Tag: 178

Projecting eigenvectors for fisher’s linear discriminant analysis

Post author By Full Stack
Post date August 2, 2020
No Comments on Projecting eigenvectors for fisher’s linear discriminant analysis

I am trying to implement this feature selection method that also allows me to classify data. Following this: paper I have tried many workarounds and ways to implement this, but always my data gets weird. The public dataset that is made available has only to aquisition 1 and 4, to which i have extracted the […]

Tags }, 0, 1, 1) #transforming the data person_1_drunk_m_t = np.dot(person_1_drunk_mean, 1) #transforming the data person_1_drunk_t = np.dot(person_1_drunk_std, 1) person_1_sober_mean = np.mean(person_1_sober, 1) person_1_sober_std = np.std(person_1_sober, 1) person_2_drunk_mean = np.mean(person_2_drunk, 1) person_2_drunk_std = np.std(person_2_drunk, 1) person_2_sober_mean = np.mean(person_2_sober, 1) person_2_sober_std = np.std(person_2_drunk, 1) person_3_drunk_mean = np.mean(person_3_drunk, 1) person_3_drunk_std = np.std(person_3_drunk, 1) person_3_sober_mean = np.mean(person_3_sober, 1) person_3_sober_std = np.std(person_3_drunk, 1))).real X_lda = np.array(X.dot(w_matrix)) le = LabelEncoder() y = le.fit_transform(df['class']) #plotting the data plt.xlabel('LD1') plt.y, 153, 168], 173, 178, 188, 189, 192, 197, 199, 2] indices = sorted(list(range(0, 201, 202, 203, 204, 205, 205)]) person_2_drunk = np.asarray([(234, 206, 207, 210)]) person_1_sober = np.asarray([(202, 211, 212, 213, 214)]) person_2_sober = np.asarray([(191, 218, 219, 221)]) person_3_sober = np.asarray([(206, 222)]) person_3_drunk = np.asarray([(226, 223, 226)]) #Finding SW for 3 persons person_1_drunk_std = np.std(person_1_drunk, 234], 235, 236, 237, 238, 239, 240, 241, 243, 244, 245, 248, alpha=0.7, axis=0) #Finding Sb for 3 persons person_1_drunk_mean = np.mean(person_1_drunk, axis=0).reshape(2, but always my data gets weird. The public dataset that is made available has only to aquisition 1 and 4, but i always seem to fail to achieve the proper projection. In the paper the scatter between is given by the sum of the means of each person, cmap='rainbow', columns=pixel_index) class_names = list('AB') target_names = ["Class_" + c for c in class_names] n_sets = df.shape[0]//5 class_col = [] for n, columns=pixel_index) y = pd.Categorical.from_codes(indices, compared to the standard deviation, cY), edgecolors='w' ) This approach yields me a eigenvalue of [80645.45889483 302.25308639] I tried following an external example and got a dif, eigen_vectors = np.linalg.eig(np.linalg.inv(Sw).dot(Sb)) pairs = [(np.abs(eigen_values[i]), eigen_vectors[:, how to calculate the features the same way the author did, I am trying to implement this feature selection method that also allows me to classify data. Following this: paper I have tried many workaro, i could compress from a 50x20 matrix to 5x2, i]) for i in range(len(eigen_values))] #Linearly transforming the data w_matrix = np.hstack((pairs[0][1].reshape(2, im trying to find a solution for the paper experiment, int(dataset_stack.shape[0]/30)))*30) df = pd.DataFrame(dataset_stack, pairs[1][1].reshape(2, person_1_drunk_mean.T) person_1_sober_m_t = np.dot(person_1_sober_mean, person_1_drunk_std.T) person_1_sober_t = np.dot(person_1_sober_std, person_1_sober, person_1_sober_m_t, person_1_sober_mean.T) person_2_drunk_m_t = np.dot(person_2_drunk_mean, person_1_sober_std.T) person_2_drunk_t = np.dot(person_2_drunk_std, person_1_sober_t, person_2_drunk, person_2_drunk_m_t, person_2_drunk_mean.T) person_2_sober_m_t = np.dot(person_2_sober_mean, person_2_drunk_std.T) person_2_sober_t = np.dot(person_2_sober_std, person_2_drunk_t, person_2_sober, person_2_sober_m_t, person_2_sober_mean.T) person_3_drunk_m_t = np.dot(person_3_drunk_mean, person_2_sober_std.T) person_3_drunk_t = np.dot(person_3_drunk_std, person_2_sober_t, person_3_drunk, person_3_drunk_m_t, person_3_drunk_mean.T) person_3_sober_m_t = np.dot(person_3_sober_mean, person_3_drunk_std.T) person_3_sober_t = np.dot(person_3_sober_std, person_3_drunk_t, person_3_sober_m_t)) #Preparing dataset dataset_stack = np.vstack((person_1_drunk, person_3_sober_mean.T) #sum of the Sb Sb = np.sum((person_1_drunk_m_t, person_3_sober_std.T) #sum of the Sw Sw = np.sum((person_1_drunk_t, person_3_sober_t), person_3_sober)) pixel_index = [1, reducing it down to from what i am trying: from matplotlib.pyplot import figure import pandas as pd import numpy as np from matplotlib impor, something like this: This is the best minimal reproducible code, specifically finding the eigen vectors and the eigen values in the same way, target_names) #extracting eigenvalues and eigenvectors eigen_values, the clusters formed by every image looked alright as the distance was greater than the standard deviation. What i want to do is have my data, the external methods of calculating yields different values, to which i have extracted the features already. My problem is when i try to project the eigenvectors the result is something like this: I ha, X_lda[:

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How to Un-Sort The legends in the graph in R?

Post author By Full Stack
Post date July 7, 2020
No Comments on How to Un-Sort The legends in the graph in R?

I am not an expert in R. I am having an issue, the legends are sorted alphabetically. I cannot arrange them manually because I have 87 patients and each patient has 300 days and the status (category) changes every day so i want to plot the graph exactly like it has in the source. ggplot(data […]

Tags `Patient Number`) Here is the result: tibble: 72 x 3 Category `Patient Number` n 1 AD, -102], -11, -182, -23, -32, -38, -41, -42L), -48, -572L), .44, .53, .97], .Label = c("AD", '137', '177', "1":66, "14", "14":7, "2":70, "3":14, "71", "Admin delay", "CP10", "CP11", "CP12", "CP13", "CP14", "CP15", "CP16", "CP17", "CP18", "CP19", "CP2", "CP20", "CP21", "CP22", "CP23", "CP24", "CP25", "CP26", "CP27", "CP28", "CP29", "CP3", "CP30", "CP31", "CP32", "CP33", "CP34", "CP35", "CP36", "CP37", "CP38", "CP39", "CP4", "CP40", "CP41", "CP42", "CP43", "CP44", "CP45", "CP46", "CP47", "CP48", "CP49", "CP5", "CP50", "CP51", "CP52", "CP53", "CP54", "CP55", "CP56", "CP6", "CP7", "CP8", "CP9", "DG", "Patient 1", "Patient 2", (64, [ "103", [ 55, [ 75, [ 82, [[ 87, [109, [132, [148, [151, [56, }, 0, 1, 10, 100, 101, 104, 105, 106]]], 107, 108, 10L), 110, 111], 112, 113, 114, 115, 116, 117, 118, 119, 11L, 12, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 12L, 13, 130, 131, 133, 134, 135, 136, 138, 139, 13L, 140, 141, 142, 143, 144, 145, 146, 149, 14L, 15, 150, 152], 153, 154, 155], 156, 157, 158, 159, 15L, 16, 160, 161, 162, 163], 164, 165, 167, 168], 169, 16L, 17, 170, 171, 172, 173, 174, 175, 176], 178, 179, 17L, 18, 180, 181, 183, 184, 185, 186, 187, 188, 189, 18L, 19, 190, 191, 192, 193, 194, 195, 196, 197, 198], 199, 19L, 1L, 2, 20, 200, 201, 202, 203, 204, 205, 206, 207, 208], 209, 20L ), 21, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 21L, 22, 220, 221, 222]], 223, 224, 225, 226, 227, 228, 229, 22L, 230, 231, 232)' } ] }, 234], 235, 236, 237, 238, 239, 23L, 24, 240, 241, 242], 243, 244, 245, 246, 247, 248, 249, 24L, 25, 250, 251, 252], 253], 254, 255, 256, 257, 258, 259, 25L, 26, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 26L, 27, 271, 272, 273, 274, 275, 276, 277, 278, 279, 27L, 28, 280, 281, 282, 283, 284, 285, 286]], 287, 288, 289, 28L, 29, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 29L, 2L, 3, 30, 300, 301), 302], 303, 304, 305, 306, 307, 308, 309, 30L, 31, 310, 311, 312, 313, 315, 31L, 32L, 33, 33L, 34, 34L, 35, 35L, 36, 36L, 37, 37L, 38L, 39, 39L, 3L, 4, 40, 40L, 41L, 42, 43, 43L, 44L, 45], 45L, 46, 46L, 47, 47L, 48L, 49, 49L, 4L, 5, 50, 50L, 51, 51L, 52, 52L, 53L, 54, 54L, 55L, 56L, 57, 57L, 58, 58L, 59, 59L, 5L, 6, 60, 60L, 61, 61L, 62, 62L, 63, 63L, 64L, 65, 65L, 66, 66L, 67, 67L, 68, 68L, 69, 6L, 7, 70], 72, 73, 74, 76, 77, 78, 79, 7L, 8, 80, 81, 83, 84), 85, 86, 88, 89, 8L), 9, 90, 91, 92, 93, 94, 95, 96, 98, 99], 9L, aes(x = `Patient Number`, aes(x = data$`Patient Number`, C=c, C|D, Category = structure(c(65L, class = "data.frame") I wanted to count the frequency for each patient's category. df=test %>% count(Category, class = "factor"), CP, Day = c(0, ECG, fill = as.factor(Category))) + geom_bar(position = position_stack(reverse = TRUE), fill = data$Category)) + geom_bar(position = position_stack(reverse = TRUE), I am not an expert in R. I am having an issue, lg, mdc, na.rm = FALSE)+ scale_fill_discrete(breaks = data$Category) structure(list(`Patient Number` = c("Patient 1", na.rm = FALSE)+scale_fill_discrete(breaks = Category), PP, row.names = c(NA, sc, spc:"", Spec, stat="identity", the legends are sorted alphabetically. I cannot arrange them manually because I have 87 patients and each patient has 300 days and the statu, y = data$count_day, y = n

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how to calculate the scatter within classes for a 50×20 matrix

Post author By Full Stack
Post date June 29, 2020
No Comments on how to calculate the scatter within classes for a 50×20 matrix

I am trying to reduce a largely dimentional matrix to only 2D, i was using an example for 2D arrays,which works, but i would need to do the same for a higher dimentional scatter. I have two classes and each classes have matrices of 50×20 dimensional feature spaces. For my example i have these 2D […]

Tags -0.25, -0.4, -1] [4, -102], -11, -182, -23, -38, -48, '<1', '177', "-0.15", "1":66, "14":7, "c"=>"blue", (c_A_array-c_A_array_mean).T) scatter_c_B_array = np.dot((c_B_array-c_B_array_mean), (c_B_array-c_B_array_mean).T) # Calculate the SW by adding the scatters within classes SW = scatter_c_A_array+scatter_c_B_array print(SW), (circles-mean_circles).T) # Calculate the SW by adding the scatters within classes SW = scatter_triangles+scatter_circles+scatter_rectangle, (triangles-mean_triangles).T) scatter_circles = np.dot((circles-mean_circles), ), {1}; {2}, <1, 0.3)", 0.35), 0.48, 0.5, 0.6, 1 0, 1 1, 1.0]]) Afterwards im finding the mean for both classes triangles and rectangles # Calculate the mean vectors per class mean_rectangles = np, 1.15, 1.3, 1.45], 1.55, 1.65, 1.75]]) triangles = np.array([[0.1, 1) mean_triangles = np.mean(triangles, 1) The value given by the means of classes rectangle and triangles, 1)-(6, 1] [5, 10, 10)) ax0 = fig.add_subplot(111) ax0.scatter(c_A_array[0], 101, 105, 12, 13, 140, 142, 143, 144, 163], 169, 17, 174, 175, 178, 179, 183, 185, 186, 188, 189, 19, 190, 191, 193, 194, 195, 196, 198], 199, 20, 20.5, 200, 201, 202, 204, 205, 206, 207, 207]] c_B_array = [[ 16, 208], 21, 211, 213, 214, 215, 216, 217, 218, 22, 220, 221, 222]], 224, 225, 226, 227, 229, 229]] #Plot the data fig = plt.figure(figsize=(10, 231, 24, 25, 26, 27, 28, 29, 30, 31, 33, 36, 37, 39, 40, 43, 46, 49, 57, 58, 59, 62, 65, 67, 76, 9, 99], axis=1) # Calculate the scatter matrices for the SW (Scatter within) and sum the elements up scatter_c_A_array = np.dot((c_A_array-c_A_arra, axis=1) c_B_array_mean = np.mean(c_A_array, axis=1).reshape(2, but i would need to do the same for a higher dimentional scatter. I have two classes and each classes have matrices of 50x20 dimensional feat, c_A_array[1], c_B_array[1], c='grey', edgecolor='black') # Calculate the mean vectors per class c_A_array_mean = np.mean(c_A_array, edgecolor='black') ax0.scatter(c_B_array[0], exactly the same way but for a larger data, I am trying to reduce a largely dimentional matrix to only 2D, i use them to calculate the scatter: scatter_triangles = np.dot((triangles-mean_triangles), i was using an example for 2D arrays, marker="o", markers, precisely for a 50x20 matrix? For reproducibility this is my code: import numpy as np import matplotlib.pyplot as plt from matplotlib i, which works

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is there nice way to find peak and valley

Post author By Full Stack
Post date June 25, 2020
No Comments on is there nice way to find peak and valley

0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,4,4,6,14,25,43,71,93,123,194,279,356,460,470,460,468,439,303,266,200,97,79,58,218,677,1193,1331,812,501,212,869,1262,1093,725,329,241,558,1077,1216,901,463,226,262,671,1103,1199,791,344,163,151,292,742,1200,1113,678,299,176,207,542,1086,1251,899,426,170,104,117,187,274,396,419,515,477,500,471,400,303,177,142,88,44,37,16,5,6,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,2,6,8,19,27,50,70,103,155,215,285,386,447,443,469,463,434,313,244,174,92,79,78,204,601,1215,1320,845,525,208,839,1321,1085,715,331,265,556,1080,1179,851,509,235,261,693,1121,1168,749,364,168,130,301,735,1152,1116,697,324,157,196,520,1092,1217,905,483,187,98,112,177,260,326,444,563,484,486,464,433,329,209,138,77,41,22,8,5,2,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,2,4,14,15,24,51,61,74,127,205,304,331,507,475,443,439,435,331,267,168,103,84,55,203,700,1250,1342,811,465,213,930,1263,1085,653,323,266,563,1053,1226,884,447,254,260,701,1103,1187,766,352,155,134,299,699,1206,1088,702,358,156,221,507,1127,1252,866,426,163,129,117,206,263,388,428,519,466,482,445,389,314,207,132,83,57,28,17,7,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,3,4,7,6,16,24,50,63,110,121,219,305,305,439,497,508,453,430,338,234,182,99,69,70,220,679,1266,1288,818,482,222,891,1279,1061,650,332,279,557,1052,1233,917,409,213,277,700,1126,1181,764,333,138,134,290,819,1258,1004,680,302,153,251,485,1092,1233,904,445,193,124,112,187,254,370,446,511,504,484,494,382,303,201,168,68,38,28,18,5,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,2,5,10,13,20,37,52,82,158,214,269,369,436,468,487,518,465,350,254,188,101,78,75,189,560,1244,1326,761,488,132,882,1253,1288,753,305,266,528,1010,1198,959,480,234,244,635,1208,1094,867,382,162,117,257,693,1203,1166,728,371,138,224,502,1044,1156,926,456,185,122,108,171,246,355,440,515,450,515,459,393,293,217,142,82,49,22,12,4,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,1,3,7,7,13,20,45,65,89,136,228,281,354,434,502,507,506,429,300,284,183,109,77,70,156,603,1188,1335,812,505,157,805,1278,1320,729,331,245,536,1024,1194,931,511,248,234,618,1108,1129,849,430,178,107,215,714,1162,1165,769,363,135,215,483,1001,1193,991,454,167,127,93,156,226,322,434,543,484,491,455,452,311,222,139,83,40,29,10,4,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,2,2,8,11,29,33,46,102,155,216,277,362,400,524,456,477,461,345,282,190,108,83,60,195,611,1198,1347,780,486,180,956,1214,1273,709,283,249,617,1054,1180,907,477,224,246,697,1179,1054,828,380,153,131,277,744,1152,1168,699,321,167,217,457,996,1195,987,475,168,113,102,161,253,303,469,474,464,497,477,389,318,234,141,103,45,40,7,6,1,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,7,7,7,20,41,80,109,158,164,325,328,436,487,478,457,480,314,304,194,108,61,78,219,591,1229,1312,745,517,152,862,1240,1305,751,289,261,581,1040,1169,901,497,229,285,628,1195,1147,783,362,170,121,261,730,1176,1160,718,340,144,219,477,1037,1230,929,438,168,121,95,161,235,357,471,500,460,471,451,383,311,247,160,85,53,27,9,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 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"71", "862", "878", ( 1056, ( 1262, ( 531, ( 718, ( 781, (-859, (1001, (1003, (1004, (1007, (1017, (1054, (1101, (1152, (1255, (1346, (338, (388, (444, (475, (489, (541, (64, (651, (660, (672, (677, (678, (679, (7+2)=9, (701, (702, (708, (735, (803, (974, (976, [ "103", [ "13:00", [ 55, [ 703], [ 75, [ 82, [ 949], [-848, ['1160', ['1201', ['1211', ["4|6|5", [(838, [[ 87, [[1270, [[1335, [[496., [1040, [109, [1191., [1202, [1264, [1273], [132, [1334, [1365], [148, [151, [56, [690, [762, [764, [767, [778, [808, [834, }, 0, 0.0, 1, 1)[:100, 10, 10:30, 10}|4{2, 10}|5{2, 10}|7{2, 10}|8{2, 10}|9{2, 100, 1000, 101, 1010, 1013), 1018), 1020, 1023, 1024], 1025, 1026, 1033, 104, 1046, 105, 1050, 106]]], 1066, 1067], 107, 1076, 108, 1080, 1081, 1086, 1095, 1098., 11:06, 11:44, 110, 1102, 1103, 1104, 1105, 1108, 1109, 111], 1113, 1115, 1116, 1117, 1118, 1119, 112, 1120, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 113, 1131, 1132, 1134, 1135, 1136, 1138, 1139, 114, 1143, 1145, 1146, 1147, 1149, 115, 1150, 1151, 1153, 1155, 1156, 1158, 1159], 116, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 117, 1170', 1172, 1174, 1175, 1176, 1177, 1178, 1179, 118, 1180, 1181, 1182, 1183, 1186, 1187, 1188, 119, 1190, 1193, 1195, 1196, 1197)', 1198, 12, 12:21, 12:46, 120, 1200, 1203, 1204, 1205, 1206, 1207, 1208, 121, 1210, 1213, 1215, 1216, 1217, 1218, 1219, 122, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 123, 1235, 1237, 1238, 1239], 124, 1240)', 1241, 1242, 1244, 1245, 1247, 1248, 1249, 125, 1250, 1251, 1252, 1253, 1254, 1256, 1258, 1259, 126, 1260, 1263, 1265), 1266, 1267, 1268, 1269, 127, 1272, 1274, 1275], 1276, 1277, 1278, 1279, 128, 1280), 1281, 1282, 1285, 1286, 1287, 1289, 129, 1290, 1291, 1292, 1293, 1295, 1296, 1297, 1298, 13, 13:08, 130, 1303, 1305, 1306, 1307, 1309, 131, 1310, 1311, 1312, 1313., 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1325, 1326, 1327, 1329], 133, 1330), 1331, 1336, 1337, 134, 1341, 1342, 1343, 1344), 1345, 1347, 1348, 135, 1350, 1351, 1353, 1354, 1356, 136, 1363, 1370, 1371, 1374, 1375, 1376, 1377, 1378, 1379, 138, 1381, 1382, 1385, 1387, 1389, 139, 1391, 1392, 1395], 1398, 140, 1400, 1401, 1403), 1406, 141, 1413, 1414, 142, 1420, 1421, 1429, 143, 1430, 144, 145, 146, 1470, 1477, 1481, 1484, 149, 1493, 15, 150, 152], 153, 154, 155], 156, 157, 158, 159, 16, 160, 161, 162, 163], 164, 165, 167, 168], 169, 17, 170, 171, 172, 173, 174, 175, 176], 178, 179, 18, 180, 181, 183, 184, 185, 186, 187, 188, 189, 19, 190, 191, 192, 193, 194, 195, 196, 197, 198], 199, 2, 20, 200, 201, 202, 203, 204, 205, 206, 207, 208], 209, 21, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 22, 220, 221, 222]], 223, 224, 225, 226, 227, 228, 229, 230, 231, 232)' } ] }, 234], 235, 236, 237, 238, 239, 24, 240, 241, 242], 243, 244, 245, 246, 247, 248, 249, 25, 250, 251, 252], 253], 254, 255, 256, 257, 258, 259, 26, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 27, 271, 272, 273, 274, 275, 276, 277, 278, 279, 28, 280, 281, 282, 283, 284, 285, 286]], 287, 288, 289, 29, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 3, 30, 300, 301), 302], 303, 304, 305, 306, 307, 308, 309, 31, 310, 311, 312, 313, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 33, 330, 331, 332, 333, 336), 337), 339), 34, 340, 341, 342), 343, 344, 345, 346, 347, 348, 349, 35, 350, 351, 352" }, 353, 354, 355, 356, 357, 358, 359, 36, 360, 361, 362, 363, 364, 365, 366), 367, 368, 369, 37, 370, 371, 372, 373, 374, 375, 377, 378" }, 379, 380, 381, 382, 383, 384, 385, 386", 387, 389, 39, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 4, 40, 400, 401, 402" }, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 415), 416, 417, 419, 42, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429 ], 43, 430, 431, 432, 433, 434, 435, 436, 437, 438, 440, 441, 443, 445, 446, 447, 448, 449, 45], 450), 451, 452, 453, 454, 455, 456, 457, 458, 459, 46, 460, 461, 462, 463, 464, 466, 467, 468, 469" }, 47, 470, 471 ], 473, 474, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 49, 490, 491, 492, 493, 494, 495, 497, 498, 499), 5, 5:10], 50, 500), 501, 502, 503, 504, 506, 507, 509, 51, 511, 512)), 513, 514, 515, 516, 517, 518, 519, 52, 520, 521, 522, 523, 524, 525, 526, 527], 528, 529, 530, 532", 533, 534, 535, 536, 537, 538]), 539, 54, 540, 542, 543, 544, 546, 547, 548, 549, 550, 551, 552), 553, 554, 555, 556, 557, 558), 559], 560, 561, 562], 563, 564, 565, 567, 568, 57, 570, 571, 572, 573, 574, 575, 576, 578, 579, 58, 581, 582, 583])), 584, 585, 588, 589, 59, 590, 591, 594], 596, 597, 598, 599]]}}, 6, 60, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 61, 610, 611, 612, 613, 615, 616, 617, 618, 62, 620, 622, 623, 624, 625, 626, 627, 628, 63, 630, 631, 632, 633" }, 634, 635, 637, 638" }, 639, 640, 641, 642, 643, 644, 645, 647, 648, 649, 65, 650, 652, 653, 654), 656), 657, 658, 659, 66, 661, 662, 663, 664, 665, 666, 667, 668, 669, 67, 670, 671, 673, 675, 676, 68, 680, 681), 682], 684, 685, 687, 688, 69, 691, 692, 693}]}, 695, 696, 697]]);, 699, 7, 7:57, 70], 700, 704, 706), 707, 709, 710, 712), 713, 715), 716, 717, 719, 72, 720, 722, 724, 725, 726, 728, 73, 731, 732, 733), 734, 738, 739, 74, 740, 741, 742, 744 ], 745, 746, 747, 748, 749", 750, 751), 752" }, 753, 754 ], 756), 76, 761" }, 765, 766, 768, 769, 77, 771), 772, 774, 775, 776", 777, 779, 78, 780), 782" }, 783, 784), 786, 787, 788, 789, 79, 790, 791, 792, 793, 794], 796, 798, 799), 8, 8*12, 80, 80:9}, 800, 801, 802, 804, 805, 806), 81, 811, 813, 815, 816, 817), 818, 819, 822, 823, 824, 825, 826, 828, 83, 832, 833, 835, 837), 839, 84), 840, 841, 842, 843, 844, 845, 846, 847, 849, 85, 850, 851, 853, 854, 855, 857, 858, 86, 861, 863, 864, 866" }, 867, 868, 869, 872, 874), 875, 876, 877)), 879'}, 88, 880, 881" }, 882, 883), 884, 885, 886], 887, 888, 889, 89, 890, 891, 892, 893, 894, 895, 896, 899, 9, 90, 900, 901, 902, 903, 904, 905), 906, 907, 91, 92, 926, 927, 928, 929, 93, 930, 931, 933, 935, 937, 938, 939, 94, 940], 941, 943, 944, 946, 948, 95, 951, 954, 955, 956, 959, 96, 960, 961}, 964" }, 965, 970, 971, 972, 973, 975, 978, 979, 98, 983, 986, 987, 988, 989, 99], 990, 991, 992)', 993, 994, 996)', 999

Database Development

How to search a table with 80 million records faster?

Post author By Jessica Alba
Post date June 24, 2020
No Comments on How to search a table with 80 million records faster?

I have a table with about 80 million records, I want to find all the activities of lists and workspaces that a user has access to. So first, I get the ids of the lists and workspaces and then I run the following query: select *, COALESCE("origin_created_at", "created_at") AS "created_at", COALESCE("updated_at", "origin_updated_at") AS "updated_at" from […]

Tags '1231', "")); }, "created_at") AS "created_at", "created_at") DESC, "id" desc limit 40; and this the execution plan Limit (cost=2446886.55..2446886.65 rows=40 width=1002) (actual time=44452.393..44452.418, "origin_updated_at") AS "updated_at" from "activities" where ("listId" in (310, [[496., 101, 1014787, 1017501, 1065915, 1104, 111], 120, 127, 1282788, 131, 1341992, 13783, 1411789, 1434677, 144564, 1515479, 155], 156, 158197, 158273, 158639, 158821, 159009, 162, 163840, 166270, 170392, 178, 178981, 179, 188, 193559, 195046, 197071, 208561, 213, 214088, 219, 220, 222]], 230, 240478, 241, 24273, 245, 260, 261, 261008, 267000, 2691, 2695, 271, 27341, 27356, 273574, 279, 281, 281105, 28434, 291171, 29271, 295312, 297964, 298318, 299145, 3, 313589, 323458, 324525, 326394, 356252, 36, 36046, 36989)) and (("isBulk" = false or "activities"."type" = 0) and "activities"."deprecated_at" is null) order by COALESCE("origin_crea, 36989}'::integer[])) Planning time: 2.882 ms Execution time: 44452.871 ms (17 rows) As stated in the plan PostgreSQL uses "Bitmap Heap Sca, 36989}'::integer[]))) Rows Removed by Index Recheck: 9072392 Filter: ((deprecated_at IS NULL) AND ((NOT "isBulk, 382159, 38236, 382843, 38739, 395721, 395873, 399844, 399845, 4, 400, 400339, 405, 408, 421035, 424284, 424760, 425935, 430658, 43233, 438101, 438106, 450540, 45765, 461, 482197, 50301, 5369, 54099, 541359, 54576, 551, 569319, 57, 57909, 57938, 58323, 58557, 587679, 620442, 628644, 631758, 632736, 632742, 632755, 632758, 642775, 642875, 643013, 643014, 643867, 64642, 65, 650332, 661, 672369, 673517, 689117, 722320, 72915, 74018) or "workspaceId" in (137, 74018}'::integer[])) -> Bitmap Index Scan on activities_workspace_id_index (cost=0.00..19702.32 rows=732444 width=0) (, 74018}'::integer[])) OR ("workspaceId" = ANY ('{137, 761351, 79, 81, 824491, 826590, 83329, 84), 841589, 847849, 886], 89043, 90857, 9092), 932660, 96660, 98818, COALESCE("origin_created_at", COALESCE("updated_at", I get the ids of the lists and workspaces and then I run the following query: select *, I have a table with about 80 million records, I want to find all the activities of lists and workspaces that a user has access to. So first, id DESC Sort Method: top-N heapsort Memory: 37kB -> Bitmap Heap Scan on activities (cost=37546.04..2414605.20 rows=10212, listid, one for each of the following columns: type, organizationId. How can I make the query faster? Or is there a better way to rewrite the query?, there are 4 indices on the table, workspaceId

Mastering Development

How to add color border or similar highlight to specifc element of heatmap in python?

Post author By Full Stack
Post date June 23, 2020
No Comments on How to add color border or similar highlight to specifc element of heatmap in python?

I want to highlight specific element in heat map(on both row and column of heatmap) Is their a way to do highlight particular element on both axis . The desire result should look like where the vector element got highlighted on both axis. Or similar to the example given below. I am using seaborn and […]

Tags -102], -11, -182, -32, -38, -442, .44, .53, '137', '177', "14", "3":14, "334", "4":72, "4|1|8"], "71", (444, (475, [ "103", [ 55, [ 75, [ 82, ["4|6|5", [[ 87, [[496., [109, [132, [148, [56, }, #annot=True, 0, 1, 100, 101, 104, 107, 108, 110, 111], 112, 113, 115, 116, 117, 118, 119, 120, 121, 123, 124, 125, 126, 128, 129, 13, 130, 131, 133, 134, 135, 136, 138, 139, 139]]) Any resource or reference will be helpful. Thanks, 140, 141, 142, 143, 144, 145, 149, 153, 154, 156, 157, 16, 160, 161, 164, 165, 167, 169, 170, 171, 172, 173, 174, 176], 178, 179, 180, 181, 183, 184, 187, 189, 19, 190, 191, 192, 193, 195, 196, 198], 199, 20, 200, 201, 202, 203, 205, 206, 207, 208], 209, 21, 211, 213, 215, 216, 217, 218, 219, 220, 221, 222]], 223, 227, 234], 236, 237, 239, 24, 240, 241, 242], 243, 244, 245, 246, 247, 248, 249, 25, 250, 251, 253], 254, 255, 256, 257, 258, 259, 262, 264, 265, 266, 267, 268, 27, 272, 273, 274, 275, 276, 277, 279, 28, 282, 284, 285, 288, 29, 290, 292, 296, 298, 30, 301), 302], 303, 304, 308, 309, 31, 310, 317, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 332, 336), 337), 339), 340, 342), 343, 344, 347, 348, 349, 351, 352" }, 353, 354, 355, 357, 359, 36, 362, 363, 364, 366), 367, 37, 370, 371, 372, 374, 377, 378" }, 379, 380, 381, 382, 383, 385, 386", 387, 389, 39, 390, 391, 392, 393, 394, 395, 398, 4, 400, 401, 402" }, 403, 404, 405, 406, 407, 409, 410, 411, 412, 415), 416, 419, 42, 421, 422, 424, 425, 426, 428, 429 ], 43, 430, 433, 435, 436, 438, 440, 443, 445, 447, 448, 45], 450), 452, 456, 458, 459, 460, 461, 462, 464, 466, 467, 469" }, 47, 470, 471 ], 473, 474, 476, 477, 478, 479, 481, 482, 483, 485, 486, 487, 488, 490, 491, 492, 493, 495, 497, 498, 499), 5, 50, 51, 52, 54, 57, 58, 59, 6, 61, 62, 63, 66, 68, 69, 7, 70], 72, 73, 74, 76, 78, 79, 8, 80, 81, 86, 89, 9, 91, 92, 93, 94, 95, 96, 99], I want to highlight specific element in heat map(on both row and column of heatmap) Is their a way to do highlight particular element on both, linewidth=0.5) Edit 2: Here is the data with columns as a b c d e vector array([[191, method="complete"), Where **PairWise ** denotes my data matrix. import seaborn as sns %matplotlib inline pairwise_corr=PairWise.corr(method="spearman") sns.clus

Cryptocurrency Ethereum Mastering Development

MainNet Truffle Contract sendTransaction request not returning transaction receipt

Post author By Crypto Questions
Post date June 20, 2020
No Comments on MainNet Truffle Contract sendTransaction request not returning transaction receipt

I just deployed my smart contract to MainNet and starting to test my DAPP. I am running my own public Open Ethereum node where I send my transactions to. I am NOT mining in this node. From what I can tell from my OE (parity) logs 1. the send_transaction request is received 2020-06-20 20:35:00 UTC […]

Tags -102], -11, -32, -41, -48, .53, .97], "14", "71", "data":"0xc2c8353200000000000000000000000000000000000000000000000000000000000000c006b3dfaec148fb1bb2b066f10ec285e7c9bf402ab32aa78a5d38e345668, "gas":"0x62d89", "gasPrice":"0x3f5476a00"}]}. 2020-06-20 20:35:00 UTC http.worker20 WARN rpc eth_sendTransaction is deprecated and will be removed in future, "id":10}. 2020-06-20 20:35:01 UTC http.worker20 TRACE rpc Request: {"jsonrpc":"2.0", "id":11}. 2020-06-20 20:35:02 UTC http.worker00 TRACE rpc Request: {"jsonrpc":"2.0", "id":12}. The transaction hash is NOT showing in etherscan.io. Where is this transaction? Has it been picked up by a miner? The gas price fo, "id":9}. 2020-06-20 20:35:00 UTC http.worker00 TRACE rpc Request: {"jsonrpc":"2.0", "method":"eth_getTransactionReceipt", "method":"eth_sendTransaction", "params":["0x5a1936a5ae943fc7f7abe17fa91aed418253cf2a30692d377aef5cb2f35c2be2"]}. 2020-06-20 20:35:00 UTC http.worker00 DEBUG rpc Response: {, "params":["0x5a1936a5ae943fc7f7abe17fa91aed418253cf2a30692d377aef5cb2f35c2be2"]}. 2020-06-20 20:35:01 UTC http.worker20 DEBUG rpc Response: {, "params":["0x5a1936a5ae943fc7f7abe17fa91aed418253cf2a30692d377aef5cb2f35c2be2"]}. 2020-06-20 20:35:02 UTC http.worker00 DEBUG rpc Response: {, "params":[{"from":"0xaaf66209133056f1f7285d6cdb61cf21d135f300", "result":"0x5a1936a5ae943fc7f7abe17fa91aed418253cf2a30692d377aef5cb2f35c2be2", "result":null, "to":"0x38dfc89a286270e7da5f6f1e8112afeb461136fd", (64, [ 75, [ 82, [109, [56, {"id":10, {"id":11, {"id":12, {"id":9, //value 0, 0, 1, 10, 100, 101, 104, 105, 108, 110, 111], 112, 114, 115, 116, 117, 118, 12, 121, 128, 129, 133, 138, 167, 174, 176], 178, 179, 185, 191, 192, 193, 194, 200, 201, 210, 223, 227, 231, 236, 241, 251, 27, 40, 42, 43, 46, 49, 50, 51, 58, 6, 65, 67, 69, 72, 74, 77, 8, 80, 83, 84), 85, 88, 93, action: Call(0x38dfc89a286270e7da5f6f1e8112afeb461136fd), because I have to respond back to my client in less than 60 seconds to inform our user that the transaction is being processed. How can I jus, condition: None } 3. I see it imported to the pool 2020-06-20 20:35:00 UTC http.worker20 DEBUG own_tx Imported to the pool (hash 0x5a1936a5, data: [194, gas_price: 17000000000, gas: 404873, hash: 0x5a1936a5ae943fc7f7abe17fa91aed418253cf2a30692d377aef5cb2f35c2be2 }, I just deployed my smart contract to MainNet and starting to test my DAPP. I am running my own public Open Ethereum node where I send my tran, public: Some(0x874833a678f3cca4a688c65ac155cbf91cc5514c9c8f6ed7f2ccfcfb387e87b83d7ba015efec002ac5de17cc1533ac7a06290a7fa1f326c24b7f027ee74a56, r: 64318481432456934412425597167761451906178142553595528239197657623645915206418, s: 1119033311313726876408734818989983190096285640462426417432701464163050876380, sender: 0xaaf66209133056f1f7285d6cdb61cf21d135f300, v: 38

Linux Mastering Development

Can no longer boot Ubuntu (dual boot setup)

Post author By Linux Questions
Post date May 13, 2020
No Comments on Can no longer boot Ubuntu (dual boot setup)

I just tried to boot my Ubuntu 16.04.5 partition of my Windows/Linux desktop machine after about 4 months of not doing so. The first thing I noticed is that I wasn’t seeing the GRUB screen at all. I’m pretty sure some Windows update in the last few months is responsible, but I guess I’m just […]

Tags (hd0, (hd1), (hd2), 178, and 142 available. 142 and 96 have a second version with .efi.signed postfixes. I don’t know which are correct or what 96 or 178 really mean, as well as ones with a .old postfix. I tried those and they don’t help either. I don’t know how to check where the symlinks point in GRUB) b, but I guess I’m just at their mercy to not break things, but I use sda1 because I’m pretty sure that’s correct based on an fdisk -l from my USB version of Ubuntu. The instructions there don’t make i, but that was the case when things were working, but to no avail. Details below. https://www.linux.com/training-tutorials/how-rescue-non-booting-grub-2-linux/ This is what I see in the GRUB, gpt3) ...and it goes on like that, gpt4), gpt5), GRUB now gives me a prompt instead of a menu to select the OS from. I’ve tried following the instructions here, however when I try to boot it, I just tried to boot my Ubuntu 16.04.5 partition of my Windows/Linux desktop machine after about 4 months of not doing so. The first thing I, I use the corresponding version number. initrd /boot/initrtd.img-4.4.0-178-generic (There are symlinks to default kernels/initrd in /boot a, it contains kernel files whereas all the others seem not to. set root=(hd1, most of them are small. It’s not hard to figure out which drives have anything interesting on them by doing ls -l The Ubuntu one seems to b, most of which goes offscreen faster than I can read. Of the bits I can read, msdos1), msdos5), msdos6 It is formatted as ext* and is the correct size of my linux partition. Furthermore, msdos6), msdos6) I have a few different options for kernels. vmlinuz-4.4.0--generic with version numbers 96, so good. I can now see my ubuntu partition in the boot menu, so I tried to figure it out myself. linux /boot/vmlinuz-4.4.0-178-generic root=/dev/sda1 For the initrd, so I’m not going to dwell on pointing fingers. I’m fairly confident that the partition and its data are still intact. I still see my drive (, so I’m not worried about it. However, that is accurate so please ELI5 if you’re willing to help out]. Let’s use the 178 one for now. I set the root as recommended in the link abov, the GRUB screen no longer gives me a list to select my OS at startup. Oh no. I made a USB boot drive containing Ubuntu and ran boot repair f, there are about 6 more. I don’t understand why I have so many of these, things that could potentially be problems: can’t find /root in /etc/fstab mounting /run on /root/run failed: No such file or directory Targe

Mastering Development

Writing 5 nested queries

I am writing 5 five nested queries and every nested connects 2 or more tables AND this is tables and data that I want to apply to nested query Please help me make the Query in SQL language Create table Doctor ( Dr_ID int not null primary key, D_nam varchar(50) not null, Dr_addres varchar(90) not […]

Tags :, '0500338811', '0500993377'); insert into INSURANCES values (2005, '0509788374', '0510529635', '05111144443', '0511223311', '0511223322', '0511552244'); insert into INSURANCES values (2004, '05222224447', '0533111222', '0544332211', '0544338888'); insert into INSURANCES values (2007, '0544443332', '0544553366', '0544554433'); insert into INSURANCES values (2009, '0544993388', '0555005588'); Create table admin ( Admin_id int not null primary key, '0555667744'); insert into INSURANCES values (2002, '0565445566', '0566665555', '0566990087', '0577446633'); insert into INSURANCES values (2006, '0577554433', '0577665544'); insert into INSURANCES values (2008, '0577766543', '0577773333', '0577777432', '0577779999', '0577887788'); insert into INSURANCES values (2010, '0577889955', '0582837531', '0583223224', '0586611224', '0586644524', '0586773322', '0587766554', '0588332946', '0588337755', '0588446655', '0588773366', '0588776622'); insert into INSURANCES values (2003, '0588779374', '0588889999', '0588899224', '0592763548', '0599008899', '0599988738', '0599988877', '05999966665', '1/1/2018'); insert into Hospital_Affiliatio values (110, '1/1/2020', '1/2/2020', '1/3/2017'); insert into Hospital_Affiliatio values (116, '10ml, '140/90mmHg', '15/1/2020', '15/2/2020', '15/6/1019'); insert into Hospital_Affiliatio values (119, '15ml, '16/2/2020', '17/1/2020', '17/2/2020', '19/2/2020', '2/2/2020', '20/2/2020', '21/2/2020', '22/1/2020', '22/12/2018'); Create table Office_visits ( patients_id int not null, '22/12/2018'); insert into Hospital_Affiliatio values (111, '22/12/2018'); insert into Hospital_Affiliatio values (112, '22/12/2018'); insert into Hospital_Affiliatio values (113, '22/12/2018'); insert into Hospital_Affiliatio values (114, '22/12/2018'); insert into Hospital_Affiliatio values (117, '22/12/2018'); insert into Hospital_Affiliatio values (118, '23/2/2020', '26/2/2020', '27/2/2020', '3/2/2020', '3/3/2020', '3001', '3002', '3003', '30mg, '35'); insert into Office_visits values (3004, '37mg, '4/1/2020', '4/2/2020', '40'); Create table PRESCRIPTIONS ( RX_ID int not null primary key, '40mg, '45mg, '54'); insert into Office_visits values (3007, '55'); insert into Office_visits values (3003, '6/1/2020', '60'); insert into Office_visits values (3005, '7/2/2020', '70'); insert into Office_visits values (3001, '70'); insert into Office_visits values (3002, '8/2/2020', '8/3/2020', '80'); insert into Office_visits values (3009, '80'); insert into Office_visits values (3010, '80/120mmHg', '85/123mmHg', '88'); insert into Office_visits values (3008, '9/2/2020', '90/125mmHg', 'a@gmail.com', 'acetaminophem', 'Ahmad', 'ahmadA@gmail.com'); insert into admin values (102, 'aion-aljua, 'airfan', 'airfan@gmail.com'); Create table DRUGS ( Drug_ID int not null primary key, 'alabtisamah', 'alabtisamah@gmail.com'); insert into Hospital values (121, 'alasiah, 'ali@gmail.com', 'aljafil', 'aljafil@gmail.com'); insert into Hospital values (151, 'aljazirah', 'aljazirah@gmail.com'); insert into Hospital values (141, 'allianz', 'almithnab, 'alnor', 'alnor@gmail.com'); insert into Hospital values (161, 'alqalij', 'alqalij@gmail.com'); insert into Hospital values (191, 'alqlimy', 'alqlimy@gmail.com'); insert into Hospital values (111, 'alriaiah', 'alriaiah@gmail.com'); insert into Hospital values (171, 'alsafwah', 'alsafwah@gmail.com'); insert into Hospital values (131, 'alsalamah', 'alsalamah@gmail.com'); insert into Hospital values (181, 'amani', 'amaniA@gmail.com'); Create table PATIENTS ( P_id int not null primary key, 'amira', 'amiraA@gmail.com'); insert into admin values (108, 'Amlodipine', 'antipyretic'); Create table INSURANCES ( Company_ID int not null primary key, 'aras, 'arras, 'asma', 'asma@gmail.com', 'Aspirin', 'assicurazioni generali', 'Atenolol', 'AXA', 'berkshire hathaway', 'buraidah, 'china live insurance', 'Cimetidine', 'dark urine, 'dehydration, 'Dental caries', 'diarrhea, 'Diclofenac', 'Doctor of Dentist'); insert into DOCTOR values (112, 'Doctor of Dentist'); insert into DOCTOR values (117, 'dry eye, 'dry mouth, 'f@gmail.com', 'fahad', 'fahadA@gmail.com'); insert into admin values (103, 'Famotidine', 'Foot fracture', 'Foot pain', 'Furosemide', 'heartburn'); insert into DRUGS values (1004, 'heartburn'); insert into DRUGS values (1005, 'Heat in the throat', 'hypertension', 'hypertension'); insert into DRUGS values (1002, 'hypertension'); insert into DRUGS values (1003, 'hypertension'); insert into DRUGS values (1008, 'hypertension'); insert into DRUGS values (1009, 'hypertension'); insert into DRUGS values (1010, 'hypertension'); insert into DRUGS values (1011, 'hypotension, 'Internist'); insert into DOCTOR values (113, 'Internist'); insert into DOCTOR values (118, 'itching, 'j@gmail.com', 'jana', 'janaA@gmail.com'); insert into admin values (100, 'japan post holding', 'joharah', 'kalid', 'kalidA@gmail.com'); insert into admin values (104, 'Keep at room temperature', 'Knee pain, 'lila', 'lila@gmail.com', 'm@gmail.com', 'mohammad', 'mohammadA@gmail.com'); insert into admin values (105, 'munich re group', 'Neurosurgeon'); Create table Hospital ( H_ID int not null primary key, 'Neurosurgeon'); insert into DOCTOR values (115, 'Nifetidine', 'norah', 'norah@gmail.com', 'nurah', 'nurahh@gmail.com', 'o@gmail.com', 'omar@gmail.com', 'orthopedist'); insert into DOCTOR values (111, 'orthopedist'); insert into DOCTOR values (116, 'othman', 'Otolaryngologist'); insert into DOCTOR values (114, 'Otolaryngologist'); insert into DOCTOR values (119, 'pain reliever'); insert into DRUGS values (1006, 'pain reliever'); insert into DRUGS values (1007, 'Propranolol', 'prudential plc', 's@gmail.com', 'sa@gmail.com', 'salih', 'salih@gmail.com', 'salma', 'sami', 'sami@gmail.com', 'sara@gmail.com', 'saraA@gmail.com'); insert into admin values (107, 'stomach pain, 't@gmail.com', 'talal', 'talalA@gmail.com'); insert into admin values (106, 'teeth pain', 'thani', 'thaniA@gmail.com'); insert into admin values (109, 'ti@gmail.com', 'Timolol', 'united health group', 'w@gmail.com', 'waad', 'yusif', 'yusif@gmail.com', 'zurich insurance group', "12-2-2020", "2001", "2002", "2003", "Bid", "Headache"]}}}, "Nausea", "Sara", [151, {"ali", 1, 100, 100); vinsert into PATIENTS values (3009, 1003); insert into PRESCRIPTIONS values (030, 1003); insert into PRESCRIPTIONS values (040, 1006); insert into PRESCRIPTIONS values (050, 1006); insert into PRESCRIPTIONS values (070, 1009); insert into PRESCRIPTIONS values (020, 101, 101); insert into PATIENTS values (3010, 1011); Create table p_doctorH ( Patient_ID int not null, 1011); insert into PRESCRIPTIONS values (010, 1011); insert into PRESCRIPTIONS values (060, 1011); insert into PRESCRIPTIONS values (080, 1011); insert into PRESCRIPTIONS values (090, 102); insert into PATIENTS values (3003, 103); insert into PATIENTS values (3002, 103); insert into PATIENTS values (3006, 104); insert into PATIENTS values (3005, 105); insert into PATIENTS values (3007, 108); insert into PATIENTS values (3004, 109); Create table Hospital_Affiliatio ( Doctor_ID int not null, 109); insert into PATIENTS values (3008, 110, 111], 112, 113, 114, 115, 116, 117, 118, 119, 121, 131, 141, 150, 160, 161, 163], 167, 170, 171, 175, 178, 181, 191, 2, 2004, 2005, 2008, 2010, 3, 30]|[10, 3004, 3006, 3007, 3008, 3009, 4, 90, abdallah street', abdoalaziz street', abdualaziz street', abdualziz street', abo talb street', abo-bakir street', address varchar(90) not null, Admin_address varchar(90) not null, Admin_Email varchar(150) not null ); insert into admin values (101, Admin_Id int not null, Admin_name varchar(30) not null, Admin_phone numeric(10) not null, Benefits varchar(200) not null ); insert into DRUGS values (1001, Blood_Pressure varchar(15), bloody stools', bronchospasm', Comments varchar(200), Company_name varchar (30), confusion, cough, D_nam varchar(50) not null, Date_of_Affiliation date, Date_of_Visit date not null, Date_of_Visit), Date_Prescribed date not null, depression, Diagnosis varchar(50), Diagnosis_Status varchar(50), dizziness', Doctor_ID, Doctor_ID int not null, Dosage varchar(40)not null, Dr_addres varchar(90) not null, Dr_Email varchar(150) not null, Dr_ID, Dr_ID int not null, Dr_phone numeric(10) not null, Dr_Specialization varchar(20) not null ); insert into DOCTOR values (110, Dru_name varchar(50) not null, Drug_ID int not null, Dura varchar(30) not null, Email varchar(150) not null, end_date date NOT NULL, flushing, foreign key (Admin_Id )references admin(Admin_Id), foreign key (Doctor_ID)references Doctor(Dr_ID ), foreign key (Dr_ID)references Doctor(Dr_ID ), foreign key (Drug_ID)references DRUGS(Drug_ID ) ); insert into PRESCRIPTIONS values (000, foreign key (Hospital_ID)references Hospital(H_ID ) ); insert into Hospital_Affiliatio values (115, foreign key (Insurance_ID)references INSURANCES(Company_ID ), foreign key (P_Dr_Id)references Doctor(Dr_ID ) ); insert into PATIENTS values (3001, foreign key (Patient_ID)references PATIENTS(P_id ), foreign key (Patient_ID)references PATIENTS(P_id ) ); insert into p_doctorH values (3006, foreign key (patients_ID)references PATIENTS(P_id ) ); insert into Office_visits values (3006, H_address varchar(90) not null, H_Email varchar(150) not null ); insert into Hospital values (101, H_name varchar(50) not null, H_phone numeric(10) not null, heartburn', Height numeric(3), Hospital_ID int null, Hospital_ID), I am writing 5 five nested queries and every nested connects 2 or more tables AND this is tables and data that I want to apply to nested qu, Initial_Diagnosi varchar(50), Insurance_ID int not null, king abdallah street', king abdoalaziz street', king abdulah street', king abdullah street', king abo talb street', king abo-bakir street', king fahad street', king kalid street', king salman street', king saud street', null, null); insert into p_doctorH values (3001, null); insert into p_doctorH values (3002, null); insert into p_doctorH values (3003, null); insert into p_doctorH values (3004, null); insert into p_doctorH values (3005, null); insert into p_doctorH values (3007, null); insert into p_doctorH values (3008, null); insert into p_doctorH values (3009, null); insert into p_doctorH values (3010, null); These are the tables I want to apply to the nested query............................................................................., Number_of_Refills numeric(9), Omar, orthostatic hypotension', P_Dr_Id int not null, p_name varchar(50) not null, Patient_ID int not null, phone numeric(10) not null, Phone numeric(9) ); insert into INSURANCES values (2001, PRIMARY KEY (Doctor_ID, PRIMARY key (Patient_ID, primary key (patients_id, PRN, Reason varchar(200), Reason_for_Leaving varchar(90), Refillable DATE not null, Shake the bottle before opening', Side_Effects varchar(200) NOT NULL, sodium retention', start_date, start_date date NOT NULL, swelling', Symptomsof_the_disease varchar(350), tid, tif, vomiting, Weight numeric(4)

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