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How to use alphabet method in hypothesis
Best Python code snippet using hypothesis
omniglot.py
Source:omniglot.py 
1import scipy.io2import numpy as np3import pickle4import os5import numpy.random as npr6from hypergrad.util import dictslice, RandomState7NUM_CHARS = 558NUM_ALPHABETS = 509NUM_EXAMPLES = 1510CURATED_ALPHABETS = [6, 10, 23, 38, 39, 8, 9, 21, 22, 41]11ROTATED_ALPHABETS = [6, 10, 23, 38, 39]12FLIPPED_ALPHABETS = [6, 10, 23, 38, 39]13def datapath(fname):14 #datadir = os.path.expanduser('../data') #Michael15 datadir = os.path.abspath('../../../data/omniglot') #Michael16 #/Users/Mike/Desktop/Vavasis/Hyperparameter_optimization/drmad-master_Mar5/cpu_ver/data 17 return os.path.join(datadir, fname)18def mat_to_pickle():19 data = scipy.io.loadmat(datapath('data_background_small1.mat'))20 images = data['data'].T.astype(np.float16) # Flattened images, (24345, 784) in range [0, 1]21 alphabet_labels = np.argmax(data['target'], axis=0) # (24345, ) ints representing alphabets22 char_labels = data['targetchar'][0, :] - 1 # (24345, ) ints representing characters23 with open(datapath("omniglot_data.pkl"), "w") as f:24 pickle.dump((images, alphabet_labels, char_labels), f, 1)25def load_data(alphabets_to_load=range(NUM_ALPHABETS)):26 one_hot = lambda x, K : np.array(x[:,None] == np.arange(K)[None, :], dtype=int)27 with open(datapath("omniglot_data.pkl")) as f:28 images, alphabet_labels, char_labels = pickle.load(f)29 # print np.min(char_labels), np.max(char_labels)30 # print np.min(alphabet_labels), np.max(alphabet_labels)31 char_labels = one_hot(char_labels, NUM_CHARS)32 alphabets = []33 for i_alphabet in alphabets_to_load:34 cur_alphabet_idxs = np.where(alphabet_labels == i_alphabet)35 alphabets.append({'X' : images[cur_alphabet_idxs],36 'T' : char_labels[cur_alphabet_idxs]})37 return alphabets38def load_data_split(num_chars, RS, num_alphabets=NUM_ALPHABETS):39 alphabets_to_load = RS.choice(range(NUM_ALPHABETS), size=num_alphabets, replace=False)40 raw_data = load_data(np.sort(alphabets_to_load))41 shuffled_data = [shuffle(alphabet, RS) for alphabet in raw_data]42 data_split = zip(*[split(alphabet, num_chars) for alphabet in shuffled_data])43 normalized_data = [subtract_mean(data_subset) for data_subset in data_split]44 return normalized_data45def load_curated_alphabets(num_chars, RS):46 raw_data = load_data(CURATED_ALPHABETS)47 shuffled_data = [shuffle(alphabet, RS) for alphabet in raw_data]48 data_split = zip(*[split(alphabet, num_chars) for alphabet in shuffled_data])49 normalized_data = [subtract_mean(data_subset) for data_subset in data_split]50 return normalized_data51# def load_rotated_alphabets(num_chars, RS):52# raw_data = load_data(ROTATED_ALPHABETS)53# shuffled_data = [shuffle(alphabet, RS) for alphabet in raw_data]54# rotated_data = [do_rotation(alphabet) for alphabet in shuffled_data]55# all_data = shuffled_data + rotated_data56# data_split = zip(*[split(alphabet, num_chars) for alphabet in all_data])57# normalized_data = [subtract_mean(data_subset) for data_subset in data_split]58# return normalized_data59def load_flipped_alphabets(RS, normalize=True):60 raw_data = load_data(FLIPPED_ALPHABETS)61 shuffled_data = [shuffle(alphabet, RS) for alphabet in raw_data]62 flipped_data = [do_flip(alphabet) for alphabet in shuffled_data]63 all_data = raw_data + flipped_data[::-1]64 if normalize:65 all_data = subtract_mean(all_data)66 return all_data67def load_rotated_alphabets(RS, normalize=True, angle=90):68 raw_data = load_data(ROTATED_ALPHABETS)69 shuffled_data = [shuffle(alphabet, RS) for alphabet in raw_data]70 rotated_data = [do_rotation(alphabet, angle=angle) for alphabet in shuffled_data]71 all_data = shuffled_data + rotated_data72 if normalize:73 all_data = subtract_mean(all_data)74 return all_data75def do_rotation(alphabet, angle):76 new_alphabet = alphabet.copy()77 old_X = alphabet['X']78 if angle == 90:79 orig_shape = old_X.shape80 new_alphabet['X'] = old_X.reshape((orig_shape[0], 28, 28))\81 .transpose([0, 2, 1])[:, :, ::-1].reshape(orig_shape)82 elif angle == 180:83 new_alphabet['X'] = old_X[:, ::-1]84 else:85 assert False, "Can't rotate by {0}".format(angle)86 return new_alphabet87def do_flip(alphabet):88 new_alphabet = alphabet.copy()89 orig_shape = alphabet['X'].shape90 num_dpts = orig_shape[0]91 new_alphabet['X'] = alphabet['X'].reshape((num_dpts, 28, 28))[:, ::-1, :].reshape(orig_shape)92 return new_alphabet93def split(alphabet, num_chars):94 cum_chars = np.cumsum(num_chars)95 def select_dataset(count):96 for i, N in enumerate(cum_chars):97 if count < N: return i98 labels = np.argmax(alphabet['T'], axis=1)99 label_counts = [0] * NUM_CHARS100 split_idxs = [[] for n in num_chars]101 for i_dpt, label in enumerate(labels):102 i_dataset = select_dataset(label_counts[label])103 split_idxs[i_dataset].append(i_dpt)104 label_counts[label] += 1105 data_splits = []106 for n, idxs in zip(num_chars, split_idxs):107 data_splits.append(dictslice(alphabet, idxs))108 totals = np.sum(data_splits[-1]['T'], axis=0)109 assert np.all(np.logical_or(totals == 0, totals == n))110 return data_splits111def random_partition(data, RS, num_chars):112 shuffled_data = [shuffle_rows(alphabet, RS) for alphabet in data]113 return zip(*[split(alphabet, num_chars) for alphabet in shuffled_data])114 115def shuffle(alphabet, RS):116 N_rows, N_cols = alphabet['T'].shape117 alphabet['T'] = alphabet['T'][:, RS.permutation(N_cols)]118 return dictslice(alphabet, RS.permutation(N_rows))119def shuffle_rows(alphabet, RS):120 N_rows, N_cols = alphabet['T'].shape121 return dictslice(alphabet, RS.permutation(N_rows))122def subtract_mean(alphabets):123 all_images = np.concatenate([alphabet['X'] for alphabet in alphabets], axis=0)124 assert np.all(all_images >= 0) and np.all(all_images <= 1)125 mean_img = np.mean(all_images, axis=0)126 for alphabet in alphabets:127 alphabet['X'] = alphabet['X'] - mean_img128 return alphabets129def show_alphabets(alphabets, ax=None, n_cols=20):130 import matplotlib as mpl131 import matplotlib.pyplot as plt132 from nn_utils import plot_images133 seed = 1134 n_rows = len(alphabets)135 full_image = np.zeros((0, n_cols * 28))136 for alphabet in alphabets:137 RS = RandomState(seed)138 char_idxs = RS.randint(alphabet['X'].shape[0], size=n_cols)139 char_ids = np.argmax(alphabet['T'][char_idxs], axis=1)140 image = alphabet['X'][char_idxs].reshape((n_cols, 28, 28))141 image = np.transpose(image, axes=[1, 0, 2]).reshape((28, n_cols * 28))142 full_image = np.concatenate((full_image, image))143 144 if ax is None:145 fig = plt.figure()146 fig.set_size_inches((8, 8 * n_rows/n_cols))147 ax = fig.add_subplot(111)148 ax.imshow(full_image, cmap=mpl.cm.binary)149 ax.set_xticks(np.array([]))150 ax.set_yticks(np.array([]))151 plt.tight_layout()152 plt.savefig("all_alphabets.png")153def show_all_alphabets(perm=None, rotate=False):154 show_alphabets(load_data)155if __name__ == "__main__":...
1. Поддержка интерфейса .py
Source:1. Поддержка интерфейса .py
1class SequenceIter:2 3 def __init__(self, seq):4 self.index = 05 self.seq = seq6 def __next__(self):7 if self.index >= len(self.seq):8 raise StopIteration9 symb = self.seq[self.index]10 self.index += 111 return symb12class Sequence:13 def __init__(self, name, seq):14 self.name = name15 self.seq = seq16 def __iter__(self):17 return SequenceIter(self.seq)18 def name ( self ) :19 return self.name20 def seq ( self ) :21 return self.seq22 def length (self) :23 length=len(seq)24 return length25class DNA(Sequence):26 name='DNA'27 alphabet=['A','C','G','T']28 def __init__(self,seq,):29 super().__init__(Sequence,seq)30 def statistic(self):31 for i in range(0,4):32 stat = self.seq.count(self.alphabet[i])33 print(alphabet[i],' - ',stat)34 def Mol(self):35 m=347 * self.seq.count(self.alphabet[0]) +\36 320.2 * self.seq.count(self.alphabet[1]) +\37 363.2 * self.seq.count(self.alphabet[2]) +\38 323.2 * self.seq.count(self.alphabet[3])39 print('ÐолекÑлÑÑÐ½Ð°Ñ Ð¼Ð°ÑÑа - ',round(m,2))40 def complementary(self):41 comp = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C'}42 comp_seq = ''43 for i in self.seq:44 comp_seq += comp[i]45 return comp_seq46 print('ÐомплеменÑаÑÐ½Ð°Ñ Ð¿Ð¾ÑледоваÑелÑноÑÑÑ - ', comp_seq)47 48 def Transcription(self):49 tran = []50 for i in range(0, len(self.seq)):51 if self.seq[i] == 'A':52 tran.append('U')53 elif self.seq[i] == 'T':54 tran.append('A')55 elif self.seq[i] == 'G':56 tran.append('C')57 elif self.seq[i] == 'C':58 tran.append('G')59 print('ТÑанÑкÑипÑÐ¸Ñ - ', tran)60class RNA(Sequence):61 name = 'RNA'62 alphabet = ['A', 'C', 'G', 'U']63 def __init__(self,seq,):64 super().__init__(Sequence,seq)65 def statistic(self):66 for i in range(0,4):67 stat = self.seq.count(alphabet[i])68 print(alphabet[i],' - ',stat)69 def Mol(self):70 m = 347 * self.seq.count(self.alphabet[0]) + 324.2 * self.seq.count(self.alphabet[1]) + 363.2 * self.seq.count(self.alphabet[2]) + 323.2 * self.seq.count(self.alphabet[3])71 print('ÐолекÑлÑÑÐ½Ð°Ñ Ð¼Ð°ÑÑа - ', round(m,2))72 def Translation(self):73 d = {'UUU': 'F', 'UUC': 'F','UUA':'L','UUG':'L','CUU':'L',74 'CUC': 'L','CUA':'L', 'CUG':'L', 'AUU':'I','AUC':'I',75 'AUA': 'I','AUG':'M','GUU':'V','GUC':'V','GUA':'V','GUG':'V',76 'UCU': 'S','UCA': 'S','UCC': 'S','UCG': 'S','CCU': 'P','CCC': 'P',77 'CCA': 'P','CCG': 'P','ACU': 'T','ACC': 'T','ACA': 'T','ACG': 'T',78 'GCU': 'A','GCC': 'A','GCA': 'A','GCG': 'A', 'UAU': 'Y','UAC': 'Y',79 'CAU': 'H','CAC': 'H','CAA': 'Q','CAG': 'Q','AAU': 'N', 'AAC': 'N',80 'AAA': 'K','AAG': 'K','GAU': 'D','GAC': 'D','GAA': 'E','GAG': 'E',81 'UGU': 'C','UGC': 'C','UGG': 'W','CGU': 'R','CGC': 'R','CGA': 'R',82 'CGG': 'R','AGU': 'S','AGC': 'S','AGA': 'R','AGG': 'R','GGU': 'G',83 'GGC': 'G','GGA': 'G','GGG': 'G','UAA': 'STOP','UAG': 'STOP',84 'UGA': 'STOP'}85 protein = []86 start = 087 for i in range(len(self.seq)):88 if (self.seq[i:i + 3] == "AUG"):89 start = i90 for j in range(start, len(self.seq) - 2, 3):91 amin = d[self.seq[j:j + 3]]92 if (cods == "STOP"):93 break94 else:95 protein.append(amin)96 print('ÐÐµÐ»ÐºÐ¾Ð²Ð°Ñ Ð¿Ð¾ÑледоваÑелÑноÑÑÑ - ', protein)97class Protein(Sequence):98 name = 'Protein'99 alphabet = ['A', 'R', 'N', 'D', 'V', 'H', 'G', 'Q', 'E', 'I', 'L',100 'K', 'M', 'P', 'S', 'Y', 'T', 'W', 'F', 'C']101 def __init__(self,seq,):102 super().__init__(Sequence,seq)103 def statistic(self):104 for i in range(0,20):105 stat = self.seq.count(alphabet[i])106 print(alphabet[i],' - ',stat)107 def Mol(self):108 m=75 * self.seq.count(self.alphabet[0]) + 89 * self.seq.count(self.alphabet[1]) +\109 117.2 * self.seq.count(self.alphabet[2]) + 131.2 * self.seq.count(self.alphabet[3]) +\110 131.2 * self.seq.count(self.alphabet[4]) + 149.2 * self.seq.count(self.alphabet[5]) +\111 115.1 * self.seq.count(self.alphabet[6]) + 165.2 * self.seq.count(self.alphabet[7]) +\112 204.2 * self.seq.count(self.alphabet[8]) + 105.9 * self.seq.count(self.alphabet[9]) +\113 119 * self.seq.count(self.alphabet[10]) + 132 * self.seq.count(self.alphabet[11]) +\114 146 * self.seq.count(self.alphabet[12]) + 181.2 * self.seq.count(self.alphabet[13]) +\115 121.2 * self.seq.count(self.alphabet[14]) + 146.2 * self.seq.count(self.alphabet[15]) +\116 174.2 * self.seq.count(self.alphabet[16]) + 155.2 * self.seq.count(self.alphabet[17]) +\117 131 * self.seq.count(self.alphabet[18]) + 147 * self.seq.count(self.alphabet[19])...
Классы .py
Source:Классы .py
1class Sequence:2 3 def __init__(self, name, seq):4 self.name = name5 self.seq = seq6 7 def name ( self ) :8 return self.name9 10 def seq ( self ) :11 return self.seq12 13 def length (self) :14 length=len(seq)15 return length16class DNA(Sequence):17 name='DNA'18 alphabet=['A','C','G','T']19 def __init__(self,seq,):20 super().__init__(Sequence,seq)21 def statistic(self):22 for i in range(0,4):23 stat = self.seq.count(self.alphabet[i])24 print(alphabet[i],' - ',stat)25 def Mol(self):26 m = 347 * self.seq.count(self.alphabet[0]) +\27 323.2 * self.seq.count(self.alphabet[1]) +\28 363.2 * self.seq.count(self.alphabet[2]) +\29 320.2 * self.seq.count(self.alphabet[3])30 print('ÐолекÑлÑÑÐ½Ð°Ñ Ð¼Ð°ÑÑа - ',round(m,2))31 def complementary(self):32 comp = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A',}33 comp_seq = ''34 for i in self.seq:35 comp_seq += comp[i]36 return comp_seq37 print('ÐомплеменÑаÑÐ½Ð°Ñ Ð¿Ð¾ÑледоваÑелÑноÑÑÑ - ', comp_seq)38 39 def Transcription(self):40 tran = []41 for i in range(0, len(self.seq)):42 if self.seq[i] == 'A':43 tran.append('U')44 elif self.seq[i] == 'C':45 tran.append('G')46 elif self.seq[i] == 'G':47 tran.append('C')48 elif self.seq[i] == 'T':49 tran.append('A')50 print('ТÑанÑкÑипÑÐ¸Ñ - ', tran)51class RNA(Sequence):52 name = 'RNA'53 alphabet = ['A', 'C', 'G', 'U']54 def __init__(self,seq,):55 super().__init__(Sequence,seq)56 def statistic(self):57 for i in range(0,4):58 stat = self.seq.count(alphabet[i])59 print(alphabet[i],' - ',stat)60 def Mol(self):61 m = 347 * self.seq.count(self.alphabet[0]) + 323.2 * self.seq.count(self.alphabet[1]) +\62 363.2 * self.seq.count(self.alphabet[2]) + 324.2 * self.seq.count(self.alphabet[3])63 print('ÐолекÑлÑÑÐ½Ð°Ñ Ð¼Ð°ÑÑа - ', round(m,2))64 def Translation(self):65 d = {'UUU': 'F', 'UUC': 'F','UUA':'L','UUG':'L','CUU':'L',66 'CUC': 'L','CUA':'L', 'CUG':'L', 'AUU':'I','AUC':'I',67 'AUA': 'I','AUG':'M','GUU':'V','GUC':'V','GUA':'V','GUG':'V',68 'UCU': 'S','UCA': 'S','UCC': 'S','UCG': 'S','CCU': 'P','CCC': 'P',69 'CCA': 'P','CCG': 'P','ACU': 'T','ACC': 'T','ACA': 'T','ACG': 'T',70 'GCU': 'A','GCC': 'A','GCA': 'A','GCG': 'A', 'UAU': 'Y','UAC': 'Y',71 'CAU': 'H','CAC': 'H','CAA': 'Q','CAG': 'Q','AAU': 'N', 'AAC': 'N',72 'AAA': 'K','AAG': 'K','GAU': 'D','GAC': 'D','GAA': 'E','GAG': 'E',73 'UGU': 'C','UGC': 'C','UGG': 'W','CGU': 'R','CGC': 'R','CGA': 'R',74 'CGG': 'R','AGU': 'S','AGC': 'S','AGA': 'R','AGG': 'R','GGU': 'G',75 'GGC': 'G','GGA': 'G','GGG': 'G','UAA': 'STOP','UAG': 'STOP',76 'UGA': 'STOP'}77 protein = []78 start = 079 for i in range(len(self.seq)):80 if (self.seq[i:i + 3] == "AUG"):81 start = i82 for j in range(start, len(self.seq) - 2, 3):83 amin = d[self.seq[j:j + 3]]84 if (cods == "STOP"):85 break86 else:87 protein.append(amin)88 print('ÐÐµÐ»ÐºÐ¾Ð²Ð°Ñ Ð¿Ð¾ÑледоваÑелÑноÑÑÑ - ', protein)89class Protein(Sequence):90 name = 'Protein'91 alphabet = ['A', 'R', 'N', 'D', 'V', 'H', 'G', 'Q', 'E', 'I', 'L',92 'K', 'M', 'P', 'S', 'Y', 'T', 'W', 'F', 'C']93 def __init__(self,seq,):94 super().__init__(Sequence,seq)95 def statistic(self):96 for i in range(0,20):97 stat = self.seq.count(alphabet[i])98 print(alphabet[i],' - ',stat)99 def Mol(self):100 m = 89.1 * self.seq.count(self.alphabet[0]) + 174.2 * self.seq.count(self.alphabet[1]) +\101 132.1 * self.seq.count(self.alphabet[2]) + 133.1 * self.seq.count(self.alphabet[3]) +\102 117.2 * self.seq.count(self.alphabet[4]) + 155.2 * self.seq.count(self.alphabet[5]) +\103 75.1 * self.seq.count(self.alphabet[6]) + 146.1 * self.seq.count(self.alphabet[7]) +\104 147.1 * self.seq.count(self.alphabet[8]) + 131.2 * self.seq.count(self.alphabet[9]) +\105 131.2 * self.seq.count(self.alphabet[10]) + 146.1 * self.seq.count(self.alphabet[11]) +\106 149.2 * self.seq.count(self.alphabet[12]) + 115.1 * self.seq.count(self.alphabet[13]) +\107 105.1 * self.seq.count(self.alphabet[14]) + 181.2 * self.seq.count(self.alphabet[15]) +\108 119.1 * self.seq.count(self.alphabet[16]) + 204.2 * self.seq.count(self.alphabet[17]) +\109 165.2 * self.seq.count(self.alphabet[18]) + 121.2 * self.seq.count(self.alphabet[19])...
dataTables.alphabetSearch.js
Source:dataTables.alphabetSearch.js
1/*! AlphabetSearch for DataTables v1.0.02 * 2014 SpryMedia Ltd - datatables.net/license3 */4/**5 * @summary AlphabetSearch6 * @description Show an set of alphabet buttons alongside a table providing7 * search input options8 * @version 1.0.09 * @file dataTables.alphabetSearch.js10 * @author SpryMedia Ltd (www.sprymedia.co.uk)11 * @contact www.sprymedia.co.uk/contact12 * @copyright Copyright 2014 SpryMedia Ltd.13 * 14 * License MIT - http://datatables.net/license/mit15 *16 * For more detailed information please see:17 * http://datatables.net/blog/2014-09-2218 */19(function(){20// Search function21$.fn.dataTable.Api.register( 'alphabetSearch()', function ( searchTerm ) {22 this.iterator( 'table', function ( context ) {23 context.alphabetSearch = searchTerm;24 } );25 return this;26} );27// Recalculate the alphabet display for updated data28$.fn.dataTable.Api.register( 'alphabetSearch.recalc()', function ( searchTerm ) {29 this.iterator( 'table', function ( context ) {30 draw(31 new $.fn.dataTable.Api( context ),32 $('div.alphabet', this.table().container())33 );34 } );35 return this;36} );37// Search plug-in38$.fn.dataTable.ext.search.push( function ( context, searchData ) {39 // Ensure that there is a search applied to this table before running it40 if ( ! context.alphabetSearch ) {41 return true;42 }43 if ( searchData[0].charAt(0) === context.alphabetSearch ) {44 return true;45 }46 return false;47} );48// Private support methods49function bin ( data ) {50 var letter, bins = {};51 for ( var i=0, ien=data.length ; i<ien ; i++ ) {52 letter = data[i].charAt(0).toUpperCase();53 if ( bins[letter] ) {54 bins[letter]++;55 }56 else {57 bins[letter] = 1;58 }59 }60 return bins;61}62function draw ( table, alphabet )63{64 alphabet.empty();65 alphabet.append( 'Search: ' );66 var columnData = table.column(0).data();67 var bins = bin( columnData );68 $('<span class="clear active"/>')69 .data( 'letter', '' )70 .data( 'match-count', columnData.length )71 .html( 'None' )72 .appendTo( alphabet );73 for ( var i=0 ; i<26 ; i++ ) {74 var letter = String.fromCharCode( 65 + i );75 $('<span/>')76 .data( 'letter', letter )77 .data( 'match-count', bins[letter] || 0 )78 .addClass( ! bins[letter] ? 'empty' : '' )79 .html( letter )80 .appendTo( alphabet );81 }82 $('<div class="alphabetInfo"></div>')83 .appendTo( alphabet );84}85$.fn.dataTable.AlphabetSearch = function ( context ) {86 var table = new $.fn.dataTable.Api( context );87 var alphabet = $('<div class="alphabet"/>');88 draw( table, alphabet );89 // Trigger a search90 alphabet.on( 'click', 'span', function () {91 alphabet.find( '.active' ).removeClass( 'active' );92 $(this).addClass( 'active' );93 table94 .alphabetSearch( $(this).data('letter') )95 .draw();96 } );97 // Mouse events to show helper information98 alphabet99 .on( 'mouseenter', 'span', function () {100 alphabet101 .find('div.alphabetInfo')102 .css( {103 opacity: 1,104 left: $(this).position().left,105 width: $(this).width()106 } )107 .html( $(this).data('match-count') );108 } )109 .on( 'mouseleave', 'span', function () {110 alphabet111 .find('div.alphabetInfo')112 .css('opacity', 0);113 } );114 // API method to get the alphabet container node115 this.node = function () {116 return alphabet;117 };118};119$.fn.DataTable.AlphabetSearch = $.fn.dataTable.AlphabetSearch;120// Register a search plug-in121$.fn.dataTable.ext.feature.push( {122 fnInit: function ( settings ) {123 var search = new $.fn.dataTable.AlphabetSearch( settings );124 return search.node();125 },126 cFeature: 'A'127} );...
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