&1\\n\\t\\t#double check to see if the new version got updated\\n\\t\\tnewlyinstalledver=\\\"$(defaults read /Applications/Adobe\\\\ Acrobat\\\\ Reader\\\\ DC.app/Contents/Info CFBundleShortVersionString)\\\"\\n \\t\\tif [ \\\"${latestvernorm}\\\" = \\\"${newlyinstalledver}\\\" ] ; then\\n\\t\\t\\techo \\\"$logStart Adobe Reader is updated/installed\\\" >> \\\"$logFile\\\"\\n\\t\\telse\\n \\t\\techo \\\"$logStart ERROR: Adobe Reader update unsuccessful.\\\" >> \\\"$logFile\\\"\\n\\t\\t\\texit\\n\\t\\tfi\\n\\telse\\n\\t\\techo \\\"$logStart Adobe Reader is already up to date.\\\" >> \\\"$logFile\\\"\\n\\tfi\\n}\\n\\n### SCRIPT\\ncheck_enabled\\nexit\\n\"\n },\n {\n \"alpha_fraction\": 0.6288492679595947,\n \"alphanum_fraction\": 0.6361426115036011,\n \"avg_line_length\": 29.850000381469727,\n \"blob_id\": \"cdc20405c7fa1fded0cd15bc490ae9459304f963\",\n \"content_id\": \"5090023562ea4756e43713f5f9f8832f04a60005\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1234,\n \"license_type\": \"permissive\",\n \"max_line_length\": 112,\n \"num_lines\": 40,\n \"path\": \"/checkChrome\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/usr/bin/python\\n# checkChrome (Python 2.7)\\n# Check if chrome is installed/up to date and install if not installed or out of date\\n# Last Edited: 6/25/18\\n\\n### IMPORTS\\nimport json\\nimport urllib2\\nimport os.path\\nimport plistlib\\n\\n### VARIABLES\\nurl = 'http://omahaproxy.appspot.com/all.json'\\nosPath = \\\"/Applications/Google Chrome.app\\\"\\n\\n### SCRIPT\\nif os.path.exists(osPath) is True:\\n resp = urllib2.urlopen(url)\\n data = json.loads(resp.read())\\n for each in data:\\n if each.get(\\\"os\\\") == \\\"mac\\\":\\n versions = each.get(\\\"versions\\\")\\n for version in versions:\\n if version.get(\\\"channel\\\") == \\\"stable\\\":\\n latest = (version.get(\\\"current_version\\\")) \\n # plist variables for comparison - if Chrome is not in /Applications, the script will create the update file\\n plistloc = \\\"/Applications/Google Chrome.app/Contents/Info.plist\\\"\\n pl = plistlib.readPlist(plistloc)\\n pver = pl[\\\"CFBundleShortVersionString\\\"]\\n if latest == pver:\\n exit\\n else:\\n file = open(\\\"/tmp/updateChrome.txt\\\",\\\"w\\\") \\n file.write(\\\"Please update chrome\\\") \\n exit\\nelse:\\n file = open(\\\"/tmp/updateChrome.txt\\\",\\\"w\\\") \\n file.write(\\\"Please update chrome\\\") \\n exit\\nexit\\n\"\n },\n {\n \"alpha_fraction\": 0.6465781331062317,\n \"alphanum_fraction\": 0.6547497510910034,\n \"avg_line_length\": 31.633333206176758,\n \"blob_id\": \"d4802bb699339ddcc0b64911d4c7904d62e9ee76\",\n \"content_id\": \"e5757e1ae0b12e184319564c5a2a410a517b50d9\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 979,\n \"license_type\": \"permissive\",\n \"max_line_length\": 88,\n \"num_lines\": 30,\n \"path\": \"/setTimezone\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# setTimezone (UNIX exec.)\\n# set timezone\\n# Last Edited: 6/25/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\nscriptEnabled=\\\"$(cat $configFile | awk '/SetTimezone/ {print $2}')\\\"\\ntimeZone=\\\"$(cat $configFile | awk '/TimeZone/ {print $2}')\\\"\\ncurrentTimeZone=\\\"$(systemsetup -gettimezone | awk '/Time Zone/ {print $(3) }')\\\"\\n\\n### SCRIPT\\nif [ \\\"$scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n echo \\\"$logStart setTimezone disabled\\\" >> \\\"$logFile\\\"\\n exit\\nelif [ \\\"$scriptEnabled\\\" == \\\"Enable\\\" ] ; then\\n if [ \\\"$currentTimeZone\\\" == \\\"$timeZone\\\" ] ; then\\n echo \\\"$logStart System timezone already set to $timeZone, exiting\\\" >> \\\"$logFile\\\"\\n exit\\n else\\n \\techo \\\"Setting timezone to $timeZone\\\" >> \\\"$logFile\\\"\\n \\tsystemsetup -settimezone \\\"$timeZone\\\" >> \\\"$logFile\\\"\\n \\tkillall SystemUIServer\\n \\techo \\\"$logStart Timezone set to $timeZone, exiting\\\" >> \\\"$logFile\\\"\\n fi\\nelse\\n echo \\\"$logStart Invalid configuration for setTimezone\\\" >> \\\"$logFile\\\"\\nfi\\nexit\\n\"\n },\n {\n \"alpha_fraction\": 0.6451191902160645,\n \"alphanum_fraction\": 0.650669276714325,\n \"avg_line_length\": 37.75949478149414,\n \"blob_id\": \"748390d50caba508055fc98b7f7397a389dec2f3\",\n \"content_id\": \"ead56d028d203823d15e8df49e3822dac223e6e3\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 3063,\n \"license_type\": \"permissive\",\n \"max_line_length\": 82,\n \"num_lines\": 79,\n \"path\": \"/powerOptions\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# powerOptions (UNIX exec.)\\n# set scheduled reboots and other power settings on macOS\\n# Last Edited: 6/25/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\nscriptEnabled=\\\"$(cat $configFile | awk '/PowerOptions/ {print $2}')\\\"\\n\\n### SCRIPT\\nif [ \\\"$scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n echo \\\"$logStart powerOptions disabled\\\" >> \\\"$logFile\\\"\\n exit\\nelif [ \\\"$scriptEnabled\\\" == \\\"Enable\\\" ] ; then\\n # display sleep time\\n displaySleepTime=\\\"$(cat $configFile | awk '/DisplaySleepTime/ {print $2}')\\\"\\n if [ \\\"$displaySleepTime\\\" != \\\"\\\" ] ; then\\n\\techo \\\"$logStart Setting display sleep to $displaySleepTime\\\" >> \\\"$logFile\\\"\\n\\tpmset -a displaysleep \\\"$displaySleepTime\\\"\\n else\\n\\techo \\\"$logStart No display sleep time specified\\\" >> \\\"$logFile\\\"\\n fi\\n # disk sleep time\\n diskSleepTime=\\\"$(cat $configFile | awk '/DiskSleepTime/ {print $2}')\\\"\\n if [ \\\"$diskSleepTime\\\" != \\\"\\\" ] ; then\\n\\techo \\\"$logStart Setting disk sleep to $diskSleepTime\\\" >> \\\"$logFile\\\"\\n\\tpmset -a disksleep \\\"$diskSleepTime\\\"\\n else\\n\\techo \\\"$logStart No disk sleep time specified\\\" >> \\\"$logFile\\\"\\n fi\\n # system sleep time\\n sysSleepTime=\\\"$(cat $configFile | awk '/SystemSleepTime/ {print $2}')\\\"\\n if [ \\\"$sysSleepTime\\\" != \\\"\\\" ] ; then\\n\\techo \\\"$logStart Setting disk sleep to $sysSleepTime\\\" >> \\\"$logFile\\\"\\n\\tpmset -a disksleep \\\"$sysSleepTime\\\"\\n else\\n\\techo \\\"$logStart No system sleep time specified\\\" >> \\\"$logFile\\\"\\n fi\\n # scheduled reboots, shutdown, sleep, and wake\\n # reboot setup --\\n rebootDays=\\\"$(cat $configFile | awk '/RebootDays/ {print $2}')\\\"\\n rebootTime=\\\"$(cat $configFile | awk '/RebootTime/ {print $2}')\\\"\\n if [ \\\"$rebootDays\\\" != \\\"\\\" ] ; then\\n\\techo \\\"$logStart Setting restart to $rebootDays at $rebootTime\\\" >> \\\"$logFile\\\"\\n\\tpmset repeat restart \\\"$rebootDays\\\" \\\"$rebootTime\\\"\\n else\\n\\techo \\\"$logStart No reboot options specified\\\" >> \\\"$logFile\\\"\\n fi\\n # shutdown setup --\\n shutdownDays=\\\"$(cat $configFile | awk '/ShutdownDays/ {print $2}')\\\"\\n shutdownTime=\\\"$(cat $configFile | awk '/ShutdownTime/ {print $2}')\\\"\\n if [ \\\"$shutdownDays\\\" != \\\"\\\" ] ; then\\n\\techo \\\"$logStart Setting shutdown to $shutdownDays at $shutdownTime\\\" >> \\\"$logFile\\\"\\n\\tpmset repeat shutdown \\\"$shutdownDays\\\" \\\"$shutdownTime\\\"\\n else\\n\\techo \\\"$logStart No shutdown options specified\\\" >> \\\"$logFile\\\"\\n fi\\n # sleep options --\\n sleepDays=\\\"$(cat $configFile | awk '/SleepDays/ {print $2}')\\\"\\n sleepTime=\\\"$(cat $configFile | awk '/SleepTime/ {print $2}')\\\"\\n if [ \\\"$sleepDays\\\" != \\\"\\\" ] ; then\\n\\techo \\\"$logStart Setting wake to $wakeDays at $wakeTime\\\" >> \\\"$logFile\\\"\\n\\tpmset repeat sleep \\\"$sleepDays\\\" \\\"$sleepTime\\\"\\n else\\n\\techo \\\"$logStart No sleep options specified\\\" >> \\\"$logFile\\\"\\n fi\\n # wake options --\\n wakeDays=\\\"$(cat $configFile | awk '/WakeDays/ {print $2}')\\\"\\n wakeTime=\\\"$(cat $configFile | awk '/WakeTime/ {print $2}')\\\"\\n if [ \\\"$wakeDays\\\" != \\\"\\\" ] ; then\\n\\tpmset repeat wakeorpoweron \\\"$wakeDays\\\" \\\"$wakeTime\\\"\\n else\\n\\techo \\\"$logStart No wake options specified\\\" >> \\\"$logFile\\\"\\n fi\\nelse\\n echo \\\"$logStart Invalid powerOptions configuration\\\" >> \\\"$logFile\\\"\\nfi\\nexit\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.674606204032898,\n \"alphanum_fraction\": 0.6780637502670288,\n \"avg_line_length\": 45.48214340209961,\n \"blob_id\": \"9a1a387349060f689ff01e28add3ce6d4d222141\",\n \"content_id\": \"7de0c609b35fb74f654220719fe56ead06282827\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 2603,\n \"license_type\": \"permissive\",\n \"max_line_length\": 128,\n \"num_lines\": 56,\n \"path\": \"/chrome\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# chrome (UNIX exec.)\\n# update/install Google Chrome\\n# Last Edited: 9/18/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\ndmgfile=\\\"googlechrome.dmg\\\"\\nvolname=\\\"Google Chrome\\\"\\nurl=\\\"https://dl.google.com/chrome/mac/stable/GGRO/googlechrome.dmg\\\"\\nscriptEnabled=\\\"$(cat $configFile | awk '/UpdateChrome/ {print $2}')\\\"\\ndeleteIfInstalled=\\\"$(cat $configFile | awk '/DeleteIfInstalled/ {print $2}')\\\"\\nincognitoMode=\\\"$(cat $configFile | awk '/IncognitoMode/ {print $2}')\\\"\\n\\n### SCRIPT\\n# check if the script is enabled in the config file\\nif [ \\\"$scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n if [ \\\"$deleteIfInstalled\\\" == \\\"Yes\\\" ] ; then\\n\\t\\techo \\\"$logStart Removing Google Chrome.app\\\" >> \\\"$logFile\\\"\\n\\t\\trm -rf /Applications/Googe\\\\ Chrome.app\\n elif [ \\\"$deleteIfInstalled\\\" == \\\"No\\\" ] ; then\\n\\t\\techo \\\"$logStart Chrome script disabled but Google Chrome allowed on this system\\\" >> \\\"$logFile\\\"\\n else\\n\\t\\techo \\\"$logStart Invalid configuration for DeleteIfInstalled key in the chrome section of the configuration file\\\" >> \\\"$logFile\\\"\\n fi\\n exit\\nelif [ \\\"$scriptEnabled\\\" == \\\"Enable\\\" ] ; then\\n # check if the .txt file created by checkChrome.py exists and if so, update chrome\\n if [ -f \\\"/tmp/updateChrome.txt\\\" ] ; then\\n \\techo \\\"$logStart Installing/updating Google Chrome\\\" >> \\\"$logFile\\\"\\n \\tcurl -s -o /tmp/$dmgfile $url\\n \\thdiutil attach /tmp/$dmgfile -nobrowse -quiet\\n \\tditto -rsrc \\\"/Volumes/$volname/Google Chrome.app\\\" \\\"/Applications/Google Chrome.app\\\"\\n \\thdiutil detach $(/bin/df | /usr/bin/grep \\\"$volname\\\" | awk '{print $1}') -quiet\\n \\trm /tmp/\\\"$dmgfile\\\"\\n # check if incognito mode is enabled or disabled\\n if [ \\\"$incognitoMode\\\" == \\\"Enabled\\\" ] ; then\\n \\t echo \\\"$logStart Incognito mode is allowed on this system\\\" >> \\\"$logFile\\\"\\n \\t rm /Library/Preferences/com.google.Chrome.plist\\n elif [ \\\"$incognitoMode\\\" == \\\"Disable\\\" ] ; then\\n \\t echo \\\"$logStart Moving com.google.Chrome.plist (disable incognito mode) into /Library/Preferences\\\" >> \\\"$logFile\\\"\\n \\t cp /usr/local/massmgmt/conf/com.google.Chrome.plist /Library/Preferences\\n \\t echo \\\"$logStart Google Chrome installed/updated and incognito mode disabled, exiting\\\" >> \\\"$logFile\\\"\\n else\\n \\t echo \\\"$logStart Unknown Incognito Mode configuration option\\\" >> \\\"$logFile\\\"\\n fi\\n # close out log and clean out /tmp/updateChrome.txt file to avoid triggering the script again after updating\\n rm /tmp/updateChrome.txt\\n else\\n echo \\\"$logStart Chrome already up to date\\\" >> \\\"$logFile\\\"\\n fi\\nelse\\n echo \\\"$logStart Invalid Chrome configuration\\\" >> \\\"$logFile\\\"\\nfi\\nexit\\n\"\n },\n {\n \"alpha_fraction\": 0.7799597382545471,\n \"alphanum_fraction\": 0.7822256088256836,\n \"avg_line_length\": 46.85542297363281,\n \"blob_id\": \"c6e060ffdfc999f5a848d60926c561ae828e645f\",\n \"content_id\": \"0a3b35ff7af9477302143877bb451526c2ee19f7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 3976,\n \"license_type\": \"permissive\",\n \"max_line_length\": 113,\n \"num_lines\": 83,\n \"path\": \"/README.md\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# massmgmt\\nVersion 2.1.4 \\nLast Edited: 06/07/21 \\n\\n# Purpose\\nThe massmgmt suite came about as an attempt to automate configuration and patch management\\non macOS laptops stored in shared-use carts and desktops in computer labs without requiring a \\ncentral server or system to manage endpoints. Massmgmt can be used in combination with Munki\\nto extend your management capabilities. The main advantage of massmgmt over other systems is\\nthat it is extremely lightweight and if the desired configuration of endpoints does not\\nfrequently change, endpoints can maintain their configuration without intervention, which \\nallows technicians to more efficiently spend their time.\\n\\n# Description\\nThe massmgmt suite uses a series of bash and python scripts to maintain a desired \\nstate configuration on macOS endpoints. Massmgmt is configured to run on startup\\nat a defined interval using a launch daemon (org.massmgmt.plist). Because the \\nsuite is built on multiple scripts, it is modular and easy for system administrators to \\nmaintain. The storage directory for the suite of scripts is /usr/local/massmgmt/bin.\\nIn addition to these scripts there are plans for endpoint log analysis and security\\nmodules.\\n\\nThe first step of each script checks if the setting is enabled and if the \\nconfiguration matches the desired state defined in the configuration file\\n/usr/local/massmgmt/massmgmt.config. Editing the configuration file and\\nredistributing it to endpoints is the most simple means of altering the desired\\nstate of managed machines.\\n\\nIf the desired state already exists no action must be taken and the script will \\nexit and the next script will run. If the setting does not match the desired \\nstate, the script will correct the discrepancy. Typically, any files that are \\ngenerated by massmgmt will be placed in /tmp unless they are meant to to be used \\nlater. Persistant and installable plist files will be stored within \\n/usr/local/massmgmt/conf.\\n\\nThe massmgmt tool has logging built into the scripts that are included. The\\nlog file is located by default at /usr/local/massmgmt/massmgmt.log. The log may \\nalso be flushed by entering the command:\\n\\n<code>massmgmt flushLog</code>\\n\\nLogs may also be retrieved to the desktop directory of the current user with:\\n\\nmassmgmt pullLog</code>\\n\\nMassmgmt can also be called so that admins can interactively call individual scripts\\nby entering the command:\\n\\n<code>massmgmt -im</code>\\n\\nThis command enters “interactive mode” and is logged as such for auditing interactions.\\nMassmgmt may also be run in its entirety by an admin or root user by entering:\\n\\n<code>massmgmt</code>\\n\\nMassmgmt also supports installing and removing configuration profiles. Profiles should\\nbe stored in /usr/local/massmgmt/mconfig so they could be installed whenever the desired\\nstate is not in place. The administrator can interactively install profiles using the\\nfollowing command:\\n\\n<code>massmgmt installProfiles</code>\\n\\nFor especially restrictive profiles, a profile removal command is available during\\ntroubleshooting:\\n\\n<code>massmgmt rmProfiles</code>\\n\\n# Requirements\\n\\nAll that is required on the client devices for massmgmt before installation is a local account \\nwith administrator privileges to run massmgmt scripts. No web servers are \\nnecessary. For maximum flexibility, massmgmt can be paired with Munki so that configurations\\ncan be changed at will or massmgmt can be updated.\\n\\n# Installation Directions\\n\\nThe easiest way to customize massmgmt for an environment is to download the zip file\\nfrom the project page, edit the configuration file with a text editor, and package it\\nwith Packages.app from Whitebox or PackageMaker.app from Apple using installmassmgmt.sh\\nas a post-install script. The installmassmgmt.sh script relies on using /tmp/massmgmt\\nas the target directory in the package.\\n\\nHappy Configuring!\\n\"\n },\n {\n \"alpha_fraction\": 0.6810513734817505,\n \"alphanum_fraction\": 0.6888976097106934,\n \"avg_line_length\": 38.21538543701172,\n \"blob_id\": \"bbf8d3041a4e581fd9cd0cecbc90e7bf5bab57d7\",\n \"content_id\": \"d37483d99b7317beca80ca149033864ab978b70b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 5098,\n \"license_type\": \"permissive\",\n \"max_line_length\": 104,\n \"num_lines\": 130,\n \"path\": \"/setFirewall\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# setFirewall (UNIX Exec.)\\n# Control the ALF firewall\\n# Last Edited: 11/15/18\\n\\n# VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\nscriptEnabled=\\\"$(cat $configFile | awk '/SetFirewall/ {print $2}')\\\"\\n\\n### FUNCTIONS\\nfunction check_scriptEnabled {\\n\\tif [ \\\"$check_scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n\\t\\techo \\\"$logStart setFirewall disabled\\\" >> \\\"$logFile\\\"\\n\\t\\texit\\n\\telse\\n\\t\\techo \\\"$logStart setFirewall is enabled\\\" >> \\\"$logFile\\\"\\n\\tfi\\n}\\n\\nfunction check_on {\\n\\tglobalState=\\\"$(sudo defaults read /Library/Preferences/com.apple.alf.plist globalstate)\\\"\\n\\tif [ \\\"$globalState\\\" == \\\"1\\\" ] ; then\\n\\t\\techo \\\"$logStart Firewall is already on\\\" >> \\\"$logFile\\\"\\n\\t\\techo \\\"$logStart Turning off ALF to make changes\\\" >> \\\"$logFile\\\"\\n\\t\\t/usr/libexec/ApplicationFirewall/socketfilterfw --setglobalstate off >> \\\"$logFile\\\" 2>&1\\n\\telse\\n\\t\\techo \\\"$logStart Firewall is off\\\" >> \\\"$logFile\\\"\\n\\tfi\\n}\\n\\nfunction check_logging {\\n\\tloggingSet=\\\"$(defaults read /Library/Preferences/com.apple.alf.plist loggingenabled)\\\"\\n\\tchkConfigLogging=\\\"$(cat $configFile | awk '/FirewallLogging/ {print $2}')\\\"\\n\\tchkConfigLevel=\\\"$(cat $configFile | awk '/LoggingLevel/ {print $2}')\\\"\\n\\tif [ \\\"$loggingSet\\\" == \\\"1\\\" ] ; then\\n\\t\\techo \\\"$logStart Logging is already ON\\\" >> \\\"$logFile\\\"\\n\\t\\t/usr/libexec/ApplicationFirewall/socketfilterfw --setloggingopt $chkConfigLevel >> \\\"$logFile\\\" 2>&1\\n\\telse\\n\\t\\techo \\\"Logging is set to OFF\\\" >> \\\"$logFile\\\"\\n\\t\\tif [ \\\"$chkConfigLogging\\\" == \\\"Enable\\\" ] ; then\\n\\t\\t\\techo \\\"$logStart Firewall logging is enabled in the config file, enabling\\\" >> \\\"$logFile\\\"\\n\\t\\t\\t/usr/libexec/ApplicationFirewall/socketfilterfw --setloggingmode on\\n\\t\\t\\techo \\\"$logStart Setting logging level to $chkConfigLevel\\\" >> \\\"$logFile\\\"\\n\\t\\t\\t/usr/libexec/ApplicationFirewall/socketfilterfw --setloggingopt $chkConfigLevel >> \\\"$logFile\\\" 2>&1\\n\\t\\telse\\n\\t\\t\\techo \\\"$logStart Firewall logging is disabled in the config file\\\" >> \\\"$logFile\\\"\\n\\t\\tfi\\n\\tfi\\n}\\n\\nfunction check_signed {\\n\\tallowSigned1=\\\"$(defaults read /Library/Preferences/com.apple.alf.plist allowdownloadsignedenabled)\\\"\\n\\tallowSigned2=\\\"$(defaults read /Library/Preferences/com.apple.alf.plist allowsignedenabled)\\\"\\n\\tconfigAllow=\\\"$(cat $configFile | awk '/AllowSignedApps/ {print $2}')\\\"\\n\\tif [ \\\"$allowSigned1\\\" == \\\"1\\\" ] ; then\\n\\t\\techo \\\"$logStart Allow download signed enabled\\\" >> \\\"$logFile\\\"\\n\\t\\tif [ \\\"$configAllow\\\" == \\\"Enable\\\" ] ; then\\n\\t \\t\\techo \\\"$logStart Allow download signed enabled in config file\\\" >> \\\"$logFile\\\"\\n\\t \\telse\\n\\t \\techo \\\"$logStart Allow download signed in config file, disabling\\\" >> \\\"$logFile\\\"\\n\\t \\t/usr/libexec/ApplicationFirewall/socketfilterfw --setallowsigned off >> \\\"$logFile\\\" 2>&1\\n\\t \\tfi\\n\\telse\\n\\t\\techo \\\"$logStart Allow download signed disabled\\\" >> \\\"$logFile\\\"\\n\\t\\tif [ \\\"$configAllow\\\" == \\\"Enable\\\" ] ; then\\n\\t \\t\\techo \\\"$logStart Allow download signed enabled in config file, enabling\\\" >> \\\"$logFile\\\"\\n\\t \\t\\t/usr/libexec/ApplicationFirewall/socketfilterfw --setallowsigned on >> \\\"$logFile\\\" 2>&1\\n\\t \\telse\\n\\t \\techo \\\"$logStart Allow download signed disabled in config file\\\" >> \\\"$logFile\\\"\\n\\t \\tfi\\n\\tfi\\n\\t#####\\n\\tif [ \\\"$allowSigned2\\\" == \\\"1\\\" ] ; then\\n\\t\\techo \\\"$logStart Allow signed enabled\\\" >> \\\"$logFile\\\"\\n\\t\\tif [ \\\"$configAllow\\\" == \\\"Enable\\\" ] ; then\\n\\t \\t\\techo \\\"$logStart Allow signed enabled in config file\\\" >> \\\"$logFile\\\"\\n\\t \\telse\\n\\t \\techo \\\"$logStart Allow signed disabled in config file, disabling\\\" >> \\\"$logFile\\\"\\n\\t \\t/usr/libexec/ApplicationFirewall/socketfilterfw --setallowsigned off >> \\\"$logFile\\\" 2>&1\\n\\t \\tfi\\n\\telse\\n\\t\\techo \\\"$logStart Allow signed disabled\\\" >> \\\"$logFile\\\"\\n\\t\\tif [ \\\"$configAllow\\\" == \\\"Enable\\\" ] ; then\\n\\t \\t\\techo \\\"$logStart Allow signed enabled in config file, enabling\\\" >> \\\"$logFile\\\"\\n\\t \\t\\t/usr/libexec/ApplicationFirewall/socketfilterfw --setallowsigned on >> \\\"$logFile\\\" 2>&1\\n\\t \\telse\\n\\t \\techo \\\"$logStart Allow download signed disabled in config file\\\" >> \\\"$logFile\\\"\\n\\t \\tfi\\n\\tfi\\n}\\n\\nfunction check_stealth {\\n\\tstealthModeState=\\\"$(defaults read /Library/Preferences/com.apple.alf.plist stealthenabled)\\\"\\n\\tconfigAllow=\\\"$(cat $configFile | awk '/StealthMode/ {print $2}')\\\"\\n\\tif [ \\\"$stealthModeState\\\" == \\\"1\\\" ] ; then\\n\\t\\techo \\\"$logStart Stealth Mode enabled\\\" >> \\\"$logFile\\\"\\n\\t\\tif [ \\\"$configAllow\\\" == \\\"Enable\\\" ] ; then\\n\\t\\t\\techo \\\"$logStart Stealth Mode enabled in config file\\\" >> \\\"$logFile\\\"\\n\\t\\telse\\n\\t\\t\\techo \\\"$logStart Stealth Mode disabled in config file\\\" >> \\\"$logFile\\\"\\n\\t\\t\\tdefaults write /Library/Preferences/com.apple.alf.plist stealthenabled -bool False >> \\\"$logFile\\\" 2>&1\\n\\t\\tfi\\n\\telse\\n\\t\\techo \\\"$logStart Stealth Mode disabled\\\" >> \\\"$logFile\\\"\\n\\t\\tif [ \\\"$configAllow\\\" == \\\"Enable\\\" ] ; then\\n\\t\\t\\techo \\\"$logStart Stealth Mode enabled in config file, enabling\\\" >> \\\"$logFile\\\"\\n\\t\\t\\tdefaults write /Library/Preferences/com.apple.alf.plist stealthenabled -bool True >> \\\"$logFile\\\" 2>&1\\n\\t\\telse\\n\\t\\t\\techo \\\"$logStart Stealth Mode disabled in config file\\\" >> \\\"$logFile\\\"\\n\\t\\tfi\\n\\tfi\\n}\\n\\nfunction turn_on {\\n\\tif [ \\\"$check_scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n\\t\\texit\\n\\telse\\n\\t\\techo \\\"$logStart Turning firewall back on\\\" >> \\\"$logFile\\\"\\n\\t\\t/usr/libexec/ApplicationFirewall/socketfilterfw --setglobalstate on >> \\\"$logFile\\\" 2>&1\\n\\tfi\\n}\\n\\n### SCRIPT\\ncheck_scriptEnabled\\ncheck_on\\ncheck_logging\\ncheck_signed\\ncheck_stealth\\nturn_on\\nexit\\n\"\n },\n {\n \"alpha_fraction\": 0.7161458134651184,\n \"alphanum_fraction\": 0.7256944179534912,\n \"avg_line_length\": 40.14285659790039,\n \"blob_id\": \"adb60ba2c514240bdde5a6d123ccaca019a51064\",\n \"content_id\": \"d4f38b82a1d1fd50a1b26a0efec6c374954398a9\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1152,\n \"license_type\": \"permissive\",\n \"max_line_length\": 112,\n \"num_lines\": 28,\n \"path\": \"/sus\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# sus (Unix Exec.)\\n# set or not set the software update server\\n# Last Edited: 6/25/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\nscriptEnabled=\\\"$(cat $configFile | awk '/SUS/ {print $2}')\\\"\\ncatalogURL=\\\"$(cat $configFile | awk '/ServerAddress/ {print $2}')\\\"\\n\\n### SCRIPT\\nif [ \\\"$scriptEnabled\\\" == \\\"Enable\\\" ] ; then\\n\\t# set to sus determined in config file\\n\\tchkConfiguredSUS=\\\"$(defaults read /Library/Preferences/com.apple.SoftwareUpdate.plist CatalogURL)\\\"\\n\\tif [ \\\"$chkConfiguredSUS\\\" == \\\"$catalogURL\\\" ] ; then\\n\\t\\techo \\\"$logStart Software Update Server already configured to $catalogURL\\\" >> \\\"$logFile\\\"\\n\\telse\\n\\t\\techo \\\"$logStart Setting Software Update Server to $catalogURL\\\" >> \\\"$logFile\\\"\\n\\t\\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate.plist CatalogURL \\\"$catalogURL\\\" >> \\\"$logFile\\\" 2>&1\\n\\tfi\\nelif [ \\\"$scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n\\techo \\\"$logStart Software Update Server configuration is disabled in the configuration file\\\" >> \\\"$logFile\\\"\\n\\tdefaults delete /Library/Preferences/com.apple.SoftwareUpdate.plist CatalogURL >> \\\"$logFile\\\" 2>&1\\nelse\\n\\techo \\\"$logStart SUS configuration is invalid\\\" >> \\\"$logFile\\\"\\nfi\\nexit\\n\"\n },\n {\n \"alpha_fraction\": 0.6620553135871887,\n \"alphanum_fraction\": 0.6798418760299683,\n \"avg_line_length\": 24.299999237060547,\n \"blob_id\": \"44febe0d3bb8d9a5293bbfeba41a7b8bf7f90525\",\n \"content_id\": \"e34e023992df5898d7f4ab9ae0c1dea11c382e31\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 506,\n \"license_type\": \"permissive\",\n \"max_line_length\": 81,\n \"num_lines\": 20,\n \"path\": \"/massmgmtlib.sh\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# massmgmtlib.sh\\n# bash library for massmgmt\\n# Last Edited: 7/2/18\\n\\n### VARIABLES\\n# files\\nconfigFile=\\\"/usr/local/massmgmt/massmgmt.config\\\"\\nlogFile=\\\"$(cat $configFile | awk '/LogFile/ {print $2}')\\\"\\n\\n# user information\\nadminUserID=\\\"$(cat $configFile | awk '/AdminUserID/ {print $2}')\\\"\\nadminUser=\\\"$(dscl . list /Users UniqueID | grep $adminUserID | awk '{print $1}')\\\"\\n\\n# system information\\nstarDate=\\\"$(date +20%y-%m-%d_%H:%M:%S)\\\"\\nhostName=\\\"$(hostname)\\\"\\n\\n# log entry\\nlogStart=\\\"$starDate $hostName\\\"\\n\"\n },\n {\n \"alpha_fraction\": 0.7381370663642883,\n \"alphanum_fraction\": 0.764938473701477,\n \"avg_line_length\": 21.097087860107422,\n \"blob_id\": \"47b5f9f928424d86844d212863e83ea67e6ac35d\",\n \"content_id\": \"7c4b93be8a203dc4b38a5c367aa4bcabab9e9d05\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 2276,\n \"license_type\": \"permissive\",\n \"max_line_length\": 71,\n \"num_lines\": 103,\n \"path\": \"/installmassmgmt.sh\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# installmassmgmt.sh\\n# install the components of the massmgmt suite in the correct locations\\n# use as a postinstall script when packaging in .pkg format\\n# Last Edited: 11/13/18\\n\\nuserName=\\\"$(whoami)\\\"\\n\\n# check if we have the appropriate privileges\\nif [ \\\"$userName\\\" != \\\"root\\\" ] ; then\\n\\techo \\\"$0 must be run using sudo\\\"\\n\\texit\\nfi\\n\\n# remove previous installation\\nif [ -e \\\"/usr/local/massmgmt\\\" ] ; then\\n rm -rf /usr/local/massmgmt\\n rm /usr/local/bin/massmgmt\\n rm /Library/LaunchDaemons/org.massmgmt.plist\\nfi\\n\\n# set up directories\\nmkdir /usr/local/massmgmt\\nmkdir /usr/local/massmgmt/bin\\nmkdir /usr/local/massmgmt/conf\\nmkdir /usr/local/massmgmt/mconfig\\nmkdir /usr/local/massmgmt/lib\\n\\n# move components into place\\ncd /tmp/massmgmt\\n\\ncp massmgmt /usr/local/massmgmt/\\n\\ncopyLoc=\\\"/usr/local/massmgmt/bin\\\"\\n\\ncp autoUpdate $copyLoc\\ncp adobeReader $copyLoc\\ncp checkChrome $copyLoc\\ncp chrome $copyLoc\\ncp firefox $copyLoc\\ncp guestAccount $copyLoc\\ncp enableSSH $copyLoc\\ncp enableARD $copyLoc\\ncp lockSysPrefs $copyLoc\\ncp munki $copyLoc\\ncp powerOptions $copyLoc\\ncp setFirewall $copyLoc\\ncp setHostname $copyLoc\\ncp setTimezone $copyLoc\\ncp sus $copyLoc\\n\\ncp com.google.Chrome.plist /usr/local/massmgmt/conf\\ncp massmgmt.config /usr/local/massmgmt\\n\\ncp massmgmtlib.sh /usr/local/massmgmt/lib\\n\\ncp org.massmgmt.plist /Library/LaunchDaemons\\n\\nif [ -d /usr/local/bin ] ; then\\n\\techo \\\"/usr/local/bin exists!\\\"\\nelse\\n\\tmkdir /usr/local/bin\\nfi\\n\\nln -s '/usr/local/massmgmt/massmgmt' '/usr/local/bin/'\\n\\n# chown\\n\\nconfigFile=\\\"/usr/local/massmgmt/massmgmt.config\\\"\\nadminUser=\\\"$(cat $configFile | awk '/AdminUserID/ {print $2}')\\\"\\n\\n# touch the massmgmt.log file so we can chown it\\ntouch /usr/local/massmgmt/massmgmt.log\\n\\n# chown the entire directory\\nchown -R $adminUser /usr/local/massmgmt\\n\\n# chmod\\nchmod 755 /usr/local/massmgmt/massmgmt\\ncd /usr/local/massmgmt/bin\\nchmod 755 autoUpdate\\nchmod 755 adobeReader\\nchmod 755 chrome\\nchmod 755 firefox\\nchmod 755 guestAccount\\nchmod 755 enableSSH\\nchmod 755 enableARD\\nchmod 755 lockSysPrefs\\nchmod 755 munki\\nchmod 755 powerOptions\\nchmod 755 setFirewall\\nchmod 755 setHostname\\nchmod 755 setTimezone\\nchmod 755 sus\\nchmod 755 checkChrome\\n\\nchmod 755 /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\nif [ -e \\\"/usr/local/massmgmt/massmgmt.config\\\" ] ; then\\n exit 0\\nelse\\n exit 1\\nfi\\n\"\n },\n {\n \"alpha_fraction\": 0.7086931467056274,\n \"alphanum_fraction\": 0.7202072739601135,\n \"avg_line_length\": 37.599998474121094,\n \"blob_id\": \"4aba57e62e9230bc80cd17222176d4167cfc3c7e\",\n \"content_id\": \"859283a4174c1d5f69d30dc8d8da4b2e95cb776a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1737,\n \"license_type\": \"permissive\",\n \"max_line_length\": 106,\n \"num_lines\": 45,\n \"path\": \"/lockSysPrefs\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# lockSysPrefs (UNIX. Exec.)\\n# Script to lock system-wide preferences from non-admin users\\n# https://derflounder.wordpress.com/2014/02/16/managing-the-authorization-database-in-os-x-mavericks/\\n# Last Edited: 6/25/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\nscriptEnabled=\\\"$(cat $configFile | awk '/LockSystemPreferences/ {print $2}')\\\"\\n\\n### FUNCTIONS\\nfunction check_enabled {\\n\\t if [ \\\"$scriptEnabled\\\" == \\\"Enable\\\" ] ; then\\n\\t\\t echo \\\"$logStart lockSysPrefs is enabled, locking\\\" >> \\\"$logFile\\\"\\n\\t\\t lock_prefs\\n\\t elif [ \\\"$scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n\\t\\t echo \\\"$logStart lockSysPrefs is disabled, unlocking\\\" >> \\\"$logFile\\\"\\n\\t\\t unlock_prefs\\n\\t else\\n\\t\\t echo \\\"$logStart lockSysPrefs has an invalid configuration in the config file, exiting\\\" >> \\\"$logFile\\\"\\n\\t\\t exit\\n\\t fi\\n}\\n\\nfunction lock_prefs {\\n\\t echo \\\"$logStart Locking system-wide preferences from non-admin users\\\" >> \\\"$logFile\\\"\\n\\t security authorizationdb read system.preferences > /tmp/system.preferences.plist 2>> \\\"$logFile\\\"\\n\\t /usr/libexec/PlistBuddy -c 'Set :shared false' /tmp/system.preferences.plist 2>> \\\"$logFile\\\"\\n \\t security authorizationdb write system.preferences < /tmp/system.preferences.plist 2>> \\\"$logFile\\\"\\n\\t rm /tmp/system.preferences.plist\\n}\\n\\nfunction unlock_prefs {\\n\\t echo \\\"$logStart Unlocking system-wide preferences from non-admin users\\\" >> \\\"$logFile\\\"\\n\\t security authorizationdb read system.preferences > /tmp/system.preferences.plist 2>> \\\"$logFile\\\"\\n\\t /usr/libexec/PlistBuddy -c 'Set :shared true' /tmp/system.preferences.plist 2>> \\\"$logFile\\\"\\n\\t security authorizationdb write system.preferences < /tmp/system.preferences.plist 2>> \\\"$logFile\\\"\\n\\t rm /tmp/system.preferences.plist\\n}\\n\\n\\n### SCRIPT\\ncheck_enabled\\nexit\\n\"\n },\n {\n \"alpha_fraction\": 0.7339877486228943,\n \"alphanum_fraction\": 0.7384049296379089,\n \"avg_line_length\": 38.95098114013672,\n \"blob_id\": \"183f54ff407b052c09be625e76461143e0687800\",\n \"content_id\": \"5ae963337b7d79b1e429aa41314235e9095a78e7\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 4075,\n \"license_type\": \"permissive\",\n \"max_line_length\": 140,\n \"num_lines\": 102,\n \"path\": \"/guestAccount\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# guestAccount\\n# disable the macOS guest account\\n# based on hxxps://github[.]com/sheagcraig/guestAccount/blob/master/guest_account by Shea Craig\\n# Last Edited: 6/26/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\nguest=\\\"$(cat $configFile | awk '/GuestAccount/ {print $2}')\\\"\\nenabled=\\\"$(defaults read /Library/Preferences/com.apple.loginwindow GuestEnabled)\\\"\\nguestPassword=\\\"$(cat $configFile | awk '/GuestPassword/ {print $2}')\\\"\\nfileServerAccess=\\\"$(cat $configFile | awk '/GuestFileServerAccess/ {print $2}')\\\"\\n\\n### FUNCTIONS\\nfunction server_access {\\n\\t# allow or deny file server access\\n\\tif [ \\\"$fileServerAccess\\\" == \\\"Allow\\\" ] ; then\\n\\t\\tdefaults write /Library/Preferences/com.apple.AppleFileServer guestAccess -bool YES\\n\\t\\tdefaults write /Library/Preferences/SystemConfiguration/com.apple.smb.server AllowGuestAccess -bool YES\\n\\telif [ \\\"$fileServerAccess\\\" == \\\"Deny\\\" ] ; then\\n\\t\\tdefaults write /Library/Preferences/com.apple.AppleFileServer guestAccess -bool NO\\n\\t\\tdefaults write /Library/Preferences/SystemConfiguration/com.apple.smb.server AllowGuestAccess -bool NO\\n\\telse\\n\\t\\techo \\\"$logStart Guest File Server Access Not Specified\\\" >> \\\"$logFile\\\"\\n\\tfi\\n}\\n\\nfunction disable_guest {\\n\\tif [ -e \\\"/Users/Guest\\\" ] ; then\\n\\t\\techo \\\"$logStart Deleting and Disabling the Guest User Account\\\" >> \\\"$logFile\\\"\\n\\t\\tdscl . -delete /Users/Guest\\n\\t\\t# write to the login window plist to disable Guest\\n\\t\\tdefaults write /Library/Preferences/com.apple.loginwindow GuestEnabled -bool NO\\n\\t\\techo \\\"$logStart Guest User Account Disabled\\\" >> \\\"$logFile\\\"\\n\\tfi\\n}\\n\\nfunction enable_guest {\\n\\techo \\\"$logStart Creating and Enabling the Guest User Account\\\"\\n\\tdscl . -create /Users/Guest\\n\\tdefaults write /Library/Preferences/com.apple.loginwindow GuestEnabled -bool YES\\n\\t\\n\\tdscl . -create /Users/Guest dsAttrTypeNative:_defaultLanguage en\\n\\tdscl . -create /Users/Guest dsAttrTypeNative:_guest true\\n\\tdscl . -create /Users/Guest dsAttrTypeNative:_writers__defaultLanguage Guest\\n\\tdscl . -create /Users/Guest dsAttrTypeNative:_writers__LinkedIdentity Guest\\n\\tdscl . -create /Users/Guest dsAttrTypeNative:_writers__UserCertificate Guest\\n\\tdscl . -create /Users/Guest AuthenticationHint ''\\n\\tdscl . -create /Users/Guest NFSHomeDirectory /Users/Guest\\n\\t# sometimes it takes a second for everything to get settled\\n\\tsleep 2\\n\\tdscl . -passwd /Users/Guest \\\"$guestPassword\\\"\\n\\tsleep 2\\n\\t\\n\\tdscl . -create /Users/Guest Picture \\\"/System/Library/CoreServices/CoreTypes.bundle/Contents/Resources/UserIcon.icns\\\"\\n\\tdscl . -create /Users/Guest PrimaryGroupID 201\\n\\tdscl . -create /Users/Guest RealName \\\"Guest User\\\"\\n\\tdscl . -create /Users/Guest RecordName Guest\\n\\tdscl . -create /Users/Guest UniqueID 201\\n\\tdscl . -create /Users/Guest UserShell /bin/bash\\n\\t#security add-generic-password -a Guest -s com.apple.loginwindow.guest-account -D \\\"application password\\\" /Library/Keychains/System.keychain\\n\\t\\n\\techo \\\"$logStart Guest User Account Enabled\\\"\\n}\\n\\n### SCRIPT\\necho \\\"$logStart Checking current status of the Guest User Account\\\" >> \\\"$logFile\\\"\\n\\nif [ \\\"$enabled\\\" == \\\"1\\\" ] ; then\\n\\t# guest account is enabled on the host\\n\\techo \\\"$logStart Guest User Account is currently enabled\\\" >> \\\"$logFile\\\"\\n\\tif [ \\\"$guest\\\" == \\\"Enable\\\" ] ; then\\n\\t\\techo \\\"$logStart Guest Account is already enabled\\\" >> \\\"$logFile\\\"\\n\\t\\texit\\n\\telif [ \\\"$guest\\\" == \\\"Disable\\\" ] ; then\\n\\t\\techo \\\"$logStart Guest Account is enabled but should be disabled\\\" >> \\\"$logFile\\\"\\n\\t\\tdisable_guest\\n\\t\\tserver_access\\n\\t\\texit\\n\\telse\\n\\t\\techo \\\"$logFile Invalid GuestAccount configuration\\\" >> \\\"$logFile\\\"\\n\\t\\texit\\n\\tfi\\nelif [ \\\"$enabled\\\" == \\\"0\\\" ] ; then\\n\\t# guest account is disabled on the host\\n\\techo \\\"$logStart Guest User Account is currently disabled\\\" >> \\\"$logFile\\\"\\n\\tif [ \\\"$guest\\\" == \\\"Enable\\\" ] ; then\\n\\t\\techo \\\"$logStart Guest Account is currently disabled but should be enabled\\\" >> \\\"$logFile\\\"\\n\\t\\tenable_guest\\n\\t\\tserver_access\\n\\t\\texit\\n\\telif [ \\\"$guest\\\" == \\\"Disable\\\" ] ; then\\n\\t\\techo \\\"$logStart Guest Account is already disabled\\\" >> \\\"$logFile\\\"\\n\\t\\texit\\n\\telse\\n\\t\\techo \\\"$logFile Invalid GuestAccount configuration\\\" >> \\\"$logFile\\\"\\n\\t\\texit\\n\\tfi\\nelse\\n\\techo \\\"$logStart Could not determine the status of the Guest User Account\\\" >> \\\"$logFile\\\"\\n\\texit\\nfi\\n\"\n },\n {\n \"alpha_fraction\": 0.6900826692581177,\n \"alphanum_fraction\": 0.6983470916748047,\n \"avg_line_length\": 36.230770111083984,\n \"blob_id\": \"2bae57fdd4f8b861b8449a14c589da39a96e93d0\",\n \"content_id\": \"1d53b5271503a99c763f9846392406f27c7d6778\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 968,\n \"license_type\": \"permissive\",\n \"max_line_length\": 87,\n \"num_lines\": 26,\n \"path\": \"/munki\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# munki (UNIX exec.)\\n# configure munki (if desired)\\n# Last Edited: 6/16/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\nscriptEnabled=\\\"$(cat $configFile | awk '/Munki/ {print $2}')\\\"\\nmunkiURL=\\\"$(cat $configFile | awk '/MunkiURL/ {print $2}')\\\"\\nmunkiClient=\\\"$(cat $configFile | awk '/MunkiClient/ {print $2}')\\\"\\n\\n### SCRIPT\\nif [ \\\"$scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n echo \\\"$logStart Munki configuration is disabled\\\" >> \\\"$logFile\\\"\\n exit\\nelif [ \\\"$scriptEnabled\\\" == \\\"Enable\\\" ] ; then\\n echo \\\"$logStart Configuring Munki URL: $munkiURL\\\" >> \\\"$logFile\\\"\\n defaults write /Library/Preferences/ManagedInstalls SoftwareRepoURL \\\"$munkiURL\\\"\\n echo \\\"$logStart Configuring Munki Client: $munkiClient\\\" >> \\\"$logFile\\\"\\n defaults write /Library/Preferences/ManagedInstalls ClientIdentifier \\\"$munkiClient\\\"\\n echo \\\"$logStart Munki configuration complete\\\" >> \\\"$logFile\\\"\\nelse\\n echo \\\"$logStart Munki configuration invalid\\\" >> \\\"$logFile\\\"\\nfi\\nexit\\n\"\n },\n {\n \"alpha_fraction\": 0.6513761281967163,\n \"alphanum_fraction\": 0.657214343547821,\n \"avg_line_length\": 34.264705657958984,\n \"blob_id\": \"1f93b894a37237dab555abe56f8d5a60cec18b16\",\n \"content_id\": \"d88e125ec04c5d2b5753a228281a1bb327646151\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1199,\n \"license_type\": \"permissive\",\n \"max_line_length\": 115,\n \"num_lines\": 34,\n \"path\": \"/setHostname\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# setName (UNIX exec.)\\n# set Hostname\\n# Last Edited: 6/25/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\nscriptEnabled=\\\"$(cat $configFile | awk '/SetHostname/ {print $2}')\\\"\\nhostnameScheme=\\\"$(cat $configFile | awk '/HostnameFormat/ {print $2}')\\\"\\n\\n### SCRIPT\\nif [ \\\"$scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n echo \\\"$logStart setHostname disabled\\\" >> \\\"$logFile\\\"\\n exit\\nelif [ \\\"$scriptEnabled\\\" == \\\"Enable\\\" ] ; then\\n echo \\\"$logStart setHostname enabled\\\" >> \\\"$logFile\\\"\\n if [ \\\"$hostnameScheme\\\" == \\\"Serial\\\" ] ; then\\n\\tserial=\\\"$(/usr/sbin/system_profiler SPHardwareDataType | /usr/bin/awk '/Serial\\\\ Number\\\\ \\\\(system\\\\)/ {print $NF}')\\\"\\n\\tif [ \\\"$hostName\\\" == \\\"$serial\\\" ] ; then\\n \\t echo \\\"$logStart The computer hostname already set to serial number, exiting\\\" >> \\\"$logFile\\\"\\n\\telse\\n \\t scutil --set ComputerName $serial\\n \\t scutil --set HostName $serial\\n \\t scutil --set LocalHostName $serial\\n \\t echo \\\"$logStart Computer hostname set to $serial, exiting\\\" >> \\\"$logFile\\\"\\n\\tfi\\n else\\n\\techo \\\"$logStart non-serial hostname format. Implement this feature\\\" >> \\\"$logFile\\\"\\n fi\\nelse\\n echo \\\"$logStart Invalid setHostname configuration\\\" >> \\\"$logFile\\\"\\nfi\\nexit\\n\"\n },\n {\n \"alpha_fraction\": 0.69749516248703,\n \"alphanum_fraction\": 0.7042388916015625,\n \"avg_line_length\": 40.52000045776367,\n \"blob_id\": \"38f7fdf805665e9955d7949873305273a678f4bd\",\n \"content_id\": \"9620c9a7171381a3af2fac39ee6b61e692073f34\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 5190,\n \"license_type\": \"permissive\",\n \"max_line_length\": 127,\n \"num_lines\": 125,\n \"path\": \"/autoUpdate\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# autoUpdateCheck (UNIX Exec.)\\n# Check to see if auto updates are set to the desired value\\n# Last Edited: 10/30/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\n# check curent settings\\nautoCheckEnabled=\\\"$(defaults read /Library/Preferences/com.apple.SoftwareUpdate | awk '/AutomaticCheckEnabled = / {print $3}')\\\"\\ncommerceAutoUpdate=\\\"$(defaults read /Library/Preferences/com.apple.commerce | awk '/AutoUpdate = / {print $3}')\\\"\\nautoMacUpdate=\\\"$(defaults read /Library/Preferences/com.apple.commerce | awk '/AutoUpdateRestartRequired = / {print $3}')\\\"\\nautoMacDownload=\\\"$(defaults read /Library/Preferences/com.apple.SoftwareUpdate | awk '/AutomaticDownload = / {print $3}')\\\"\\nsysDataFiles1=\\\"$(defaults read /Library/Preferences/com.apple.SoftwareUpdate | awk '/CriticalUpdateInstall = / {print $3}')\\\"\\nsysDataFiles2=\\\"$(defaults read /Library/Preferences/com.apple.SoftwareUpdate | awk '/ConfigDataInstall = / {print $3}')\\\"\\n\\n# check config file for desired settings\\nautoCheck=\\\"$(cat $configFile | awk '/AutomaticCheck/ {print $2}')\\\"\\nappUpdate=\\\"$(cat $configFile | awk '/AppUpdate/ {print $2}')\\\"\\nmacUpdate=\\\"$(cat $configFile | awk '/MacOSUpdate/ {print $2}')\\\"\\nautoDownload=\\\"$(cat $configFile | awk '/AutomaticDownload/ {print $2}')\\\"\\nsystemFiles=\\\"$(cat $configFile | awk '/SystemDataFile/ {print $2}')\\\"\\n\\n# assign value for plist based on config file --------------------------------\\n# automatically check for updates\\nif [ \\\"$autoCheck\\\" == \\\"Enable\\\" ] ; then\\n\\tautoCheck=\\\"1;\\\"\\nelse\\n\\tautoCheck=\\\"0;\\\"\\nfi\\n\\n# update macOS apps\\nif [ \\\"$appUpdate\\\" == \\\"Enable\\\" ] ; then\\n\\tappUpdate=\\\"1;\\\"\\nelse\\n\\tappUpdate=\\\"0;\\\"\\nfi\\n\\n# update macOS automatically\\nif [ \\\"$macUpdate\\\" == \\\"Enable\\\" ] ; then\\n\\tmacUpdate=\\\"1;\\\"\\nelse\\n\\tmacUpdate=\\\"0;\\\"\\nfi\\n\\n# download updates automatically for installation\\nif [ \\\"$autoDownload\\\" == \\\"Enable\\\" ] ; then\\n\\tautoDownload=\\\"1;\\\"\\nelse\\n\\tautoDownload=\\\"0;\\\"\\nfi\\n\\n# install system files automatically\\nif [ \\\"$systemFiles\\\" == \\\"Enable\\\" ] ; then\\n\\tsystemFiles=\\\"1;\\\"\\nelse\\n\\tsystemFiles=\\\"0;\\\"\\nfi\\n\\n##### set parameters\\n# parameter that specifies scheduled checking for updates\\nif [ \\\"$autoCheck\\\" == \\\"$autoCheckEnabled\\\" ] ; then\\n\\techo \\\"$logStart Desired state in place for automatic checking for updates\\\" >> \\\"$logFile\\\"\\nelse\\n\\tif [ \\\"$autoCheck\\\" == \\\"0;\\\" ] ; then # if current value is false (off), turn on\\n \\techo \\\"$logStart Setting Software Update scheduled checks --> On\\\" >> \\\"$logFile\\\"\\n \\t\\tsoftwareupdate --schedule on\\n \\telse # if current value is true (on), turn off\\n\\t\\techo \\\"$logStart Setting Software Update scheduled checks --> Off\\\" >> \\\"$logFile\\\"\\n\\t\\tsoftwareupdate --schedule off\\n \\tfi\\nfi\\n# parameter for automatic app updates\\nif [ \\\"$appUpdate\\\" == \\\"$commerceAutoUpdate\\\" ] ; then\\n\\techo \\\"$logStart Desired state in place for app store automatic updates\\\" >> \\\"$logFile\\\"\\nelse\\n\\tif [ \\\"$appUpdate\\\" == \\\"0;\\\" ] ; then\\n \\t\\techo \\\"$logStart Setting automatic app updates --> On\\\" >> \\\"$logFile\\\"\\n \\t\\tdefaults write /Library/Preferences/com.apple.commerce AutoUpdate -bool TRUE\\n \\telse\\n \\t\\techo \\\"$logStart Setting automatic app updates --> Off\\\" >> \\\"$logFile\\\"\\n \\t\\tdefaults write /Library/Preferences/com.apple.commerce AutoUpdate -bool FALSE\\n \\tfi\\nfi\\n# parameter for macOS updates\\nif [ \\\"$macUpdate\\\" == \\\"$autoMacUpdate\\\" ] ; then\\n\\techo \\\"$logStart Desired state in place for automatic macOS updates\\\" >> \\\"$logFile\\\"\\nelse\\n\\tif [ \\\"$macUpdate\\\" == \\\"0;\\\" ] ; then\\n \\techo \\\"$logStart Setting automatic macOS updates --> On\\\" >> \\\"$logFile\\\"\\n \\t\\tdefaults write /Library/Preferences/com.apple.commerce AutoUpdateRestartRequired -bool TRUE\\n \\telse\\n \\t\\techo \\\"$logStart Setting automatic macOS updates --> Off\\\" >> \\\"$logFile\\\"\\n \\t\\tdefaults write /Library/Preferences/com.apple.commerce AutoUpdateRestartRequired -bool FALSE\\n \\tfi\\nfi\\n# parameter for automatic download of available updates\\nif [ \\\"$autoDownload\\\" == \\\"$autoMacDownload\\\" ] ; then\\n\\techo \\\"$logStart Desired state in place for automatic downloads of macOS updates\\\" >> \\\"$logFile\\\"\\nelse\\n\\tif [ \\\"$autoMacDownload\\\" == \\\"0;\\\" ] ; then\\n \\t\\techo \\\"$logStart Setting automatic download --> On\\\" >> \\\"$logFile\\\"\\n \\t\\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate AutomaticDownload -bool TRUE\\n \\telse\\n \\t\\techo \\\"$logStart Setting automatic download --> Off\\\" >> \\\"$logFile\\\"\\n \\t\\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate AutomaticDownload -bool FALSE\\n \\tfi\\nfi\\n# \\\"install system data files\\\"\\nif [ \\\"$systemFiles\\\" == \\\"$sysDataFiles1\\\" ] ; then\\n\\techo \\\"$logStart Desired state in place for automatic macOS updates\\\" >> \\\"$logFile\\\"\\nelse\\n\\tif [ \\\"$systemFiles\\\" == \\\"0;\\\" ] ; then\\n \\t\\techo \\\"$logStart Setting install system data files --> On\\\" >> \\\"$logFile\\\"\\n \\t\\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate CriticalUpdateInstall -bool TRUE\\n \\t\\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate ConfigDataInstall -bool TRUE\\n \\telse\\n \\t\\techo \\\"$logStart Setting install system data files --> Off ***Not Recommended\\\" >> \\\"$logFile\\\"\\n \\t\\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate CriticalUpdateInstall -bool False\\n \\t\\tdefaults write /Library/Preferences/com.apple.SoftwareUpdate ConfigDataInstall -bool False\\n \\tfi\\nfi\\n\\necho \\\"$logStart All parameters set, exiting\\\" >> \\\"$logFile\\\"\\nexit\\n\"\n },\n {\n \"alpha_fraction\": 0.6153255105018616,\n \"alphanum_fraction\": 0.6187824010848999,\n \"avg_line_length\": 27.927778244018555,\n \"blob_id\": \"bcaf83c7a5de3cea6e921fab9c1074e46c49bb8d\",\n \"content_id\": \"3a869a0f3ee9a85f5b16005a222665031b930b5c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 5207,\n \"license_type\": \"permissive\",\n \"max_line_length\": 96,\n \"num_lines\": 180,\n \"path\": \"/massmgmt\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# massmgmt\\n# launchd-based macOS desired-state management\\n# Last Edited: 6/25/18\\n\\n### VARIABLES\\nconfigFile=\\\"/usr/local/massmgmt/massmgmt.config\\\"\\n\\n# define admin user\\nadminUser=\\\"$(cat $configFile | awk '/AdminUserID/ {print $2}')\\\"\\n\\n# log file\\nlogFile=\\\"$(cat $configFile | awk '/LogFile/ {print $2}')\\\"\\nstarDate=\\\"$(date)\\\"\\nhostName=\\\"$(hostname)\\\"\\n\\n# define location of help page\\nhelpPage=\\\"$(cat $configFile | awk '/HelpPage/ {print $2}')\\\"\\n\\n# define bin location for scripts\\nbinLoc=\\\"$(cat $configFile | awk '/ScriptLocation/ {print $2}')\\\"\\n\\n# define directory for configuration profiles\\nprofLoc=\\\"$(cat $configFile | awk '/MobileConfigLocation/ {print $2}')\\\"\\n\\n### FUNCTIONS\\nfunction massmgmtRun {\\n $binLoc/autoUpdate\\n $binLoc/adobeReader\\n $binLoc/checkChrome\\n $binLoc/chrome\\n $binLoc/enableARD\\n $binLoc/enableSSH\\n $binLoc/firefox\\n $binLoc/guestAccount\\n $binLoc/lockSysPrefs\\n $binLoc/munki\\n $binLoc/powerOptions\\n $binLoc/setFirewall\\n $binLoc/setHostname\\n $binLoc/setTimezone\\n $binLoc/sus\\n}\\n\\nfunction interactiveMode {\\n echo\\n ls -l \\\"$binLoc\\\" | awk '/-/ {print $9}'\\n echo\\n printf \\\"Run which script? Or enter 'q' to quit -> \\\"\\n read A\\n if [ \\\"$A\\\" == \\\"q\\\" ] ; then\\n \\texit\\n elif [ \\\"$A\\\" == \\\"autoUpdate\\\" ] ; then\\n\\techo \\\"$logStart -- Running autoUpdate interactively\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/autoUpdate\\n elif [ \\\"$A\\\" == \\\"adobeReader\\\" ] ; then\\n \\techo \\\"$logStart -- Running adobeReader interactively\\\" | tee \\\"$logFile\\\"\\n\\t$binLoc/adobeReader\\n elif [ \\\"$A\\\" == \\\"chrome\\\" ] ; then\\n\\techo \\\"$logStart -- Running chrome interactively\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/checkChrome\\n\\t$binLoc/chrome\\n elif [ \\\"$A\\\" == \\\"enableARD\\\" ] ; then\\n\\techo \\\"$logStart -- Running enableARD interactively\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/enableARD\\n elif [ \\\"$A\\\" == \\\"enableSSH\\\" ] ; then\\n\\techo \\\"$logStart -- Running enableSSH interactively\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/enableSSH \\n elif [ \\\"$A\\\" == \\\"firefox\\\" ] ; then\\n \\techo \\\"$logStart -- Running firefox interactively\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/firefox\\n elif [ \\\"$A\\\" == \\\"guestAccount\\\" ] ; then\\n \\techo \\\"$logStart -- Running guestAccount interactively\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/guestAccount\\n elif [ \\\"$A\\\" == \\\"munki\\\" ] ; then\\n \\techo \\\"$logStart -- Running munki interactively\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/munki\\n elif [ \\\"$A\\\" == \\\"lockSysPrefs\\\" ] ; then\\n \\techo \\\"$logStart --Running lockSysPrefs interactively\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/lockSysPrefs\\n elif [ \\\"$A\\\" == \\\"powerOptions\\\" ] ; then\\n \\techo \\\"$logStart -- Running powerOptions interactively\\\" | tee \\\"$logFile\\\"\\n\\t$binLoc/powerOptions\\n elif [ \\\"$A\\\" == \\\"setFirewall\\\" ] ; then\\n \\techo \\\"$logStart -- Running setFirewall\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/setFirewall\\n elif [ \\\"$A\\\" == \\\"setHostname\\\" ] ; then\\n \\techo \\\"$logStart -- Running setHostname interactively\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/setHostname\\n elif [ \\\"$A\\\" == \\\"setTimeZone\\\" ] ; then\\n \\techo \\\"$logStart -- Running setTimezone interactively\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/setTimezone\\n elif [ \\\"$A\\\" == \\\"sus\\\" ] ; then\\n \\techo \\\"$logStart -- Running sus interactively\\\" | tee \\\"$logFile\\\"\\n \\t$binLoc/sus\\n else\\n \\tinteractiveMode\\n fi\\n}\\n\\nfunction flushLog {\\n printf \\\"Are you sure you want to flush the log directory? (y/n) -> \\\"\\n read A\\n if [ \\\"$A\\\" == \\\"n\\\" ] ; then\\n exit\\n elif [ \\\"$A\\\" == \\\"y\\\" ] ; then\\n echo \\\"Removing $logFile\\\"\\n rm \\\"$logFile\\\"\\n touch \\\"$logFile\\\"\\n echo \\\"Log flushed\\\"\\n exit\\n else\\n echo \\\"Invalid option\\\"\\n fi\\n}\\n\\nfunction pullLog {\\n currentUser=\\\"$(id -un)\\\"\\n echo \\\"Pulling log\\\"\\n cp \\\"$logFile\\\" \\\"/Users/$currentUser/Desktop/\\\"\\n if [ -e \\\"/Users/$currentUser/Desktop/massmgmt.log\\\" ] ; then\\n \\topen \\\"/Users/$currentUser/Desktop\\\"\\n\\topen \\\"/Users/$currentUser/Desktop/massmgmt.log\\\"\\n \\techo \\\"Process Complete\\\"\\n else\\n \\texit\\n fi\\n}\\n\\nfunction rmProfiles {\\n echo \\\"Removing all configuration profiles installed on this computer.\\\"\\n profiles -D\\n}\\n\\nfunction installProfiles {\\n printf \\\"Install all configuration profiles stored in /usr/local/massmgmt/mconfig? (y/n) -> \\\"\\n read B\\n if [ \\\"$B\\\" == \\\"n\\\" ] ; then\\n exit\\n elif [ \\\"B\\\" == \\\"y\\\" ] ; then\\n echo \\\"Installing all configuration profiles stored in $profLoc\\\"\\n cd \\\"$profLoc\\\"\\n ls -l | awk '/-rw/ {print $(9) }' >> \\\"$profLoc/profiles.txt\\\"\\n cat \\\"$profLoc/profiles.txt\\\" | while read line\\n do\\n profiles -I \\\"$profLoc/$line\\\"\\n echo \\\"Profiles installed.\\\"\\n done\\n else\\n exit\\n fi\\n}\\n\\n### SCRIPT\\n# check if we are running with root privs\\nif [ \\\"$(whoami)\\\" != \\\"root\\\" ] ; then\\n echo \\\"$logStart Attempt to run as non-root -> $(whoami)\\\" >> \\\"$logFile\\\"\\n exit\\nfi\\n\\n# check arguments\\n# options that can be called using: $ massmgmt <option>\\nif [ \\\"$1\\\" == \\\"flushLog\\\" ] ; then\\n flushLog\\nelif [ \\\"$1\\\" == \\\"pullLog\\\" ] ; then\\n pullLog\\nelif [ \\\"$1\\\" == \\\"rmProfiles\\\" ] ; then\\n rmProfiles\\nelif [ \\\"$1\\\" == \\\"installProfiles\\\" ] ; then\\n installProfiles\\nelif [ \\\"$1\\\" == \\\"help\\\" ] ; then\\n cat $helpPage\\nelif [ \\\"$1\\\" == \\\"-im\\\" ] ; then\\n echo \\\"Entering interactive mode...\\\"\\n interactiveMode\\nelse\\n # if no option is specified, $ massmgmt will run the tool\\n massmgmtRun\\nfi\\nexit\\n\"\n },\n {\n \"alpha_fraction\": 0.6886409521102905,\n \"alphanum_fraction\": 0.6942190527915955,\n \"avg_line_length\": 40.08333206176758,\n \"blob_id\": \"49818240bf56f107d418e4995bd3f70ae31b2468\",\n \"content_id\": \"ef63162742c14ffffcf3deba87fc6df49ded249e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Shell\",\n \"length_bytes\": 1972,\n \"license_type\": \"permissive\",\n \"max_line_length\": 114,\n \"num_lines\": 48,\n \"path\": \"/enableSSH\",\n \"repo_name\": \"ez3r0sec/massmgmt\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#!/bin/bash\\n# enableSSH (UNIX exec.)\\n# enables ssh for $adminUser\\n# Last Edited: 6/25/18\\n\\n### VARIABLES\\nsource /usr/local/massmgmt/lib/massmgmtlib.sh\\n\\nsshCheck=\\\"$(systemsetup getremotelogin | awk '/Remote/ {print $(3) }')\\\"\\nscriptEnabled=\\\"$(cat $configFile | awk '/EnableSSH/ {print $2}')\\\"\\n\\n### SCRIPT\\n# check if the script is enabled in the config file\\nif [ \\\"$scriptEnabled\\\" == \\\"Disable\\\" ] ; then\\n\\techo \\\"$logStart enableSSH is disabled in the configuration file\\\" >> \\\"$logFile\\\"\\n\\tif [ \\\"$sshCheck\\\" == \\\"On\\\" ] ; then\\n \\t\\techo \\\"$logStart SSH disabled but is on, turning off SSH\\\" >> \\\"$logFile\\\"\\n\\t\\tsystemsetup setremotelogin off\\n\\t\\tsystemsetup setremotelogin off\\n\\telse\\n\\t\\techo \\\"$logStart SSH is disabled\\\" >> \\\"$logFile\\\"\\n\\tfi\\n\\texit\\nelif [ \\\"$scriptEnabled\\\" == \\\"Enable\\\" ] ; then\\n\\tif [ \\\"$sshCheck\\\" == \\\"On\\\" ] ; then\\n\\t\\techo \\\"$logStart SSH was already set to On\\\" >> \\\"$logFile\\\"\\n\\telif [ \\\"$sshCheck\\\" == \\\"Off\\\" ] ; then\\n\\t\\techo \\\"$logStart SSH set to Off\\\" >> \\\"$logFile\\\"\\n\\t\\tuserID=\\\"$(/usr/bin/dscl localhost -read /Local/Default/Users/$adminUser | grep GeneratedUID | awk '{print $2}')\\\"\\n\\t\\tif [ \\\"$userID\\\" != \\\"\\\" ] ; then\\n\\t\\t\\techo \\\"$logStart Granting SSH Access for $adminUser with GUID $userID\\\" >> \\\"$logFile\\\"\\n\\t\\t\\t/usr/bin/dscl localhost -create /Local/Default/Groups/com.apple.access_ssh\\n\\t\\t\\t/usr/bin/dscl localhost -append /Local/Default/Groups/com.apple.access_ssh GroupMembership \\\"$adminUser\\\"\\n\\t\\t\\t/usr/bin/dscl localhost -append /Local/Default/Groups/com.apple.access_ssh GroupMembers \\\"$userID\\\"\\n\\t\\t\\t/usr/bin/dscl localhost -append /Local/Default/Groups/com.apple.access_ssh RealName 'Remote Login Group'\\n\\t\\t\\t/usr/bin/dscl localhost -append /Local/Default/Groups/com.apple.access_ssh PrimaryGroupID 104\\n\\t\\t\\tsystemsetup setremotelogin on\\n\\t\\t\\techo \\\"$logStart SSH set to On\\\" >> \\\"$logFile\\\"\\n\\t\\telse\\n\\t\\t\\techo \\\"$logStart No record was found for $adminUser\\\" >> \\\"$logFile\\\"\\n\\t\\tfi\\n\\telse\\n \\techo \\\"$logStart Failed, exiting\\\" >> \\\"$logFile\\\"\\n\\tfi\\nelse\\n\\techo \\\"$logStart Invalid enableSSH configuration\\\" >> \\\"$logFile\\\"\\nfi\\nexit\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":19,"string":"19"}}},{"rowIdx":2979194,"cells":{"repo_name":{"kind":"string","value":"JakubSzwajka/ant_colony"},"repo_url":{"kind":"string","value":"https://github.com/JakubSzwajka/ant_colony"},"snapshot_id":{"kind":"string","value":"efc1af4e42f5b2f66f3634a5d99425f06746b8df"},"revision_id":{"kind":"string","value":"5a3ec7ebd20ac6af9b5a869ee41aa235f4b3504b"},"directory_id":{"kind":"string","value":"bd6791a8d45e4cd357a234a08af00dd1e2757712"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-01-13T12:47:50.914503","string":"2023-01-13T12:47:50.914503"},"revision_date":{"kind":"timestamp","value":"2020-11-19T10:35:05","string":"2020-11-19T10:35:05"},"committer_date":{"kind":"timestamp","value":"2020-11-19T10:35:05","string":"2020-11-19T10:35:05"},"github_id":{"kind":"number","value":213933196,"string":"213,933,196"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.4817880690097809,"alphanum_fraction":0.498344361782074,"avg_line_length":27.66666603088379,"blob_id":"1741754a0978e833e2d223ad970a80b78f94a5e3","content_id":"e8fea807d5478c56df4af8015ac3f871d491610b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":604,"license_type":"no_license","max_line_length":59,"num_lines":21,"path":"/city.py","repo_name":"JakubSzwajka/ant_colony","src_encoding":"UTF-8","text":"\n\nclass City( ):\n def __init__(self, ind, x , y):\n self.index = ind\n self.x = x\n self.y = y \n self.my_cities = []\n self.visited = False\n\n def add_cities( self, cities):\n for city in cities:\n if city.index != self.index:\n route = [city , 1000 ] # 1000 is pheromone \n self.my_cities.append( route )\n\n def set_pheromone_to_city(self, city_to, phermonoe ):\n for city in self.my_cities:\n if city[0] == city_to:\n city[1] += phermonoe\n\n def reset(self):\n self.visited = False "},{"alpha_fraction":0.56175297498703,"alphanum_fraction":0.5776892304420471,"avg_line_length":24,"blob_id":"cf10e2bd81b89e093d9b238e5b19f9f952a2877b","content_id":"831f51c742f539d8cf6c22d14ba2692875251f14","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":753,"license_type":"no_license","max_line_length":104,"num_lines":30,"path":"/utils.py","repo_name":"JakubSzwajka/ant_colony","src_encoding":"UTF-8","text":"\nimport math\nimport networkx as nx\nimport matplotlib.pyplot as plt\n\n\ndef count_distance( point_a, point_b):\n if point_a == point_b:\n return float(1) \n else:\n distance = float(math.sqrt( pow(point_b.y - point_a.y , 2 ) + pow( point_b.x - point_a.x , 2))) \n # print (distance)\n return distance\n\ndef sum_pheromone(city_from , city_to ):\n pheromone = 0\n for city in city_from.my_cities:\n if city[0] == city_to:\n pheromone += city[1]\n\n for city in city_to.my_cities:\n if city[0] == city_from:\n pheromone += city[1]\n\n return pheromone\n\ndef pheromone_leak(cities):\n\n for city in cities: \n for cit in city.my_cities:\n cit[1] = cit[1] * 0.7 #pheromone leak \n\n"},{"alpha_fraction":0.6031582355499268,"alphanum_fraction":0.6086508631706238,"avg_line_length":37.813331604003906,"blob_id":"be92bec35fdc3266d2788903998b7e2d4c1fbe6a","content_id":"227fa5e9cd25a51b84cff464646912cde801c1d4","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2913,"license_type":"no_license","max_line_length":130,"num_lines":75,"path":"/ant.py","repo_name":"JakubSzwajka/ant_colony","src_encoding":"UTF-8","text":"\nimport utils\nimport random\nimport city\nimport numpy\n\nclass Ant( ):\n def __init__ (self, nodes):\n self.not_visited_locations = []\n self.all_loc_visited = False\n self.distance_travelled = 0.0\n self.route = nodes\n self.current_location = numpy.random.choice(self.route) #self.route[0]\n self.start_location = self.route.index(self.current_location)\n self.next_location = self.route[self.start_location]\n self.alpha = .5\n self.beta = 1.2 #1.2\n self.memory = []\n \n def run(self):\n self.not_visited_locations = self.route\n \n while True:\n self.current_location.visited = True\n self.memory.append(self.current_location.index)\n self.next_location = self.find_path( )\n if self.all_loc_visited == True or self.next_location == None:\n self.go_to( self.current_location, self.route[self.start_location] )\n self.memory.append(self.current_location.index)\n break\n\n if self.current_location != self.next_location:\n self.go_to( self.current_location , self.next_location) \n\n def find_path(self):\n\n attractiveness = dict()\n sum_total = 0.0\n for possible_next_location in self.not_visited_locations:\n if possible_next_location.visited == False:\n pheromone_amount = float(utils.sum_pheromone(possible_next_location , self.current_location))\n distance = float(utils.count_distance(possible_next_location , self.current_location))\n \n attractiveness[possible_next_location] = pow(pheromone_amount, self.alpha)*pow(1/distance, self.beta)\n sum_total += attractiveness[possible_next_location]\n \n import random\n toss = random.random()\n \n cummulative = 0\n \n self.all_loc_visited = True\n for possible_next_location in attractiveness:\n if possible_next_location.visited == False:\n self.all_loc_visited = False\n weight = (attractiveness[possible_next_location] / sum_total)\n if toss <= weight + cummulative:\n return possible_next_location\n cummulative += weight\n \n\n def go_to(self, current_location , next_location):\n self.distance_travelled = self.distance_travelled + utils.count_distance( current_location, next_location)\n self.current_location.set_pheromone_to_city(next_location , 1000 / utils.count_distance( current_location, next_location))\n self.current_location.visited = True\n self.current_location = next_location\n \n\n def return_cities(self):\n return self.route\n\n def set_cities(self, cities):\n self.route = cities\n\n def get_memory(self):\n return self.memory, self.distance_travelled\n\n"},{"alpha_fraction":0.5227272510528564,"alphanum_fraction":0.5340909361839294,"avg_line_length":28,"blob_id":"fa38f2d6f967e205ee13777d5dbce3c2550a5991","content_id":"a2454d2c515c8b852cd432a7b882a6d213e14856","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":176,"license_type":"no_license","max_line_length":44,"num_lines":6,"path":"/route.py","repo_name":"JakubSzwajka/ant_colony","src_encoding":"UTF-8","text":"\n\nclass Route( ):\n def __init__(self, city_from, city_to ):\n self.cityA = city_from\n self.cityB = city_to\n self.distance = 0\n self.pheromone = 0 "},{"alpha_fraction":0.84375,"alphanum_fraction":0.84375,"avg_line_length":31,"blob_id":"77011e0875ed08f9e30201c6b9c17e1e60f6952c","content_id":"70ae9750f4fde7ec54340d33abb70282c5533e30","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":64,"license_type":"no_license","max_line_length":50,"num_lines":2,"path":"/README.md","repo_name":"JakubSzwajka/ant_colony","src_encoding":"UTF-8","text":"# ant_colony\nTraveling salesman problem ant colony optimization\n"},{"alpha_fraction":0.54356849193573,"alphanum_fraction":0.5580912828445435,"avg_line_length":20.186813354492188,"blob_id":"f7659647e3f924bdbc6b689d32bad5e44b63f178","content_id":"ca48cc5e22b7a9db61ebe1c0b933c49c6d83751b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1930,"license_type":"no_license","max_line_length":78,"num_lines":91,"path":"/main.py","repo_name":"JakubSzwajka/ant_colony","src_encoding":"UTF-8","text":"import utils \nimport city\nimport route\nfrom ant import Ant\nfrom operator import itemgetter\nfrom string import ascii_uppercase\nimport networkx as nx\nimport matplotlib.pyplot as plt\n\n\n# punkt startowy ze wzgledu na numer indeksu 3 – B\nA = [1,2,'A']\nB = [3,1,'B']\nC = [3,6,'C']\nD = [6,7,'D']\nE = [5,2,'E']\n\nnodes = [A,B,C,D,E]\n\nants = []\ncities = []\nnumerator = 1\nmem = []\nshortest_way = []\nshortest_distance = 0.0\nshortest_iterator = 1\ndistances = []\nstorage = []\n\nfor node in nodes: \n cities.append( city.City(node[2] , node[0], node[1] ))\n numerator += 1\n\nfor city in cities: \n city.add_cities( cities )\n\nfor i in range(1000):\n ants.append( Ant( cities ))\n\n#####################################################################\n\nfor ant in ants:\n ant.set_cities(cities)\n ant.run()\n cities = ant.return_cities()\n mem , distance = ant.get_memory( )\n # print('memory: ',mem, ' distance: ', distance)\n\n utils.pheromone_leak(cities)\n\n for city in cities:\n city.reset() # set the city as not visited yet\n \n storage.append(distance)\n\n if distance <= shortest_distance or shortest_distance == 0:\n shortest_distance = distance\n shortest_way.append(mem)\n if distance == shortest_distance:\n shortest_iterator += 1\n \n \nfor item in storage:\n setter = [ item , storage.count(item)]\n if setter not in distances:\n distances.append(setter)\n\nprint( '===========')\nprint( 'Shortest distnce:', shortest_distance , 'Times: ' , shortest_iterator)\nprint( 'Shortest path: ' , mem )\nprint( 'Begin nodes: ', nodes)\nprint( '===========')\n\ndistances.sort() \nfor dist in distances:\n print(dist)\n\n\n############################################################################\n\nx = []\ny = []\n\nfor node in mem:\n for nod in nodes:\n if nod[2] == node:\n x.append(nod[0])\n y.append(nod[1])\n\nplt.plot(x,y,'-or')\nplt.show()\n"}],"string":"[\n {\n \"alpha_fraction\": 0.4817880690097809,\n \"alphanum_fraction\": 0.498344361782074,\n \"avg_line_length\": 27.66666603088379,\n \"blob_id\": \"1741754a0978e833e2d223ad970a80b78f94a5e3\",\n \"content_id\": \"e8fea807d5478c56df4af8015ac3f871d491610b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 604,\n \"license_type\": \"no_license\",\n \"max_line_length\": 59,\n \"num_lines\": 21,\n \"path\": \"/city.py\",\n \"repo_name\": \"JakubSzwajka/ant_colony\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\nclass City( ):\\n def __init__(self, ind, x , y):\\n self.index = ind\\n self.x = x\\n self.y = y \\n self.my_cities = []\\n self.visited = False\\n\\n def add_cities( self, cities):\\n for city in cities:\\n if city.index != self.index:\\n route = [city , 1000 ] # 1000 is pheromone \\n self.my_cities.append( route )\\n\\n def set_pheromone_to_city(self, city_to, phermonoe ):\\n for city in self.my_cities:\\n if city[0] == city_to:\\n city[1] += phermonoe\\n\\n def reset(self):\\n self.visited = False \"\n },\n {\n \"alpha_fraction\": 0.56175297498703,\n \"alphanum_fraction\": 0.5776892304420471,\n \"avg_line_length\": 24,\n \"blob_id\": \"cf10e2bd81b89e093d9b238e5b19f9f952a2877b\",\n \"content_id\": \"831f51c742f539d8cf6c22d14ba2692875251f14\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 753,\n \"license_type\": \"no_license\",\n \"max_line_length\": 104,\n \"num_lines\": 30,\n \"path\": \"/utils.py\",\n \"repo_name\": \"JakubSzwajka/ant_colony\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport math\\nimport networkx as nx\\nimport matplotlib.pyplot as plt\\n\\n\\ndef count_distance( point_a, point_b):\\n if point_a == point_b:\\n return float(1) \\n else:\\n distance = float(math.sqrt( pow(point_b.y - point_a.y , 2 ) + pow( point_b.x - point_a.x , 2))) \\n # print (distance)\\n return distance\\n\\ndef sum_pheromone(city_from , city_to ):\\n pheromone = 0\\n for city in city_from.my_cities:\\n if city[0] == city_to:\\n pheromone += city[1]\\n\\n for city in city_to.my_cities:\\n if city[0] == city_from:\\n pheromone += city[1]\\n\\n return pheromone\\n\\ndef pheromone_leak(cities):\\n\\n for city in cities: \\n for cit in city.my_cities:\\n cit[1] = cit[1] * 0.7 #pheromone leak \\n\\n\"\n },\n {\n \"alpha_fraction\": 0.6031582355499268,\n \"alphanum_fraction\": 0.6086508631706238,\n \"avg_line_length\": 37.813331604003906,\n \"blob_id\": \"be92bec35fdc3266d2788903998b7e2d4c1fbe6a\",\n \"content_id\": \"227fa5e9cd25a51b84cff464646912cde801c1d4\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2913,\n \"license_type\": \"no_license\",\n \"max_line_length\": 130,\n \"num_lines\": 75,\n \"path\": \"/ant.py\",\n \"repo_name\": \"JakubSzwajka/ant_colony\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport utils\\nimport random\\nimport city\\nimport numpy\\n\\nclass Ant( ):\\n def __init__ (self, nodes):\\n self.not_visited_locations = []\\n self.all_loc_visited = False\\n self.distance_travelled = 0.0\\n self.route = nodes\\n self.current_location = numpy.random.choice(self.route) #self.route[0]\\n self.start_location = self.route.index(self.current_location)\\n self.next_location = self.route[self.start_location]\\n self.alpha = .5\\n self.beta = 1.2 #1.2\\n self.memory = []\\n \\n def run(self):\\n self.not_visited_locations = self.route\\n \\n while True:\\n self.current_location.visited = True\\n self.memory.append(self.current_location.index)\\n self.next_location = self.find_path( )\\n if self.all_loc_visited == True or self.next_location == None:\\n self.go_to( self.current_location, self.route[self.start_location] )\\n self.memory.append(self.current_location.index)\\n break\\n\\n if self.current_location != self.next_location:\\n self.go_to( self.current_location , self.next_location) \\n\\n def find_path(self):\\n\\n attractiveness = dict()\\n sum_total = 0.0\\n for possible_next_location in self.not_visited_locations:\\n if possible_next_location.visited == False:\\n pheromone_amount = float(utils.sum_pheromone(possible_next_location , self.current_location))\\n distance = float(utils.count_distance(possible_next_location , self.current_location))\\n \\n attractiveness[possible_next_location] = pow(pheromone_amount, self.alpha)*pow(1/distance, self.beta)\\n sum_total += attractiveness[possible_next_location]\\n \\n import random\\n toss = random.random()\\n \\n cummulative = 0\\n \\n self.all_loc_visited = True\\n for possible_next_location in attractiveness:\\n if possible_next_location.visited == False:\\n self.all_loc_visited = False\\n weight = (attractiveness[possible_next_location] / sum_total)\\n if toss <= weight + cummulative:\\n return possible_next_location\\n cummulative += weight\\n \\n\\n def go_to(self, current_location , next_location):\\n self.distance_travelled = self.distance_travelled + utils.count_distance( current_location, next_location)\\n self.current_location.set_pheromone_to_city(next_location , 1000 / utils.count_distance( current_location, next_location))\\n self.current_location.visited = True\\n self.current_location = next_location\\n \\n\\n def return_cities(self):\\n return self.route\\n\\n def set_cities(self, cities):\\n self.route = cities\\n\\n def get_memory(self):\\n return self.memory, self.distance_travelled\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5227272510528564,\n \"alphanum_fraction\": 0.5340909361839294,\n \"avg_line_length\": 28,\n \"blob_id\": \"fa38f2d6f967e205ee13777d5dbce3c2550a5991\",\n \"content_id\": \"a2454d2c515c8b852cd432a7b882a6d213e14856\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 176,\n \"license_type\": \"no_license\",\n \"max_line_length\": 44,\n \"num_lines\": 6,\n \"path\": \"/route.py\",\n \"repo_name\": \"JakubSzwajka/ant_colony\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\nclass Route( ):\\n def __init__(self, city_from, city_to ):\\n self.cityA = city_from\\n self.cityB = city_to\\n self.distance = 0\\n self.pheromone = 0 \"\n },\n {\n \"alpha_fraction\": 0.84375,\n \"alphanum_fraction\": 0.84375,\n \"avg_line_length\": 31,\n \"blob_id\": \"77011e0875ed08f9e30201c6b9c17e1e60f6952c\",\n \"content_id\": \"70ae9750f4fde7ec54340d33abb70282c5533e30\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 64,\n \"license_type\": \"no_license\",\n \"max_line_length\": 50,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"JakubSzwajka/ant_colony\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# ant_colony\\nTraveling salesman problem ant colony optimization\\n\"\n },\n {\n \"alpha_fraction\": 0.54356849193573,\n \"alphanum_fraction\": 0.5580912828445435,\n \"avg_line_length\": 20.186813354492188,\n \"blob_id\": \"f7659647e3f924bdbc6b689d32bad5e44b63f178\",\n \"content_id\": \"ca48cc5e22b7a9db61ebe1c0b933c49c6d83751b\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1930,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 91,\n \"path\": \"/main.py\",\n \"repo_name\": \"JakubSzwajka/ant_colony\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import utils \\nimport city\\nimport route\\nfrom ant import Ant\\nfrom operator import itemgetter\\nfrom string import ascii_uppercase\\nimport networkx as nx\\nimport matplotlib.pyplot as plt\\n\\n\\n# punkt startowy ze wzgledu na numer indeksu 3 – B\\nA = [1,2,'A']\\nB = [3,1,'B']\\nC = [3,6,'C']\\nD = [6,7,'D']\\nE = [5,2,'E']\\n\\nnodes = [A,B,C,D,E]\\n\\nants = []\\ncities = []\\nnumerator = 1\\nmem = []\\nshortest_way = []\\nshortest_distance = 0.0\\nshortest_iterator = 1\\ndistances = []\\nstorage = []\\n\\nfor node in nodes: \\n cities.append( city.City(node[2] , node[0], node[1] ))\\n numerator += 1\\n\\nfor city in cities: \\n city.add_cities( cities )\\n\\nfor i in range(1000):\\n ants.append( Ant( cities ))\\n\\n#####################################################################\\n\\nfor ant in ants:\\n ant.set_cities(cities)\\n ant.run()\\n cities = ant.return_cities()\\n mem , distance = ant.get_memory( )\\n # print('memory: ',mem, ' distance: ', distance)\\n\\n utils.pheromone_leak(cities)\\n\\n for city in cities:\\n city.reset() # set the city as not visited yet\\n \\n storage.append(distance)\\n\\n if distance <= shortest_distance or shortest_distance == 0:\\n shortest_distance = distance\\n shortest_way.append(mem)\\n if distance == shortest_distance:\\n shortest_iterator += 1\\n \\n \\nfor item in storage:\\n setter = [ item , storage.count(item)]\\n if setter not in distances:\\n distances.append(setter)\\n\\nprint( '===========')\\nprint( 'Shortest distnce:', shortest_distance , 'Times: ' , shortest_iterator)\\nprint( 'Shortest path: ' , mem )\\nprint( 'Begin nodes: ', nodes)\\nprint( '===========')\\n\\ndistances.sort() \\nfor dist in distances:\\n print(dist)\\n\\n\\n############################################################################\\n\\nx = []\\ny = []\\n\\nfor node in mem:\\n for nod in nodes:\\n if nod[2] == node:\\n x.append(nod[0])\\n y.append(nod[1])\\n\\nplt.plot(x,y,'-or')\\nplt.show()\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":6,"string":"6"}}},{"rowIdx":2979195,"cells":{"repo_name":{"kind":"string","value":"amal-krm/SendMail"},"repo_url":{"kind":"string","value":"https://github.com/amal-krm/SendMail"},"snapshot_id":{"kind":"string","value":"9493bcceb6012d92d6e9230bdb3d1c6bf9de63b5"},"revision_id":{"kind":"string","value":"8ae56eab601c3ff009157d075d850bd6f8f5aabe"},"directory_id":{"kind":"string","value":"6edc2f45b1677e7bc27cc7db89cfc01ee0e985ce"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-08-31T15:33:49.354680","string":"2022-08-31T15:33:49.354680"},"revision_date":{"kind":"timestamp","value":"2020-05-31T21:43:09","string":"2020-05-31T21:43:09"},"committer_date":{"kind":"timestamp","value":"2020-05-31T21:43:09","string":"2020-05-31T21:43:09"},"github_id":{"kind":"number","value":267991128,"string":"267,991,128"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6410714387893677,"alphanum_fraction":0.6476190686225891,"avg_line_length":27.965517044067383,"blob_id":"947668306b12e4f0802faa04ca0c33bd1bc6eb14","content_id":"372d44ec10699f2fb04978b030ff82c126a1bd82","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1680,"license_type":"no_license","max_line_length":123,"num_lines":58,"path":"/SendAutomaticMail.py","repo_name":"amal-krm/SendMail","src_encoding":"UTF-8","text":"from datetime import datetime\ndef send_email_pdf_figs(path_to_pdf, subject, message,destination):\n ## credits: http://linuxcursor.com/python-programming/06-how-to-send-pdf-ppt-attachment-with-html-body-in-python-script\n from socket import gethostname\n #import email\n from email.mime.application import MIMEApplication\n from email.mime.multipart import MIMEMultipart\n from email.mime.text import MIMEText\n import smtplib\n import json\n\n server = smtplib.SMTP('smtp.gmail.com', 587)\n server.starttls()\n server.login('mail', 'mdp')\n # Craft message (obj)\n msg = MIMEMultipart()\n\n\n msg['Subject'] = subject\n msg['From'] = ''\n msg['To'] = destination\n # Insert the text to the msg going by e-mail\n msg.attach(MIMEText(message, \"plain\"))\n # Attach the pdf to the msg going by e-mail\n with open(path_to_pdf, \"rb\") as f:\n #attach = email.mime.application.MIMEApplication(f.read(),_subtype=\"pdf\")\n attach = MIMEApplication(f.read(),_subtype=\"pdf\")\n attach.add_header('Content-Disposition','attachment',filename=str(path_to_pdf))\n msg.attach(attach)\n # send msg\n server.send_message(msg)\n\n\nfile1 = open('emails.txt', 'r')\nLines = file1.readlines()\nMsg = \"\"\ncount = 0\n# Strips the newline character\nnow = datetime.now()\n\n\n\n# dd/mm/YY H:M:S\ndt_string = now.strftime(\"%d/%m/%Y %H:%M:%S\")\nprint(\"date 1 === >\", dt_string)\nfor line in Lines:\n send_email_pdf_figs(\"\", \"\", Msg, line.strip())\n count=count+1\n print(count)\n\nprint(\"nbre d'emails === > \",count)\nnow = datetime.now()\n\n\n\n# dd/mm/YY H:M:S\ndt_string = now.strftime(\"%d/%m/%Y %H:%M:%S\")\nprint(\"date 2 === >\", dt_string)\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6410714387893677,\n \"alphanum_fraction\": 0.6476190686225891,\n \"avg_line_length\": 27.965517044067383,\n \"blob_id\": \"947668306b12e4f0802faa04ca0c33bd1bc6eb14\",\n \"content_id\": \"372d44ec10699f2fb04978b030ff82c126a1bd82\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1680,\n \"license_type\": \"no_license\",\n \"max_line_length\": 123,\n \"num_lines\": 58,\n \"path\": \"/SendAutomaticMail.py\",\n \"repo_name\": \"amal-krm/SendMail\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from datetime import datetime\\ndef send_email_pdf_figs(path_to_pdf, subject, message,destination):\\n ## credits: http://linuxcursor.com/python-programming/06-how-to-send-pdf-ppt-attachment-with-html-body-in-python-script\\n from socket import gethostname\\n #import email\\n from email.mime.application import MIMEApplication\\n from email.mime.multipart import MIMEMultipart\\n from email.mime.text import MIMEText\\n import smtplib\\n import json\\n\\n server = smtplib.SMTP('smtp.gmail.com', 587)\\n server.starttls()\\n server.login('mail', 'mdp')\\n # Craft message (obj)\\n msg = MIMEMultipart()\\n\\n\\n msg['Subject'] = subject\\n msg['From'] = ''\\n msg['To'] = destination\\n # Insert the text to the msg going by e-mail\\n msg.attach(MIMEText(message, \\\"plain\\\"))\\n # Attach the pdf to the msg going by e-mail\\n with open(path_to_pdf, \\\"rb\\\") as f:\\n #attach = email.mime.application.MIMEApplication(f.read(),_subtype=\\\"pdf\\\")\\n attach = MIMEApplication(f.read(),_subtype=\\\"pdf\\\")\\n attach.add_header('Content-Disposition','attachment',filename=str(path_to_pdf))\\n msg.attach(attach)\\n # send msg\\n server.send_message(msg)\\n\\n\\nfile1 = open('emails.txt', 'r')\\nLines = file1.readlines()\\nMsg = \\\"\\\"\\ncount = 0\\n# Strips the newline character\\nnow = datetime.now()\\n\\n\\n\\n# dd/mm/YY H:M:S\\ndt_string = now.strftime(\\\"%d/%m/%Y %H:%M:%S\\\")\\nprint(\\\"date 1 === >\\\", dt_string)\\nfor line in Lines:\\n send_email_pdf_figs(\\\"\\\", \\\"\\\", Msg, line.strip())\\n count=count+1\\n print(count)\\n\\nprint(\\\"nbre d'emails === > \\\",count)\\nnow = datetime.now()\\n\\n\\n\\n# dd/mm/YY H:M:S\\ndt_string = now.strftime(\\\"%d/%m/%Y %H:%M:%S\\\")\\nprint(\\\"date 2 === >\\\", dt_string)\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979196,"cells":{"repo_name":{"kind":"string","value":"Rutujakadam0204/video_conferencing"},"repo_url":{"kind":"string","value":"https://github.com/Rutujakadam0204/video_conferencing"},"snapshot_id":{"kind":"string","value":"b7d4be1c375facd93b5b08ead4126bea4d495061"},"revision_id":{"kind":"string","value":"7c0ea87d16195bc565fc7fd7f389a431a3e47df2"},"directory_id":{"kind":"string","value":"ffcedc794046c735df5b179d65f7fd0104d791c3"},"branch_name":{"kind":"string","value":"refs/heads/main"},"visit_date":{"kind":"timestamp","value":"2023-06-25T23:28:42.116800","string":"2023-06-25T23:28:42.116800"},"revision_date":{"kind":"timestamp","value":"2021-07-30T12:03:09","string":"2021-07-30T12:03:09"},"committer_date":{"kind":"timestamp","value":"2021-07-30T12:03:09","string":"2021-07-30T12:03:09"},"github_id":{"kind":"number","value":390248790,"string":"390,248,790"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.6073394417762756,"alphanum_fraction":0.6073394417762756,"avg_line_length":32.030303955078125,"blob_id":"02f5319fe9d17e8f26f27af5e18187cc717e8947","content_id":"d4e201d09bc6df73496b60b63ee5061d73d07ae3","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2180,"license_type":"permissive","max_line_length":85,"num_lines":66,"path":"/chat/consumers.py","repo_name":"Rutujakadam0204/video_conferencing","src_encoding":"UTF-8","text":"# clients are gonna connect to our websockets and will connect through this consumers\nimport json\nfrom channels.generic.websocket import AsyncWebsocketConsumer\nfrom django.dispatch.dispatcher import receiver\n\n\nclass ChatConsumer(AsyncWebsocketConsumer):\n\n # will be triggered whenever a client connects to consumer\n async def connect(self): \n self.room_group_name = 'Test_Room'\n\n await self.channel_layer.group_add(\n self.room_group_name,\n self.channel_name\n )\n\n await self.accept()\n\n # will be triggered whenever a client disconnects to consumer\n async def disconnect(self, close_code): \n await self.channel_layer.group_discard(\n self.room_group_name,\n self.channel_name\n )\n print('Disconnected')\n\n # whenever a msg is received from a client\n async def receive(self, text_data):\n #most of the code for sending sdp and notifying every peer wnenevr sent sdp\n #receive code from js in dict format or json so deserialize json\n receive_dict = json.loads(text_data)\n message = receive_dict['message']\n action = receive_dict['action']\n\n if (action == 'new-pffer') or (action == 'new-answer'):\n receiver_channel_name = receive_dict['message']['receiver_channel_name']\n\n receive_dict['message']['receiver_channel_name'] = self.channel_name\n await self.channel_layer.send(\n receiver_channel_name,\n {\n 'type': 'send.sdp',\n 'receive_dict':receive_dict\n }\n )\n return\n\n receive_dict['message']['receiver_channel_name'] = self.channel_name\n \n #send msg to all the other peers\n await self.channel_layer.group_send(\n self.room_group_name,\n {\n 'type': 'send.sdp',\n 'receive_dict':receive_dict\n }\n )\n\n async def send_sdp(self, event):\n receive_dict = event['receive_dict']\n\n #send msg to other peer\n await self.send(text_data=json.dumps(receive_dict))\n\n #dumps bcz converting from python to json\n"},{"alpha_fraction":0.6715885996818542,"alphanum_fraction":0.6802443861961365,"avg_line_length":34.07143020629883,"blob_id":"ac6e9bdcd6387bebd1b4f25ed6041e7e0898afbb","content_id":"769dbb3572983a7fb3b865a285ce379a3862ec0a","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1964,"license_type":"permissive","max_line_length":74,"num_lines":56,"path":"/cam/models.py","repo_name":"Rutujakadam0204/video_conferencing","src_encoding":"UTF-8","text":"from django.db import models\n# from camus import models\nfrom slugify import slugify\nimport uuid\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom datetime import datetime\n\n# Create your models here.\nclass Room(models.Model):\n # __tablename__ = 'rooms'\n # id = models.(models.Integer, primary_key=True)\n name = models.CharField(unique=True, max_length=100, blank=True)\n slug = models.SlugField(editable=False, blank=True)\n password_hash = models.CharField(max_length=10000, blank=True)\n guest_limit = models.IntegerField(default=100)\n is_public = models.BooleanField(default=False)\n created = models.DateTimeField(default=datetime.utcnow)\n active = models.DateTimeField(default=datetime.utcnow)\n\n # clients = models.ManyToManyField('Client', backref='room')\n\n def __repr__(self):\n return '<Room {}>'.format(self.name)\n\n def set_name(self, name):\n self.name = name\n self.slug = slugify(name)\n\n def set_password(self, password):\n self.password_hash = generate_password_hash(password)\n\n \n def is_full(self):\n \"\"\"Check whether the room's guest limit has been reached.\n\n Returns True if the guest limit has been reached, False otherwise.\n \"\"\"\n return self.guest_limit and len(self.clients) >= self.guest_limit\n\n def active_ago(self):\n \"\"\"The number of minutes ago that the room was last active.\"\"\"\n now = datetime.utcnow().timestamp()\n return int((now - self.active.timestamp()) / 60)\n\n\nclass Client(models.Model):\n # __tablename__ = 'clients'\n # id = models.Column(models.Integer, primary_key=True)\n name = models.CharField(max_length=100)\n # uuid = models.UUIDField(unique=True, default=uuid.uuid4().hex)\n seen = models.DateTimeField(default=datetime.utcnow)\n\n room_id = models.ForeignKey(Room, on_delete=models.DO_NOTHING)\n\n def __repr__(self):\n return '<Client {}>'.format(self.uuid)\n"},{"alpha_fraction":0.6334866285324097,"alphanum_fraction":0.6394373774528503,"avg_line_length":28.576000213623047,"blob_id":"92b5d35d32086da416e465ce9b1d108d5173b1c0","content_id":"f54a8f1b8c929ac6f8f7b109f34f34958e003960","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3697,"license_type":"permissive","max_line_length":102,"num_lines":125,"path":"/cam/views.py","repo_name":"Rutujakadam0204/video_conferencing","src_encoding":"UTF-8","text":"from django.shortcuts import render\n\n# Create your views here.\nimport asyncio\nimport logging\n\nimport sqlalchemy\nfrom quart import (Blueprint, copy_current_websocket_context, flash, redirect,\n render_template, session, websocket)\n\nfrom camus import db, message_handler\nfrom .forms import *\nfrom .models import Client, Room\nfrom .util import *\n\nbp = Blueprint('main', __name__)\n\n\n@bp.route('/about')\nasync def about():\n return redirect('/#why-camus')\n\n\n# @bp.route('/', methods=['GET', 'POST'])\ndef index(request):\n create_room_form = CreateRoomForm()\n if request.method == 'POST':\n create_room_form = CreateRoomForm(request.POST)\n if create_room_form.is_valid():\n a = create_room_form.save()\n a.slug = slugify(str(a.id))\n a.save()\n print(a.id)\n return redirect(f\"/room/{a.id}\")\n return render(request, 'chat.html', {'create_room_form':create_room_form})\n\n\n# The `/chat` route is deprecated.\n# @bp.route('/chat')\n# async def chat_index():\n# return redirect('/', code=307)\n\n\n# The `/chat/` route is deprecated. Prefer`/room/` instead.\n# @bp.route('/chat/<room_id>', methods=['GET', 'POST'])\n# @bp.route('/room/<room_id>', methods=['GET', 'POST'])\ndef room(request,room_id):\n pass\n room = Room.objects.filter(id=room_id)\n room1 = Room.objects.get(id=room_id)\n name = request.POST.get('name')\n # if room.is_full():\n # return 'Guest limit already reached', 418\n\n # # No password is required to join the room\n # client = room.authenticate()\n if request.method == 'POST':\n a = Client.objects.create(name=name, room_id=room1)\n request.session['id'] = a.name\n\n return render(request,\n 'chatroom.html')\n\n # # A password is required to join the room\n # status_code = 200\n form = JoinRoomForm()\n \n # await flash('Invalid password')\n\n return render(request, 'join-room.html', {'title':'Camus | Join a room','form':form, 'room':room})\n\n\n# The `/chat/` route is deprecated. Prefer`/room/` instead.\n# @bp.websocket('/chat/<room_id>/ws')\n# @bp.websocket('/room/<room_id>/ws')\nasync def room_ws(request,room_id):\n # Verify that the room exists\n Room.objects.filter(id=room_id).first_or_404()\n\n # Verify the client using a secure cookie\n client = Client.objects.filter(name=request.session['id'])\n client1 = Client.objects.get(name=request.session['id'])\n\n if client:\n client = Client.objects.get(name=request.session['id'])\n logging.info(f'Accepted websocket connection for client {client1.name}')\n await websocket.accept()\n else:\n return 'Forbidden', 403\n\n inbox, outbox = message_handler.inbox, message_handler.outbox\n\n send_task = asyncio.create_task(\n copy_current_websocket_context(ws_send)(outbox[client1.name]),\n )\n receive_task = asyncio.create_task(\n copy_current_websocket_context(ws_receive)(client1.name, inbox),\n )\n try:\n await asyncio.gather(send_task, receive_task)\n finally:\n logging.info(f'Terminating websocket connection for client {client1.name}')\n send_task.cancel()\n receive_task.cancel()\n\n\n@bp.route('/public')\nasync def public():\n public_rooms = Room.query.filter_by(is_public=True).all()\n\n return await render_template(\n 'public.html', title='Camus Video Chat | Public Rooms',\n public_rooms=public_rooms)\n\n\nasync def ws_send(queue):\n while True:\n message = await queue.get()\n await websocket.send(message)\n\n\nasync def ws_receive(client_id, queue):\n while True:\n message = await websocket.receive()\n await queue.put((client_id, message))\n"},{"alpha_fraction":0.6208961606025696,"alphanum_fraction":0.6220053434371948,"avg_line_length":24.76285743713379,"blob_id":"7c895392ae893756887e14c23bdd467feb973584","content_id":"bbb6d8c182cb620e52a0cd29b7e2625fc31209a2","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"JavaScript","length_bytes":9016,"license_type":"permissive","max_line_length":121,"num_lines":350,"path":"/static/js/main.js","repo_name":"Rutujakadam0204/video_conferencing","src_encoding":"UTF-8","text":"console.log(\"In main.js\");\n\n\n\nvar mapPeers = {};\n\nvar usernameInput = document.getElementById('username')\nvar btnJoin = document.querySelector('#btn-join')\n\nvar username;\nvar webSocket;\n\nfunction webSocketOnMessage(event) {\n var parseData = JSON.parse(event.data);\n var peerUsername = parseData['peer'];\n var action = parseData['action'];\n\n if (username == peerUsername) {\n return;\n }\n\n var receiver_channel_name = parseData['message']['receiver_channel_name'];\n if (action == 'new-peer') {\n createOfferer(peerUsername, receiver_channel_name);\n return;\n }\n\n if (action == 'new-offer') {\n var offer = parseData['message']['sdp'];\n createAnswerer(offer, peerUsername, receiver_channel_name);\n return;\n }\n\n if (action == 'new-answer') {\n var answer = parseData['message']['sdp'];\n\n var peer = mapPeers[peerUsername][0];\n peer.setRemoteDescription(answer);\n return;\n }\n}\n\n\nbtnJoin.addEventListener('click', () => {\n username = usernameInput.value;\n console.log(username)\n if (username == '') {\n return;\n }\n\n usernameInput.value = '';\n usernameInput.disabled = true;\n usernameInput.style.visibility = 'hidden';\n\n usernameInput.disabled = true;\n usernameInput.style.visibility = 'hidden';\n\n btnJoin.disabled = true;\n btnJoin.style.visibility = 'hidden';\n\n var labelUsername = document.querySelector('#label-username')\n labelUsername.innerHTML = username;\n\n var loc = window.location;\n var wsStart = 'ws://';\n\n if (loc.protocol == 'https:') {\n wsStart = 'wss://';\n }\n\n var endPoint = wsStart + loc.host + loc.pathname + '/';\n\n console.log('endPoint', endPoint);\n\n webSocket = new WebSocket(endPoint);\n\n webSocket.addEventListener('open', (e) => {\n console.log('Connection Opened !')\n\n sendSignal('new-peer', {});\n\n });\n webSocket.addEventListener('message', webSocketOnMessage);\n webSocket.addEventListener('close', (e) => {\n console.log('Connection closed !')\n });\n webSocket.addEventListener('error', (e) => {\n console.log('Error occured !')\n });\n\n})\n\n\nvar localStream = new MediaStream();\n\n// const constraints = {\n// 'video': true,\n// 'audio': true\n// }\nconst constraints = window.constraints = {\n audio: true,\n video: true\n};\n\nconst localVideo = document.querySelector('#local-video');\n\nconst btnToggleAudio = document.querySelector('#btn-toggle-audio');\nconst btnToggleVideo = document.querySelector('#btn-toggle-video');\n\n\n\nvar userMedia = navigator.mediaDevices.getUserMedia(constraints)\n .then(stream => {\n localStream = stream;\n localVideo.srcObject = localStream;\n localVideo.muted = true;\n\n var audioTracks = stream.getAudioTracks();\n var videoTracks = stream.getVideoTracks();\n\n audioTracks[0].enabled = true;\n videoTracks[0].enabled = true;\n\n btnToggleAudio.addEventListener('click', () => {\n audioTracks[0].enabled = !audioTracks[0].enabled;\n if (audioTracks[0].enabled) {\n btnToggleAudio.innerHTML = 'Audio Mute';\n return;\n }\n btnToggleAudio.innerHTML = 'Audio Unmute';\n });\n\n btnToggleVideo.addEventListener('click', () => {\n videoTracks[0].enabled = !videoTracks[0].enabled;\n if (videoTracks[0].enabled) {\n btnToggleVideo.innerHTML = 'Video off';\n return;\n }\n btnToggleVideo.innerHTML = 'Video On';\n });\n\n })\n\n.catch(error => {\n console.log('error accessing media device', error)\n});\n\n\n\n\nvar btnSendMsg = document.querySelector('#btn-send-msg');\nvar messageList = document.querySelector('#message-list');\nvar messageInput = document.querySelector('#msg');\n\nbtnSendMsg.addEventListener('click', sendMsgOnClick);\n\nfunction sendMsgOnClick() {\n var message = messageInput.value;\n\n var li = document.createElement('li');\n li.appendChild(document.createTextNode('Me: ' + message));\n messageList.appendChild(li);\n\n var dataChannels = getDataChannels();\n\n message = username + ': ' + message;\n\n for (index in dataChannels) {\n dataChannels[index].send(message);\n }\n\n messageInput.value = '';\n}\n\n\n\nfunction sendSignal(action, message) {\n var jsonStr = JSON.stringify({\n 'peer': username,\n 'action': action,\n 'message': message,\n });\n\n webSocket.send(jsonStr);\n\n}\n\n\nfunction createOfferer(peerUsername, receiver_channel_name) {\n var peer = new RTCPeerConnection(null);\n\n addLocalTracks(peer);\n\n var dc = peer.createDataChannel('channel');\n dc.addEventListener('open', () => {\n console.log('Connection open')\n })\n dc.addEventListener('message', dcOnMessage);\n\n var remoteVideo = createVideo(peerUsername);\n setOnTrack(peer, remoteVideo);\n mapPeers[peerUsername] = [peer, dc];\n peer.addEventListener('iceconnectionstatechange', () => {\n var iceConnectionState = peer.iceConnectionState;\n if (iceConnectionState == 'failed' || iceConnectionState === 'disconnected' || iceConnectionState === 'Closed') {\n delete mapPeers[peerUsername];\n if (iceConnectionState != 'closed') {\n peer.close();\n }\n removeVideo(remoteVideo);\n }\n });\n\n peer.addEventListener('icecandidate', (event) => {\n if (event.candidate) {\n console.log('New ice candidate', JSON.stringify(peer.localDescription));\n return;\n }\n\n sendSignal('new-offer', {\n 'sdp': peer.localDescription,\n 'receiver_channel_name': receiver_channel_name\n })\n });\n\n peer.createOffer()\n .then(o => peer.setLocalDescription(o))\n .then(() => {\n console.log('Local Description set successfully')\n })\n}\n\nfunction createAnswerer(offer, peerUsername, receiver_channel_name) {\n var peer = new RTCPeerConnection(null);\n\n addLocalTracks(peer);\n\n var remoteVideo = createVideo(peerUsername);\n setOnTrack(peer, remoteVideo);\n\n peer.addEventListener('datachannel', e => {\n peer.dc = e.channel;\n peer.dc.addEventListener('open', () => {\n console.log('Connection open')\n })\n peer.dc.addEventListener('message', dcOnMessage);\n\n mapPeers[peerUsername] = [peer, peer.dc];\n\n })\n\n peer.addEventListener('iceconnectionstatechange', () => {\n var iceConnectionState = peer.iceConnectionState;\n if (iceConnectionState == 'failed' || iceConnectionState === 'disconnected' || iceConnectionState === 'Closed') {\n delete mapPeers[peerUsername];\n if (iceConnectionState != 'closed') {\n peer.close();\n }\n removeVideo(remoteVideo);\n }\n });\n\n peer.addEventListener('icecandidate', (event) => {\n if (event.candidate) {\n console.log('New ice candidate', JSON.stringify(peer.localDescription));\n return;\n }\n\n sendSignal('new-answer', {\n 'sdp': peer.localDescription,\n 'receiver_channel_name': receiver_channel_name\n })\n });\n\n peer.setRemoteDescription(offer)\n .then(() => {\n console.log('remote description set successfully for %s.', peerUsername);\n return peer.createAnswer();\n })\n\n .then(a => {\n console.log('Answer created !')\n\n peer.setLocalDescription(a);\n })\n\n}\n\nfunction addLocalTracks(peer) {\n\n localStream.getTracks().forEach(track => {\n peer.addTrack(track, localStream);\n });\n\n return;\n\n}\n\nvar messageList = document.querySelector('#message-list');\n\nfunction dcOnMessage(event) {\n var message = event.data;\n\n var li = document.createElement('li')\n li.appendChild(document.createTextNode(message));\n messageList.appendChild(li);\n}\n\nfunction createVideo(peerUsername) {\n var videoContainer = document.querySelector('#video-container');\n var remoteVideo = document.createElement('video');\n remoteVideo.id = peerUsername + '-video';\n remoteVideo.autoplay = true;\n remoteVideo.playsInline = true;\n remoteVideo.controls = true;\n\n var videoWrapper = document.createElement('div');\n videoContainer.appendChild(videoWrapper);\n videoWrapper.appendChild(remoteVideo);\n return remoteVideo;\n}\n\nfunction setOnTrack(peer, remoteVideo) {\n var remoteStream = new MediaStream();\n\n remoteVideo.srcObject = remoteStream;\n\n peer.addEventListener('track', async(event) => {\n remoteStream.addTrack(event.track, remoteStream);\n });\n}\n\n\nfunction removeVideo(video) {\n var videoWrapper = video.parentNode;\n\n videoWrapper.parentNode.removeChild(videoWrapper);\n}\n\nfunction getDataChannels() {\n var dataChannels = [];\n\n for (peerUsername in mapPeers) {\n var dataChannel = mapPeers[peerUsername][1];\n\n dataChannels.push(dataChannel);\n }\n\n return dataChannels;\n}"},{"alpha_fraction":0.7166666388511658,"alphanum_fraction":0.7166666388511658,"avg_line_length":34,"blob_id":"964d626111b25b85d3f61dd280217510726b9bd4","content_id":"e7b51fd366500536bef8cf10383ea1e35c42363b","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":420,"license_type":"permissive","max_line_length":63,"num_lines":12,"path":"/cam/urls.py","repo_name":"Rutujakadam0204/video_conferencing","src_encoding":"UTF-8","text":"from django.contrib import admin\nfrom django.urls import path\n# from django.contrib.auth.decorators import login_required\nfrom .views import *\n# from rest_framework.urlpatterns import format_suffix_patterns\n\nurlpatterns = [\n path('', index, name=''),\n path('room/<int:room_id>/', room, name='room'),\n path('room/<int:room_id>/ws', room_ws, name='room_ws'),\n]\n# urlpatterns = format_suffix_patterns(urlpatterns)\n"},{"alpha_fraction":0.5681378841400146,"alphanum_fraction":0.5681378841400146,"avg_line_length":31.824390411376953,"blob_id":"703624533077adc5f017c5eac229dba0425d3dae","content_id":"ced79ef7d16a16e5b6e216b192b7683690dfa9f4","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":6729,"license_type":"permissive","max_line_length":101,"num_lines":205,"path":"/cam/message_handler.py","repo_name":"Rutujakadam0204/video_conferencing","src_encoding":"UTF-8","text":"import asyncio\nimport datetime\nimport json\nimport logging\nfrom collections import defaultdict\n\nfrom camus import db\nfrom .models import Client\nfrom .util import commit_database, get_ice_servers\n\n\nclass MessageHandler:\n \"\"\"\n Receives, processes, and sends messages.\n Messages are received via the inbox queue and sent via an outbox queue.\n Received messages are handled according to the destination and are either\n forwarded to another client, broadcast to a room, or processed locally.\n \"\"\"\n\n def __init__(self):\n self._address = 'ground control'\n self.inbox = None\n self.outbox = defaultdict(asyncio.Queue)\n self._inbox_task = None\n\n def start(self):\n self.inbox = asyncio.Queue()\n self._inbox_task = asyncio.create_task(self._process_inbox())\n\n def stop(self):\n if self._inbox_task:\n self._inbox_task.cancel()\n\n def send(self, message):\n \"\"\"Add a message to the outbox.\"\"\"\n receiver = message.receiver\n data = message.json()\n self.outbox[receiver].put_nowait(data)\n\n def broadcast(self, room, message):\n \"\"\"Add a message to the outbox for every client in the given room.\"\"\"\n for client in room.clients:\n msg = Message(message.json())\n msg.receiver = client.uuid\n self.send(msg)\n\n async def _process_inbox(self):\n \"\"\"Remove and process messages from the inbox.\"\"\"\n\n while True:\n try:\n client_uuid, data = await self.inbox.get()\n message = Message(data)\n message.sender = client_uuid\n\n # Update seen & active timestamps for client and room\n client = Client.query.filter_by(uuid=client_uuid).first()\n client.seen = client.room.active = datetime.datetime.utcnow()\n # commit_database()\n\n # Message intended for the server\n if message.receiver == self._address:\n self._handle_local_message(message)\n\n # Message to be sent to all clients in the room\n elif message.receiver == 'room':\n self.broadcast(client.room, message)\n\n # Message to another client\n else:\n # TODO: validate receiver is in same room\n self.send(message)\n\n except Exception as e:\n logging.exception('Error processing inbox')\n\n def _handle_local_message(self, message):\n \"\"\"Handle a message according to its type.\"\"\"\n\n reply = Message()\n reply.sender = self._address\n reply.receiver = message.sender\n\n if message.type == 'ping':\n reply.type = 'pong'\n reply.data = message.data\n logging.info('got ping')\n\n elif message.type == 'pong':\n logging.info('Got pong {} from client {}'.format(message.data, message.sender))\n reply = None\n\n elif message.type == 'profile':\n logging.info('got profile')\n client = Client.query.filter_by(uuid=message.sender).first()\n\n username = message.data.get('username')\n if username:\n client.name = username\n # commit_database()\n\n reply.type = 'room-info'\n reply.data = self._room_info(client.room)\n self.broadcast(client.room, reply)\n reply = None\n\n elif message.type == 'get-room-info':\n logging.info('got get-room-info')\n client = Client.query.filter_by(uuid=message.sender).first()\n reply.type = 'room-info'\n reply.data = self._room_info(client.room)\n\n elif message.type == 'get-ice-servers':\n logging.info('got get-ice-servers')\n reply.type = 'ice-servers'\n reply.data = get_ice_servers(message.sender)\n logging.info('\\t-> Done getting ice servers: {}'.format(reply.data))\n\n elif message.type == 'greeting':\n logging.info('Greeting received from client {}: {}'.format(message.sender, message.data))\n reply = None\n\n elif message.type == 'bye':\n logging.info('got bye')\n\n client = Client.query.filter_by(uuid=message.sender).first()\n room = client.room\n\n # Delete client object from db\n db.session.delete(client)\n # commit_database()\n\n # Remove message queues associated with the client\n self.outbox.pop(client.uuid, None)\n\n # Broadcast updated room info to remaining clients\n reply.type = 'room-info'\n reply.data = self._room_info(room)\n self.broadcast(client.room, reply)\n reply = None\n\n else:\n reply.type = 'error'\n reply.data = 'Unknown message type: {}'.format(message.type)\n\n if reply:\n self.send(reply)\n\n def _room_info(self, room):\n \"\"\"\n Get information about the room, consisting of the room ID and a list of\n connected clients.\n \"\"\"\n\n clients = [{'id': client.uuid, 'username': client.name}\n for client in room.clients]\n\n return {'room_id': room.slug, 'clients': clients}\n\n def send_ping(self, receiver):\n \"\"\"Send a ping message to the given receiver.\"\"\"\n ping = Message()\n ping.type = 'ping'\n ping.sender = self._address\n ping.receiver = receiver\n ping.data = datetime.datetime.utcnow().timestamp()\n self.send(ping)\n\n def send_bye(self, receiver):\n \"\"\"Send a bye message to the given receiver.\"\"\"\n bye = Message()\n bye.type = 'bye'\n bye.sender = self._address\n bye.receiver = receiver\n bye.data = datetime.datetime.utcnow().timestamp()\n self.send(bye)\n\n def broadcast_room_info(self, room):\n \"\"\"Send a room-info message to clients in the given room.\"\"\"\n info = Message()\n info.type = 'room-info'\n info.sender = self._address\n info.data = self._room_info(room)\n self.broadcast(room, info)\n\n\nclass Message:\n \"\"\"A structured message that can be sent to a client.\"\"\"\n\n def __init__(self, message=None):\n if isinstance(message, str):\n _json = json.loads(message)\n elif message is None:\n _json = {}\n else:\n _json = message\n\n self.sender = _json.get('sender')\n self.receiver = _json.get('receiver')\n self.type = _json.get('type')\n self.data = _json.get('data')\n\n def json(self):\n \"\"\"Get the JSON-encoded representation of the message.\"\"\"\n return json.dumps(self.__dict__)\n"},{"alpha_fraction":0.737864077091217,"alphanum_fraction":0.737864077091217,"avg_line_length":33.22222137451172,"blob_id":"6fb34381e120d092d2c6099b6417b8e7bf75e874","content_id":"8f38235907b9eb58f5cbcd6b95c8ecc50d95b91f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":309,"license_type":"permissive","max_line_length":63,"num_lines":9,"path":"/chat/urls.py","repo_name":"Rutujakadam0204/video_conferencing","src_encoding":"UTF-8","text":"from django.contrib import admin\nfrom django.urls import path\n# from django.contrib.auth.decorators import login_required\n# from rest_framework.urlpatterns import format_suffix_patterns\nfrom . views import *\nurlpatterns = [\n # path('admin/', admin.site.urls),\n path('', main_view, name='main_view'),\n]\n\n"},{"alpha_fraction":0.5636561512947083,"alphanum_fraction":0.5756256580352783,"avg_line_length":37.29166793823242,"blob_id":"fb304243d9aa2d88b371c8f4c72630ce9d0125f0","content_id":"710ff376d21ad79adeb6ba5f8d8a8c1a3e582a65","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":919,"license_type":"permissive","max_line_length":99,"num_lines":24,"path":"/cam/forms.py","repo_name":"Rutujakadam0204/video_conferencing","src_encoding":"UTF-8","text":"from django.forms import ModelForm, models\nfrom wtforms import BooleanField, HiddenField, PasswordField, SelectField, StringField, SubmitField\nfrom wtforms.validators import DataRequired\nfrom . models import *\n\nclass CreateRoomForm(ModelForm):\n # room_name = StringField('Room name', validators=[DataRequired()])\n # password = PasswordField('Password')\n # public = SelectField('Public', coerce=lambda x: x == 'Yes',\n # choices=[('No'), ('Yes')])\n # guest_limit = SelectField('Guest limit', coerce=int,\n # choices=[(0, 'None'), (2, '2'), (3, '3'),\n # (5, '5'), (10, '10')])\n # submit = SubmitField('Create')\n class Meta:\n model = Room\n exclude = ['slug','created','active']\n \n\n\nclass JoinRoomForm(ModelForm):\n class Meta:\n model = Client\n exclude = ['uuid','room_id','seen']\n"},{"alpha_fraction":0.731582760810852,"alphanum_fraction":0.7385120391845703,"avg_line_length":42.507938385009766,"blob_id":"74e6060f37b6df4cc1e46eb90348f950d5510bee","content_id":"e3bd980b9bdf9c037562c29a723d6be7dd53e0dd","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":2742,"license_type":"permissive","max_line_length":138,"num_lines":63,"path":"/Readme.md","repo_name":"Rutujakadam0204/video_conferencing","src_encoding":"UTF-8","text":"\n\n<h1>Video call and chat in Django</h1>\n<div>\nUsing Django, Django-channels, js, js - websockets, Daphne ASGI server ( built specially for django channels),\nredis server (installed locally using docker or on GCP using redis memory cache service)\n</div>\n\n<h4> Deploying this app on google app engine flex </h4>\n\n* We dont use/need any sql db. This app has default sqlite db in settings.py.\n\n* Clone this repository to your local machine. cd into this repo- `cd django_video/`\n\n* create a python virtual env `python3.8 -m venv venv`\n\n * Install all dependencies required: `pip install -r requirements.txt`\n\n* Create a GCP (google cloud platform lol) project.\n\n* Deploy a test app on gcp app engine flex to get an idea - \n<a href='https://cloud.google.com/python/django/flexible-environment'> Link</a>\n\n * You can ignore the cloud sql creation step as we dont need sql in our app.\n \n * In settings.py update STATIC_URL with your gcp public bucket name that you created \n when deploying test app to GAE flex\n\n* Create a redis instance on default VPC network in same gcp project - \n<a href='https://cloud.google.com/memorystore/docs/redis/creating-managing-instances#creating_a_redis_instance_on_a_vpc_network'>Link </a>\n * This Redis instance should be in same region as app engine flex.\n \n * Perhaps every gcp project comes with a default VPC network, hence we choose default and dont have to create \n another VPC.\n \n * In app.yaml file update env variables - REDISHOST,REDISPORT from your redis instance.\n\n\n<h4>You can run this project locally through your venv that you created above. I run it on Ubuntu. \nif you are using windows, god save you. </h4>\n\n* we will need a redis server locally: Hoping you have docker installed on your machine. \nif not then install it on your own.\n * `docker run -p 6379:6379 -d redis:5`\n * `daphne django_video.asgi:application`\n * Once you are done using this app, you can stop the redis container:\n \n * `sudo systemctl restart docker.socket docker.service`\n \n * `docker ps` -- shows which containers are running\n \n * `docker rm <container id>` -- stop the container\n\n<h4>Deploy your project to google app engine flex:</h4>\n\n* `gcloud app deploy` -- call this from root dir that contains app.yaml file.\n\n* If this is your first time deploying app to GAE, you will be asked to select a region and app engine type.\n * Pick the same region as your redis instance.\n * Pick flexible app engine option\n \nGAE Flex deployment takes lot of time compared to GAE standard deployment, but GAE standard doesnt support websockets\n(which are required for peer to peer connections for video and chat functionality.)\n\nVideo calling doesnt work on localhost if your VPN is on."}],"string":"[\n {\n \"alpha_fraction\": 0.6073394417762756,\n \"alphanum_fraction\": 0.6073394417762756,\n \"avg_line_length\": 32.030303955078125,\n \"blob_id\": \"02f5319fe9d17e8f26f27af5e18187cc717e8947\",\n \"content_id\": \"d4e201d09bc6df73496b60b63ee5061d73d07ae3\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2180,\n \"license_type\": \"permissive\",\n \"max_line_length\": 85,\n \"num_lines\": 66,\n \"path\": \"/chat/consumers.py\",\n \"repo_name\": \"Rutujakadam0204/video_conferencing\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# clients are gonna connect to our websockets and will connect through this consumers\\nimport json\\nfrom channels.generic.websocket import AsyncWebsocketConsumer\\nfrom django.dispatch.dispatcher import receiver\\n\\n\\nclass ChatConsumer(AsyncWebsocketConsumer):\\n\\n # will be triggered whenever a client connects to consumer\\n async def connect(self): \\n self.room_group_name = 'Test_Room'\\n\\n await self.channel_layer.group_add(\\n self.room_group_name,\\n self.channel_name\\n )\\n\\n await self.accept()\\n\\n # will be triggered whenever a client disconnects to consumer\\n async def disconnect(self, close_code): \\n await self.channel_layer.group_discard(\\n self.room_group_name,\\n self.channel_name\\n )\\n print('Disconnected')\\n\\n # whenever a msg is received from a client\\n async def receive(self, text_data):\\n #most of the code for sending sdp and notifying every peer wnenevr sent sdp\\n #receive code from js in dict format or json so deserialize json\\n receive_dict = json.loads(text_data)\\n message = receive_dict['message']\\n action = receive_dict['action']\\n\\n if (action == 'new-pffer') or (action == 'new-answer'):\\n receiver_channel_name = receive_dict['message']['receiver_channel_name']\\n\\n receive_dict['message']['receiver_channel_name'] = self.channel_name\\n await self.channel_layer.send(\\n receiver_channel_name,\\n {\\n 'type': 'send.sdp',\\n 'receive_dict':receive_dict\\n }\\n )\\n return\\n\\n receive_dict['message']['receiver_channel_name'] = self.channel_name\\n \\n #send msg to all the other peers\\n await self.channel_layer.group_send(\\n self.room_group_name,\\n {\\n 'type': 'send.sdp',\\n 'receive_dict':receive_dict\\n }\\n )\\n\\n async def send_sdp(self, event):\\n receive_dict = event['receive_dict']\\n\\n #send msg to other peer\\n await self.send(text_data=json.dumps(receive_dict))\\n\\n #dumps bcz converting from python to json\\n\"\n },\n {\n \"alpha_fraction\": 0.6715885996818542,\n \"alphanum_fraction\": 0.6802443861961365,\n \"avg_line_length\": 34.07143020629883,\n \"blob_id\": \"ac6e9bdcd6387bebd1b4f25ed6041e7e0898afbb\",\n \"content_id\": \"769dbb3572983a7fb3b865a285ce379a3862ec0a\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1964,\n \"license_type\": \"permissive\",\n \"max_line_length\": 74,\n \"num_lines\": 56,\n \"path\": \"/cam/models.py\",\n \"repo_name\": \"Rutujakadam0204/video_conferencing\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.db import models\\n# from camus import models\\nfrom slugify import slugify\\nimport uuid\\nfrom werkzeug.security import generate_password_hash, check_password_hash\\nfrom datetime import datetime\\n\\n# Create your models here.\\nclass Room(models.Model):\\n # __tablename__ = 'rooms'\\n # id = models.(models.Integer, primary_key=True)\\n name = models.CharField(unique=True, max_length=100, blank=True)\\n slug = models.SlugField(editable=False, blank=True)\\n password_hash = models.CharField(max_length=10000, blank=True)\\n guest_limit = models.IntegerField(default=100)\\n is_public = models.BooleanField(default=False)\\n created = models.DateTimeField(default=datetime.utcnow)\\n active = models.DateTimeField(default=datetime.utcnow)\\n\\n # clients = models.ManyToManyField('Client', backref='room')\\n\\n def __repr__(self):\\n return '<Room {}>'.format(self.name)\\n\\n def set_name(self, name):\\n self.name = name\\n self.slug = slugify(name)\\n\\n def set_password(self, password):\\n self.password_hash = generate_password_hash(password)\\n\\n \\n def is_full(self):\\n \\\"\\\"\\\"Check whether the room's guest limit has been reached.\\n\\n Returns True if the guest limit has been reached, False otherwise.\\n \\\"\\\"\\\"\\n return self.guest_limit and len(self.clients) >= self.guest_limit\\n\\n def active_ago(self):\\n \\\"\\\"\\\"The number of minutes ago that the room was last active.\\\"\\\"\\\"\\n now = datetime.utcnow().timestamp()\\n return int((now - self.active.timestamp()) / 60)\\n\\n\\nclass Client(models.Model):\\n # __tablename__ = 'clients'\\n # id = models.Column(models.Integer, primary_key=True)\\n name = models.CharField(max_length=100)\\n # uuid = models.UUIDField(unique=True, default=uuid.uuid4().hex)\\n seen = models.DateTimeField(default=datetime.utcnow)\\n\\n room_id = models.ForeignKey(Room, on_delete=models.DO_NOTHING)\\n\\n def __repr__(self):\\n return '<Client {}>'.format(self.uuid)\\n\"\n },\n {\n \"alpha_fraction\": 0.6334866285324097,\n \"alphanum_fraction\": 0.6394373774528503,\n \"avg_line_length\": 28.576000213623047,\n \"blob_id\": \"92b5d35d32086da416e465ce9b1d108d5173b1c0\",\n \"content_id\": \"f54a8f1b8c929ac6f8f7b109f34f34958e003960\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3697,\n \"license_type\": \"permissive\",\n \"max_line_length\": 102,\n \"num_lines\": 125,\n \"path\": \"/cam/views.py\",\n \"repo_name\": \"Rutujakadam0204/video_conferencing\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.shortcuts import render\\n\\n# Create your views here.\\nimport asyncio\\nimport logging\\n\\nimport sqlalchemy\\nfrom quart import (Blueprint, copy_current_websocket_context, flash, redirect,\\n render_template, session, websocket)\\n\\nfrom camus import db, message_handler\\nfrom .forms import *\\nfrom .models import Client, Room\\nfrom .util import *\\n\\nbp = Blueprint('main', __name__)\\n\\n\\n@bp.route('/about')\\nasync def about():\\n return redirect('/#why-camus')\\n\\n\\n# @bp.route('/', methods=['GET', 'POST'])\\ndef index(request):\\n create_room_form = CreateRoomForm()\\n if request.method == 'POST':\\n create_room_form = CreateRoomForm(request.POST)\\n if create_room_form.is_valid():\\n a = create_room_form.save()\\n a.slug = slugify(str(a.id))\\n a.save()\\n print(a.id)\\n return redirect(f\\\"/room/{a.id}\\\")\\n return render(request, 'chat.html', {'create_room_form':create_room_form})\\n\\n\\n# The `/chat` route is deprecated.\\n# @bp.route('/chat')\\n# async def chat_index():\\n# return redirect('/', code=307)\\n\\n\\n# The `/chat/` route is deprecated. Prefer`/room/` instead.\\n# @bp.route('/chat/<room_id>', methods=['GET', 'POST'])\\n# @bp.route('/room/<room_id>', methods=['GET', 'POST'])\\ndef room(request,room_id):\\n pass\\n room = Room.objects.filter(id=room_id)\\n room1 = Room.objects.get(id=room_id)\\n name = request.POST.get('name')\\n # if room.is_full():\\n # return 'Guest limit already reached', 418\\n\\n # # No password is required to join the room\\n # client = room.authenticate()\\n if request.method == 'POST':\\n a = Client.objects.create(name=name, room_id=room1)\\n request.session['id'] = a.name\\n\\n return render(request,\\n 'chatroom.html')\\n\\n # # A password is required to join the room\\n # status_code = 200\\n form = JoinRoomForm()\\n \\n # await flash('Invalid password')\\n\\n return render(request, 'join-room.html', {'title':'Camus | Join a room','form':form, 'room':room})\\n\\n\\n# The `/chat/` route is deprecated. Prefer`/room/` instead.\\n# @bp.websocket('/chat/<room_id>/ws')\\n# @bp.websocket('/room/<room_id>/ws')\\nasync def room_ws(request,room_id):\\n # Verify that the room exists\\n Room.objects.filter(id=room_id).first_or_404()\\n\\n # Verify the client using a secure cookie\\n client = Client.objects.filter(name=request.session['id'])\\n client1 = Client.objects.get(name=request.session['id'])\\n\\n if client:\\n client = Client.objects.get(name=request.session['id'])\\n logging.info(f'Accepted websocket connection for client {client1.name}')\\n await websocket.accept()\\n else:\\n return 'Forbidden', 403\\n\\n inbox, outbox = message_handler.inbox, message_handler.outbox\\n\\n send_task = asyncio.create_task(\\n copy_current_websocket_context(ws_send)(outbox[client1.name]),\\n )\\n receive_task = asyncio.create_task(\\n copy_current_websocket_context(ws_receive)(client1.name, inbox),\\n )\\n try:\\n await asyncio.gather(send_task, receive_task)\\n finally:\\n logging.info(f'Terminating websocket connection for client {client1.name}')\\n send_task.cancel()\\n receive_task.cancel()\\n\\n\\n@bp.route('/public')\\nasync def public():\\n public_rooms = Room.query.filter_by(is_public=True).all()\\n\\n return await render_template(\\n 'public.html', title='Camus Video Chat | Public Rooms',\\n public_rooms=public_rooms)\\n\\n\\nasync def ws_send(queue):\\n while True:\\n message = await queue.get()\\n await websocket.send(message)\\n\\n\\nasync def ws_receive(client_id, queue):\\n while True:\\n message = await websocket.receive()\\n await queue.put((client_id, message))\\n\"\n },\n {\n \"alpha_fraction\": 0.6208961606025696,\n \"alphanum_fraction\": 0.6220053434371948,\n \"avg_line_length\": 24.76285743713379,\n \"blob_id\": \"7c895392ae893756887e14c23bdd467feb973584\",\n \"content_id\": \"bbb6d8c182cb620e52a0cd29b7e2625fc31209a2\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"JavaScript\",\n \"length_bytes\": 9016,\n \"license_type\": \"permissive\",\n \"max_line_length\": 121,\n \"num_lines\": 350,\n \"path\": \"/static/js/main.js\",\n \"repo_name\": \"Rutujakadam0204/video_conferencing\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"console.log(\\\"In main.js\\\");\\n\\n\\n\\nvar mapPeers = {};\\n\\nvar usernameInput = document.getElementById('username')\\nvar btnJoin = document.querySelector('#btn-join')\\n\\nvar username;\\nvar webSocket;\\n\\nfunction webSocketOnMessage(event) {\\n var parseData = JSON.parse(event.data);\\n var peerUsername = parseData['peer'];\\n var action = parseData['action'];\\n\\n if (username == peerUsername) {\\n return;\\n }\\n\\n var receiver_channel_name = parseData['message']['receiver_channel_name'];\\n if (action == 'new-peer') {\\n createOfferer(peerUsername, receiver_channel_name);\\n return;\\n }\\n\\n if (action == 'new-offer') {\\n var offer = parseData['message']['sdp'];\\n createAnswerer(offer, peerUsername, receiver_channel_name);\\n return;\\n }\\n\\n if (action == 'new-answer') {\\n var answer = parseData['message']['sdp'];\\n\\n var peer = mapPeers[peerUsername][0];\\n peer.setRemoteDescription(answer);\\n return;\\n }\\n}\\n\\n\\nbtnJoin.addEventListener('click', () => {\\n username = usernameInput.value;\\n console.log(username)\\n if (username == '') {\\n return;\\n }\\n\\n usernameInput.value = '';\\n usernameInput.disabled = true;\\n usernameInput.style.visibility = 'hidden';\\n\\n usernameInput.disabled = true;\\n usernameInput.style.visibility = 'hidden';\\n\\n btnJoin.disabled = true;\\n btnJoin.style.visibility = 'hidden';\\n\\n var labelUsername = document.querySelector('#label-username')\\n labelUsername.innerHTML = username;\\n\\n var loc = window.location;\\n var wsStart = 'ws://';\\n\\n if (loc.protocol == 'https:') {\\n wsStart = 'wss://';\\n }\\n\\n var endPoint = wsStart + loc.host + loc.pathname + '/';\\n\\n console.log('endPoint', endPoint);\\n\\n webSocket = new WebSocket(endPoint);\\n\\n webSocket.addEventListener('open', (e) => {\\n console.log('Connection Opened !')\\n\\n sendSignal('new-peer', {});\\n\\n });\\n webSocket.addEventListener('message', webSocketOnMessage);\\n webSocket.addEventListener('close', (e) => {\\n console.log('Connection closed !')\\n });\\n webSocket.addEventListener('error', (e) => {\\n console.log('Error occured !')\\n });\\n\\n})\\n\\n\\nvar localStream = new MediaStream();\\n\\n// const constraints = {\\n// 'video': true,\\n// 'audio': true\\n// }\\nconst constraints = window.constraints = {\\n audio: true,\\n video: true\\n};\\n\\nconst localVideo = document.querySelector('#local-video');\\n\\nconst btnToggleAudio = document.querySelector('#btn-toggle-audio');\\nconst btnToggleVideo = document.querySelector('#btn-toggle-video');\\n\\n\\n\\nvar userMedia = navigator.mediaDevices.getUserMedia(constraints)\\n .then(stream => {\\n localStream = stream;\\n localVideo.srcObject = localStream;\\n localVideo.muted = true;\\n\\n var audioTracks = stream.getAudioTracks();\\n var videoTracks = stream.getVideoTracks();\\n\\n audioTracks[0].enabled = true;\\n videoTracks[0].enabled = true;\\n\\n btnToggleAudio.addEventListener('click', () => {\\n audioTracks[0].enabled = !audioTracks[0].enabled;\\n if (audioTracks[0].enabled) {\\n btnToggleAudio.innerHTML = 'Audio Mute';\\n return;\\n }\\n btnToggleAudio.innerHTML = 'Audio Unmute';\\n });\\n\\n btnToggleVideo.addEventListener('click', () => {\\n videoTracks[0].enabled = !videoTracks[0].enabled;\\n if (videoTracks[0].enabled) {\\n btnToggleVideo.innerHTML = 'Video off';\\n return;\\n }\\n btnToggleVideo.innerHTML = 'Video On';\\n });\\n\\n })\\n\\n.catch(error => {\\n console.log('error accessing media device', error)\\n});\\n\\n\\n\\n\\nvar btnSendMsg = document.querySelector('#btn-send-msg');\\nvar messageList = document.querySelector('#message-list');\\nvar messageInput = document.querySelector('#msg');\\n\\nbtnSendMsg.addEventListener('click', sendMsgOnClick);\\n\\nfunction sendMsgOnClick() {\\n var message = messageInput.value;\\n\\n var li = document.createElement('li');\\n li.appendChild(document.createTextNode('Me: ' + message));\\n messageList.appendChild(li);\\n\\n var dataChannels = getDataChannels();\\n\\n message = username + ': ' + message;\\n\\n for (index in dataChannels) {\\n dataChannels[index].send(message);\\n }\\n\\n messageInput.value = '';\\n}\\n\\n\\n\\nfunction sendSignal(action, message) {\\n var jsonStr = JSON.stringify({\\n 'peer': username,\\n 'action': action,\\n 'message': message,\\n });\\n\\n webSocket.send(jsonStr);\\n\\n}\\n\\n\\nfunction createOfferer(peerUsername, receiver_channel_name) {\\n var peer = new RTCPeerConnection(null);\\n\\n addLocalTracks(peer);\\n\\n var dc = peer.createDataChannel('channel');\\n dc.addEventListener('open', () => {\\n console.log('Connection open')\\n })\\n dc.addEventListener('message', dcOnMessage);\\n\\n var remoteVideo = createVideo(peerUsername);\\n setOnTrack(peer, remoteVideo);\\n mapPeers[peerUsername] = [peer, dc];\\n peer.addEventListener('iceconnectionstatechange', () => {\\n var iceConnectionState = peer.iceConnectionState;\\n if (iceConnectionState == 'failed' || iceConnectionState === 'disconnected' || iceConnectionState === 'Closed') {\\n delete mapPeers[peerUsername];\\n if (iceConnectionState != 'closed') {\\n peer.close();\\n }\\n removeVideo(remoteVideo);\\n }\\n });\\n\\n peer.addEventListener('icecandidate', (event) => {\\n if (event.candidate) {\\n console.log('New ice candidate', JSON.stringify(peer.localDescription));\\n return;\\n }\\n\\n sendSignal('new-offer', {\\n 'sdp': peer.localDescription,\\n 'receiver_channel_name': receiver_channel_name\\n })\\n });\\n\\n peer.createOffer()\\n .then(o => peer.setLocalDescription(o))\\n .then(() => {\\n console.log('Local Description set successfully')\\n })\\n}\\n\\nfunction createAnswerer(offer, peerUsername, receiver_channel_name) {\\n var peer = new RTCPeerConnection(null);\\n\\n addLocalTracks(peer);\\n\\n var remoteVideo = createVideo(peerUsername);\\n setOnTrack(peer, remoteVideo);\\n\\n peer.addEventListener('datachannel', e => {\\n peer.dc = e.channel;\\n peer.dc.addEventListener('open', () => {\\n console.log('Connection open')\\n })\\n peer.dc.addEventListener('message', dcOnMessage);\\n\\n mapPeers[peerUsername] = [peer, peer.dc];\\n\\n })\\n\\n peer.addEventListener('iceconnectionstatechange', () => {\\n var iceConnectionState = peer.iceConnectionState;\\n if (iceConnectionState == 'failed' || iceConnectionState === 'disconnected' || iceConnectionState === 'Closed') {\\n delete mapPeers[peerUsername];\\n if (iceConnectionState != 'closed') {\\n peer.close();\\n }\\n removeVideo(remoteVideo);\\n }\\n });\\n\\n peer.addEventListener('icecandidate', (event) => {\\n if (event.candidate) {\\n console.log('New ice candidate', JSON.stringify(peer.localDescription));\\n return;\\n }\\n\\n sendSignal('new-answer', {\\n 'sdp': peer.localDescription,\\n 'receiver_channel_name': receiver_channel_name\\n })\\n });\\n\\n peer.setRemoteDescription(offer)\\n .then(() => {\\n console.log('remote description set successfully for %s.', peerUsername);\\n return peer.createAnswer();\\n })\\n\\n .then(a => {\\n console.log('Answer created !')\\n\\n peer.setLocalDescription(a);\\n })\\n\\n}\\n\\nfunction addLocalTracks(peer) {\\n\\n localStream.getTracks().forEach(track => {\\n peer.addTrack(track, localStream);\\n });\\n\\n return;\\n\\n}\\n\\nvar messageList = document.querySelector('#message-list');\\n\\nfunction dcOnMessage(event) {\\n var message = event.data;\\n\\n var li = document.createElement('li')\\n li.appendChild(document.createTextNode(message));\\n messageList.appendChild(li);\\n}\\n\\nfunction createVideo(peerUsername) {\\n var videoContainer = document.querySelector('#video-container');\\n var remoteVideo = document.createElement('video');\\n remoteVideo.id = peerUsername + '-video';\\n remoteVideo.autoplay = true;\\n remoteVideo.playsInline = true;\\n remoteVideo.controls = true;\\n\\n var videoWrapper = document.createElement('div');\\n videoContainer.appendChild(videoWrapper);\\n videoWrapper.appendChild(remoteVideo);\\n return remoteVideo;\\n}\\n\\nfunction setOnTrack(peer, remoteVideo) {\\n var remoteStream = new MediaStream();\\n\\n remoteVideo.srcObject = remoteStream;\\n\\n peer.addEventListener('track', async(event) => {\\n remoteStream.addTrack(event.track, remoteStream);\\n });\\n}\\n\\n\\nfunction removeVideo(video) {\\n var videoWrapper = video.parentNode;\\n\\n videoWrapper.parentNode.removeChild(videoWrapper);\\n}\\n\\nfunction getDataChannels() {\\n var dataChannels = [];\\n\\n for (peerUsername in mapPeers) {\\n var dataChannel = mapPeers[peerUsername][1];\\n\\n dataChannels.push(dataChannel);\\n }\\n\\n return dataChannels;\\n}\"\n },\n {\n \"alpha_fraction\": 0.7166666388511658,\n \"alphanum_fraction\": 0.7166666388511658,\n \"avg_line_length\": 34,\n \"blob_id\": \"964d626111b25b85d3f61dd280217510726b9bd4\",\n \"content_id\": \"e7b51fd366500536bef8cf10383ea1e35c42363b\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 420,\n \"license_type\": \"permissive\",\n \"max_line_length\": 63,\n \"num_lines\": 12,\n \"path\": \"/cam/urls.py\",\n \"repo_name\": \"Rutujakadam0204/video_conferencing\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib import admin\\nfrom django.urls import path\\n# from django.contrib.auth.decorators import login_required\\nfrom .views import *\\n# from rest_framework.urlpatterns import format_suffix_patterns\\n\\nurlpatterns = [\\n path('', index, name=''),\\n path('room/<int:room_id>/', room, name='room'),\\n path('room/<int:room_id>/ws', room_ws, name='room_ws'),\\n]\\n# urlpatterns = format_suffix_patterns(urlpatterns)\\n\"\n },\n {\n \"alpha_fraction\": 0.5681378841400146,\n \"alphanum_fraction\": 0.5681378841400146,\n \"avg_line_length\": 31.824390411376953,\n \"blob_id\": \"703624533077adc5f017c5eac229dba0425d3dae\",\n \"content_id\": \"ced79ef7d16a16e5b6e216b192b7683690dfa9f4\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 6729,\n \"license_type\": \"permissive\",\n \"max_line_length\": 101,\n \"num_lines\": 205,\n \"path\": \"/cam/message_handler.py\",\n \"repo_name\": \"Rutujakadam0204/video_conferencing\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import asyncio\\nimport datetime\\nimport json\\nimport logging\\nfrom collections import defaultdict\\n\\nfrom camus import db\\nfrom .models import Client\\nfrom .util import commit_database, get_ice_servers\\n\\n\\nclass MessageHandler:\\n \\\"\\\"\\\"\\n Receives, processes, and sends messages.\\n Messages are received via the inbox queue and sent via an outbox queue.\\n Received messages are handled according to the destination and are either\\n forwarded to another client, broadcast to a room, or processed locally.\\n \\\"\\\"\\\"\\n\\n def __init__(self):\\n self._address = 'ground control'\\n self.inbox = None\\n self.outbox = defaultdict(asyncio.Queue)\\n self._inbox_task = None\\n\\n def start(self):\\n self.inbox = asyncio.Queue()\\n self._inbox_task = asyncio.create_task(self._process_inbox())\\n\\n def stop(self):\\n if self._inbox_task:\\n self._inbox_task.cancel()\\n\\n def send(self, message):\\n \\\"\\\"\\\"Add a message to the outbox.\\\"\\\"\\\"\\n receiver = message.receiver\\n data = message.json()\\n self.outbox[receiver].put_nowait(data)\\n\\n def broadcast(self, room, message):\\n \\\"\\\"\\\"Add a message to the outbox for every client in the given room.\\\"\\\"\\\"\\n for client in room.clients:\\n msg = Message(message.json())\\n msg.receiver = client.uuid\\n self.send(msg)\\n\\n async def _process_inbox(self):\\n \\\"\\\"\\\"Remove and process messages from the inbox.\\\"\\\"\\\"\\n\\n while True:\\n try:\\n client_uuid, data = await self.inbox.get()\\n message = Message(data)\\n message.sender = client_uuid\\n\\n # Update seen & active timestamps for client and room\\n client = Client.query.filter_by(uuid=client_uuid).first()\\n client.seen = client.room.active = datetime.datetime.utcnow()\\n # commit_database()\\n\\n # Message intended for the server\\n if message.receiver == self._address:\\n self._handle_local_message(message)\\n\\n # Message to be sent to all clients in the room\\n elif message.receiver == 'room':\\n self.broadcast(client.room, message)\\n\\n # Message to another client\\n else:\\n # TODO: validate receiver is in same room\\n self.send(message)\\n\\n except Exception as e:\\n logging.exception('Error processing inbox')\\n\\n def _handle_local_message(self, message):\\n \\\"\\\"\\\"Handle a message according to its type.\\\"\\\"\\\"\\n\\n reply = Message()\\n reply.sender = self._address\\n reply.receiver = message.sender\\n\\n if message.type == 'ping':\\n reply.type = 'pong'\\n reply.data = message.data\\n logging.info('got ping')\\n\\n elif message.type == 'pong':\\n logging.info('Got pong {} from client {}'.format(message.data, message.sender))\\n reply = None\\n\\n elif message.type == 'profile':\\n logging.info('got profile')\\n client = Client.query.filter_by(uuid=message.sender).first()\\n\\n username = message.data.get('username')\\n if username:\\n client.name = username\\n # commit_database()\\n\\n reply.type = 'room-info'\\n reply.data = self._room_info(client.room)\\n self.broadcast(client.room, reply)\\n reply = None\\n\\n elif message.type == 'get-room-info':\\n logging.info('got get-room-info')\\n client = Client.query.filter_by(uuid=message.sender).first()\\n reply.type = 'room-info'\\n reply.data = self._room_info(client.room)\\n\\n elif message.type == 'get-ice-servers':\\n logging.info('got get-ice-servers')\\n reply.type = 'ice-servers'\\n reply.data = get_ice_servers(message.sender)\\n logging.info('\\\\t-> Done getting ice servers: {}'.format(reply.data))\\n\\n elif message.type == 'greeting':\\n logging.info('Greeting received from client {}: {}'.format(message.sender, message.data))\\n reply = None\\n\\n elif message.type == 'bye':\\n logging.info('got bye')\\n\\n client = Client.query.filter_by(uuid=message.sender).first()\\n room = client.room\\n\\n # Delete client object from db\\n db.session.delete(client)\\n # commit_database()\\n\\n # Remove message queues associated with the client\\n self.outbox.pop(client.uuid, None)\\n\\n # Broadcast updated room info to remaining clients\\n reply.type = 'room-info'\\n reply.data = self._room_info(room)\\n self.broadcast(client.room, reply)\\n reply = None\\n\\n else:\\n reply.type = 'error'\\n reply.data = 'Unknown message type: {}'.format(message.type)\\n\\n if reply:\\n self.send(reply)\\n\\n def _room_info(self, room):\\n \\\"\\\"\\\"\\n Get information about the room, consisting of the room ID and a list of\\n connected clients.\\n \\\"\\\"\\\"\\n\\n clients = [{'id': client.uuid, 'username': client.name}\\n for client in room.clients]\\n\\n return {'room_id': room.slug, 'clients': clients}\\n\\n def send_ping(self, receiver):\\n \\\"\\\"\\\"Send a ping message to the given receiver.\\\"\\\"\\\"\\n ping = Message()\\n ping.type = 'ping'\\n ping.sender = self._address\\n ping.receiver = receiver\\n ping.data = datetime.datetime.utcnow().timestamp()\\n self.send(ping)\\n\\n def send_bye(self, receiver):\\n \\\"\\\"\\\"Send a bye message to the given receiver.\\\"\\\"\\\"\\n bye = Message()\\n bye.type = 'bye'\\n bye.sender = self._address\\n bye.receiver = receiver\\n bye.data = datetime.datetime.utcnow().timestamp()\\n self.send(bye)\\n\\n def broadcast_room_info(self, room):\\n \\\"\\\"\\\"Send a room-info message to clients in the given room.\\\"\\\"\\\"\\n info = Message()\\n info.type = 'room-info'\\n info.sender = self._address\\n info.data = self._room_info(room)\\n self.broadcast(room, info)\\n\\n\\nclass Message:\\n \\\"\\\"\\\"A structured message that can be sent to a client.\\\"\\\"\\\"\\n\\n def __init__(self, message=None):\\n if isinstance(message, str):\\n _json = json.loads(message)\\n elif message is None:\\n _json = {}\\n else:\\n _json = message\\n\\n self.sender = _json.get('sender')\\n self.receiver = _json.get('receiver')\\n self.type = _json.get('type')\\n self.data = _json.get('data')\\n\\n def json(self):\\n \\\"\\\"\\\"Get the JSON-encoded representation of the message.\\\"\\\"\\\"\\n return json.dumps(self.__dict__)\\n\"\n },\n {\n \"alpha_fraction\": 0.737864077091217,\n \"alphanum_fraction\": 0.737864077091217,\n \"avg_line_length\": 33.22222137451172,\n \"blob_id\": \"6fb34381e120d092d2c6099b6417b8e7bf75e874\",\n \"content_id\": \"8f38235907b9eb58f5cbcd6b95c8ecc50d95b91f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 309,\n \"license_type\": \"permissive\",\n \"max_line_length\": 63,\n \"num_lines\": 9,\n \"path\": \"/chat/urls.py\",\n \"repo_name\": \"Rutujakadam0204/video_conferencing\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.contrib import admin\\nfrom django.urls import path\\n# from django.contrib.auth.decorators import login_required\\n# from rest_framework.urlpatterns import format_suffix_patterns\\nfrom . views import *\\nurlpatterns = [\\n # path('admin/', admin.site.urls),\\n path('', main_view, name='main_view'),\\n]\\n\\n\"\n },\n {\n \"alpha_fraction\": 0.5636561512947083,\n \"alphanum_fraction\": 0.5756256580352783,\n \"avg_line_length\": 37.29166793823242,\n \"blob_id\": \"fb304243d9aa2d88b371c8f4c72630ce9d0125f0\",\n \"content_id\": \"710ff376d21ad79adeb6ba5f8d8a8c1a3e582a65\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 919,\n \"license_type\": \"permissive\",\n \"max_line_length\": 99,\n \"num_lines\": 24,\n \"path\": \"/cam/forms.py\",\n \"repo_name\": \"Rutujakadam0204/video_conferencing\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from django.forms import ModelForm, models\\nfrom wtforms import BooleanField, HiddenField, PasswordField, SelectField, StringField, SubmitField\\nfrom wtforms.validators import DataRequired\\nfrom . models import *\\n\\nclass CreateRoomForm(ModelForm):\\n # room_name = StringField('Room name', validators=[DataRequired()])\\n # password = PasswordField('Password')\\n # public = SelectField('Public', coerce=lambda x: x == 'Yes',\\n # choices=[('No'), ('Yes')])\\n # guest_limit = SelectField('Guest limit', coerce=int,\\n # choices=[(0, 'None'), (2, '2'), (3, '3'),\\n # (5, '5'), (10, '10')])\\n # submit = SubmitField('Create')\\n class Meta:\\n model = Room\\n exclude = ['slug','created','active']\\n \\n\\n\\nclass JoinRoomForm(ModelForm):\\n class Meta:\\n model = Client\\n exclude = ['uuid','room_id','seen']\\n\"\n },\n {\n \"alpha_fraction\": 0.731582760810852,\n \"alphanum_fraction\": 0.7385120391845703,\n \"avg_line_length\": 42.507938385009766,\n \"blob_id\": \"74e6060f37b6df4cc1e46eb90348f950d5510bee\",\n \"content_id\": \"e3bd980b9bdf9c037562c29a723d6be7dd53e0dd\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 2742,\n \"license_type\": \"permissive\",\n \"max_line_length\": 138,\n \"num_lines\": 63,\n \"path\": \"/Readme.md\",\n \"repo_name\": \"Rutujakadam0204/video_conferencing\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\n\\n<h1>Video call and chat in Django</h1>\\n<div>\\nUsing Django, Django-channels, js, js - websockets, Daphne ASGI server ( built specially for django channels),\\nredis server (installed locally using docker or on GCP using redis memory cache service)\\n</div>\\n\\n<h4> Deploying this app on google app engine flex </h4>\\n\\n* We dont use/need any sql db. This app has default sqlite db in settings.py.\\n\\n* Clone this repository to your local machine. cd into this repo- `cd django_video/`\\n\\n* create a python virtual env `python3.8 -m venv venv`\\n\\n * Install all dependencies required: `pip install -r requirements.txt`\\n\\n* Create a GCP (google cloud platform lol) project.\\n\\n* Deploy a test app on gcp app engine flex to get an idea - \\n<a href='https://cloud.google.com/python/django/flexible-environment'> Link</a>\\n\\n * You can ignore the cloud sql creation step as we dont need sql in our app.\\n \\n * In settings.py update STATIC_URL with your gcp public bucket name that you created \\n when deploying test app to GAE flex\\n\\n* Create a redis instance on default VPC network in same gcp project - \\n<a href='https://cloud.google.com/memorystore/docs/redis/creating-managing-instances#creating_a_redis_instance_on_a_vpc_network'>Link </a>\\n * This Redis instance should be in same region as app engine flex.\\n \\n * Perhaps every gcp project comes with a default VPC network, hence we choose default and dont have to create \\n another VPC.\\n \\n * In app.yaml file update env variables - REDISHOST,REDISPORT from your redis instance.\\n\\n\\n<h4>You can run this project locally through your venv that you created above. I run it on Ubuntu. \\nif you are using windows, god save you. </h4>\\n\\n* we will need a redis server locally: Hoping you have docker installed on your machine. \\nif not then install it on your own.\\n * `docker run -p 6379:6379 -d redis:5`\\n * `daphne django_video.asgi:application`\\n * Once you are done using this app, you can stop the redis container:\\n \\n * `sudo systemctl restart docker.socket docker.service`\\n \\n * `docker ps` -- shows which containers are running\\n \\n * `docker rm <container id>` -- stop the container\\n\\n<h4>Deploy your project to google app engine flex:</h4>\\n\\n* `gcloud app deploy` -- call this from root dir that contains app.yaml file.\\n\\n* If this is your first time deploying app to GAE, you will be asked to select a region and app engine type.\\n * Pick the same region as your redis instance.\\n * Pick flexible app engine option\\n \\nGAE Flex deployment takes lot of time compared to GAE standard deployment, but GAE standard doesnt support websockets\\n(which are required for peer to peer connections for video and chat functionality.)\\n\\nVideo calling doesnt work on localhost if your VPN is on.\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":9,"string":"9"}}},{"rowIdx":2979197,"cells":{"repo_name":{"kind":"string","value":"Astolatt/hangman"},"repo_url":{"kind":"string","value":"https://github.com/Astolatt/hangman"},"snapshot_id":{"kind":"string","value":"51ef3aa82f476e6cf58b195a00b39c75122f6b1b"},"revision_id":{"kind":"string","value":"e467ef376fa82537bc5da0eaef36a88e789817b5"},"directory_id":{"kind":"string","value":"8a95cbe632124397a68d435c345800cb008401ef"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-03-17T17:51:27.419949","string":"2020-03-17T17:51:27.419949"},"revision_date":{"kind":"timestamp","value":"2018-05-18T14:24:32","string":"2018-05-18T14:24:32"},"committer_date":{"kind":"timestamp","value":"2018-05-18T14:24:32","string":"2018-05-18T14:24:32"},"github_id":{"kind":"number","value":133804625,"string":"133,804,625"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.46113306283950806,"alphanum_fraction":0.4736495316028595,"avg_line_length":23.095237731933594,"blob_id":"9ce57956976fc1355775db0b0d1dfb31ccfda5cd","content_id":"9567f66ad45313fc5e2c48b6a6915cfd31a8a057","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1518,"license_type":"no_license","max_line_length":88,"num_lines":63,"path":"/hangman.py","repo_name":"Astolatt/hangman","src_encoding":"UTF-8","text":"from random import randint\n\na=[r\" -----|\", r\" | \\| \", r\" |\", r\" |\", r\" |\", r\" |\", \" / \\\\\"]\nparts=0\nword=\"WORD\"\n\nfile=open(\"hangman.txt\", 'r')\nwords=file.read().splitlines()\nfile.close()\n\nword=words[randint(0,len(words)-1)]\nused=set()\nknows=[]\ncryptic=\"\"\n\ndef draw_word():\n global cryptic\n for i in word:\n if i in knows: cryptic+=i\n elif i==' ': cryptic+=i\n else: cryptic+='_'\n if not '_' in cryptic:\n print(' '.join(cryptic)+'\\n')\n print('\\n'.join(a)+'\\n')\n print(\"You won, motherfucker!\")\n exit()\n print(' '+' '.join(cryptic)+'\\n')\n cryptic=\"\"\n\ndef draw_part():\n print(\"Wrong, bitch!\"+'\\n')\n global a, parts\n if parts==0: a[2]=\" O |\"\n elif parts==1: a[3]=\" X |\"\n elif parts==2: a[4]=\" / |\"\n elif parts==3: a[4]=r\" / \\ |\"\n elif parts==4: a[3]=r\" X\\ |\"\n elif parts>=5:\n a[3]=r\" /X\\ |\"\n print(\"You lost, fucker!\")\n print(' '+' '.join(word))\n print('\\n'.join(a)+'\\n')\n exit()\n parts+=1\n\nwhile True:\n print('#'*(2*len(word)+1))\n draw_word()\n print('\\n'.join(a)+'\\n')\n print(\"Type a letter: \",)\n ch=input()\n if not ch.isalpha() or len(ch)>1:\n print(\"Not a letter. Try again.\")\n continue\n ch=ch.upper()\n if ch in set(used) or ch in knows:\n print(\"You already tried that. Try again.\")\n continue\n else:\n if ch in word: knows.append(ch)\n else:\n used.add(ch)\n draw_part()\n"}],"string":"[\n {\n \"alpha_fraction\": 0.46113306283950806,\n \"alphanum_fraction\": 0.4736495316028595,\n \"avg_line_length\": 23.095237731933594,\n \"blob_id\": \"9ce57956976fc1355775db0b0d1dfb31ccfda5cd\",\n \"content_id\": \"9567f66ad45313fc5e2c48b6a6915cfd31a8a057\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1518,\n \"license_type\": \"no_license\",\n \"max_line_length\": 88,\n \"num_lines\": 63,\n \"path\": \"/hangman.py\",\n \"repo_name\": \"Astolatt/hangman\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from random import randint\\n\\na=[r\\\" -----|\\\", r\\\" | \\\\| \\\", r\\\" |\\\", r\\\" |\\\", r\\\" |\\\", r\\\" |\\\", \\\" / \\\\\\\\\\\"]\\nparts=0\\nword=\\\"WORD\\\"\\n\\nfile=open(\\\"hangman.txt\\\", 'r')\\nwords=file.read().splitlines()\\nfile.close()\\n\\nword=words[randint(0,len(words)-1)]\\nused=set()\\nknows=[]\\ncryptic=\\\"\\\"\\n\\ndef draw_word():\\n global cryptic\\n for i in word:\\n if i in knows: cryptic+=i\\n elif i==' ': cryptic+=i\\n else: cryptic+='_'\\n if not '_' in cryptic:\\n print(' '.join(cryptic)+'\\\\n')\\n print('\\\\n'.join(a)+'\\\\n')\\n print(\\\"You won, motherfucker!\\\")\\n exit()\\n print(' '+' '.join(cryptic)+'\\\\n')\\n cryptic=\\\"\\\"\\n\\ndef draw_part():\\n print(\\\"Wrong, bitch!\\\"+'\\\\n')\\n global a, parts\\n if parts==0: a[2]=\\\" O |\\\"\\n elif parts==1: a[3]=\\\" X |\\\"\\n elif parts==2: a[4]=\\\" / |\\\"\\n elif parts==3: a[4]=r\\\" / \\\\ |\\\"\\n elif parts==4: a[3]=r\\\" X\\\\ |\\\"\\n elif parts>=5:\\n a[3]=r\\\" /X\\\\ |\\\"\\n print(\\\"You lost, fucker!\\\")\\n print(' '+' '.join(word))\\n print('\\\\n'.join(a)+'\\\\n')\\n exit()\\n parts+=1\\n\\nwhile True:\\n print('#'*(2*len(word)+1))\\n draw_word()\\n print('\\\\n'.join(a)+'\\\\n')\\n print(\\\"Type a letter: \\\",)\\n ch=input()\\n if not ch.isalpha() or len(ch)>1:\\n print(\\\"Not a letter. Try again.\\\")\\n continue\\n ch=ch.upper()\\n if ch in set(used) or ch in knows:\\n print(\\\"You already tried that. Try again.\\\")\\n continue\\n else:\\n if ch in word: knows.append(ch)\\n else:\\n used.add(ch)\\n draw_part()\\n\"\n }\n]","isConversational":false},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2979198,"cells":{"repo_name":{"kind":"string","value":"aamustapha/django-recip"},"repo_url":{"kind":"string","value":"https://github.com/aamustapha/django-recip"},"snapshot_id":{"kind":"string","value":"91957929f9ea7d318045efc920f4133935ea5848"},"revision_id":{"kind":"string","value":"6dd412ad835c85fc3b7057ff5a9de43f9b4cb96b"},"directory_id":{"kind":"string","value":"7cedf47069bf12f6a964b8851f3df096e0ff5eba"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-11-27T11:11:29.757935","string":"2020-11-27T11:11:29.757935"},"revision_date":{"kind":"timestamp","value":"2019-12-22T20:41:49","string":"2019-12-22T20:41:49"},"committer_date":{"kind":"timestamp","value":"2019-12-22T20:41:49","string":"2019-12-22T20:41:49"},"github_id":{"kind":"number","value":229416099,"string":"229,416,099"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5910364389419556,"alphanum_fraction":0.5910364389419556,"avg_line_length":27.559999465942383,"blob_id":"fbb4945f1aa234bd8545812d9dc5356900574f32","content_id":"b1c15078db586445c21c94f49f3f12ca3dc31641","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1428,"license_type":"permissive","max_line_length":79,"num_lines":50,"path":"/app/core/tests/test_models.py","repo_name":"aamustapha/django-recip","src_encoding":"UTF-8","text":"\"\"\"\nCreates test case for app models\n\"\"\"\nfrom django.test import TestCase\nfrom django.contrib.auth import get_user_model\n\n\nclass ModelTests(TestCase):\n\n def test_create_user_with_email_successful(self):\n \"\"\"\n Method tests if creating a user with valid email and passwords succeeds\n \"\"\"\n email = 'amustapha@gmail.com'\n password = 'SecurePasswordWith1234Numbers'\n user = get_user_model().objects.create_user(\n email=email,\n password=password\n )\n\n self.assertEqual(user.email, email)\n self.assertTrue(user.check_password(password))\n\n def test_new_user_email_normalized(self):\n \"\"\"Method to test user emaail is normalized\"\"\"\n email = \"amustapha@GMAIL.COM\"\n user = get_user_model().objects.create_user(\n email=email,\n password='falsepassword123'\n )\n self.assertEqual(user.email, email.lower())\n\n def test_new_user_invalid_email(self):\n \"\"\"\n Test creaating new user without email throws error\n \"\"\"\n with self.assertRaises(ValueError):\n get_user_model().objects.create_user(None)\n\n def test_create_superuser(self):\n \"\"\"\n Tests creation of superuser\n \"\"\"\n\n user = get_user_model().objects.create_superuser(\n email='amustapha@hooli.ng',\n password='fake`12323'\n )\n self.assertTrue(user.is_superuser)\n self.assertTrue(user.is_staff)\n"},{"alpha_fraction":0.804347813129425,"alphanum_fraction":0.804347813129425,"avg_line_length":22,"blob_id":"3df5dd89ffbd5b20072c0af7f1f453c27c95c772","content_id":"579d3bee0230bd55d2559d82e0e4f4088318737c","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":46,"license_type":"permissive","max_line_length":30,"num_lines":2,"path":"/README.md","repo_name":"aamustapha/django-recip","src_encoding":"UTF-8","text":"# django-recip\nUdemy's Advanced Django Course\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5910364389419556,\n \"alphanum_fraction\": 0.5910364389419556,\n \"avg_line_length\": 27.559999465942383,\n \"blob_id\": \"fbb4945f1aa234bd8545812d9dc5356900574f32\",\n \"content_id\": \"b1c15078db586445c21c94f49f3f12ca3dc31641\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1428,\n \"license_type\": \"permissive\",\n \"max_line_length\": 79,\n \"num_lines\": 50,\n \"path\": \"/app/core/tests/test_models.py\",\n \"repo_name\": \"aamustapha/django-recip\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nCreates test case for app models\\n\\\"\\\"\\\"\\nfrom django.test import TestCase\\nfrom django.contrib.auth import get_user_model\\n\\n\\nclass ModelTests(TestCase):\\n\\n def test_create_user_with_email_successful(self):\\n \\\"\\\"\\\"\\n Method tests if creating a user with valid email and passwords succeeds\\n \\\"\\\"\\\"\\n email = 'amustapha@gmail.com'\\n password = 'SecurePasswordWith1234Numbers'\\n user = get_user_model().objects.create_user(\\n email=email,\\n password=password\\n )\\n\\n self.assertEqual(user.email, email)\\n self.assertTrue(user.check_password(password))\\n\\n def test_new_user_email_normalized(self):\\n \\\"\\\"\\\"Method to test user emaail is normalized\\\"\\\"\\\"\\n email = \\\"amustapha@GMAIL.COM\\\"\\n user = get_user_model().objects.create_user(\\n email=email,\\n password='falsepassword123'\\n )\\n self.assertEqual(user.email, email.lower())\\n\\n def test_new_user_invalid_email(self):\\n \\\"\\\"\\\"\\n Test creaating new user without email throws error\\n \\\"\\\"\\\"\\n with self.assertRaises(ValueError):\\n get_user_model().objects.create_user(None)\\n\\n def test_create_superuser(self):\\n \\\"\\\"\\\"\\n Tests creation of superuser\\n \\\"\\\"\\\"\\n\\n user = get_user_model().objects.create_superuser(\\n email='amustapha@hooli.ng',\\n password='fake`12323'\\n )\\n self.assertTrue(user.is_superuser)\\n self.assertTrue(user.is_staff)\\n\"\n },\n {\n \"alpha_fraction\": 0.804347813129425,\n \"alphanum_fraction\": 0.804347813129425,\n \"avg_line_length\": 22,\n \"blob_id\": \"3df5dd89ffbd5b20072c0af7f1f453c27c95c772\",\n \"content_id\": \"579d3bee0230bd55d2559d82e0e4f4088318737c\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 46,\n \"license_type\": \"permissive\",\n \"max_line_length\": 30,\n \"num_lines\": 2,\n \"path\": \"/README.md\",\n \"repo_name\": \"aamustapha/django-recip\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# django-recip\\nUdemy's Advanced Django Course\\n\"\n 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like","value":[{"alpha_fraction":0.6043285131454468,"alphanum_fraction":0.6376248598098755,"avg_line_length":35.775508880615234,"blob_id":"c6b39527244cf02ac62dd00d9388af8bc104380c","content_id":"52df1f35ed1f879bf0b69febb0c63f9ef0c2718a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1802,"license_type":"no_license","max_line_length":121,"num_lines":49,"path":"/summarizer/sentence_encoder.py","repo_name":"ram1988/text_summarization","src_encoding":"UTF-8","text":"# TensorFlow and tf.keras\nimport tensorflow as tf\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.models import *\n\n\n\n# https://towardsdatascience.com/first-contact-with-tensorflow-estimator-69a5e072998d\n# https://www.tensorflow.org/api_docs/python/tf/contrib/estimator/RNNEstimator\nclass SentenceEncoder:\n\n def buildRNN(self, x):\n print(x)\n # bidirectional rnn to get the sentence vectors\n # https://blog.myyellowroad.com/unsupervised-sentence-representation-with-deep-learning-104b90079a93\n # https://medium.com/tensorflow/standardizing-on-keras-guidance-on-high-level-apis-in-tensorflow-2-0-bad2b04c819a\n '''\n model = Sequential()\n model.add(Embedding(50000, 100, input_length=30))\n model.add(Bidirectional(LSTM(10, return_sequences=True), input_shape=(30, 100)))\n model.add(Dense(5))\n model.add(Activation('softmax'))\n '''\n encoder_model = tf.keras.Sequential([\n tf.keras.layers.Embedding(200000, 64),\n tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64))\n ])\n\n target_model = tf.keras.Sequential([\n tf.keras.layers.Dense(64, activation='relu'),\n tf.keras.layers.Dense(3, activation='softmax')\n ])\n\n total_model = tf.keras.Sequential([\n encoder_model,target_model\n ])\n\n return encoder_model,total_model\n\n def getEncoder(self, x_train, y_train):\n encoder, model = self.buildRNN(x_train)\n print(encoder)\n y_train = tf.keras.utils.to_categorical(y_train,num_classes=3)\n print(y_train)\n model.compile(loss='MSE',\n optimizer='adam',\n metrics=['accuracy'])\n model.fit(x_train, y_train, epochs=1)\n return encoder\n"},{"alpha_fraction":0.78742516040802,"alphanum_fraction":0.78742516040802,"avg_line_length":36,"blob_id":"e6f22ab18c52b08843b48e7bb18c0f60787c7639","content_id":"7a7e6998ec0b1c93612ac4a37e70685129499fab","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":334,"license_type":"no_license","max_line_length":133,"num_lines":9,"path":"/README.md","repo_name":"ram1988/text_summarization","src_encoding":"UTF-8","text":"# Text Summarization\n\nTrained the RNN LSTM model to generate the Sentence Encoder(SE).\n\nSentences would be passed to the SE to generate the sentence vectors. \n\nWith the sentence vectors, k-means clustering is performed to infer the top 'n' sentences which are closer to the cluster centroids. \n\nHence, the text summary is generated. \n"},{"alpha_fraction":0.5996732115745544,"alphanum_fraction":0.6119281053543091,"avg_line_length":34.911766052246094,"blob_id":"362223d8559ac5a74d46c87a075aa1f6cc769524","content_id":"648fe685c878c991d379eaa614ead9b54bca203b","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1224,"license_type":"no_license","max_line_length":126,"num_lines":34,"path":"/summarizer/kmeans_clusterer.py","repo_name":"ram1988/text_summarization","src_encoding":"UTF-8","text":"from sklearn.cluster import KMeans\nfrom sklearn.metrics import pairwise_distances_argmin_min\n\nimport numpy as np\n\n#https://stackoverflow.com/questions/26795535/output-50-samples-closest-to-each-cluster-center-using-scikit-learn-k-means-libr\n#https://stackabuse.com/k-means-clustering-with-scikit-learn/\nclass TextSummarizer:\n def summarize(self,sentences,sentence_vectors,top_n):\n n_clusters = int(np.ceil(len(sentence_vectors) ** 0.5))\n kmeans = KMeans(n_clusters=n_clusters, random_state=0)\n kmeans = kmeans.fit(sentence_vectors)\n closest = []\n '''\n for j in range(n_clusters):\n idx = np.where(kmeans.labels_ == j)[0]\n avg.append(np.mean(idx))\n '''\n closest, _ = pairwise_distances_argmin_min(kmeans.cluster_centers_, \\\n sentence_vectors)\n print(\"summ....\")\n print(len(sentence_vectors))\n print(n_clusters)\n print(closest)\n\n closest = sorted(closest)\n\n if top_n < len(closest):\n closest = closest[0:top_n]\n\n summary = ' '.join([sentences[idx] for idx in closest])\n\n print('Summarization Finished')\n return summary\n\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.6043285131454468,\n \"alphanum_fraction\": 0.6376248598098755,\n \"avg_line_length\": 35.775508880615234,\n \"blob_id\": \"c6b39527244cf02ac62dd00d9388af8bc104380c\",\n \"content_id\": \"52df1f35ed1f879bf0b69febb0c63f9ef0c2718a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1802,\n \"license_type\": \"no_license\",\n \"max_line_length\": 121,\n \"num_lines\": 49,\n \"path\": \"/summarizer/sentence_encoder.py\",\n \"repo_name\": \"ram1988/text_summarization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# TensorFlow and tf.keras\\nimport tensorflow as tf\\nfrom tensorflow.keras.layers import *\\nfrom tensorflow.keras.models import *\\n\\n\\n\\n# https://towardsdatascience.com/first-contact-with-tensorflow-estimator-69a5e072998d\\n# https://www.tensorflow.org/api_docs/python/tf/contrib/estimator/RNNEstimator\\nclass SentenceEncoder:\\n\\n def buildRNN(self, x):\\n print(x)\\n # bidirectional rnn to get the sentence vectors\\n # https://blog.myyellowroad.com/unsupervised-sentence-representation-with-deep-learning-104b90079a93\\n # https://medium.com/tensorflow/standardizing-on-keras-guidance-on-high-level-apis-in-tensorflow-2-0-bad2b04c819a\\n '''\\n model = Sequential()\\n model.add(Embedding(50000, 100, input_length=30))\\n model.add(Bidirectional(LSTM(10, return_sequences=True), input_shape=(30, 100)))\\n model.add(Dense(5))\\n model.add(Activation('softmax'))\\n '''\\n encoder_model = tf.keras.Sequential([\\n tf.keras.layers.Embedding(200000, 64),\\n tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64))\\n ])\\n\\n target_model = tf.keras.Sequential([\\n tf.keras.layers.Dense(64, activation='relu'),\\n tf.keras.layers.Dense(3, activation='softmax')\\n ])\\n\\n total_model = tf.keras.Sequential([\\n encoder_model,target_model\\n ])\\n\\n return encoder_model,total_model\\n\\n def getEncoder(self, x_train, y_train):\\n encoder, model = self.buildRNN(x_train)\\n print(encoder)\\n y_train = tf.keras.utils.to_categorical(y_train,num_classes=3)\\n print(y_train)\\n model.compile(loss='MSE',\\n optimizer='adam',\\n metrics=['accuracy'])\\n model.fit(x_train, y_train, epochs=1)\\n return encoder\\n\"\n },\n {\n \"alpha_fraction\": 0.78742516040802,\n \"alphanum_fraction\": 0.78742516040802,\n 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kmeans.fit(sentence_vectors)\\n closest = []\\n '''\\n for j in range(n_clusters):\\n idx = np.where(kmeans.labels_ == j)[0]\\n avg.append(np.mean(idx))\\n '''\\n closest, _ = pairwise_distances_argmin_min(kmeans.cluster_centers_, \\\\\\n sentence_vectors)\\n print(\\\"summ....\\\")\\n print(len(sentence_vectors))\\n print(n_clusters)\\n print(closest)\\n\\n closest = sorted(closest)\\n\\n if top_n < len(closest):\\n closest = closest[0:top_n]\\n\\n summary = ' '.join([sentences[idx] for idx in closest])\\n\\n print('Summarization Finished')\\n return summary\\n\\n\\n\\n\"\n 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esteve/rosidl | https://github.com/esteve/rosidl | 7dbf827eb7600d2195fb80dc0daa7cb5442b0986 | 6f12fa5a08e461f02aa5586dc9d64978dba20099 | 4a8097242c0e950fe45bd49384e21a45cbde6c39 | refs/heads/master | 2023-01-28T01:56:34.344922 | 2016-10-28T23:09:50 | 2016-10-28T23:09:50 | 52,849,188 | 0 | 0 | null | 2016-03-01T04:57:56 | 2016-01-15T00:44:02 | 2016-02-26T03:17:51 | null | [
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burhankangsi/HandwrittenTextRecognition | https://github.com/burhankangsi/HandwrittenTextRecognition | 9764116cfb74701b6d943dede8820b03c2365e2d | 5ab1fa172316df9e8ea66a2f8d7ce94ecf4367b5 | 86b5ba7c8415ea82c70056428587a45af177d796 | refs/heads/master | 2021-06-03T11:20:29.632930 | 2020-04-09T10:05:26 | 2020-04-09T10:05:26 | 254,335,685 | 2 | 0 | null | null | null | null | null | [
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iosmichael/GURU_video_processor | https://github.com/iosmichael/GURU_video_processor | fd6166d2dec2cface1518208e2fa9aac27d037ec | f099b832b2267a88268eae3971c0b2075dfb66f3 | 0537f223301bc66c9dde86dd27a072a81f029ca4 | refs/heads/master | 2020-07-10T09:52:06.643755 | 2019-09-08T03:12:05 | 2019-09-08T03:12:05 | 204,235,326 | 0 | 0 | null | null | null | null | null | [
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rgn216/meetup-insight | https://github.com/rgn216/meetup-insight | 41d217814b1d6e3c5a23f898752a6628cd16fb32 | c726410092f3ce13b62cbec5cb797f146d5bab91 | 09a3f6ac29e0a993dace9f68b348f36fa84de898 | refs/heads/master | 2021-01-19T08:17:57.194435 | 2015-07-01T22:47:58 | 2015-07-01T22:47:58 | 37,613,975 | 0 | 0 | null | null | null | null | null | [
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laligafilipina/falcon-framework | https://github.com/laligafilipina/falcon-framework | b236f4d08d78bd060092d98426ac4383e3f88074 | 6b57309bd4c09077cd911e698ad9fea4aac0acbf | f04e471c3d75aba5bfeae39d9c9da35dfb01af9e | refs/heads/main | 2023-06-10T09:44:52.486784 | 2021-07-01T03:04:13 | 2021-07-01T03:04:13 | 366,954,809 | 1 | 0 | null | null | null | null | null | [
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294010390/PicVerify | https://github.com/294010390/PicVerify | ffd4f070419b7e82f835c8b5ba07bb66f53a7220 | f0a0bae7429406c8dd7b3ee102f1cd08bd7e86fd | 3f7c4c44fe5c725c50e8914f7cba695fff897783 | refs/heads/master | 2020-03-21T00:12:09.414326 | 2018-07-01T13:09:38 | 2018-07-01T13:09:38 | 137,882,804 | 0 | 0 | null | null | null | null | null | [
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ProfDevAnderson/introducao-python | https://github.com/ProfDevAnderson/introducao-python | 41b3517387a390c9a8d44db4a8c17e80b4b73e40 | 6c4110058a03daa61001011994457e31776795f9 | ef44aec81400e19595c9354e42c4b121fe06177b | refs/heads/master | 2023-07-12T06:08:31.621344 | 2021-08-18T14:11:55 | 2021-08-18T14:11:55 | 391,444,462 | 0 | 1 | null | null | null | null | null | [
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EEExphon/Tik_Tok_Toe | https://github.com/EEExphon/Tik_Tok_Toe | 707055479886e32598d5070e369504d6bf4690f4 | 8f3ef6c5d0680881c5a23867d18697c04246f52d | 4a96d9825f96d3e7833efb19507b2d6bbc1470d5 | refs/heads/master | 2022-09-19T13:09:46.172467 | 2020-06-03T08:57:55 | 2020-06-03T08:57:55 | 269,039,142 | 2 | 0 | null | null | null | null | null | [
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mvidalgarcia/places-server | https://github.com/mvidalgarcia/places-server | dce908166e3a6941e888d63a3d870ee5605c10ef | dbc5c451f31822ca6ed8becaf5ed06c46ed24ac2 | 96c09c27e3ed92e6ffbad991127ac53e29a58d5f | refs/heads/master | 2016-09-06T08:27:53.397204 | 2015-02-21T22:49:07 | 2015-02-21T22:49:07 | 30,366,216 | 0 | 0 | null | null | null | null | null | [
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DeekshaKodieur/python_programs | https://github.com/DeekshaKodieur/python_programs | 42b5cea3128226768ce95bf5887cc6943c6d8c92 | 3408ef89345ec6ff8ab654c581bb92b42abc246c | 10cb91efb7d0f8267f1782760ae501e42cd47cb9 | refs/heads/master | 2020-04-13T20:02:46.169119 | 2018-12-28T14:43:14 | 2018-12-28T14:43:14 | 163,419,500 | 0 | 0 | null | null | null | null | null | [
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incafox/test_server | https://github.com/incafox/test_server | 3ce2b6c39c86f75ca75387a04ecf4b1ea0ad7b8f | c37bae5d20b099544b6e2fed14e197a574b68c01 | a87e22a4ed9233130a59ea3edee20073cec1cfc9 | refs/heads/master | 2021-05-17T17:57:35.454008 | 2020-06-29T15:56:13 | 2020-06-29T15:56:13 | 250,906,815 | 0 | 0 | null | null | null | null | null | [
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saiabhishek28/india-covid-tracker | https://github.com/saiabhishek28/india-covid-tracker | 03761c4280d179345c794099248c04024352f937 | 445a3a4bd16a51f39e7fa6c09f19ca331bf8af1a | 735f1de1f36ea583e69b84b2671feb57f39094fa | refs/heads/main | 2023-01-07T12:20:53.862576 | 2020-11-03T12:12:10 | 2020-11-03T12:12:10 | null | 0 | 0 | null | null | null | null | null | [
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QDkAc/Decaf-Version2 | https://github.com/QDkAc/Decaf-Version2 | 16834f0b1249d754b7c69ac1151298bea4c35382 | bddadc10ab0f925276fdcf6a25714ed617479293 | 2ccf704fa2bc5956a4b55bb80755f8f1f7d0b584 | refs/heads/master | 2021-01-10T16:09:03.018837 | 2016-01-15T15:41:52 | 2016-01-15T15:41:52 | 49,501,920 | 0 | 0 | null | null | null | null | null | [
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RYUHYEREE/openfinal | https://github.com/RYUHYEREE/openfinal | fb338684e977eccbac15f95e901ca66ca198bcfa | 487a78a7eda1b55d31a2f652c529906eaedb0afb | fd7ef7b30eeb6b254131d0a91bf8684a041c6b18 | refs/heads/master | 2022-11-07T07:32:15.897802 | 2020-06-24T01:19:19 | 2020-06-24T01:19:19 | 274,532,889 | 0 | 0 | null | null | null | null | null | [
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MyGateGlobal/SampleCode | https://github.com/MyGateGlobal/SampleCode | d0072fdbe6ceb8df5d3762016000bb2e205e03e9 | 353da16fe784279d2189037c9a25c3f64e6bf0ac | 26abe7d39bac921f99d134d8c27f03add9f2c7d3 | refs/heads/master | 2021-05-04T10:19:06.123702 | 2016-05-04T11:51:46 | 2016-05-04T11:51:46 | 36,375,015 | 0 | 1 | null | null | null | null | null | [
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s0ap/array2latex | https://github.com/s0ap/array2latex | c309870b2cb2dc68a76b49090a048e7b159c3229 | a77eac21f8c70a0a50e65b2eb4f7844cdd0a2ffa | 75abd1910b883c85283539dccdfb2f0eed76583e | refs/heads/master | 2021-06-13T14:37:22.286783 | 2016-12-12T12:32:09 | 2016-12-12T12:32:09 | null | 0 | 0 | null | null | null | null | null | [
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webclinic017/machinelearning | https://github.com/webclinic017/machinelearning | f33816be7069b6b5d398196457b048660aa41b9f | 58848714000e7a9bf51bb44676629a39e71ab1bc | 3140f59579aed56ed3085d31cecc9d1c7acfbfcd | refs/heads/main | 2023-09-02T21:57:52.924555 | 2021-11-10T13:45:24 | 2021-11-10T13:45:24 | 427,091,250 | 1 | 0 | null | 2021-11-11T17:44:56 | 2021-11-10T13:45:54 | 2021-11-10T13:45:52 | null | [
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m1chuang/pychef | https://github.com/m1chuang/pychef | 97002340d3387458faa1736ef81d8c5607bc61b9 | fa5ceb6827e687993bd344dda39027ac6cca2c6e | f19c63535daf3cc3622b561f89c1547cefb380b1 | refs/heads/master | 2021-01-11T01:55:09.521309 | 2016-10-15T00:32:55 | 2016-10-15T00:32:55 | 70,836,412 | 0 | 0 | null | 2016-10-13T18:32:47 | 2016-10-07T13:29:35 | 2016-09-20T13:41:11 | null | [
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youngmoney/session | https://github.com/youngmoney/session | f7a16e7e7f3ed4e88f4fb078f0ea3283f49c15f0 | 59b68fc52cdc0b61b6e7b64988ccbb95b7cdbbfd | f7ccb9215b55bb6e954d7e470f59a1eaf2b411f5 | refs/heads/master | 2022-11-05T06:48:51.713232 | 2020-06-23T04:20:25 | 2020-06-23T04:20:25 | 274,307,629 | 0 | 0 | null | null | null | null | null | [
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mfcardenas/mongodb-vagrant-box | https://github.com/mfcardenas/mongodb-vagrant-box | 1ce529547eb8982128ed9c18e8f9c7cbecbe2025 | 5abbc4b240358ac3e10940a8c4938b1a31bc3cef | e3e241f662d0501c3f3ddbeb962c1148f1557ac6 | refs/heads/master | 2022-07-17T20:59:35.645559 | 2020-05-17T01:04:53 | 2020-05-17T01:04:53 | 263,735,760 | 0 | 0 | null | null | null | null | null | [
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jchernus/CAN-Bus-Datalogger | https://github.com/jchernus/CAN-Bus-Datalogger | 429df32725b47c7ede96d92985469cc971ef6b8a | 39c20ca9c9008f0eec1bdce76759c5ca28dc6760 | 517db48364aa173dcc26858f414032eafead5c38 | refs/heads/master | 2021-01-17T07:20:40.764414 | 2017-04-18T20:02:33 | 2017-04-18T20:02:33 | 31,286,155 | 12 | 1 | null | null | null | null | null | [
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nicole7han/exaplainableAI_nfb | https://github.com/nicole7han/exaplainableAI_nfb | 8ee9a957b53430fcbc42972d2018da0a036bf7b9 | a0c1562f15a2641b71398b12c4173f0d8c40349c | 037b8d58f1aeedcea23a8d8dd4c26390adbfe013 | refs/heads/main | 2023-07-08T15:08:46.312367 | 2021-08-13T16:47:37 | 2021-08-13T16:47:37 | 395,722,921 | 0 | 0 | null | null | null | null | null | [
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a9s2w5/ufc_stats | https://github.com/a9s2w5/ufc_stats | edf0213e655c223f625bf2305627345cf96ccefc | 0f2d270c3fa6728117fc4b25b9a86662ee88f2b3 | 7bdbf919a556fe9d0b3b8630cdaf58777867c679 | refs/heads/main | 2023-08-25T01:16:15.250664 | 2021-10-30T00:15:09 | 2021-10-30T00:15:09 | null | 0 | 0 | null | null | null | null | null | [
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PikachuPikachuHAHA/Secure-instant-messaging | https://github.com/PikachuPikachuHAHA/Secure-instant-messaging | 817554654e8333f8bae2d6f2e385f1dffbca32c2 | 3e1dc9bd2d824f3ee2ce01575c6635e8d7d9738a | f7b53b1459dda569a7151809e723e5144dabcbd9 | refs/heads/master | 2021-06-08T23:20:48.819447 | 2016-12-02T22:50:12 | 2016-12-02T22:50:12 | null | 0 | 0 | null | null | null | null | null | [
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dallaszkorben/akoteka-folder_hierarchy | https://github.com/dallaszkorben/akoteka-folder_hierarchy | e11583ef6da78c044b9c97e3a14624404d71be85 | 1ced37cc87c216ec563c24e51de46eb19c44a64c | 2c976b807e609642c1e2b477b982d8cc6186c1cb | refs/heads/master | 2022-11-19T05:50:51.570746 | 2022-11-13T21:34:32 | 2022-11-13T21:34:32 | 163,203,533 | 0 | 0 | null | null | null | null | null | [
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karanajith2096/SML-Project | https://github.com/karanajith2096/SML-Project | c06266a0caaa7465a9752b28ce56af5b6f7cc701 | 95f042ebcde1420ad35343e6da1c9f8d17ae7518 | d9637f39666ea0bcd9a0b7120492132bc71b0d07 | refs/heads/master | 2020-08-02T04:20:57.577047 | 2019-12-09T04:41:45 | 2019-12-09T04:41:45 | 211,232,312 | 0 | 0 | null | null | null | null | null | [
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geasyheart/voice_change | https://github.com/geasyheart/voice_change | d0d663a4f9e87bc2e6bf44723d1f0b922db4d1dd | 72cc2d7da36d5472da59e5ae38b30b23f967faff | 425ebca03e41716e61b795ec00b8012316699a9d | refs/heads/master | 2023-02-10T03:56:39.246843 | 2019-10-24T13:00:00 | 2019-10-24T13:00:00 | 192,755,360 | 7 | 0 | null | 2019-06-19T15:07:07 | 2022-06-21T00:21:08 | 2023-02-02T06:39:54 | Dart | [
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Rudra705/counting-teller | https://github.com/Rudra705/counting-teller | e1af869f368f83b3a259bbc89e78a9415d63ef19 | 239018a1f7eb1fb6dc374367834876f052ae918b | 66bcb8d948aa4c3cdd4ee57c969f2a8a598a66ee | refs/heads/master | 2023-08-30T06:28:02.891989 | 2021-11-05T06:29:28 | 2021-11-05T06:29:28 | 424,848,840 | 0 | 0 | null | null | null | null | null | [
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DarthDanAmesh/Malaika | https://github.com/DarthDanAmesh/Malaika | b313944b339d7c915e54ac1dd0c0eedcc273401b | 396964061dd6696350292aee8f600681f2ef0608 | 4e6d15fcab2c48d6d27a337128c249519bd03a47 | refs/heads/master | 2021-04-15T11:50:13.148706 | 2017-05-19T13:19:30 | 2017-05-19T13:19:30 | null | 0 | 0 | null | null | null | null | null | [
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BayCEER/python-baySerial | https://github.com/BayCEER/python-baySerial | a18685a4a900a1b708f5d40a02f15f834e514dd7 | 6c193a475c764f92ba15de90dc2bf96983767ed1 | 04275cdc67729399643eeffd5f33269ee44807f3 | refs/heads/master | 2020-06-27T03:26:23.335648 | 2019-08-01T06:04:16 | 2019-08-01T06:04:16 | 199,831,537 | 0 | 0 | null | null | null | null | null | [
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openstack/charm-heat | https://github.com/openstack/charm-heat | 4be73c2a923c9cbb249e982b1cb0f979b63beb0c | af1bfc7eda22984978801dd8b0b439b6c49e8067 | ae1a7e0d8f3735e2dc0b966f25d164819f737225 | refs/heads/master | 2023-08-26T15:56:31.061431 | 2023-08-04T16:16:42 | 2023-08-14T10:05:30 | 52,858,756 | 16 | 10 | null | null | null | null | null | [
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zhanglian-boob/zhanglian_Home1 | https://github.com/zhanglian-boob/zhanglian_Home1 | 8555196c907006201fd7887a010aed1dccc35fb9 | da31448f987aba57076f29444fa103d803199e26 | 8d9791ead2142aaaf6cd5eab2708395e9827c41d | refs/heads/master | 2020-09-24T22:09:41.960731 | 2019-12-05T15:07:27 | 2019-12-05T15:07:27 | 225,854,760 | 0 | 0 | null | null | null | null | null | [
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reshavcodes/empty_directory_killer | https://github.com/reshavcodes/empty_directory_killer | 0c0b237d0cce4ca32b3c43cbc188204158c7cd58 | 8d71d4aa537519572ad1c5cd2b43fe41e1fa4110 | 13cfa31834c6cc27ba1e295452ed1a69f95b4674 | refs/heads/main | 2023-05-05T22:52:56.248179 | 2021-05-21T16:21:46 | 2021-05-21T16:21:46 | 369,590,088 | 0 | 0 | null | null | null | null | null | [
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priyankark/LSI | https://github.com/priyankark/LSI | 7aac559bd514463e37503521a0e8f060f386c7a0 | c4eea605c55905d2c776ba5aa78e9e33dbc02ca5 | dedb7d6451e37965e4f25e26c96ec6718aed250e | refs/heads/master | 2021-01-13T05:36:14.708119 | 2017-06-23T14:30:29 | 2017-06-23T14:30:29 | 95,101,068 | 0 | 0 | null | null | null | null | null | [
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akankshanb/BeagleBone-Black-IOT | https://github.com/akankshanb/BeagleBone-Black-IOT | 2e7fbf98c815be8ae64506d9ca55e6e64f09f737 | 89e16196713a173f3dc2e6b291c7926a6533fe1e | 485eabf72cd25707307468539e455936fd4345bb | refs/heads/master | 2021-10-19T00:46:18.141765 | 2019-02-16T06:02:17 | 2019-02-16T06:02:17 | 105,971,953 | 0 | 0 | null | null | null | null | null | [
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neuralian/Spider | https://github.com/neuralian/Spider | f5a6f7df5a106c6b79f07d054ba3809025b619a0 | 456f061640ad210e8f35f9c2b46fed49cd835d73 | 16006a05ef6da4bc678d92772ce8ff2c63600f1d | refs/heads/master | 2022-12-02T11:35:45.769040 | 2020-08-17T08:21:26 | 2020-08-17T08:21:26 | 269,482,371 | 0 | 0 | null | null | null | null | null | [
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jnphilipp/infohub | https://github.com/jnphilipp/infohub | 99c8bd5a0d26aa94c4dc2402d411ae2932987e49 | 0ac449a31073fa437ec9a8fc1f1b0a88eda33ad7 | d056134245558897ad4412f1b8f99e4ffa3b38b2 | refs/heads/master | 2021-01-22T09:48:24.353060 | 2018-10-15T12:23:47 | 2018-10-15T12:23:47 | 19,138,723 | 0 | 0 | null | null | null | null | null | [
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pdillon222/FretPositions | https://github.com/pdillon222/FretPositions | 17a6afca41f3c98f032466725501d56351405f6b | dd6f17d7e0cacebc6c9d9fb36b72816514f147c6 | 88461806ea62c38e6792bc2e4ada7c06f0f352ac | refs/heads/master | 2016-08-31T06:23:13.151726 | 2016-07-20T20:17:36 | 2016-07-20T20:17:36 | 50,479,268 | 0 | 0 | null | null | null | null | null | [
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huisso97/baekjoon_learning | https://github.com/huisso97/baekjoon_learning | c67377f05bf6598f9e317b616dd5e8812c3819ea | c7c5d3c76b3d8661aa9327a98be2cb8ced35a55c | fa89bb8764dfd5ece61505ed98fbd9bc7a41a6a8 | refs/heads/master | 2023-07-14T16:56:03.705297 | 2023-02-26T13:17:29 | 2023-02-26T13:17:29 | 397,442,123 | 0 | 0 | null | null | null | null | null | [
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gambini16/proy_ventas | https://github.com/gambini16/proy_ventas | c440a5ecca97bf2c54feb474a7644249331f15aa | b9d76c9c084e920535e8b413cea51f7a50b210c1 | f82e09d09a04bfe504a21c15845e01c4548942db | refs/heads/master | 2021-01-22T21:27:29.431018 | 2017-08-31T06:01:12 | 2017-08-31T06:01:12 | 100,669,673 | 0 | 0 | null | null | null | null | null | [
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adtech-scott/samsara-sfmta | https://github.com/adtech-scott/samsara-sfmta | 698030d643f9a0849897ff5619af65b81b38e389 | 27b784d48a71a860b3b05dcbe5f5ef1b649f6891 | a7a5746155925dbebc295b2f8dceecd8206b9805 | refs/heads/master | 2020-03-10T01:43:35.422975 | 2018-04-11T15:48:07 | 2018-04-11T15:48:07 | 129,117,470 | 0 | 0 | null | 2018-04-11T15:45:45 | 2017-06-21T18:17:28 | 2017-12-22T00:58:14 | null | [
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scottherold/django_login_and_registration | https://github.com/scottherold/django_login_and_registration | 067ff9d15b35defb1512dc5a4d8863425e5ee839 | 9f8d081ea6bf995a2fbb1448b0917cb8528b4ee7 | 4bc876b62d26e4096376677ddc9763e705d81f26 | refs/heads/master | 2020-04-02T23:19:54.818513 | 2018-10-26T16:34:47 | 2018-10-26T16:34:47 | 154,863,721 | 0 | 0 | null | null | null | null | null | [
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samesense/msCentipede_example | https://github.com/samesense/msCentipede_example | 23efa4884ce1f57ed7b96bb309a8c51d5061e9ca | 6eb87504ad9eb0bdb8affbf2d36f945ef8925173 | 010aa85fc600d8e4c8b2a73940f26b729beedd9c | refs/heads/master | 2019-05-13T00:18:45.836167 | 2016-07-28T18:22:50 | 2016-07-28T18:22:50 | 64,418,353 | 0 | 0 | null | null | null | null | null | [
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Subik25/Interesting | https://github.com/Subik25/Interesting | 1f4ac9402ca89bb316d5346b7bf3bd4e7a94d8af | e78aca7b7c92f675cc887413f9908942336b6eea | 1fd0edecc3499fef8b342cd984fa38fa3b242899 | refs/heads/main | 2023-08-24T23:02:21.976790 | 2021-10-21T16:06:08 | 2021-10-21T16:06:08 | null | 0 | 0 | null | null | null | null | null | [
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Joao-Maria-Janeiro/SmartTicket | https://github.com/Joao-Maria-Janeiro/SmartTicket | 01442dece3046cc3105d615157f8908be32e256d | 557429278ea94b4b35d9ff15b856da055719854c | c69df28453ad90e050f54e3c3e72e2971b24aa27 | refs/heads/master | 2020-04-13T19:45:02.407684 | 2019-01-17T12:29:00 | 2019-01-17T12:29:00 | 163,411,730 | 1 | 0 | null | null | null | null | null | [
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guxuan123456/DSG-SEG | https://github.com/guxuan123456/DSG-SEG | 93810dd94ba5af7fe3d6a07466c5b168f54f8d96 | 5ddeaedd8575abd0db72db9a41f6b64442203d6c | c27d3112cbff44602cf7a65e9d5bd14599f8846a | refs/heads/master | 2023-03-31T10:03:02.069007 | 2021-04-01T06:36:15 | 2021-04-01T06:36:15 | null | 0 | 0 | null | null | null | null | null | [
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etiennechabert/kolibree_backend_test | https://github.com/etiennechabert/kolibree_backend_test | 3befeb1ac1bb50ffd03964ec60758c217987466d | 6c8fcd503420ff25718beb20bf94804c138c27d5 | 9a8a2caf73060d1b4ae1bf2b54ba03f189a0ed56 | refs/heads/master | 2021-01-16T21:50:36.164180 | 2015-10-15T13:06:54 | 2015-10-15T13:06:54 | null | 0 | 0 | null | null | null | null | null | [
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D45H7/KunjungAN | https://github.com/D45H7/KunjungAN | 7a095d4faff0e8a22eb56a705fc510d4e96001d2 | e656963764687bb878d3c139db508914a5d957e2 | cca095cd18155402b33045811adeb40fb9500b5c | refs/heads/main | 2023-02-05T04:53:01.192161 | 2020-12-29T23:06:24 | 2020-12-29T23:06:24 | 325,404,369 | 2 | 0 | null | null | null | null | null | [
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es3a10/fuente_web | https://github.com/es3a10/fuente_web | d12f8c5c9442dd240997e2b2e3ccf44d50e7608f | 69fc338dbad104207e3df39fe6351a4c452a2cfc | a48c7de2585c5bcfb644c528e0203bd7d54b5e84 | refs/heads/master | 2021-01-15T15:41:52.247773 | 2016-11-15T18:40:10 | 2016-11-15T18:40:10 | 39,762,240 | 0 | 0 | null | null | null | null | null | [
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justinlundy/illumos-live | https://github.com/justinlundy/illumos-live | ee6761d8170353ecd5ebb73538ebf7012c2c873f | 0edca8475368b6450fe94516828e6c5bd3e946d6 | 1352c2d85f47719c1b1c49edd450bf98a8e024ec | refs/heads/master | 2016-09-25T02:13:31.291755 | 2011-02-23T17:59:48 | 2011-02-23T17:59:54 | 1,830,750 | 0 | 1 | null | null | null | null | null | [
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Laischon/P7M-Extract | https://github.com/Laischon/P7M-Extract | 474385efcf756b3d2e88e5a864de5f2468210faf | dcaeba35e53c064f8c7c5c1203b02adcf29fd237 | c51ee0cfc3775bc2976e28a1e9664aa6003a254a | refs/heads/master | 2020-08-01T03:29:07.661083 | 2019-09-25T13:06:02 | 2019-09-25T13:06:02 | 210,845,649 | 1 | 1 | null | null | null | null | null | [
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faircloth-lab/alfred_zotquery | https://github.com/faircloth-lab/alfred_zotquery | 4635649607402bc3786e93ef7a6d27fd7e2e431b | 964d9c7575b8ff57e4c3d2a0c485aa6d76e88fb7 | 52dfef197b882e58bb088558a80e3d3178653db1 | refs/heads/master | 2021-01-18T12:17:16.919171 | 2014-06-07T20:09:52 | 2014-06-07T20:09:52 | null | 0 | 0 | null | null | null | null | null | [
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sophiiasun/ViBot-Submission | https://github.com/sophiiasun/ViBot-Submission | aa21226004863b1ad72a983f1c51963ec2504252 | 684ab538a7b5497775a49f28b5db5b71eb0a4435 | 5d1ab08e8f91d9fd9dab009d66d44dc92b53dfaf | refs/heads/main | 2023-05-10T17:39:50.327640 | 2021-05-30T14:18:59 | 2021-05-30T14:18:59 | 361,263,173 | 0 | 1 | null | null | null | null | null | [
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LittleBlackSubmarine/rgrv_lv4 | https://github.com/LittleBlackSubmarine/rgrv_lv4 | 567754c07f1339971721a246c0477f931fc53258 | ce6ae7e136ec734ca40a8d3a6f81fd23a1f35ffd | df1b21a56b1a20603d8c10f2576b70c5b9266f31 | refs/heads/master | 2020-11-25T14:18:49.959782 | 2019-12-17T22:14:38 | 2019-12-17T22:14:38 | 228,709,475 | 0 | 0 | null | null | null | null | null | [
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Rajatjaiswa/Rajat-jaiswal | https://github.com/Rajatjaiswa/Rajat-jaiswal | 90a4cf765a8ee48308c940ffe9d4498732292dfb | 5ebdea11a3ddadf6c8349400043667122a8bd4d5 | 6b130e0a67851b12a06123c8df000ebba99cd0dd | refs/heads/main | 2023-08-13T15:37:10.858407 | 2021-10-11T08:43:57 | 2021-10-11T08:43:57 | 415,843,346 | 0 | 0 | null | null | null | null | null | [
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johnlinp/falcon-raml | https://github.com/johnlinp/falcon-raml | 796ffc022f4103c4e2509c971fa9911382ce25cf | ef15f83dfa0e295de58d0d83bd4ff35c36f769db | 7f4ff1025bfa3c8253e61f500d5b17e4ed0c500e | refs/heads/master | 2022-12-10T00:09:41.317869 | 2022-12-08T22:24:29 | 2022-12-08T22:24:29 | 77,845,528 | 0 | 0 | BSD-3-Clause | 2017-01-02T16:11:54 | 2022-10-28T19:22:20 | 2022-11-04T02:49:55 | Python | [
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qiaocci/django-forbidden-ip | https://github.com/qiaocci/django-forbidden-ip | 43d3cc5b8ce31ba155b7cdad8c30d8ddd59b951a | dd5be0d7592d619f142a1fcdd951f9aecba36c41 | 4886ab27c5a4e935c2732f1da5ee5097a93f92bd | refs/heads/master | 2020-03-26T21:26:30.711229 | 2018-08-20T08:53:37 | 2018-08-20T08:53:37 | null | 0 | 0 | null | null | null | null | null | [
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AjManzoor/WebScraping | https://github.com/AjManzoor/WebScraping | a6ca199f485c567e0a853eced0a0f8c2a99f262c | 80fd7937a9528d066c2f89c864b0d7409ecfee08 | f13913b5e027bb79d189a880c1d750af67ffbdae | refs/heads/master | 2020-03-22T21:36:47.276627 | 2018-07-12T10:54:38 | 2018-07-12T10:54:38 | 140,700,556 | 0 | 0 | null | null | null | null | null | [
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zyrxvo/GR-and-Long-term-Stability | https://github.com/zyrxvo/GR-and-Long-term-Stability | eff98b74ca08f2a6cc22a798e7cbb5239f978a6e | 03a75e98de44215a261efcec9594888f144344b2 | 09a6b88fd97bb635b4a639ff6faefe27221368a9 | refs/heads/main | 2023-04-16T20:10:48.217824 | 2023-04-05T15:22:44 | 2023-04-05T15:22:44 | 597,187,040 | 3 | 0 | null | null | null | null | null | [
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LHGames-2017/nospace | https://github.com/LHGames-2017/nospace | cba0b70d84b9682881fcf27d69a157c540936014 | 1f36fb980ee51cdc576b765eff2c4ad5533ea0e3 | 91453217692354c88c63aa7969f919b0070e2136 | refs/heads/master | 2021-01-16T22:22:58.698362 | 2017-10-08T00:13:28 | 2017-10-08T00:13:28 | 106,107,920 | 0 | 0 | null | null | null | null | null | [
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yeguacelestial/django-microsoft-auth | https://github.com/yeguacelestial/django-microsoft-auth | 22ced96187a12ef10486bf2a5f47d6cc8ca6bb8f | a3747ed0b941953cca22d23b839e4905904ad7f3 | af01d038d9bb4fbf510f56d23a45764d838d166a | refs/heads/main | 2023-03-07T22:24:16.432472 | 2021-02-09T00:04:35 | 2021-02-09T00:04:35 | 336,100,701 | 0 | 0 | null | null | null | null | null | [
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quakehub/discord-metro | https://github.com/quakehub/discord-metro | ab571c0358bb16a4e1ec4945292292c4e93f5a8f | 2c8eeeb3f5b7f20f4bf4b57d7279bddb4d2f7ca7 | 280f83c238f90734a62b425b0f6f3bfded2295dd | refs/heads/master | 2023-01-31T18:48:00.512027 | 2020-07-13T01:32:38 | 2020-07-13T01:32:38 | null | 0 | 0 | null | null | null | null | null | [
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BradDoddsProjects/kMeans | https://github.com/BradDoddsProjects/kMeans | d5a0bc13824600a052e33a169b797f959508e04f | 307081fd856ae6188642f0d2ea75318d6b1cd1ae | 0162f87f61c91a7d7c7eeee57b704a0a9cacd651 | refs/heads/master | 2021-01-06T18:24:15.572682 | 2020-02-20T18:16:04 | 2020-02-20T18:16:04 | 241,438,391 | 0 | 0 | null | null | null | null | null | [
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xingchenwan/Sentiment | https://github.com/xingchenwan/Sentiment | 05d923e52e1b468b423e81631a034675deab850d | 0798565fa788b969ab381920082312dc98e6f9ab | 089591b265e53641e7d7cac88958b05dec4b9546 | refs/heads/master | 2021-06-26T06:51:24.608816 | 2019-07-12T16:19:05 | 2019-07-12T16:19:05 | 147,643,193 | 1 | 0 | null | null | null | null | null | [
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HenryJobst/bowling_ml | https://github.com/HenryJobst/bowling_ml | 706e5c70edf90f57b01fcacd27bdf230d75b4daf | f52bff897c823dc6f43913414c2ec21dcb921b35 | 374ea3aa01ca1d9d20e41136fc9c392f94008f2b | refs/heads/master | 2020-04-21T22:15:36.634297 | 2019-02-09T19:35:57 | 2019-02-09T19:35:57 | 169,905,223 | 0 | 0 | null | null | null | null | null | [
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manuelbenedicto/Emotions_Towards_COVID-19_on_Reddit | https://github.com/manuelbenedicto/Emotions_Towards_COVID-19_on_Reddit | 922dc41cd524c172dd0add9b34509e60bf405b57 | f1ec64e35ee47c9da0dfb34a9a9ec105f7f709de | 30b741c2c5c69ebbe628ff072c6f8843e40dbf89 | refs/heads/master | 2022-06-11T17:21:51.546083 | 2020-05-06T11:18:16 | 2020-05-06T11:18:16 | 255,926,824 | 1 | 2 | null | null | null | null | null | [
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CuTePsYcHo110/AI_Robotic_Arm_Code | https://github.com/CuTePsYcHo110/AI_Robotic_Arm_Code | 3748e979c538bf5320b45d8c71bc67449fb0d4a3 | 40d2f08b55fd92b9a2e20150a74ee38241cb2378 | 9e3635402fd4b8e09d1aa74d9be207ed2c216eed | refs/heads/main | 2023-07-18T16:40:40.836740 | 2021-09-21T16:49:39 | 2021-09-21T16:49:39 | null | 0 | 0 | null | null | null | null | null | [
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franzpoeschel/openPMD-pipe | https://github.com/franzpoeschel/openPMD-pipe | c7817a01645381410d0e4f3d17bc35720421cdfd | d4a56a2be647ccccb85f752bb84632093e8b6f1d | b8fa870f757a5c944313ba26d2449431be9f14dd | refs/heads/master | 2023-01-08T23:46:18.822490 | 2020-11-12T14:21:06 | 2020-11-12T14:23:52 | 306,111,080 | 0 | 0 | null | null | null | null | null | [
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jeneigabor/github | https://github.com/jeneigabor/github | 3462b967c5ebb6d38a341d3c19536eef52698c56 | 74543e04988d43a766523db40f3faf2e8f3a3f71 | 6507d565f7cd4bb89d7cc03b92366e304d78891e | refs/heads/master | 2020-06-13T14:20:08.855163 | 2017-02-02T17:24:44 | 2017-02-02T17:24:44 | 75,368,427 | 0 | 0 | null | null | null | null | null | [
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kelajin/djangochat | https://github.com/kelajin/djangochat | 6b15fab6c26f8f67bbd5aebe3ede3d2c037bd54b | 08fc1dcfd0e15adee194f1788e829598d4781545 | 49158bb1726173cbf1bad4463b30175088bfdb94 | refs/heads/master | 2018-09-28T10:33:55.439405 | 2018-06-18T06:48:48 | 2018-06-18T06:48:48 | 133,458,789 | 0 | 0 | null | null | null | null | null | [
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dattv/RetinaNet_TF14 | https://github.com/dattv/RetinaNet_TF14 | 2350d33038430057626a748ff6f0e30e058e7b1d | 03631421078b2340da1387298542188d6a0014b0 | 967458b1638f7e6005e471c29e8450c24d3496b4 | refs/heads/main | 2023-06-19T05:50:54.488309 | 2021-07-14T16:57:58 | 2021-07-14T16:57:58 | 347,784,170 | 0 | 0 | null | null | null | null | null | [
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mertoenjosh/Lux-Tech-BootCamp | https://github.com/mertoenjosh/Lux-Tech-BootCamp | 1929b708e2b39f9aba68e065573dee742244fe0a | 4150dcafd6dd6013b2bca6058a9ae95373f84bb7 | 75387bd21aab148a3f91fce58c87fc2b61ed89f8 | refs/heads/main | 2023-07-11T10:20:43.465616 | 2021-08-15T02:23:27 | 2021-08-15T02:23:27 | 388,427,743 | 0 | 0 | null | null | null | null | null | [
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arjangroen/corona_dynamics | https://github.com/arjangroen/corona_dynamics | 27f751837508c91a3954a0918cfb6d4714c4b57e | bd09eb08b75f2bb0274a4013a071ff170de16a5e | 7ade0a5687a9420a8285d802ad60fa20876e3a5d | refs/heads/master | 2021-03-21T16:42:34.790363 | 2020-03-19T17:15:28 | 2020-03-19T17:15:28 | 247,312,584 | 0 | 0 | null | null | null | null | null | [
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jinhw1989/Leetcode_Python | https://github.com/jinhw1989/Leetcode_Python | d02d4e81b7dd5a4048de1707370ffe8f06641cb7 | a6243e8b7b108a6dbee7c559feda7745c78d7281 | 3475848e0c253a8db0be3dbdbe356aae0c2b190d | refs/heads/master | 2016-08-06T21:46:37.774395 | 2015-06-11T21:34:12 | 2015-06-11T21:34:12 | 21,712,065 | 0 | 1 | null | null | null | null | null | [
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MrTrustworthy/typeutils | https://github.com/MrTrustworthy/typeutils | 9e75d429fc3d9e8320a0f8bfd22a105bbbae4570 | 8c65987eae3a396081063e9c6dc0e1f7f6ada8a2 | 506cfb6eda98c81b7fd44ba36949925933f5b843 | refs/heads/master | 2020-03-12T09:24:56.471693 | 2018-04-22T09:18:59 | 2018-04-22T09:18:59 | 130,552,246 | 0 | 0 | null | null | null | null | null | [
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ram1988/text_summarization | https://github.com/ram1988/text_summarization | d56bd8fe03f5d0224b522f9dac9c68228e7623dd | 3878b5b97e51c14a706b8cc06c86378c5204ddec | c0c94710d1c53eda6bab2a1fbf10f983510db35a | refs/heads/master | 2020-04-29T13:23:44.534994 | 2019-06-03T21:27:40 | 2019-06-03T21:27:40 | 176,167,687 | 1 | 0 | null | null | null | null | null | [
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