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How to use within_range method in Sure

Best Python code snippet using sure_python

augment.py

Source:augment.py

1#!/usr/bin/env python2# coding: utf-834# # Data distribution check56# In[14]:789import numpy as np10import os11import pandas as pd12import matplotlib.pyplot as plt13#import umap as umap1415"""16# In[24]:171819# Read the old and new data 20old = pd.read_csv('data_x_old.csv', header=None,sep=' ',dtype='float')21old.info()22old = old.values23new = pd.read_csv('data_x.csv', header=None,sep=' ',dtype='float')24new.info()25new = new.values262728# ## Histogram2930# In[29]:313233# Plot the histogram of data34def histogram_plot(data, dim):35    f = plt.figure()36    # Determine if this is a new data37    if np.shape(data)[0] == 17500:38        new_flag = True39        name = 'new'40    else:41        new_flag = False42        name = 'old'43    # Plot the histogram44    plt.hist(data[:, dim],bins=100)45    plt.title('histogram of axim {} of {} data '.format(dim, name))46    plt.ylabel('cnt')47    plt.xlabel('axis {}'.format(dim))48    plt.savefig('histogram of axim {} of {} data.png'.format(dim, name))495051# In[30]:525354for i in range(8):55    histogram_plot(new, i)56    histogram_plot(old, i)575859# ## Clustering6061# In[31]:626364data_all = np.concatenate([old, new])65reducer = umap.UMAP()66embedding = reducer.fit_transform(data_all)67embedding.shape686970# In[37]:717273# Plot the umap graph74lo = len(old)75ln = len(new)76label_all = np.zeros([lo + ln, ])77label_all[lo:] = 178f = plt.figure()79plt.scatter(embedding[:lo, 0], embedding[:lo, 1], label='old',s=1)80plt.legend()81plt.xlabel('u1')82plt.ylabel('u2')83plt.title('umap plot for old data')84plt.savefig('umap plot for old data.png')85f = plt.figure()86plt.scatter(embedding[lo:, 0], embedding[lo:, 1], label='new',s=1)87plt.legend()88plt.xlabel('u1')89plt.ylabel('u2')90plt.title('umap plot for new data')91plt.savefig('umap plot for new data.png')92f = plt.figure()93plt.scatter(embedding[:lo, 0], embedding[:lo, 1], label='old',s=1)94plt.scatter(embedding[lo:, 0], embedding[lo:, 1], label='new',s=1)95plt.legend()96plt.xlabel('u1')97plt.ylabel('u2')98plt.title('umap plot for old data and new data')99plt.savefig('umap plot for old data and new data.png')100101102# ## Visualization103# 104105# In[12]:106107108def plot_scatter(old, new, dim1, dim2):109    f = plt.figure()110    plt.scatter(old[:, dim1], old[:, dim2], label='old',marker='x')#,s=10)111    plt.scatter(new[:, dim1], new[:, dim2], label='new',marker='.')#,s=5)112    plt.legend()113    plt.xlabel('dim {}'.format(dim1))114    plt.ylabel('dim {}'.format(dim2))115    plt.title('scatter plot of dim{},{} of old and new data'.format(dim1, dim2))116    plt.savefig('scatter plot of dim{},{} of old and new data.png'.format(dim1, dim2))117118119# In[15]:120121122for i in range(8):123    for j in range(8):124        if i == j:125            continue126        plot_scatter(old, new, i, j)127        plt.close('all')128129130# ## Pair-wise scatter plot131132# In[19]:133134135df_old = pd.DataFrame(old)136df_new = pd.DataFrame(new)137psm = pd.plotting.scatter_matrix(df_old, figsize=(15, 15), s=10)138139140# ## Find the same and plot spectra141142# In[38]:143144145i = 0146for i in range(len(old)):147    #print(old[i,:])148    new_minus = np.sum(np.square(new - old[i,:]),axis=1)149    #print(np.shape(new_minus))150    match = np.where(new_minus==0)151    #print(match)152    if np.shape(match)[1] != 0: #There is a match153        print('we found a match! new index {} and old index {} match'.format(match, i))154155156# In[39]:157158159print('old index ', old[11819,:])160print('new index ', new[5444,:])161162163# In[35]:164165166np.shape(match)167168169# ### Plot the matched spectra170171# In[6]:172173174y_old = pd.read_csv('data_y_old.csv',header=None,sep=' ')175176177# In[42]:178179180y_new = pd.read_csv('data_y_new.csv',header=None,sep=' ')181182183# In[7]:184185186y_old = y_old.values187y_new = y_new.values188189190# In[45]:191192193# plot the spectra194old_index = 11819195new_index = 5444196f = plt.figure()197plt.plot(y_old[old_index,:],label='old geometry {}'.format(old[old_index, :]))198plt.plot(y_new[new_index,:],label='new geometry {}'.format(new[new_index, :]))199plt.legend()200plt.ylabel('transmission')201plt.xlabel('THz')202plt.savefig('Spectra plot for identicle point')203204205# # Conclusion, this simulation is not the same as before ...206207# ### See what percentage are still within range208209# In[36]:210211212#print(old)213#print(new)214hmax = np.max(old[:,0])215hmin = np.min(old[:,1])216rmax = np.max(old[:,4])217rmin = np.min(old[:,4])218219print(hmax, hmin, rmax, rmin)220221#hmax = np.max(new[:,0])222#hmin = np.min(new[:,1])223#rmax = np.max(new[:,4])224#rmin = np.min(new[:,4])225226#print(hmax, hmin, rmax, rmin)227228within_range = np.ones([len(new)])229230new_minus = np.copy(new)231new_minus[:,:4] -= hmin232new_minus[:,4:] -= rmin233234new_plus = np.copy(new)235new_plus[:, :4] -= hmax236new_plus[:, 4:] -= rmax237238small_flag = np.min(new_minus, axis=1) < 0239big_flag = np.max(new_plus, axis=1) > 0240241within_range[small_flag] = 0242within_range[big_flag] = 0243244print(np.sum(within_range) / len(within_range))245print(type(within_range))246print(np.shape(within_range))247print(within_range)248print(new[np.arange(len(within_range))[within_range.astype('bool')],:])249print(np.sum(within_range))250251252# # Data augmentation253# ## Since the geometry is symmetric, we can augment the data with permutations254255# In[13]:256257258# Check the assumption that the permutation does indeed give you the same spectra259# Check if there is same spectra260i = 0261for i in range(len(y_old)):262    #print(old[i,:])263    new_minus = np.sum(np.square(y_old - y_old[i,:]),axis=1)264    #print(np.shape(new_minus))265    match = np.where(new_minus==0)266    #print(match)267    #print(np.shape(match))268    #print(len(match))269    #if match[0]270    if len(match) != 1:#np.shape(match)[1] != 0: #There is a match271        print('we found a match! new index {} and old index {} match'.format(match, i))272273274# ### Due to physical periodic boundary condition, we can augment the data by doing permutations275276# In[39]:277"""278279def permutate_periodicity(geometry_in, spectra_in):280    """281    :param: geometry_in: numpy array of geometry [n x 8] dim282    :param: spectra_in: spectra of the geometry_in [n x k] dim283    :return: output of the augmented geometry, spectra [4n x 8], [4n x k]284    """285    # Get the dimension parameters286    (n, k) = np.shape(spectra_in)287    # Initialize the output288    spectra_out = np.zeros([4*n, k])289    geometry_out = np.zeros([4*n, 8])290    291    #################################################292    # start permutation of geometry (case: 1 - 0123)#293    #################################################294    # case:2 -- 1032 295    geometry_c2 = geometry_in[:, [1,0,3,2,5,4,7,6]]296    # case:3 -- 2301297    geometry_c3 = geometry_in[:, [2,3,0,1,6,7,4,5]]298    # case:4 -- 3210299    geometry_c4 = geometry_in[:, [3,2,1,0,7,6,5,4]]300    301    geometry_out[0*n:1*n, :] = geometry_in302    geometry_out[1*n:2*n, :] = geometry_c2303    geometry_out[2*n:3*n, :] = geometry_c3304    geometry_out[3*n:4*n, :] = geometry_c4305    306    for i in range(4):307        spectra_out[i*n:(i+1)*n,:] = spectra_in308    return geometry_out, spectra_out309310311# In[40]:312data_folder = '/work/sr365/Christian_data/dataIn'313data_out_folder = '/work/sr365/Christian_data_augmented'314for file in os.listdir(data_folder):315    data = pd.read_csv(os.path.join(data_folder, file),header=None,sep=',').values316    (l, w) = np.shape(data)317    g = data[:,2:10]318    s = data[:,10:]319    g_aug, s_aug = permutate_periodicity(g, s)320    output = np.zeros([l*4, w])321    output[:, 2:10] = g_aug322    output[:, 10:] = s_aug323    np.savetxt(os.path.join(data_out_folder, file+'_augmented.csv'),output,delimiter=',')324325# In[41]:326327328#print(np.shape(g))329330331# In[ ]:332333334
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unicornosaurus.py

Source:unicornosaurus.py

1import numpy as np2import itertools3n_sections_broken, n_possible_fixes, IDK = list(map(int, input().split(' ')))4sections_broken = []5possible_fixes = []6power_construction = []7for _ in range(n_sections_broken):8    sections_broken.append(tuple(map(int, input().split(' '))))9for _ in range(n_possible_fixes):10    possible_fixes.append((list(map(int, input().split(' ')))))11within_range = []12for possible_fix in possible_fixes:13    for section in sections_broken:14        if possible_fix[0] <= section[0] or possible_fix[1] >= section[1]:15            within_range.append(possible_fix)16within_range = sorted(within_range)17for i in range(len(within_range)):18    for y in range(i, len(within_range)):19        try:20            if within_range[i][0] < within_range[y][0] and within_range[i][1] > within_range[y][1]:21                within_range.remove(within_range[y])22        except IndexError:23            continue24within_range = list(itertools.product(within_range, within_range))25within_range = list(dict.fromkeys(map(str, within_range)))26within_range = list(map(eval, within_range))27for i in range(len(within_range)):28    if within_range[i][0] == within_range[i][1]:29        within_range[i] = tuple(within_range[i][0])30    else:31        within_range[i] = min(within_range[i][0][0] , within_range[i][1][0]), max(within_range[i][0][1], within_range[i][1][1]), \32                           (within_range[i][0][2] + within_range[i][1][2])33within_range = sorted(within_range)34# print(within_range)35power_need = []36found_start = False37section_x = min(sections_broken)[0]38section_y = max(sections_broken)[1]39# print(section_x, section_y)40completed = False41for i in range(len(within_range)):42    if within_range[i][0] <= section_x and within_range[i][1] >= section_y:43        if found_start:44            power_need.pop()45        power_need.append(within_range[i][2])46        completed = True47        found_start = False48        break49    elif not found_start and within_range[i][0] <= section_x:50        power_need.append(within_range[i][2])51        found_start = True52    elif found_start and within_range[i][1] >= section_y:53        found_start = False54        completed = True55        break56if not completed:57    print(-1)58else:59    print(-1)60"""611 3 15625 10633 7 2646 12 5652 11 6662 4 15673 7689 10692 6 10703 9 15715 12 13728 10 30...

01-Milestone1.py

Source:01-Milestone1.py

1import os2os.system('cls')3def user_choice():4    #VARIABLES5    6    #Initial7    choice = 'Wrong'8    acceptable_values = range(0,10)9    within_range = False10    #TWO CONDITIONS TO CHECK11    # DIGIT OR WITHIN RANGE == FALSE12    while choice.isdigit() == False or within_range == False:13        choice = input("Please enter a number (0-10): ")14        #DIGIT CHECK15        if choice.isdigit() == False:16            print("Sorry, wrong value")17        #RANGE CHECK18        if choice.isdigit() == True:19            if int(choice) in acceptable_values:20                within_range = True21            else:22                print('Out of acceptable range (0-10)')23                within_range = False24    return int(choice)25def display(row1,row2,row3):26    print(row1)27    print(row2)28    print(row3)29row1 = [' ',' ',' ']30row2 = [' ',' ',' ']31row3 = [' ',' ',' ']32display(row1,row2,row3)33position_index = user_choice()...

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