paulo037/starcoder · Datasets at Hugging Face

max_stars_repo_path stringlengths 4 237	max_stars_repo_name stringlengths 6 117	max_stars_count int64 0 95.2k	id stringlengths 1 7	content stringlengths 12 593k	input_ids sequencelengths 7 549k
Formatting_Strings/What_day_is_today?.py	RKiddle/python_reg_expressions	0	164755	<reponame>RKiddle/python_reg_expressions<filename>Formatting_Strings/What_day_is_today?.py # Import datetime from datetime import datetime # Assign date to get_date get_date = datetime.now() # Add named placeholders with format specifiers message = "Good morning. Today is {today:%B %d, %Y}. It's {today:%H:%M} ... time to work!" # Format date print(message.format(today=get_date))	[ 1, 529, 276, 1112, 420, 29958, 29934, 29968, 2632, 29914, 4691, 29918, 1727, 29918, 17073, 1080, 29966, 9507, 29958, 2500, 23980, 29918, 26545, 29914, 5618, 29918, 3250, 29918, 275, 29918, 27765, 9808, 2272, 13, 29937, 16032, 12865, 29871, 13, 3166, 12865, 1053, 12865, 13, 13, 29937, 4007, 647, 2635, 304, 679, 29918, 1256, 13, 657, 29918, 1256, 353, 12865, 29889, 3707, 580, 13, 13, 29937, 3462, 4257, 2058, 8948, 414, 411, 3402, 1580, 14903, 13, 4906, 353, 376, 18420, 7250, 29889, 20628, 338, 426, 27765, 16664, 29933, 1273, 29881, 29892, 1273, 29979, 1836, 739, 29915, 29879, 426, 27765, 16664, 29950, 16664, 29924, 29913, 2023, 931, 304, 664, 3850, 13, 13, 29937, 19191, 2635, 13, 2158, 29898, 4906, 29889, 4830, 29898, 27765, 29922, 657, 29918, 1256, 876, 13, 2 ]
ripiu/djangocms_aoxomoxoa/admin/options/grid_panel.py	ripiu/djangocms_aoxomoxoa	0	44283	<filename>ripiu/djangocms_aoxomoxoa/admin/options/grid_panel.py from django.contrib import admin from django.utils.translation import ugettext_lazy as _ class GridPanelUniteOptionsAdmin(admin.ModelAdmin): ''' Grid theme ''' fieldsets = ( (_('Strip panel options'), { 'classes': ('collapse',), 'fields': ( 'gridpanel_vertical_scroll', 'gridpanel_grid_align', ( 'gridpanel_padding_border_top', 'gridpanel_padding_border_bottom', ), ( 'gridpanel_padding_border_left', 'gridpanel_padding_border_right', ), 'gridpanel_arrows_skin', ('gridpanel_arrows_align_vert', 'gridpanel_arrows_align_hor',), ( 'gridpanel_arrows_padding_vert', 'gridpanel_arrows_padding_hor', ), 'gridpanel_space_between_arrows', 'gridpanel_arrows_always_on', 'gridpanel_enable_handle', 'gridpanel_handle_align', 'gridpanel_handle_offset', 'gridpanel_handle_skin', 'gridpanel_background_color', 'grid_panes_direction', 'grid_num_cols', 'grid_space_between_cols', 'grid_space_between_rows', 'grid_transition_duration', 'grid_transition_easing', 'grid_carousel', ) }), )	[ 1, 529, 9507, 29958, 374, 1631, 29884, 29914, 19776, 574, 542, 1516, 29918, 29874, 2251, 290, 2251, 19807, 29914, 6406, 29914, 6768, 29914, 7720, 29918, 15119, 29889, 2272, 13, 3166, 9557, 29889, 21570, 1053, 4113, 13, 3166, 9557, 29889, 13239, 29889, 3286, 18411, 1053, 318, 657, 726, 29918, 433, 1537, 408, 903, 13, 13, 13, 1990, 11657, 7490, 2525, 568, 5856, 12754, 29898, 6406, 29889, 3195, 12754, 1125, 13, 1678, 14550, 13, 1678, 11657, 10929, 13, 1678, 14550, 13, 1678, 1746, 7224, 353, 313, 13, 4706, 9423, 877, 855, 6472, 9451, 3987, 5477, 426, 13, 9651, 525, 13203, 2396, 6702, 27756, 742, 511, 13, 9651, 525, 9621, 2396, 313, 13, 18884, 525, 7720, 15119, 29918, 18575, 29918, 10510, 742, 13, 18884, 525, 7720, 15119, 29918, 7720, 29918, 2520, 742, 13, 18884, 313, 13, 462, 1678, 525, 7720, 15119, 29918, 12791, 29918, 11466, 29918, 3332, 742, 13, 462, 1678, 525, 7720, 15119, 29918, 12791, 29918, 11466, 29918, 8968, 742, 13, 18884, 10353, 13, 18884, 313, 13, 462, 1678, 525, 7720, 15119, 29918, 12791, 29918, 11466, 29918, 1563, 742, 13, 462, 1678, 525, 7720, 15119, 29918, 12791, 29918, 11466, 29918, 1266, 742, 13, 18884, 10353, 13, 13, 18884, 525, 7720, 15119, 29918, 2936, 29879, 29918, 808, 262, 742, 13, 18884, 6702, 7720, 15119, 29918, 2936, 29879, 29918, 2520, 29918, 1765, 742, 525, 7720, 15119, 29918, 2936, 29879, 29918, 2520, 29918, 2015, 742, 511, 13, 18884, 313, 13, 462, 1678, 525, 7720, 15119, 29918, 2936, 29879, 29918, 12791, 29918, 1765, 742, 13, 462, 1678, 525, 7720, 15119, 29918, 2936, 29879, 29918, 12791, 29918, 2015, 742, 13, 18884, 10353, 13, 13, 18884, 525, 7720, 15119, 29918, 3493, 29918, 14811, 29918, 2936, 29879, 742, 13, 18884, 525, 7720, 15119, 29918, 2936, 29879, 29918, 21936, 29918, 265, 742, 13, 13, 18884, 525, 7720, 15119, 29918, 12007, 29918, 8411, 742, 13, 18884, 525, 7720, 15119, 29918, 8411, 29918, 2520, 742, 13, 18884, 525, 7720, 15119, 29918, 8411, 29918, 10289, 742, 13, 18884, 525, 7720, 15119, 29918, 8411, 29918, 808, 262, 742, 13, 13, 18884, 525, 7720, 15119, 29918, 7042, 29918, 2780, 742, 13, 13, 18884, 525, 7720, 29918, 8357, 267, 29918, 20845, 742, 13, 18884, 525, 7720, 29918, 1949, 29918, 22724, 742, 13, 18884, 525, 7720, 29918, 3493, 29918, 14811, 29918, 22724, 742, 13, 18884, 525, 7720, 29918, 3493, 29918, 14811, 29918, 5727, 742, 13, 18884, 525, 7720, 29918, 20543, 29918, 19708, 742, 13, 18884, 525, 7720, 29918, 20543, 29918, 29872, 5832, 742, 13, 18884, 525, 7720, 29918, 4287, 21299, 742, 13, 9651, 1723, 13, 4706, 500, 511, 13, 1678, 1723, 13, 2 ]
p_030_039/problem31.py	ericgreveson/projecteuler	0	3235	<gh_stars>0 class CoinArray(list): """ Coin list that is hashable for storage in sets The 8 entries are [1p count, 2p count, 5p count, ... , 200p count] """ def __hash__(self): """ Hash this as a string """ return hash(" ".join([str(i) for i in self])) def main(): """ Entry point """ # Important: sorted smallest to largest coins = [1, 2, 5, 10, 20, 50, 100, 200] coin_index = {coin: index for index, coin in enumerate(coins)} # How many ways are there of making each number from 1 to 200 from these values? # Building up from 1 means we can re-use earlier results # e.g.: # 1p: [{1}] # 2p: [{1,1}, {2}] # 3p: [{1,1,1}, {2,1}] # 4p: [{1,1,1,1}, {2,1,1}, {2,2}] # etc way_sets = [None] for i in range(1, 201): way_set_i = set() # Try using 1 of each coin and then all the ways of the remainder, if > 0 for coin in coins: remainder = i - coin if remainder == 0: # We can make this with exactly this coin alone - but no larger coins coin_count = [0 for i in coins] coin_count[coin_index[coin]] = 1 way_set_i.add(CoinArray(coin_count)) break elif remainder > 0: # We can use this coin and whatever the options for the smaller value are for rem_list in way_sets[remainder]: new_coin_count = [c for c in rem_list] new_coin_count[coin_index[coin]] += 1 way_set_i.add(CoinArray(new_coin_count)) else: # Can't use any bigger coins break way_sets.append(way_set_i) print(f"Number of ways of making £2: {len(way_sets[200])}") return if __name__ == "__main__": main()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 1990, 3189, 262, 2588, 29898, 1761, 1125, 13, 1678, 9995, 13, 1678, 3189, 262, 1051, 393, 338, 6608, 519, 363, 8635, 297, 6166, 13, 1678, 450, 29871, 29947, 9976, 526, 518, 29896, 29886, 2302, 29892, 29871, 29906, 29886, 2302, 29892, 29871, 29945, 29886, 2302, 29892, 2023, 1919, 29871, 29906, 29900, 29900, 29886, 2302, 29962, 13, 1678, 9995, 13, 1678, 822, 4770, 8568, 12035, 1311, 1125, 13, 4706, 9995, 13, 4706, 11874, 445, 408, 263, 1347, 13, 4706, 9995, 13, 4706, 736, 6608, 703, 11393, 7122, 4197, 710, 29898, 29875, 29897, 363, 474, 297, 1583, 12622, 13, 13, 1753, 1667, 7295, 13, 1678, 9995, 13, 1678, 28236, 1298, 13, 1678, 9995, 13, 1678, 396, 16032, 424, 29901, 12705, 19087, 304, 10150, 13, 1678, 1302, 1144, 353, 518, 29896, 29892, 29871, 29906, 29892, 29871, 29945, 29892, 29871, 29896, 29900, 29892, 29871, 29906, 29900, 29892, 29871, 29945, 29900, 29892, 29871, 29896, 29900, 29900, 29892, 29871, 29906, 29900, 29900, 29962, 13, 1678, 19480, 29918, 2248, 353, 426, 1111, 262, 29901, 2380, 363, 2380, 29892, 19480, 297, 26985, 29898, 1111, 1144, 2915, 13, 268, 13, 1678, 396, 1128, 1784, 5837, 526, 727, 310, 3907, 1269, 1353, 515, 29871, 29896, 304, 29871, 29906, 29900, 29900, 515, 1438, 1819, 29973, 13, 1678, 396, 17166, 701, 515, 29871, 29896, 2794, 591, 508, 337, 29899, 1509, 8859, 2582, 13, 1678, 396, 321, 29889, 29887, 4898, 13, 1678, 396, 29871, 29896, 29886, 29901, 15974, 29896, 6525, 13, 1678, 396, 29871, 29906, 29886, 29901, 15974, 29896, 29892, 29896, 1118, 426, 29906, 6525, 13, 1678, 396, 29871, 29941, 29886, 29901, 15974, 29896, 29892, 29896, 29892, 29896, 1118, 426, 29906, 29892, 29896, 6525, 13, 1678, 396, 29871, 29946, 29886, 29901, 15974, 29896, 29892, 29896, 29892, 29896, 29892, 29896, 1118, 426, 29906, 29892, 29896, 29892, 29896, 1118, 426, 29906, 29892, 29906, 6525, 13, 1678, 396, 2992, 13, 1678, 982, 29918, 7224, 353, 518, 8516, 29962, 13, 1678, 363, 474, 297, 3464, 29898, 29896, 29892, 29871, 29906, 29900, 29896, 1125, 13, 4706, 982, 29918, 842, 29918, 29875, 353, 731, 580, 13, 13, 4706, 396, 3967, 773, 29871, 29896, 310, 1269, 19480, 322, 769, 599, 278, 5837, 310, 278, 21162, 29892, 565, 1405, 29871, 29900, 13, 4706, 363, 19480, 297, 1302, 1144, 29901, 13, 9651, 21162, 353, 474, 448, 19480, 13, 13, 9651, 565, 21162, 1275, 29871, 29900, 29901, 13, 18884, 396, 1334, 508, 1207, 445, 411, 3721, 445, 19480, 7432, 448, 541, 694, 7200, 1302, 1144, 13, 18884, 19480, 29918, 2798, 353, 518, 29900, 363, 474, 297, 1302, 1144, 29962, 13, 18884, 19480, 29918, 2798, 29961, 1111, 262, 29918, 2248, 29961, 1111, 262, 5262, 353, 29871, 29896, 13, 18884, 982, 29918, 842, 29918, 29875, 29889, 1202, 29898, 7967, 262, 2588, 29898, 1111, 262, 29918, 2798, 876, 13, 18884, 2867, 13, 9651, 25342, 21162, 1405, 29871, 29900, 29901, 13, 18884, 396, 1334, 508, 671, 445, 19480, 322, 6514, 278, 3987, 363, 278, 7968, 995, 526, 13, 18884, 363, 1083, 29918, 1761, 297, 982, 29918, 7224, 29961, 1745, 475, 672, 5387, 13, 462, 1678, 716, 29918, 1111, 262, 29918, 2798, 353, 518, 29883, 363, 274, 297, 1083, 29918, 1761, 29962, 13, 462, 1678, 716, 29918, 1111, 262, 29918, 2798, 29961, 1111, 262, 29918, 2248, 29961, 1111, 262, 5262, 4619, 29871, 29896, 13, 462, 1678, 982, 29918, 842, 29918, 29875, 29889, 1202, 29898, 7967, 262, 2588, 29898, 1482, 29918, 1111, 262, 29918, 2798, 876, 13, 9651, 1683, 29901, 13, 18884, 396, 1815, 29915, 29873, 671, 738, 16600, 1302, 1144, 13, 18884, 2867, 13, 13, 4706, 982, 29918, 7224, 29889, 4397, 29898, 1582, 29918, 842, 29918, 29875, 29897, 13, 13, 1678, 1596, 29898, 29888, 29908, 4557, 310, 5837, 310, 3907, 15151, 29906, 29901, 426, 2435, 29898, 1582, 29918, 7224, 29961, 29906, 29900, 29900, 2314, 27195, 13, 1678, 736, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1667, 580, 13, 2 ]
scanpy/tools/paga.py	LuckyMD/scanpy	0	31033	from collections import namedtuple import numpy as np import scipy as sp from scipy.sparse.csgraph import minimum_spanning_tree from .. import logging as logg from ..neighbors import Neighbors from .. import utils from .. import settings def paga( adata, groups='louvain', use_rna_velocity=False, copy=False): """\ Generate cellular maps of differentiation manifolds with complex topologies [Wolf17i]_. Partition-based graph abstraction (PAGA) quantifies the connectivities of partitions of a neighborhood graph of single cells, thereby generating a much simpler abstracted graph whose nodes label the partitions. Together with a random walk-based distance measure, this generates a partial coordinatization of data useful for exploring and explaining its variation. Parameters ---------- adata : :class:`~scanpy.api.AnnData` Annotated data matrix. groups : categorical annotation of observations or 'louvain_groups', optional (default: 'louvain_groups') Criterion to determine the resulting partitions of the single-cell graph. 'louvain_groups' uses the Louvain algorithm and optimizes modularity of the graph. You can also pass your predefined groups by choosing any categorical annotation of observations (`adata.obs`). use_rna_velocity : `bool` (default: `False`) Use RNA velocity to orient edges in the abstracted graph and estimate transitions. copy : `bool`, optional (default: `False`) Copy `adata` before computation and return a copy. Otherwise, perform computation inplace and return `None`. Returns ------- Returns or updates `adata` depending on `copy` with connectivities : np.ndarray (adata.uns['connectivities']) The full adjacency matrix of the abstracted graph, weights correspond to connectivities. confidence : np.ndarray (adata.uns['confidence']) The full adjacency matrix of the abstracted graph, weights correspond to confidence in the presence of an edge. confidence_tree : sc.sparse csr matrix (adata.uns['confidence_tree']) The adjacency matrix of the tree-like subgraph that best explains the topology. """ if 'neighbors' not in adata.uns: raise ValueError( 'You need to run `pp.neighbors` first to compute a neighborhood graph.') adata = adata.copy() if copy else adata utils.sanitize_anndata(adata) logg.info('running partition-based graph abstraction (PAGA)', reset=True) paga = PAGA(adata, groups, use_rna_velocity=use_rna_velocity) paga.compute() # only add if not present if 'paga' not in adata.uns: adata.uns['paga'] = {} if not use_rna_velocity: adata.uns['paga']['connectivities'] = paga.connectivities_coarse adata.uns['paga']['confidence'] = paga.confidence adata.uns['paga']['confidence_tree'] = paga.confidence_tree adata.uns[groups + '_sizes'] = np.array(paga.vc.sizes()) else: adata.uns['paga']['transitions_confidence'] = paga.transitions_confidence adata.uns['paga']['transitions_ttest'] = paga.transitions_ttest adata.uns['paga']['groups'] = groups logg.info(' finished', time=True, end=' ' if settings.verbosity > 2 else '\n') if use_rna_velocity: logg.hint( 'added\n' ' \'paga/transitions_confidence\', confidence adjacency (adata.uns)\n' ' \'paga/transitions_ttest\', confidence subtree (adata.uns)') else: logg.hint( 'added\n' ' \'paga/connectivities\', connectivities adjacency (adata.uns)\n' ' \'paga/confidence\', confidence adjacency (adata.uns)\n' ' \'paga/confidence_tree\', confidence subtree (adata.uns)') return adata if copy else None class PAGA(Neighbors): def __init__(self, adata, groups, use_rna_velocity=False, tree_based_confidence=False): super(PAGA, self).__init__(adata) self._groups = groups self._tree_based_confidence = tree_based_confidence self._use_rna_velocity = use_rna_velocity def compute(self): if self._use_rna_velocity: self.compute_transitions_coarse() else: self.compute_connectivities_coarse() self.compute_confidence() def compute_connectivities_coarse(self): import igraph ones = self.connectivities.copy() # graph where edges carry weight 1 ones.data = np.ones(len(ones.data)) g = utils.get_igraph_from_adjacency(ones) self.vc = igraph.VertexClustering( g, membership=self._adata.obs[self._groups].cat.codes.values) cg = self.vc.cluster_graph(combine_edges='sum') self.connectivities_coarse = utils.get_sparse_from_igraph(cg, weight_attr='weight')/2 def compute_confidence(self): """Translates the connectivities_coarse measure into a confidence measure. """ pseudo_distance = self.connectivities_coarse.copy() pseudo_distance.data = 1./pseudo_distance.data connectivities_coarse_tree = minimum_spanning_tree(pseudo_distance) connectivities_coarse_tree.data = 1./connectivities_coarse_tree.data connectivities_coarse_tree_indices = [ connectivities_coarse_tree[i].nonzero()[1] for i in range(connectivities_coarse_tree.shape[0])] # inter- and intra-cluster based confidence if not self._tree_based_confidence: total_n = self.n_neighbors * np.array(self.vc.sizes()) maximum = self.connectivities_coarse.max() confidence = self.connectivities_coarse.copy() # initializing for i in range(self.connectivities_coarse.shape[0]): for j in range(i+1, self.connectivities_coarse.shape[1]): if self.connectivities_coarse[i, j] > 0: geom_mean = np.sqrt(total_n[i] * total_n[j]) confidence[i, j] = self.connectivities_coarse[i, j] / geom_mean confidence[j, i] = confidence[i, j] # tree-based confidence else: median_connectivities_coarse_tree = np.median(connectivities_coarse_tree.data) confidence = self.connectivities_coarse.copy() confidence.data[self.connectivities_coarse.data >= median_connectivities_coarse_tree] = 1 connectivities_coarse_adjusted = self.connectivities_coarse.copy() connectivities_coarse_adjusted.data -= median_connectivities_coarse_tree connectivities_coarse_adjusted.data = np.exp(connectivities_coarse_adjusted.data) index = self.connectivities_coarse.data < median_connectivities_coarse_tree confidence.data[index] = connectivities_coarse_adjusted.data[index] confidence_tree = self.compute_confidence_tree( confidence, connectivities_coarse_tree_indices) self.confidence = confidence self.confidence_tree = confidence_tree def compute_confidence_tree( self, confidence, connectivities_coarse_tree_indices): confidence_tree = sp.sparse.lil_matrix(confidence.shape, dtype=float) for i, neighbors in enumerate(connectivities_coarse_tree_indices): if len(neighbors) > 0: confidence_tree[i, neighbors] = confidence[i, neighbors] return confidence_tree.tocsr() def compute_transitions_coarse(self): # analogous code using networkx # membership = adata.obs['clusters'].cat.codes.tolist() # partition = defaultdict(list) # for n, p in zip(list(range(len(G))), membership): # partition[p].append(n) # partition = partition.values() # g_abstracted = nx.quotient_graph(g, partition, relabel=True) # for some reason, though, edges aren't oriented in the quotient # graph... import igraph g = utils.get_igraph_from_adjacency( self._adata.uns['velocyto_transitions'], directed=True) vc = igraph.VertexClustering( g, membership=self._adata.obs[self._groups].cat.codes.values) cg_full = vc.cluster_graph(combine_edges=False) g_bool = utils.get_igraph_from_adjacency( self._adata.uns['velocyto_transitions'].astype('bool'), directed=True) vc_bool = igraph.VertexClustering( g_bool, membership=self._adata.obs[self._groups].cat.codes.values) cg_bool = vc_bool.cluster_graph(combine_edges='sum') # collapsed version transitions_coarse = utils.get_sparse_from_igraph(cg_bool, weight_attr='weight') # translate this into a confidence measure # the number of outgoing edges # total_n = np.zeros(len(vc.sizes())) # # (this is not the convention of standard stochastic matrices) # total_outgoing = transitions_coarse.sum(axis=1) # for i in range(len(total_n)): # total_n[i] = vc.subgraph(i).ecount() # total_n[i] += total_outgoing[i, 0] # use the topology based reference, the velocity one might have very small numbers total_n = self.n_neighbors * np.array(vc_bool.sizes()) transitions_ttest = transitions_coarse.copy() transitions_confidence = transitions_coarse.copy() from scipy.stats import ttest_1samp for i in range(transitions_coarse.shape[0]): # no symmetry in transitions_coarse, hence we should not restrict to # upper triangle neighbors = transitions_coarse[i].nonzero()[1] for j in neighbors: forward = cg_full.es.select(_source=i, _target=j)['weight'] backward = cg_full.es.select(_source=j, _target=i)['weight'] # backward direction: add minus sign values = np.array(list(forward) + list(-np.array(backward))) # require some minimal number of observations if len(values) < 5: transitions_ttest[i, j] = 0 transitions_ttest[j, i] = 0 transitions_confidence[i, j] = 0 transitions_confidence[j, i] = 0 continue t, prob = ttest_1samp(values, 0.0) if t > 0: # number of outgoing edges greater than number of ingoing edges # i.e., transition from i to j transitions_ttest[i, j] = -np.log10(max(prob, 1e-10)) transitions_ttest[j, i] = 0 else: transitions_ttest[j, i] = -np.log10(max(prob, 1e-10)) transitions_ttest[i, j] = 0 # geom_mean geom_mean = np.sqrt(total_n[i] * total_n[j]) diff = (len(forward) - len(backward)) / geom_mean if diff > 0: transitions_confidence[i, j] = diff transitions_confidence[j, i] = 0 else: transitions_confidence[j, i] = -diff transitions_confidence[i, j] = 0 transitions_ttest.eliminate_zeros() transitions_confidence.eliminate_zeros() # transpose in order to match convention of stochastic matrices # entry ij means transition from j to i self.transitions_ttest = transitions_ttest.T self.transitions_confidence = transitions_confidence.T def paga_degrees(adata): """Compute the degree of each node in the abstracted graph. Parameters ---------- adata : AnnData Annotated data matrix. Returns ------- degrees : list List of degrees for each node. """ import networkx as nx g = nx.Graph(adata.uns['paga']['confidence']) degrees = [d for _, d in g.degree(weight='weight')] return degrees def paga_expression_entropies(adata): """Compute the median expression entropy for each node-group. Parameters ---------- adata : AnnData Annotated data matrix. Returns ------- entropies : list Entropies of median expressions for each node. """ from scipy.stats import entropy groups_order, groups_masks = utils.select_groups( adata, key=adata.uns['paga']['groups']) entropies = [] for mask in groups_masks: X_mask = adata.X[mask] x_median = np.median(X_mask, axis=0) x_probs = (x_median - np.min(x_median)) / (np.max(x_median) - np.min(x_median)) entropies.append(entropy(x_probs)) return entropies def paga_compare_paths(adata1, adata2, adjacency_key='confidence', adjacency_key2=None): """Compare paths in abstracted graphs in two datasets. Compute the fraction of consistent paths between leafs, a measure for the topological similarity between graphs. By increasing the verbosity to level 4 and 5, the paths that do not agree and the paths that agree are written to the output, respectively. The PAGA "groups key" needs to be the same in both objects. Parameters ---------- adata1, adata2 : AnnData Annotated data matrices to compare. adjacency_key : str Key for indexing the adjacency matrices in `.uns['paga']` to be used in adata1 and adata2. adjacency_key2 : str, None If provided, used for adata2. Returns ------- OrderedTuple with attributes ``n_steps`` (total number of steps in paths) and ``frac_steps`` (fraction of consistent steps), ``n_paths`` and ``frac_paths``. """ import networkx as nx g1 = nx.Graph(adata1.uns['paga'][adjacency_key]) g2 = nx.Graph(adata2.uns['paga'][adjacency_key2 if adjacency_key2 is not None else adjacency_key]) leaf_nodes1 = [str(x) for x in g1.nodes() if g1.degree(x) == 1] logg.msg('leaf nodes in graph 1: {}'.format(leaf_nodes1), v=5, no_indent=True) paga_groups = adata1.uns['paga']['groups'] asso_groups1 = utils.identify_groups(adata1.obs[paga_groups].values, adata2.obs[paga_groups].values) asso_groups2 = utils.identify_groups(adata2.obs[paga_groups].values, adata1.obs[paga_groups].values) orig_names1 = adata1.obs[paga_groups].cat.categories orig_names2 = adata2.obs[paga_groups].cat.categories import itertools n_steps = 0 n_agreeing_steps = 0 n_paths = 0 n_agreeing_paths = 0 # loop over all pairs of leaf nodes in the reference adata1 for (r, s) in itertools.combinations(leaf_nodes1, r=2): r2, s2 = asso_groups1[r][0], asso_groups1[s][0] orig_names = [orig_names1[int(i)] for i in [r, s]] orig_names += [orig_names2[int(i)] for i in [r2, s2]] logg.msg('compare shortest paths between leafs ({}, {}) in graph1 and ({}, {}) in graph2:' .format(*orig_names), v=4, no_indent=True) no_path1 = False try: path1 = [str(x) for x in nx.shortest_path(g1, int(r), int(s))] except nx.NetworkXNoPath: no_path1 = True no_path2 = False try: path2 = [str(x) for x in nx.shortest_path(g2, int(r2), int(s2))] except nx.NetworkXNoPath: no_path2 = True if no_path1 and no_path2: # consistent behavior n_paths += 1 n_agreeing_paths += 1 n_steps += 1 n_agreeing_steps += 1 logg.msg('there are no connecting paths in both graphs', v=5, no_indent=True) continue elif no_path1 or no_path2: # non-consistent result n_paths += 1 n_steps += 1 continue if len(path1) >= len(path2): path_mapped = [asso_groups1[l] for l in path1] path_compare = path2 path_compare_id = 2 path_compare_orig_names = [[orig_names2[int(s)] for s in l] for l in path_compare] path_mapped_orig_names = [[orig_names2[int(s)] for s in l] for l in path_mapped] else: path_mapped = [asso_groups2[l] for l in path2] path_compare = path1 path_compare_id = 1 path_compare_orig_names = [[orig_names1[int(s)] for s in l] for l in path_compare] path_mapped_orig_names = [[orig_names1[int(s)] for s in l] for l in path_mapped] n_agreeing_steps_path = 0 ip_progress = 0 for il, l in enumerate(path_compare[:-1]): for ip, p in enumerate(path_mapped): if ip >= ip_progress and l in p: # check whether we can find the step forward of path_compare in path_mapped if (ip + 1 < len(path_mapped) and path_compare[il + 1] in path_mapped[ip + 1]): # make sure that a step backward leads us to the same value of l # in case we "jumped" logg.msg('found matching step ({} -> {}) at position {} in path{} and position {} in path_mapped' .format(l, path_compare_orig_names[il + 1], il, path_compare_id, ip), v=6) consistent_history = True for iip in range(ip, ip_progress, -1): if l not in path_mapped[iip - 1]: consistent_history = False if consistent_history: # here, we take one step further back (ip_progress - 1); it's implied that this # was ok in the previous step logg.msg(' step(s) backward to position(s) {} in path_mapped are fine, too: valid step' .format(list(range(ip - 1, ip_progress - 2, -1))), v=6) n_agreeing_steps_path += 1 ip_progress = ip + 1 break n_steps_path = len(path_compare) - 1 n_agreeing_steps += n_agreeing_steps_path n_steps += n_steps_path n_paths += 1 if n_agreeing_steps_path == n_steps_path: n_agreeing_paths += 1 # only for the output, use original names path1_orig_names = [orig_names1[int(s)] for s in path1] path2_orig_names = [orig_names2[int(s)] for s in path2] logg.msg(' path1 = {},\n' 'path_mapped = {},\n' ' path2 = {},\n' '-> n_agreeing_steps = {} / n_steps = {}.' .format(path1_orig_names, [list(p) for p in path_mapped_orig_names], path2_orig_names, n_agreeing_steps_path, n_steps_path), v=5, no_indent=True) Result = namedtuple('paga_compare_paths_result', ['frac_steps', 'n_steps', 'frac_paths', 'n_paths']) return 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297, 2224, 29918, 18307, 29962, 13, 9651, 2224, 29918, 655, 2986, 29918, 12683, 29918, 7039, 353, 5519, 12683, 29918, 7039, 29906, 29961, 524, 29898, 29879, 4638, 363, 269, 297, 301, 29962, 363, 301, 297, 2224, 29918, 655, 2986, 29962, 13, 4706, 1683, 29901, 13, 9651, 2224, 29918, 655, 2986, 353, 518, 9979, 29918, 13155, 29906, 29961, 29880, 29962, 363, 301, 297, 2224, 29906, 29962, 13, 9651, 2224, 29918, 18307, 353, 2224, 29896, 13, 9651, 2224, 29918, 18307, 29918, 333, 353, 29871, 29896, 13, 9651, 2224, 29918, 18307, 29918, 12683, 29918, 7039, 353, 5519, 12683, 29918, 7039, 29896, 29961, 524, 29898, 29879, 4638, 363, 269, 297, 301, 29962, 363, 301, 297, 2224, 29918, 18307, 29962, 13, 9651, 2224, 29918, 655, 2986, 29918, 12683, 29918, 7039, 353, 5519, 12683, 29918, 7039, 29896, 29961, 524, 29898, 29879, 4638, 363, 269, 297, 301, 29962, 363, 301, 297, 2224, 29918, 655, 2986, 29962, 13, 4706, 302, 29918, 351, 929, 292, 29918, 24530, 29918, 2084, 353, 29871, 29900, 13, 4706, 10377, 29918, 18035, 353, 29871, 29900, 13, 4706, 363, 980, 29892, 301, 297, 26985, 29898, 2084, 29918, 18307, 7503, 29899, 29896, 29962, 1125, 13, 9651, 363, 10377, 29892, 282, 297, 26985, 29898, 2084, 29918, 655, 2986, 1125, 13, 18884, 565, 10377, 6736, 10377, 29918, 18035, 322, 301, 297, 282, 29901, 13, 462, 1678, 396, 1423, 3692, 591, 508, 1284, 278, 4331, 6375, 310, 2224, 29918, 18307, 297, 2224, 29918, 655, 2986, 13, 462, 1678, 565, 313, 666, 718, 29871, 29896, 529, 7431, 29898, 2084, 29918, 655, 2986, 29897, 13, 462, 4706, 322, 13, 462, 4706, 2224, 29918, 18307, 29961, 309, 718, 29871, 29896, 29962, 297, 2224, 29918, 655, 2986, 29961, 666, 718, 29871, 29896, 29962, 1125, 13, 462, 4706, 396, 1207, 1854, 393, 263, 4331, 1250, 1328, 11981, 502, 304, 278, 1021, 995, 310, 301, 13, 462, 4706, 396, 297, 1206, 591, 376, 29926, 3427, 287, 29908, 13, 462, 4706, 1480, 29887, 29889, 7645, 877, 11940, 9686, 4331, 313, 8875, 1599, 426, 1800, 472, 2602, 6571, 297, 2224, 8875, 322, 2602, 6571, 297, 2224, 29918, 655, 2986, 29915, 13, 462, 1669, 869, 4830, 29898, 29880, 29892, 2224, 29918, 18307, 29918, 12683, 29918, 7039, 29961, 309, 718, 29871, 29896, 1402, 980, 29892, 2224, 29918, 18307, 29918, 333, 29892, 10377, 511, 325, 29922, 29953, 29897, 13, 462, 4706, 13747, 29918, 18434, 353, 5852, 13, 462, 4706, 363, 474, 666, 297, 3464, 29898, 666, 29892, 10377, 29918, 18035, 29892, 448, 29896, 1125, 13, 462, 9651, 565, 301, 451, 297, 2224, 29918, 655, 2986, 29961, 29875, 666, 448, 29871, 29896, 5387, 13, 462, 18884, 13747, 29918, 18434, 353, 7700, 13, 462, 4706, 565, 13747, 29918, 18434, 29901, 13, 462, 9651, 396, 1244, 29892, 591, 2125, 697, 4331, 4340, 1250, 313, 666, 29918, 18035, 448, 29871, 29896, 416, 372, 29915, 29879, 2411, 2957, 393, 445, 13, 462, 9651, 396, 471, 3431, 297, 278, 3517, 4331, 13, 462, 9651, 1480, 29887, 29889, 7645, 877, 1678, 4331, 29898, 29879, 29897, 1250, 1328, 304, 2602, 29898, 29879, 29897, 6571, 297, 2224, 29918, 655, 2986, 526, 2691, 29892, 2086, 29901, 2854, 4331, 29915, 13, 462, 462, 259, 869, 4830, 29898, 1761, 29898, 3881, 29898, 666, 448, 29871, 29896, 29892, 10377, 29918, 18035, 448, 29871, 29906, 29892, 448, 29896, 876, 511, 325, 29922, 29953, 29897, 13, 462, 9651, 302, 29918, 351, 929, 292, 29918, 24530, 29918, 2084, 4619, 29871, 29896, 13, 462, 9651, 10377, 29918, 18035, 353, 10377, 718, 29871, 29896, 13, 462, 9651, 2867, 13, 4706, 302, 29918, 24530, 29918, 2084, 353, 7431, 29898, 2084, 29918, 18307, 29897, 448, 29871, 29896, 13, 4706, 302, 29918, 351, 929, 292, 29918, 24530, 4619, 302, 29918, 351, 929, 292, 29918, 24530, 29918, 2084, 13, 4706, 302, 29918, 24530, 4619, 302, 29918, 24530, 29918, 2084, 13, 4706, 302, 29918, 24772, 4619, 29871, 29896, 13, 4706, 565, 302, 29918, 351, 929, 292, 29918, 24530, 29918, 2084, 1275, 302, 29918, 24530, 29918, 2084, 29901, 302, 29918, 351, 929, 292, 29918, 24772, 4619, 29871, 29896, 13, 13, 4706, 396, 871, 363, 278, 1962, 29892, 671, 2441, 2983, 13, 4706, 2224, 29896, 29918, 12683, 29918, 7039, 353, 518, 12683, 29918, 7039, 29896, 29961, 524, 29898, 29879, 4638, 363, 269, 297, 2224, 29896, 29962, 13, 4706, 2224, 29906, 29918, 12683, 29918, 7039, 353, 518, 12683, 29918, 7039, 29906, 29961, 524, 29898, 29879, 4638, 363, 269, 297, 2224, 29906, 29962, 13, 4706, 1480, 29887, 29889, 7645, 877, 418, 2224, 29896, 353, 426, 7570, 29876, 29915, 13, 1669, 525, 2084, 29918, 655, 2986, 353, 426, 7570, 29876, 29915, 13, 1669, 525, 418, 2224, 29906, 353, 426, 7570, 29876, 29915, 13, 1669, 525, 976, 302, 29918, 351, 929, 292, 29918, 24530, 353, 6571, 847, 302, 29918, 24530, 353, 426, 1836, 29915, 13, 1669, 869, 4830, 29898, 2084, 29896, 29918, 12683, 29918, 7039, 29892, 13, 462, 539, 518, 1761, 29898, 29886, 29897, 363, 282, 297, 2224, 29918, 655, 2986, 29918, 12683, 29918, 7039, 1402, 13, 462, 539, 2224, 29906, 29918, 12683, 29918, 7039, 29892, 13, 462, 539, 302, 29918, 351, 929, 292, 29918, 24530, 29918, 2084, 29892, 302, 29918, 24530, 29918, 2084, 511, 325, 29922, 29945, 29892, 694, 29918, 12860, 29922, 5574, 29897, 13, 1678, 7867, 353, 4257, 23583, 877, 29886, 7781, 29918, 18307, 29918, 24772, 29918, 2914, 742, 13, 462, 4706, 6024, 1154, 29918, 24530, 742, 525, 29876, 29918, 24530, 742, 525, 1154, 29918, 24772, 742, 525, 29876, 29918, 24772, 11287, 13, 1678, 736, 7867, 29898, 1154, 29918, 24530, 29922, 29876, 29918, 351, 929, 292, 29918, 24530, 29914, 29876, 29918, 24530, 565, 302, 29918, 24530, 1405, 29871, 29900, 1683, 7442, 29889, 13707, 29892, 13, 462, 29871, 302, 29918, 24530, 29922, 29876, 29918, 24530, 565, 302, 29918, 24530, 1405, 29871, 29900, 1683, 7442, 29889, 13707, 29892, 13, 462, 29871, 285, 945, 29918, 24772, 29922, 29876, 29918, 351, 929, 292, 29918, 24772, 29914, 29876, 29918, 24772, 565, 302, 29918, 24530, 1405, 29871, 29900, 1683, 7442, 29889, 13707, 29892, 13, 462, 29871, 302, 29918, 24772, 29922, 29876, 29918, 24772, 565, 302, 29918, 24530, 1405, 29871, 29900, 1683, 7442, 29889, 13707, 29897, 13, 2 ]
wholesomebot/__main__.py	bnb32/wholesome_bot	0	1612892	from wholesomebot.connection import WebSocketClient, IrcSocketClient from wholesomebot.misc import get_logger import asyncio logger = get_logger() ircClient = IrcSocketClient() webClient = WebSocketClient() try: loop = asyncio.new_event_loop() loop = asyncio.get_event_loop() loop.create_task(ircClient.listen_forever()) loop.create_task(webClient.listen_forever()) loop.run_forever() except KeyboardInterrupt: logger.warning("Exiting wholesomebot!") exit()	[ 1, 515, 377, 6544, 608, 7451, 29889, 9965, 1053, 2563, 11373, 4032, 29892, 306, 2214, 11373, 4032, 13, 3166, 377, 6544, 608, 7451, 29889, 29885, 10669, 1053, 679, 29918, 21707, 13, 13, 5215, 408, 948, 3934, 13, 13, 21707, 353, 679, 29918, 21707, 580, 13, 539, 13, 2076, 4032, 353, 306, 2214, 11373, 4032, 580, 13, 2676, 4032, 353, 2563, 11373, 4032, 580, 13, 268, 13, 2202, 29901, 13, 268, 13, 1678, 2425, 353, 408, 948, 3934, 29889, 1482, 29918, 3696, 29918, 7888, 580, 13, 1678, 2425, 353, 408, 948, 3934, 29889, 657, 29918, 3696, 29918, 7888, 580, 13, 1678, 2425, 29889, 3258, 29918, 7662, 29898, 2076, 4032, 29889, 20631, 29918, 1079, 369, 3101, 13, 1678, 2425, 29889, 3258, 29918, 7662, 29898, 2676, 4032, 29889, 20631, 29918, 1079, 369, 3101, 13, 1678, 2425, 29889, 3389, 29918, 1079, 369, 580, 13, 13, 19499, 7670, 3377, 4074, 6685, 29901, 13, 1678, 17927, 29889, 27392, 703, 1252, 11407, 377, 6544, 608, 7451, 29991, 1159, 13, 1678, 6876, 580, 13, 2 ]
test/sample.py	HyechurnJang/umbi	0	94302	# -- coding: utf-8 -- ''' Created on 2017. 10. 12. @author: HyechurnJang ''' import json from umbi import UmbiAPI def v(data): print json.dumps(data, indent=2) api = UmbiAPI('85aaa4d3-333e-4212-a7bd-b64300321193') # print 'Domains' # v(api.Domains.Categorization('hecloud.org', 'google.com')) # v(api.Domains.Categories()) # v(api.Domains.Score('hecloud.org', 'google.com')) # v(api.Domains.LatestTags('hecloud.org')) # print '' # print 'Timeline' # v(api.Timeline('domain.com')) # print '' # print 'URL' # v(api.URL.Classifiers('hecloud.org')) # v(api.URL.Info('hecloud.org')) # print '' # print 'Search' # v(api.Search('hecloud.org', '-30days', True)) # print '' # print 'Recommendations' # v(api.Recommendations.Name('hecloud.org')) # print '' # print 'Links' # v(api.Links.Name('hecloud.org')) # print '' # print 'Security' # v(api.Security.Name('hecloud.org')) # print '' # print 'DnsDB' # v(api.DnsDB.Name('hecloud.org')) # v(api.DnsDB.IP('8.8.8.8')) # print '' # print 'BGP' # v(api.BgpRoutes.IP('8.8.8.8')) # v(api.BgpRoutes.ASN(11425)) # print 'WhoIs' # v(api.WhoIs('hecloud.org')) # v(api.WhoIs.Emails('<EMAIL>', '<EMAIL>', limit=2, offset=10, sort='created')) # v(api.WhoIs.Nameservers('ns1.google.com', 'ns2.google.com', limit=2, offset=10, sort='created')) # print '' # print 'IPs' # v(api.IPs.LatestDomains('192.168.3.11')) # print '' # print 'TopMillion' # v(api.TopMillion(limit=100)) # print '' print 'Samples' v(api.Samples('192.168.3.11')) print ''	[ 1, 396, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 30004, 13, 12008, 30004, 13, 20399, 373, 29871, 29906, 29900, 29896, 29955, 29889, 29871, 29896, 29900, 29889, 29871, 29896, 29906, 22993, 13, 29992, 8921, 29901, 379, 4099, 305, 595, 29967, 574, 30004, 13, 12008, 30004, 13, 30004, 13, 5215, 4390, 30004, 13, 3166, 1922, 5365, 1053, 6379, 5365, 8787, 30004, 13, 30004, 13, 1753, 325, 29898, 1272, 1125, 30004, 13, 1678, 1596, 4390, 29889, 29881, 17204, 29898, 1272, 29892, 29536, 29922, 29906, 8443, 13, 30004, 13, 2754, 353, 6379, 5365, 8787, 877, 29947, 29945, 7340, 29874, 29946, 29881, 29941, 29899, 29941, 29941, 29941, 29872, 29899, 29946, 29906, 29896, 29906, 29899, 29874, 29955, 6448, 29899, 29890, 29953, 29946, 29941, 29900, 29900, 29941, 29906, 29896, 29896, 29929, 29941, 1495, 30004, 13, 30004, 13, 29937, 1596, 525, 11096, 2708, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 11096, 2708, 29889, 29907, 20440, 2133, 877, 354, 9274, 29889, 990, 742, 525, 3608, 29889, 510, 8785, 30004, 13, 29937, 325, 29898, 2754, 29889, 11096, 2708, 29889, 29907, 14404, 3101, 30004, 13, 29937, 325, 29898, 2754, 29889, 11096, 2708, 29889, 20097, 877, 354, 9274, 29889, 990, 742, 525, 3608, 29889, 510, 8785, 30004, 13, 29937, 325, 29898, 2754, 29889, 11096, 2708, 29889, 13992, 342, 28089, 877, 354, 9274, 29889, 990, 8785, 30004, 13, 29937, 1596, 6629, 30004, 13, 30004, 13, 29937, 1596, 525, 13711, 5570, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 13711, 5570, 877, 7247, 29889, 510, 8785, 30004, 13, 29937, 1596, 6629, 30004, 13, 30004, 13, 29937, 1596, 525, 4219, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 4219, 29889, 2385, 14903, 877, 354, 9274, 29889, 990, 8785, 30004, 13, 29937, 325, 29898, 2754, 29889, 4219, 29889, 3401, 877, 354, 9274, 29889, 990, 8785, 30004, 13, 29937, 1596, 6629, 30004, 13, 30004, 13, 29937, 1596, 525, 7974, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 7974, 877, 354, 9274, 29889, 990, 742, 17411, 29941, 29900, 16700, 742, 5852, 876, 30004, 13, 29937, 1596, 6629, 30004, 13, 30004, 13, 29937, 1596, 525, 1123, 2055, 355, 800, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 1123, 2055, 355, 800, 29889, 1170, 877, 354, 9274, 29889, 990, 8785, 30004, 13, 29937, 1596, 6629, 30004, 13, 30004, 13, 29937, 1596, 525, 6595, 29879, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 6595, 29879, 29889, 1170, 877, 354, 9274, 29889, 990, 8785, 30004, 13, 29937, 1596, 6629, 30004, 13, 30004, 13, 29937, 1596, 525, 13228, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 13228, 29889, 1170, 877, 354, 9274, 29889, 990, 8785, 30004, 13, 29937, 1596, 6629, 30004, 13, 30004, 13, 29937, 1596, 525, 29928, 1983, 4051, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 29928, 1983, 4051, 29889, 1170, 877, 354, 9274, 29889, 990, 8785, 30004, 13, 29937, 325, 29898, 2754, 29889, 29928, 1983, 4051, 29889, 5690, 877, 29947, 29889, 29947, 29889, 29947, 29889, 29947, 8785, 30004, 13, 29937, 1596, 6629, 30004, 13, 30004, 13, 29937, 1596, 525, 29933, 19903, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 29933, 29887, 29886, 24254, 267, 29889, 5690, 877, 29947, 29889, 29947, 29889, 29947, 29889, 29947, 8785, 30004, 13, 29937, 325, 29898, 2754, 29889, 29933, 29887, 29886, 24254, 267, 29889, 3289, 29940, 29898, 29896, 29896, 29946, 29906, 29945, 876, 30004, 13, 30004, 13, 29937, 1596, 525, 22110, 3624, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 22110, 3624, 877, 354, 9274, 29889, 990, 8785, 30004, 13, 29937, 325, 29898, 2754, 29889, 22110, 3624, 29889, 9823, 29879, 877, 29966, 26862, 6227, 29958, 742, 12801, 26862, 6227, 29958, 742, 4046, 29922, 29906, 29892, 9210, 29922, 29896, 29900, 29892, 2656, 2433, 11600, 8785, 30004, 13, 29937, 325, 29898, 2754, 29889, 22110, 3624, 29889, 8659, 261, 874, 877, 1983, 29896, 29889, 3608, 29889, 510, 742, 525, 1983, 29906, 29889, 3608, 29889, 510, 742, 4046, 29922, 29906, 29892, 9210, 29922, 29896, 29900, 29892, 2656, 2433, 11600, 8785, 30004, 13, 29937, 1596, 6629, 30004, 13, 30004, 13, 29937, 1596, 525, 5690, 29879, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 5690, 29879, 29889, 13992, 342, 11096, 2708, 877, 29896, 29929, 29906, 29889, 29896, 29953, 29947, 29889, 29941, 29889, 29896, 29896, 8785, 30004, 13, 29937, 1596, 6629, 30004, 13, 30004, 13, 29937, 1596, 525, 7031, 19169, 291, 29915, 30004, 13, 29937, 325, 29898, 2754, 29889, 7031, 19169, 291, 29898, 13400, 29922, 29896, 29900, 29900, 876, 30004, 13, 29937, 1596, 6629, 30004, 13, 30004, 13, 2158, 525, 29903, 9422, 29915, 30004, 13, 29894, 29898, 2754, 29889, 29903, 9422, 877, 29896, 29929, 29906, 29889, 29896, 29953, 29947, 29889, 29941, 29889, 29896, 29896, 8785, 30004, 13, 2158, 6629, 30004, 13, 2 ]
third_party/blink/tools/blinkpy/w3c/wpt_uploader.py	chromium/chromium	14,668	175393	# Copyright 2021 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Uploads Wpt test results from Chromium to wpt.fyi.""" import argparse import gzip import json import logging import os import requests import tempfile from blinkpy.common.net.rpc import Rpc from blinkpy.common.system.log_utils import configure_logging _log = logging.getLogger(__name__) class WptReportUploader(object): def __init__(self, host): self._host = host self._rpc = Rpc(host) self._dry_run = False configure_logging(logging_level=logging.INFO, include_time=True) def main(self, argv=None): """Pull wpt_report.json from latest CI runs, merge the reports and upload that to wpt.fyi. Returns: A boolean: True if success, False if there were any failures. """ options = self.parse_args(argv) if options.verbose: configure_logging(logging_level=logging.DEBUG, include_time=True) self._dry_run = options.dry_run rv = 0 builders = [("chromium", "ci", "android-webview-pie-x86-wpt-fyi-rel")] for builder in builders: reports = [] _log.info("Uploading report for %s" % builder[2]) build = self.fetch_latest_complete_build(builder) if build: _log.info("Find latest completed build %d" % build.get("number")) urls = self.fetch_wpt_report_urls(build.get("id")) for url in urls: _log.info("Fetching wpt report from %s" % url) res = self._host.web.request("GET", url) if res.getcode() == 200: body = res.read() reports.append(json.loads(body)) else: _log.error("Failed to fetch wpt report.") merged_report = self.merge_reports(reports) with tempfile.TemporaryDirectory() as tmpdir: path = os.path.join(tmpdir, "reports.json.gz") with gzip.open(path, 'wt', encoding="utf-8") as zipfile: json.dump(merged_report, zipfile) rv = rv \| self.upload_report(path) _log.info(" ") return rv def fetch_wpt_report_urls(self, build_id): """Get a list of fetchUrl for wpt-report from given build. This uses the QueryArtifacts rpc format specified in https://source.chromium.org/chromium/infra/infra/+/main:go/src/go.chromium.org/luci/resultdb/proto/v1/resultdb.proto The response is a list of dicts of the following form: { 'artifacts': [ { 'name': 'some name', 'artifactId': 'wpt_reports_dada.json', 'fetchUrl': 'https://something...', 'fetchUrlExpiration': 'some future time', 'sizeBytes': '8472164' }, ... more artifacts ] } Returns a list of URLs for wpt report """ invocation = "invocations/build-%s" % build_id data = { "invocations": [invocation], "predicate": { "followEdges": {"includedInvocations": True} } } url = 'https://results.api.cr.dev/prpc/luci.resultdb.v1.ResultDB/QueryArtifacts' res = self._rpc.luci_rpc(url, data) artifacts = res.get("artifacts") if res else None if not artifacts: return [] rv = [] for artifact in artifacts: if artifact.get("artifactId").startswith("wpt_reports"): rv.append(artifact.get("fetchUrl")) return rv def fetch_latest_complete_build(self, project, bucket, builder_name): """Gets latest successful build from a CI builder. This uses the SearchBuilds rpc format specified in https://cs.chromium.org/chromium/infra/go/src/go.chromium.org/luci/buildbucket/proto/rpc.proto The response is a list of dicts of the following form: { "builds": [ { "id": "8828280326907235505", "builder": { "builder": "android-webview-pie-x86-wpt-fyi-rel" }, "status": "SUCCESS" }, ... more builds } This method returns the latest finished build. """ data = { "predicate": { "builder": { "project": project, "bucket": bucket, "builder": builder_name }, "status": "SUCCESS" }, "fields": "builds..builder.builder,builds..number,builds..status,builds.*.id", "pageSize": 10 } url = 'https://cr-buildbucket.appspot.com/prpc/buildbucket.v2.Builds/SearchBuilds' raw_results_json = self._rpc.luci_rpc(url, data) if 'builds' not in raw_results_json: return None builds = raw_results_json['builds'] return builds[0] if builds else None def upload_report(self, path_to_report): """Upload the wpt report to wpt.fyi The Api is defined at: https://github.com/web-platform-tests/wpt.fyi/tree/main/api#results-creation """ username = "chromium-ci-results-uploader" password = <PASSWORD>" url = "https://staging.wpt.fyi/api/results/upload" session = requests.Session() session.auth = (username, password) with open(path_to_report, 'rb') as fp: files = {'result_file': fp} if self._dry_run: _log.info("Dry run, no report uploaded.") return 0 res = session.post(url=url, files=files) if res.status_code == 200: _log.info("Successfully uploaded wpt report with response: " + res.text) report_id = res.text.split()[1] _log.info("Report uploaded to https://staging.wpt.fyi/api/runs?run_id=%s" % report_id) return 0 else: _log.error("Upload wpt report failed with status code: %d", res.status_code) return 1 def merge_reports(self, reports): if not reports: return {} merged_report = {} merged_report['run_info'] = reports[0]['run_info'] merged_report['time_start'] = reports[0]['time_start'] merged_report['results'] = [] merged_report['time_end'] = reports[0]['time_end'] for report in reports: merged_report['time_start'] = min(merged_report['time_start'], report['time_start']) merged_report['results'].extend(report['results']) merged_report['time_end'] = max(merged_report['time_end'], report['time_end']) return merged_report def parse_args(self, argv): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( '-v', 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authors/apps/articles/tests/test_tags.py	Tittoh/blog-API	1	73587	import json from django.urls import reverse from rest_framework.test import APITestCase from rest_framework import status from authors.apps.authentication.models import User from authors.apps.authentication.tests.utils import TEST_USER from django.core import mail from authors.apps.authentication.views import VerifyAccount from django.utils.http import urlsafe_base64_encode from django.utils.encoding import force_bytes from authors.apps.authentication.utils import generate_token from rest_framework.test import force_authenticate from rest_framework.test import APIRequestFactory from authors.apps.articles.models import Article user = { "user": { "username": "test", "email": "<EMAIL>", "password": "<PASSWORD>." } } class TestTags(APITestCase): """ This class defines the test suite for the tags of an article. """ def setUp(self): """Define the test client and other test variables.""" self.article1 = { "article": { "title": "How to train your dragon", "description": "Ever wonder how?", "body": "You have to believe", "tagList": ["django-rest", "python"] } } self.bad_tag_list = { "article": { "title": "How to train your dragon", "description": "Ever wonder how?", "body": "You have to believe", "tagList": "django-rest, python" } } self.tag_tuple = { "article": { "title": "How to train your dragon", "description": "Ever wonder how?", "body": "You have to believe", "tagList": ("django-rest, python") } } self.user = { "user": { "username": "test", "email": "<EMAIL>", "password": "<PASSWORD>." } } def login_user(self, user=user): """ login user """ response = self.client.post( reverse("authentication:login"), user, format='json') response.render() user = json.loads(response.content) return user def create_a_user(self, username='test', email='<EMAIL>', password='<PASSWORD>.'): """ Create a test user """ user = User.objects.create_user(username, email, password) user.save() return user def create_article(self): """ Create a test article """ tags = ["django-rest", "python"] user = User.objects.get() article = Article.objects.create( title="How to train your dragon", description="Ever wonder how?", body="You have to believe", tagList=tags, author=user.profile) article.save() return article def verify_user(self, user): """Verify user""" token = generate_token.make_token(user) uid = urlsafe_base64_encode(force_bytes(user.pk)) request = APIRequestFactory().get( reverse("authentication:verify", args=[uid, token])) verify_account = VerifyAccount.as_view() verify_account(request, uidb64=uid, token=token) return user #Test cases def test_tagList_added(self): """Test a tagList is added when an article is created""" user = self.create_a_user() self.verify_user(user) auth_user = self.login_user() user = User.objects.get() response = self.client.post('/api/articles/', self.article1, HTTP_AUTHORIZATION='Bearer ' + auth_user['user']['token'], format='json' ) self.assertIn("django-rest", response.content.decode()), self.assertIn("python", response.content.decode()) def test_tagList_returned(self): """Test api can return a taglist with an article""" user = self.create_a_user() self.verify_user(user) auth_user = self.login_user() self.client.post('/api/articles/', self.article1, HTTP_AUTHORIZATION='Bearer ' + auth_user['user']['token'], format='json' ) response = self.client.get('/api/articles/', format='json' ) self.assertIn("django-rest", response.content.decode()), self.assertIn("python", response.content.decode()) def test_get_tagList(self): """ Test api can get a tagList """ user = self.create_a_user() self.verify_user(user) auth_user = self.login_user() self.client.post('/api/articles/', self.article1, HTTP_AUTHORIZATION='Bearer ' + auth_user['user']['token'], format='json' ) response = self.client.get('/api/tags/', format='json' ) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_tagList_object(self): """ Test a tagList cannot be a string """ user = self.create_a_user() self.verify_user(user) auth_user = self.login_user() response = self.client.post('/api/articles/', self.bad_tag_list, HTTP_AUTHORIZATION='Bearer ' + auth_user['user']['token'], format='json' ) self.assertNotIn("django-rest", response.content.decode()), self.assertNotIn("python", response.content.decode()) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_tagList_data_structure(self): """ Test a tagList cannot be a tuple """ user = self.create_a_user() self.verify_user(user) auth_user = self.login_user() response = self.client.post('/api/articles/', self.tag_tuple, HTTP_AUTHORIZATION='Bearer ' + auth_user['user']['token'], format='json' ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)	[ 1, 1053, 4390, 13, 3166, 9557, 29889, 26045, 1053, 11837, 13, 3166, 1791, 29918, 4468, 29889, 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gpytorch/lazy/non_lazy_tensor.py	Sciguymjm/gpytorch	0	192371	#!/usr/bin/env python3 import torch from .. import settings from ..lazy import LazyTensor class NonLazyTensor(LazyTensor): def __init__(self, tsr): """ Not a lazy tensor Args: - tsr (Tensor: matrix) a Tensor """ if not torch.is_tensor(tsr): raise RuntimeError("NonLazyTensor must take a torch.Tensor; got {}".format(tsr.__class__.__name__)) super(NonLazyTensor, self).__init__(tsr) self.tensor = tsr def _matmul(self, rhs): return torch.matmul(self.tensor, rhs) def _t_matmul(self, rhs): return torch.matmul(self.tensor.transpose(-1, -2), rhs) def _quad_form_derivative(self, left_vecs, right_vecs): res = left_vecs.matmul(right_vecs.transpose(-1, -2)) return (res,) def _size(self): return self.tensor.size() def _transpose_nonbatch(self): return NonLazyTensor(self.tensor.transpose(-1, -2)) def _get_indices(self, left_indices, right_indices, batch_indices): return self.tensor.__getitem__((batch_indices, left_indices, right_indices)) def diag(self): if self.tensor.ndimension() < 3: return self.tensor.diag() else: row_col_iter = torch.arange(0, self.matrix_shape[-1], dtype=torch.long, device=self.device) return self.tensor[..., row_col_iter, row_col_iter].view(self.batch_shape, -1) def evaluate(self): return self.tensor def __getitem__(self, index): # Make sure index is a list if not isinstance(index, tuple): index = [index] else: index = list(index) # Handle the ellipsis # Find the index of the ellipsis ellipsis_locs = [index for index, item in enumerate(index) if item is Ellipsis] if settings.debug.on(): if len(ellipsis_locs) > 1: raise RuntimeError( "Cannot have multiple ellipsis in a __getitem__ call. LazyTensor {} " " received index {}.".format(self, index) ) if len(ellipsis_locs) == 1: ellipsis_loc = ellipsis_locs[0] num_to_fill_in = self.ndimension() - (len(index) - 1) index = index[:ellipsis_loc] + [slice(None, None, None)] num_to_fill_in + index[ellipsis_loc + 1:] # Make the index a tuple again index = tuple(index) # Perform the __getitem__ res = self.tensor[index] # Determine if we should return a LazyTensor or a Tensor if len(index) >= self.ndimension() - 1: row_index = index[self.ndimension() - 2] if isinstance(row_index, int) or torch.is_tensor(row_index): return res if len(index) == self.ndimension(): col_index = index[self.ndimension() - 1] if isinstance(col_index, int) or torch.is_tensor(col_index): return res return NonLazyTensor(res)	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 13, 5215, 4842, 305, 13, 3166, 6317, 1053, 6055, 13, 3166, 6317, 433, 1537, 1053, 19575, 29891, 29911, 6073, 13, 13, 13, 1990, 10050, 29931, 24683, 29911, 6073, 29898, 29931, 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openbatlib/model.py	fastrockstar/openBatLib	4	173858	import numpy as np import numba as nb from dataclasses import dataclass from numba import types from numba.typed import Dict from numba import njit import pandas as pd import time import datetime import csv from openpyxl import load_workbook from pyModbusTCP.client import ModbusClient from pyModbusTCP import utils @dataclass class Data: Ppv: np.array Pbat: np.array Pperi: np.array soc: np.array soc0: int Pbs0: int E: dict class BatModDC(object): """Performance Simulation Class for DC-coupled PV-Battery systems :param parameter: PV battery system parameters :type parameter: dict :param d: array containing parameters :type d: numpy array :param ppv: normalized DC power output of the PV generator :type ppv: numpy array :param pl: AC load power :type pl: numpy array :param Pr: Residual power for battery charging :type Pr: numpy array :param Prpv: AC residual power :type Pr: numpy array :param Ppv: DC power output of the PV generator :type Ppv: numpy array :param ppv2ac: Normalized AC output power of the PV2AC conversion pathway to cover the AC power demand :type ppv2ac: numpy array :param Ppv2ac_out: Target AC output power of the PV2AC conversion pathway :type Ppv2ac_out: numpy array :param dt: time step width in seconds :type dt: integer """ _version = 0.1 def __init__(self, parameter, d, ppv, pl, dt): """Constructor method """ self.parameter = parameter self.d = d self.ppv = ppv self.pl = pl self.dt = dt self.th = False # Start threshold for the recharging of the battery self.spi = float() # Initialization and preallocation self.Real.Pr, self.Real.Prpv, self.Real.Ppv, self.Real.ppv2ac, self.Real.Ppv2ac_out = max_self_consumption(parameter, ppv, pl, pvmod=True) self.Real.Ppv2ac_out0 = 0 self.Real.Ppv2bat_in0 = 0 self.Real.Pbat = np.zeros_like(self.ppv) # DC power of the battery in W self.Real.soc = np.zeros_like(self.ppv) # State of charge of the battery self.Real.soc0 = 0 # State of charge of the battery in the first time step # Input power of the PV2BAT conversion pathway in W self.Real.Ppv2bat_in = np.zeros_like(self.ppv) # Output power of the BAT2AC conversion pathway in W self.Real.Pbat2ac_out = np.zeros_like(self.ppv) self.Real.Pbat2ac_out0 = 0 # AC power of the PV-battery system in W self.Real.Ppvbs = np.zeros_like(self.ppv) # Additional power consumption of other system components (e.g. AC power meter) in W self.Real.Pperi = np.ones(self.ppv.size) * self.parameter['P_PERI_AC'] self.Ideal.Ppv = np.maximum(0, self.ppv) * self.parameter['P_PV'] * 1000 self.Ideal.Pr = self.Ideal.Ppv - self.pl self.Ideal.Pbat = np.zeros_like(self.ppv) self.Ideal.soc = np.zeros_like(self.ppv) self.Ideal.Ppv2bat_in = np.zeros_like(self.ppv) self.Ideal.Ppv2bat_in = np.zeros_like(self.ppv) self.Ideal.Pbat2ac_out = np.zeros_like(self.ppv) self.Ideal.Ppvbs = np.zeros_like(self.ppv) @dataclass class Real(Data): Pr : np.array Prpv : np.array ppv2ac : np.array Ppv2ac_out : np.array Ppv2ac_out0 : int Ppv2bat_in : np.array Pbat2ac_out : np.array Ppvbs : np.array @dataclass class Ideal(Real): def __init__(self): super().__init__() def simulation(self, pvmod=True): """Manages the Performance Simulation Model for AC-coupled PV-Battery Systems """ self.Real.Ppv2ac_out, self.Real.Ppv2bat_in, self.Real.Ppv2bat_in0, self.Real.Pbat2ac_out, self.Real.Pbat2ac_out0, self.Real.Ppvbs, self.Real.Pbat, self.Real.soc, self.Real.soc0 = batmod_dc( self.d, self.dt, self.Real.soc0, self.Real.soc, self.Real.Pr, self.Real.Prpv, self.Real.Ppv, self.Real.Ppv2bat_in0, self.Real.Ppv2bat_in, self.Real.Pbat2ac_out0, self.Real.Pbat2ac_out, self.Real.Ppv2ac_out, self.Real.Ppvbs, self.Real.Pbat) self.Ideal.Pbat, self.Ideal.soc, self.Ideal.soc0 = batmod_dc_ideal(self.d, self.dt, self.Ideal.soc0, self.Ideal.soc, self.Ideal.Pr, self.Ideal.Pbat) # Define missing parameters self.Real.Ppv2ac = self.Real.Ppv2ac_out # AC output power of the PV2AC conversion pathway self.Real.Ppv2bat = self.Real.Ppv2bat_in # DC input power of the PV2BAT conversion pathway self.Ideal.Ppvbs = self.Ideal.Ppv - np.maximum(0, self.Ideal.Pbat) - (np.minimum(0, self.Ideal.Pbat)) # Realized AC power of the PV-battery system self.Ideal.Ppv2ac = self.Ideal.Ppv - np.maximum(0, self.Ideal.Pbat) # AC output power of the PV2AC conversion pathway self.Ideal.Ppv2bat = np.maximum(0, self.Ideal.Pbat) # DC input power of the PV2BAT conversion pathway print() def bat_mod_res(self): """Function to calculate the power flows and energy sums including curtailment of PV power """ self.Real.E = bat_res_mod(self.parameter, self.pl, self.Real.Ppv, self.Real.Pbat, self.dt, self.Real.Ppv2ac, self.Real.Ppv2bat, self.Real.Ppvbs, self.Real.Pperi) self.Ideal.E = bat_res_mod_ideal(self.parameter, self.pl, self.Ideal.Ppv, self.Ideal.Pbat, self.dt, self.Ideal.Ppv2ac, self.Ideal.Ppv2bat, self.Ideal.Ppvbs, self.Ideal.Pperi) def calculate_spi(self): self.spi = calculate_spi(_E_real=self.Real.E, _E_ideal=self.Ideal.E) def get_E(self): """Returns the energy sums of the simulation :return: Energy sums of the simulation in MWh :rtype: dict """ return self.Real.E, self.Ideal.E def get_soc(self): """Returns the state of charge of the battery :return: state of charge of the battery :rtype: numpy array """ return self.soc def get_Pbat(self): """Returns the DC power of the battery in W :return: DC power of the battery in W :rtype: numpy array """ return self.Pbat def get_SPI(self): return self.spi class BatModAC(object): """Performance Simulation Class for AC-coupled PV-Battery systems :param parameter: PV battery system parameters :type parameter: dict :param d: array containing parameters :type d: numpy array :param ppv: normalized DC power output of the PV generator :type ppv: numpy array :param pl: AC load power :type pl: numpy array :param Pr: AC residual power :type Pr: numpy array :param Ppv: DC power output of the PV generator :type Ppv: numpy array :param Ppvs: AC power output of the PV inverter taking into account the conversion losses and maximum output power of the PV inverter :type Ppvs: numpy array :param Pperi: Additional power consumption of other system components (e.g. AC power meter) in W :type Pperi: numpy array :param dt: time step width in seconds :type dt: integer """ _version = '0.1' def __init__(self, parameter, d, ppv, pl, dt): """Constructor method """ self.parameter = parameter self.d = d self.ppv = ppv self.pl = pl self.dt = dt self.spi = float() self.th = False # Start threshold for the recharging of the battery # Initialization and preallocation self.Real.Pr, self.Real.Ppv, self.Real.Ppvs, self.Real.Pperi = max_self_consumption(parameter, ppv, pl, pvmod=True) self.Real.Pbat = np.zeros_like(self.ppv) # DC power of the battery in W self.Real.Pbs = np.zeros_like(self.ppv) # AC power of the battery system in W self.Real.soc = np.zeros_like(self.ppv) # State of charge of the battery self.Real.soc0 = 0 # State of charge of the battery in the first time step self.Real.Pbs0 = 0 # State of the battery storage in the previous time step self.Ideal.Ppv = np.maximum(0, ppv) * parameter['P_PV'] * 1000 self.Ideal.Pr = self.Ideal.Ppv - pl self.Ideal.Pbat = np.zeros_like(self.ppv) self.Ideal.Pbs = np.zeros_like(self.ppv) self.Ideal.Pbs0 = 0 self.Ideal.soc = np.zeros_like(self.ppv) self.Ideal.soc0 = 0 self.Ideal.Ppvs = self.Ideal.Ppv self.Ideal.Pperi = np.zeros_like(self.ppv) @dataclass class Real(Data): Pr : np.array Ppvs : np.array Pbs : np.array @dataclass class Ideal(Real): def __init__(self): super().__init__() def simulation(self): """Manages the Performance Simulation Model for AC-coupled PV-Battery Systems """ self.Real.Pbat, self.Real.Pbs, self.Real.soc, self.Real.soc0, self.Real.Pbs0 = batmod_ac( self.d, self.dt, self.Real.soc0, self.Real.soc, self.Real.Pr, self.Real.Pbs0, self.Real.Pbs, self.Real.Pbat) self.Ideal.Pbs, self.Ideal.Pbat, self.Ideal.soc0, self.Ideal.soc = batmod_ac_ideal( self.d, self.dt, self.Ideal.soc0, self.Ideal.soc, self.Ideal.Pr, self.Ideal.Pbat) def bat_mod_res(self): """Function to calculate the power flows and energy sums including curtailment of PV power """ self.Real.E = bat_res_mod( self.parameter, self.pl, self.Real.Ppv, self.Real.Pbat, self.dt, self.Real.Ppvs, self.Real.Pbs, self.Real.Pperi) self.Ideal.E = bat_res_mod_ideal( self.parameter, self.pl, self.Ideal.Ppv, self.Ideal.Pbat, self.dt, self.Ideal.Ppvs, self.Ideal.Pbs, self.Ideal.Pperi) def calculate_spi(self): self.spi = calculate_spi(_E_real=self.Real.E, _E_ideal=self.Ideal.E) def get_E(self): """Returns the energy sums of the simulation :return: Energy sums of the simulation in MWh :rtype: dict """ return self.Real.E, self.Ideal.E def get_soc(self): """Returns the state of charge of the battery :return: state of charge of the battery :rtype: numpy array """ return self.soc def get_Pbat(self): """Returns the DC power of the battery in W :return: DC power of the battery in W :rtype: numpy array """ return self.Pbat def get_Pbs(self): """Returns the AC power of the battery system in W :return: AC power of the battery system in W :rtype: numpy array """ return self.Pbs def get_SPI(self): return self.spi class BatModPV(object): """Performance Simulation Class for PV-coupled PV-Battery systems :param parameter: PV battery system parameters :type parameter: dict :param d: array containing parameters :type d: numpy array :param ppv: normalized DC power output of the PV generator :type ppv: numpy array :param pl: AC load power :type pl: numpy array :param Pac: Power demand on the AC side :type Pac: numpy array :param Ppv: DC power output of the PV generator :type Ppv: numpy array :param Pperi: Additional power consumption of other system components (e.g. AC power meter) in W :type Pperi: numpy array :param dt: time step width in seconds :type dt: integer """ _version = '0.1' def __init__(self, parameter, d, ppv, pl, Pac, Ppv, Pperi, dt): """Constructor method """ self.parameter = parameter self.d = d self.ppv = ppv self.pl = pl self.Pac = Pac self.Ppv = Ppv self.Pperi = Pperi self.dt = dt # Initialization and preallocation self.Pbat = np.zeros_like(self.ppv) # DC power of the battery in W self.soc = np.zeros_like(self.ppv) # State of charge of the battery # Output power of the PV2AC conversion pathway in W self.Ppv2ac_out = np.zeros_like(self.ppv) # Input power of the PV2BAT conversion pathway in W self.Ppv2bat_in = np.zeros_like(self.ppv) self.Ppv2bat_in0 = 0 # Output power of the BAT2PV conversion pathway in W self.Pbat2pv_out = np.zeros_like(self.ppv) self.Pbat2pv_out0 = 0 # AC power of the PV-battery system in W self.Ppvbs = np.zeros_like(self.ppv) self.simulation() self.bat_mod_res() def simulation(self, pvmod=True): """Manages the Performance Simulation Model for AC-coupled PV-Battery Systems """ self.th = 0 # Start threshold for the recharging of the battery self.soc0 = 0 # Initial state of charge of the battery in the first time step # Simulation of the battery system #start = time.process_time() self.soc, self.soc0, self.Ppv, self.Ppvbs, self.Pbat, self.Ppv2ac_out, self.Pbat2pv_out, self.Ppv2bat_in = batmod_pv(self.d, self.dt, self.soc0, self.soc, self.Ppv, self.Pac, self.Ppv2bat_in0, self.Ppv2bat_in, self.Ppv2ac_out, self.Pbat2pv_out0, self.Pbat2pv_out, self.Ppvbs, self.Pbat) #print(time.process_time()-start) # Define missing parameters self.Ppv2ac = self.Ppv2ac_out # AC output power of the PV2AC conversion pathway self.Ppv2bat = self.Ppv2bat_in # DC input power of the PV2BAT conversion pathway def bat_mod_res(self): """Function to calculate the power flows and energy sums including curtailment of PV power """ self.E = bat_res_mod(self.parameter, self.pl, self.Ppv, self.Pbat, self.dt, self.Ppv2ac, self.Ppv2bat, self.Ppvbs, self.Pperi) def get_E(self): """Returns the energy sums of the simulation :return: Energy sums of the simulation in MWh :rtype: dict """ return self.E def get_soc(self): """Returns the state of charge of the battery :return: state of charge of the battery :rtype: numpy array """ return self.soc def get_Pbat(self): """Returns the DC power of the battery in W :return: DC power of the battery in W :rtype: numpy array """ return self.Pbat class ModBus(object): """Establishes connection to a battery system via ModBus protocol :param host: IP address of the host :type host: string :param port: Server port of the host :type port: integer :param unit_id: Unit-ID of the host :type unit_id: integer """ def __init__(self, host, port, unit_id, input_vals, dt, fname): """Constructor method """ self.host = host self.port = port self.unit_id = unit_id self.dt = dt self.input_vals = input_vals self.fname = fname self.open_connection() self.create_csv_file() self.start_loop() def open_connection(self): """Opens the connection to the host """ # Open ModBus connection try: self.c = ModbusClient(host=self.host, port=self.port, unit_id=self.unit_id, auto_open=True, auto_close=True) except ValueError: print("Error with host: {}, port: {} or unit-ID: {} params".format( self.host, self.port, self.unit_id)) def start_loop(self): """Starts the writing and reading process """ # Transform the array to fit the 1 minute time duration #self.set_vals = np.repeat(self.input_vals, self.dt * 60) i = 0 idx = pd.date_range(start=datetime.datetime.now(), periods=(self.input_vals.size), freq='S') while i < len(idx): if datetime.datetime.now().second == idx[i].second: # Set chrging value self.set_val = int(self.input_vals[i]) if self.set_val < 0: # Write negative value to battery charge power (AC) setpoint register self.c.write_single_register(1024, self.set_val & 0xFFFF) # Log writing time self.set_time = datetime.datetime.now() else: # Write positive value to battery charge power (AC) setpoint to register self.c.write_single_register(1024, self.set_val) # Log writing time self.set_time = datetime.datetime.now() try: # Read total AC power value from register _P_ac = self.c.read_holding_registers(172, 2) self.read_time_P_ac = datetime.datetime.now() except: print('Could not read register 172!') try: # Read actual battery charge/discharge power value from register _P_bat = self.c.read_holding_registers(582, 1) self.read_time_P_bat = datetime.datetime.now() except: print('Could not read register 582!') # Load content of two registers into a single float value zregs = utils.word_list_to_long(_P_ac, big_endian=False) # Decode and store float value of the AC-power self.P_ac = utils.decode_ieee(zregs) # Store the DC charging power self.P_bat = np.int16(_P_bat) # Read actual soc self.soc0 = self.read_soc(210) try: # Save the values to a csv file self.save_to_csv() except: print('Could not save to csv!') i += 1 def read_soc(self, reg): """Reads the state of charge of the battery """ # Load the actual state fo charge of the battery regs = self.c.read_holding_registers(reg, 2) # Load content of two registers into a single float value zregs = utils.word_list_to_long(regs, big_endian=False) return utils.decode_ieee(zregs) def create_csv_file(self): """Creates a csv file from set and read values """ # Create a new csv-file with open(self.fname, 'w') as f: writer = csv.writer(f, dialect='excel') writer.writerow(['set_time', 'read_time_P_ac', 'read_time_P_bat', 'soc', 'set_value', 'P_ac', 'P_bat']) def save_to_csv(self): """Saves the set and read values to s csv file """ # Save the read values to a csv file with open(self.fname, "a") as f: wr = csv.writer(f, dialect='excel') wr.writerow([self.set_time, self.read_time_P_ac, self.read_time_P_bat, self.soc0, self.set_val, self.P_ac, self.P_bat]) def max_self_consumption(parameter, ppv, pl, pvmod=True, ideal=False): """Function for maximizing self consumption :param parameter: PV battery system parameters :type parameter: dict :param ppv: normalized DC power output of the PV generator :type ppv: numpy array :param pl: AC load power :type pl: numpy array """ # Maximize self consumption for AC-coupled systems if parameter['Top'] == 'AC': # DC power output of the PV generator if pvmod: # ppv: Normalized DC power output of the PV generator in kW/kWp if ideal: Ppv = np.maximum(0, ppv ) parameter['P_PV'] * 1000 else: Ppv = np.minimum(ppv * parameter['P_PV'], parameter['P_PV2AC_in']) * 1000 else: # ppv: DC power output of the PV generator in W if ideal: Ppv = np.maximum(0, ppv) else: Ppv = np.minimum(ppv, parameter['P_PV2AC_in'] * 1000) # Normalized input power of the PV inverter ppvinvin = Ppv / parameter['P_PV2AC_in'] / 1000 # AC power output of the PV inverter taking into account the conversion losses and maximum # output power of the PV inverter Ppvs = np.minimum(np.maximum(0, Ppv-(parameter['PV2AC_a_in'] * ppvinvin * ppvinvin + parameter['PV2AC_b_in'] * ppvinvin + parameter['PV2AC_c_in'])), parameter['P_PV2AC_out'] * 1000) # 3.2 Residual power # Additional power consumption of other system components (e.g. AC power meter) in W Pperi = np.ones_like(ppv) * parameter['P_PERI_AC'] # Adding the standby consumption of the PV inverter in times without any AC power output of the PV system # to the additional power consumption Pperi[Ppvs == 0] += parameter['P_PVINV_AC'] # Residual power if ideal: Pr = Ppv - pl else: Pr = Ppvs - pl - Pperi return Pr, Ppv, Ppvs, Pperi # Maximize self consumption for DC-coupled systems elif parameter['Top'] == 'DC': # Initialization and preallocation Ppv2ac_in_ac = np.zeros_like(ppv) Ppv = np.empty_like(ppv) # DC power output of the PV generator if pvmod: # ppv: Normalized DC power output of the PV generator in kW/kWp Ppv = ppv * parameter['P_PV'] * 1000 else: Ppv = ppv # DC power output of the PV generator taking into account the maximum # DC input power of the PV2AC conversion pathway Ppv = np.minimum(Ppv, parameter['P_PV2AC_in'] * 1000) # Residual power # Power demand on the AC side Pac = pl + parameter['P_PERI_AC'] # Normalized AC output power of the PV2AC conversion pathway to cover the AC # power demand ppv2ac = np.minimum( Pac, parameter['P_PV2AC_out'] * 1000) / parameter['P_PV2AC_out'] / 1000 # Target DC input power of the PV2AC conversion pathway Ppv2ac_in_ac = np.minimum(Pac, parameter['P_PV2AC_out'] * 1000) + ( parameter['PV2AC_a_out'] * ppv2ac*2 + parameter['PV2AC_b_out'] ppv2ac + parameter['PV2AC_c_out']) # Normalized DC input power of the PV2AC conversion pathway TODO 1 ppv2ac = Ppv / parameter['P_PV2AC_in'] / 1000 # Target AC output power of the PV2AC conversion pathway Ppv2ac_out = np.maximum( 0, Ppv - (parameter['PV2AC_a_in'] * ppv2ac*2 + parameter['PV2AC_b_in'] ppv2ac + parameter['PV2AC_c_in'])) # Residual power for battery charging Prpv = Ppv - Ppv2ac_in_ac # Residual power for battery discharging Pr = Ppv2ac_out - Pac return Pr, Prpv, Ppv, ppv2ac, Ppv2ac_out # Maximize self consumption for PV-coupled systems elif parameter['Top'] == 'PV': # Preallocation # Pbat = np.zeros_like(ppv) # DC power of the battery in W # soc = np.zeros_like(ppv) # State of charge of the battery # Ppv2ac_out = np.zeros_like(ppv) # Output power of the PV2AC conversion pathway in W # Ppv2bat_in = np.zeros_like(ppv) # Input power of the PV2BAT conversion pathway in W # Pbat2pv_out = np.zeros_like(ppv) # Output power of the BAT2PV conversion pathway in W # Ppvbs = np.zeros_like(ppv) # AC power of the PV-battery system in W Ppv = np.empty_like(ppv) # DC power output of the PV generator # Additional power consumption of other system components (e.g. AC power meter) in W Pperi = np.ones_like(ppv) * parameter['P_PERI_AC'] # dt = 1 # Time increment in s # th = 0 # Start threshold for the recharging of the battery # soc0 = 0 # State of charge of the battery in the first time step # DC power output of the PV generator if pvmod: # ppv: Normalized DC power output of the PV generator in kW/kWp Ppv = ppv * parameter['P_PV'] * 1000 else: # ppv: DC power output of the PV generator in W Ppv = ppv # Power demand on the AC side Pac = pl + Pperi return Pac, Ppv, Pperi @nb.jit(nopython=True) def batmod_ac(d, _dt, _soc0, _soc, _Pr, _Pbs0, _Pbs, _Pbat): """Performance Simulation function for AC-coupled battery systems :param d: array containing parameters :type d: numpy array :param dt: time step width :type dt: integer :param soc0: state of charge in the previous time step :type soc0: float :param Pr: residual power :type Pr: numpy array :param Pbs0: AC-power of the battery system in the previous time step :type Pbs0: float :param Pbs: AC-power of the battery syste :type Pbs: numpy array :param Pbat: DC-power oof the battery :type Pbat: numpy array """ # Loading of particular variables _E_BAT = d[0] _eta_BAT = d[1] _t_CONSTANT = d[2] _P_SYS_SOC0_DC = d[3] _P_SYS_SOC0_AC = d[4] _P_SYS_SOC1_DC = d[5] _P_SYS_SOC1_AC = d[6] _AC2BAT_a_in = d[7] _AC2BAT_b_in = d[8] _AC2BAT_c_in = d[9] _BAT2AC_a_out = d[10] _BAT2AC_b_out = d[11] _BAT2AC_c_out = d[12] _P_AC2BAT_DEV = d[13] _P_BAT2AC_DEV = d[14] _P_BAT2AC_out = d[15] _P_AC2BAT_in = d[16] _t_DEAD = int(round(d[17])) _SOC_h = d[18] _P_AC2BAT_min = _AC2BAT_c_in _P_BAT2AC_min = _BAT2AC_c_out # Correction factor to avoid over charge and discharge the battery corr = 0.1 # Initialization of particular variables _tde = _t_CONSTANT > 0 # Binary variable to activate the first-order time delay element # Factor of the first-order time delay element _ftde = 1 - np.exp(-_dt / _t_CONSTANT) # First time step with regard to the dead time of the system control _tstart = np.maximum(2, 1 + _t_DEAD) _tend = int(_Pr.size) _th = 0 # Capacity of the battery, conversion from kWh to Wh _E_BAT = 1000 # Effiency of the battery in percent _eta_BAT /= 100 # Check if the dead or settling time can be ignored and set flags accordingly if _dt >= (3 _t_CONSTANT) or _tend == 1: _tstart = 1 T_DEAD = False else: T_DEAD = True if _dt >= _t_DEAD + 3 * _t_CONSTANT: SETTLING = False else: SETTLING = True for t in range(_tstart - 1, _tend): # Energy content of the battery in the previous time step E_b0 = _soc0 * _E_BAT # Calculate the AC power of the battery system from the residual power # with regard to the dead time of the system control if T_DEAD: P_bs = _Pr[t - _t_DEAD] else: P_bs = _Pr[t] # Check if the battery holds enough unused capacity for charging or discharging # Estimated amount of energy in Wh that is supplied to or discharged from the storage unit. E_bs_est = P_bs * _dt / 3600 # Reduce P_bs to avoid over charging of the battery if E_bs_est > 0 and E_bs_est > (_E_BAT - E_b0): P_bs = (_E_BAT - E_b0) * 3600 / _dt # When discharging take the correction factor into account elif E_bs_est < 0 and np.abs(E_bs_est) > (E_b0): P_bs = (E_b0 * 3600 / _dt) * (1-corr) # Adjust the AC power of the battery system due to the stationary # deviations taking the minimum charging and discharging power into # account if P_bs > _P_AC2BAT_min: P_bs = np.maximum(_P_AC2BAT_min, P_bs + _P_AC2BAT_DEV) elif P_bs < -_P_BAT2AC_min: P_bs = np.minimum(-_P_BAT2AC_min, P_bs - _P_BAT2AC_DEV) else: P_bs = 0 # Limit the AC power of the battery system to the rated power of the # battery converter P_bs = np.maximum(-_P_BAT2AC_out * 1000, np.minimum(_P_AC2BAT_in * 1000, P_bs)) # Adjust the AC power of the battery system due to the settling time # (modeled by a first-order time delay element) Hier hat der Schritt vorher eine Null? # Muss der vorherige Wert mit übergeben werden? if SETTLING: if t > 0: P_bs = _tde * _Pbs[t-1] + _tde * (P_bs - _Pbs[t-1]) * _ftde + P_bs * (not _tde) else: P_bs = _tde * _Pbs0 + _tde * (P_bs - _Pbs0) * _ftde + P_bs * (not _tde) # Decision if the battery should be charged or discharged if P_bs > 0 and _soc0 < 1 - _th * (1 - _SOC_h): # The last term th(1-SOC_h) avoids the alternation between # charging and standby mode due to the DC power consumption of the # battery converter when the battery is fully charged. The battery # will not be recharged until the SOC falls below the SOC-threshold # (SOC_h) for recharging from PV. # Normalized AC power of the battery system p_bs = P_bs / _P_AC2BAT_in / 1000 # DC power of the battery affected by the AC2BAT conversion losses # of the battery converter P_bat = np.maximum( 0, P_bs - (_AC2BAT_a_in p_bs * p_bs + _AC2BAT_b_in * p_bs + _AC2BAT_c_in)) elif P_bs < 0 and _soc0 > 0: # Normalized AC power of the battery system p_bs = np.abs(P_bs / _P_BAT2AC_out / 1000) # DC power of the battery affected by the BAT2AC conversion losses # of the battery converter P_bat = P_bs - (_BAT2AC_a_out * p_bs * p_bs + _BAT2AC_b_out * p_bs + _BAT2AC_c_out) else: # Neither charging nor discharging of the battery # Set the DC power of the battery to zero P_bat = 0 # Decision if the standby mode is active if P_bat == 0 and _soc0 <= 0: # Standby mode in discharged state # DC and AC power consumption of the battery converter P_bat = -np.maximum(0, _P_SYS_SOC0_DC) P_bs = _P_SYS_SOC0_AC elif P_bat == 0 and _soc0 > 0: # Standby mode in fully charged state # DC and AC power consumption of the battery converter P_bat = -np.maximum(0, _P_SYS_SOC1_DC) P_bs = _P_SYS_SOC1_AC # Transfer the realized AC power of the battery system and # the DC power of the battery _Pbs0 = P_bs _Pbs[t] = P_bs _Pbat[t] = P_bat # Change the energy content of the battery from Ws to Wh conversion if P_bat > 0: E_b = E_b0 + P_bat * np.sqrt(_eta_BAT) * _dt / 3600 elif P_bat < 0: E_b = E_b0 + P_bat / np.sqrt(_eta_BAT) * _dt / 3600 else: E_b = E_b0 # Calculate the state of charge of the battery _soc0 = E_b / (_E_BAT) _soc[t] = _soc0 # Adjust the hysteresis threshold to avoid alternation # between charging and standby mode due to the DC power # consumption of the battery converter. if _th and _soc[t] > _SOC_h or _soc[t] > 1: _th = True else: _th = False return _Pbat, _Pbs, _soc, _soc0, _Pbs0 @nb.jit(nopython=True) def batmod_ac_ideal(d, _dt, _soc0, _soc, _Pr, _Pbat): _E_BAT = d[0] for t in range(_Pr.size): # Energy content of the battery in the previous time step E_b0 = _soc0 * _E_BAT * 1000 # Calculate the DC power of the battery from the residual power P_bat = _Pr[t] # Decision if the battery should be charged or discharged if P_bat > 0 and _soc0 < 1: # Battery charging E_b = E_b0 + P_bat * _dt / 3600 # Change the energy content of the battery elif P_bat < 0 and _soc0 > 0: # Battery discharging # Change the energy content of the battery E_b = E_b0 + P_bat * _dt / 3600 else: # Neither charging nor discharging of the battery # Set the DC power of the battery to zero P_bat = 0 # No change in the energy content of the battery E_b = E_b0 # Transfer the realized DC power of the battery _Pbat[t] = P_bat # Calculate the state of charge of the battery _soc0 = E_b / (_E_BAT * 1000) _soc[t] = _soc0 # Define missing parameters _Pbs = _Pbat # Realized AC power of the battery system return _Pbs, _Pbat, _soc0, _soc @nb.jit(nopython=True) def batmod_dc(d, _dt, _soc0, _soc, _Pr, _Prpv, _Ppv, _Ppv2bat_in0, _Ppv2bat_in, _Pbat2ac_out0, _Pbat2ac_out, _Ppv2ac_out, _Ppvbs, _Pbat): """Performance simulation function for DC-coupled battery systems :param d: array containing parameters :type d: numpy array :param dt: time step width :type dt: integer :param soc0: state of charge in the previous time step :type soc0: float :param Pr: residual power :type Pr: numpy array :param Prpv: residual power of the PV-system :type Prpv: numpy array :param Ppv: PV-power :type Ppv: numpy array :param Ppv2bat_in0: AC input power of the battery system in the previous time step :type Ppv2bat_in0: float :param Ppv2bat_in: AC input power of the battery system :type Ppv2bat_in: numpy array :param Pbat2ac_out0: AC output power of the battery system in the previous time step :type Pbat2ac_out0: float :param Pbat2ac_out: AC output power of the battery system :type Pbat2ac_out: numpy array :param Ppv2ac_out0: AC output power of the PV inverter in the previous time step :type Ppv2ac_out0: float :param Ppv2ac_out: AC output power of the PV inverter :type Ppv2ac_out: numpy array :param Ppvbs: AC power from the PV system to the battery system :type Ppvbs: numpy array :param Pbat: DC power of the battery :type Pbat: float """ _E_BAT = d[0] _P_PV2AC_in = d[1] _P_PV2AC_out = d[2] _P_PV2BAT_in = d[3] _P_BAT2AC_out = d[4] _PV2AC_a_in = d[5] _PV2AC_b_in = d[6] _PV2AC_c_in = d[7] _PV2BAT_a_in = d[8] _PV2BAT_b_in = d[9] _BAT2AC_a_out = d[10] _BAT2AC_b_out = d[11] _BAT2AC_c_out = d[12] _eta_BAT = d[13] _SOC_h = d[14] _P_PV2BAT_DEV = d[15] _P_BAT2AC_DEV = d[16] _t_DEAD = int(round(d[17])) _t_CONSTANT = d[18] _P_SYS_SOC1_DC = d[19] _P_SYS_SOC0_AC = d[20] _P_SYS_SOC0_DC = d[21] _P_PV2AC_min = _PV2AC_c_in # Capacity of the battery, conversion from kWh to Wh _E_BAT = 1000 # Effiency of the battery in percent _eta_BAT /= 100 # Initialization of particular variables # _P_PV2AC_min = _parameter['PV2AC_c_in'] # Minimum input power of the PV2AC conversion pathway _tde = _t_CONSTANT > 0 # Binary variable to activate the first-order time delay element # Factor of the first-order time delay element _ftde = 1 - np.exp(-_dt / _t_CONSTANT) # First time step with regard to the dead time of the system control _tstart = np.maximum(2, 1 + _t_DEAD) _tend = int(_Pr.size) _th = 0 corr = 0.1 # Check if the dead or settling time can be ignored and set flags accordingly if _dt >= (3 _t_CONSTANT) or _tend == 1: _tstart = 1 T_DEAD = False else: T_DEAD = True if _dt >= _t_DEAD + 3 * _t_CONSTANT: SETTLING = False else: SETTLING = True for t in range(_tstart - 1, _tend): # Energy content of the battery in the previous time step E_b0 = _soc0 * _E_BAT # Residual power with regard to the dead time of the system control if T_DEAD: P_rpv = _Prpv[t - _t_DEAD] P_r = _Pr[t - _t_DEAD] else: P_rpv = _Prpv[t] P_r = _Pr[t] # Check if the battery holds enough unused capacity for charging or discharging # Estimated amount of energy that is supplied to or discharged from the storage unit. E_bs_rpv = P_rpv * _dt / 3600 E_bs_r = P_r * _dt / 3600 # Reduce P_bs to avoid over charging of the battery if E_bs_rpv > 0 and E_bs_rpv > (_E_BAT - E_b0): P_rpv = (_E_BAT - E_b0) * 3600 / _dt # When discharging take the correction factor into account elif E_bs_r < 0 and np.abs(E_bs_r) > (E_b0): P_r = ((E_b0) * 3600 / _dt) * (1-corr) # Decision if the battery should be charged or discharged if P_rpv > 0 and _soc0 < 1 - _th * (1 - _SOC_h): ''' The last term th(1-SOC_h) avoids the alternation between charging and standby mode due to the DC power consumption of the battery converter when the battery is fully charged. The battery will not be recharged until the SOC falls below the SOC-threshold (SOC_h) for recharging from PV. ''' # Charging power P_pv2bat_in = P_rpv # Adjust the charging power due to the stationary deviations P_pv2bat_in = np.maximum(0, P_pv2bat_in + _P_PV2BAT_DEV) # Limit the charging power to the maximum charging power P_pv2bat_in = np.minimum(P_pv2bat_in, _P_PV2BAT_in 1000) # Adjust the charging power due to the settling time # (modeled by a first-order time delay element) if SETTLING: if t > 0: P_pv2bat_in = _tde * _Ppv2bat_in[(t-1)] + _tde * ( P_pv2bat_in - _Ppv2bat_in[(t-1)]) * _ftde + P_pv2bat_in * (not _tde) else: P_pv2bat_in = _tde * _Ppv2bat_in0 + _tde * \ (P_pv2bat_in - _Ppv2bat_in0) * \ _ftde + P_pv2bat_in * (not _tde) # Limit the charging power to the current power output of the PV generator P_pv2bat_in = np.minimum(P_pv2bat_in, _Ppv[t]) # Normalized charging power ppv2bat = P_pv2bat_in / _P_PV2BAT_in / 1000 # DC power of the battery affected by the PV2BAT conversion losses # (the idle losses of the PV2BAT conversion pathway are not taken # into account) P_bat = np.maximum( 0, P_pv2bat_in - (_PV2BAT_a_in * ppv2bat*2 + _PV2BAT_b_in ppv2bat)) # Realized DC input power of the PV2AC conversion pathway P_pv2ac_in = _Ppv[t] - P_pv2bat_in # Normalized DC input power of the PV2AC conversion pathway _ppv2ac = P_pv2ac_in / _P_PV2AC_in / 1000 # Realized AC power of the PV-battery system P_pv2ac_out = np.maximum( 0, P_pv2ac_in - (_PV2AC_a_in * _ppv2ac*2 + _PV2AC_b_in _ppv2ac + _PV2AC_c_in)) P_pvbs = P_pv2ac_out # Transfer the final values _Ppv2ac_out[t] = P_pv2ac_out _Ppv2bat_in0 = P_pv2bat_in _Ppv2bat_in[t] = P_pv2bat_in elif P_rpv < 0 and _soc0 > 0: # Discharging power P_bat2ac_out = P_r * -1 # Adjust the discharging power due to the stationary deviations P_bat2ac_out = np.maximum(0, P_bat2ac_out + _P_BAT2AC_DEV) # Adjust the discharging power to the maximum discharging power P_bat2ac_out = np.minimum(P_bat2ac_out, _P_BAT2AC_out * 1000) # Adjust the discharging power due to the settling time # (modeled by a first-order time delay element) if SETTLING: if t > 0: P_bat2ac_out = _tde * _Pbat2ac_out[t-1] + _tde * ( P_bat2ac_out - _Pbat2ac_out[t-1]) * _ftde + P_bat2ac_out * (not _tde) else: P_bat2ac_out = _tde * _Pbat2ac_out0 + _tde * \ (P_bat2ac_out - _Pbat2ac_out0) * \ _ftde + P_bat2ac_out * (not _tde) # Limit the discharging power to the maximum AC power output of the PV-battery system P_bat2ac_out = np.minimum( _P_PV2AC_out * 1000 - _Ppv2ac_out[t], P_bat2ac_out) # Normalized discharging power ppv2bat = P_bat2ac_out / _P_BAT2AC_out / 1000 # DC power of the battery affected by the BAT2AC conversion losses # (if the idle losses of the PV2AC conversion pathway are covered by # the PV generator, the idle losses of the BAT2AC conversion pathway # are not taken into account) if _Ppv[t] > _P_PV2AC_min: P_bat = -1 * (P_bat2ac_out + (_BAT2AC_a_out * ppv2bat*2 + _BAT2AC_b_out ppv2bat)) else: P_bat = -1 * (P_bat2ac_out + (_BAT2AC_a_out * ppv2bat ** 2 + _BAT2AC_b_out * ppv2bat + _BAT2AC_c_out)) + _Ppv[t] # Realized AC power of the PV-battery system P_pvbs = _Ppv2ac_out[t] + P_bat2ac_out # Transfer the final values _Pbat2ac_out0 = P_bat2ac_out _Pbat2ac_out[t] = P_bat2ac_out else: # Neither charging nor discharging of the battery # Set the DC power of the battery to zero P_bat = 0 # Realized AC power of the PV-battery system P_pvbs = _Ppv2ac_out[t] # Decision if the standby mode is active if P_bat == 0 and P_pvbs == 0 and _soc0 <= 0: # Standby mode in discharged state # DC and AC power consumption of the PV-battery inverter P_bat = -np.maximum(0, _P_SYS_SOC0_DC) P_pvbs = -_P_SYS_SOC0_AC elif P_bat == 0 and P_pvbs > 0 and _soc0 > 0: # Standby mode in fully charged state # DC power consumption of the PV-battery inverter P_bat = -np.maximum(0, _P_SYS_SOC1_DC) # Transfer the realized AC power of the PV-battery system and the DC power of the battery _Ppvbs[t] = P_pvbs _Pbat[t] = P_bat # Change the energy content of the battery Wx to Wh conversion if P_bat > 0: E_b = E_b0 + P_bat * np.sqrt(_eta_BAT) * _dt / 3600 elif P_bat < 0: E_b = E_b0 + P_bat / np.sqrt(_eta_BAT) * _dt / 3600 else: E_b = E_b0 # Calculate the state of charge of the battery _soc0 = E_b / _E_BAT _soc[t] = _soc0 # Adjust the hysteresis threshold to avoid alternation between charging # and standby mode due to the DC power consumption of the # PV-battery inverter if _th and _soc[t] > _SOC_h or _soc[t] > 1: _th = True else: _th = False return _Ppv2ac_out, _Ppv2bat_in, _Ppv2bat_in0, _Pbat2ac_out, _Pbat2ac_out0, _Ppvbs, _Pbat, _soc, _soc0 @nb.jit(nopython=True) def batmod_dc_ideal(d, _dt, _soc0, _soc, _Pr, _Pbat): _E_BAT = d[0] for t in range(_Pr.size): # Energy content of the battery in the previous time step E_b0 = _soc0 * _E_BAT * 1000 P_bat = _Pr[t] if P_bat > 0 and _soc0 < 1: # Battery charging # Change the energy content of the battery E_b = E_b0 + P_bat * _dt / 3600 elif P_bat < 0 and _soc0 > 0: # Battery discharging # Change the energy content of the battery E_b = E_b0 + P_bat * _dt / 3600 else: # Neither charging nor discharging of the battery P_bat = 0 E_b = E_b0 _Pbat[t] = P_bat _soc0 = E_b / (_E_BAT * 1000) _soc[t] = _soc0 return _Pbat, _soc, _soc0 @nb.jit(nopython=True) def batmod_pv(d, _dt, _soc0, _soc, _Ppv, _Pac, _Ppv2bat_in0, _Ppv2bat_in, _Ppv2ac_out, _Pbat2pv_out0, _Pbat2pv_out, _Ppvbs, _Pbat): """Performance simulation function for PV-coupled battery systems :param d: array containing parameters :type d: numpy array :param dt: time step width :type dt: integer :param soc0: state of charge of the battery in the previous time step :type soc0: float :param soc: state of charge of the battery :type soc: numpy array :param Pr: residual power :type Pr: numpy array :param Ppv: PV-power :type Ppv: numpy array :param Pac: AC output power of the PV inverter :type Pac: numpy array :param Ppv2bat_in: AC input power of the battery system :type Ppv2bat_in: numpy array :param Ppv2bat_in0: AC input power of the battery system in the previous time step :type Ppv2bat_in0: float :param Pbat2pv_out0: AC output power of the battery system in the previous time step :type Pbat2pv_out0: float :param Pbat2pv_out: AC output power of the battery system :type Pbat2pv_out: numpy array :param Ppvbs: AC power from the PV system to the battery system :type Ppvbs: numpy array :param Pbat: DC power of the battery :type Pbat: float """ # Initialization of particular variables _E_BAT = d[0] _P_PV2AC_in = d[1] _P_PV2AC_out = d[2] _P_PV2BAT_in = d[3] _P_BAT2PV_out = d[4] _PV2AC_a_in = d[5] _PV2AC_b_in = d[6] _PV2AC_c_in = d[7] _PV2BAT_a_in = d[8] _PV2BAT_b_in = d[9] _PV2BAT_c_in = d[10] _PV2AC_a_out = d[11] _PV2AC_b_out = d[12] _PV2AC_c_out = d[13] _BAT2PV_a_out = d[14] _BAT2PV_b_out = d[15] _BAT2PV_c_out = d[16] _eta_BAT = d[17] _SOC_h = d[18] _P_PV2BAT_DEV = d[19] _P_BAT2AC_DEV = d[20] _P_SYS_SOC1_DC = d[21] _P_SYS_SOC0_AC = d[22] _P_SYS_SOC0_DC = d[23] _t_DEAD = int(round(d[24])) _t_CONSTANT = d[25] # Correction factor to avoid over charge and discharge the battery corr = 0.1 _P_PV2BAT_min = _PV2BAT_c_in # Minimum DC charging power _P_BAT2PV_min = _BAT2PV_c_out # Minimum DC discharging power # Initialization of particular variables _tde = _t_CONSTANT > 0 # Binary variable to activate the first-order time delay element # Factor of the first-order time delay element _ftde = 1 - np.exp(-_dt / _t_CONSTANT) # First time step with regard to the dead time of the system control _tstart = np.maximum(2, 1 + _t_DEAD) _tend = int(_Ppv.size) _th = 0 _E_BAT = 1000 # Conversion from W to kW _eta_BAT /= 100 # Check if the dead or settling time can be ignored and set flags accordingly if _dt >= (3 _t_CONSTANT) or _tend == 1: _tstart = 1 T_DEAD = False else: T_DEAD = True if _dt >= _t_DEAD + 3 * _t_CONSTANT: SETTLING = False else: SETTLING = True for t in range(_tstart - 1, _tend): # Energy content of the battery in the previous time step E_b0 = _soc0 * _E_BAT # Target AC output power of the PV-battery system to cover the AC power demand if T_DEAD: P_pvbs = np.minimum(_Pac[t - _t_DEAD], _P_PV2AC_out * 1000) else: P_pvbs = np.minimum(_Pac[t], _P_PV2AC_out * 1000) # Normalized AC output power of the PV2AC conversion pathway ppv2ac = P_pvbs / _P_PV2AC_out / 1000 # Target DC input power of the PV2AC conversion pathway P_pv2ac_in = P_pvbs + (_PV2AC_a_out * ppv2ac ** 2 + _PV2AC_b_out * ppv2ac + _PV2AC_c_out) # Residual power if T_DEAD: P_rpv = _Ppv[t - _t_DEAD] - P_pv2ac_in else: P_rpv = _Ppv[t] - P_pv2ac_in # Check if the battery holds enough unused capacity for charging or discharging # Estimated amount of energy that is supplied to or discharged from the storage unit. E_bs_rpv = P_rpv * _dt / 3600 # Reduce P_bs to avoid over charging of the battery if E_bs_rpv > 0 and E_bs_rpv > (_E_BAT - E_b0): P_rpv = ((_E_BAT - E_b0) * 3600) / _dt # When charging take the correction factor into account elif E_bs_rpv < 0 and np.abs(E_bs_rpv) > (E_b0): P_rpv = ((E_b0) * 3600 / _dt) * (1-corr) # Decision if the battery should be charged or discharged if P_rpv > _P_PV2BAT_min and _soc0 < 1 - _th * (1 - _SOC_h): ''' The last term th(1-SOC_h) avoids the alternation between charging and standby mode due to the DC power consumption of the battery converter when the battery is fully charged. The battery will not be recharged until the SOC falls below the SOC-threshold (SOC_h) for recharging from PV. ''' # Charging power P_pv2bat_in = P_rpv # Adjust the charging power due to stationary deviations P_pv2bat_in = np.maximum(0, P_pv2bat_in + _P_PV2BAT_DEV) # Limit the charging power to the maximum charging power P_pv2bat_in = np.minimum(P_pv2bat_in, _P_PV2BAT_in 1000) # Adjust the charging power due to the settling time # (modeled by a first-order time delay element) if SETTLING: if t > 0: P_pv2bat_in = _tde * _Ppv2bat_in[t-1] + _tde * ( P_pv2bat_in - _Ppv2bat_in[t-1]) * _ftde + P_pv2bat_in * (not _tde) else: P_pv2bat_in = _tde * _Ppv2bat_in0 + _tde * \ (P_pv2bat_in - _Ppv2bat_in0) * \ _ftde + P_pv2bat_in * (not _tde) # Limit the charging power to the current power output of the PV generator P_pv2bat_in = np.minimum(P_pv2bat_in, _Ppv[t]) # Normalized charging power ppv2bat = P_pv2bat_in / _P_PV2BAT_in / 1000 # DC power of the battery P_bat = np.maximum(0, P_pv2bat_in - (_PV2BAT_a_in * ppv2bat*2 + _PV2BAT_b_in ppv2bat + _PV2BAT_c_in)) # Realized DC input power of the PV2AC conversion pathway P_pv2ac_in = _Ppv[t] - P_pv2bat_in # Limit the DC input power of the PV2AC conversion pathway P_pv2ac_in = np.minimum(P_pv2ac_in, _P_PV2AC_in * 1000) # Recalculate Ppv(t) with limited PV2AC input power _Ppv[t] = P_pv2ac_in + P_pv2bat_in # Normalized DC input power of the PV2AC conversion pathway ppv2ac = P_pv2ac_in / _P_PV2AC_in / 1000 # Realized AC power of the PV-battery system P_pv2ac_out = np.maximum( 0, P_pv2ac_in - (_PV2AC_a_in * ppv2ac*2 + _PV2AC_b_in ppv2ac + _PV2AC_c_in)) P_pvbs = P_pv2ac_out # Transfer the final values _Ppv2ac_out[t] = P_pv2ac_out _Ppv2bat_in0 = P_pv2bat_in _Ppv2bat_in[t] = P_pv2bat_in elif P_rpv < -_P_BAT2PV_min and _soc0 > 0: # Target discharging power of the battery P_bat2pv_out = np.abs(P_rpv) # Adjust the discharging power due to the stationary deviations P_bat2pv_out = np.maximum(0, P_bat2pv_out + _P_BAT2AC_DEV) # Adjust the discharging power to the maximum discharging power P_bat2pv_out = np.minimum(P_bat2pv_out, _P_BAT2PV_out * 1000) # Adjust the discharging power due to the settling time # (modeled by a first-order time delay element) if SETTLING: if t > 0: P_bat2pv_out = _tde * _Pbat2pv_out[t-1] + _tde * (P_bat2pv_out - _Pbat2pv_out[t-1]) * _ftde + P_bat2pv_out * (not _tde) else: P_bat2pv_out = _tde * _Pbat2pv_out0 + _tde * (P_bat2pv_out - _Pbat2pv_out0) * _ftde + P_bat2pv_out * (not _tde) # Recalculate Ppv(t) with limited PV2AC input power _Ppv[t] = np.minimum(_P_PV2AC_in * 1000, _Ppv[t]) # Limit the discharging power to the maximum AC power output of the PV-battery system P_bat2pv_out = np.minimum(_P_PV2AC_in * 1000 - _Ppv[t], P_bat2pv_out) # Normalized discharging power pbat2pv = P_bat2pv_out / _P_BAT2PV_out / 1000 # DC power of the battery affected by the BAT2PV conversion losses P_bat = -1(P_bat2pv_out+(_BAT2PV_a_out pbat2pv*2 + _BAT2PV_b_out pbat2pv + _BAT2PV_c_out)) # Realized DC input power of the PV2AC conversion pathway P_pv2ac_in = _Ppv[t] + P_bat2pv_out # Normalized DC input power of the PV2AC conversion pathway ppv2ac = P_pv2ac_in / _P_PV2AC_in / 1000 # AC power of the PV-battery system P_pvbs = np.maximum(0, P_pv2ac_in-(_PV2AC_a_in * ppv2ac*2 + _PV2AC_b_in ppv2ac + _PV2AC_c_in)) P_pv2ac_out = P_pvbs # Transfer the final values _Ppv2ac_out[t] = P_pv2ac_out _Pbat2pv_out0 = P_bat2pv_out _Pbat2pv_out[t] = P_bat2pv_out else: # Neither charging nor discharging of the battery # Set the DC power of the battery to zero P_bat = 0 # Limit the power output of the PV generator to the maximum input power # of the PV inverter _Ppv[t] = np.minimum(_Ppv[t], _P_PV2AC_in * 1000) # Normalized DC input power of the PV2AC conversion pathway ppv2ac = _Ppv[t] / _P_PV2AC_in / 1000 # Realized AC power of the PV-battery system P_pvbs = np.maximum(0, _Ppv[t] - (_PV2AC_a_in * ppv2ac*2 + _PV2AC_b_in ppv2ac + _PV2AC_c_in)) # Transfer the final values _Ppv2ac_out[t] = P_pvbs # Decision if the standby mode is active if P_bat == 0 and _soc0 <= 0: # Standby mode in discharged state # DC power consumption of the battery converter P_bat = -np.maximum(0, _P_SYS_SOC0_DC) if P_pvbs == 0: P_pvbs = -_P_SYS_SOC0_AC elif P_bat == 0 and P_pvbs > 0 and _soc0 > 0: # Standby mode in fully charged state # DC power consumption of the battery converter P_bat = -np.maximum(0, _P_SYS_SOC1_DC) # Transfer the realized AC power of the battery system and # the DC power of the battery _Ppvbs[t] = P_pvbs _Pbat[t] = P_bat # Change the energy content of the battery Wx to Wh conversio if P_bat > 0: E_b = E_b0 + P_bat * np.sqrt(_eta_BAT) * _dt / 3600 elif P_bat < 0: E_b = E_b0 + P_bat / np.sqrt(_eta_BAT) * _dt / 3600 else: E_b = E_b0 # Calculate the state of charge of the battery _soc0 = E_b / (_E_BAT) _soc[t] = _soc0 # Adjust the hysteresis threshold to avoid alternation # between charging and standby mode due to the DC power # consumption of the battery converter. if _th and _soc[t] > _SOC_h or _soc[t] > 1: _th = True else: _th = False return _soc, _soc0, _Ppv, _Ppvbs, _Pbat, _Ppv2ac_out, _Pbat2pv_out, _Ppv2bat_in def bat_res_mod(_parameter, _Pl, _Ppv, _Pbat, _dt, args): """Function for calculating energy sums :param _parameter: parameter of the system :type _parameter: dict :param _Pl: load power :type _Pl: numpy array :param _Ppv: output power of the PV generator :type _Ppv: numpy array :param _Pbat: DC power of the battery :type _Pbat: numpy array :param _dt: time step width :type _dt: integer :return: energy sums :rtype: dict """ _E = dict() if _parameter['Top'] == 'AC': # AC-coupled systems _Ppvs = args[0] # AC output power of the PV system _Pbs = args[1] # AC power of the battery system # Additional power consumption of the other system components _Pperi = args[2] elif _parameter['Top'] == 'DC' or _parameter['Top'] == 'PV': # DC- and PV-coupled systems _Ppv2ac = args[0] # AC output power of the PV2AC conversion pathway _Ppv2bat_in = args[1] # Input power of the PV2BAT conversion pathway _Ppvbs = args[2] # AC power of the PV-battery system # Additional power consumption of the other system components _Pperi = args[3] _Ppv2ac_in = _Ppv - _Ppv2bat_in # Input power of the PV2AC conversion pathway # Total load including the power consumption of the other system components _Plt = _Pl + _Pperi # DC input power of the battery (charged) _Pbatin = np.maximum(0, _Pbat) # DC output power of the battery (discharged) _Pbatout = np.minimum(0, _Pbat) # Maximum PV feed-in power _P_ac2g_max = _parameter['p_ac2g_max'] _parameter['P_PV'] * 1000 if _parameter['Top'] == 'AC': # AC-coupled systems # Residual power without curtailment _Pr = _Ppvs - _Plt # AC input power of the battery system _Pac2bs = np.maximum(0, _Pbs) # AC output power of the battery system _Pbs2ac = np.minimum(0, _Pbs) # Negative residual power (residual load demand) _Prn = np.minimum(0, _Pr) # Positive residual power (surplus PV power) _Prp = np.maximum(0, _Pr) # Direct use of PV power by the load _Ppvs2l = np.minimum(_Ppvs, _Plt) # PV charging power _Ppvs2bs = np.minimum(_Prp, _Pac2bs) # Grid charging power _Pg2bs = np.maximum(_Pac2bs - _Prp, 0) # Grid supply power of the load _Pg2l = np.minimum(_Prn - _Pbs2ac, 0) # Battery supply power of the load _Pbs2l = np.maximum(_Prn, _Pbs2ac) # Battery feed-in power _Pbs2g = np.minimum(_Pbs2ac - _Prn, 0) # PV feed-in power including curtailment _Ppvs2g = np.minimum(np.maximum(_Prp - _Pac2bs, 0), _P_ac2g_max) # Power demand from the grid _Pg2ac = _Pg2l - _Pg2bs # Feed-in power to the grid _Pac2g = _Ppvs2g - _Pbs2g # Grid power _Pg = _Pac2g + _Pg2ac # Curtailed PV power (AC output power) _Pct = np.maximum(_Prp - _Pac2bs, 0) - _Ppvs2g # AC output power of the PV system including curtailment _Ppvs = _Ppvs - _Pct # Residual power including curtailment _Pr = _Ppvs - _Plt # Index for PV curtailment _idx = np.where(_Pct > 0)[0] for i in range(len(_idx)): _tct = _idx[i] # Normalized output power of the PV inverter _ppvinvout = _Ppvs[_tct] / _parameter['P_PV2AC_out'] / 1000 # DC output power of the PV generator taking into account the # conversion and curtailment losses _Ppv[_tct] = _Ppvs[_tct] + (_parameter['PV2AC_a_out'] * _ppvinvout ** 2 + _parameter['PV2AC_b_out'] * _ppvinvout + _parameter['PV2AC_c_out']) elif _parameter['Top'] == 'DC' or _parameter['Top'] == 'PV': # DC- and PV-coupled systems # Grid power demand of the PV-battery system _Pg2pvbs = np.minimum(0, _Ppvbs) # AC input power of the PV-battery system _Pac2pvbs = _Pg2pvbs # AC output power of the PV-battery system _Ppvbs2ac = np.maximum(0, _Ppvbs) # Load supply power by the PV-battery system _Ppvbs2l = np.minimum(_Plt, _Ppvbs2ac) # Load supply power by the grid _Pg2l = _Plt - _Ppvbs2l # Direct use of PV power by the load _Ppv2l = np.minimum(_Plt, _Ppv2ac) # PV feed-in power including curtailment _Ppv2g = np.minimum(_Ppv2ac - _Ppv2l, _P_ac2g_max) # Curtailed PV power (AC output power) _Pct = _Ppv2ac - _Ppv2l - _Ppv2g if np.sum(_Pct) > 0: # Power of the PV-battery system including curtailment _Ppvbs = _Ppvbs - _Pct # AC output power of the PV-battery system including curtailment _Ppvbs2ac = np.maximum(0, _Ppvbs) # AC output power of the PV2AC conversion pathway including curtailment _Ppv2ac = _Ppv2ac - _Pct # Index for PV curtailment _idx = np.where(_Pct > 0)[0] for i in range(len(_idx)): _tct = _idx[i] # Specific AC output power of the PV2AC conversion pathway _ppv2ac = _Ppv2ac[_tct] / _parameter['P_PV2AC_out'] / 1000 # DC input power of the PV2AC conversion pathway including curtailment _Ppv2ac_in[_tct] = _Ppv2ac[_tct] + (_parameter['PV2AC_a_out'] * _ppv2ac ** 2 + _parameter['PV2AC_b_out'] * _ppv2ac + _parameter['PV2AC_c_out']) # DC output power of the PV generator including curtailment _Ppv = _Ppv2ac_in + _Ppv2bat_in # Grid power including curtailment _Pg = _Ppvbs-_Plt # Feed-in power to the grid including curtailment _Pac2g = np.maximum(0, _Pg) # Power demand from the grid _Pg2ac = np.minimum(0, _Pg) # Energy sums in MWH # Electrical demand including the energy consumption of the other system components _E['El'] = np.sum(np.abs(_Plt)) * _dt / 3.6e9 # DC output of the PV generator including curtailment _E['Epv'] = np.sum(np.abs(_Ppv)) * _dt / 3.6e9 # DC input of the battery (charged) _E['Ebatin'] = np.sum(np.abs(_Pbatin)) * _dt / 3.6e9 # DC output of the battery (discharged) _E['Ebatout'] = np.sum(np.abs(_Pbatout)) * _dt / 3.6e9 # Grid feed-in _E['Eac2g'] = np.sum(np.abs(_Pac2g)) * _dt / 3.6e9 # Grid demand _E['Eg2ac'] = np.sum(np.abs(_Pg2ac)) * _dt / 3.6e9 # Load supply by the grid _E['Eg2l'] = np.sum(np.abs(_Pg2l)) * _dt / 3.6e9 # Demand of the other system components _E['Eperi'] = np.sum(np.abs(_Pperi)) * _dt / 3.6e9 # Curtailed PV energy _E['Ect'] = np.sum(np.abs(_Pct)) * _dt / 3.6e9 if _parameter['Top'] == 'AC': # AC-coupled systems # AC output of the PV system including curtailment _E['Epvs'] = np.sum(np.abs(_Ppvs)) * _dt / 3.6e9 # AC input of the battery system _E['Eac2bs'] = np.sum(np.abs(_Pac2bs)) * _dt / 3.6e9 # AC output of the battery system _E['Ebs2ac'] = np.sum(np.abs(_Pbs2ac)) * _dt / 3.6e9 # Direct use of PV energy _E['Epvs2l'] = np.sum(np.abs(_Ppvs2l)) * _dt / 3.6e9 # PV charging _E['Epvs2bs'] = np.sum(np.abs(_Ppvs2bs)) * _dt / 3.6e9 # Grid charging _E['Eg2bs'] = np.sum(np.abs(_Pg2bs)) * _dt / 3.6e9 # PV feed-in _E['Epvs2g'] = np.sum(np.abs(_Ppvs2g)) * _dt / 3.6e9 # Load supply by the battery system _E['Ebs2l'] = np.sum(np.abs(_Pbs2l)) * _dt / 3.6e9 # Battery feed-in _E['Ebs2g'] = np.sum(np.abs(_Pbs2g)) * _dt / 3.6e9 elif _parameter['Top'] == 'DC' or _parameter['Top'] == 'PV': # DC- and PV-coupled systems # Grid demand of the PV-battery system _E['Eg2pvbs'] = np.sum(np.abs(_Pg2pvbs)) * _dt / 3.6e9 # AC input of the PV-battery system _E['Eac2pvbs'] = np.sum(np.abs(_Pac2pvbs)) * _dt / 3.6e9 # AC output of the PV-battery system _E['Epvbs2ac'] = np.sum(np.abs(_Ppvbs2ac)) * _dt / 3.6e9 # Load supply by the PV-battery system _E['Epvbs2l'] = np.sum(np.abs(_Ppvbs2l)) * _dt / 3.6e9 return _E def bat_res_mod_ideal(_parameter, _Pl, _Ppv, _Pbat, _dt, args): E = dict() # Dictionary to store energy sums if _parameter['Top'] == 'AC': Ppvs = args[0] # AC output power of the PV system Pbs = args[1] # AC power of the battery system Pperi = args[2] # Additional power consumption of the other system components elif _parameter['Top'] == 'DC': Ppv2ac = args[0] Ppv2bat_in = args[1] Ppvbs = args[2] Pperi = args[3] Ppv2ac_in = _Ppv - Ppv2bat_in # Additional power consumption of the other system components Pperi = np.zeros_like(_Ppv) # Total load including the power consumption of the other system components Plt = _Pl # DC input power of the battery (charged) Pbatin = np.maximum(0, _Pbat) # DC output power of the battery (discharged) Pbatout = np.minimum(0, _Pbat) if _parameter['Top'] == 'AC': # Grid power Pg = Ppvs - _Pl - Pbs # Residual power Pr = Ppvs - Plt # AC input power of the battery system Pac2bs = np.maximum(0, Pbs) # AC output power of the battery system Pbs2ac = np.minimum(0, Pbs) # Negative residual power (residual load demand) Prn = np.minimum(0, Pr) # Positive residual power (surplus PV power) Prp = np.maximum(0, Pr) # Direct use of PV power by the load Ppvs2l = np.minimum(Ppvs, Plt) # PV charging power Ppvs2bs=np.minimum(Prp, Pac2bs) # Grid charging power Pg2bs=np.maximum(Pac2bs - Prp, 0) # Grid supply power of the load Pg2l=np.minimum(Prn - Pbs2ac, 0) # Battery supply power of the load Pbs2l=np.maximum(Prn, Pbs2ac) # Battery feed-in power Pbs2g=np.minimum(Pbs2ac - Prn, 0) # PV feed-in power Ppvs2g=np.maximum(Prp - Pac2bs, 0) elif _parameter['Top'] == 'DC': # Grid power Pg = Ppvbs - _Pl # Grid power demand of the PV-battery system Pg2pvbs = np.minimum(0, Ppvbs) # AC input power of the PV-battery system Pac2pvbs = Pg2pvbs # AC output power of the PV-battery system Ppvbs2ac = np.maximum(0, Ppvbs) # Load supply power by the PV-battery system Ppvbs2l = np.minimum(_Pl, Ppvbs2ac) # Load supply power by the grid Pg2l = (Plt - Ppvbs2l) # Curtailed PV power (AC output power) Pct = np.zeros_like(_Ppv) # Power demand from the grid Pg2ac = np.minimum(0, Pg) # Feed-in power to the grid Pac2g=np.maximum(0, Pg) # Energy sums # Electrical demand including the energy consumption of the other system components E['El'] = np.sum(np.abs(Plt)) / 3.6e9 # DC output of the PV generator including curtailment E['Epv'] = np.sum(np.abs(_Ppv)) / 3.6e9 # DC input of the battery (charged) E['Ebatin'] = np.sum(np.abs(Pbatin)) / 3.6e9 # DC output of the battery (discharged) E['Ebatout'] = np.sum(np.abs(Pbatout)) / 3.6e9 # Grid feed-in E['Eac2g'] = np.sum(np.abs(Pac2g)) / 3.6e9 # Grid demand E['Eg2ac'] = np.sum(np.abs(Pg2ac)) / 3.6e9 # Load supply by the grid E['Eg2l'] = np.sum(np.abs(Pg2l)) / 3.6e9 # Demand of the other system components E['Eperi'] = np.sum(np.abs(Pperi)) / 3.6e9 # Curtailed PV energy E['Ect'] = np.sum(np.abs(Pct)) / 3.6e9 if _parameter['Top'] == 'AC': # AC output of the PV system including curtailment E['Epvs']=np.sum(np.abs(Ppvs)) / 3.6e9 # AC input of the battery system E['Eac2bs']=np.sum(np.abs(Pac2bs)) / 3.6e9 # AC output of the battery system E['Ebs2ac']=np.sum(np.abs(Pbs2ac)) / 3.6e9 # Direct use of PV energy E['Epvs2l']=np.sum(np.abs(Ppvs2l)) / 3.6e9 # PV charging E['Epvs2bs']=np.sum(np.abs(Ppvs2bs)) / 3.6e9 # Grid charging E['Eg2bs']=np.sum(np.abs(Pg2bs)) / 3.6e9 # PV feed-in E['Epvs2g']=np.sum(np.abs(Ppvs2g)) / 3.6e9 # Load supply by the battery system E['Ebs2l']=np.sum(np.abs(Pbs2l)) / 3.6e9 # Battery feed-in E['Ebs2g']=np.sum(np.abs(Pbs2g)) / 3.6e9 elif _parameter['Top'] == 'DC': # Grid demand of the PV-battery system E['Eg2pvbs'] = np.sum(np.abs(Pg2pvbs)) / 3.6e9 # AC input of the PV-battery system E['Eac2pvbs'] = np.sum(np.abs(Pac2pvbs)) / 3.6e9 # AC output of the PV-battery system E['Epvbs2ac'] = np.sum(np.abs(Ppvbs2ac)) / 3.6e9 # Load supply by the PV-battery system E['Epvbs2l'] = np.sum(np.abs(Ppvbs2l)) / 3.6e9 return E def load_parameter(fname, col_name): """Loads system parameter from excel file :param fname: Path to the excel file :type fname: string :param col_name: Column to read data from :type col_name: string :return: Dictionary holding parameters from the Excel sheet :rtype: dict """ wb = load_workbook(fname, data_only=True) ws = wb['Data'] # Load Data sheet of excel file # read keys and values from Excel sheet keys = (c.value for c in ws['E'][1:]) values = (c.value if c.value != 'ns' else None for c in ws[col_name][1:]) parameter = dict(zip(keys, values)) # deletes entries where key is None del parameter[None] # Assign specific parameters parameter['P_PV2AC_out_PVINV'] = ws[col_name][15].value parameter['P_PV2AC_out'] = ws[col_name][24].value parameter['P_AC2BAT_in_DCC'] = ws[col_name][25].value parameter['P_AC2BAT_in'] = ws[col_name][26].value parameter['P_BAT2AC_out'] = ws[col_name][27].value parameter['P_BAT2AC_out_DCC'] = ws[col_name][28].value # Set refrence case values to boolean if parameter['ref_1'] == 'yes': parameter['ref_1'] = True elif parameter['ref_1'] == 'no': parameter['ref_1'] = False if parameter['ref_2'] == 'yes': parameter['ref_2'] = True elif parameter['ref_2'] == 'no': parameter['ref_2'] = False # Specific parameters of DC-coupled systems if parameter['Top'] == 'DC': parameter['P_AC2BAT_in'] = parameter['P_AC2BAT_in_DCC'] # Nominal charging power (AC) in kW parameter['P_BAT2AC_out'] = parameter['P_BAT2AC_out_DCC'] # Specific parameters of PV inverters and AC-coupled systems if parameter['Top'] == 'PVINV' or parameter['Top'] == 'AC' and parameter['P_PV2AC_out_PVINV'] is not None: parameter['P_PV2AC_out'] = parameter['P_PV2AC_out_PVINV'] # Specific parameters of PV-coupled systems if parameter['Top'] == 'PV': parameter['P_BAT2PV_in'] = parameter['P_BAT2AC_in'] parameter['P_BAT2AC_out'] = parameter['P_BAT2AC_out_DCC'] # replace 'ns', 'o' and 'c' entries to None for key, value in parameter.items(): if value == 'ns' or value == 'o' or value == 'c' or value == ' ': parameter[key] = None # Convert to kW convert_to_kw = ['P_PV2AC_in', 'P_PV2AC_out_PVINV','P_PV2AC_out','P_AC2BAT_in_DCC','P_AC2BAT_in','P_BAT2AC_out', 'P_BAT2AC_out_DCC','P_PV2BAT_in','P_BAT2PV_out','P_PV2BAT_out','P_BAT2AC_in'] for par in convert_to_kw: if parameter[par]: parameter[par] /= 1000 return parameter def eta2abc(parameter): """Function to calculate the parameters of the power loss functions (quadratic equations) from the path efficiencies :param parameter: Holds parameters of the system :type parameter: dict :return: Dictionary holding parameters from the Excel sheet :rtype: dict """ # PV2AC conversion pathway TODO if parameter['Top'] == 'DC' or parameter['Top'] == 'PVINV' or parameter['Top'] == 'PV' and parameter['P_PV2AC_out'] is not None or parameter['Top'] == 'AC' and parameter['P_PV2AC_out'] is not None: # Create variables for the sampling points and corresponding efficiencies TODO p_pv2ac = np.fromiter((value for key, value in parameter.items() if 'p_PV2AC_' in key and value is not None), float) eta_pv2ac = np.fromiter((value / 100 for key, value in parameter.items() if 'eta_PV2AC_' in key and value is not None), float) # Absolute input and output power in W p_pv2ac_out = parameter['P_PV2AC_out'] p_pv2ac * 1000 p_pv2ac_in = p_pv2ac_out / eta_pv2ac # Absolute power loss in W P_l_pv2ac_in = (1 - eta_pv2ac) * p_pv2ac_in P_l_pv2ac_out = (1 / eta_pv2ac - 1) * p_pv2ac_out # Polynomial curve fitting parameters of the power loss functions in W # Based on input power p = np.polyfit(p_pv2ac_in / parameter['P_PV2AC_in'] / 1000, P_l_pv2ac_in, 2) parameter['PV2AC_a_in'] = p[0] parameter['PV2AC_b_in'] = p[1] parameter['PV2AC_c_in'] = p[2] # Based on output power p = np.polyfit(p_pv2ac, P_l_pv2ac_out, 2) parameter['PV2AC_a_out'] = p[0] parameter['PV2AC_b_out'] = p[1] parameter['PV2AC_c_out'] = p[2] # PV2BAT conversion pathway if parameter['Top'] == 'DC' or parameter['Top'] == 'PV': # Create variables for the sampling points and corresponding efficiencies p_pv2bat = np.array([value for key, value in parameter.items() if 'p_PV2BAT_' in key]) eta_pv2bat = np.array([value / 100 for key, value in parameter.items() if 'eta_PV2BAT_' in key]) # Create missing variables # Nominal input power of the PV2BAT conversion pathway of DC-coupled systems if parameter['P_PV2BAT_in'] is None: parameter['P_PV2BAT_in'] = parameter['P_PV2BAT_out'] / (parameter['eta_PV2BAT_100'] / 100) # Absolute input and output power in W p_pv2bat_out = parameter['P_PV2BAT_out'] * p_pv2bat * 1000 p_pv2bat_in = p_pv2bat_out / eta_pv2bat # Absolute power loss in W P_l_pv2bat_in = (1 - eta_pv2bat) * p_pv2bat_in P_l_pv2bat_out = (1 / eta_pv2bat - 1) * p_pv2bat_out # Polynomial curve fitting parameters of the power loss functions in W # Based on input power p = np.polyfit(p_pv2bat_in / parameter['P_PV2BAT_in'] / 1000, P_l_pv2bat_in, 2) parameter['PV2BAT_a_in'] = p[0] parameter['PV2BAT_b_in'] = p[1] parameter['PV2BAT_c_in'] = p[2] # Based on output power p = np.polyfit(p_pv2bat, P_l_pv2bat_out, 2) parameter['PV2BAT_a_out'] = p[0] parameter['PV2BAT_b_out'] = p[1] parameter['PV2BAT_c_out'] = p[2] # AC2BAT conversion pathway if parameter['Top'] == 'AC' or parameter['Top'] == 'DC' and parameter['P_AC2BAT_in'] is not None: # Create variables for the sampling points and corresponding efficiencies TODO p_ac2bat = np.fromiter((value for key, value in parameter.items() if 'p_AC2BAT_' in key), float) eta_ac2bat = np.fromiter((value / 100 for key, value in parameter.items() if 'eta_AC2BAT_' in key), float) # Absolute input and output power in W p_ac2bat_out = parameter['P_PV2BAT_out'] * p_ac2bat * 1000 p_ac2bat_in = p_ac2bat_out / eta_ac2bat # Absolute power loss in W P_l_ac2bat_in = (1 - eta_ac2bat) * p_ac2bat_in P_l_ac2bat_out = (1 / eta_ac2bat - 1) * p_ac2bat_out # Polynomial curve fitting parameters of the power loss functions in W # Based on input power p = np.polyfit(p_ac2bat_in / parameter['P_AC2BAT_in'] / 1000, P_l_ac2bat_in, 2) parameter['AC2BAT_a_in'] = p[0] parameter['AC2BAT_b_in'] = p[1] parameter['AC2BAT_c_in'] = p[2] # Based on output power p = np.polyfit(p_ac2bat, P_l_ac2bat_out, 2) parameter['AC2BAT_a_out'] = p[0] parameter['AC2BAT_b_out'] = p[1] parameter['AC2BAT_c_out'] = p[2] # BAT2AC conversion pathway if parameter['Top'] =='AC' or parameter['Top'] =='DC' or parameter['Top'] =='PV' and parameter['P_BAT2AC_out'] is not None: # Create variables for the sampling points and corresponding efficiencies TODO p_bat2ac = np.fromiter((value for key, value in parameter.items() if 'p_BAT2AC_' in key), float) eta_bat2ac = np.fromiter((value / 100 for key, value in parameter.items() if 'eta_BAT2AC_' in key), float) # Absolute input and output power in W p_bat2ac_out = parameter['P_BAT2AC_out'] * p_bat2ac * 1000 p_bat2ac_in = p_bat2ac_out / eta_bat2ac # Absolute power loss in W P_l_bat2ac_in = (1 - eta_bat2ac) * p_bat2ac_in P_l_bat2ac_out = (1 / eta_bat2ac - 1) * p_bat2ac_out # Polynomial curve fitting parameters of the power loss functions in W # Based on input power p = np.polyfit(p_bat2ac_in / parameter['P_BAT2AC_in'] / 1000, P_l_bat2ac_in, 2) parameter['BAT2AC_a_in'] = p[0] parameter['BAT2AC_b_in'] = p[1] parameter['BAT2AC_c_in'] = p[2] # Based on output power p = np.polyfit(p_bat2ac, P_l_bat2ac_out, 2) parameter['BAT2AC_a_out'] = p[0] parameter['BAT2AC_b_out'] = p[1] parameter['BAT2AC_c_out'] = p[2] # BAT2PV conversion pathway if parameter['Top'] =='PV': # Create variables for the sampling points and corresponding efficiencies TODO p_bat2pv = np.fromiter((value for key, value in parameter.items() if 'p_BAT2PV_' in key), float) eta_bat2pv = np.fromiter((value / 100 for key, value in parameter.items() if 'eta_BAT2PV_' in key), float) # Absolute input and output power in W p_bat2pv_out = parameter['P_BAT2PV_out'] * p_bat2pv * 1000 p_bat2pv_in = p_bat2pv_out / eta_bat2pv # Absolute power loss in W P_l_bat2pv_in = (1 - eta_bat2pv) * p_bat2pv_in P_l_bat2pv_out = (1 / eta_bat2pv - 1) * p_bat2pv_out # Polynomial curve fitting parameters of the power loss functions in W # Based on input power TODO p = np.polyfit(p_bat2pv_in / parameter['P_BAT2AC_in'] / 1000, P_l_bat2pv_in, 2) parameter['BAT2PV_a_in'] = p[0] parameter['BAT2PV_b_in'] = p[1] parameter['BAT2PV_c_in'] = p[2] # Based on output power p = np.polyfit(p_bat2pv, P_l_bat2pv_out, 2) parameter['BAT2PV_a_out'] = p[0] parameter['BAT2PV_b_out'] = p[1] parameter['BAT2PV_c_out'] = p[2] # Additional parameters # Mean battery capacity in kWh try: parameter['E_BAT'] = (parameter['E_BAT_usable'] / parameter['eta_BAT'] * 100 + parameter['E_BAT_usable']) / 2 except: parameter['E_BAT'] = None # Mean stationary deviation of the charging power in W try: parameter['P_PV2BAT_DEV'] = parameter['P_PV2BAT_DEV_IMPORT'] - parameter['P_PV2BAT_DEV_EXPORT'] except: parameter['P_PV2BAT_DEV'] = None if parameter['Top'] == 'AC': parameter['P_AC2BAT_DEV'] = parameter['P_PV2BAT_DEV'] # Mean stationary deviation of the discharging power in W try: parameter['P_BAT2AC_DEV'] = parameter['P_BAT2AC_DEV_EXPORT'] - parameter['P_BAT2AC_DEV_IMPORT'] except: parameter['P_BAT2AC_DEV'] = None # Time constant for the first-order time delay element in s try: parameter['t_CONSTANT'] = (parameter['t_SETTLING'] - round(parameter['t_DEAD'])) / 3 except: parameter['t_CONSTANT'] = None # Hysteresis threshold for the recharging of the battery parameter['SOC_h'] = 0.98 # Feed-in power limit in kW/kWp parameter['p_ac2g_max'] = 0.7 return parameter def load_ref_case(fname, name): """Loads PV power or Load from the reference cases :param fname: Path to mat file :type fname: string :param name: Identifier for PV Power or Load :type name: string :return: Returns PV power or load from the reference case :rtype: numpy array """ with open(fname, 'rb') as f: a = np.load(f) data = a[name] return data def resample_data_frame(df): """Function for resampling data frames :param df: data frame :type df: pandas data frame :return: data frame :rtype: pandas data frame """ df_rs = df.resample('15min').mean() return df_rs def transform_dict_to_array(parameter): """Function for transforming a dict to an numpy array :param parameter: dict of system parameters :type parameter: dict :return: array of system parameters :rtype: numpy array """ if parameter['Top'] == 'AC': d = np.array(parameter['E_BAT']) # 0 d = np.append(d, parameter['eta_BAT']) # 1 d = np.append(d, parameter['t_CONSTANT']) # 2 d = np.append(d, parameter['P_SYS_SOC0_DC']) # 3 d = np.append(d, parameter['P_SYS_SOC0_AC']) # 4 d = np.append(d, parameter['P_SYS_SOC1_DC']) # 5 d = np.append(d, parameter['P_SYS_SOC1_AC']) # 6 d = np.append(d, parameter['AC2BAT_a_in']) # 7 d = np.append(d, parameter['AC2BAT_b_in']) # 8 d = np.append(d, parameter['AC2BAT_c_in']) # 9 d = np.append(d, parameter['BAT2AC_a_out']) # 10 d = np.append(d, parameter['BAT2AC_b_out']) # 11 d = np.append(d, parameter['BAT2AC_c_out']) # 12 d = np.append(d, parameter['P_AC2BAT_DEV']) # 13 d = np.append(d, parameter['P_BAT2AC_DEV']) # 14 d = np.append(d, parameter['P_BAT2AC_out']) # 15 d = np.append(d, parameter['P_AC2BAT_in']) # 16 d = np.append(d, parameter['t_DEAD']) # 17 d = np.append(d, parameter['SOC_h']) # 18 if parameter['Top'] == 'DC': d = np.array(parameter['E_BAT']) # 1 d = np.append(d, parameter['P_PV2AC_in']) # 2 d = np.append(d, parameter['P_PV2AC_out']) # 3 d = np.append(d, parameter['P_PV2BAT_in']) # 4 d = np.append(d, parameter['P_BAT2AC_out']) # 5 d = np.append(d, parameter['PV2AC_a_in']) # 6 d = np.append(d, parameter['PV2AC_b_in']) # 7 d = np.append(d, parameter['PV2AC_c_in']) # 8 d = np.append(d, parameter['PV2BAT_a_in']) # 9 d = np.append(d, parameter['PV2BAT_b_in']) # 10 d = np.append(d, parameter['BAT2AC_a_out']) # 11 d = np.append(d, parameter['BAT2AC_b_out']) # 12 d = np.append(d, parameter['BAT2AC_c_out']) # 13 d = np.append(d, parameter['eta_BAT']) # 14 d = np.append(d, parameter['SOC_h']) # 15 d = np.append(d, parameter['P_PV2BAT_DEV']) # 16 d = np.append(d, parameter['P_BAT2AC_DEV']) # 17 d = np.append(d, parameter['t_DEAD']) # 18 d = np.append(d, parameter['t_CONSTANT']) # 19 d = np.append(d, parameter['P_SYS_SOC1_DC']) # 20 d = np.append(d, parameter['P_SYS_SOC0_AC']) # 21 d = np.append(d, parameter['P_SYS_SOC0_DC']) # 22 if parameter['Top'] == 'PV': d = np.array(parameter['E_BAT']) d = np.append(d, parameter['P_PV2AC_in']) d = np.append(d, parameter['P_PV2AC_out']) d = np.append(d, parameter['P_PV2BAT_in']) d = np.append(d, parameter['P_BAT2PV_out']) d = np.append(d, parameter['PV2AC_a_in']) d = np.append(d, parameter['PV2AC_b_in']) d = np.append(d, parameter['PV2AC_c_in']) d = np.append(d, parameter['PV2BAT_a_in']) d = np.append(d, parameter['PV2BAT_b_in']) d = np.append(d, parameter['PV2BAT_c_in']) d = np.append(d, parameter['PV2AC_a_out']) d = np.append(d, parameter['PV2AC_b_out']) d = np.append(d, parameter['PV2AC_c_out']) d = np.append(d, parameter['BAT2PV_a_out']) d = np.append(d, parameter['BAT2PV_b_out']) d = np.append(d, parameter['BAT2PV_c_out']) d = np.append(d, parameter['eta_BAT']) d = np.append(d, parameter['SOC_h']) d = np.append(d, parameter['P_PV2BAT_DEV']) d = np.append(d, parameter['P_BAT2AC_DEV']) d = np.append(d, parameter['P_SYS_SOC1_DC']) d = np.append(d, parameter['P_SYS_SOC0_AC']) d = np.append(d, parameter['P_SYS_SOC0_DC']) d = np.append(d, parameter['t_DEAD']) d = np.append(d, parameter['t_CONSTANT']) return d def calculate_spi(_E_real, _E_ideal): # SPI calculation for the reference case: # Grid electricity price in Euro/kWh pg2ac = 0.30 # Grid feed-in tariff in Euro/kWh pac2g = 0.12 # Grid electricity costs without a PV-battery system in Euro/a Cref = _E_ideal['El'] * pg2ac * 1000 # Net grid electricity costs with the lossless PV-battery system in Euro/a Cideal = _E_ideal['Eg2ac'] * pg2ac * 1000 - _E_ideal['Eac2g'] * pac2g * 1000 # Net grid electricity costs with the real PV-battery system in Euro/a Creal = _E_real['Eg2ac'] * pg2ac * 1000 - _E_real['Eac2g'] * pac2g * 1000 # Reduction of the net grid electricity costs by the lossless PV-battery system in Euro/a dCideal = Cref - Cideal # Reduction of the net grid electricity costs by the real PV-battery system in Euro/a dCreal = Cref - Creal # System Performance Index (SPI) spi = dCreal / dCideal return spi	[ 1, 1053, 12655, 408, 7442, 30004, 13, 5215, 954, 2291, 408, 302, 29890, 30004, 13, 3166, 848, 13203, 1053, 848, 1990, 30004, 13, 3166, 954, 2291, 1053, 4072, 30004, 13, 3166, 954, 2291, 29889, 1017, 9795, 1053, 360, 919, 30004, 13, 3166, 954, 2291, 1053, 20199, 277, 30004, 13, 5215, 11701, 408, 10518, 30004, 13, 5215, 931, 30004, 13, 5215, 12865, 30004, 13, 5215, 11799, 30004, 13, 3166, 1722, 2272, 15524, 1053, 2254, 29918, 1287, 2909, 30004, 13, 3166, 11451, 2111, 8262, 29911, 6271, 29889, 4645, 1053, 3382, 8262, 4032, 30004, 13, 3166, 11451, 2111, 8262, 29911, 6271, 1053, 3667, 29879, 30004, 13, 30004, 13, 29992, 1272, 1990, 30004, 13, 1990, 3630, 29901, 30004, 13, 1678, 349, 29886, 29894, 29901, 7442, 29889, 2378, 30004, 13, 1678, 349, 10222, 29901, 7442, 29889, 2378, 30004, 13, 1678, 349, 546, 29875, 29901, 7442, 29889, 2378, 30004, 13, 1678, 17874, 29901, 7442, 29889, 2378, 30004, 13, 1678, 17874, 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tests/test_operations.py	HumanCellAtlas/data-store	46	154788	#!/usr/bin/env python # coding: utf-8 import io import os import sys import uuid import json import argparse import unittest import string import random import copy import datetime import tempfile from collections import namedtuple from unittest import mock from boto3.s3.transfer import TransferConfig from botocore.exceptions import ClientError from cloud_blobstore import BlobNotFoundError pkg_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) # noqa sys.path.insert(0, pkg_root) # noqa from tests import skip_on_travis from tests.infra import testmode from dss.operations import DSSOperationsCommandDispatch from dss.operations.util import map_bucket_results from dss.operations import checkout, storage, sync, secrets, lambda_params, iam, events from dss.operations.lambda_params import get_deployed_lambdas, fix_ssm_variable_prefix from dss.operations.iam import IAMSEPARATOR from dss.operations.secrets import SecretsChecker from dss.logging import configure_test_logging from dss.config import BucketConfig, Config, Replica, override_bucket_config from dss.storage.hcablobstore import FileMetadata, compose_blob_key from dss.util.version import datetime_to_version_format from tests import CaptureStdout, SwapStdin from tests.test_bundle import TestBundleApiMixin from tests.infra import get_env, DSSUploadMixin, TestAuthMixin, DSSAssertMixin from tests.infra.server import ThreadedLocalServer def setUpModule(): configure_test_logging() def random_alphanumeric_string(N=10): return ''.join(random.choices(string.ascii_uppercase + string.digits, k=N)) @testmode.standalone class TestOperations(unittest.TestCase): def test_dispatch(self): with self.subTest("dispatch without mutually exclusive arguments"): self._test_dispatch() with self.subTest("dispatch with mutually exclusive arguments"): self._test_dispatch(mutually_exclusive=True) with self.subTest("dispatch with action overrides"): self._test_dispatch(action_overrides=True) def _test_dispatch(self, mutually_exclusive=None, action_overrides=False): dispatch = DSSOperationsCommandDispatch() target = dispatch.target( "my_target", arguments={ "foo": dict(default="george", type=int), "--argument-a": None, "--argument-b": dict(default="bar"), }, mutually_exclusive=(["--argument-a", "--argument-b"] if mutually_exclusive else None) ) if action_overrides: @target.action("my_action", arguments={"foo": None, "--bar": dict(default="bars")}) def my_action(argv, args): self.assertEqual(args.argument_b, "LSDKFJ") self.assertEqual(args.foo, "24") self.assertEqual(args.bar, "bars") else: @target.action("my_action") def my_action(argv, args): self.assertEqual(args.argument_b, "LSDKFJ") self.assertEqual(args.foo, 24) dispatch(["my_target", "my_action", "24", "--argument-b", "LSDKFJ"]) def test_map_bucket(self): with override_bucket_config(BucketConfig.TEST_FIXTURE): for replica in Replica: with self.subTest(replica=replica): handle = Config.get_blobstore_handle(replica) count_list = 0 for key in handle.list(replica.bucket, prefix="bundles/"): count_list += 1 def counter(keys): count = 0 for key in keys: count += 1 return count total = 0 for count in map_bucket_results(counter, handle, replica.bucket, "bundles/", 2): total += count self.assertGreater(count_list, 0) self.assertEqual(count_list, total) def test_repair_blob_metadata(self): uploader = {Replica.aws: self._put_s3_file, Replica.gcp: self._put_gs_file} with override_bucket_config(BucketConfig.TEST): for replica in Replica: handle = Config.get_blobstore_handle(replica) key = str(<KEY>()) file_metadata = { FileMetadata.SHA256: "foo", FileMetadata.SHA1: "foo", FileMetadata.S3_ETAG: "foo", FileMetadata.CRC32C: "foo", FileMetadata.CONTENT_TYPE: "foo" } blob_key = compose_blob_key(file_metadata) uploader[replica](key, json.dumps(file_metadata).encode("utf-8"), "application/json") uploader[replica](blob_key, b"123", "bar") args = argparse.Namespace(keys=[key], entity_type="files", job_id="", replica=replica.name) with self.subTest("Blob content type repaired", replica=replica): storage.repair_file_blob_metadata([], args).process_key(key) self.assertEqual(handle.get_content_type(replica.bucket, blob_key), file_metadata[FileMetadata.CONTENT_TYPE]) with self.subTest("Should handle arbitrary exceptions", replica=replica): with mock.patch("dss.operations.storage.StorageOperationHandler.log_error") as log_error: with mock.patch("dss.config.Config.get_native_handle") as thrower: thrower.side_effect = Exception() storage.repair_file_blob_metadata([], args).process_key(key) log_error.assert_called() self.assertEqual(log_error.call_args[0][0], "Exception") with self.subTest("Should handle missing file metadata", replica=replica): with mock.patch("dss.operations.storage.StorageOperationHandler.log_warning") as log_warning: storage.repair_file_blob_metadata([], args).process_key("wrong key") self.assertEqual(log_warning.call_args[0][0], "BlobNotFoundError") with self.subTest("Should handle missing blob", replica=replica): with mock.patch("dss.operations.storage.StorageOperationHandler.log_warning") as log_warning: file_metadata[FileMetadata.SHA256] = "wrong" uploader[replica](key, json.dumps(file_metadata).encode("utf-8"), "application/json") storage.repair_file_blob_metadata([], args).process_key(key) self.assertEqual(log_warning.call_args[0][0], "BlobNotFoundError") with self.subTest("Should handle corrupt file metadata", replica=replica): with mock.patch("dss.operations.storage.StorageOperationHandler.log_warning") as log_warning: uploader[replica](key, b"this is not json", "application/json") storage.repair_file_blob_metadata([], args).process_key(key) self.assertEqual(log_warning.call_args[0][0], "JSONDecodeError") def test_bundle_reference_list(self): class MockHandler: mock_file_data = {"uuid": "987", "version": "987", "sha256": "256k", "sha1": "1thing", "s3-etag": "s34me", "crc32c": "wthisthis"} mock_bundle_metadata = {"files": [mock_file_data]} mock_bundle_key = 'bundles/123.456' handle = mock.Mock() def get(self, bucket, key): return json.dumps(self.mock_bundle_metadata) for replica in Replica: with self.subTest("Test Bundle Reference"): with override_bucket_config(BucketConfig.TEST): with mock.patch("dss.operations.storage.Config") as mock_handle: mock_handle.get_blobstore_handle = mock.MagicMock(return_value=MockHandler()) args = argparse.Namespace(keys=[MockHandler.mock_bundle_key], replica=replica.name, entity_type='bundles', job_id="") res = storage.build_reference_list([], args).process_key(MockHandler.mock_bundle_key) self.assertIn(MockHandler.mock_bundle_key, res) self.assertIn(f'files/{MockHandler.mock_file_data["uuid"]}.' f'{MockHandler.mock_file_data["version"]}', res) self.assertIn(compose_blob_key(MockHandler.mock_file_data), res) def test_update_content_type(self): TestCase = namedtuple("TestCase", "replica upload size update initial_content_type expected_content_type") with override_bucket_config(BucketConfig.TEST): key = f"operations/{uuid.uuid4()}" large_size = 64 * 1024 * 1024 + 1 tests = [ TestCase(Replica.aws, self._put_s3_file, 1, storage.update_aws_content_type, "a", "b"), TestCase(Replica.aws, self._put_s3_file, large_size, storage.update_aws_content_type, "a", "b"), TestCase(Replica.gcp, self._put_gs_file, 1, storage.update_gcp_content_type, "a", "b"), ] for test in tests: data = os.urandom(test.size) with self.subTest(test.replica.name): handle = Config.get_blobstore_handle(test.replica) native_handle = Config.get_native_handle(test.replica) test.upload(key, data, test.initial_content_type) old_checksum = handle.get_cloud_checksum(test.replica.bucket, key) test.update(native_handle, test.replica.bucket, key, test.expected_content_type) self.assertEqual(test.expected_content_type, handle.get_content_type(test.replica.bucket, key)) self.assertEqual(handle.get(test.replica.bucket, key), data) self.assertEqual(old_checksum, handle.get_cloud_checksum(test.replica.bucket, key)) def test_verify_blob_replication(self): key = "blobs/alsdjflaskjdf" from_handle = mock.Mock() to_handle = mock.Mock() from_handle.get_size = mock.Mock(return_value=10) to_handle.get_size = mock.Mock(return_value=10) with self.subTest("no replication error"): res = sync.verify_blob_replication(from_handle, to_handle, "", "", key) self.assertEqual(res, list()) with self.subTest("Unequal size blobs reports error"): to_handle.get_size = mock.Mock(return_value=11) res = sync.verify_blob_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("mismatch", res[0].anomaly) with self.subTest("Missing target blob reports error"): to_handle.get_size.side_effect = BlobNotFoundError res = sync.verify_blob_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("missing", res[0].anomaly) def test_verify_file_replication(self): key = "blobs/alsdjflaskjdf" from_handle = mock.Mock() to_handle = mock.Mock() file_metadata = json.dumps({'sha256': "", 'sha1': "", 's3-etag': "", 'crc32c': ""}) from_handle.get = mock.Mock(return_value=file_metadata) to_handle.get = mock.Mock(return_value=file_metadata) with self.subTest("no replication error"): with mock.patch("dss.operations.sync.verify_blob_replication") as vbr: vbr.return_value = list() res = sync.verify_file_replication(from_handle, to_handle, "", "", key) self.assertEqual(res, list()) with self.subTest("Unequal file metadata"): to_handle.get.return_value = "{}" res = sync.verify_file_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("mismatch", res[0].anomaly) with self.subTest("Missing file metadata"): to_handle.get.side_effect = BlobNotFoundError res = sync.verify_file_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("missing", res[0].anomaly) def test_verify_bundle_replication(self): key = "blobs/alsdjflaskjdf" from_handle = mock.Mock() to_handle = mock.Mock() bundle_metadata = json.dumps({ "creator_uid": 8008, "files": [{"uuid": None, "version": None}] }) from_handle.get = mock.Mock(return_value=bundle_metadata) to_handle.get = mock.Mock(return_value=bundle_metadata) with mock.patch("dss.operations.sync.verify_file_replication") as vfr: with self.subTest("replication ok"): vfr.return_value = list() res = sync.verify_bundle_replication(from_handle, to_handle, "", "", key) self.assertEqual(res, []) with self.subTest("replication problem"): vfr.return_value = [sync.ReplicationAnomaly(key="", anomaly="")] res = sync.verify_bundle_replication(from_handle, to_handle, "", "", key) self.assertEqual(res, vfr.return_value) with self.subTest("Unequal bundle metadata"): to_handle.get.return_value = "{}" res = sync.verify_bundle_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("mismatch", res[0].anomaly) with self.subTest("Missing destination bundle metadata"): to_handle.get.side_effect = BlobNotFoundError res = sync.verify_bundle_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("missing on target", res[0].anomaly) with self.subTest("Missing source bundle metadata"): from_handle.get.side_effect = BlobNotFoundError res = sync.verify_bundle_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("missing on source", res[0].anomaly) def _put_s3_file(self, key, data, content_type="blah", part_size=None): s3 = Config.get_native_handle(Replica.aws) with io.BytesIO(data) as fh: s3.upload_fileobj(Bucket=Replica.aws.bucket, Key=key, Fileobj=fh, ExtraArgs=dict(ContentType=content_type), Config=TransferConfig(multipart_chunksize=64 * 1024 * 1024)) def _put_gs_file(self, key, data, content_type="blah"): gs = Config.get_native_handle(Replica.gcp) gs_bucket = gs.bucket(Replica.gcp.bucket) gs_blob = gs_bucket.blob(key, chunk_size=1 * 1024 * 1024) with io.BytesIO(data) as fh: gs_blob.upload_from_file(fh, content_type="application/octet-stream") def test_iam_aws_list_policies(self): def _get_aws_list_policies_kwargs(*kwargs): # Set default kwarg values, then set any user-specified kwargs custom_kwargs = dict( cloud_provider="aws", group_by=None, output=None, force=False, include_managed=False, exclude_headers=False, quiet=True ) for kw, val in kwargs.items(): custom_kwargs[kw] = val return custom_kwargs def _get_fake_policy_document(): """Utility function to get a fake policy document for mocking the AWS API""" return { "Version": "2000-01-01", "Statement": [ { "Effect": "Allow", "Action": ["fakeservice:"], "Resource": [ "arn:aws:fakeservice:us-east-1:861229788715:foo:bar", "arn:aws:fakeservice:us-east-1:861229788715:foo:bar/baz", ], } ], } with self.subTest("List AWS policies"): with mock.patch("dss.operations.iam.iam_client") as iam_client: # calling list_policies() will call list_aws_policies() # which will call extract_aws_policies() # which will call get_paginator("list_policies") # which will call paginate() to ask for each page, # and ask for ["Policies"] for the page items, # and ["PolicyName"] for the items class MockPaginator(object): def paginate(self, args, kwargs): # Return a mock page from the mock paginator return [{"Policies": [{"PolicyName": "fake-policy"}]}] # Plain call to list_policies iam_client.get_paginator.return_value = MockPaginator() with CaptureStdout() as output: kwargs = _get_aws_list_policies_kwargs() iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn("fake-policy", output) # Check write to output file temp_prefix = "dss-test-operations-iam-aws-list-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) iam_client.get_paginator.return_value = MockPaginator() kwargs = _get_aws_list_policies_kwargs(output=fname, force=True) iam.list_policies([], argparse.Namespace(kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn("fake-policy", output) # Define utility functions and classes to help test the --group-by flags def _get_detail_lists(asset_type): """Utility function to return a fake user detail list for mocking AWS API""" if asset_type not in ['users', 'groups', 'roles']: raise RuntimeError("Error: invalid asset type given, cannot mock AWS API") user_detail_list = [ { "UserName": "fake-user-1", "UserId": random_alphanumeric_string(N=21).upper(), "AttachedManagedPolicies": [], "UserPolicyList": [ { "PolicyName": "fake-policy-attached-to-fake-user-1", "PolicyDocument": _get_fake_policy_document(), } ], } ] group_detail_list = [ { "GroupName": "fake-group-1", "GroupId": random_alphanumeric_string(N=21).upper(), "AttachedManagedPolicies": [], "GroupPolicyList": [ { "PolicyName": "fake-policy-attached-to-fake-group-1", "PolicyDocument": _get_fake_policy_document(), } ], } ] role_detail_list = [ { "RoleName": "fake-role-1", "RoleId": random_alphanumeric_string(N=21).upper(), "AttachedManagedPolicies": [], "RolePolicyList": [ { "PolicyName": "fake-policy-attached-to-fake-role-1", "PolicyDocument": _get_fake_policy_document(), } ], } ] return { "GroupDetailList": group_detail_list if asset_type == "groups" else [], "RoleDetailList": role_detail_list if asset_type == "roles" else [], "UserDetailList": user_detail_list if asset_type == "users" else [], } class MockPaginator_UserPolicies(object): def paginate(self, args, *kwargs): yield _get_detail_lists("users") class MockPaginator_GroupPolicies(object): def paginate(self, args, *kwargs): yield _get_detail_lists("groups") class MockPaginator_RolePolicies(object): def paginate(self, args, kwargs): yield _get_detail_lists("roles") with self.subTest("List AWS policies grouped by user"): with mock.patch("dss.operations.iam.iam_client") as iam_client: # this will call list_aws_user_policies() # which will call list_aws_policies_grouped() # which will call get_paginator("get_account_authorization_details") # (this is what we mock) # then it calls paginate() to ask for each page, # which we mock in the mock classes above. iam_client.get_paginator.return_value = MockPaginator_UserPolicies() # Plain call to list_policies with CaptureStdout() as output: kwargs = _get_aws_list_policies_kwargs(group_by="users") iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn(IAMSEPARATOR.join(["fake-user-1", "fake-policy-attached-to-fake-user-1"]), output) # Check write to output file temp_prefix = "dss-test-operations-iam-aws-list-users-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) iam_client.get_paginator.return_value = MockPaginator_UserPolicies() kwargs = _get_aws_list_policies_kwargs(group_by="users", output=fname, force=True) iam.list_policies([], argparse.Namespace(kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn( IAMSEPARATOR.join(["fake-user-1", "fake-policy-attached-to-fake-user-1"]), output ) with self.subTest("List AWS policies grouped by user"): with mock.patch("dss.operations.iam.iam_client") as iam_client: # calls list_aws_group_policies # then list_aws_policies_grouped # then get_paginator("get_account_authorization_details") # (this is what we mock) iam_client.get_paginator.return_value = MockPaginator_GroupPolicies() # Plain call to list_policies with CaptureStdout() as output: kwargs = _get_aws_list_policies_kwargs(group_by="groups") iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn( IAMSEPARATOR.join(["fake-group-1", "fake-policy-attached-to-fake-group-1"]), output ) # Check write to output file temp_prefix = "dss-test-operations-iam-aws-list-groups-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) iam_client.get_paginator.return_value = MockPaginator_GroupPolicies() kwargs = _get_aws_list_policies_kwargs(group_by="groups", output=fname, force=True) iam.list_policies([], argparse.Namespace(kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn( IAMSEPARATOR.join(["fake-group-1", "fake-policy-attached-to-fake-group-1"]), output ) with self.subTest("List AWS policies grouped by role"): with mock.patch("dss.operations.iam.iam_client") as iam_client: # calls list_aws_group_policies # then list_aws_policies_grouped # then get_paginator("get_account_authorization_details") # (this is what we mock) iam_client.get_paginator.return_value = MockPaginator_RolePolicies() # Plain call to list_policies with CaptureStdout() as output: kwargs = _get_aws_list_policies_kwargs(group_by="roles") iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn( IAMSEPARATOR.join(["fake-role-1", "fake-policy-attached-to-fake-role-1"]), output ) # Check write to output file temp_prefix = "dss-test-operations-iam-aws-list-roles-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) iam_client.get_paginator.return_value = MockPaginator_RolePolicies() kwargs = _get_aws_list_policies_kwargs(group_by="roles", output=fname, force=True) iam.list_policies([], argparse.Namespace(kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn( IAMSEPARATOR.join(["fake-role-1", "fake-policy-attached-to-fake-role-1"]), output ) # Make sure we can't overwrite without --force with self.assertRaises(RuntimeError): kwargs = _get_aws_list_policies_kwargs(group_by="roles", output=fname, force=False) iam.list_policies([], argparse.Namespace(kwargs)) # Test error-handling and exceptions last with self.subTest("Test exceptions and error-handling for AWS IAM functions in dss-ops"): with self.assertRaises(RuntimeError): kwargs = _get_aws_list_policies_kwargs(cloud_provider="invalid-cloud-provider") iam.list_policies([], argparse.Namespace(kwargs)) with self.assertRaises(RuntimeError): kwargs = _get_aws_list_policies_kwargs(group_by="another-invalid-choice") iam.list_policies([], argparse.Namespace(kwargs)) def test_iam_fus_list_policies(self): def _get_fus_list_policies_kwargs(*kwargs): # Set default kwargs values, then set user-specified kwargs custom_kwargs = dict( cloud_provider="fusillade", group_by=None, output=None, force=False, exclude_headers=False, include_managed=False, quiet=True ) for kw, val in kwargs.items(): custom_kwargs[kw] = val return custom_kwargs with self.subTest("Fusillade client"): with mock.patch("dss.operations.iam.DCPServiceAccountManager") as SAM, \ mock.patch("dss.operations.iam.requests") as req: # Mock the service account manager so it won't hit the fusillade server class FakeServiceAcctMgr(object): def get_authorization_header(self, args, *kwargs): return {} SAM.from_secrets_manager = mock.MagicMock(return_value=FakeServiceAcctMgr()) # Create fake API response (one page) class FakeResponse(object): def __init__(self): self.headers = {} def raise_for_status(self, args, *kwargs): pass def json(self, args, *kwargs): return {"key": "value"} # Test call_api() req.get = mock.MagicMock(return_value=FakeResponse()) client = iam.FusilladeClient("testing") result = client.call_api("/foobar", "key") self.assertEqual(result, "value") # Mock paginated responses with and without Link header class FakePaginatedResponse(object): def __init__(self): self.headers = {} def raise_for_status(self, args, *kwargs): pass def json(self, args, *kwargs): return {"key": ["values", "values"]} class FakePaginatedResponseWithLink(FakePaginatedResponse): def __init__(self): self.headers = {"Link": "<https://api.github.com/user/repos?page=3&per_page=100>;"} # Test paginate() req.get = mock.MagicMock(side_effect=[FakePaginatedResponseWithLink(), FakePaginatedResponse()]) result = client.paginate("/foobar", "key") self.assertEqual(result, ["values"] 4) def _wrap_policy(policy_doc): """Wrap a policy doc the way Fusillade stores/returns them""" return {"IAMPolicy": policy_doc} def _repatch_fus_client(fus_client): """ Re-patch a mock Fusillade client with the proper responses for no --group-by flag or for the --group-by users flag. """ # When we call list_policies(), which calls list_fus_user_policies(), # it calls the paginate() method to get a list of all users, # then the paginate() method twice for each user (once for groups, once for roles), side_effects = [ [ "<EMAIL>", "<EMAIL>" ], ["fake-group"], ["fake-role"], ["fake-group-2"], ["fake-role-2"] ] fus_client().paginate = mock.MagicMock(side_effect=side_effects) # Once we have called the paginate() methods, # we call the call_api() method to get IAM policies attached to roles and groups policy_docs = [ '{"Id": "fake-group-policy"}', '{"Id": "fake-role-policy"}', '{"Id": "fake-group-2-policy"}', '{"Id": "fake-role-2-policy"}', ] fus_client().call_api = mock.MagicMock(side_effect=[_wrap_policy(doc) for doc in policy_docs]) with self.subTest("List Fusillade policies"): with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: # Note: Need to call _repatch_fus_client() before each test # Plain call to list_fus_policies with CaptureStdout() as output: _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs() iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn("fake-group-policy", output) self.assertIn("fake-role-policy", output) self.assertIn("fake-group-2-policy", output) self.assertIn("fake-role-2-policy", output) # Check exclude headers with CaptureStdout() as output: _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs(exclude_headers=True) iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn("fake-group-policy", output) self.assertIn("fake-role-policy", output) self.assertIn("fake-group-2-policy", output) self.assertIn("fake-role-2-policy", output) # Check write to output file temp_prefix = "dss-test-operations-iam-fus-list-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs(output=fname, force=True) iam.list_policies([], argparse.Namespace(kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn("fake-group-policy", output) self.assertIn("fake-role-policy", output) self.assertIn("fake-group-2-policy", output) self.assertIn("fake-role-2-policy", output) with self.subTest("List Fusillade policies grouped by users"): with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: # List fusillade policies grouped by user with CaptureStdout() as output: _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="users") iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-2-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-2-policy" ]), output) # Check exclude headers with CaptureStdout() as output: _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="users", exclude_headers=True) iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-2-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-2-policy" ]), output) # Check write to output file temp_prefix = "dss-test-operations-iam-fus-list-users-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="users", output=fname, force=True) iam.list_policies([], argparse.Namespace(kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-2-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-2-policy" ]), output) with self.subTest("List Fusillade policies grouped by groups"): # We can't use _repatch_fus_client() to repatch, # since grouping by groups makes different function calls def _repatch_fus_client_groups(fus_client): """Re-patch a mock Fusillade client with the proper responses for using the --group-by groups flag""" # When we call list_policies(), which calls list_fus_group_policies(), # it calls paginate() to get all groups, # then calls paginate() to get roles for each group responses = [["fake-group", "fake-group-2"], ["fake-role"], ["fake-role-2"]] fus_client().paginate = mock.MagicMock(side_effect=responses) # For each role, list_fus_group_policies() calls get_fus_role_attached_policies(), # which calls call_api() on each role and returns a corresponding policy document # @chmreid TODO: should this be calling get policy on each group, too? (inline policies) policy_docs = ['{"Id": "fake-role-policy"}', '{"Id": "fake-role-2-policy"}'] fus_client().call_api = mock.MagicMock(side_effect=[_wrap_policy(doc) for doc in policy_docs]) with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: # List fusillade policies grouped by groups with CaptureStdout() as output: _repatch_fus_client_groups(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="groups") iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn(IAMSEPARATOR.join(["fake-group", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-group-2", "fake-role-2-policy"]), output) # Check exclude headers with CaptureStdout() as output: _repatch_fus_client_groups(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="groups", exclude_headers=True) iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn(IAMSEPARATOR.join(["fake-group", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-group-2", "fake-role-2-policy"]), output) # Check write to output file temp_prefix = "dss-test-operations-iam-fus-list-groups-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) _repatch_fus_client_groups(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="groups", output=fname, force=True) iam.list_policies([], argparse.Namespace(kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn(IAMSEPARATOR.join(["fake-group", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-group-2", "fake-role-2-policy"]), output) with self.subTest("List Fusillade policies grouped by roles"): # repatch the fusillade client for calling a list of policies grouped by roles def _repatch_fus_client_roles(fus_client): """Re-patch a mock Fusillade client with the proper responses for using the --group-by roles flag""" # When we call list_policies, which calls list_fus_role_policies(), # it calls paginate() to get the list of all roles, side_effects = [["fake-role", "fake-role-2"]] fus_client().paginate = mock.MagicMock(side_effect=side_effects) # list_fus_role_policies then calls get_fus_role_attached_policies() # to get a list of policies attached to the role, # which calls call_api() for each role returned by the paginate command policy_docs = ['{"Id": "fake-role-policy"}', '{"Id": "fake-role-2-policy"}'] fus_client().call_api = mock.MagicMock(side_effect=[_wrap_policy(doc) for doc in policy_docs]) with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: # List fusillade policies grouped by roles with CaptureStdout() as output: _repatch_fus_client_roles(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="roles") iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn(IAMSEPARATOR.join(["fake-role", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-role-2", "fake-role-2-policy"]), output) # Check exclude headers with CaptureStdout() as output: _repatch_fus_client_roles(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="roles", exclude_headers=True) iam.list_policies([], argparse.Namespace(kwargs)) self.assertIn(IAMSEPARATOR.join(["fake-role", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-role-2", "fake-role-2-policy"]), output) # Check write to output file temp_prefix = "dss-test-operations-iam-list-roles-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) _repatch_fus_client_roles(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="roles", output=fname, force=True) iam.list_policies([], argparse.Namespace(kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn(IAMSEPARATOR.join(["fake-role", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-role-2", "fake-role-2-policy"]), output) def test_iam_aws_list_assets(self): def _get_aws_list_assets_kwargs(*kwargs): # Set default kwargs values, then set user-specified kwargs custom_kwargs = dict( cloud_provider="aws", output=None, force=False, exclude_headers=False, ) for kw, val in kwargs.items(): custom_kwargs[kw] = val return custom_kwargs with self.subTest("AWS list users"): with mock.patch("dss.operations.iam.iam_client") as iam_client: class MockPaginator_Users(object): def paginate(self, args, kwargs): return [{"Users": [ {"UserName": "<EMAIL>"}, {"UserName": "<EMAIL>"} ]}] iam_client.get_paginator.return_value = MockPaginator_Users() with CaptureStdout() as output: kwargs = _get_aws_list_assets_kwargs() iam.list_users([], argparse.Namespace(kwargs)) self.assertIn("<EMAIL>", output) self.assertIn("<EMAIL>", output) with self.subTest("AWS list groups"): with mock.patch("dss.operations.iam.iam_client") as iam_client: class MockPaginator_Groups(object): def paginate(self, args, kwargs): return [{"Groups": [{"GroupName": "fake-group-1"}, {"GroupName": "fake-group-2"}]}] iam_client.get_paginator.return_value = MockPaginator_Groups() with CaptureStdout() as output: kwargs = _get_aws_list_assets_kwargs() iam.list_groups([], argparse.Namespace(kwargs)) self.assertIn("fake-group-1", output) self.assertIn("fake-group-2", output) with self.subTest("AWS list roles"): with mock.patch("dss.operations.iam.iam_client") as iam_client: class MockPaginator_Roles(object): def paginate(self, args, kwargs): return [{"Roles": [{"RoleName": "fake-role-1"}, {"RoleName": "fake-role-2"}]}] iam_client.get_paginator.return_value = MockPaginator_Roles() with CaptureStdout() as output: kwargs = _get_aws_list_assets_kwargs() iam.list_roles([], argparse.Namespace(kwargs)) self.assertIn("fake-role-1", output) self.assertIn("fake-role-2", output) def test_iam_fus_list_assets(self): def _get_fus_list_assets_kwargs(kwargs): # Set default kwargs values, then set user-specified kwargs custom_kwargs = dict( cloud_provider="fusillade", output=None, force=False, exclude_headers=False, ) for kw, val in kwargs.items(): custom_kwargs[kw] = val return custom_kwargs with self.subTest("Fusillade list users"): with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: side_effects = [[ "<EMAIL>", "<EMAIL>" ]] fus_client().paginate = mock.MagicMock(side_effect=side_effects) kwargs = _get_fus_list_assets_kwargs() with CaptureStdout() as output: iam.list_users([], argparse.Namespace(kwargs)) self.assertIn("<EMAIL>", output) self.assertIn("<EMAIL>", output) with self.subTest("Fusillade list groups"): with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: side_effects = [["fake-group-1", "fake-group-2"]] fus_client().paginate = mock.MagicMock(side_effect=side_effects) kwargs = _get_fus_list_assets_kwargs() with CaptureStdout() as output: iam.list_groups([], argparse.Namespace(kwargs)) self.assertIn("fake-group-1", output) self.assertIn("fake-group-2", output) with self.subTest("Fusillade list roles"): with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: side_effects = [["fake-role-1", "fake-role-2"]] fus_client().paginate = mock.MagicMock(side_effect=side_effects) kwargs = _get_fus_list_assets_kwargs() with CaptureStdout() as output: iam.list_roles([], argparse.Namespace(kwargs)) self.assertIn("fake-role-1", output) self.assertIn("fake-role-2", output) def test_secrets_crud(self): # CRUD (create read update delete) test procedure: # - create new secret # - list secrets and verify new secret shows up # - get secret value and verify it is correct # - update secret value # - get secret value and verify it is correct # - delete secret which_stage = os.environ["DSS_DEPLOYMENT_STAGE"] which_store = os.environ["DSS_SECRETS_STORE"] secret_name = random_alphanumeric_string() testvar_name = f"{which_store}/{which_stage}/{secret_name}" testvar_value = "Hello world!" testvar_value2 = "Goodbye world!" unusedvar_name = f"{which_store}/{which_stage}/admin_user_emails" with self.subTest("Create a new secret"): # Monkeypatch the secrets manager with mock.patch("dss.operations.secrets.sm_client") as sm: # Creating a new variable will first call get, which will not find it sm.get_secret_value = mock.MagicMock(return_value=None, side_effect=ClientError({}, None)) # Next we will use the create secret command sm.create_secret = mock.MagicMock(return_value=None) # Create initial secret value: # Dry run first with SwapStdin(testvar_value): secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=True, infile=None, quiet=True, force=True ), ) # Provide secret via stdin with SwapStdin(testvar_value): secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=False, infile=None, quiet=True, force=True ), ) # Provide secret via infile with tempfile.NamedTemporaryFile(prefix='dss-test-operations-new-secret-temp-input', mode='w') as f: f.write(testvar_value) secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=False, infile=f.name, force=True, quiet=True ), ) # Check error-catching with non-existent infile mf = 'this-file-is-not-here' with self.assertRaises(RuntimeError): secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=False, infile=mf, force=True, quiet=True ), ) with self.subTest("List secrets"): with mock.patch("dss.operations.secrets.sm_client") as sm: # Listing secrets requires creating a paginator first, # so mock what the paginator returns class MockPaginator(object): def paginate(self): # Return a mock page from the mock paginator return [{"SecretList": [{"Name": testvar_name}, {"Name": unusedvar_name}]}] sm.get_paginator.return_value = MockPaginator() # Non-JSON output first with CaptureStdout() as output: secrets.list_secrets([], argparse.Namespace(json=False)) self.assertIn(testvar_name, output) # JSON output with CaptureStdout() as output: secrets.list_secrets([], argparse.Namespace(json=True)) all_secrets_output = json.loads("\n".join(output)) self.assertIn(testvar_name, all_secrets_output) with self.subTest("Get secret value"): with mock.patch("dss.operations.secrets.sm_client") as sm: # Requesting the variable will try to get secret value and succeed sm.get_secret_value.return_value = {"SecretString": testvar_value} # Now run get secret value in JSON mode and non-JSON mode # and verify variable name/value is in both. # New output file with tempfile.NamedTemporaryFile(prefix='dss-test-operations-get-secret-temp-output', mode='w') as f: # Try to overwrite outfile without --force with self.assertRaises(RuntimeError): secrets.get_secret( [], argparse.Namespace(secret_name=testvar_name, outfile=f.name, force=False) ) # Overwrite outfile with --force secrets.get_secret( [], argparse.Namespace(secret_name=testvar_name, outfile=f.name, force=True) ) with open(f.name, 'r') as fr: file_contents = fr.read() self.assertIn(testvar_value, file_contents) # Output secret to stdout with CaptureStdout() as output: secrets.get_secret( [], argparse.Namespace(secret_name=testvar_name, outfile=None, force=False) ) self.assertIn(testvar_value, "\n".join(output)) with self.subTest("Update existing secret"): with mock.patch("dss.operations.secrets.sm_client") as sm: # Updating the variable will try to get secret value and succeed sm.get_secret_value = mock.MagicMock(return_value={"SecretString": testvar_value}) # Next we will call the update secret command sm.update_secret = mock.MagicMock(return_value=None) # Update secret: # Dry run first with SwapStdin(testvar_value2): secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=True, infile=None, force=True, quiet=True ), ) # Use stdin with SwapStdin(testvar_value2): secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=False, infile=None, force=True, quiet=True ), ) # Use input file with tempfile.NamedTemporaryFile(prefix='dss-test-operations-update-secret-temp-input', mode='w') as f: f.write(testvar_value2) secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=False, infile=f.name, force=True, quiet=True ), ) with self.subTest("Delete secret"): with mock.patch("dss.operations.secrets.sm_client") as sm: # Deleting the variable will try to get secret value and succeed sm.get_secret_value = mock.MagicMock(return_value={"SecretString": testvar_value}) sm.delete_secret = mock.MagicMock(return_value=None) # Delete secret # Dry run first secrets.del_secret( [], argparse.Namespace(secret_name=testvar_name, force=True, dry_run=True, quiet=True) ) # Real thing secrets.del_secret( [], argparse.Namespace(secret_name=testvar_name, force=True, dry_run=False, quiet=True) ) def test_ssmparams_utilities(self): prefix = f"/{os.environ['DSS_PARAMETER_STORE']}/{os.environ['DSS_DEPLOYMENT_STAGE']}" gold_var = f"{prefix}/dummy_variable" var = "dummy_variable" new_var = fix_ssm_variable_prefix(var) self.assertEqual(new_var, gold_var) var = "/dummy_variable" new_var = fix_ssm_variable_prefix(var) self.assertEqual(new_var, gold_var) var = f"{prefix}/dummy_variable" new_var = fix_ssm_variable_prefix(var) self.assertEqual(new_var, gold_var) var = f"{prefix}/dummy_variable/" new_var = fix_ssm_variable_prefix(var) self.assertEqual(new_var, gold_var) def test_ssmparams_crud(self): # CRUD (create read update delete) test for setting environment variables in SSM param store testvar_name = random_alphanumeric_string() testvar_value = "Hello world!" # Assemble environment to return old_env = {"DUMMY_VARIABLE": "dummy_value"} new_env = dict(old_env) new_env[testvar_name] = testvar_value ssm_new_env = self._wrap_ssm_env(new_env) with self.subTest("Print the SSM environment"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm: # listing params will call ssm.get_parameter to get the entire environment ssm.get_parameter = mock.MagicMock(return_value=ssm_new_env) # Now call our params.py module. Output var=value on each line. with CaptureStdout() as output: lambda_params.ssm_environment([], argparse.Namespace(json=False)) self.assertIn(f"{testvar_name}={testvar_value}", output) def test_lambdaparams_crud(self): # CRUD (create read update delete) test for setting lambda function environment variables testvar_name = random_alphanumeric_string() testvar_value = "Hello world!" testvar_value2 = "Goodbye world!" # Assemble an old and new environment to return old_env = {"DUMMY_VARIABLE": "dummy_value"} new_env = dict(old_env) new_env[testvar_name] = testvar_value ssm_old_env = self._wrap_ssm_env(old_env) ssm_new_env = self._wrap_ssm_env(new_env) lam_old_env = self._wrap_lambda_env(old_env) lam_new_env = self._wrap_lambda_env(new_env) with self.subTest("Create a new lambda parameter"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm, \ mock.patch("dss.operations.lambda_params.lambda_client") as lam: # If this is not a dry run, lambda_set in params.py # will update the SSM first, so we mock those first. # Before we have set the new test variable for the # first time, we will see the old environment. ssm.put_parameter = mock.MagicMock(return_value=None) ssm.get_parameter = mock.MagicMock(return_value=ssm_old_env) # The lambda_set func in params.py will update lambdas, # so we mock the calls that those will make too. lam.get_function = mock.MagicMock(return_value=None) lam.get_function_configuration = mock.MagicMock(return_value=lam_old_env) lam.update_function_configuration = mock.MagicMock(return_value=None) with SwapStdin(testvar_value): lambda_params.lambda_set( [], argparse.Namespace(name=testvar_name, dry_run=True, quiet=True) ) with SwapStdin(testvar_value): lambda_params.lambda_set( [], argparse.Namespace(name=testvar_name, dry_run=False, quiet=True) ) with self.subTest("List lambda parameters"): with mock.patch("dss.operations.lambda_params.lambda_client") as lam: # The lambda_list func in params.py calls get_deployed_lambas, which calls lam.get_function() # using daemon folder names (this function is called only to ensure no exception is thrown) lam.get_function = mock.MagicMock(return_value=None) # Next we call get_deployed_lambda_environment(), which calls lam.get_function_configuration # (this returns the mocked new env vars json) lam.get_function_configuration = mock.MagicMock(return_value=lam_new_env) # Used to specify a lambda by name stage = os.environ["DSS_DEPLOYMENT_STAGE"] # Non-JSON fmt with CaptureStdout() as output: lambda_params.lambda_list([], argparse.Namespace(json=False)) # Check that all deployed lambdas are present for lambda_name in lambda_params.get_deployed_lambdas(quiet=True): self.assertIn(f"{lambda_name}", output) # JSON fmt with CaptureStdout() as output: lambda_params.lambda_list([], argparse.Namespace(json=True)) # Check that all deployed lambdas are present all_lams_output = json.loads("\n".join(output)) for lambda_name in lambda_params.get_deployed_lambdas(quiet=True): self.assertIn(lambda_name, all_lams_output) with self.subTest("Get environments of each lambda function"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm, \ mock.patch("dss.operations.lambda_params.lambda_client") as lam: # lambda_environment() function in dss/operations/lambda_params.py calls get_deployed_lambdas() # (which only does local operations) # then it calls get_deployed_lambda_environment() on every lambda, # which calls lambda_client.get_function() (only called to ensure no exception is thrown) lam.get_function = mock.MagicMock(return_value=None) # then calls lambda_client.get_function_configuration() lam.get_function_configuration = mock.MagicMock(return_value=lam_new_env) # TODO: reduce copypasta # Non-JSON, no lambda name specified with CaptureStdout() as output: lambda_params.lambda_environment([], argparse.Namespace(lambda_name=None, json=False)) # Check that all deployed lambdas are present output = "\n".join(output) for lambda_name in lambda_params.get_deployed_lambdas(quiet=True): self.assertIn(lambda_name, output) # Non-JSON, lambda name specified with CaptureStdout() as output: lambda_params.lambda_environment([], argparse.Namespace(lambda_name=f"dss-{stage}", json=False)) output = "\n".join(output) self.assertIn(f"dss-{stage}", output) # JSON, no lambda name specified with CaptureStdout() as output: lambda_params.lambda_environment([], argparse.Namespace(lambda_name=None, json=True)) # Check that all deployed lambdas are present all_lams_output = json.loads("\n".join(output)) for lambda_name in lambda_params.get_deployed_lambdas(quiet=True): self.assertIn(lambda_name, all_lams_output) # JSON, lambda name specified with CaptureStdout() as output: lambda_params.lambda_environment([], argparse.Namespace(lambda_name=f"dss-{stage}", json=True)) all_lams_output = json.loads("\n".join(output)) self.assertIn(f"dss-{stage}", all_lams_output) with self.subTest("Update (set) existing lambda parameters"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm, \ mock.patch("dss.operations.lambda_params.lambda_client") as lam: # Mock the same way we did for create new param above. # First we mock the SSM param store ssm.get_parameter = mock.MagicMock(return_value=ssm_new_env) ssm.put_parameter = mock.MagicMock(return_value=None) # Next we mock the lambda client lam.get_function = mock.MagicMock(return_value=None) lam.get_function_configuration = mock.MagicMock(return_value=lam_new_env) lam.update_function_configuration = mock.MagicMock(return_value=None) # Dry run then real (mocked) thing with SwapStdin(testvar_value2): lambda_params.lambda_set( [], argparse.Namespace(name=testvar_name, dry_run=True, quiet=True) ) with SwapStdin(testvar_value2): lambda_params.lambda_set( [], argparse.Namespace(name=testvar_name, dry_run=False, quiet=True) ) with self.subTest("Update lambda environment stored in SSM store under $DSS_DEPLOYMENT_STAGE/environment"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm, \ mock.patch("dss.operations.lambda_params.lambda_client") as lam, \ mock.patch("dss.operations.lambda_params.es_client") as es, \ mock.patch("dss.operations.lambda_params.sm_client") as sm, \ mock.patch("dss.operations.lambda_params.set_ssm_environment") as set_ssm: # If we call lambda_update in dss/operations/lambda_params.py, # it calls get_local_lambda_environment() # (local operations only) # lambda_update() then calls set_ssm_environment(), # which we mocked above into set_ssm set_ssm = mock.MagicMock(return_value=None) # noqa ssm.put_parameter = mock.MagicMock(return_value=None) # get_elasticsearch_endpoint() calls es.describe_elasticsearch_domain() es_endpoint_secret = { "DomainStatus": { "Endpoint": "this-invalid-es-endpoint-value-comes-from-dss-test-operations" } } es.describe_elasticsearch_domain = mock.MagicMock( return_value=es_endpoint_secret ) # get_admin_emails() calls sm.get_secret_value() several times: # - google service acct secret (json string) # - admin email secret # use side_effect when returning multiple values google_service_acct_secret = json.dumps( {"client_email": "this-invalid-email-comes-from-dss-test-operations"} ) admin_email_secret = "this-invalid-email-list-comes-from-dss-test-operations" # Finally, we call set_ssm_environment # which calls ssm.put_parameter() # (mocked above). # If we also update deployed lambdas: # get_deployed_lambdas() -> lam_client.get_function() # get_deployed_lambda_environment() -> lam_client.get_function_configuration() # set_deployed_lambda_environment() -> lam_client.update_function_configuration() lam.get_function = mock.MagicMock(return_value=None) lam.get_function_configuration = mock.MagicMock(return_value=lam_new_env) lam.update_function_configuration = mock.MagicMock(return_value=None) # The function sm.get_secret_value() must return things in the right order # Re-mock it before each call email_side_effect = [ self._wrap_secret(google_service_acct_secret), self._wrap_secret(admin_email_secret), ] # Dry run, then real (mocked) thing sm.get_secret_value = mock.MagicMock(side_effect=email_side_effect) lambda_params.lambda_update( [], argparse.Namespace(update_deployed=False, dry_run=True, force=True, quiet=True) ) sm.get_secret_value = mock.MagicMock(side_effect=email_side_effect) lambda_params.lambda_update( [], argparse.Namespace(update_deployed=False, dry_run=False, force=True, quiet=True) ) sm.get_secret_value = mock.MagicMock(side_effect=email_side_effect) lambda_params.lambda_update( [], argparse.Namespace(update_deployed=True, dry_run=False, force=True, quiet=True) ) with self.subTest("Unset lambda parameters"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm, \ mock.patch("dss.operations.lambda_params.lambda_client") as lam: # If this is not a dry run, lambda_set in params.py # will update the SSM first, so we mock those first. # Before we have set the new test variable for the # first time, we will see the old environment. ssm.put_parameter = mock.MagicMock(return_value=None) # use deepcopy here to prevent delete operation from being permanent ssm.get_parameter = mock.MagicMock(return_value=copy.deepcopy(ssm_new_env)) # The lambda_set func in params.py will update lambdas, # so we mock the calls that those will make too. lam.get_function = mock.MagicMock(return_value=None) # use side effect here, and copy the environment for each lambda, so that deletes won't be permanent lam.get_function_configuration = mock.MagicMock( side_effect=[copy.deepcopy(lam_new_env) for j in get_deployed_lambdas()] ) lam.update_function_configuration = mock.MagicMock(return_value=None) lambda_params.lambda_unset([], argparse.Namespace(name=testvar_name, dry_run=True, quiet=True)) lambda_params.lambda_unset([], argparse.Namespace(name=testvar_name, dry_run=False, quiet=True)) def test_events_operations_journal(self): with self.subTest("Should forward to lambda when `starting_journal_id` is `None`"): self._test_events_operations_journal(None, 0, 2) with self.subTest("Should execute journaling when `starting_journal_id` is not `None`"): self._test_events_operations_journal("blah", 1, 0) def _test_events_operations_journal(self, starting_journal_id: str, expected_journal_flashflood_calls: int, expected_sqs_messenger_calls: int): sqs_messenger = mock.MagicMock() with mock.patch("dss.operations.events.SQSMessenger", return_value=sqs_messenger), \ mock.patch("dss.operations.events.list_new_flashflood_journals"), \ mock.patch("dss.operations.events.journal_flashflood") as journal_flashflood, \ mock.patch("dss.operations.events.monitor_logs"): args = argparse.Namespace(prefix="pfx", number_of_events=5, starting_journal_id=starting_journal_id, job_id=None) events.journal([], args) self.assertEqual(expected_journal_flashflood_calls, len(journal_flashflood.mock_calls)) self.assertEqual(expected_sqs_messenger_calls, len(sqs_messenger.mock_calls)) def _wrap_ssm_env(self, e): """ Package up the SSM environment the way AWS returns it. :param dict e: the dict containing the environment to package up and send to SSM store at $DSS_DEPLOYMENT_STAGE/environment. """ # Value should be serialized JSON ssm_e = {"Parameter": {"Name": "environment", "Value": json.dumps(e)}} return ssm_e def _wrap_lambda_env(self, e): """ Package up the lambda environment (a.k.a. function configuration) the way AWS returns it. :param dict e: the dict containing the lambda function's environment variables """ # Value should be a dict (NOT a string) lam_e = {"Environment": {"Variables": e}} return lam_e def _wrap_secret(self, val): """ Package up the secret the way AWS returns it. """ return {"SecretString": val} @testmode.integration class TestOperationsIntegration(TestBundleApiMixin): @classmethod def setUpClass(cls): cls.app = ThreadedLocalServer() cls.app.start() @classmethod def tearDownClass(cls): cls.app.shutdown() def setUp(self): Config.set_config(BucketConfig.TEST) self.s3_test_bucket = get_env("DSS_S3_BUCKET_TEST") self.gs_test_bucket = get_env("DSS_GS_BUCKET_TEST") self.s3_test_fixtures_bucket = get_env("DSS_S3_BUCKET_TEST_FIXTURES") self.gs_test_fixtures_bucket = get_env("DSS_GS_BUCKET_TEST_FIXTURES") def test_checkout_operations(self): with override_bucket_config(BucketConfig.TEST): for replica, fixture_bucket in [(Replica['aws'], self.s3_test_fixtures_bucket), (Replica['gcp'], self.gs_test_fixtures_bucket)]: bundle, bundle_uuid = self._create_bundle(replica, fixture_bucket) args = argparse.Namespace(replica=replica.name, keys=[f'bundles/{bundle_uuid}.{bundle["version"]}']) checkout_status = checkout.Verify([], args).process_keys() for key in args.keys: self.assertIn(key, checkout_status) checkout.Remove([], args).process_keys() checkout_status = checkout.Verify([], args).process_keys() for key in args.keys: for file in checkout_status[key]: self.assertIs(False, file['bundle_checkout']) self.assertIs(False, file['blob_checkout']) checkout.Add([], args).process_keys() checkout_status = checkout.Verify([], args).process_keys() for key in args.keys: for file in checkout_status[key]: self.assertIs(True, file['bundle_checkout']) self.assertIs(True, file['blob_checkout']) self.delete_bundle(replica, bundle_uuid) def _create_bundle(self, replica: Replica, fixtures_bucket: str): schema = replica.storage_schema bundle_uuid = str(uuid.uuid4()) file_uuid = str(uuid.uuid4()) resp_obj = self.upload_file_wait( f"{schema}://{fixtures_bucket}/test_good_source_data/0", replica, file_uuid, bundle_uuid=bundle_uuid, ) file_version = resp_obj.json['version'] bundle_version = datetime_to_version_format(datetime.datetime.utcnow()) resp_obj = self.put_bundle(replica, bundle_uuid, [(file_uuid, file_version, "LICENSE")], bundle_version) return resp_obj.json, bundle_uuid @testmode.integration class TestSecretsChecker(unittest.TestCase): """Test the SecretsChecker class defined in dss/operations/secrets.py""" @skip_on_travis def test_check_secrets(self): """Check that the current stage's secrets conform to expected values""" secrets.check_secrets([], argparse.Namespace()) @skip_on_travis def test_custom_stage_secrets(self): """ The SecretsChecker should not test stages that are not in the list: dev, staging, integration, prod. 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tests/test_handlers_item_builder.py	MacHu-GWU/afwf_aws_tools-project	6	59329	<reponame>MacHu-GWU/afwf_aws_tools-project<filename>tests/test_handlers_item_builder.py # -- coding: utf-8 -- import pytest from workflow.workflow3 import Workflow3 from aws_tools.handlers.item_builder import item_builders class TestItemBuilders: wf = Workflow3() def setup_method(self): self.wf = Workflow3() def test_select_aws_profile_as_default(self): item_builders.select_aws_profile_as_default( wf=self.wf, aws_profile_list=["a", "b", "c"], set_default_aws_profile_handler_id="handler_id" ) def test_select_aws_profile_for_mfa(self): item_builders.select_aws_profile_for_mfa( wf=self.wf, aws_profile_list=["a", "b", "c"], execute_mfa_auth_handler_id="handler_id" ) def test_set_aws_profile_as_aws_tools_default(self): item_builders.set_aws_profile_as_aws_tools_default( wf=self.wf, aws_profile_list=["a", "b", "c"], set_aws_profile_as_aws_tools_default_handler_id="handler_id" ) if __name__ == "__main__": import os basename = os.path.basename(__file__) pytest.main([basename, "-s", "--tb=native"])	[ 1, 529, 276, 1112, 420, 29958, 15735, 29950, 29884, 29899, 29954, 29956, 29965, 29914, 2142, 29893, 29888, 29918, 10467, 29918, 8504, 29899, 4836, 29966, 9507, 29958, 21150, 29914, 1688, 29918, 3179, 9306, 29918, 667, 29918, 16409, 29889, 2272, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 13, 5215, 11451, 1688, 13, 3166, 27321, 29889, 1287, 1731, 29941, 1053, 5244, 1731, 29941, 13, 3166, 25879, 29918, 8504, 29889, 3179, 9306, 29889, 667, 29918, 16409, 1053, 2944, 29918, 4282, 414, 13, 13, 13, 1990, 4321, 2001, 8893, 414, 29901, 13, 1678, 281, 29888, 353, 5244, 1731, 29941, 580, 13, 13, 1678, 822, 6230, 29918, 5696, 29898, 1311, 1125, 13, 4706, 1583, 29889, 29893, 29888, 353, 5244, 1731, 29941, 580, 13, 13, 1678, 822, 1243, 29918, 2622, 29918, 10467, 29918, 10185, 29918, 294, 29918, 4381, 29898, 1311, 1125, 13, 4706, 2944, 29918, 4282, 414, 29889, 2622, 29918, 10467, 29918, 10185, 29918, 294, 29918, 4381, 29898, 13, 9651, 281, 29888, 29922, 1311, 29889, 29893, 29888, 29892, 13, 9651, 25879, 29918, 10185, 29918, 1761, 29922, 3366, 29874, 613, 376, 29890, 613, 376, 29883, 12436, 13, 9651, 731, 29918, 4381, 29918, 10467, 29918, 10185, 29918, 13789, 29918, 333, 543, 13789, 29918, 333, 29908, 13, 4706, 1723, 13, 13, 1678, 822, 1243, 29918, 2622, 29918, 10467, 29918, 10185, 29918, 1454, 29918, 29885, 5444, 29898, 1311, 1125, 13, 4706, 2944, 29918, 4282, 414, 29889, 2622, 29918, 10467, 29918, 10185, 29918, 1454, 29918, 29885, 5444, 29898, 13, 9651, 281, 29888, 29922, 1311, 29889, 29893, 29888, 29892, 13, 9651, 25879, 29918, 10185, 29918, 1761, 29922, 3366, 29874, 613, 376, 29890, 613, 376, 29883, 12436, 13, 9651, 6222, 29918, 29885, 5444, 29918, 5150, 29918, 13789, 29918, 333, 543, 13789, 29918, 333, 29908, 13, 4706, 1723, 13, 13, 1678, 822, 1243, 29918, 842, 29918, 10467, 29918, 10185, 29918, 294, 29918, 10467, 29918, 8504, 29918, 4381, 29898, 1311, 1125, 13, 4706, 2944, 29918, 4282, 414, 29889, 842, 29918, 10467, 29918, 10185, 29918, 294, 29918, 10467, 29918, 8504, 29918, 4381, 29898, 13, 9651, 281, 29888, 29922, 1311, 29889, 29893, 29888, 29892, 13, 9651, 25879, 29918, 10185, 29918, 1761, 29922, 3366, 29874, 613, 376, 29890, 613, 376, 29883, 12436, 13, 9651, 731, 29918, 10467, 29918, 10185, 29918, 294, 29918, 10467, 29918, 8504, 29918, 4381, 29918, 13789, 29918, 333, 543, 13789, 29918, 333, 29908, 13, 4706, 1723, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 1678, 1053, 2897, 13, 13, 1678, 2362, 3871, 353, 2897, 29889, 2084, 29889, 6500, 3871, 22168, 1445, 1649, 29897, 13, 1678, 11451, 1688, 29889, 3396, 4197, 6500, 3871, 29892, 11663, 29879, 613, 376, 489, 22625, 29922, 11487, 20068, 13, 2 ]
kirppuauth/urls.py	Glowieh/kirppu	0	156190	<filename>kirppuauth/urls.py from django.conf.urls import patterns, include, url urlpatterns = patterns('kirppuauth.views', url(r'^addclerk$', 'register_clerk', name='kirppu_register_clerk'), )	[ 1, 529, 9507, 29958, 14166, 407, 29884, 5150, 29914, 26045, 29889, 2272, 13, 3166, 9557, 29889, 5527, 29889, 26045, 1053, 15038, 29892, 3160, 29892, 3142, 13, 13, 2271, 11037, 29879, 353, 15038, 877, 14166, 407, 29884, 5150, 29889, 7406, 742, 13, 1678, 3142, 29898, 29878, 29915, 29985, 1202, 695, 5968, 29938, 742, 525, 9573, 29918, 695, 5968, 742, 1024, 2433, 14166, 407, 29884, 29918, 9573, 29918, 695, 5968, 5477, 13, 29897, 13, 2 ]
praw_memories/prawdebug.py	elnuno/praw_memories	1	50951	"""Debugging facilities for PRAW-based projects.""" import datetime import json import logging import time import praw import prawcore	[ 1, 9995, 11862, 3460, 23330, 363, 349, 4717, 29956, 29899, 6707, 9279, 1213, 15945, 13, 13, 5215, 12865, 13, 5215, 4390, 13, 5215, 12183, 13, 5215, 931, 13, 13, 5215, 20467, 13, 5215, 20467, 3221, 13, 2 ]
Intro_to_Recommendation_Systems/api.py	NunoEdgarGFlowHub/sciblog_support	668	174503	import cherrypy from paste.translogger import TransLogger from flask import Flask, request, abort, jsonify, make_response import json import os from utils import decode_string from DeepRecommender.reco_encoder.data import input_layer_api, input_layer from DeepRecommender.reco_encoder.model import model import torch from torch.autograd import Variable from parameters import * # app app = Flask(__name__) BAD_REQUEST = 400 STATUS_OK = 200 NOT_FOUND = 404 SERVER_ERROR = 500 @app.errorhandler(BAD_REQUEST) def bad_request(error): return make_response(jsonify({'error': 'Bad request'}), BAD_REQUEST) @app.errorhandler(NOT_FOUND) def not_found(error): return make_response(jsonify({'error': 'Not found'}), NOT_FOUND) @app.errorhandler(SERVER_ERROR) def server_error(error): return make_response(jsonify({'error': 'Server Internal Error'}), SERVER_ERROR) def run_server(): # Enable WSGI access logging via Paste app_logged = TransLogger(app) # Mount the WSGI callable object (app) on the root directory cherrypy.tree.graft(app_logged, '/') # Set the configuration of the web server cherrypy.config.update({ 'engine.autoreload_on': True, 'log.screen': True, 'log.access_file': 'access.log', 'log.error_file': "cherrypy.log", 'server.socket_port': 5000, 'server.socket_host': '0.0.0.0', 'server.thread_pool': 50, # 10 is default }) # Start the CherryPy WSGI web server cherrypy.engine.start() cherrypy.engine.block() def load_model_weights(model_architecture, weights_path): if os.path.isfile(weights_path): cherrypy.log("CHERRYPYLOG Loading model from: {}".format(weights_path)) model_architecture.load_state_dict(torch.load(weights_path)) else: raise ValueError("Path not found {}".format(weights_path)) def load_recommender(vector_dim, hidden, activation, dropout, weights_path): rencoder_api = model.AutoEncoder(layer_sizes=[vector_dim] + [int(l) for l in hidden.split(',')], nl_type=activation, is_constrained=False, dp_drop_prob=dropout, last_layer_activations=False) load_model_weights(rencoder_api, weights_path) rencoder_api.eval() rencoder_api = rencoder_api.cuda() return rencoder_api def load_train_data(data_dir): params = dict() params['batch_size'] = 1 params['data_dir'] = data_dir params['major'] = 'users' params['itemIdInd'] = 1 params['userIdInd'] = 0 cherrypy.log("CHERRYPYLOG Loading training data") data_layer = input_layer.UserItemRecDataProvider(params=params) return data_layer def manage_query(dict_query, data_layer): params = dict() params['batch_size'] = 1 params['data_dict'] = dict_query params['major'] = 'users' params['itemIdInd'] = 1 params['userIdInd'] = 0 data_api = input_layer_api.UserItemRecDataProviderAPI(params=params, user_id_map=data_layer.userIdMap, item_id_map=data_layer.itemIdMap) cherrypy.log("CHERRYPYLOG Input data: {}".format(data_api.data)) data_api.src_data = data_layer.data return data_api def evaluate_model(rencoder_api, data_api): result = dict() for i, ((out, src), major_ind) in enumerate(data_api.iterate_one_epoch_eval(for_inf=True)): inputs = Variable(src.cuda().to_dense()) targets_np = out.to_dense().numpy()[0, :] non_zeros = targets_np.nonzero()[0].tolist() outputs = rencoder_api(inputs).cpu().data.numpy()[0, :] for ind in non_zeros: result[ind] = outputs[ind] return result @app.route("/") def index(): return "Yeah, yeah, I highly recommend it" @app.route("/recommend", methods=['POST']) def recommend(): if not request.is_json: abort(BAD_REQUEST) dict_query = request.get_json() dict_query = dict((decode_string(k), decode_string(v)) for k, v in dict_query.items()) data_api = manage_query(dict_query, data_layer) result = evaluate_model(rencoder_api, data_api) cherrypy.log("CHERRYPYLOG Result: {}".format(result)) result = dict((str(k), str(v)) for k,v in result.items()) return make_response(jsonify(result), STATUS_OK) #Load data and model as global variables data_layer = load_train_data(TRAIN) rencoder_api = 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downstream/tinypersons/tests/test_onnx/__init__.py	bwconrad/solo-learn	326	174461	<filename>downstream/tinypersons/tests/test_onnx/__init__.py from .utils import ort_validate __all__ = ['ort_validate']	[ 1, 529, 9507, 29958, 3204, 5461, 29914, 29873, 262, 1478, 414, 787, 29914, 21150, 29914, 1688, 29918, 3409, 29916, 29914, 1649, 2344, 26914, 2272, 13, 3166, 869, 13239, 1053, 20289, 29918, 15480, 13, 13, 1649, 497, 1649, 353, 6024, 441, 29918, 15480, 2033, 13, 2 ]
access_restricted/tests/test_fields_get.py	trojikman/access-addons	0	91915	from odoo.tests.common import TransactionCase, tagged from odoo.addons.base.models.res_users import name_selection_groups @tagged("at_install", "post_install") class TestFieldsGet(TransactionCase): def test_base(self): demo_user = self.env.ref("base.user_demo") group_erp_manager = self.env.ref("base.group_erp_manager") group_system = self.env.ref("base.group_system") demo_user.write({"groups_id": [(3, group_system.id)]}) group_system.write({"users": [(3, demo_user.id)]}) demo_user.write({"groups_id": [(4, group_erp_manager.id)]}) view_users_form = self.env.ref("base.view_users_form") res = ( self.env["res.users"] .with_user(demo_user) .with_context({"uid": demo_user.id}) .load_views([[view_users_form.id, "form"]]) ) sel_groups = name_selection_groups([group_erp_manager.id]) res = self.env["res.users"].with_user(demo_user).fields_get() self.assertTrue(res.get(sel_groups)) demo_user.write({"groups_id": [(4, group_system.id)]}) sel_groups = name_selection_groups([group_erp_manager.id, group_system.id]) res = self.env["res.users"].with_user(demo_user).fields_get() self.assertTrue(res.get(sel_groups))	[ 1, 515, 288, 1867, 29877, 29889, 21150, 29889, 9435, 1053, 4103, 2467, 8259, 29892, 4055, 3192, 13, 13, 3166, 288, 1867, 29877, 29889, 1202, 787, 29889, 3188, 29889, 9794, 29889, 690, 29918, 7193, 1053, 1024, 29918, 21731, 29918, 13155, 13, 13, 13, 29992, 4039, 3192, 703, 271, 29918, 6252, 613, 376, 2490, 29918, 6252, 1159, 13, 1990, 4321, 14256, 2577, 29898, 12460, 8259, 1125, 13, 1678, 822, 1243, 29918, 3188, 29898, 1311, 1125, 13, 4706, 13455, 29918, 1792, 353, 1583, 29889, 6272, 29889, 999, 703, 3188, 29889, 1792, 29918, 17482, 1159, 13, 4706, 2318, 29918, 261, 29886, 29918, 12847, 353, 1583, 29889, 6272, 29889, 999, 703, 3188, 29889, 2972, 29918, 261, 29886, 29918, 12847, 1159, 13, 4706, 2318, 29918, 5205, 353, 1583, 29889, 6272, 29889, 999, 703, 3188, 29889, 2972, 29918, 5205, 1159, 13, 13, 4706, 13455, 29918, 1792, 29889, 3539, 3319, 29908, 13155, 29918, 333, 1115, 17288, 29941, 29892, 2318, 29918, 5205, 29889, 333, 4638, 1800, 13, 4706, 2318, 29918, 5205, 29889, 3539, 3319, 29908, 7193, 1115, 17288, 29941, 29892, 13455, 29918, 1792, 29889, 333, 4638, 1800, 13, 4706, 13455, 29918, 1792, 29889, 3539, 3319, 29908, 13155, 29918, 333, 1115, 17288, 29946, 29892, 2318, 29918, 261, 29886, 29918, 12847, 29889, 333, 4638, 1800, 13, 13, 4706, 1776, 29918, 7193, 29918, 689, 353, 1583, 29889, 6272, 29889, 999, 703, 3188, 29889, 1493, 29918, 7193, 29918, 689, 1159, 13, 4706, 620, 353, 313, 13, 9651, 1583, 29889, 6272, 3366, 690, 29889, 7193, 3108, 13, 9651, 869, 2541, 29918, 1792, 29898, 17482, 29918, 1792, 29897, 13, 9651, 869, 2541, 29918, 4703, 3319, 29908, 5416, 1115, 13455, 29918, 1792, 29889, 333, 1800, 13, 9651, 869, 1359, 29918, 7406, 4197, 29961, 1493, 29918, 7193, 29918, 689, 29889, 333, 29892, 376, 689, 3108, 2314, 13, 4706, 1723, 13, 13, 4706, 5535, 29918, 13155, 353, 1024, 29918, 21731, 29918, 13155, 4197, 2972, 29918, 261, 29886, 29918, 12847, 29889, 333, 2314, 13, 4706, 620, 353, 1583, 29889, 6272, 3366, 690, 29889, 7193, 16862, 2541, 29918, 1792, 29898, 17482, 29918, 1792, 467, 9621, 29918, 657, 580, 13, 4706, 1583, 29889, 9294, 5574, 29898, 690, 29889, 657, 29898, 2838, 29918, 13155, 876, 13, 13, 4706, 13455, 29918, 1792, 29889, 3539, 3319, 29908, 13155, 29918, 333, 1115, 17288, 29946, 29892, 2318, 29918, 5205, 29889, 333, 4638, 1800, 13, 13, 4706, 5535, 29918, 13155, 353, 1024, 29918, 21731, 29918, 13155, 4197, 2972, 29918, 261, 29886, 29918, 12847, 29889, 333, 29892, 2318, 29918, 5205, 29889, 333, 2314, 13, 4706, 620, 353, 1583, 29889, 6272, 3366, 690, 29889, 7193, 16862, 2541, 29918, 1792, 29898, 17482, 29918, 1792, 467, 9621, 29918, 657, 580, 13, 4706, 1583, 29889, 9294, 5574, 29898, 690, 29889, 657, 29898, 2838, 29918, 13155, 876, 13, 2 ]
tests/tools/test_rm.py	Vikash-Kothary/aem-cmd-python	2	147959	# coding: utf-8 from mock import patch from httmock import urlmatch, HTTMock from nose.tools import eq_ from acmd import tool_repo, Server from test_utils.compat import StringIO @urlmatch(netloc='localhost:4502', method='DELETE') def service_rm(url, request): return { 'content': '', 'status_code': 204 } @patch('sys.stdout', new_callable=StringIO) @patch('sys.stderr', new_callable=StringIO) def test_rm(stderr, stdout): with HTTMock(service_rm): tool = tool_repo.get_tool('rm') server = Server('localhost') status = tool.execute(server, ['rm', '/content/path/node']) eq_(0, status) eq_('/content/path/node\n', stdout.getvalue()) @patch('sys.stdout', new_callable=StringIO) @patch('sys.stderr', new_callable=StringIO) @patch('sys.stdin', new=StringIO('/content/path/node\n')) def test_rm_stdin(stderr, stdout): with HTTMock(service_rm): tool = tool_repo.get_tool('rm') server = Server('localhost') status = tool.execute(server, ['rm']) eq_(0, status) eq_('/content/path/node\n', stdout.getvalue())	[ 1, 396, 14137, 29901, 23616, 29899, 29947, 13, 3166, 11187, 1053, 13261, 13, 3166, 1143, 17640, 1053, 3142, 4352, 29892, 3154, 29911, 18680, 13, 3166, 26414, 29889, 8504, 1053, 11594, 29918, 13, 13, 3166, 1274, 3487, 1053, 5780, 29918, 20095, 29892, 5656, 13, 13, 3166, 1243, 29918, 13239, 29889, 12667, 1053, 1714, 5971, 13, 13, 29992, 2271, 4352, 29898, 1212, 2029, 2433, 7640, 29901, 29946, 29945, 29900, 29906, 742, 1158, 2433, 2287, 18476, 1495, 13, 1753, 2669, 29918, 1758, 29898, 2271, 29892, 2009, 1125, 13, 1678, 736, 426, 13, 4706, 525, 3051, 2396, 15516, 13, 4706, 525, 4882, 29918, 401, 2396, 29871, 29906, 29900, 29946, 13, 1678, 500, 13, 13, 13, 29992, 5041, 877, 9675, 29889, 25393, 742, 716, 29918, 4804, 519, 29922, 1231, 5971, 29897, 13, 29992, 5041, 877, 9675, 29889, 303, 20405, 742, 716, 29918, 4804, 519, 29922, 1231, 5971, 29897, 13, 1753, 1243, 29918, 1758, 29898, 303, 20405, 29892, 27591, 1125, 13, 1678, 411, 3154, 29911, 18680, 29898, 5509, 29918, 1758, 1125, 13, 4706, 5780, 353, 5780, 29918, 20095, 29889, 657, 29918, 10154, 877, 1758, 1495, 13, 4706, 1923, 353, 5656, 877, 7640, 1495, 13, 4706, 4660, 353, 5780, 29889, 7978, 29898, 2974, 29892, 6024, 1758, 742, 8207, 3051, 29914, 2084, 29914, 3177, 11287, 13, 4706, 11594, 23538, 29900, 29892, 4660, 29897, 13, 4706, 11594, 29918, 11219, 3051, 29914, 2084, 29914, 3177, 29905, 29876, 742, 27591, 29889, 657, 1767, 3101, 13, 13, 13, 29992, 5041, 877, 9675, 29889, 25393, 742, 716, 29918, 4804, 519, 29922, 1231, 5971, 29897, 13, 29992, 5041, 877, 9675, 29889, 303, 20405, 742, 716, 29918, 4804, 519, 29922, 1231, 5971, 29897, 13, 29992, 5041, 877, 9675, 29889, 4172, 262, 742, 716, 29922, 1231, 5971, 11219, 3051, 29914, 2084, 29914, 3177, 29905, 29876, 8785, 13, 1753, 1243, 29918, 1758, 29918, 4172, 262, 29898, 303, 20405, 29892, 27591, 1125, 13, 1678, 411, 3154, 29911, 18680, 29898, 5509, 29918, 1758, 1125, 13, 4706, 5780, 353, 5780, 29918, 20095, 29889, 657, 29918, 10154, 877, 1758, 1495, 13, 4706, 1923, 353, 5656, 877, 7640, 1495, 13, 4706, 4660, 353, 5780, 29889, 7978, 29898, 2974, 29892, 6024, 1758, 11287, 13, 4706, 11594, 23538, 29900, 29892, 4660, 29897, 13, 4706, 11594, 29918, 11219, 3051, 29914, 2084, 29914, 3177, 29905, 29876, 742, 27591, 29889, 657, 1767, 3101, 13, 2 ]
data_visualizer.py	austinmdillow/natural-selection-simulator	0	94648	from environment import Environment from species import Species import matplotlib.pyplot as plt import matplotlib matplotlib.use('TkAgg') # <-- THIS MAKES IT FAST! class DataVisualizer(): def __init__(self, env): self.environments = [env] plt.ion() plt.show() print("Init of Data Visualizer complete") def plotPopulations(self): plt.clf() plt.plot(self.environments[0].population_log[Species.Rabbit]) #plt.draw() plt.pause(0.0001)	[ 1, 515, 5177, 1053, 16738, 13, 3166, 6606, 1053, 21807, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 5215, 22889, 13, 2922, 17357, 29889, 1509, 877, 29911, 29895, 29909, 1505, 1495, 396, 529, 489, 3446, 3235, 14861, 29968, 2890, 13315, 13515, 1254, 29991, 13, 1990, 3630, 16227, 3950, 7295, 13, 29871, 822, 4770, 2344, 12035, 1311, 29892, 8829, 1125, 13, 1678, 1583, 29889, 21813, 1860, 353, 518, 6272, 29962, 13, 1678, 14770, 29889, 291, 580, 13, 1678, 14770, 29889, 4294, 580, 13, 1678, 1596, 703, 6644, 310, 3630, 9249, 3950, 4866, 1159, 13, 13, 29871, 822, 6492, 12310, 8250, 29898, 1311, 1125, 13, 1678, 14770, 29889, 695, 29888, 580, 13, 1678, 14770, 29889, 5317, 29898, 1311, 29889, 21813, 1860, 29961, 29900, 1822, 7323, 2785, 29918, 1188, 29961, 10649, 2478, 29889, 29934, 370, 2966, 2314, 13, 1678, 396, 572, 29873, 29889, 4012, 580, 13, 1678, 14770, 29889, 29886, 1071, 29898, 29900, 29889, 29900, 29900, 29900, 29896, 29897, 13, 268, 2 ]
lk_lambdex/traceback_.py	likianta/lk-lambdex	0	67171	from textwrap import dedent from textwrap import indent def format_exception( direct_caller_frame, lambdex_definition_place, traceback_obj, source_code_block, ) -> str: # caller: see `.main.lambdex.<vars:code>.<code_template>.<except_clause>`. def _show_place(filename, lineno): return '{}:{}'.format(filename, lineno) # pycharm console font color: https://blog.csdn.net/mouday/article/details # /79152994 return dedent(''' An error is raised when executing lambda function within lk-lambdex. Here's the detailed infomation: Lambdex definition start: {lambdex_definition_place_head} Lambdex definition body: {lambdex_definition_place_body} \033[37m{source_lines}\033[0m -> value: \033[36m{value}\033[0m -> error: \033[31m{error_info}!\033[0m Direct caller: {direct_caller_place} ''').format( lambdex_definition_place_head=_show_place( lambdex_definition_place[0], lambdex_definition_place[1], ), lambdex_definition_place_body=_show_place( lambdex_definition_place[0], lambdex_definition_place[1] + traceback_obj.tb_lineno - 6, ), source_lines=_traceback_source_lines( source_code_block, lineno=traceback_obj.tb_lineno - 6, indentation=12 ), value={k: v for k, v in traceback_obj.tb_frame.f_locals.items() if k not in ('e', 'sys', 'somefunc')}, error_info=_pretty_error( text=traceback_obj.tb_frame.f_locals['e'].__repr__(), indentation=8 + 10 # (8 + 10): 8 is the indentation of '-> error', 10 is the string # length of '-> error: ', (8 + 10) is made for aligning the # succeeding lines to look more pretty. ), direct_caller_place=_show_place( direct_caller_frame.f_code.co_filename, direct_caller_frame.f_lineno ), ).strip() def _traceback_source_lines( source_code_block, lineno, charno=-1, indentation=0, extend_lineno_range=True, lineno_range=3 ) -> str: if extend_lineno_range: temp = [] while lineno >= 0 and len(temp) < lineno_range: line = source_code_block[lineno] if line.strip(): temp.append(line) lineno -= 1 source_lines = dedent('\n'.join(reversed(temp))) else: source_lines = source_code_block[lineno].strip() if charno == -1: return indent(source_lines, ' ' * indentation).lstrip() else: return indent(source_lines, ' ' * indentation).lstrip() + '\n' \ + ' ' * indentation + ' ' * charno + '^' def _pretty_error(text: str, indentation: int): if '\\n' not in text: return text lines = text.split('\\n') return '\n'.join( ( lines[0], indent(dedent('\n'.join(lines[1:])), ' ' * indentation) ) )	[ 1, 515, 1426, 6312, 1053, 28262, 296, 13, 3166, 1426, 6312, 1053, 29536, 13, 13, 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29961, 29896, 29901, 2314, 511, 525, 525, 334, 1399, 9233, 29897, 13, 4706, 1723, 13, 1678, 1723, 13, 2 ]
potok/tabular/utils.py	ksetherg/Chereda	1	90531	<filename>potok/tabular/utils.py import scipy.stats as sst import numpy as np import pandas as pd from potok.tabular import TabularData class SyntheticData: def __init__(self, problem='regression', pdf_train=sst.norm(loc=-1, scale=2), pdf_test=sst.norm(loc=1, scale=3), seed=None): assert problem in ['regression', 'classification'] self.problem = problem self.pdf_train = pdf_train self.pdf_test = pdf_test self.seed = seed def _create_sample_(self, pdf, size): x = pdf.rvs(size=size, random_state=self.seed) if self.problem == 'regression': y = 2*x + 1 if self.problem == 'classification': y = (x > np.mean(x)).astype(int) df = pd.DataFrame({'X': x, 'Target': y}, index=list(range(size))) return df def create_train(self, size=10): df_train = self._create_sample_(self.pdf_train, size) return TabularData(df_train, target=['Target']) def create_test(self, size=10): data_test = self._create_sample_(self.pdf_test, size) return TabularData(data_test, target=['Target'])	[ 1, 529, 9507, 29958, 17765, 554, 29914, 9456, 29914, 13239, 29889, 2272, 13, 5215, 4560, 2272, 29889, 16202, 408, 269, 303, 13, 5215, 12655, 408, 7442, 13, 5215, 11701, 408, 10518, 13, 13, 3166, 3104, 554, 29889, 9456, 1053, 11090, 1070, 1469, 13, 13, 13, 1990, 10829, 386, 7492, 1469, 29901, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 13, 462, 1108, 2433, 276, 11476, 742, 13, 462, 13552, 29918, 14968, 29922, 29879, 303, 29889, 12324, 29898, 2029, 10457, 29896, 29892, 6287, 29922, 29906, 511, 13, 462, 13552, 29918, 1688, 29922, 29879, 303, 29889, 12324, 29898, 2029, 29922, 29896, 29892, 6287, 29922, 29941, 511, 13, 462, 16717, 29922, 8516, 1125, 13, 13, 4706, 4974, 1108, 297, 6024, 276, 11476, 742, 525, 1990, 2450, 2033, 13, 4706, 1583, 29889, 17199, 353, 1108, 13, 13, 4706, 1583, 29889, 5140, 29918, 14968, 353, 13552, 29918, 14968, 13, 4706, 1583, 29889, 5140, 29918, 1688, 353, 13552, 29918, 1688, 13, 4706, 1583, 29889, 26776, 353, 16717, 13, 13, 1678, 822, 903, 3258, 29918, 11249, 23538, 1311, 29892, 13552, 29892, 2159, 1125, 13, 4706, 921, 353, 13552, 29889, 29878, 4270, 29898, 2311, 29922, 2311, 29892, 4036, 29918, 3859, 29922, 1311, 29889, 26776, 29897, 13, 4706, 565, 1583, 29889, 17199, 1275, 525, 276, 11476, 2396, 13, 9651, 343, 353, 29871, 29906, 29930, 29916, 718, 29871, 29896, 13, 4706, 565, 1583, 29889, 17199, 1275, 525, 1990, 2450, 2396, 13, 9651, 343, 353, 313, 29916, 1405, 7442, 29889, 12676, 29898, 29916, 8106, 579, 668, 29898, 524, 29897, 13, 13, 4706, 4489, 353, 10518, 29889, 17271, 3319, 29915, 29990, 2396, 921, 29892, 525, 8667, 2396, 343, 1118, 2380, 29922, 1761, 29898, 3881, 29898, 2311, 4961, 13, 4706, 736, 4489, 13, 13, 1678, 822, 1653, 29918, 14968, 29898, 1311, 29892, 2159, 29922, 29896, 29900, 1125, 13, 4706, 4489, 29918, 14968, 353, 1583, 3032, 3258, 29918, 11249, 23538, 1311, 29889, 5140, 29918, 14968, 29892, 2159, 29897, 13, 4706, 736, 11090, 1070, 1469, 29898, 2176, 29918, 14968, 29892, 3646, 29922, 1839, 8667, 11287, 13, 13, 1678, 822, 1653, 29918, 1688, 29898, 1311, 29892, 2159, 29922, 29896, 29900, 1125, 13, 4706, 848, 29918, 1688, 353, 1583, 3032, 3258, 29918, 11249, 23538, 1311, 29889, 5140, 29918, 1688, 29892, 2159, 29897, 13, 4706, 736, 11090, 1070, 1469, 29898, 1272, 29918, 1688, 29892, 3646, 29922, 1839, 8667, 11287, 13, 2 ]
idfield/fields.py	lis-space/django-idfield	0	55106	<gh_stars>0 from django.db import models import random class IDField(models.Field): def __init__(self, alphabet=None, readable=False, args, kwargs): self.max_length = kwargs.get('max_length', 10) self.readable = readable self.alphabet = alphabet if not self.alphabet: if self.readable: self.alphabet = "ABCDEFGHJKMNPQRSTUVWXYZ23456789" else: self.alphabet = "abcdefghijklmnopqrstuvwxyz0123456789" super(IDField, self).__init__(args, kwargs) def db_type(self, connection): return 'char({})'.format(self.max_length) def pre_save(self, model_instance, add): value = super(IDField, self).pre_save(model_instance, add) if not value: value = '' for i in range(0, self.max_length): value += random.choice(self.alphabet) setattr(model_instance, self.attname, value) return value def formfield(self, kwargs): return super(IDField, self).formfield(**kwargs) def deconstruct(self): return super(IDField, self).deconstruct()	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 9557, 29889, 2585, 1053, 4733, 13, 5215, 4036, 13, 13, 13, 1990, 3553, 3073, 29898, 9794, 29889, 3073, 1125, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 22968, 29922, 8516, 29892, 19909, 29922, 8824, 29892, 334, 5085, 29892, 3579, 19290, 1125, 13, 4706, 1583, 29889, 3317, 29918, 2848, 353, 9049, 5085, 29889, 657, 877, 3317, 29918, 2848, 742, 29871, 29896, 29900, 29897, 13, 4706, 1583, 29889, 949, 519, 353, 19909, 13, 4706, 1583, 29889, 284, 17416, 353, 22968, 13, 4706, 565, 451, 1583, 29889, 284, 17416, 29901, 13, 9651, 565, 1583, 29889, 949, 519, 29901, 13, 18884, 1583, 29889, 284, 17416, 353, 376, 19658, 24405, 29954, 29950, 29967, 29968, 29924, 25500, 29984, 29934, 1254, 29965, 29963, 29956, 18454, 29999, 29906, 29941, 29946, 29945, 29953, 29955, 29947, 29929, 29908, 13, 9651, 1683, 29901, 13, 18884, 1583, 29889, 284, 17416, 353, 376, 10736, 1753, 12443, 823, 6321, 23521, 459, 29939, 29878, 303, 4090, 29893, 20230, 29900, 29896, 29906, 29941, 29946, 29945, 29953, 29955, 29947, 29929, 29908, 13, 4706, 2428, 29898, 1367, 3073, 29892, 1583, 467, 1649, 2344, 1649, 10456, 5085, 29892, 3579, 19290, 29897, 13, 13, 1678, 822, 4833, 29918, 1853, 29898, 1311, 29892, 3957, 1125, 13, 4706, 736, 525, 3090, 3319, 1800, 4286, 4830, 29898, 1311, 29889, 3317, 29918, 2848, 29897, 13, 13, 1678, 822, 758, 29918, 7620, 29898, 1311, 29892, 1904, 29918, 8758, 29892, 788, 1125, 13, 4706, 995, 353, 2428, 29898, 1367, 3073, 29892, 1583, 467, 1457, 29918, 7620, 29898, 4299, 29918, 8758, 29892, 788, 29897, 13, 4706, 565, 451, 995, 29901, 13, 9651, 995, 353, 6629, 13, 9651, 363, 474, 297, 3464, 29898, 29900, 29892, 1583, 29889, 3317, 29918, 2848, 1125, 995, 4619, 4036, 29889, 16957, 29898, 1311, 29889, 284, 17416, 29897, 13, 9651, 731, 5552, 29898, 4299, 29918, 8758, 29892, 1583, 29889, 1131, 978, 29892, 995, 29897, 13, 4706, 736, 995, 13, 13, 1678, 822, 883, 2671, 29898, 1311, 29892, 3579, 19290, 1125, 13, 4706, 736, 2428, 29898, 1367, 3073, 29892, 1583, 467, 689, 2671, 29898, 1068, 19290, 29897, 13, 13, 1678, 822, 316, 11433, 29898, 1311, 1125, 13, 4706, 736, 2428, 29898, 1367, 3073, 29892, 1583, 467, 311, 11433, 580, 13, 2 ]
docs/streammarker_connected_sensor/streammarker_connected_sensor_reader.py	urlgrey/streammarker-collector	0	96035	#!/usr/bin/python import os import httplib import serial import re import json ser = serial.Serial('/dev/ttyAMA0', 9600) headers = {"Content-type": "application/json", "Accept": "application/json"} if 'STREAMMARKER_API_KEY' in os.environ: headers["X-API-KEY"] = os.environ['STREAMMARKER_API_KEY'] r_unwanted = re.compile("[\n\r]") while True: try: msg = ser.readline() msg = r_unwanted.sub("", msg) readings = msg.split(',') body = json.dumps({"timestamp": 0,"relay_id": "3AC91DEC-B0DD-4DB5-B56D-7682B7C9B28C","status": "ok","sensors": [{"id":"31E541A7-815A-4527-88FC-CEA39808FCAC","readings": [{"timestamp": 0,"measurements": [{"name":"soil_moisture_1","value": float(readings[0]),"unit":"VWC"}, {"name":"soil_temperature_1","value": float(readings[1]),"unit":"Celsius"}]}]}]}) print body except KeyboardInterrupt: break except: # catch-all e = sys.exc_info()[0] print(e) ser.close()	[ 1, 18787, 4855, 29914, 2109, 29914, 4691, 13, 13, 5215, 2897, 13, 5215, 1732, 1982, 13, 5215, 7797, 13, 5215, 337, 13, 5215, 4390, 13, 13, 643, 353, 7797, 29889, 9125, 11219, 3359, 29914, 4349, 29909, 1529, 29900, 742, 29871, 29929, 29953, 29900, 29900, 29897, 13, 13, 13662, 353, 8853, 3916, 29899, 1853, 1115, 376, 6214, 29914, 3126, 613, 13, 965, 376, 23965, 1115, 376, 6214, 29914, 3126, 9092, 13, 361, 525, 1254, 1525, 5194, 1529, 29934, 29968, 1001, 29918, 8787, 29918, 10818, 29915, 297, 2897, 29889, 21813, 29901, 13, 29871, 9066, 3366, 29990, 29899, 8787, 29899, 10818, 3108, 353, 2897, 29889, 21813, 1839, 1254, 1525, 5194, 1529, 29934, 29968, 1001, 29918, 8787, 29918, 10818, 2033, 13, 13, 29878, 29918, 348, 29893, 9714, 353, 337, 29889, 12198, 703, 7110, 29876, 29905, 29878, 29962, 1159, 13, 8000, 5852, 29901, 13, 1678, 1018, 29901, 13, 4706, 10191, 353, 724, 29889, 949, 1220, 580, 13, 4706, 10191, 353, 364, 29918, 348, 29893, 9714, 29889, 1491, 703, 613, 10191, 29897, 13, 4706, 1303, 886, 353, 10191, 29889, 5451, 29317, 1495, 13, 4706, 3573, 353, 4390, 29889, 29881, 17204, 3319, 29908, 16394, 1115, 29871, 29900, 1699, 2674, 388, 29918, 333, 1115, 376, 29941, 2477, 29929, 29896, 2287, 29907, 29899, 29933, 29900, 7858, 29899, 29946, 4051, 29945, 29899, 29933, 29945, 29953, 29928, 29899, 29955, 29953, 29947, 29906, 29933, 29955, 29907, 29929, 29933, 29906, 29947, 29907, 3284, 4882, 1115, 376, 554, 3284, 23149, 943, 1115, 518, 6377, 333, 4710, 29941, 29896, 29923, 29945, 29946, 29896, 29909, 29955, 29899, 29947, 29896, 29945, 29909, 29899, 29946, 29945, 29906, 29955, 29899, 29947, 29947, 8610, 29899, 4741, 29909, 29941, 29929, 29947, 29900, 29947, 8610, 2477, 3284, 949, 886, 1115, 518, 6377, 16394, 1115, 29871, 29900, 1699, 26658, 1860, 1115, 518, 6377, 978, 4710, 578, 309, 29918, 4346, 391, 545, 29918, 29896, 3284, 1767, 1115, 5785, 29898, 949, 886, 29961, 29900, 11724, 29908, 5441, 4710, 29963, 29956, 29907, 10758, 8853, 978, 4710, 578, 309, 29918, 12863, 1535, 29918, 29896, 3284, 1767, 1115, 5785, 29898, 949, 886, 29961, 29896, 11724, 29908, 5441, 4710, 29907, 295, 1039, 375, 29908, 6525, 6525, 6525, 1800, 13, 4706, 1596, 3573, 13, 13, 1678, 5174, 7670, 3377, 4074, 6685, 29901, 13, 4706, 2867, 13, 1678, 5174, 29901, 396, 4380, 29899, 497, 13, 4706, 321, 353, 10876, 29889, 735, 29883, 29918, 3888, 580, 29961, 29900, 29962, 13, 4706, 1596, 29898, 29872, 29897, 13, 13, 643, 29889, 5358, 580, 2 ]
examples/04_sweep_wind_directions.py	ElieKadoche/floris	0	12814	# Copyright 2022 NREL # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of # the License at http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. # See https://floris.readthedocs.io for documentation import matplotlib.pyplot as plt import numpy as np from floris.tools import FlorisInterface from floris.tools.visualization import visualize_cut_plane """ 04_sweep_wind_directions This example demonstrates vectorization of wind direction. A vector of wind directions is passed to the intialize function and the powers of the two simulated turbines is computed for all wind directions in one call The power of both turbines for each wind direction is then plotted """ # Instantiate FLORIS using either the GCH or CC model fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 # fi = FlorisInterface("inputs/cc.yaml") # New CumulativeCurl model # Define a two turbine farm D = 126. layout_x = np.array([0, D*6]) layout_y = [0, 0] fi.reinitialize(layout = [layout_x, layout_y]) # Sweep wind speeds but keep wind direction fixed wd_array = np.arange(250,291,1.) fi.reinitialize(wind_directions=wd_array) # Define a matrix of yaw angles to be all 0 # Note that yaw angles is now specified as a matrix whose dimesions are # wd/ws/turbine num_wd = len(wd_array) # Number of wind directions num_ws = 1 # Number of wind speeds num_turbine = len(layout_x) # Number of turbines yaw_angles = np.zeros((num_wd, num_ws, num_turbine)) # Calculate fi.calculate_wake(yaw_angles=yaw_angles) # Collect the turbine powers turbine_powers = fi.get_turbine_powers() / 1E3 # In kW # Pull out the power values per turbine pow_t0 = turbine_powers[:,:,0].flatten() pow_t1 = turbine_powers[:,:,1].flatten() # Plot fig, ax = plt.subplots() ax.plot(wd_array,pow_t0,color='k',label='Upstream Turbine') ax.plot(wd_array,pow_t1,color='r',label='Downstream Turbine') ax.grid(True) ax.legend() ax.set_xlabel('Wind Direction (deg)') ax.set_ylabel('Power (kW)') plt.show()	[ 1, 396, 14187, 1266, 29871, 29906, 29900, 29906, 29906, 405, 1525, 29931, 13, 13, 29937, 10413, 21144, 1090, 278, 13380, 19245, 29892, 10079, 29871, 29906, 29889, 29900, 313, 1552, 376, 29931, 293, 1947, 1496, 366, 1122, 451, 13, 29937, 671, 445, 934, 5174, 297, 752, 13036, 411, 278, 19245, 29889, 887, 1122, 4017, 263, 3509, 310, 13, 29937, 278, 19245, 472, 1732, 597, 1636, 29889, 4288, 29889, 990, 29914, 506, 11259, 29914, 27888, 1430, 1660, 29899, 29906, 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learnMongo/mongoInsert.py	huobingli/pyAlgorithm	0	24764	import pymongo from bson.objectid import ObjectId # mongo 增加 def main(): client = pymongo.MongoClient(host='172.16.17.32', port=27017) db = client.test collection = db.students # 插入一条数据 student = { 'id': '20170101', 'name': 'Kevin', 'age': 20, 'gender': 'male' } # 每条数据其实都有一个 _id 属性来唯一标识。 # 如果没有显式指明该属性，MongoDB 会自动产生一个 ObjectId 类型的 _id 属性。 # insert() 方法会在执行后返回_id 值。 result = collection.insert(student) print(result) # 插入多条数据 student1 = { 'id': '20170101', 'name': 'Jordan', 'age': 20, 'gender': 'male' } student2 = { 'id': '20170202', 'name': 'Mike', 'age': 20, 'gender': 'male' } # 这里用列表方式传入 # 返回结果也是列表方式 result = collection.insert([student1, student2]) print(result) # pymongo 官方不推荐使用insert # 推荐使用 insert_one 和 insert_many 插入一条或者多条记录 student = { 'id': '20170101', 'name': 'Jordan', 'age': 20, 'gender': 'male' } result = collection.insert_one(student) print(result) print(result.inserted_id) student1 = { 'id': '20170101', 'name': 'Jordan', 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cpgan_data.py	basilevh/object-discovery-cp-gan	14	18657	# <NAME>, March 2020 # Common code for PyTorch implementation of Copy-Pasting GAN import copy import itertools import matplotlib.pyplot as plt import numpy as np import os, platform, time import torch import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.transforms as transforms from PIL import Image, ImageDraw from torch.utils.data import Dataset from tqdm import tqdm def read_image_robust(img_path, monochromatic=False): ''' Returns an image that meets conditions along with a success flag, in order to avoid crashing. ''' try: # image = plt.imread(img_path).copy() image = np.array(Image.open(img_path)).copy() # always uint8 success = True if np.any(np.array(image.strides) < 0): success = False # still negative stride elif not(monochromatic) and (image.ndim != 3 or image.shape[2] != 3): success = False # not RGB elif monochromatic: # width, height = image.shape[1], image.shape[0] # image = np.broadcast_to(x[:, :, np.newaxis], (height, width, 3)) image = image[:, :, np.newaxis] # one channel <=> only one ground truth except IOError: # Probably corrupt file image = None success = False return image, success def paint_squares(image, noisy=False, channels=10): ''' Paints one or more squares at random locations to create an artificial foreground image. Generates multiple associated ground truth masks; one per object. ''' width, height = image.shape[1], image.shape[0] image = image.copy() # do not overwrite background object_count = np.random.randint(1, 5) # [1, 4] inclusive masks = np.zeros((height, width, channels), dtype=np.uint8) for i in range(object_count): sq_w, sq_h = 9, 9 x1 = np.random.randint(0, width - sq_w + 1) y1 = np.random.randint(0, height - sq_h + 1) x2 = x1 + sq_w y2 = y1 + sq_h masks[y1:y2, x1:x2, i] = 255 if not(noisy): # Pick one fixed (not necessarily saturated) color for the whole square clr = np.random.randint(0, 256, 3) image[y1:y2, x1:x2] = clr else: # Pick a random fully saturated (extremal) color for every pixel image[y1:y2, x1:x2] = np.random.choice([0, 255], (sq_h, sq_w, 3)) return image, masks, object_count def create_random_gfake_mask(width, height): ''' See Appendix D. ''' x0, y0 = np.random.rand(2) * 0.8 + 0.1 num_verts = np.random.randint(4, 7) # TODO possible improvement: allow up to more vertices? # TODO possible improvement: encourage convex (currently many "sharp" objects) radii = np.random.rand(num_verts) * 0.4 + 0.1 # radii = np.random.rand(num_verts) * 0.8 + 0.2 # TODO: not very clear from paper angles = np.sort(np.random.rand(num_verts)) * 2.0 * np.pi poly_polar = list(zip(radii, angles)) poly_cart = [(int(width * (x0 + r * np.cos(a)) / 1), int(height * (y0 + r * np.sin(a)) / 1)) for (r, a) in poly_polar] # poly_cart = [(x1, y1), (x2, y2), ...] img = Image.new('L', (width, height), 0) ImageDraw.Draw(img).polygon(poly_cart, outline=1, fill=255) mask = np.array(img, dtype='uint8') assert(mask.shape == (height, width)) return mask def apply_border_zero(masks): ndim = len(masks.shape) if ndim == 2: masks[0, :] = 0 masks[-1, :] = 0 masks[:, 0] = 0 masks[:, -1] = 0 elif ndim == 3: masks[:, 0, :] = 0 masks[:, -1, :] = 0 masks[:, :, 0] = 0 masks[:, :, -1] = 0 elif ndim == 4: masks[:, :, 0, :] = 0 masks[:, :, -1, :] = 0 masks[:, :, :, 0] = 0 masks[:, :, :, -1] = 0 else: raise Exception('Mask has too many dimensions') return masks def copy_paste(fores, masks, backs, border_zero=True): # TODO possible improvement: poisson blending # if hard_thres > 0: # used_masks = (masks > hard_thres).float() # force binary # else: used_masks = masks.clone() # Border zeroing implemented in April 2020 if border_zero: used_masks = apply_border_zero(used_masks) return used_masks * fores + (1.0 - used_masks) * backs class MyCopyPasteDataset(Dataset): ''' Custom dataset class with foreground, background, and optional mask folders as image sources. Only one object may appear per image, since the object count is not kept track of. Returns irrelevant foreground anti-shortcuts as well. Enforces color (RGB) images. ''' def __init__(self, fore_dir, back_dir, mask_dir=None, rand_horz_flip=True, post_resize=-1, center_crop=False): self.fore_dir = fore_dir self.back_dir = back_dir self.rand_horz_flip = rand_horz_flip if post_resize <= 0: self.post_tf = transforms.ToTensor() # converts [0, 255] to [0.0, 1.0] elif center_crop: # Resize + square center crop self.post_tf = transforms.Compose([ transforms.ToPILImage(), transforms.Resize(post_resize), transforms.CenterCrop(post_resize), transforms.ToTensor() ]) else: # Resize both dimensions, possibly distorting the images self.post_tf = transforms.Compose([ transforms.ToPILImage(), transforms.Resize((post_resize, post_resize)), transforms.ToTensor() ]) self.has_masks = (mask_dir is not None) # Load all file paths; file names must be the same across all 2 or 3 given directories # self.all_fore_files = [] # self.all_mask_files = [] # self.all_back_files = [] # for fn in os.listdir(fore_dir): # fore_fp = os.path.join(fore_dir, fn) # if os.path.isfile(fore_fp): # back_fp = os.path.join(back_dir, fn) # assert(os.path.isfile(back_fp)) # self.all_fore_files.append(fore_fp) # self.all_back_files.append(back_fp) # if self.has_masks: # mask_fp = os.path.join(mask_dir, fn) # assert(os.path.isfile(mask_fp)) # self.all_mask_files.append(mask_fp) # Load all file paths; file names must be the same across foreground and segmentation masks self.all_fore_files = [] self.all_mask_files = [] self.all_back_files = [] for fn in os.listdir(fore_dir): fore_fp = os.path.join(fore_dir, fn) self.all_fore_files.append(fore_fp) if self.has_masks: mask_fp_jpg = os.path.join(mask_dir, fn[:-4] + '.jpg') mask_fp_png = os.path.join(mask_dir, fn[:-4] + '.png') if os.path.isfile(mask_fp_jpg): self.all_mask_files.append(mask_fp_jpg) elif os.path.isfile(mask_fp_png): self.all_mask_files.append(mask_fp_png) else: raise Exception('No matching mask file found for ' + fore_fp) for fn in os.listdir(back_dir): back_fp = os.path.join(back_dir, fn) self.all_back_files.append(back_fp) self.fore_count = len(self.all_fore_files) self.back_count = len(self.all_back_files) print('Image file count: ' + str(self.fore_count) + ' foreground, ' + str(self.back_count) + ' background, has masks: ' + str(self.has_masks)) def __len__(self): return self.fore_count def __getitem__(self, idx): # Force randomness (especially if num_workers > 0) np.random.seed(idx + int((time.time() * 654321) % 123456)) # Read random pair of images from file system success = False while not(success): file_idx = np.random.choice(self.fore_count) fp = self.all_fore_files[file_idx] fore, success = read_image_robust(fp) if not(success): continue if self.has_masks: fp = self.all_mask_files[file_idx] mask, success = read_image_robust(fp, monochromatic=True) assert(success) # must match fore # mask = ((mask > 0) * 255.0).astype('uint8') # convert soft masks to hard else: mask = None # Read random background image success = False while not(success): file_idx2 = np.random.choice(self.back_count) fp = self.all_back_files[file_idx2] back, success = read_image_robust(fp) # Read irrelevant foreground image success = False while not(success): file_idx3 = np.random.choice(self.fore_count) if file_idx3 == file_idx: continue # try again, cannot pick same image fp = self.all_fore_files[file_idx3] irrel, success = read_image_robust(fp) # Transform foregrounds (+ masks) and backgrounds # NOTE: identical random choices must be made for some images if self.rand_horz_flip: if np.random.rand() < 0.5: fore = fore[:, ::-1, :].copy() if self.has_masks: mask = mask[:, ::-1, :].copy() if np.random.rand() < 0.5: irrel = irrel[:, ::-1, :].copy() if np.random.rand() < 0.5: back = back[:, ::-1, :].copy() fore = self.post_tf(fore) irrel = self.post_tf(irrel) back = self.post_tf(back) if self.has_masks: mask = self.post_tf(mask) # Verify sizes assert(fore.shape[1:] == irrel.shape[1:]) assert(fore.shape[1:] == back.shape[1:]) if self.has_masks: assert(fore.shape[1:] == mask.shape[1:]) # Create grounded fake mask and composite width, height = fore.shape[2], fore.shape[1] # fore is (C, H, W) gfake_mask = self.post_tf(create_random_gfake_mask(width, height)) comp_gfake = copy_paste(fore, gfake_mask, back) # Construct dictionary; object count is unknown result = {'fore': fore, 'back': back, 'irrel': irrel, 'object_cnt': 1, 'gfake_mask': gfake_mask, 'comp_gfake': comp_gfake} if self.has_masks: result['mask'] = mask # don't set None, otherwise crash return result class MySquaresDataset(Dataset): ''' Custom dataset class with just a collection of background images as source. One or more artificial objects are painted to create a foreground, keeping track of object count. Returns irrelevant foreground anti-shortcuts as well. Enforces color (RGB) images. ''' def __init__(self, back_dir, rand_horz_flip=True, noisy=False, max_objects=10): self.back_dir = back_dir self.rand_horz_flip = rand_horz_flip self.post_tf = transforms.ToTensor() # converts [0, 255] to [0.0, 1.0] self.noisy = noisy self.max_objects = max_objects # Load all file paths; file names must be the same across all 2 or 3 given directories self.all_back_files = [] for fn in os.listdir(back_dir): back_fp = os.path.join(back_dir, fn) self.all_back_files.append(back_fp) self.file_count = len(self.all_back_files) print('Image file count: ' + str(self.file_count) + ', noisy: ' + str(self.noisy) + ', max objects: ' + str(self.max_objects)) def __len__(self): return self.file_count def __getitem__(self, idx): # Read a random triplet (relevant + background + irrelevant) of non-overlapping backgrounds from file system success = False while not(success): file_idx = np.random.choice(self.file_count) fp = self.all_back_files[file_idx] fore, success = read_image_robust(fp) success = False while not(success): file_idx2 = np.random.choice(self.file_count) if file_idx2 == file_idx: continue # try again, cannot pick same image fp = self.all_back_files[file_idx2] back, success = read_image_robust(fp) success = False while not(success): file_idx3 = np.random.choice(self.file_count) if file_idx3 == file_idx or file_idx3 == file_idx2: continue # try again, cannot pick same image fp = self.all_back_files[file_idx3] irrel, success = read_image_robust(fp) # Create corresponding foregrounds and masks; leave actual background unchanged fore, masks, object_cnt = paint_squares(fore, noisy=self.noisy, channels=self.max_objects) irrel, _, _ = paint_squares(irrel, noisy=self.noisy, channels=self.max_objects) # Transform foregrounds (+ masks) and backgrounds # NOTE: identical random choices must be made for some images if self.rand_horz_flip: if np.random.rand() < 0.5: fore = fore[:, ::-1, :].copy() masks = masks[:, ::-1, :].copy() if np.random.rand() < 0.5: irrel = irrel[:, ::-1, :].copy() if np.random.rand() < 0.5: back = back[:, ::-1, :].copy() fore = self.post_tf(fore) masks = self.post_tf(masks) irrel = self.post_tf(irrel) back = self.post_tf(back) # Create grounded fake mask and composite width, height = fore.shape[2], fore.shape[1] # fore is (C, H, W) gfake_mask = self.post_tf(create_random_gfake_mask(width, height)) comp_gfake = copy_paste(fore, gfake_mask, back) # Construct dictionary result = {'fore': fore, 'back': back, 'irrel': irrel, 'mask': masks, 'object_cnt': object_cnt, 'gfake_mask': gfake_mask, 'comp_gfake': comp_gfake} return result	[ 1, 396, 529, 5813, 10202, 4779, 29871, 29906, 29900, 29906, 29900, 13, 29937, 13103, 775, 363, 10772, 29911, 25350, 5314, 310, 14187, 29899, 29925, 579, 292, 402, 2190, 13, 13, 5215, 3509, 13, 5215, 4256, 8504, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 13, 5215, 12655, 408, 7442, 13, 5215, 2897, 29892, 7481, 29892, 931, 13, 5215, 4842, 305, 13, 5215, 4842, 305, 29889, 15755, 408, 302, 29876, 13, 5215, 4842, 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dpx2ffv1/test.py	DerouineauNicolas/dpx_to_ffv1	0	1605438	<reponame>DerouineauNicolas/dpx_to_ffv1<filename>dpx2ffv1/test.py<gh_stars>0 from unittest import TestCase from dpx2ffv1.dpx2ffv1 import dpx2ffv1 class TestJoke(TestCase): def test_main_function(self): out = dpx2ffv1('./test/', 'out.mkv', 24) assert(out == 0) if __name__ == '__main__': unittest.main()	[ 1, 529, 276, 1112, 420, 29958, 15383, 283, 457, 585, 29940, 5283, 294, 29914, 29881, 1756, 29918, 517, 29918, 600, 29894, 29896, 29966, 9507, 29958, 29881, 1756, 29906, 600, 29894, 29896, 29914, 1688, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 3166, 443, 27958, 1053, 4321, 8259, 13, 3166, 270, 1756, 29906, 600, 29894, 29896, 29889, 29881, 1756, 29906, 600, 29894, 29896, 1053, 270, 1756, 29906, 600, 29894, 29896, 13, 13, 1990, 4321, 29967, 6946, 29898, 3057, 8259, 1125, 13, 1678, 822, 1243, 29918, 3396, 29918, 2220, 29898, 1311, 1125, 13, 4706, 714, 353, 270, 1756, 29906, 600, 29894, 29896, 877, 6904, 1688, 29914, 742, 525, 449, 29889, 11256, 29894, 742, 29871, 29906, 29946, 29897, 13, 4706, 4974, 29898, 449, 1275, 29871, 29900, 29897, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 443, 27958, 29889, 3396, 580, 2 ]
RSA_keys.py	davarbri/jpegForensics	0	59093	<filename>RSA_keys.py # !/usr/bin/python3 # RSA_keys.py # <NAME> # January 2022 # usage: python RSA_keys.py <bitslenght> # -------------------------------------------- # The script generates a public and private # RSA key pair of the desired length. # Only 1024, 2048 and 4096 bits are possible # -------------------------------------------- import sys from Crypto.PublicKey import RSA def main(): # possible key length in bits valid_length_keys = [1024,2048,4096] try: length = int(sys.argv[1]) if (length in valid_length_keys): generate_key(length) else: print("That was no valid length") except ValueError: print("Oops! That was no valid input. Try again...") def generate_key(length): print("[] Generating", length, "bits RSA key pair...") key = RSA.generate(length) # Private key private_key = key.export_key() try: file_out = open("private.pem", "wb") file_out.write(private_key) print(" [+] Private keyring file written...") except IOError: msg = "Unable to create Private Key File, private.pem, on disk" print(msg) finally: file_out.close() # Public key public_key = key.publickey().export_key() try: file_out = open("public.pem", "wb") file_out.write(public_key) print(" [+] Public keyring file written...") except IOError: msg = "Unable to create Public Key File, public.pem, on disk" print(msg) finally: file_out.close() if __name__ == "__main__": if len(sys.argv) != 2: print("usage: python RSA_keys.py <bitslenght>") else: main()	[ 1, 529, 9507, 29958, 29934, 8132, 29918, 8149, 29889, 2272, 13, 29937, 1738, 29914, 4855, 29914, 2109, 29914, 4691, 29941, 30004, 13, 29937, 390, 8132, 29918, 8149, 29889, 2272, 30004, 13, 29937, 529, 5813, 3238, 13, 29937, 5490, 29871, 29906, 29900, 29906, 29906, 30004, 13, 29937, 8744, 29901, 3017, 390, 8132, 29918, 8149, 29889, 2272, 529, 14836, 29880, 996, 400, 3238, 13, 29937, 448, 2683, 2683, 1378, 5634, 30004, 13, 29937, 450, 2471, 16785, 263, 970, 322, 2024, 6756, 13, 29937, 390, 8132, 1820, 5101, 310, 278, 7429, 3309, 22993, 13, 29937, 9333, 259, 29896, 29900, 29906, 29946, 29892, 29871, 29906, 29900, 29946, 29947, 322, 29871, 29946, 29900, 29929, 29953, 9978, 526, 1950, 30004, 13, 29937, 448, 2683, 2683, 1378, 5634, 30004, 13, 30004, 13, 30004, 13, 5215, 10876, 30004, 13, 3166, 315, 17929, 29889, 19858, 2558, 1053, 390, 8132, 30004, 13, 30004, 13, 1753, 1667, 7295, 30004, 13, 1678, 396, 1950, 1820, 3309, 297, 9978, 30004, 13, 1678, 2854, 29918, 2848, 29918, 8149, 353, 518, 29896, 29900, 29906, 29946, 29892, 29906, 29900, 29946, 29947, 29892, 29946, 29900, 29929, 29953, 29962, 30004, 13, 1678, 1018, 29901, 30004, 13, 4706, 3309, 353, 938, 29898, 9675, 29889, 19218, 29961, 29896, 2314, 30004, 13, 4706, 565, 313, 2848, 297, 2854, 29918, 2848, 29918, 8149, 1125, 30004, 13, 9651, 5706, 29918, 1989, 29898, 2848, 29897, 539, 6756, 13, 4706, 1683, 29901, 30004, 13, 9651, 1596, 703, 7058, 471, 694, 2854, 3309, 1159, 30004, 13, 1678, 5174, 7865, 2392, 29901, 30004, 13, 4706, 1596, 703, 29949, 3554, 29991, 2193, 471, 694, 2854, 1881, 29889, 3967, 1449, 856, 1159, 30004, 13, 30004, 13, 418, 6756, 13, 1753, 5706, 29918, 1989, 29898, 2848, 1125, 30004, 13, 1678, 1596, 703, 2636, 3251, 1218, 613, 3309, 29892, 376, 14836, 390, 8132, 1820, 5101, 856, 1159, 30004, 13, 1678, 1820, 353, 390, 8132, 29889, 17158, 29898, 2848, 8443, 13, 4706, 6756, 13, 1678, 396, 12230, 1820, 30004, 13, 1678, 2024, 29918, 1989, 353, 1820, 29889, 15843, 29918, 1989, 26471, 13, 1678, 1018, 29901, 30004, 13, 4706, 934, 29918, 449, 353, 1722, 703, 9053, 29889, 29886, 331, 613, 376, 29893, 29890, 1159, 30004, 13, 4706, 934, 29918, 449, 29889, 3539, 29898, 9053, 29918, 1989, 8443, 13, 4706, 1596, 703, 1678, 518, 29974, 29962, 12230, 1820, 5393, 934, 3971, 856, 1159, 30004, 13, 1678, 5174, 10663, 2392, 29901, 30004, 13, 4706, 10191, 353, 376, 2525, 519, 304, 1653, 12230, 7670, 3497, 29892, 2024, 29889, 29886, 331, 29892, 373, 8086, 19451, 13, 4706, 1596, 29898, 7645, 8443, 13, 1678, 7146, 29901, 30004, 13, 4706, 934, 29918, 449, 29889, 5358, 26471, 13, 30004, 13, 1678, 396, 5236, 1820, 30004, 13, 1678, 970, 29918, 1989, 353, 1820, 29889, 3597, 1989, 2141, 15843, 29918, 1989, 26471, 13, 1678, 1018, 29901, 30004, 13, 4706, 934, 29918, 449, 353, 1722, 703, 3597, 29889, 29886, 331, 613, 376, 29893, 29890, 1159, 30004, 13, 4706, 934, 29918, 449, 29889, 3539, 29898, 3597, 29918, 1989, 8443, 13, 4706, 1596, 703, 1678, 518, 29974, 29962, 5236, 1820, 5393, 934, 3971, 856, 1159, 30004, 13, 1678, 5174, 10663, 2392, 29901, 30004, 13, 4706, 10191, 353, 376, 2525, 519, 304, 1653, 5236, 7670, 3497, 29892, 970, 29889, 29886, 331, 29892, 373, 8086, 19451, 13, 4706, 1596, 29898, 7645, 8443, 13, 1678, 7146, 29901, 30004, 13, 4706, 934, 29918, 449, 29889, 5358, 26471, 13, 30004, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 30004, 13, 1678, 565, 7431, 29898, 9675, 29889, 19218, 29897, 2804, 29871, 29906, 29901, 30004, 13, 4706, 1596, 703, 21125, 29901, 3017, 390, 8132, 29918, 8149, 29889, 2272, 529, 14836, 29880, 996, 400, 29958, 1159, 30004, 13, 1678, 1683, 29901, 30004, 13, 4706, 1667, 580, 2 ]
test/test_vlan_group.py	nrfta/python-netbox-client	0	16388	<filename>test/test_vlan_group.py # coding: utf-8 """ NetBox API API to access NetBox # noqa: E501 OpenAPI spec version: 2.8 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import netbox_client from netbox_client.models.vlan_group import VLANGroup # noqa: E501 from netbox_client.rest import ApiException class TestVLANGroup(unittest.TestCase): """VLANGroup unit test stubs""" def setUp(self): pass def tearDown(self): pass def testVLANGroup(self): """Test VLANGroup""" # FIXME: construct object with mandatory attributes with example values # model = netbox_client.models.vlan_group.VLANGroup() # noqa: E501 pass if __name__ == '__main__': unittest.main()	[ 1, 529, 9507, 29958, 1688, 29914, 1688, 29918, 29894, 6468, 29918, 2972, 29889, 2272, 13, 29937, 14137, 29901, 23616, 29899, 29947, 13, 13, 15945, 29908, 13, 1678, 12670, 3313, 3450, 13, 13, 1678, 3450, 304, 2130, 12670, 3313, 29871, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 13, 1678, 4673, 8787, 1580, 1873, 29901, 29871, 29906, 29889, 29947, 13, 268, 13, 1678, 3251, 630, 491, 29901, 2045, 597, 3292, 29889, 510, 29914, 2774, 9921, 29899, 2754, 29914, 2774, 9921, 29899, 401, 1885, 29889, 5559, 13, 15945, 29908, 13, 13, 13, 3166, 4770, 29888, 9130, 1649, 1053, 8380, 29918, 5215, 13, 13, 5215, 443, 27958, 13, 13, 5215, 7787, 1884, 29918, 4645, 13, 3166, 7787, 1884, 29918, 4645, 29889, 9794, 29889, 29894, 6468, 29918, 2972, 1053, 478, 29931, 2190, 4782, 29871, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 3166, 7787, 1884, 29918, 4645, 29889, 5060, 1053, 29749, 2451, 13, 13, 13, 1990, 4321, 29963, 29931, 2190, 4782, 29898, 348, 27958, 29889, 3057, 8259, 1125, 13, 1678, 9995, 29963, 29931, 2190, 4782, 5190, 1243, 19281, 29879, 15945, 29908, 13, 13, 1678, 822, 731, 3373, 29898, 1311, 1125, 13, 4706, 1209, 13, 13, 1678, 822, 734, 279, 6767, 29898, 1311, 1125, 13, 4706, 1209, 13, 13, 1678, 822, 1243, 29963, 29931, 2190, 4782, 29898, 1311, 1125, 13, 4706, 9995, 3057, 478, 29931, 2190, 4782, 15945, 29908, 13, 4706, 396, 383, 6415, 2303, 29901, 3386, 1203, 411, 9619, 7606, 8393, 411, 1342, 1819, 13, 4706, 396, 1904, 353, 7787, 1884, 29918, 4645, 29889, 9794, 29889, 29894, 6468, 29918, 2972, 29889, 29963, 29931, 2190, 4782, 580, 29871, 396, 694, 25621, 29901, 382, 29945, 29900, 29896, 13, 4706, 1209, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 443, 27958, 29889, 3396, 580, 13, 2 ]
Linked List/ListNode.py	paulpatault/Algorithmic	0	154208	<gh_stars>0 class ListNode(object): def __init__(self, x): self.val = x self.next = None def __repr__(self): node = self output = "" while node != None: output += str(node.val) output += " " node = node.next return output	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29900, 13, 1990, 2391, 4247, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 921, 1125, 13, 4706, 1583, 29889, 791, 353, 921, 13, 4706, 1583, 29889, 4622, 353, 6213, 13, 13, 1678, 822, 4770, 276, 558, 12035, 1311, 1125, 13, 4706, 2943, 353, 1583, 13, 4706, 1962, 353, 5124, 13, 4706, 1550, 2943, 2804, 6213, 29901, 13, 9651, 1962, 4619, 851, 29898, 3177, 29889, 791, 29897, 13, 9651, 1962, 4619, 376, 376, 13, 9651, 2943, 353, 2943, 29889, 4622, 13, 4706, 736, 1962, 13, 2 ]
project/repository/user.py	tobiasaditya/fastapi-blog	0	21890	from typing import List from fastapi import APIRouter from fastapi.params import Depends from fastapi import HTTPException, status from sqlalchemy.orm.session import Session from project import schema, models, database, hashing router = APIRouter( prefix="/user", tags=['Users'] ) @router.post('/new') def create_user(request:schema.User, db:Session = Depends(database.get_db)): hashed_pass = hashing.get_password_hash(request.password) new_user = models.User(name = request.name,username = request.username, password = hashed_pass) db.add(new_user) db.commit() db.refresh(new_user) return request @router.get('/find', response_model= List[schema.showUser]) def show_user_all(db:Session=Depends(database.get_db)): all_users = db.query(models.User).all() return all_users @router.get('/find/{id}',response_model= schema.showUser) def show_user_id(id:int, db:Session = Depends(database.get_db)): selected_project = db.query(models.User).filter(models.User.id == id).first() if not selected_project: raise HTTPException(status_code=status.HTTP_404_NOT_FOUND,detail=f"User {id} not found.") return selected_project # @router.put('/{id}') # def update_project_id(id:int,request:schema.Project,db:Session = Depends(database.get_db)): # #Search for projects' id # selected_project = db.query(models.Project).filter(models.Project.id == id) # if not selected_project.first(): # raise HTTPException(status_code=status.HTTP_404_NOT_FOUND,detail=f"Project {id} not found.") # selected_project.update(dict(request)) # return {'status':f'project {id} updated'} # @router.delete('/{id}') # def delete_project_id(id:int,db:Session = Depends(database.get_db)): # selected_project = db.query(models.Project).filter(models.Project.id == id).first() # if not selected_project: # raise HTTPException(status_code=status.HTTP_404_NOT_FOUND,detail=f"Project {id} not found.") # db.delete(selected_project) # db.commit() # return {'status':f'delete project_id {id} successful'}	[ 1, 515, 19229, 1053, 2391, 13, 3166, 5172, 2754, 1053, 3450, 23971, 13, 3166, 5172, 2754, 29889, 7529, 1053, 10034, 1975, 13, 3166, 5172, 2754, 1053, 7331, 2451, 29892, 4660, 13, 3166, 4576, 284, 305, 6764, 29889, 555, 29889, 7924, 1053, 16441, 13, 3166, 2060, 1053, 10938, 29892, 4733, 29892, 2566, 29892, 756, 2790, 13, 13, 15140, 353, 3450, 23971, 29898, 13, 1678, 10944, 13802, 1792, 613, 13, 1678, 8282, 29922, 1839, 5959, 2033, 13, 29897, 13, 13, 29992, 15140, 29889, 2490, 11219, 1482, 1495, 13, 1753, 1653, 29918, 1792, 29898, 3827, 29901, 11010, 29889, 2659, 29892, 4833, 29901, 7317, 353, 10034, 1975, 29898, 9803, 29889, 657, 29918, 2585, 22164, 13, 1678, 6608, 287, 29918, 3364, 353, 756, 2790, 29889, 657, 29918, 5630, 29918, 8568, 29898, 3827, 29889, 5630, 29897, 13, 1678, 716, 29918, 1792, 353, 4733, 29889, 2659, 29898, 978, 353, 2009, 29889, 978, 29892, 6786, 353, 2009, 29889, 6786, 29892, 4800, 353, 6608, 287, 29918, 3364, 29897, 13, 1678, 4833, 29889, 1202, 29898, 1482, 29918, 1792, 29897, 13, 1678, 4833, 29889, 15060, 580, 13, 1678, 4833, 29889, 22379, 29898, 1482, 29918, 1792, 29897, 13, 1678, 736, 2009, 13, 13, 29992, 15140, 29889, 657, 11219, 2886, 742, 2933, 29918, 4299, 29922, 2391, 29961, 11010, 29889, 4294, 2659, 2314, 13, 1753, 1510, 29918, 1792, 29918, 497, 29898, 2585, 29901, 7317, 29922, 8498, 1975, 29898, 9803, 29889, 657, 29918, 2585, 22164, 13, 1678, 599, 29918, 7193, 353, 4833, 29889, 1972, 29898, 9794, 29889, 2659, 467, 497, 580, 13, 1678, 736, 599, 29918, 7193, 13, 13, 29992, 15140, 29889, 657, 11219, 2886, 19248, 333, 29913, 742, 5327, 29918, 4299, 29922, 10938, 29889, 4294, 2659, 29897, 13, 1753, 1510, 29918, 1792, 29918, 333, 29898, 333, 29901, 524, 29892, 4833, 29901, 7317, 353, 10034, 1975, 29898, 9803, 29889, 657, 29918, 2585, 22164, 13, 1678, 4629, 29918, 4836, 353, 4833, 29889, 1972, 29898, 9794, 29889, 2659, 467, 4572, 29898, 9794, 29889, 2659, 29889, 333, 1275, 1178, 467, 4102, 580, 13, 13, 1678, 565, 451, 4629, 29918, 4836, 29901, 13, 4706, 12020, 7331, 2451, 29898, 4882, 29918, 401, 29922, 4882, 29889, 10493, 29918, 29946, 29900, 29946, 29918, 12256, 29918, 5800, 18783, 29892, 16432, 29922, 29888, 29908, 2659, 426, 333, 29913, 451, 1476, 23157, 13, 13, 1678, 736, 4629, 29918, 4836, 13, 13, 29937, 732, 15140, 29889, 649, 11219, 29912, 333, 29913, 1495, 13, 29937, 822, 2767, 29918, 4836, 29918, 333, 29898, 333, 29901, 524, 29892, 3827, 29901, 11010, 29889, 7653, 29892, 2585, 29901, 7317, 353, 10034, 1975, 29898, 9803, 29889, 657, 29918, 2585, 22164, 13, 29937, 268, 396, 7974, 363, 9279, 29915, 1178, 13, 29937, 268, 4629, 29918, 4836, 353, 4833, 29889, 1972, 29898, 9794, 29889, 7653, 467, 4572, 29898, 9794, 29889, 7653, 29889, 333, 1275, 1178, 29897, 13, 13, 29937, 268, 565, 451, 4629, 29918, 4836, 29889, 4102, 7295, 13, 29937, 308, 12020, 7331, 2451, 29898, 4882, 29918, 401, 29922, 4882, 29889, 10493, 29918, 29946, 29900, 29946, 29918, 12256, 29918, 5800, 18783, 29892, 16432, 29922, 29888, 29908, 7653, 426, 333, 29913, 451, 1476, 23157, 13, 13, 13, 29937, 268, 4629, 29918, 4836, 29889, 5504, 29898, 8977, 29898, 3827, 876, 13, 13, 29937, 268, 736, 11117, 4882, 2396, 29888, 29915, 4836, 426, 333, 29913, 4784, 10827, 13, 13, 13, 13, 29937, 732, 15140, 29889, 8143, 11219, 29912, 333, 29913, 1495, 13, 29937, 822, 5217, 29918, 4836, 29918, 333, 29898, 333, 29901, 524, 29892, 2585, 29901, 7317, 353, 10034, 1975, 29898, 9803, 29889, 657, 29918, 2585, 22164, 13, 29937, 268, 4629, 29918, 4836, 353, 4833, 29889, 1972, 29898, 9794, 29889, 7653, 467, 4572, 29898, 9794, 29889, 7653, 29889, 333, 1275, 1178, 467, 4102, 580, 13, 13, 29937, 268, 565, 451, 4629, 29918, 4836, 29901, 13, 29937, 308, 12020, 7331, 2451, 29898, 4882, 29918, 401, 29922, 4882, 29889, 10493, 29918, 29946, 29900, 29946, 29918, 12256, 29918, 5800, 18783, 29892, 16432, 29922, 29888, 29908, 7653, 426, 333, 29913, 451, 1476, 23157, 13, 13, 29937, 268, 4833, 29889, 8143, 29898, 8391, 29918, 4836, 29897, 13, 29937, 268, 4833, 29889, 15060, 580, 13, 13, 29937, 268, 736, 11117, 4882, 2396, 29888, 29915, 8143, 2060, 29918, 333, 426, 333, 29913, 9150, 10827, 13, 13, 2 ]
test_mersenne.py	Crulzor/algorithms-python-intro-ex	0	26134	<reponame>Crulzor/algorithms-python-intro-ex<filename>test_mersenne.py from mersenne import generatePotentialMP, isPrime def test_generatePotentialMP(): assert(generatePotentialMP(2) == 3) assert(generatePotentialMP(1) ==1) def test_isPrime(): assert(isPrime(7)) assert(isPrime(2)) assert(isPrime(3)) assert(isPrime(4) == False)	[ 1, 529, 276, 1112, 420, 29958, 20647, 352, 29015, 29914, 9564, 12404, 29899, 4691, 29899, 23333, 29899, 735, 29966, 9507, 29958, 1688, 29918, 13269, 4584, 29889, 2272, 13, 3166, 286, 414, 4584, 1053, 5706, 29925, 327, 2556, 3580, 29892, 338, 4040, 603, 13, 13, 1753, 1243, 29918, 17158, 29925, 327, 2556, 3580, 7295, 13, 1678, 4974, 29898, 17158, 29925, 327, 2556, 3580, 29898, 29906, 29897, 1275, 29871, 29941, 29897, 13, 1678, 4974, 29898, 17158, 29925, 327, 2556, 3580, 29898, 29896, 29897, 1275, 29896, 29897, 13, 13, 1753, 1243, 29918, 275, 4040, 603, 7295, 13, 1678, 4974, 29898, 275, 4040, 603, 29898, 29955, 876, 13, 1678, 4974, 29898, 275, 4040, 603, 29898, 29906, 876, 13, 1678, 4974, 29898, 275, 4040, 603, 29898, 29941, 876, 13, 1678, 4974, 29898, 275, 4040, 603, 29898, 29946, 29897, 1275, 7700, 29897, 2 ]
tests/test_files/kwonly_arg_annotated.py	misteraverin/flake8-annotations-coverage	29	159131	<reponame>misteraverin/flake8-annotations-coverage def foo(): pass def bar(*args, kwonly_arg: str = None): pass	[ 1, 529, 276, 1112, 420, 29958, 29885, 1531, 12483, 262, 29914, 29888, 433, 446, 29947, 29899, 6735, 800, 29899, 11911, 482, 13, 1753, 7953, 7295, 13, 1678, 1209, 13, 13, 13, 1753, 2594, 10456, 5085, 29892, 9049, 6194, 29918, 1191, 29901, 851, 353, 6213, 1125, 13, 1678, 1209, 13, 2 ]
setup.py	NoamNol/gematriapy	1	96771	<reponame>NoamNol/gematriapy import setuptools import gematriapy with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() setuptools.setup( name="gematriapy", version=gematriapy.__version__, author="<NAME>", author_email="<EMAIL>", description=gematriapy.__doc__, long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/NoamNol/gematriapy", packages=['gematriapy'], license="MIT", classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", 'Development Status :: 4 - Beta', ], keywords="hebrew gematria gematriapy", python_requires='>=3.7', )	[ 1, 529, 276, 1112, 420, 29958, 3782, 314, 29940, 324, 29914, 29887, 4579, 374, 27580, 13, 5215, 731, 21245, 8789, 13, 5215, 330, 4579, 374, 27580, 13, 13, 2541, 1722, 703, 16310, 2303, 29889, 3487, 613, 376, 29878, 613, 8025, 543, 9420, 29899, 29947, 1159, 408, 285, 29882, 29901, 13, 1678, 1472, 29918, 8216, 353, 285, 29882, 29889, 949, 580, 13, 13, 842, 21245, 8789, 29889, 14669, 29898, 13, 1678, 1024, 543, 29887, 4579, 374, 27580, 613, 13, 1678, 1873, 29922, 29887, 4579, 374, 27580, 17255, 3259, 1649, 29892, 13, 1678, 4148, 543, 29966, 5813, 28341, 13, 1678, 4148, 29918, 5269, 543, 29966, 26862, 6227, 28341, 13, 1678, 6139, 29922, 29887, 4579, 374, 27580, 17255, 1514, 1649, 29892, 13, 1678, 1472, 29918, 8216, 29922, 5426, 29918, 8216, 29892, 13, 1678, 1472, 29918, 8216, 29918, 3051, 29918, 1853, 543, 726, 29914, 3502, 3204, 613, 13, 1678, 3142, 543, 991, 597, 3292, 29889, 510, 29914, 3782, 314, 29940, 324, 29914, 29887, 4579, 374, 27580, 613, 13, 1678, 9741, 29922, 1839, 29887, 4579, 374, 27580, 7464, 13, 1678, 19405, 543, 26349, 613, 13, 1678, 770, 14903, 11759, 13, 4706, 376, 9283, 4056, 17088, 4761, 5132, 4761, 29871, 29941, 613, 13, 4706, 376, 29931, 293, 1947, 4761, 438, 5425, 28268, 1490, 4761, 341, 1806, 19245, 613, 13, 4706, 376, 7094, 1218, 2184, 4761, 6570, 25266, 613, 13, 4706, 525, 21956, 358, 16034, 4761, 29871, 29946, 448, 350, 1187, 742, 13, 1678, 21251, 13, 1678, 29361, 543, 354, 1030, 29893, 330, 4579, 2849, 330, 4579, 374, 27580, 613, 13, 1678, 3017, 29918, 276, 339, 2658, 2433, 18572, 29941, 29889, 29955, 742, 13, 29897, 13, 2 ]
upcfcardsearch/c106.py	ProfessorSean/Kasutamaiza	0	192069	<filename>upcfcardsearch/c106.py import discord from discord.ext import commands from discord.utils import get class c106(commands.Cog, name="c106"): def __init__(self, bot: commands.Bot): self.bot = bot @commands.command(name='Golden_Statue_in_the_Sky', aliases=['c106']) async def example_embed(self, ctx): embed = discord.Embed(title='Golden Statue in the Sky', color=0xA086B7) embed.set_thumbnail(url='https://www.duelingbook.com/images/custom-pics/2300000/2321563.jpg') embed.add_field(name='Status (Archetype)', value='Casual:3/Tournament:3', inline=True) embed.add_field(name='Type (Attribute)', value='Rock/Fusion (LIGHT)', inline=False) embed.add_field(name='Level (ATK/DEF)', value='8 (1000/3500)', inline=False) embed.add_field(name='Monster Effect', value='1 Fairy monster + 1 Rock monster', inline=False) embed.set_footer(text='Set Code: ANCF') await ctx.send(embed=embed) def setup(bot: commands.Bot): bot.add_cog(c106(bot))	[ 1, 529, 9507, 29958, 786, 6854, 7543, 4478, 29914, 29883, 29896, 29900, 29953, 29889, 2272, 13, 5215, 2313, 536, 13, 3166, 2313, 536, 29889, 1062, 1053, 8260, 13, 3166, 2313, 536, 29889, 13239, 1053, 679, 13, 13, 1990, 274, 29896, 29900, 29953, 29898, 26381, 29889, 29907, 468, 29892, 1024, 543, 29883, 29896, 29900, 29953, 29908, 1125, 13, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 9225, 29901, 8260, 29889, 29933, 327, 1125, 13, 4706, 1583, 29889, 7451, 353, 9225, 13, 1678, 732, 26381, 29889, 6519, 29898, 978, 2433, 29954, 1025, 264, 29918, 9513, 434, 29918, 262, 29918, 1552, 29918, 29903, 3459, 742, 14430, 2129, 29922, 1839, 29883, 29896, 29900, 29953, 11287, 13, 1678, 7465, 822, 1342, 29918, 17987, 29898, 1311, 29892, 12893, 1125, 13, 4706, 8297, 353, 2313, 536, 29889, 6026, 2580, 29898, 3257, 2433, 29954, 1025, 264, 6666, 434, 297, 278, 16572, 742, 13, 462, 795, 2927, 29922, 29900, 29916, 29909, 29900, 29947, 29953, 29933, 29955, 29897, 13, 4706, 8297, 29889, 842, 29918, 386, 21145, 29898, 2271, 2433, 991, 597, 1636, 29889, 700, 14067, 2909, 29889, 510, 29914, 8346, 29914, 6341, 29899, 29886, 1199, 29914, 29906, 29941, 29900, 29900, 29900, 29900, 29900, 29914, 29906, 29941, 29906, 29896, 29945, 29953, 29941, 29889, 6173, 1495, 13, 13, 4706, 8297, 29889, 1202, 29918, 2671, 29898, 978, 2433, 5709, 313, 13197, 300, 668, 29897, 742, 995, 2433, 29907, 294, 950, 29901, 29941, 29914, 29911, 2905, 1166, 29901, 29941, 742, 10583, 29922, 5574, 29897, 13, 4706, 8297, 29889, 1202, 29918, 2671, 29898, 978, 2433, 1542, 313, 6708, 29897, 742, 995, 2433, 29934, 1698, 29914, 29943, 3958, 313, 5265, 29954, 3912, 29897, 742, 10583, 29922, 8824, 29897, 13, 4706, 8297, 29889, 1202, 29918, 2671, 29898, 978, 2433, 10108, 313, 1299, 29968, 29914, 24405, 29897, 742, 995, 2433, 29947, 313, 29896, 29900, 29900, 29900, 29914, 29941, 29945, 29900, 29900, 29897, 742, 10583, 29922, 8824, 29897, 13, 4706, 8297, 29889, 1202, 29918, 2671, 29898, 978, 2433, 7185, 2475, 26475, 742, 995, 2433, 29896, 13822, 29891, 1601, 2475, 718, 29871, 29896, 8027, 1601, 2475, 742, 10583, 29922, 8824, 29897, 13, 4706, 8297, 29889, 842, 29918, 21720, 29898, 726, 2433, 2697, 5920, 29901, 13764, 9207, 1495, 13, 13, 4706, 7272, 12893, 29889, 6717, 29898, 17987, 29922, 17987, 29897, 13, 13, 1753, 6230, 29898, 7451, 29901, 8260, 29889, 29933, 327, 1125, 13, 1678, 9225, 29889, 1202, 29918, 29883, 468, 29898, 29883, 29896, 29900, 29953, 29898, 7451, 876, 2 ]
backoffice/views.py	chervaliery/tv_show_fetcher	0	1600272	from django.http import HttpResponse from models import * from utils import fetch_show, get_show # Create your views here. def home(request): return HttpResponse("Bonjour monde!")	[ 1, 515, 9557, 29889, 1124, 1053, 9056, 5103, 13, 13, 3166, 4733, 1053, 334, 13, 3166, 3667, 29879, 1053, 6699, 29918, 4294, 29892, 679, 29918, 4294, 13, 13, 29937, 6204, 596, 8386, 1244, 29889, 13, 13, 13, 1753, 3271, 29898, 3827, 1125, 13, 1678, 736, 9056, 5103, 703, 29933, 265, 29926, 473, 9122, 29991, 1159, 13, 2 ]
tnia/viewing/napari_helper.py	True-North-Intelligent-Algorithms/tnia-python	3	104456	import napari def show_image(im, title, viewer=None, label=False): """ convenience helper function to show image in Napari Args: im (numpy array): image to show title (string): title of image viewer (napari instance, optional): pre-existing Napari label (bool, optional): True if a label Returns: [napari instance]: a Napari instance """ if viewer==None: viewer = napari.Viewer() if label==False: viewer.add_image(im,name=title) else: viewer.add_labels(im,name=title) return viewer def show_image_and_label(im, label, title, viewer=None): """[summary] Args: im (numpy array): image to show label (numpy integer array): labels to show title (string): title of image viewer (napari instance, optional): pre-existing Napari Returns: [napari instance]: a Napari instance """ viewer = show_image(im, title, viewer, False) show_image(label, title+' label', viewer, True) return viewer	[ 1, 1053, 9709, 1306, 13, 13, 1753, 1510, 29918, 3027, 29898, 326, 29892, 3611, 29892, 6316, 556, 29922, 8516, 29892, 3858, 29922, 8824, 1125, 13, 1678, 9995, 29703, 16876, 740, 304, 1510, 1967, 297, 8344, 1306, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 527, 313, 23749, 1409, 1125, 29871, 1967, 304, 1510, 13, 4706, 3611, 313, 1807, 1125, 3611, 310, 1967, 13, 4706, 6316, 556, 313, 8971, 1306, 2777, 29892, 13136, 1125, 758, 29899, 735, 15423, 8344, 1306, 13, 4706, 3858, 313, 11227, 29892, 13136, 1125, 5852, 565, 263, 3858, 13, 13, 1678, 16969, 29901, 13, 4706, 518, 8971, 1306, 2777, 5387, 263, 8344, 1306, 2777, 13, 1678, 9995, 13, 1678, 565, 6316, 556, 1360, 8516, 29901, 13, 4706, 6316, 556, 353, 9709, 1306, 29889, 29963, 15580, 580, 13, 268, 13, 1678, 565, 3858, 1360, 8824, 29901, 13, 4706, 6316, 556, 29889, 1202, 29918, 3027, 29898, 326, 29892, 978, 29922, 3257, 29897, 13, 1678, 1683, 29901, 13, 4706, 6316, 556, 29889, 1202, 29918, 21134, 29898, 326, 29892, 978, 29922, 3257, 29897, 13, 13, 1678, 736, 6316, 556, 13, 13, 1753, 1510, 29918, 3027, 29918, 392, 29918, 1643, 29898, 326, 29892, 3858, 29892, 3611, 29892, 6316, 556, 29922, 8516, 1125, 13, 1678, 9995, 29961, 7727, 29962, 13, 13, 1678, 826, 3174, 29901, 13, 4706, 527, 313, 23749, 1409, 1125, 29871, 1967, 304, 1510, 13, 4706, 3858, 313, 23749, 6043, 1409, 1125, 11073, 304, 1510, 13, 4706, 3611, 313, 1807, 1125, 3611, 310, 1967, 13, 4706, 6316, 556, 313, 8971, 1306, 2777, 29892, 13136, 1125, 758, 29899, 735, 15423, 8344, 1306, 13, 13, 1678, 16969, 29901, 13, 4706, 518, 8971, 1306, 2777, 5387, 263, 8344, 1306, 2777, 13, 1678, 9995, 13, 1678, 6316, 556, 353, 1510, 29918, 3027, 29898, 326, 29892, 3611, 29892, 6316, 556, 29892, 7700, 29897, 13, 1678, 1510, 29918, 3027, 29898, 1643, 29892, 3611, 23097, 3858, 742, 6316, 556, 29892, 5852, 29897, 13, 13, 1678, 736, 6316, 556, 2 ]
time_calculator.py	yashvarshney003/Time-Calculator-Freecodecamp	0	192439	<filename>time_calculator.py def add_time(start, duration, day=None): time, period = start.split() initial_period = period hr_start, min_start = time.split(':') hr_duration, min_duration = duration.split(':') min_new = int(min_start) + int(min_duration) hr_new = int(hr_start) + int(hr_duration) periods_later = 0 days_later = 0 DAYS_OF_WEEK = [ "Saturday", "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday" ] if min_new > 59: min_new -= 60 hr_new += 1 hr_new_period = hr_new while hr_new > 12: hr_new -= 12 while hr_new_period > 11: hr_new_period -= 12 period = "PM" if period == "AM" else "AM" periods_later += 1 if periods_later % 2 != 0: if initial_period == "PM": periods_later += 1 else: periods_later -= 1 days_later = periods_later / 2 new_time = f"{hr_new}:{str(min_new).zfill(2)} {period}" if day: day_index = DAYS_OF_WEEK.index(day.title()) new_day_index = int((day_index + days_later) % 7) new_time += f", {DAYS_OF_WEEK[new_day_index]}" if days_later == 1: new_time += " (next day)" if days_later > 1: new_time += f" ({int(days_later)} days later)" return new_time	[ 1, 529, 9507, 29958, 2230, 29918, 15807, 1061, 29889, 2272, 13, 1753, 788, 29918, 2230, 29898, 2962, 29892, 14385, 29892, 2462, 29922, 8516, 1125, 13, 29871, 931, 29892, 3785, 353, 1369, 29889, 5451, 580, 13, 29871, 2847, 29918, 19145, 353, 3785, 13, 259, 13, 29871, 22157, 29918, 2962, 29892, 1375, 29918, 2962, 353, 931, 29889, 5451, 877, 29901, 1495, 13, 29871, 22157, 29918, 19708, 29892, 1375, 29918, 19708, 353, 14385, 29889, 5451, 877, 29901, 1495, 13, 13, 29871, 1375, 29918, 1482, 353, 938, 29898, 1195, 29918, 2962, 29897, 718, 938, 29898, 1195, 29918, 19708, 29897, 13, 29871, 22157, 29918, 1482, 353, 938, 29898, 1092, 29918, 2962, 29897, 718, 938, 29898, 1092, 29918, 19708, 29897, 13, 13, 29871, 23704, 29918, 29880, 1008, 353, 29871, 29900, 13, 29871, 3841, 29918, 29880, 1008, 353, 29871, 29900, 13, 13, 29871, 21330, 21554, 29918, 9800, 29918, 8851, 29923, 29968, 353, 518, 13, 1678, 376, 29903, 20510, 613, 13, 1678, 376, 29903, 870, 388, 613, 13, 1678, 376, 29924, 898, 388, 613, 13, 1678, 376, 29911, 1041, 3250, 613, 13, 1678, 376, 29956, 287, 4515, 3250, 613, 13, 1678, 376, 1349, 1295, 3250, 613, 13, 1678, 376, 29943, 2429, 388, 29908, 13, 1678, 4514, 13, 13, 29871, 565, 1375, 29918, 1482, 1405, 29871, 29945, 29929, 29901, 13, 1678, 1375, 29918, 1482, 22361, 29871, 29953, 29900, 13, 1678, 22157, 29918, 1482, 4619, 29871, 29896, 13, 13, 29871, 22157, 29918, 1482, 29918, 19145, 353, 22157, 29918, 1482, 13, 13, 29871, 1550, 22157, 29918, 1482, 1405, 29871, 29896, 29906, 29901, 13, 1678, 22157, 29918, 1482, 22361, 29871, 29896, 29906, 13, 13, 29871, 1550, 22157, 29918, 1482, 29918, 19145, 1405, 29871, 29896, 29896, 29901, 259, 13, 1678, 22157, 29918, 1482, 29918, 19145, 22361, 29871, 29896, 29906, 13, 1678, 3785, 353, 376, 13427, 29908, 565, 3785, 1275, 376, 5194, 29908, 1683, 376, 5194, 29908, 13, 1678, 23704, 29918, 29880, 1008, 4619, 29871, 29896, 13, 13, 29871, 565, 23704, 29918, 29880, 1008, 1273, 29871, 29906, 2804, 29871, 29900, 29901, 13, 1678, 565, 2847, 29918, 19145, 1275, 376, 13427, 1115, 13, 418, 23704, 29918, 29880, 1008, 4619, 29871, 29896, 13, 1678, 1683, 29901, 13, 418, 23704, 29918, 29880, 1008, 22361, 29871, 29896, 13, 13, 29871, 3841, 29918, 29880, 1008, 353, 23704, 29918, 29880, 1008, 847, 29871, 29906, 13, 13, 29871, 716, 29918, 2230, 353, 285, 29908, 29912, 1092, 29918, 1482, 6177, 29912, 710, 29898, 1195, 29918, 1482, 467, 29920, 5589, 29898, 29906, 2915, 426, 19145, 5038, 13, 13, 29871, 565, 2462, 29901, 13, 1678, 2462, 29918, 2248, 353, 21330, 21554, 29918, 9800, 29918, 8851, 29923, 29968, 29889, 2248, 29898, 3250, 29889, 3257, 3101, 13, 1678, 716, 29918, 3250, 29918, 2248, 353, 938, 3552, 3250, 29918, 2248, 718, 3841, 29918, 29880, 1008, 29897, 1273, 29871, 29955, 29897, 13, 1678, 716, 29918, 2230, 4619, 285, 613, 426, 7698, 21554, 29918, 9800, 29918, 8851, 29923, 29968, 29961, 1482, 29918, 3250, 29918, 2248, 29962, 5038, 29871, 13, 13, 29871, 565, 3841, 29918, 29880, 1008, 1275, 29871, 29896, 29901, 13, 1678, 716, 29918, 2230, 4619, 376, 313, 4622, 2462, 5513, 13, 13, 29871, 565, 3841, 29918, 29880, 1008, 1405, 29871, 29896, 29901, 13, 1678, 716, 29918, 2230, 4619, 285, 29908, 21313, 524, 29898, 16700, 29918, 29880, 1008, 2915, 3841, 2678, 5513, 13, 13, 29871, 736, 716, 29918, 2230, 2 ]
boto-python-workshop/01_list_objects.py	shy950521/ecs-samples	28	186798	<reponame>shy950521/ecs-samples<gh_stars>10-100 ''' Same stuff as 00_getting_started. If you've pasted all this into your python interpreter already, you can skip past this. ''' from boto.s3.connection import S3Connection, OrdinaryCallingFormat from boto.s3.key import Key from boto.s3.prefix import Prefix host = "object.ecstestdrive.com" port = 443 secure = port == 9021 or port == 443 access_key_id = 'user1' secret_key = '' debug_level = 2 calling_format = OrdinaryCallingFormat() s3 = S3Connection(aws_access_key_id=access_key_id, aws_secret_access_key=secret_key, is_secure=secure, port=port, host=host, debug=debug_level, calling_format=calling_format) bucket_name = "mybucket" bucket = s3.create_bucket(bucket_name) ''' Create a bunch of keys to test with. ''' key_names = ["20151102/account-12345/bill.xml", "20151102/account-12345/bill.pdf", "20151102/account-12345/bill.html", "20151102/account-55555/bill.xml", "20151102/account-55555/bill.pdf", "20151102/account-55555/bill.html", "20151102/account-77777/bill.xml", "20151102/account-77777/bill.pdf", "20151102/account-77777/bill.html", "20151103/account-11111/bill.xml", "20151103/account-11111/bill.pdf", "20151103/account-11111/bill.html", "20151103/account-11122/bill.xml", "20151103/account-11122/bill.pdf", "20151103/account-11122/bill.html", "20151103/account-11133/bill.xml", "20151103/account-11133/bill.pdf", "20151103/account-11133/bill.html", "20141103/account-11111/bill.xml", "20141103/account-11111/bill.pdf", "20141103/account-11111/bill.html", "20141103/account-11122/bill.xml", "20141103/account-11122/bill.pdf", "20141103/account-11122/bill.html", "20141103/account-11133/bill.xml", "20141103/account-11133/bill.pdf", "20141103/account-11133/bill.html"] for key_name in key_names: key = bucket.new_key(key_name) key.set_contents_from_string("data for key %s" % key_name, {"content-type": "text/plain"}) def bucket_list(): """ Start a loop to experiment with bucket listing. """ running = True while running: prefix = raw_input("Prefix: ") delimiter = raw_input("Delimiter: ") marker = raw_input("Marker: ") results = bucket.list(prefix, delimiter, marker) print "%30s %10s %s" % ("LastModified", "Size", "Key") print "------------------------------ ---------- ------------------------------------------" for result in results: if type(result) is Key: print "%30s %10d %s" % (result.last_modified, result.size, result.name) if type(result) is Prefix: print "%30s %10s %s" % ("", "(PREFIX)", result.name) again = raw_input("Again? 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auxiliary/auxiliary.py	amageh/respy-tut	0	76491	<reponame>amageh/respy-tut """This file contains auxiliary function that we use throughout the respy tutorial.""" import matplotlib.pyplot as plt import pandas as pd def plot_fishing_grounds(df): """Plot choice shares for observables example.""" fig, ax = plt.subplots(1, 2, figsize=(10, 4)) for i, observable in enumerate(["rich", "poor"]): df.query("Fishing_Grounds == @observable").groupby( "Period" ).Choice.value_counts(normalize=True,).unstack().plot.bar( width=0.4, stacked=True, rot=0, legend=False, ax=ax[i] ) ax[i].set_title("Fishing grounds: " + observable, pad=10) ax[i].xaxis.label.set_visible(False) plt.legend(loc="lower center", bbox_to_anchor=(-0.15, -0.3), ncol=2) plt.suptitle("Robinson's choices by period", y=1.05) plt.show() def plot_choice_shares(df, friday=False): """Plot choice shares.""" if friday: color = ["C0", "C2", "C1"] choices = 3 else: color = ["C0", "C1"] choices = 2 fig, ax = plt.subplots() df.groupby("Period").Choice.value_counts(normalize=True).unstack().plot.bar( stacked=True, ax=ax, color=color ) plt.xticks(rotation="horizontal") plt.legend(loc="lower center", bbox_to_anchor=(0.5, -0.275), ncol=choices) plt.show() plt.close() def plot_choice_prob_and_exp_level(df): """Plot choices and experience levels.""" fig, axs = plt.subplots(1, 2, figsize=(10, 4)) ( df.groupby("Period") .Choice.value_counts(normalize=True) .unstack() .plot.bar(ax=axs[0], stacked=True, rot=0, title="Choice Probabilities") ) ( df.groupby("Period") .Experience_Fishing.value_counts(normalize=True) .unstack() .plot.bar( ax=axs[1], stacked=True, rot=0, title="Share of Experience Level per Period", cmap="Blues", ) ) axs[0].legend(loc="lower center", bbox_to_anchor=(0.5, -0.4), ncol=2) axs[1].legend(loc="right", bbox_to_anchor=(1.3, 0.5), ncol=1, title="Experience") plt.show() def plot_choice_prob(df): """Plot choices.""" fig, ax = plt.subplots() df.groupby("Period").Choice.value_counts(normalize=True).unstack().plot.bar( stacked=True, ax=ax ) plt.xticks(rotation="horizontal") plt.legend(loc="lower center", bbox_to_anchor=(0.5, -0.275), ncol=2) plt.show() plt.close() def plot_choices_kw(example="1994"): """Plot models from Keane and Wolpin (1994,1997,2000).""" if example == "1994": fig, ax = plt.subplots(1, 3, figsize=(15, 5)) models = ["kw_94_one", "kw_94_two", "kw_94_three"] if example == "1997": fig, ax = plt.subplots(1, 2, figsize=(15, 5)) models = ["kw_97_basic", "kw_97_extended"] for i, model in enumerate(models): df = pd.read_pickle(f"data/choices_{model}.pkl") df.plot( alpha=0.8, stacked=True, ax=ax[i], legend=False, kind="area", rot=0, title=f"Example model: {model}", linewidth=0, ) df.plot(stacked=True, ax=ax[i], legend=False, color="white", linewidth=1, rot=0) ax[i].margins(x=0) ax[i].spines["right"].set_visible(False) ax[i].spines["top"].set_visible(False) handles, labels = plt.gca().get_legend_handles_labels() by_label = dict(zip(labels, handles)) if len(ax) % 2 == 1: move_left = -0.7 else: move_left = -0.2 plt.legend( by_label.values(), by_label.keys(), loc="lower center", ncol=len(df.columns), bbox_to_anchor=(move_left, -0.25), fontsize=12, ) plt.show() def plot_kw_97_comparison(): """Plot comparison of KW 97 models and observed data.""" models = ["kw_97_obs", "kw_97_basic", "kw_97_extended"] colors = ["tab:blue", "tab:orange", "tab:green", "tab:red", "tab:purple"] data = {} for model in models: df = pd.read_pickle(f"data/choices_{model}.pkl") data[model] = df fig, ax = plt.subplots(2, 3, figsize=(15, 9)) ax = ax.ravel() for i, (choice, color) in enumerate(zip(df.columns, colors)): ax[i].plot( data["kw_97_obs"][choice], linestyle="dashed", color="k", label="Observed" ) ax[i].plot( data["kw_97_basic"][choice], linestyle="dotted", alpha=0.8, color=color, label="kw_97_basic", ) ax[i].plot( data["kw_97_extended"][choice], color=color, alpha=0.8, label="kw_97_extended", ) 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monitor/rotation.py	UWCubeSat/bairing	0	116388	import serial import struct import time import numpy as np from ahrs.filters import Madgwick from cube import CubeRenderer sync = b'\xEF\xBE\xAD\xDE' def reset(ser): ser.write(sync) ser.write(b'\x01\x00') ser.write(b'\x00') def begin(ser): ser.write(sync) ser.write(b'\x03\x00') ser.write(b'\x10') ser.write(b'\x32\0x00') # 50 ms period def receive(ser): recognized = False pattern = [ 0, 0, 0, 0 ] while not recognized: pattern.append(ser.read(1)) # sync pattern pattern.pop(0) recognized = True for i in range(0, 4): recognized = recognized and (pattern[i] == sync[i:i+1]) rcvd = ser.read(2) len = struct.unpack('<H', rcvd)[0] # packet length cmd = ser.read(1) data = ser.read(len - 1) return [cmd, data] def main(): connecting = True while connecting: try: ser = serial.Serial('COM4') connecting = False except serial.serialutil.SerialException: print("Connection to COM4 timed out, retrying...") reset(ser) print("resetting") time.sleep(3) begin(ser) # q_last = np.array([0.7071, -0.7071, 0.0, 0.0]) q_last = np.array([1., 0., 0., 0.]) madgwick = Madgwick(Dt = 0.05, q0 = q_last) renderer = CubeRenderer() while True: [cmd, data] = receive(ser) if cmd == b'\x10': raw = struct.unpack('<fffffffff', data) # 3 float vectors, 9 floats mag = raw[0:3] acc = raw[3:6] gyro = raw[6:9] # q_last = madgwick.updateMARG(q_last, gyr=gyro, acc=acc, mag=mag) q_last = madgwick.updateIMU(q_last, gyr=gyro, acc=acc) print(q_last) renderer.render(q_last) if __name__ == '__main__': main()	[ 1, 1053, 7797, 13, 5215, 2281, 13, 5215, 931, 13, 5215, 12655, 408, 7442, 13, 3166, 263, 1092, 29879, 29889, 26705, 1053, 4104, 29887, 6669, 13, 3166, 28704, 1053, 315, 4003, 21323, 13, 13, 16593, 353, 289, 12764, 29916, 29638, 29905, 29916, 15349, 29905, 29916, 3035, 29905, 29916, 2287, 29915, 13, 13, 1753, 10092, 29898, 643, 1125, 13, 29871, 724, 29889, 3539, 29898, 16593, 29897, 13, 29871, 724, 29889, 3539, 29898, 29890, 12764, 29916, 29900, 29896, 29905, 29916, 29900, 29900, 1495, 13, 29871, 724, 29889, 3539, 29898, 29890, 12764, 29916, 29900, 29900, 1495, 13, 13, 1753, 3380, 29898, 643, 1125, 13, 29871, 724, 29889, 3539, 29898, 16593, 29897, 13, 29871, 724, 29889, 3539, 29898, 29890, 12764, 29916, 29900, 29941, 29905, 29916, 29900, 29900, 1495, 13, 29871, 724, 29889, 3539, 29898, 29890, 12764, 29916, 29896, 29900, 1495, 13, 29871, 724, 29889, 3539, 29898, 29890, 12764, 29916, 29941, 29906, 29905, 29900, 29916, 29900, 29900, 1495, 29871, 396, 29871, 29945, 29900, 10887, 3785, 13, 259, 13, 1753, 7150, 29898, 643, 1125, 13, 29871, 14831, 353, 7700, 13, 29871, 4766, 353, 518, 29871, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 4514, 13, 29871, 1550, 451, 14831, 29901, 13, 1678, 4766, 29889, 4397, 29898, 643, 29889, 949, 29898, 29896, 876, 29871, 396, 16523, 4766, 13, 1678, 4766, 29889, 7323, 29898, 29900, 29897, 13, 268, 13, 1678, 14831, 353, 5852, 13, 1678, 363, 474, 297, 3464, 29898, 29900, 29892, 29871, 29946, 1125, 13, 418, 14831, 353, 14831, 322, 313, 11037, 29961, 29875, 29962, 1275, 16523, 29961, 29875, 29901, 29875, 29974, 29896, 2314, 13, 13, 29871, 364, 11023, 29881, 353, 724, 29889, 949, 29898, 29906, 29897, 13, 29871, 7431, 353, 2281, 29889, 348, 4058, 877, 29966, 29950, 742, 364, 11023, 29881, 9601, 29900, 29962, 29871, 396, 18203, 3309, 13, 259, 13, 29871, 9920, 353, 724, 29889, 949, 29898, 29896, 29897, 13, 13, 29871, 848, 353, 724, 29889, 949, 29898, 2435, 448, 29871, 29896, 29897, 13, 259, 13, 29871, 736, 518, 9006, 29892, 848, 29962, 13, 268, 13, 1753, 1667, 7295, 13, 29871, 16791, 353, 5852, 13, 29871, 1550, 16791, 29901, 13, 1678, 1018, 29901, 13, 418, 724, 353, 7797, 29889, 9125, 877, 19795, 29946, 1495, 13, 418, 16791, 353, 7700, 13, 1678, 5174, 7797, 29889, 15550, 4422, 29889, 9125, 2451, 29901, 13, 418, 1596, 703, 5350, 304, 23353, 29946, 5335, 287, 714, 29892, 337, 2202, 292, 856, 1159, 13, 259, 13, 29871, 10092, 29898, 643, 29897, 13, 29871, 1596, 703, 12071, 1259, 1159, 13, 13, 29871, 931, 29889, 17059, 29898, 29941, 29897, 13, 259, 13, 29871, 3380, 29898, 643, 29897, 13, 13, 29871, 396, 3855, 29918, 4230, 353, 7442, 29889, 2378, 4197, 29900, 29889, 29955, 29900, 29955, 29896, 29892, 448, 29900, 29889, 29955, 29900, 29955, 29896, 29892, 29871, 29900, 29889, 29900, 29892, 29871, 29900, 29889, 29900, 2314, 13, 29871, 3855, 29918, 4230, 353, 7442, 29889, 2378, 4197, 29896, 1696, 29871, 29900, 1696, 29871, 29900, 1696, 29871, 29900, 29889, 2314, 13, 29871, 10395, 29887, 6669, 353, 4104, 29887, 6669, 29898, 29928, 29873, 353, 29871, 29900, 29889, 29900, 29945, 29892, 3855, 29900, 353, 3855, 29918, 4230, 29897, 13, 259, 13, 29871, 4050, 261, 353, 315, 4003, 21323, 580, 13, 259, 13, 29871, 1550, 5852, 29901, 13, 1678, 518, 9006, 29892, 848, 29962, 353, 7150, 29898, 643, 29897, 13, 1678, 565, 9920, 1275, 289, 12764, 29916, 29896, 29900, 2396, 13, 418, 10650, 353, 2281, 29889, 348, 4058, 877, 29966, 17156, 17156, 29888, 742, 848, 29897, 29871, 396, 29871, 29941, 5785, 12047, 29892, 29871, 29929, 5685, 1446, 13, 418, 2320, 353, 10650, 29961, 29900, 29901, 29941, 29962, 13, 418, 1035, 353, 10650, 29961, 29941, 29901, 29953, 29962, 13, 418, 10966, 307, 353, 10650, 29961, 29953, 29901, 29929, 29962, 13, 539, 13, 418, 396, 3855, 29918, 4230, 353, 10395, 29887, 6669, 29889, 5504, 1529, 29934, 29954, 29898, 29939, 29918, 4230, 29892, 330, 4316, 29922, 1927, 307, 29892, 1035, 29922, 5753, 29892, 2320, 29922, 11082, 29897, 13, 418, 3855, 29918, 4230, 353, 10395, 29887, 6669, 29889, 5504, 7833, 29965, 29898, 29939, 29918, 4230, 29892, 330, 4316, 29922, 1927, 307, 29892, 1035, 29922, 5753, 29897, 13, 418, 1596, 29898, 29939, 29918, 4230, 29897, 13, 418, 4050, 261, 29889, 9482, 29898, 29939, 29918, 4230, 29897, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 29871, 1667, 580, 2 ]
src/train.py	saikrishnaklu/Kaggle_Categorical_Feature_Encoding-Challenge_II	1	103442	from __future__ import print_function import os from sklearn import preprocessing from sklearn import ensemble from sklearn.metrics import accuracy_score,roc_auc_score import pandas as pd import joblib import numpy as np #import dispatcher folds={ 0:[1,2,3,4], 1:[0,2,3,4], 2:[1,0,3,4], 3:[1,2,0,4], 4:[1,2,3,0], } TRAINING="input/train_folds.csv" TEST="input/test.csv" FOLD=2 MODEL="randomforest" MODEL2="logisticregression" """ TRAINING=os.getenv(['TRAINING']) TEST=os.getenv(['TEST']) FOLD=os.getenv(['FOLD']) MODEL=os.getenv(['MODEL']) """ MODELS={ "randomforest":ensemble.RandomForestClassifier(n_estimators=200,n_jobs=-1,verbose=2), "extratreesclassfier":ensemble.ExtraTreesClassifier(n_estimators=200,n_jobs=-1,verbose=2) } if __name__=="__main__": df_train_main=pd.read_csv(TRAINING) df_test=pd.read_csv(TEST) df_train=df_train_main[df_train_main.kfold.isin(folds.get(FOLD))].reset_index(drop=True) df_val=df_train[df_train_main.kfold==0].reset_index(drop=True) df_y_train=df_train.target.values df_y_val=df_val.target.values df_train=df_train.drop(['id','target','kfold'],axis=1) df_val=df_val.drop(['id','target','kfold'],axis=1) df_val=df_val[df_train.columns] label_encoders={} for c in df_train.columns: label_encoder=preprocessing.LabelEncoder() label_encoder.fit(df_train[c].values.tolist()+df_val[c].values.tolist()+df_test[c].values.tolist()) onehot_encoder=preprocessing.OneHotEncoder() df_train.loc[:,c]=label_encoder.transform(df_train[c].values.tolist()) df_val.loc[:,c]=label_encoder.transform(df_val[c].values.tolist()) label_encoders[c]=label_encoder model=MODELS[MODEL] model.fit(df_train,df_y_train) pred=model.predict_proba(df_val)[:,1] pred2=model.predict(df_val) print(roc_auc_score(df_y_val,pred)) print(accuracy_score(df_y_val,pred2)) joblib.dump(label_encoders,f"models/{MODEL}_{FOLD}_label_encoder.pkl") joblib.dump(model,f"models/{MODEL}_{FOLD}.pkl") 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notebooks/__code/_utilities/array.py	mabrahamdevops/python_notebooks	0	128582	<reponame>mabrahamdevops/python_notebooks import numpy as np def exclude_y_value_when_error_is_nan(axis, error_axis): axis_cleaned = [] error_axis_cleaned = [] for _x, _error in zip(axis, error_axis): if (_x == "None") or (_error == "None") or (_x is None) or (_error is None): axis_cleaned.append(np.NaN) error_axis_cleaned.append(np.NaN) else: axis_cleaned.append(np.float(_x)) error_axis_cleaned.append(np.float(_error)) return axis_cleaned, error_axis_cleaned def check_size(x_axis=None, y_axis=None): size_x = len(x_axis) size_y = len(y_axis) min_len = np.min([size_x, size_y]) return x_axis[:min_len], y_axis[:min_len] def get_n_random_int_of_max_value_m(n=1, max=10): list_random = np.random.random(n) * max list_random_int = [np.int(_value) for _value in list_random] return list_random_int def reject_outliers(array=None): if len(array) < 3: return array new_array = [_value for _value in array if (not (_value == np.min(array))) and (not (_value == np.max(array)))] return new_array def reject_n_outliers(array=None, n=1): set_array = list(array) set_array.sort() return set_array[n: -n] def find_nearest_index(array, value): idx = (np.abs(np.array(array) - value)).argmin() return idx def get_closest_index(array=None, value=None): idx = (np.abs(np.array(array) - value)).argmin() return idx def get_nearest_index(array=None, value=None): return get_closest_index(array=array, value=value)	[ 1, 529, 276, 1112, 420, 29958, 29885, 370, 22084, 3359, 3554, 29914, 4691, 29918, 1333, 19273, 29879, 13, 5215, 12655, 408, 7442, 13, 13, 13, 1753, 19060, 29918, 29891, 29918, 1767, 29918, 8256, 29918, 2704, 29918, 275, 29918, 13707, 29898, 8990, 29892, 1059, 29918, 8990, 1125, 13, 1678, 9685, 29918, 14941, 287, 353, 5159, 13, 1678, 1059, 29918, 8990, 29918, 14941, 287, 353, 5159, 13, 13, 1678, 363, 903, 29916, 29892, 903, 2704, 297, 14319, 29898, 8990, 29892, 1059, 29918, 8990, 1125, 13, 4706, 565, 9423, 29916, 1275, 376, 8516, 1159, 470, 9423, 2704, 1275, 376, 8516, 1159, 470, 9423, 29916, 338, 6213, 29897, 470, 9423, 2704, 338, 6213, 1125, 13, 9651, 9685, 29918, 14941, 287, 29889, 4397, 29898, 9302, 29889, 19377, 29897, 13, 9651, 1059, 29918, 8990, 29918, 14941, 287, 29889, 4397, 29898, 9302, 29889, 19377, 29897, 13, 4706, 1683, 29901, 13, 9651, 9685, 29918, 14941, 287, 29889, 4397, 29898, 9302, 29889, 7411, 7373, 29916, 876, 13, 9651, 1059, 29918, 8990, 29918, 14941, 287, 29889, 4397, 29898, 9302, 29889, 7411, 7373, 2704, 876, 13, 13, 1678, 736, 9685, 29918, 14941, 287, 29892, 1059, 29918, 8990, 29918, 14941, 287, 13, 13, 13, 1753, 1423, 29918, 2311, 29898, 29916, 29918, 8990, 29922, 8516, 29892, 343, 29918, 8990, 29922, 8516, 1125, 13, 1678, 2159, 29918, 29916, 353, 7431, 29898, 29916, 29918, 8990, 29897, 13, 1678, 2159, 29918, 29891, 353, 7431, 29898, 29891, 29918, 8990, 29897, 13, 1678, 1375, 29918, 2435, 353, 7442, 29889, 1195, 4197, 2311, 29918, 29916, 29892, 2159, 29918, 29891, 2314, 13, 1678, 736, 921, 29918, 8990, 7503, 1195, 29918, 2435, 1402, 343, 29918, 8990, 7503, 1195, 29918, 2435, 29962, 13, 13, 13, 1753, 679, 29918, 29876, 29918, 8172, 29918, 524, 29918, 974, 29918, 3317, 29918, 1767, 29918, 29885, 29898, 29876, 29922, 29896, 29892, 4236, 29922, 29896, 29900, 1125, 13, 1678, 1051, 29918, 8172, 353, 7442, 29889, 8172, 29889, 8172, 29898, 29876, 29897, 334, 4236, 13, 1678, 1051, 29918, 8172, 29918, 524, 353, 518, 9302, 29889, 524, 7373, 1767, 29897, 363, 903, 1767, 297, 1051, 29918, 8172, 29962, 13, 1678, 736, 1051, 29918, 8172, 29918, 524, 13, 13, 13, 1753, 12560, 29918, 449, 27801, 29898, 2378, 29922, 8516, 1125, 13, 1678, 565, 7431, 29898, 2378, 29897, 529, 29871, 29941, 29901, 13, 4706, 736, 1409, 13, 13, 1678, 716, 29918, 2378, 353, 23160, 1767, 13, 462, 363, 903, 1767, 297, 1409, 13, 462, 565, 313, 1333, 9423, 1767, 1275, 7442, 29889, 1195, 29898, 2378, 4961, 13, 462, 322, 313, 1333, 9423, 1767, 1275, 7442, 29889, 3317, 29898, 2378, 876, 4638, 13, 1678, 736, 716, 29918, 2378, 13, 13, 13, 1753, 12560, 29918, 29876, 29918, 449, 27801, 29898, 2378, 29922, 8516, 29892, 302, 29922, 29896, 1125, 13, 1678, 731, 29918, 2378, 353, 1051, 29898, 2378, 29897, 13, 1678, 731, 29918, 2378, 29889, 6605, 580, 13, 1678, 736, 731, 29918, 2378, 29961, 29876, 29901, 448, 29876, 29962, 13, 13, 13, 1753, 1284, 29918, 28502, 342, 29918, 2248, 29898, 2378, 29892, 995, 1125, 13, 1678, 22645, 353, 313, 9302, 29889, 6897, 29898, 9302, 29889, 2378, 29898, 2378, 29897, 448, 995, 8106, 1191, 1195, 580, 13, 1678, 736, 22645, 13, 13, 13, 1753, 679, 29918, 11291, 342, 29918, 2248, 29898, 2378, 29922, 8516, 29892, 995, 29922, 8516, 1125, 13, 1678, 22645, 353, 313, 9302, 29889, 6897, 29898, 9302, 29889, 2378, 29898, 2378, 29897, 448, 995, 8106, 1191, 1195, 580, 13, 1678, 736, 22645, 13, 13, 13, 1753, 679, 29918, 28502, 342, 29918, 2248, 29898, 2378, 29922, 8516, 29892, 995, 29922, 8516, 1125, 13, 1678, 736, 679, 29918, 11291, 342, 29918, 2248, 29898, 2378, 29922, 2378, 29892, 995, 29922, 1767, 29897, 13, 2 ]
chapter_3/ex_3-10.py	akshaymoharir/PythonCrashCourse	0	62899	<reponame>akshaymoharir/PythonCrashCourse<filename>chapter_3/ex_3-10.py ## Python Crash Course # Exercise 3.10: Every Function: # Think of something you could store in a list. # For example, you could make a list of mountains, rivers, countries, cities, languages, or any- thing else you’d like. # Write a program that creates a list containing these items and then uses each function introduced in this chapter at least once . def main(): print("Skipping this exercise since this is redundant exercise to use previously used functions again.") if __name__ == '__main__': main()	[ 1, 529, 276, 1112, 420, 29958, 557, 845, 388, 29885, 1148, 279, 381, 29914, 11980, 20647, 1161, 29907, 10242, 29966, 9507, 29958, 27349, 29918, 29941, 29914, 735, 29918, 29941, 29899, 29896, 29900, 29889, 2272, 13, 13, 2277, 5132, 6781, 1161, 6325, 344, 13, 13, 29937, 1222, 6269, 895, 29871, 29941, 29889, 29896, 29900, 29901, 7569, 6680, 29901, 259, 13, 29937, 18884, 25086, 310, 1554, 366, 1033, 3787, 297, 263, 1051, 29889, 29871, 13, 29937, 18884, 1152, 1342, 29892, 366, 1033, 1207, 263, 1051, 310, 19223, 29892, 27515, 29892, 10916, 29892, 14368, 29892, 10276, 29892, 470, 738, 29899, 2655, 1683, 366, 30010, 29881, 763, 29889, 29871, 13, 29937, 18884, 14350, 263, 1824, 393, 10017, 263, 1051, 6943, 1438, 4452, 322, 769, 3913, 1269, 740, 9129, 297, 445, 16385, 472, 3203, 2748, 869, 259, 13, 632, 13, 13, 1753, 1667, 7295, 13, 268, 13, 1678, 1596, 703, 29903, 1984, 3262, 445, 15058, 1951, 445, 338, 28005, 15058, 304, 671, 9251, 1304, 3168, 1449, 23157, 13, 13, 13, 361, 4770, 978, 1649, 1275, 525, 1649, 3396, 1649, 2396, 13, 1678, 1667, 580, 13, 13, 2 ]
fred_crawler.py	Tyromancer/economics.py	0	46646	# Usage: # Find and download fred series data # then find the specific data on yyyy-mm-dd import requests # used for downloading text files from FRED import os # used for removing temporary text files # Usage : download series data in the form of text file and extract data on given date # param: # series: the series number # year : target year. Format: 4 digit YYYY # month : target month. Format: 2 digit MM # day : target day. Format: 2 digit DD def fred_crawler(series, year, month, day): r = requests.get('https://fred.stlouisfed.org/data/' + series + '.txt') fname = series + 'txt' with open(fname, 'wb') as f: f.write(r.content) with open(fname, 'r', encoding='utf-8') as f: lines = f.readlines() data_file = dict() for line in lines: if not line[0].isdigit(): continue else: temp = line.rstrip().split() data_file[temp[0]] = temp[1] if len(month) == 1: month = '0' + mm if len(day) == 1: day = '0' + day date = year + '-' + month + '-' + day # print('The result is: {}'.format(data_file[date])) print("{} {}: {}".format(series.upper(), date, data_file[date])) os.remove(fname) if __name__ == "__main__": query = input('Please enter the series ID ==> ').upper() yyyy = input('Year == > ') mm = input('Month ==> ') dd = input('Day ==> ') fred_crawler(query, yyyy, mm, dd)	[ 1, 396, 10783, 482, 29901, 13, 29937, 10987, 322, 5142, 285, 1127, 3652, 848, 13, 29937, 769, 1284, 278, 2702, 848, 373, 343, 8071, 29891, 29899, 4317, 29899, 1289, 13, 13, 5215, 7274, 29871, 396, 1304, 363, 28536, 1426, 2066, 515, 383, 19386, 13, 5215, 2897, 4706, 396, 1304, 363, 11077, 13201, 1426, 2066, 13, 13, 13, 29937, 10783, 482, 584, 5142, 3652, 848, 297, 278, 883, 310, 1426, 934, 322, 6597, 848, 373, 2183, 2635, 13, 29937, 1828, 29901, 13, 29937, 308, 3652, 29901, 278, 3652, 1353, 13, 29937, 308, 1629, 29871, 584, 3646, 1629, 29889, 19191, 29901, 29871, 29946, 13615, 612, 14995, 29979, 13, 29937, 308, 4098, 584, 3646, 4098, 29889, 19191, 29901, 29871, 29906, 13615, 28880, 13, 29937, 308, 2462, 259, 584, 3646, 2462, 29889, 29871, 19191, 29901, 259, 29906, 13615, 360, 29928, 13, 1753, 285, 1127, 29918, 29883, 1610, 1358, 29898, 13757, 29892, 1629, 29892, 4098, 29892, 2462, 1125, 13, 12, 29878, 353, 7274, 29889, 657, 877, 991, 597, 18447, 29889, 303, 23581, 4492, 287, 29889, 990, 29914, 1272, 22208, 718, 3652, 718, 15300, 3945, 1495, 13, 12, 29888, 978, 353, 3652, 718, 525, 3945, 29915, 13, 13, 12, 2541, 1722, 29898, 29888, 978, 29892, 525, 29893, 29890, 1495, 408, 285, 29901, 13, 12, 12, 29888, 29889, 3539, 29898, 29878, 29889, 3051, 29897, 13, 13, 12, 2541, 1722, 29898, 29888, 978, 29892, 525, 29878, 742, 8025, 2433, 9420, 29899, 29947, 1495, 408, 285, 29901, 13, 12, 12, 9012, 353, 285, 29889, 949, 9012, 580, 13, 13, 12, 12, 1272, 29918, 1445, 353, 9657, 580, 13, 12, 12, 1454, 1196, 297, 3454, 29901, 13, 12, 12, 12, 361, 451, 1196, 29961, 29900, 1822, 275, 26204, 7295, 13, 12, 12, 12, 12, 19878, 13, 12, 12, 12, 2870, 29901, 13, 12, 12, 12, 12, 7382, 353, 1196, 29889, 29878, 17010, 2141, 5451, 580, 13, 12, 12, 12, 12, 1272, 29918, 1445, 29961, 7382, 29961, 29900, 5262, 353, 5694, 29961, 29896, 29962, 13, 13, 12, 361, 7431, 29898, 10874, 29897, 1275, 29871, 29896, 29901, 13, 12, 12, 10874, 353, 525, 29900, 29915, 718, 5654, 13, 13, 12, 361, 7431, 29898, 3250, 29897, 1275, 29871, 29896, 29901, 13, 12, 12, 3250, 353, 525, 29900, 29915, 718, 2462, 13, 13, 12, 1256, 353, 1629, 718, 17411, 29915, 718, 4098, 718, 17411, 29915, 718, 2462, 13, 12, 29937, 1596, 877, 1576, 1121, 338, 29901, 6571, 4286, 4830, 29898, 1272, 29918, 1445, 29961, 1256, 12622, 13, 12, 2158, 703, 8875, 426, 6177, 6571, 1642, 4830, 29898, 13757, 29889, 21064, 3285, 2635, 29892, 848, 29918, 1445, 29961, 1256, 12622, 13, 12, 359, 29889, 5992, 29898, 29888, 978, 29897, 13, 13, 13, 361, 4770, 978, 1649, 1275, 376, 1649, 3396, 1649, 1115, 13, 12, 1972, 353, 1881, 877, 12148, 3896, 278, 3652, 3553, 25230, 525, 467, 21064, 580, 13, 12, 18855, 353, 1881, 877, 12883, 1275, 1405, 25710, 13, 12, 4317, 353, 1881, 877, 13953, 25230, 25710, 13, 12, 1289, 353, 1881, 877, 12742, 25230, 25710, 13, 13, 12, 18447, 29918, 29883, 1610, 1358, 29898, 1972, 29892, 343, 8071, 29891, 29892, 5654, 29892, 24488, 29897, 13, 2 ]
pyscf/nao/test/test_0003_na2_nao.py	robert-anderson/pyscf	3	6101	<filename>pyscf/nao/test/test_0003_na2_nao.py # Copyright 2014-2018 The PySCF Developers. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest from pyscf.nao.m_siesta_utils import get_siesta_command, get_pseudo class KnowValues(unittest.TestCase): def test_siesta2sv_df(self): import subprocess import os siesta_fdf = """ xml.write .true. PAO.EnergyShift 100 meV %block ChemicalSpeciesLabel 1 11 Na %endblock ChemicalSpeciesLabel NumberOfAtoms 2 NumberOfSpecies 1 %block AtomicCoordinatesAndAtomicSpecies 0.77573521 0.00000000 0.00000000 1 -0.77573521 0.00000000 0.00000000 1 %endblock AtomicCoordinatesAndAtomicSpecies MD.NumCGsteps 0 COOP.Write .true. WriteDenchar .true. """ label = 'siesta' fi = open(label+'.fdf', 'w') print(siesta_fdf, file=fi) fi.close() for sp in ['Na']: try: os.remove(sp+'.psf') except : pass try: pppath = get_pseudo(sp) except: print('get_pseudo( '+sp+' ) is not working--> skip siesta run' ) return os.symlink(pppath, sp+'.psf') errorcode = subprocess.call(get_siesta_command(label), shell=True) if errorcode: raise RuntimeError('siesta returned an error: {0}'.format(errorcode)) # run test system_vars from pyscf.nao import mf sv = mf(label=label) self.assertEqual(sv.norbs, 10) self.assertTrue( sv.diag_check() ) self.assertTrue( sv.overlap_check()) if __name__ == "__main__": unittest.main()	[ 1, 529, 9507, 29958, 29886, 952, 6854, 29914, 1056, 29877, 29914, 1688, 29914, 1688, 29918, 29900, 29900, 29900, 29941, 29918, 1056, 29906, 29918, 1056, 29877, 29889, 2272, 13, 29937, 14187, 1266, 29871, 29906, 29900, 29896, 29946, 29899, 29906, 29900, 29896, 29947, 450, 10772, 7187, 29943, 10682, 414, 29889, 2178, 26863, 2538, 9841, 29889, 13, 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scripts/add_probs.py	sharmaeklavya2/ekuOJ	10	109871	""" This module has functions which syncs problems data in database with data in OJ_data """ import os FILE_PATH = os.path.abspath(__file__) BASE_DIR = os.path.dirname(os.path.dirname(FILE_PATH)) import sys if BASE_DIR not in sys.path: sys.path.append(BASE_DIR) import json def add_prob(prob_path, contest=None): pcode = os.path.basename(prob_path) if contest==None: ccode = os.path.basename(os.path.dirname(prob_path)) contest = Contest.objects.get_or_create(ccode=ccode)[0] (problem, created) = Problem.objects.get_or_create(pcode=pcode, contest=contest) title_path = os.path.join(prob_path, "title.txt") data_path = os.path.join(prob_path, "data.json") try: problem.title = open(title_path).read().strip() except FileNotFoundError: pass try: prob_data = json.load(open(data_path)) problem.source_lim = prob_data.get("source_lim", None) problem.time_lim_s = prob_data.get("time_lim_s", None) problem.mem_lim_k = prob_data.get("mem_lim_k", None) problem.output_lim_k = prob_data.get("output_lim_k", None) except FileNotFoundError: pass except ValueError as e: print("The following error was encountered while reading", os.path.join(ccode, pcode, "data.json")+".", "The file has been ignored.") print(e) problem.save() return created def add_contest(contest_path): # create contest object in db ccode = os.path.basename(contest_path) (contest, created) = Contest.objects.get_or_create(ccode=ccode) title_path = os.path.join(contest_path, "title.txt") try: contest.title = open(title_path).read().strip() except FileNotFoundError: pass contest.save() # add problems to db pcodes = os.listdir(contest_path) for pcode in pcodes: prob_path = os.path.join(contest_path, pcode) add_prob(prob_path, contest) return created FILE_NAME = os.path.basename(FILE_PATH) if __name__=="__main__": USAGE_STR = "usage: python3 " + FILE_NAME + " <contest_codes>" else: USAGE_STR = "No contests specified" CONTESTS_DIR_PATH = os.path.join(BASE_DIR, "OJ_data", "contests") import sys def main(args): if len(args)==0: print(USAGE_STR, file=sys.stderr) else: for ccode in args: contest_path = os.path.join(CONTESTS_DIR_PATH, ccode) created = add_contest(contest_path) if created: print("Added contest", ccode) else: print("Updated contest", ccode) if __name__=="__main__": # set up django os.environ.setdefault("DJANGO_SETTINGS_MODULE", "project_conf.settings") print("Setting up Django ...", flush=True, end='') import django django.setup() print(" done") from main.models import Problem, Contest if __name__=="__main__": main((sys.argv[1:]))	[ 1, 9995, 13, 4013, 3883, 756, 3168, 607, 5222, 2395, 4828, 848, 297, 2566, 411, 848, 297, 438, 29967, 29918, 1272, 13, 15945, 29908, 13, 13, 5215, 2897, 13, 7724, 29918, 10145, 353, 2897, 29889, 2084, 29889, 370, 1028, 493, 22168, 1445, 1649, 29897, 13, 25416, 29918, 9464, 353, 2897, 29889, 2084, 29889, 25721, 29898, 359, 29889, 2084, 29889, 25721, 29898, 7724, 29918, 10145, 876, 13, 5215, 10876, 13, 361, 350, 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hydrobox/geostat/variogram.py	VForWaTer/hydrobox	4	183855	<filename>hydrobox/geostat/variogram.py import numpy as np import skgstat as skg import gstools as gs from hydrobox.geostat import typing from hydrobox.plotting import plot_function_loader def variogram( coordinates: np.ndarray, values: np.ndarray, estimator: typing.Estimator = 'matheron', model: typing.Model = 'spherical', dist_func: typing.DistFunc = 'euclidean', bin_func: typing.BinFunc = 'even', fit_method: typing.FitMethod = 'trf', fit_sigma: typing.FitSigma = None, use_nugget: bool = False, maxlag: typing.Maxlag = None, n_lags: typing.Union[int, None] = 10, return_type: typing.Literal['object', 'describe', 'plot', 'distance_difference', 'location_trend', 'scattergram'] = 'object', kwargs ) -> skg.Variogram: """ Uses scikit-gstat to estimate a Variogram. Refer to the :class:`Variogram <skgstat.Variogram>` class to learn about the different Parameters. Parameters ---------- return_type : str Specify how the Variogram instance should be returned. Object will return the actual instance. 'describe' is the dictionary output generated by the class, which is serializable. All other options return the different plot types Returns ------- variogram : skgstat.Variogram If the return type is `'object'` plot : plotly.Figure, matplotlib.pyplot.Figure If the return type is one of the plots description : dict If the return type is `'describe'` """ # create the variogram v = skg.Variogram( coordinates=coordinates, values=values, estimator=estimator, model=model, dist_func=dist_func, bin_func=bin_func, fit_method=fit_method, fit_sigma=fit_sigma, use_nugget=use_nugget, maxlag=maxlag, n_lags=n_lags, kwargs ) if return_type == 'object': return v elif return_type == 'describe': return v.describe(short=False, flat=False) # otherwise create a plot pfunc = plot_function_loader('variogram') fig = pfunc( func_args=dict( variogram=v, plot_type=return_type ), plot_args=kwargs ) return fig	[ 1, 529, 9507, 29958, 29882, 11279, 1884, 29914, 479, 520, 271, 29914, 5927, 13342, 29889, 2272, 13, 5215, 12655, 408, 7442, 13, 5215, 2071, 29887, 6112, 408, 2071, 29887, 13, 5215, 330, 303, 8789, 408, 330, 29879, 13, 13, 3166, 17546, 1884, 29889, 479, 520, 271, 1053, 19229, 13, 3166, 17546, 1884, 29889, 5317, 1259, 1053, 6492, 29918, 2220, 29918, 12657, 13, 13, 13, 1753, 1197, 13342, 29898, 13, 1678, 10350, 29901, 7442, 29889, 299, 2378, 29892, 13, 1678, 1819, 29901, 7442, 29889, 299, 2378, 29892, 13, 1678, 4844, 1061, 29901, 19229, 29889, 12787, 326, 1061, 353, 525, 755, 261, 265, 742, 13, 1678, 1904, 29901, 19229, 29889, 3195, 353, 525, 29879, 8096, 936, 742, 13, 1678, 1320, 29918, 9891, 29901, 19229, 29889, 13398, 14400, 353, 525, 29872, 27511, 742, 13, 1678, 9016, 29918, 9891, 29901, 19229, 29889, 29933, 262, 14400, 353, 525, 11884, 742, 13, 1678, 6216, 29918, 5696, 29901, 19229, 29889, 29943, 277, 4062, 353, 525, 509, 29888, 742, 13, 1678, 6216, 29918, 3754, 29901, 19229, 29889, 29943, 277, 10142, 353, 6213, 29892, 13, 1678, 671, 29918, 29876, 688, 657, 29901, 6120, 353, 7700, 29892, 13, 1678, 4236, 3110, 29901, 19229, 29889, 7976, 3110, 353, 6213, 29892, 13, 1678, 302, 29918, 29880, 810, 29901, 19229, 29889, 19986, 29961, 524, 29892, 6213, 29962, 353, 29871, 29896, 29900, 29892, 13, 1678, 736, 29918, 1853, 29901, 19229, 29889, 24938, 284, 1839, 3318, 742, 525, 2783, 29581, 742, 525, 5317, 742, 525, 19244, 29918, 29881, 17678, 742, 525, 5479, 29918, 509, 355, 742, 525, 1557, 2620, 1393, 2033, 353, 525, 3318, 742, 13, 1678, 3579, 19290, 13, 29897, 1599, 2071, 29887, 29889, 10444, 13342, 29901, 13, 1678, 9995, 13, 1678, 10783, 267, 4560, 7354, 29899, 29887, 6112, 304, 12678, 263, 9586, 13342, 29889, 29871, 13, 1678, 4118, 304, 278, 584, 1990, 18078, 10444, 13342, 529, 808, 29887, 6112, 29889, 10444, 13342, 13885, 770, 13, 1678, 304, 5110, 1048, 278, 1422, 12662, 2699, 29889, 13, 13, 1678, 12662, 2699, 13, 1678, 448, 1378, 29899, 13, 1678, 736, 29918, 1853, 584, 851, 13, 4706, 12048, 1598, 920, 278, 9586, 13342, 2777, 881, 367, 4133, 29889, 4669, 674, 13, 4706, 736, 278, 3935, 2777, 29889, 525, 2783, 29581, 29915, 338, 278, 8600, 1962, 13, 4706, 5759, 491, 278, 770, 29892, 607, 338, 7797, 13902, 29889, 2178, 916, 3987, 13, 4706, 736, 278, 1422, 6492, 4072, 13, 13, 1678, 16969, 13, 1678, 448, 22158, 13, 1678, 1197, 13342, 584, 2071, 29887, 6112, 29889, 10444, 13342, 13, 4706, 960, 278, 736, 1134, 338, 16218, 3318, 11120, 13, 1678, 6492, 584, 6492, 368, 29889, 13080, 545, 29892, 22889, 29889, 2272, 5317, 29889, 13080, 545, 13, 4706, 960, 278, 736, 1134, 338, 697, 310, 278, 24580, 13, 1678, 6139, 584, 9657, 13, 4706, 960, 278, 736, 1134, 338, 16218, 2783, 29581, 11120, 13, 13, 1678, 9995, 13, 1678, 396, 1653, 278, 1197, 13342, 13, 1678, 325, 353, 2071, 29887, 29889, 10444, 13342, 29898, 13, 4706, 10350, 29922, 1111, 24266, 29892, 13, 4706, 1819, 29922, 5975, 29892, 13, 4706, 4844, 1061, 29922, 342, 326, 1061, 29892, 13, 4706, 1904, 29922, 4299, 29892, 13, 4706, 1320, 29918, 9891, 29922, 5721, 29918, 9891, 29892, 13, 4706, 9016, 29918, 9891, 29922, 2109, 29918, 9891, 29892, 13, 4706, 6216, 29918, 5696, 29922, 9202, 29918, 5696, 29892, 13, 4706, 6216, 29918, 3754, 29922, 9202, 29918, 3754, 29892, 13, 4706, 671, 29918, 29876, 688, 657, 29922, 1509, 29918, 29876, 688, 657, 29892, 13, 4706, 4236, 3110, 29922, 3317, 3110, 29892, 13, 4706, 302, 29918, 29880, 810, 29922, 29876, 29918, 29880, 810, 29892, 13, 4706, 3579, 19290, 13, 1678, 1723, 13, 13, 1678, 565, 736, 29918, 1853, 1275, 525, 3318, 2396, 13, 4706, 736, 325, 13, 1678, 25342, 736, 29918, 1853, 1275, 525, 2783, 29581, 2396, 13, 4706, 736, 325, 29889, 2783, 29581, 29898, 12759, 29922, 8824, 29892, 12151, 29922, 8824, 29897, 13, 268, 13, 1678, 396, 6467, 1653, 263, 6492, 13, 1678, 282, 9891, 353, 6492, 29918, 2220, 29918, 12657, 877, 5927, 13342, 1495, 13, 1678, 2537, 353, 282, 9891, 29898, 13, 4706, 3653, 29918, 5085, 29922, 8977, 29898, 13, 9651, 1197, 13342, 29922, 29894, 29892, 13, 9651, 6492, 29918, 1853, 29922, 2457, 29918, 1853, 13, 4706, 10353, 29871, 13, 4706, 6492, 29918, 5085, 29922, 19290, 13, 1678, 1723, 13, 1678, 736, 2537, 13, 13, 13, 2 ]
seeds/plugins/action/StopOnConvergence.py	briandconnelly/seeds	11	146974	<reponame>briandconnelly/seeds # -- coding: utf-8 -- """ Stop the experiment when the number of existing Cell types in the population drops below a threshold. This allows experiments to end earlier than the configured number if epochs have passed or experiments to not specify the total number of epochs to observe. This is accomplished by calling the Experiment's end method. """ __author__ = "<NAME> <<EMAIL>>" __credits__ = "<NAME>" from seeds.Action import * from seeds.Plugin import * from seeds.SEEDSError import * class StopOnConvergence(Action, Plugin): """ Stop the experiment when the number of exisiting cell types in the population drops below the configured threshold. Configuration is done in the [StopOnConvergence] section Configuration Options: epoch_start The epoch at which to start executing (default: 0) epoch_end The epoch at which to stop executing (default: end of experiment) frequency The frequency (epochs) at which to execute (default: 1) priority The priority of this action. Actions with higher priority get run first. (default: 0) threshold The number of cell types at or below which to stop the experiment (default: 0) Configuration Example: [StopOnConvergence] threshold = 2 epoch_start = 0 epoch_end = 100 frequency = 1 priority = 0 """ __name__ = "StopOnConvergence" __version__ = (1,0) __author__ = "<NAME> <<EMAIL>>" __credits__ = "<NAME>" __description__ = "Stop the experiment when the number of existiing cell types in the population falls below the given threshold" __type__ = 4 __requirements__ = [] def __init__(self, experiment, label=None): """Initialize the StopOnConvergence Action""" super(StopOnConvergence, self).__init__(experiment, name="StopOnConvergence", label=label) self.threshold = self.experiment.config.getint(self.config_section, 'threshold', 0) self.epoch_start = self.experiment.config.getint(self.config_section, 'epoch_start', 0) self.epoch_end = self.experiment.config.getint(self.config_section, 'epoch_end', default=self.experiment.config.getint('Experiment', 'epochs', default=-1)) self.frequency = self.experiment.config.getint(self.config_section, 'frequency', 1) self.priority = self.experiment.config.getint(self.config_section, 'priority', 0) if self.threshold < 0: raise ConfigurationError("StopOnConvergence: threshold value must be at least 0") def update(self): """Execute the Action""" if self.skip_update(): return converged = True num_active_types = 0 for tcount in self.experiment.data['population']['type_count']: if tcount > 0: num_active_types += 1 if num_active_types >= self.threshold: converged = False if converged: self.experiment.end()	[ 1, 529, 276, 1112, 420, 29958, 29890, 374, 392, 535, 9139, 368, 29914, 344, 5779, 13, 29937, 448, 29930, 29899, 14137, 29901, 23616, 29899, 29947, 448, 29930, 29899, 13, 15945, 29908, 22303, 278, 7639, 746, 278, 1353, 310, 5923, 19413, 4072, 297, 278, 13, 7323, 2785, 4441, 567, 2400, 263, 16897, 29889, 29871, 910, 6511, 15729, 304, 1095, 8859, 13, 27603, 278, 13252, 1353, 565, 21502, 12168, 505, 4502, 470, 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examples/chanlun/feature/03/ex_setup_commands.py	garywangiam02/vnpy	0	67069	# coding=utf-8 from pytdx.parser.base import BaseParser from pytdx.helper import get_datetime, get_volume, get_price from collections import OrderedDict import struct class ExSetupCmd1(BaseParser): def setup(self): # self.send_pkg = bytearray.fromhex("01 01 48 65 00 01 52 00 52 00 54 24 1f 32 c6 e5" # "d5 3d fb 41 1f 32 c6 e5 d5 3d fb 41 1f 32 c6 e5" # "d5 3d fb 41 1f 32 c6 e5 d5 3d fb 41 1f 32 c6 e5" # "d5 3d fb 41 1f 32 c6 e5 d5 3d fb 41 1f 32 c6 e5" # "d5 3d fb 41 1f 32 c6 e5 d5 3d fb 41 cc e1 6d ff" # "d5 ba 3f b8 cb c5 7a 05 4f 77 48 ea") self.send_pkg = bytearray.fromhex("01 01 48 65 00 01 52 00 52 00 54 24" "FC F0 0E 92 F3 C8 37 83 1F 32 C6 E5 D5 3D FB 41 CD 9C" "F2 07 FC D0 3C F6 F2 F7 A4 77 47 83 1D 59 9D CC 1F 91" "D5 55 82 DC 09 07 EE 29 DD FE 4C 28 1F 32 C6 E5 D5 3D" "FB 41 1F 32 C6 E5 D5 3D FB 41 F3 43 87 E6 68 A9 2A A3" "70 11 E4 9C D2 6E B0 1A") def parseResponse(self, body_buf): pass	[ 1, 396, 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asyncupbankapi/models/__init__.py	unchartedshark/async-up-bank-api	1	95562	<gh_stars>1-10 """Typed python client for interacting with Up's banking API.""" from asyncupbankapi.models.accounts import Account, Accounts from asyncupbankapi.models.categories import Category, Categories from asyncupbankapi.models.tags import Tags from asyncupbankapi.models.transactions import Transaction, Transactions from asyncupbankapi.models.utility import Ping from asyncupbankapi.models.webhooks import Webhook, WebhookEvent, WebhookLogs, Webhooks	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29899, 29896, 29900, 13, 15945, 29908, 24933, 287, 3017, 3132, 363, 16254, 292, 411, 5020, 29915, 29879, 9124, 292, 3450, 1213, 15945, 30004, 13, 3166, 7465, 786, 9157, 2754, 29889, 9794, 29889, 10149, 29879, 1053, 16535, 29892, 16535, 29879, 30004, 13, 3166, 7465, 786, 9157, 2754, 29889, 9794, 29889, 20683, 1053, 17943, 29892, 315, 14404, 30004, 13, 3166, 7465, 786, 9157, 2754, 29889, 9794, 29889, 11338, 1053, 917, 30004, 13, 3166, 7465, 786, 9157, 2754, 29889, 9794, 29889, 3286, 7387, 1053, 4103, 2467, 29892, 4103, 7387, 30004, 13, 3166, 7465, 786, 9157, 2754, 29889, 9794, 29889, 329, 1793, 1053, 349, 292, 30004, 13, 3166, 7465, 786, 9157, 2754, 29889, 9794, 29889, 2676, 1251, 12117, 1053, 2563, 20849, 29892, 2563, 20849, 2624, 29892, 2563, 20849, 3403, 29879, 29892, 2563, 1251, 12117, 30004, 13, 2 ]
hikari/api/special_endpoints.py	patchwork-systems/hikari	0	195915	<reponame>patchwork-systems/hikari # -- coding: utf-8 -- # cython: language_level=3 # Copyright (c) 2020 Nekokatt # Copyright (c) 2021-present davfsa # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """Special additional endpoints used by the REST API.""" from __future__ import annotations __all__: typing.List[str] = [ "ActionRowBuilder", "ButtonBuilder", "CommandBuilder", "SlashCommandBuilder", "ContextMenuCommandBuilder", "ComponentBuilder", "TypingIndicator", "GuildBuilder", "InteractionAutocompleteBuilder", "InteractionDeferredBuilder", "InteractionResponseBuilder", "InteractionMessageBuilder", "InteractiveButtonBuilder", "LinkButtonBuilder", "SelectMenuBuilder", "SelectOptionBuilder", ] import abc import typing from hikari import undefined if typing.TYPE_CHECKING: import types from hikari import channels from hikari import colors from hikari import commands from hikari import embeds as embeds_ from hikari import emojis from hikari import files from hikari import guilds from hikari import messages from hikari import permissions as permissions_ from hikari import snowflakes from hikari import users from hikari import voices from hikari.api import entity_factory as entity_factory_ from hikari.interactions import base_interactions from hikari.internal import data_binding from hikari.internal import time _T = typing.TypeVar("_T") _SelectMenuBuilderT = typing.TypeVar("_SelectMenuBuilderT", bound="SelectMenuBuilder[typing.Any]") _ContainerT = typing.TypeVar("_ContainerT") class TypingIndicator(abc.ABC): """Result type of `hikari.api.rest.RESTClient.trigger_typing`. This is an object that can either be awaited like a coroutine to trigger the typing indicator once, or an async context manager to keep triggering the typing indicator repeatedly until the context finishes. !!! note This is a helper class that is used by `hikari.api.rest.RESTClient`. You should only ever need to use instances of this class that are produced by that API. """ __slots__: typing.Sequence[str] = () @abc.abstractmethod def __await__(self) -> typing.Generator[None, typing.Any, None]: ... @abc.abstractmethod async def __aenter__(self) -> None: ... @abc.abstractmethod async def __aexit__( self, exception_type: typing.Type[BaseException], exception: BaseException, exception_traceback: types.TracebackType, ) -> None: ... class GuildBuilder(abc.ABC): """Result type of `hikari.api.rest.RESTClient.guild_builder`. This is used to create a guild in a tidy way using the HTTP API, since the logic behind creating a guild on an API level is somewhat confusing and detailed. !!! note This is a helper class that is used by `hikari.api.rest.RESTClient`. You should only ever need to use instances of this class that are produced by that API, thus, any details about the constructor are omitted from the following examples for brevity. Examples -------- Creating an empty guild. ```py guild = await rest.guild_builder("My Server!").create() ``` Creating a guild with an icon ```py from hikari.files import WebResourceStream guild_builder = rest.guild_builder("My Server!") guild_builder.icon = WebResourceStream("cat.png", "http://...") guild = await guild_builder.create() ``` Adding roles to your guild. ```py from hikari.permissions import Permissions guild_builder = rest.guild_builder("My Server!") everyone_role_id = guild_builder.add_role("@everyone") admin_role_id = guild_builder.add_role("Admins", permissions=Permissions.ADMINISTRATOR) await guild_builder.create() ``` !!! warning The first role must always be the `@everyone` role. !!! note If you call `add_role`, the default roles provided by discord will be created. This also applies to the `add_` functions for text channels/voice channels/categories. !!! note Functions that return a `hikari.snowflakes.Snowflake` do not provide the final ID that the object will have once the API call is made. The returned IDs are only able to be used to re-reference particular objects while building the guild format. This is provided to allow creation of channels within categories, and to provide permission overwrites. Adding a text channel to your guild. ```py guild_builder = rest.guild_builder("My Server!") category_id = guild_builder.add_category("My safe place") channel_id = guild_builder.add_text_channel("general", parent_id=category_id) await guild_builder.create() ``` """ __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def name(self) -> str: """Name of the guild to create. Returns ------- builtins.str The guild name. """ @property @abc.abstractmethod def default_message_notifications(self) -> undefined.UndefinedOr[guilds.GuildMessageNotificationsLevel]: """Default message notification level that can be overwritten. If not overridden, this will use the Discord default level. Returns ------- hikari.undefined.UndefinedOr[hikari.guilds.GuildMessageNotificationsLevel] The default message notification level, if overwritten. """ # noqa: D401 - Imperative mood @default_message_notifications.setter def default_message_notifications( self, default_message_notifications: undefined.UndefinedOr[guilds.GuildMessageNotificationsLevel], / ) -> None: raise NotImplementedError @property @abc.abstractmethod def explicit_content_filter_level(self) -> undefined.UndefinedOr[guilds.GuildExplicitContentFilterLevel]: """Explicit content filter level that can be overwritten. If not overridden, this will use the Discord default level. Returns ------- hikari.undefined.UndefinedOr[hikari.guilds.GuildExplicitContentFilterLevel] The explicit content filter level, if overwritten. """ @explicit_content_filter_level.setter def explicit_content_filter_level( self, explicit_content_filter_level: undefined.UndefinedOr[guilds.GuildExplicitContentFilterLevel], / ) -> None: raise NotImplementedError @property @abc.abstractmethod def icon(self) -> undefined.UndefinedOr[files.Resourceish]: """Guild icon to use that can be overwritten. If not overridden, the guild will not have an icon. Returns ------- hikari.undefined.UndefinedOr[hikari.files.Resourceish] The guild icon to use, if overwritten. """ @icon.setter def icon(self, icon: undefined.UndefinedOr[files.Resourceish], /) -> None: raise NotImplementedError @property @abc.abstractmethod def verification_level(self) -> undefined.UndefinedOr[typing.Union[guilds.GuildVerificationLevel, int]]: """Verification level required to join the guild that can be overwritten. If not overridden, the guild will use the default verification level for Returns ------- hikari.undefined.UndefinedOr[typing.Union[hikari.guilds.GuildVerificationLevel, builtins.int]] The verification level required to join the guild, if overwritten. """ @verification_level.setter def verification_level( self, verification_level: undefined.UndefinedOr[typing.Union[guilds.GuildVerificationLevel, int]], / ) -> None: raise NotImplementedError @abc.abstractmethod async def create(self) -> guilds.RESTGuild: """Send the request to Discord to create the guild. The application user will be added to this guild as soon as it is created. All IDs that were provided when building this guild will become invalid and will be replaced with real IDs. Returns ------- hikari.guilds.RESTGuild The created guild. Raises ------ hikari.errors.BadRequestError If any values set in the guild builder are invalid. hikari.errors.UnauthorizedError If you are unauthorized to make the request (invalid/missing token). hikari.errors.ForbiddenError If you are already in 10 guilds. hikari.errors.InternalServerError If an internal error occurs on Discord while handling the request. """ @abc.abstractmethod def add_role( self, name: str, /, , permissions: undefined.UndefinedOr[permissions_.Permissions] = undefined.UNDEFINED, color: undefined.UndefinedOr[colors.Colorish] = undefined.UNDEFINED, colour: undefined.UndefinedOr[colors.Colorish] = undefined.UNDEFINED, hoist: undefined.UndefinedOr[bool] = undefined.UNDEFINED, mentionable: undefined.UndefinedOr[bool] = undefined.UNDEFINED, position: undefined.UndefinedOr[int] = undefined.UNDEFINED, ) -> snowflakes.Snowflake: """Create a role. !!! warning The first role you create must always be the `@everyone` role. Parameters ---------- name : builtins.str The role's name. Other Parameters ---------------- permissions : hikari.undefined.UndefinedOr[hikari.permissions.Permissions] If provided, the permissions for the role. color : hikari.undefined.UndefinedOr[hikari.colors.Colorish] If provided, the role's color. colour : hikari.undefined.UndefinedOr[hikari.colors.Colorish] An alias for `color`. hoist : hikari.undefined.UndefinedOr[builtins.bool] If provided, whether to hoist the role. mentionable : hikari.undefined.UndefinedOr[builtins.bool] If provided, whether to make the role mentionable. reason : hikari.undefined.UndefinedOr[builtins.str] If provided, the reason that will be recorded in the audit logs. Maximum of 512 characters. Returns ------- hikari.snowflakes.Snowflake The dummy ID for this role that can be used temporarily to refer to this object while designing the guild layout. When the guild is created, this will be replaced with a different ID. Raises ------ builtins.ValueError If you are defining the first role, but did not name it `@everyone`. builtins.TypeError If you specify both `color` and `colour` together or if you try to specify `color`, `colour`, `hoisted`, `mentionable` or `position` for the `@everyone` role. """ @abc.abstractmethod def add_category( self, name: str, /, , position: undefined.UndefinedOr[int] = undefined.UNDEFINED, permission_overwrites: undefined.UndefinedOr[ typing.Collection[channels.PermissionOverwrite] ] = undefined.UNDEFINED, ) -> snowflakes.Snowflake: """Create a category channel. Parameters ---------- name : builtins.str The channels name. Must be between 2 and 1000 characters. Other Parameters ---------------- position : hikari.undefined.UndefinedOr[builtins.int] If provided, the position of the category. permission_overwrites : hikari.undefined.UndefinedOr[typing.Sequence[hikari.channels.PermissionOverwrite]] If provided, the permission overwrites for the category. Returns ------- hikari.snowflakes.Snowflake The dummy ID for this channel that can be used temporarily to refer to this object while designing the guild layout. When the guild is created, this will be replaced with a different ID. """ # noqa: E501 - Line too long @abc.abstractmethod def add_text_channel( self, name: str, /, , parent_id: undefined.UndefinedOr[snowflakes.Snowflake] = undefined.UNDEFINED, topic: undefined.UndefinedOr[str] = undefined.UNDEFINED, rate_limit_per_user: undefined.UndefinedOr[time.Intervalish] = undefined.UNDEFINED, position: undefined.UndefinedOr[int] = undefined.UNDEFINED, permission_overwrites: undefined.UndefinedOr[ typing.Collection[channels.PermissionOverwrite] ] = undefined.UNDEFINED, nsfw: undefined.UndefinedOr[bool] = undefined.UNDEFINED, ) -> snowflakes.Snowflake: """Create a text channel. Parameters ---------- name : builtins.str The channels name. Must be between 2 and 1000 characters. Other Parameters ---------------- position : hikari.undefined.UndefinedOr[builtins.int] If provided, the position of the channel (relative to the category, if any). topic : hikari.undefined.UndefinedOr[builtins.str] If provided, the channels topic. Maximum 1024 characters. nsfw : hikari.undefined.UndefinedOr[builtins.bool] If provided, whether to mark the channel as NSFW. rate_limit_per_user : hikari.undefined.UndefinedOr[builtins.int] If provided, the amount of seconds a user has to wait before being able to send another message in the channel. Maximum 21600 seconds. permission_overwrites : hikari.undefined.UndefinedOr[typing.Sequence[hikari.channels.PermissionOverwrite]] If provided, the permission overwrites for the channel. category : hikari.undefined.UndefinedOr[hikari.snowflakes.SnowflakeishOr[hikari.channels.GuildCategory]] The category to create the channel under. This may be the object or the ID of an existing category. Returns ------- hikari.snowflakes.Snowflake The dummy ID for this channel that can be used temporarily to refer to this object while designing the guild layout. When the guild is created, this will be replaced with a different ID. """ # noqa: E501 - Line too long @abc.abstractmethod def add_voice_channel( self, name: str, /, , parent_id: undefined.UndefinedOr[snowflakes.Snowflake] = undefined.UNDEFINED, bitrate: undefined.UndefinedOr[int] = undefined.UNDEFINED, video_quality_mode: undefined.UndefinedOr[typing.Union[channels.VideoQualityMode, int]] = undefined.UNDEFINED, position: undefined.UndefinedOr[int] = undefined.UNDEFINED, permission_overwrites: undefined.UndefinedOr[ typing.Collection[channels.PermissionOverwrite] ] = undefined.UNDEFINED, region: undefined.UndefinedNoneOr[typing.Union[voices.VoiceRegion, str]], user_limit: undefined.UndefinedOr[int] = undefined.UNDEFINED, ) -> snowflakes.Snowflake: """Create a voice channel. Parameters ---------- name : builtins.str The channels name. Must be between 2 and 1000 characters. Other Parameters ---------------- position : hikari.undefined.UndefinedOr[builtins.int] If provided, the position of the channel (relative to the category, if any). user_limit : hikari.undefined.UndefinedOr[builtins.int] If provided, the maximum users in the channel at once. Must be between 0 and 99 with 0 meaning no limit. bitrate : hikari.undefined.UndefinedOr[builtins.int] If provided, the bitrate for the channel. Must be between 8000 and 96000 or 8000 and 128000 for VIP servers. video_quality_mode: hikari.undefined.UndefinedOr[typing.Union[hikari.channels.VideoQualityMode, builtins.int]] If provided, the new video quality mode for the channel. permission_overwrites : hikari.undefined.UndefinedOr[typing.Sequence[hikari.channels.PermissionOverwrite]] If provided, the permission overwrites for the channel. region : hikari.undefined.UndefinedOr[typing.Union[hikari.voices.VoiceRegion, builtins.str]] If provided, the voice region to for this channel. Passing `builtins.None` here will set it to "auto" mode where the used region will be decided based on the first person who connects to it when it's empty. category : hikari.undefined.UndefinedOr[hikari.snowflakes.SnowflakeishOr[hikari.channels.GuildCategory]] The category to create the channel under. This may be the object or the ID of an existing category. Returns ------- hikari.snowflakes.Snowflake The dummy ID for this channel that can be used temporarily to refer to this object while designing the guild layout. When the guild is created, this will be replaced with a different ID. """ # noqa: E501 - Line too long @abc.abstractmethod def add_stage_channel( self, name: str, /, *, parent_id: undefined.UndefinedOr[snowflakes.Snowflake] = undefined.UNDEFINED, bitrate: undefined.UndefinedOr[int] = undefined.UNDEFINED, position: undefined.UndefinedOr[int] = undefined.UNDEFINED, permission_overwrites: undefined.UndefinedOr[ typing.Collection[channels.PermissionOverwrite] ] = undefined.UNDEFINED, region: undefined.UndefinedNoneOr[typing.Union[voices.VoiceRegion, str]], user_limit: undefined.UndefinedOr[int] = undefined.UNDEFINED, ) -> snowflakes.Snowflake: """Create a stage channel. Parameters ---------- name : builtins.str The channels name. Must be between 2 and 1000 characters. Other Parameters ---------------- position : hikari.undefined.UndefinedOr[builtins.int] If provided, the position of the channel (relative to the category, if any). user_limit : hikari.undefined.UndefinedOr[builtins.int] If provided, the maximum users in the channel at once. Must be between 0 and 99 with 0 meaning no limit. bitrate : hikari.undefined.UndefinedOr[builtins.int] If provided, the bitrate for the channel. Must be between 8000 and 96000 or 8000 and 128000 for VIP servers. permission_overwrites : hikari.undefined.UndefinedOr[typing.Sequence[hikari.channels.PermissionOverwrite]] If provided, the permission overwrites for the channel. region : hikari.undefined.UndefinedOr[typing.Union[hikari.voices.VoiceRegion, builtins.str]] If provided, the voice region to for this channel. Passing `builtins.None` here will set it to "auto" mode where the used region will be decided based on the first person who connects to it when it's empty. category : hikari.undefined.UndefinedOr[hikari.snowflakes.SnowflakeishOr[hikari.channels.GuildCategory]] The category to create the channel under. This may be the object or the ID of an existing category. Returns ------- hikari.snowflakes.Snowflake The dummy ID for this channel that can be used temporarily to refer to this object while designing the guild layout. When the guild is created, this will be replaced with a different ID. """ class InteractionResponseBuilder(abc.ABC): """Base class for all interaction response builders used in the interaction server.""" @property @abc.abstractmethod def type(self) -> typing.Union[int, base_interactions.ResponseType]: """Return the type of this response. Returns ------- typing.Union[builtins.int, hikari.interactions.base_interactions.ResponseType] The type of response this is. """ @abc.abstractmethod def build(self, entity_factory: entity_factory_.EntityFactory, /) -> data_binding.JSONObject: """Build a JSON object from this builder. Parameters ---------- entity_factory : hikari.api.entity_factory.EntityFactory The entity factory to use to serialize entities within this builder. Returns ------- hikari.internal.data_binding.JSONObject The built json object representation of this builder. """ class InteractionDeferredBuilder(InteractionResponseBuilder, abc.ABC): """Interface of a deferred message interaction response builder.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def type(self) -> base_interactions.DeferredResponseTypesT: """Return the type of this response. Returns ------- hikari.interactions.base_interactions.DeferredResponseTypesT The type of response this is. """ @property @abc.abstractmethod def flags(self) -> typing.Union[undefined.UndefinedType, int, messages.MessageFlag]: """Message flags this response should have. !!! note As of writing the only message flag which can be set here is `hikari.messages.MessageFlag.EPHEMERAL`. Returns ------- typing.Union[hikari.undefined.UndefinedType, builtins.int, hikari.messages.MessageFlag] The message flags this response should have if set else `hikari.undefined.UNDEFINED`. """ @abc.abstractmethod def set_flags(self: _T, flags: typing.Union[undefined.UndefinedType, int, messages.MessageFlag], /) -> _T: """Set message flags for this response. !!! note As of writing, the only message flag which can be set is EPHEMERAL. Parameters ---------- flags : typing.Union[hikari.undefined.UndefinedType, builtins.int, hikari.messages.MessageFlag] The message flags to set for this response. Returns ------- InteractionMessageBuilder Object of this builder. """ class InteractionAutocompleteBuilder(InteractionResponseBuilder, abc.ABC): """Interface of an autocomplete interaction response builder.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def choices(self) -> typing.Sequence[commands.CommandChoice]: """Return autocomplete choices.""" @abc.abstractmethod def set_choices(self: _T, choices: typing.Sequence[commands.CommandChoice], /) -> _T: """Set autocomplete choices. Returns ------- InteractionAutocompleteBuilder Object of this builder. """ class InteractionMessageBuilder(InteractionResponseBuilder, abc.ABC): """Interface of an interaction message response builder used within REST servers. This can be returned by the listener registered to `hikari.api.interaction_server.InteractionServer` as a response to the interaction create. """ __slots__: typing.Sequence[str] = () # Required fields @property @abc.abstractmethod def type(self) -> base_interactions.MessageResponseTypesT: """Return the type of this response. Returns ------- hikari.interactions.base_interactions.MessageResponseTypesT The type of response this is. """ # Extendable fields @property @abc.abstractmethod def components(self) -> typing.Sequence[ComponentBuilder]: """Sequence of up to 5 component builders to send in this response.""" @property @abc.abstractmethod def embeds(self) -> typing.Sequence[embeds_.Embed]: """Sequence of up to 10 of the embeds included in this response. Returns ------- typing.Sequence[hikari.embeds.Embed] A sequence of up to 10 of the embeds included in this response. """ # Settable fields @property @abc.abstractmethod def content(self) -> undefined.UndefinedOr[str]: """Response's message content. Returns ------- hikari.undefined.UndefinedOr[builtins.str] The response's message content, if set. """ @property @abc.abstractmethod def flags(self) -> typing.Union[undefined.UndefinedType, int, messages.MessageFlag]: """Message flags this response should have. !!! note As of writing the only message flag which can be set here is `hikari.messages.MessageFlag.EPHEMERAL`. Returns ------- typing.Union[hikari.undefined.UndefinedType, builtins.int, hikari.messages.MessageFlag] The message flags this response should have if set else `hikari.undefined.UNDEFINED`. """ @property @abc.abstractmethod def is_tts(self) -> undefined.UndefinedOr[bool]: """Whether this response's content should be treated as text-to-speech. Returns ------- builtins.bool Whether this response's content should be treated as text-to-speech. If left as `hikari.undefined.UNDEFINED` then this will be disabled. """ @property @abc.abstractmethod def mentions_everyone(self) -> undefined.UndefinedOr[bool]: """Whether @everyone and @here mentions should be enabled for this response. Returns ------- hikari.undefined.UndefinedOr[builtins.bool] Whether @everyone mentions should be enabled for this response. If left as `hikari.undefined.UNDEFINED` then they will be disabled. """ @property @abc.abstractmethod def role_mentions( self, ) -> undefined.UndefinedOr[typing.Union[snowflakes.SnowflakeishSequence[guilds.PartialRole], bool]]: """Whether and what role mentions should be enabled for this response. Returns ------- hikari.undefined.UndefinedOr[typing.Union[hikari.snowflakes.SnowflakeishSequence[hikari.users.PartialUser], builtins.bool]] Either a sequence of object/IDs of the roles mentions should be enabled for, `builtins.False` or `hikari.undefined.UNDEFINED` to disallow any role mentions or `True` to allow all role mentions. """ # noqa: E501 - Line too long @property @abc.abstractmethod def user_mentions( self, ) -> undefined.UndefinedOr[typing.Union[snowflakes.SnowflakeishSequence[users.PartialUser], bool]]: """Whether and what user mentions should be enabled for this response. Returns ------- hikari.undefined.UndefinedOr[typing.Union[hikari.snowflakes.SnowflakeishSequence[hikari.users.PartialUser], builtins.bool]] Either a sequence of object/IDs of the users mentions should be enabled for, `builtins.False` or `hikari.undefined.UNDEFINED` to disallow any user mentions or `True` to allow all user mentions. """ # noqa: E501 - Line too long @abc.abstractmethod def add_component(self: _T, component: ComponentBuilder, /) -> _T: """Add a component to this response. Parameters ---------- component : ComponentBuilder The component builder to add to this response. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def add_embed(self: _T, embed: embeds_.Embed, /) -> _T: """Add an embed to this response. Parameters ---------- embed : hikari.embeds.Embed Object of the embed to add to this response. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def set_content(self: _T, content: undefined.UndefinedOr[str], /) -> _T: """Set the response's message content. Parameters ---------- content : hikari.undefined.UndefinedOr[builtins.str] The message content to set for this response. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def set_flags(self: _T, flags: typing.Union[undefined.UndefinedType, int, messages.MessageFlag], /) -> _T: """Set message flags for this response. !!! note As of writing, the only message flag which can be set is EPHEMERAL. Parameters ---------- flags : typing.Union[hikari.undefined.UndefinedType, builtins.int, hikari.messages.MessageFlag] The message flags to set for this response. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def set_tts(self: _T, tts: undefined.UndefinedOr[bool], /) -> _T: """Set whether this response should trigger text-to-speech processing. Parameters ---------- tts : Whether this response should trigger text-to-speech processing. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def set_mentions_everyone(self: _T, mentions: undefined.UndefinedOr[bool] = undefined.UNDEFINED, /) -> _T: """Set whether this response should be able to mention @everyone/@here. Parameters ---------- mentions : hikari.undefined.UndefinedOr[builtins.bool] Whether this response should be able to mention @everyone/@here. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def set_role_mentions( self: _T, mentions: undefined.UndefinedOr[ typing.Union[snowflakes.SnowflakeishSequence[guilds.PartialRole], bool] ] = undefined.UNDEFINED, /, ) -> _T: """Set whether and what role mentions should be possible for this response. Parameters ---------- mentions : hikari.undefined.UndefinedOr[typing.Union[hikari.snowflakes.SnowflakeishSequence[hikari.users.PartialUser], builtins.bool]] Either a sequence of object/IDs of the roles mentions should be enabled for, `builtins.False` or `hikari.undefined.UNDEFINED` to disallow any role mentions or `True` to allow all role mentions. Returns ------- InteractionMessageBuilder Object of this builder. """ # noqa: E501 - Line too long @abc.abstractmethod def set_user_mentions( self: _T, mentions: undefined.UndefinedOr[ typing.Union[snowflakes.SnowflakeishSequence[users.PartialUser], bool] ] = undefined.UNDEFINED, /, ) -> _T: """Set whether and what user mentions should be possible for this response. Parameters ---------- mentions: hikari.undefined.UndefinedOr[typing.Union[hikari.snowflakes.SnowflakeishSequence[hikari.users.PartialUser], builtins.bool]] Either a sequence of object/IDs of the users mentions should be enabled for, `builtins.False` or `hikari.undefined.UNDEFINED` to disallow any user mentions or `True` to allow all user mentions. Returns ------- InteractionMessageBuilder Object of this builder. """ # noqa: E501 - Line too long class CommandBuilder(abc.ABC): """Interface of a command builder used when bulk creating commands over REST.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def name(self) -> str: r"""Name to set for this command. !!! warning This should match the regex `^[\w-]{1,32}$` in Unicode mode and must be lowercase. Returns ------- builtins.str The name to set for this command. """ @property @abc.abstractmethod def type(self) -> undefined.UndefinedOr[commands.CommandType]: """Return the type of this command. Returns ------- hikari.commands.CommandType The type of this command. """ @property @abc.abstractmethod def id(self) -> undefined.UndefinedOr[snowflakes.Snowflake]: """ID of this command. Returns ------- hikari.undefined.UndefinedOr[hikari.snowflakes.Snowflake] The ID of this command if set. """ @property @abc.abstractmethod def default_permission(self) -> undefined.UndefinedOr[bool]: """Whether the command should be enabled by default (without any permissions). Defaults to `builtins.bool`. Returns ------- undefined.UndefinedOr[builtins.bool] Whether the command should be enabled by default (without any permissions). """ @abc.abstractmethod def set_id(self: _T, id_: undefined.UndefinedOr[snowflakes.Snowflakeish], /) -> _T: """Set the ID of this command. Parameters ---------- id_ : hikari.undefined.UndefinedOr[hikari.snowflakes.Snowflake] The ID to set for this command. Returns ------- CommandBuilder Object of this command builder. """ @abc.abstractmethod def set_default_permission(self: _T, state: undefined.UndefinedOr[bool], /) -> _T: """Whether this command should be enabled by default (without any permissions). Parameters ---------- state : hikari.undefined.UndefinedOr[builtins.bool] Whether this command should be enabled by default. Returns ------- CommandBuilder Object of this command builder for chained calls. """ @abc.abstractmethod def build(self, entity_factory: entity_factory_.EntityFactory, /) -> data_binding.JSONObject: """Build a JSON object from this builder. Parameters ---------- entity_factory : hikari.api.entity_factory.EntityFactory The entity factory to use to serialize entities within this builder. Returns ------- hikari.internal.data_binding.JSONObject The built json object representation of this builder. """ class SlashCommandBuilder(CommandBuilder): """SlashCommandBuilder.""" @property @abc.abstractmethod def description(self) -> str: """Return the description to set for this command. !!! warning This should be inclusively between 1-100 characters in length. Returns ------- builtins.str The description to set for this command. """ @property @abc.abstractmethod def options(self) -> typing.Sequence[commands.CommandOption]: """Sequence of up to 25 of the options set for this command. Returns ------- typing.Sequence[hikari.commands.CommandOption] A sequence of up to 25 of the options set for this command. """ @abc.abstractmethod def add_option(self: _T, option: commands.CommandOption) -> _T: """Add an option to this command. !!! note A command can have up to 25 options. Parameters ---------- option : hikari.commands.CommandOption The option to add to this command. Returns ------- CommandBuilder Object of this command builder. """ class ContextMenuCommandBuilder(CommandBuilder): """ContextMenuCommandBuilder.""" class ComponentBuilder(abc.ABC): """Base class for all component builder classes.""" __slots__: typing.Sequence[str] = () @abc.abstractmethod def build(self) -> data_binding.JSONObject: """Build a JSON object from this builder. Returns ------- hikari.internal.data_binding.JSONObject The built json object representation of this builder. """ class ButtonBuilder(ComponentBuilder, abc.ABC, typing.Generic[_ContainerT]): """Builder class for a message button component.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def style(self) -> typing.Union[messages.ButtonStyle, int]: """Button's style. Returns ------- typing.Union[builtins.int, hikari.messages.ButtonStyle] The button's style. """ @property @abc.abstractmethod def emoji(self) -> typing.Union[snowflakes.Snowflakeish, emojis.Emoji, str, undefined.UndefinedType]: """Emoji which should appear on this button. Returns ------- typing.Union[hikari.snowflakes.Snowflakeish, hikari.emojis.Emoji, builtins.str, hikari.undefined.UndefinedType] Object or ID or raw string of the emoji which should be displayed on this button if set. """ @property @abc.abstractmethod def label(self) -> undefined.UndefinedOr[str]: """Text label which should appear on this button. !!! note The text label to that should appear on this button. This may be up to 80 characters long. Returns ------- hikari.undefined.UndefinedOr[builtins.str] Text label which should appear on this button. """ @property @abc.abstractmethod def is_disabled(self) -> bool: """Whether the button should be marked as disabled. !!! note Defaults to `builtins.False`. Returns ------- builtins.bool Whether the button should be marked as disabled. """ @abc.abstractmethod def set_emoji( self: _T, emoji: typing.Union[snowflakes.Snowflakeish, emojis.Emoji, str, undefined.UndefinedType], / ) -> _T: """Set the emoji to display on this button. Parameters ---------- emoji : typing.Union[hikari.snowflakes.Snowflakeish, hikari.emojis.Emoji, builtins.str, hikari.undefined.UndefinedType] Object, ID or raw string of the emoji which should be displayed on this button. Returns ------- ButtonBuilder The builder object to enable chained calls. """ # noqa E501 - Line too long @abc.abstractmethod def set_label(self: _T, label: undefined.UndefinedOr[str], /) -> _T: """Set the text label which should be displayed on this button. Parameters ---------- label : hikari.undefined.UndefinedOr[builtins.str] The text label to show on this button. This may be up to 80 characters long. Returns ------- ButtonBuilder The builder object to enable chained calls. """ @abc.abstractmethod def set_is_disabled(self: _T, state: bool, /) -> _T: """Set whether this button should be disabled. Parameters ---------- state : bool Whether this button should be disabled. Returns ------- ButtonBuilder The builder object to enable chained calls. """ @abc.abstractmethod def add_to_container(self) -> _ContainerT: """Add this button to the container component it belongs to. This is used as the finalising call during chained calls. Returns ------- _ContainerT The container component that owns this button. """ class LinkButtonBuilder(ButtonBuilder[_ContainerT], abc.ABC): """Builder interface for link buttons.""" @property @abc.abstractmethod def url(self) -> str: """Url this button should link to when pressed. Returns ------- builtins.str Url this button should link to when pressed. """ class InteractiveButtonBuilder(ButtonBuilder[_ContainerT], abc.ABC): """Builder interface for interactive buttons.""" @property @abc.abstractmethod def custom_id(self) -> str: """Developer set custom ID used for identifying interactions with this button. Returns ------- builtins.str Developer set custom ID used for identifying interactions with this button. """ class SelectOptionBuilder(ComponentBuilder, abc.ABC, typing.Generic[_SelectMenuBuilderT]): """Builder class for select menu options.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def label(self) -> str: """User-facing name of the option, max 100 characters. Returns ------- builtins.str User-facing name of the option. """ @property @abc.abstractmethod def value(self) -> str: """Developer-defined value of the option, max 100 characters. Returns ------- builtins.str Developer-defined value of the option. """ @property @abc.abstractmethod def description(self) -> undefined.UndefinedOr[str]: """Return the description of the option, max 100 characters. Returns ------- hikari.undefined.UndefinedOr[builtins.str] Description of the option, if set. """ @property @abc.abstractmethod def emoji(self) -> typing.Union[snowflakes.Snowflakeish, emojis.Emoji, str, undefined.UndefinedType]: """Emoji which should appear on this option. Returns ------- typing.Union[hikari.snowflakes.Snowflakeish, hikari.emojis.Emoji, builtins.str, hikari.undefined.UndefinedType] Object or ID or raw string of the emoji which should be displayed on this option if set. """ @property @abc.abstractmethod def is_default(self) -> bool: """Whether this option should be marked as selected by default. Defaults to `builtins.False`. Returns ------- builtins.bool Whether this option should be marked as selected by default. """ @abc.abstractmethod def set_description(self: _T, value: undefined.UndefinedOr[str], /) -> _T: """Set the option's description. Parameters ---------- value : hikari.undefined.UndefinedOr[builtins.str] Description to set for this option. This can be up to 100 characters long. Returns ------- SelectOptionBuilder The builder object to enable chained calls. """ @abc.abstractmethod def set_emoji( self: _T, emoji: typing.Union[snowflakes.Snowflakeish, emojis.Emoji, str, undefined.UndefinedType], / ) -> _T: """Set the emoji to display on this option. Parameters ---------- emoji : typing.Union[hikari.snowflakes.Snowflakeish, hikari.emojis.Emoji, builtins.str, hikari.undefined.UndefinedType] Object, ID or raw string of the emoji which should be displayed on this option. Returns ------- SelectOptionBuilder The builder object to enable chained calls. """ # noqa E501 - Line too long @abc.abstractmethod def set_is_default(self: _T, state: bool, /) -> _T: """Set whether this option should be selected by default. Parameters ---------- state : builtins.bool Whether this option should be selected by default. Returns ------- SelectOptionBuilder The builder object to enable chained calls. """ @abc.abstractmethod def add_to_menu(self) -> _SelectMenuBuilderT: """Add this option to the menu component it belongs to. This is used as the finalising call during chained calls. Returns ------- _SelectMenuBuilderT The menu component that owns this button. """ class SelectMenuBuilder(ComponentBuilder, abc.ABC, typing.Generic[_ContainerT]): """Builder class for select menu options.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def custom_id(self) -> str: """Developer set custom ID used for identifying interactions with this menu. Returns ------- builtins.str Developer set custom ID used for identifying interactions with this menu. """ @property @abc.abstractmethod def is_disabled(self) -> bool: """Whether the select menu should be marked as disabled. !!! note Defaults to `builtins.False`. Returns ------- builtins.bool Whether the select menu should be marked as disabled. """ @property @abc.abstractmethod def options(self: _SelectMenuBuilderT) -> typing.Sequence[SelectOptionBuilder[_SelectMenuBuilderT]]: """Sequence of the options set for this select menu. Returns ------- typing.Sequence[SelectOptionBuilder[Self]] Sequence of the options set for this select menu. """ @property @abc.abstractmethod def placeholder(self) -> undefined.UndefinedOr[str]: """Return the placeholder text to display when no options are selected. Returns ------- hikari.undefined.UndefinedOr[builtins.str] Placeholder text to display when no options are selected, if defined. """ @property @abc.abstractmethod def min_values(self) -> int: """Minimum number of options which must be chosen. Defaults to 1. Must be less than or equal to `SelectMenuBuilder.max_values` and greater than or equal to 0. Returns ------- builtins.str Minimum number of options which must be chosen. """ @property @abc.abstractmethod def max_values(self) -> int: """Maximum number of options which can be chosen. Defaults to 1. Must be greater than or equal to `SelectMenuBuilder.min_values` and less than or equal to 25. Returns ------- builtins.str Maximum number of options which can be chosen. """ @abc.abstractmethod def add_option(self: _SelectMenuBuilderT, label: str, value: str, /) -> SelectOptionBuilder[_SelectMenuBuilderT]: """Add an option to this menu. Parameters ---------- label : builtins.str The user-facing name of this option, max 100 characters. value : builtins.str The developer defined value of this option, max 100 characters. Returns ------- SelectOptionBuilder[Self] Option builder object. This should be finalised by calling `SelectOptionBuilder.add_to_menu`. """ @abc.abstractmethod def set_is_disabled(self: _T, state: bool, /) -> _T: """Set whether this option is disabled. Defaults to `builtins.False`. Parameters ---------- state : builtins.bool Whether this option is disabled. Returns ------- SelectMenuBuilder The builder object to enable chained calls. """ @abc.abstractmethod def set_placeholder(self: _T, value: undefined.UndefinedOr[str], /) -> _T: """Set place-holder text to be shown when no option is selected. Parameters ---------- value : hikari.undefined.UndefinedOr[builtins.str] Place-holder text to be displayed when no option is selected. Max 100 characters. Returns ------- SelectMenuBuilder The builder object to enable chained calls. """ @abc.abstractmethod def set_min_values(self: _T, value: int, /) -> _T: """Set the minimum amount of options which need to be selected for this menu. !!! note This defaults to 1 if not set and must be greater than or equal to 0 and less than or equal to `SelectMenuBuilder.max_values`. Parameters ---------- value : builtins.int The minimum amount of options which need to be selected for this menu. Returns ------- SelectMenuBuilder The builder object to enable chained calls. """ @abc.abstractmethod def set_max_values(self: _T, value: int, /) -> _T: """Set the maximum amount of options which can be selected for this menu. !!! note This defaults to 1 if not set and must be less than or equal to 25 and greater than or equal to `SelectMenuBuilder.min_values`. Parameters ---------- value : builtins.int The maximum amount of options which can selected for this menu. Returns ------- SelectMenuBuilder The builder object to enable chained calls. """ @abc.abstractmethod def add_to_container(self) -> _ContainerT: """Finalise this builder by adding it to its parent container component. Returns ------- _ContainerT The parent container component builder. """ class ActionRowBuilder(ComponentBuilder, abc.ABC): """Builder class for action row components.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def components(self) -> typing.Sequence[ComponentBuilder]: """Sequence of the component builders registered within this action row. Returns ------- typing.Sequence[ComponentBuilder] Sequence of the component builders registered within this action row. """ @abc.abstractmethod def add_component( self: _T, component: ComponentBuilder, /, ) -> _T: """Add a component to this action row builder. !!! warning It is generally better to use `ActionRowBuilder.add_button` and `ActionRowBuilder.add_select_menu` to add your component to the builder. Those methods utilize this one. Parameters ---------- component : ComponentBuilder The component builder to add to the action row. Returns ------- ActionRowBuilder The builder object to enable chained calls. """ @typing.overload @abc.abstractmethod def add_button( self: _T, style: messages.InteractiveButtonTypesT, custom_id: str, / ) -> InteractiveButtonBuilder[_T]: ... @typing.overload @abc.abstractmethod def add_button(self: _T, style: typing.Literal[messages.ButtonStyle.LINK, 5], url: str, /) -> LinkButtonBuilder[_T]: ... @typing.overload @abc.abstractmethod def add_button( self: _T, style: typing.Union[int, messages.ButtonStyle], url_or_custom_id: str, / ) -> typing.Union[LinkButtonBuilder[_T], InteractiveButtonBuilder[_T]]: ... @abc.abstractmethod def add_button( self: _T, style: typing.Union[int, messages.ButtonStyle], url_or_custom_id: str, / ) -> typing.Union[LinkButtonBuilder[_T], InteractiveButtonBuilder[_T]]: """Add a button component to this action row builder. Parameters ---------- style : typing.Union[builtins.int, hikari.messages.ButtonStyle] The button's style. url_or_custom_id : builtins.str For interactive button styles this is a developer-defined custom identifier used to identify which button triggered component interactions. For Link button styles this is the URL the link button should redirect to. Returns ------- typing.Union[LinkButtonBuilder[Self], InteractiveButtonBuilder[Self]] Button builder object. `ButtonBuilder.add_to_container` should be called to finalise the button. """ @abc.abstractmethod def add_select_menu(self: _T, custom_id: str, /) -> SelectMenuBuilder[_T]: """Add a select menu component to this action row builder. Parameters ---------- custom_id : builtins.str A developer-defined custom identifier used to identify which menu triggered component interactions. Returns ------- SelectMenuBuilder[Self] Select menu builder object. `SelectMenuBuilder.add_to_container` should be called to finalise the button. 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demo/mislabelled_data_demo.py	bottydim/dginn	0	1615850	<reponame>bottydim/dginn<gh_stars>0 if __name__ == '__main__': import tensorflow as tf tf.compat.v1.enable_eager_execution() from aggregator_utils import get_count_aggregators, uncertainty_pred, compute_dg from core import * from dataset_utils import filter_dataset from mnist_loader import load_mnist, get_mnist_model """ Things to try: - Dependency graph computation approach (dg_relevance) - Aggregation approach + similarity to aggregator - Number of aggregator samples - Depth of the model """ def main(): # Load dataset train_x, train_y, test_x, test_y = load_mnist() # Filter out subset of classes selected_classes = [0, 1, 2, 3] train_x, train_y = filter_dataset((train_x, train_y), selected_classes) test_x, test_y = filter_dataset((test_x, test_y), selected_classes) # Create model model = get_mnist_model(train_x, train_y) # Obtain subset of incorrectly labelled training points preds = np.argmax(model.predict(train_x), axis=1) # @Dima union of array & Iterable got boolean? incorrects = np.nonzero(preds != train_y)[0] incorrect_samples, incorrect_labels = train_x[incorrects], train_y[incorrects] incorrect_samples, incorrect_labels = incorrect_samples[:100], incorrect_labels[:100] # Select random set of samples n_mislabelled = 100 indices = np.random.choice(train_x.shape[0], n_mislabelled, replace=False) random_x, random_y = train_x[indices], train_y[indices] # Combine two sets of samples combined_x, combined_y = np.concatenate((incorrect_samples, random_x), axis=0), \ np.concatenate((incorrect_labels, random_y), axis=0) print("# samples: ", combined_x.shape[0]) # Get sample aggregators aggregators = get_count_aggregators(train_x, train_y, model, 100) sim_scores = {} agg_labels = {} compute_fx = Activations_Computer(model=model, agg_data_points=True) for i, sample in enumerate(combined_x): sample = np.expand_dims(sample, axis=0) dg = compute_dg(sample,compute_fx) pred_label, sim_score = uncertainty_pred(dg, aggregators) agg_labels[i] = False sim_scores[i] = sim_score y_pred = np.argmax(model.predict(sample), axis=1) # TODO: remove after testing # Temporary printing information if pred_label == combined_y[i]: agg_labels[i] = True print("correct sample ", i) if pred_label != y_pred: print("disagreement ", i, " predicted: ", pred_label, " model: ", y_pred) # Sort samples by similarity sorted_keys = sorted(sim_scores, key=sim_scores.get) print(sorted_keys) labels = [agg_labels[i] for i in sorted_keys] scores = [sim_scores[i] for i in sorted_keys] print(labels) print(scores) # Count how many mislabelled samples are in the second half of the sorted keys # (i.e. have high similarity) n_incorrect = len(incorrect_labels) mislabelled = sum([1 for key in sorted_keys[n_incorrect:] if key >= n_incorrect]) print("Mislabelled: ", mislabelled) return sorted_keys if __name__ == '__main__': main() """ Observations: 1) High certainty corresponds to higher chance of a correct prediction 2) Mislabelled 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tools/pycocoEval.py	Manthata/hrnet	80	171187	import matplotlib.pyplot as plt from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval import numpy as np import skimage.io as io import ipdb;pdb=ipdb.set_trace from collections import OrderedDict # markdown format output def _print_name_value(name_value, full_arch_name): names = name_value.keys() values = name_value.values() num_values = len(name_value) print( '\| Arch ' + ' '.join(['\| {}'.format(name) for name in names]) + ' \|' ) print('\|---' * (num_values+1) + '\|') if len(full_arch_name) > 15: full_arch_name = full_arch_name[:8] + '...' print( '\| ' + full_arch_name + ' ' + ' '.join(['\| {:.3f}'.format(value) for value in values]) + ' \|' ) gt_anns = 'data/coco/annotations/person_keypoints_val2017.json' # dt_anns = '/home/xyliu/2D_pose/deep-high-resolution-net.pytorch/person_keypoints.json' dt_anns = '/home/xyliu/2D_pose/simple-pose-estimation/person_keypoints.json' annType = 'keypoints' cocoGt=COCO(gt_anns) cocoDt=cocoGt.loadRes(dt_anns) cocoEval = COCOeval(cocoGt,cocoDt,annType) cocoEval.evaluate() cocoEval.accumulate() cocoEval.summarize() stats_names = ['AP', 'Ap .5', 'AP .75', 'AP (M)', 'AP (L)', 'AR', 'AR .5', 'AR .75', 'AR (M)', 'AR (L)'] info_str = [] for ind, name in enumerate(stats_names): info_str.append((name, cocoEval.stats[ind])) name_values = OrderedDict(info_str) model_name = 'openpose' if isinstance(name_values, list): for name_value in name_values: _print_name_value(name_value, model_name) else: _print_name_value(name_values, model_name)	[ 1, 1053, 22889, 29889, 2272, 5317, 408, 14770, 13, 3166, 282, 11078, 542, 327, 8789, 29889, 29883, 6235, 1053, 4810, 3217, 13, 3166, 282, 11078, 542, 327, 8789, 29889, 29883, 6235, 14513, 1053, 4810, 3217, 14513, 13, 5215, 12655, 408, 7442, 13, 5215, 2071, 3027, 29889, 601, 408, 12013, 13, 5215, 10377, 2585, 29936, 29886, 2585, 29922, 666, 2585, 29889, 842, 29918, 15003, 13, 3166, 16250, 1053, 8170, 287, 21533, 13, 13, 29937, 2791, 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image_classification/token_labeling/tlt/models/lvvit.py	AK391/UniFormer	367	89498	<reponame>AK391/UniFormer<filename>image_classification/token_labeling/tlt/models/lvvit.py import torch import torch.nn as nn from timm.models.helpers import load_pretrained from timm.models.registry import register_model from timm.models.layers import trunc_normal_ from timm.models.resnet import resnet26d, resnet50d, resnet101d import numpy as np from .layers import * def _cfg(url='', kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, 'crop_pct': .9, 'interpolation': 'bicubic', 'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225), 'classifier': 'head', kwargs } default_cfgs = { 'LV_ViT_Tiny': _cfg(), 'LV_ViT': _cfg(), 'LV_ViT_Medium': _cfg(crop_pct=1.0), 'LV_ViT_Large': _cfg(crop_pct=1.0), } def get_block(block_type, kargs): if block_type=='mha': # multi-head attention block return MHABlock(kargs) elif block_type=='ffn': # feed forward block return FFNBlock(kargs) elif block_type=='tr': # transformer block return Block(kargs) def rand_bbox(size, lam): W = size[2] H = size[3] cut_rat = np.sqrt(1. - lam) cut_w = np.int(W * cut_rat) cut_h = np.int(H * cut_rat) # uniform cx = np.random.randint(W) cy = np.random.randint(H) bbx1 = np.clip(cx - cut_w // 2, 0, W) bby1 = np.clip(cy - cut_h // 2, 0, H) bbx2 = np.clip(cx + cut_w // 2, 0, W) bby2 = np.clip(cy + cut_h // 2, 0, H) return bbx1, bby1, bbx2, bby2 def get_dpr(drop_path_rate,depth,drop_path_decay='linear'): if drop_path_decay=='linear': # linear dpr decay dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule elif drop_path_decay=='fix': # use fixed dpr dpr= [drop_path_rate]depth else: # use predefined drop_path_rate list assert len(drop_path_rate)==depth dpr=drop_path_rate return dpr class LV_ViT(nn.Module): """ Vision Transformer with tricks Arguements: p_emb: different conv based position embedding (default: 4 layer conv) skip_lam: residual scalar for skip connection (default: 1.0) order: which order of layers will be used (default: None, will override depth if given) mix_token: use mix token augmentation for batch of tokens (default: False) return_dense: whether to return feature of all tokens with an additional aux_head (default: False) """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., drop_path_decay='linear', hybrid_backbone=None, norm_layer=nn.LayerNorm, p_emb='4_2', head_dim = None, skip_lam = 1.0,order=None, mix_token=False, return_dense=False): super().__init__() self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.output_dim = embed_dim if num_classes==0 else num_classes if hybrid_backbone is not None: self.patch_embed = HybridEmbed( hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim) else: if p_emb=='4_2': patch_embed_fn = PatchEmbed4_2 elif p_emb=='4_2_128': patch_embed_fn = PatchEmbed4_2_128 else: patch_embed_fn = PatchEmbedNaive self.patch_embed = patch_embed_fn(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) if order is None: dpr=get_dpr(drop_path_rate, depth, drop_path_decay) self.blocks = nn.ModuleList([ Block( dim=embed_dim, num_heads=num_heads, head_dim=head_dim, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, skip_lam=skip_lam) for i in range(depth)]) else: # use given order to sequentially generate modules dpr=get_dpr(drop_path_rate, len(order), drop_path_decay) self.blocks = nn.ModuleList([ get_block(order[i], dim=embed_dim, num_heads=num_heads, head_dim=head_dim, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, skip_lam=skip_lam) for i in range(len(order))]) self.norm = norm_layer(embed_dim) self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() self.return_dense=return_dense self.mix_token=mix_token if return_dense: self.aux_head=nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() if mix_token: self.beta = 1.0 assert return_dense, "always return all features when mixtoken is enabled" trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, GroupLinear): trunc_normal_(m.group_weight, std=.02) if isinstance(m, GroupLinear) and m.group_bias is not None: nn.init.constant_(m.group_bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token'} def get_classifier(self): return self.head def reset_classifier(self, num_classes, global_pool=''): self.num_classes = num_classes self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() def forward_embeddings(self,x): x = self.patch_embed(x) return x def forward_tokens(self, x): B = x.shape[0] cls_tokens = self.cls_token.expand(B, -1, -1) x = torch.cat((cls_tokens, x), dim=1) x = x + self.pos_embed x = self.pos_drop(x) for blk in self.blocks: x = blk(x) x = self.norm(x) return x def forward_features(self,x): # simple forward to obtain feature map (without mixtoken) x = self.forward_embeddings(x) x = x.flatten(2).transpose(1, 2) x = self.forward_tokens(x) return x def forward(self, x): x = self.forward_embeddings(x) # token level mixtoken augmentation if self.mix_token and self.training: lam = np.random.beta(self.beta, self.beta) patch_h, patch_w = x.shape[2],x.shape[3] bbx1, bby1, bbx2, bby2 = rand_bbox(x.size(), lam) temp_x = x.clone() temp_x[:, :, bbx1:bbx2, bby1:bby2] = x.flip(0)[:, :, bbx1:bbx2, bby1:bby2] x = temp_x else: bbx1, bby1, bbx2, bby2 = 0,0,0,0 x = x.flatten(2).transpose(1, 2) x = self.forward_tokens(x) x_cls = self.head(x[:,0]) if self.return_dense: x_aux = self.aux_head(x[:,1:]) if not self.training: return x_cls+0.5x_aux.max(1)[0] # recover the mixed part if self.mix_token and self.training: x_aux = x_aux.reshape(x_aux.shape[0],patch_h, patch_w,x_aux.shape[-1]) temp_x = x_aux.clone() temp_x[:, bbx1:bbx2, bby1:bby2, :] = x_aux.flip(0)[:, bbx1:bbx2, bby1:bby2, :] x_aux = temp_x x_aux = x_aux.reshape(x_aux.shape[0],patch_hpatch_w,x_aux.shape[-1]) return x_cls, x_aux, (bbx1, bby1, bbx2, bby2) return x_cls @register_model def vit(pretrained=False, kwargs): model = LV_ViT(patch_size=16, embed_dim=384, depth=16, num_heads=6, mlp_ratio=3., p_emb=1, kwargs) model.default_cfg = default_cfgs['LV_ViT'] return model @register_model def lvvit(pretrained=False, kwargs): model = LV_ViT(patch_size=16, embed_dim=384, depth=16, num_heads=6, mlp_ratio=3., p_emb='4_2',skip_lam=2., kwargs) model.default_cfg = default_cfgs['LV_ViT'] return model @register_model def lvvit_s(pretrained=False, kwargs): model = LV_ViT(patch_size=16, embed_dim=384, depth=16, num_heads=6, mlp_ratio=3., p_emb='4_2',skip_lam=2., return_dense=True,mix_token=True, kwargs) model.default_cfg = default_cfgs['LV_ViT'] return model @register_model def lvvit_m(pretrained=False, kwargs): model = LV_ViT(patch_size=16, embed_dim=512, depth=20, num_heads=8, mlp_ratio=3., p_emb='4_2',skip_lam=2., return_dense=True,mix_token=True, kwargs) model.default_cfg = default_cfgs['LV_ViT_Medium'] return model @register_model def 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gputools/denoise/denoise_filter2.py	VolkerH/gputools	0	119149	<reponame>VolkerH/gputools<gh_stars>0 """ A collection of some denoising algorithms in 2d MW, 2014 """ from __future__ import print_function, unicode_literals, absolute_import, division import numpy as np import subprocess import logging logger = logging.getLogger(__name__) def bilateral(data, fSize, sigma, sigma_x = 10., dev= None): """bilateral filter """ if dev is None: dev = imgtools.__DEFAULT_OPENCL_DEVICE__ if dev is None: raise ValueError("no OpenCLDevice found...") dtype = data.dtype.type dtypes_kernels = {np.float32:"run2d_float", np.uint16:"run2d_short"} if not dtype in dtypes_kernels: logger.error("data type %s not supported yet, casting to float:"%dtype,list(dtypes_kernels.keys())) return img = dev.createImage_like(data) buf = dev.createBuffer(data.size,dtype = dtype) dev.writeImage(img,data) proc = OCLProcessor(dev,absPath("kernels/bilateral.cl")) proc.runKernel(dtypes_kernels[dtype],img.shape,None,img,buf, np.int32(img.shape[0]),np.int32(img.shape[1]), np.int32(fSize),np.float32(sigma_x),np.float32(sigma)) return dev.readBuffer(buf,dtype=dtype).reshape(data.shape) def test_bilateral(): from imgtools import test_images, calcPSNR data = test_images.lena() data = 100.data/np.amax(data) y = data + np.random.normal(0,20,data.shape) y = y.astype(np.float32) outs = [calcPSNR(data,bilateral(y,2,sigs)) for sigs in np.linspace(1.,200,20)] return outs # def bilateralBuffer(clDev, data, fSize, sigmaX, sigmaP): # clProc = OCLProcessor(clDev,absPath("kernels/bilateral.cl")) # clBufIn = clDev.createBuffer(data.size, mem_flag = cl.mem_flags.READ_ONLY) # clBufOut = clDev.createBuffer(data.size) # clDev.writeBuffer(clBufIn,data.astype(np.uint16)) # clProc.runKernel("run2dBuf",data.shape[::-1],None,clBufIn,clBufOut, # np.int32(data.shape[1]),np.int32(data.shape[0]), # np.int32(fSize),np.float32(sigmaX),np.float32(sigmaP)) # return clDev.readBuffer(clBufOut).reshape(data.shape) # def bilateralAdapt(clDev, data, sensor, fSize, sigmaX, fac): # clProc = OCLProcessor(clDev, absPath("kernels/bilateralAdapt.cl")) # clImg = clDev.createImage(data.shape[::-1]) # clImgSensor = clDev.createImage(sensor.shape[::-1]) # clBuf = clDev.createBuffer(data.size) # clDev.writeImage(clImg,data.astype(np.uint16)) # clDev.writeImage(clImgSensor,sensor.astype(np.uint16)) # clProc.runKernel("run2d",clImg.shape,None,clImg,clImgSensor,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]), # np.int32(fSize),np.float32(sigmaX),np.float32(fac)) # return clDev.readBuffer(clBuf).reshape(data.shape) # def nlMeansShared(clDev, data, fSize, bSize, sigma): # locSize = 16 # clProc = OCLProcessor(clDev,absPath("kernels/nlmeans.cl"),options=["-D FS=%d -D BS=%d -D GS=%d"%(fSize,bSize,locSize)]) # clImg = clDev.createImage(data.shape[::-1]) # clBuf = clDev.createBuffer(data.size) # clDev.writeImage(clImg,data.astype(np.uint16)) # clProc.runKernel("run2d_SHARED",clImg.shape,(locSize,locSize),clImg,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]),np.float32(sigma)) # return clDev.readBuffer(clBuf).reshape(data.shape) def nlm(data, fSize, bSize, sigma, dev = None, proc = None): if dev is None: dev = imgtools.__DEFAULT_OPENCL_DEVICE__ if dev is None: raise ValueError("no OpenCLDevice found...") dtype = data.dtype.type dtypes_kernels = {np.float32:"run2d_float", np.uint16:"run2d_short"} if not dtype in dtypes_kernels: logger.error("data type %s not supported yet, please convert to:"%dtype,list(dtypes_kernels.keys())) return img = dev.createImage_like(data) buf = dev.createBuffer(data.size,dtype = dtype) dev.writeImage(img,data) if proc is None: proc = OCLProcessor(dev,absPath("kernels/nlmeans.cl")) proc.runKernel(dtypes_kernels[dtype],img.shape,None,img,buf, np.int32(img.shape[0]),np.int32(img.shape[1]), np.int32(fSize), np.int32(bSize),np.float32(sigma)).wait() return dev.readBuffer(buf,dtype=dtype).reshape(data.shape) def nlm_fast(data,FS,BS,sigma,dev = None, proc = None): """for noise level (and FS,BS = 2,3) of sigma_0, choose sigma = 1.5sigma_0 """ if dev is None: dev = imgtools.__DEFAULT_OPENCL_DEVICE__ if dev is None: raise ValueError("no OpenCLDevice found...") if proc is None: proc = OCLProcessor(dev,absPath("kernels/nlm_fast.cl"),options="-D FS=%i -D BS=%i"%(FS,BS)) img = dev.createImage_like(data) distImg = dev.createImage_like(data) distImg = dev.createImage_like(data, mem_flags = "READ_WRITE") tmpImg = dev.createImage_like(data, mem_flags = "READ_WRITE") tmpImg2 = dev.createImage_like(data, mem_flags = "READ_WRITE") accBuf = dev.createBuffer(data.size, mem_flags = cl.mem_flags.READ_WRITE, dtype = np.float32) weightBuf = dev.createBuffer(data.size, mem_flags = cl.mem_flags.READ_WRITE, dtype = np.float32) dev.writeImage(img,data); dev.writeBuffer(weightBuf,np.zeros_like(data,dtype=np.float32)); for dx in range(BS+1): for dy in range(-BS,BS+1): proc.runKernel("dist",img.shape,None,img,tmpImg,np.int32(dx),np.int32(dy)) proc.runKernel("convolve",img.shape,None,tmpImg,tmpImg2,np.int32(1)) proc.runKernel("convolve",img.shape,None,tmpImg2,distImg,np.int32(2)) proc.runKernel("computePlus",img.shape,None,img,distImg,accBuf,weightBuf, np.int32(img.shape[0]),np.int32(img.shape[1]), np.int32(dx),np.int32(dy),np.float32(sigma)) if any([dx,dy]): proc.runKernel("computeMinus",img.shape,None,img,distImg,accBuf,weightBuf, np.int32(img.shape[0]),np.int32(img.shape[1]), np.int32(dx),np.int32(dy),np.float32(sigma)) acc = dev.readBuffer(accBuf,dtype=np.float32).reshape(data.shape) weights = dev.readBuffer(weightBuf,dtype=np.float32).reshape(data.shape) return acc/weights def test_nlm(): from imgtools import test_images, calcPSNR data = test_images.lena() data = 100.data/np.amax(data) y = data + np.random.normal(0,20,data.shape) y = y.astype(np.float32) out = nlm(y,2,3,25.) sigs = np.linspace(2,70,10) outs = [calcPSNR(data,nlm(y,2,3,s)) for s in sigs] print("nlm: sig_max = %s" %sigs[np.argmax(outs)]) return outs def test_nlm_fast(): from imgtools import test_images, calcPSNR data = test_images.lena() data = 100.data/np.amax(data) y = data + np.random.normal(0,20,data.shape) y = y.astype(np.float32) out = nlm_fast(y,2,3,5.) sigs = np.linspace(3,70,30) outs = [calcPSNR(data,nlm_fast(y,2,3,s)) for s in sigs] bests = [] for s0 in np.linspace(2,20,10): y = data + np.random.normal(0,s0,data.shape) y = y.astype(np.float32) ind=np.argmax([calcPSNR(data,nlm_fast(y,2,3,s)) for s in sigs]) print(ind) bests.append([s0,sigs[ind]]) print("nlm fast: sig_max = %s" %sigs[np.argmax(outs)]) return bests # def nlMeansBuffer(clDev, data, fSize, bSize, sigma): # clProc = OCLProcessor(clDev,absPath("kernels/nlmeans.cl")) # clBufIn = clDev.createBuffer(data.size, mem_flag = cl.mem_flags.READ_ONLY) # clBufOut = clDev.createBuffer(data.size) # clDev.writeBuffer(clBufIn,data.astype(np.uint16)) # clProc.runKernel("run2dBuf",(data.size,1),None,clBufIn,clBufOut, # np.int32(data.shape[1]),np.int32(data.shape[0]), # np.int32(fSize), np.int32(bSize),np.float32(sigma)) # return clDev.readBuffer(clBufOut).reshape(data.shape) # def nlMeansPrefetch(clDev, data, fSize, bSize, sigma): # clProc = OCLProcessor(clDev,absPath("kernels/nlmeans.cl"),options=["-D FS=%d -D BS=%d"%(fSize,bSize)]) # clImg = clDev.createImage(data.shape[::-1]) # clBuf = clDev.createBuffer(data.size) # clDev.writeImage(clImg,data.astype(np.uint16)) # clProc.runKernel("run2d_FIXED",clImg.shape,None,clImg,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]),np.float32(sigma)) # return clDev.readBuffer(clBuf).reshape(data.shape) # def nlMeansProjected(clDev, data, fSize, bSize, sigma, proc = None): # clImg = clDev.createImage(data.shape[::-1]) # patchImg = clDev.createImage(data.shape[::-1], mem_flags = cl.mem_flags.READ_WRITE, channel_order = cl.channel_order.RGBA, channel_type=cl.channel_type.FLOAT) # clBuf = clDev.createBuffer(data.size) # if proc ==None: # proc = OCLProcessor(clDev,absPath("kernels/patch_kernel.cl")) # clDev.writeImage(clImg,data.astype(np.uint16)) # proc.runKernel("project4",clImg.shape,None,clImg,patchImg, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]),np.int32(fSize)) # proc.runKernel("nlm2dProject",clImg.shape,None,clImg,patchImg,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]), # np.int32(fSize), np.int32(bSize),np.float32(sigma)) # return clDev.readBuffer(clBuf).reshape(data.shape) # def nlMeansProjected2(clDev, data, fSize, bSize, sigma, proc = None): # clImg = clDev.createImage(data.shape[::-1]) # patchImg = clDev.createImage(data.shape[::-1], mem_flags = cl.mem_flags.READ_WRITE, channel_order = cl.channel_order.RGBA, channel_type=cl.channel_type.FLOAT) # clBuf = clDev.createBuffer(data.size) # if proc ==None: # proc = OCLProcessor(clDev,absPath("kernels/nlmeans_projected.cl")) # clDev.writeImage(clImg,data.astype(np.uint16)) # from time import time # t = time() # proc.runKernel("project4",clImg.shape,None,clImg,patchImg, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]),np.int32(fSize)) # proc.runKernel("nlm2dProject",clImg.shape,None,clImg,patchImg,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]), # np.int32(fSize), np.int32(bSize),np.float32(sigma)) # out= clDev.readBuffer(clBuf).reshape(data.shape) # print time()-t ,"second" # return out # def nlMeansProjectedSensor(clDev, data, sensor, fSize, bSize, sigma, proc = None): # clImg = clDev.createImage(data.shape[::-1]) # patchImg = clDev.createImage(data.shape[::-1], mem_flags = cl.mem_flags.READ_WRITE, channel_order = cl.channel_order.RGBA, channel_type=cl.channel_type.FLOAT) # sensorImg = clDev.createImage(data.shape[::-1], mem_flags = cl.mem_flags.READ_WRITE, channel_order = cl.channel_order.R, channel_type=cl.channel_type.FLOAT) # clBuf = clDev.createBuffer(data.size) # if proc ==None: # proc = OCLProcessor(clDev,absPath("kernels/patch_kernel.cl")) # clDev.writeImage(clImg,data.astype(np.uint16)) # clDev.writeImage(sensorImg,sensor.astype(np.float32)) # proc.runKernel("project4",clImg.shape,None,clImg,patchImg, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]),np.int32(fSize)) # proc.runKernel("nlm2dProjectSensor",clImg.shape,None,clImg,patchImg,sensorImg,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]), # np.int32(fSize), np.int32(bSize),np.float32(sigma)) # return clDev.readBuffer(clBuf).reshape(data.shape) def bm3d(data,sigma): from scipy.misc import imsave, imread # meanData = np.mean(data) fName = "0123456789_TMP.png" imsave(fName,data) subprocess.call([os.path.join(os.path.dirname(__file__), "cxx_code/bm3d/bm3d"),fName,str(sigma),fName], stdout=subprocess.PIPE) out = imread(fName) return out # return out(1.meanData/np.mean(out)) def dct_denoising(data,sigma): from scipy.misc import imsave, imread meanData = np.mean(data) fName = "0123456789_TMP.png" imsave(fName,data) subprocess.call([os.path.join(os.path.dirname(__file__), "cxx_code/dct/demo_DCTdenoising"),fName,str(sigma),fName]) out = imread(fName) return out(1.meanData/np.mean(out)) def roundUp(n,k): #rounds to the next number divisible by k return np.int(np.ceil(1.n/k)k) def roundDown(n,k): #rounds to the next number divisible by k return np.int(np.floor(1.n/k)k) def dct_8x8(data,sigma,dev = None, proc=None): if dev is None: dev = imgtools.__DEFAULT_OPENCL_DEVICE__ if dev is None: raise ValueError("no OpenCLDevice found...") if not proc: proc = OCLProcessor(dev,absPath("kernels/dct_8x8.cl")) src = dev.createImage(data.shape[::-1],channel_type = cl.channel_type.FLOAT,mem_flags = cl.mem_flags.READ_ONLY) acc = dev.createBuffer(roundUp(data.shape[0],8)roundUp(data.shape[1],8),dtype=np.float32) dst = dev.createImage(data.shape[::-1],channel_type = cl.channel_type.FLOAT,mem_flags = cl.mem_flags.WRITE_ONLY) dev.writeImage(src,data.astype(np.float32)) astride = roundDown(dst.width,8) for j in range(8): for i in range(8): proc.runKernel("dct_denoise_8x8_r",(roundDown(src.width-i,8),roundDown(src.height-j,8)),(8,8), src,acc, np.float32(sigma),np.int32(i),np.int32(j),np.int32(astride),np.int32(i==0 and j==0)) proc.runKernel("dct_denoise_normalise_r",(roundDown(src.width,8),roundDown(src.height,8)),(8,8), acc, dst,np.int32(astride)) out = dev.readBuffer(acc,dtype=np.float32) return np.maximum(0,dev.readImage(dst,dtype=np.float32)).astype(data.dtype) def test_dct(): from imgtools import test_images, calcPSNR data = test_images.lena() data = 100.data/np.amax(data) y = data + np.random.normal(0,20,data.shape) y = y.astype(np.float32) out = dct_8x8(y,60) outs = [calcPSNR(data,dct_8x8(y,s)) for s in np.linspace(10,60,20)] return out def test_filter(): from functools import partial import time data = np.random.uniform(0,1,[28,28]).astype(np.float32) print("running on image shape : {} \n\n".format(data.shape)) fs = { "bilateral":lambda:bilateral(data,3,10,10), "nlMeans":lambda:nlm_fast(data,3,7,10), # "nlMeans S":lambda:nlMeansShared(clDev,data,3,7,10), # "nlMeans P":lambda:nlMeansProjected(clDev,data,3,7,10), "bm3d ":lambda:bm3d(data,10), "dct ":lambda:dct_denoising(data,10), "dct_8x8 ":lambda:dct_8x8(data,10), # "nlMeans prefetched":lambda:nlMeansPrefetch(clDev,data,3,5,10), #"nlMeans test":lambda:nlMeansTest(clDev,data,3,5,10), } t = time.time() for f in fs.keys: fs[f]() print("%s \t: \t %.2f s"%(f,time.time()-t)) t = time.time() if __name__ == '__main__': test_filter() # out1 = test_bilateral() # out = test_nlm_fast() # out3 = test_nlm() # out4 = test_dct() # test_filter() # from SpimUtils import test_images # data0 = test_images.lena() # y = np.maximum(0,1.*data0+np.random.normal(0,20,data0.shape)).astype(np.uint16) # dev = OCLDevice() # out = nlMeansProjected2(dev,y,3,4,35)	[ 1, 529, 276, 1112, 420, 29958, 13072, 3946, 29950, 29914, 29887, 649, 8789, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 15945, 29908, 13, 29909, 4333, 310, 777, 972, 29877, 5921, 14009, 297, 29871, 29906, 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tests/strategies/base.py	lycantropos/clipping	4	47439	from fractions import Fraction from functools import partial from hypothesis import strategies MAX_NUMBER = 10 ** 10 MIN_NUMBER = -MAX_NUMBER coordinates_strategies_factories = { float: partial(strategies.floats, allow_nan=False, allow_infinity=False), Fraction: partial(strategies.fractions, max_denominator=MAX_NUMBER), int: strategies.integers, } coordinates_strategies = strategies.sampled_from( [factory(MIN_NUMBER, MAX_NUMBER) for factory in coordinates_strategies_factories.values()])	[ 1, 515, 5227, 1953, 1053, 7347, 428, 13, 3166, 2090, 312, 8789, 1053, 7687, 13, 13, 3166, 20051, 1053, 16650, 583, 13, 13, 12648, 29918, 23207, 353, 29871, 29896, 29900, 3579, 29871, 29896, 29900, 13, 16173, 29918, 23207, 353, 448, 12648, 29918, 23207, 13, 1111, 24266, 29918, 710, 1845, 583, 29918, 17028, 3842, 353, 426, 13, 1678, 5785, 29901, 7687, 29898, 710, 1845, 583, 29889, 29888, 417, 1446, 29892, 13, 462, 259, 2758, 29918, 13707, 29922, 8824, 29892, 13, 462, 259, 2758, 29918, 262, 4951, 537, 29922, 8824, 511, 13, 1678, 7347, 428, 29901, 7687, 29898, 710, 1845, 583, 29889, 20910, 1953, 29892, 13, 462, 418, 4236, 29918, 1145, 5817, 1061, 29922, 12648, 29918, 23207, 511, 13, 1678, 938, 29901, 16650, 583, 29889, 6693, 5743, 29892, 13, 29913, 13, 1111, 24266, 29918, 710, 1845, 583, 353, 16650, 583, 29889, 11249, 29881, 29918, 3166, 29898, 13, 4706, 518, 14399, 29898, 16173, 29918, 23207, 29892, 18134, 29918, 23207, 29897, 13, 308, 363, 12529, 297, 10350, 29918, 710, 1845, 583, 29918, 17028, 3842, 29889, 5975, 580, 2314, 13, 2 ]
speech_to_text_azure.py	jaykasundra2/Speech_To_Text	0	180496	# Convert the differnt audio formats into .wav format import subprocess, os os.chdir(r'D:\STT\src') # audio file formats are converted using ffmpeg tool # set the path where the executable (.exe) file is located # download ffmpeg build from https://ffmpeg.zeranoe.com/builds/ and place it in your project folder # ffmpeg source code is available at https://www.ffmpeg.org/download.html FFMPEG_PATH = r'D:\STT\ffmpeg\bin\ffmpeg' import glob # Below is the folder structure for audio files. Multiple speakers each having individual folder of his/her recordings # recordings # - speaker 1 # - speaker 2 # - ... # set the path where audio files are located folder_path = r'..\data\recordings' for speaker_folder in glob.glob(folder_path + "/"): speaker = speaker_folder.split("\\")[-1] print(speaker) for file in glob.glob(speaker_folder + "/"): file_name = file.split("\\")[-1].split(".")[0] # output file name output_file_name = folder_path + '\\' + speaker + '_' + file_name + '.wav' print(output_file_name) # ffmpeg command to convert the audio file format command = [FFMPEG_PATH, '-i', file,output_file_name] subprocess.call(command) ################### All files converted to .wav fomrat ######################### ################### Generate transcript from this audio files ################## import time import azure.cognitiveservices.speech as speechsdk # configure API keys and service region speech_key, service_region = "53f0b034226a41c18e8ab5639604b74f", "centralindia" # configure speechadk object speech_config = speechsdk.SpeechConfig(subscription=speech_key, region=service_region) # create an empty output output = "" def combine_text(evt): """ this function will combine the transcript that is generated by multiple utterances/sentences from a audio file""" global output output = output + evt.result.text print(evt.result.text) def speech_recognize_continuous_from_file(): """performs continuous speech recognition with input from an audio file""" done = False def stop_cb(evt): """callback that stops continuous recognition upon receiving an event `evt`""" speech_recognizer.stop_continuous_recognition() nonlocal done done = True speech_recognizer.recognized.connect(lambda evt: combine_text(evt)) speech_recognizer.session_stopped.connect(stop_cb) speech_recognizer.canceled.connect(stop_cb) # Start continuous speech recognition speech_recognizer.start_continuous_recognition() while not done: time.sleep(.5) # get a list of files that have the extension wav filelist = filter(lambda f: f.split('.')[-1] in ['wav'], os.listdir(folder_path)) filelist = sorted(filelist) # generate transcript for each file for file in filelist: audio_config = speechsdk.audio.AudioConfig(filename=folder_path+"\\"+file) speech_recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config, audio_config=audio_config) speech_recognize_continuous_from_file() # save the transcript to a text file f= open(r'..\data\recordings\\'+file.split(".")[0] +".txt","w+") f.write(output) f.close() # reset the output to blank for next file output = ""	[ 1, 396, 14806, 278, 1163, 593, 10348, 21971, 964, 869, 29893, 485, 3402, 29871, 13, 5215, 1014, 5014, 29892, 2897, 13, 359, 29889, 305, 3972, 29898, 29878, 29915, 29928, 3583, 1254, 29911, 29905, 4351, 1495, 29871, 13, 13, 29937, 10348, 934, 21971, 526, 11543, 773, 14336, 20856, 5780, 13, 29937, 731, 278, 2224, 988, 278, 16813, 14544, 8097, 29897, 934, 338, 5982, 13, 29937, 5142, 14336, 20856, 2048, 515, 2045, 597, 600, 20856, 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shop/admin.py	Zadigo/mycommerce	0	162772	from django.contrib import admin from jsonschema import ValidationError from shop.models import (Image, Like, Product, Video, Wishlist) import random from shop.utils import create_product_slug @admin.register(Product) class ProductAdmin(admin.ModelAdmin): list_display = ['name', 'color', 'category', 'unit_price', 'active'] filter_horizontal = ['images'] list_filter = ['active'] date_hiearchy = 'created_on' search_fields = ['name', 'slug'] fieldsets = [ ['General', {'fields': ['name', 'sku']}], ['Variant', {'fields': ['color', 'category']}], ['Media', {'fields': ['images', 'video']}], ['Pricing', {'fields': ['unit_price', 'sale_value', 'sale_price', 'on_sale']}], ['Other', {'fields': ['display_new', 'active', 'slug']}] ] actions = ['activate', 'deactivate', 'download_csv', 'copy_products', 'create_default_sizes'] def activate(self, request, queryset): queryset.update(active=True) def deactivate(self, request, queryset): queryset.update(active=False) def download_csv(self, request, queryset): queryset = None def copy_products(self, request, queryset): new_products = [] for product in queryset: new_name = f"{product.name}{random.randrange(1, 999)}" slug = create_product_slug(new_name, product.color) new_products.append(Product(name=new_name, color=product.color, category=product.category, unit_price=product.unit_price, slug=slug)) Product.objects.bulk_create(new_products) def create_default_sizes(self, request, queryset): """For selected products, create three basic sizes XS, S and M which are generally the average size for clothes""" sizes = ['XS', 'S', 'M'] for product in queryset: for size in sizes: try: product.size_set.create(product=product, name=size, availability=False) except ValidationError: # If the size already exists for a given # product, just fail silently pass self.message_user(request, f'Created default sizes for {len(queryset)} products') @admin.register(Image) class ImageAdmin(admin.ModelAdmin): list_display = ['name', 'original', 'created_on', 'is_main_image'] date_hiearchy = 'created_on' search_fields = ['name', 'original'] list_filter = ['is_main_image'] @admin.register(Video) class VideoAdmin(admin.ModelAdmin): list_display = ['name', 'content'] search_fields = ['name'] # @admin.register(AdditionalVariant) # class AdditionalVariantAdmin(admin.ModelAdmin): # list_display = ['reference', 'in_stock', 'active'] # list_filter = ['active', 'in_stock'] @admin.register(Wishlist) class WishlistAdmin(admin.ModelAdmin): list_display = ['user', 'name', 'created_on'] date_hiearchy = 'created_on' @admin.register(Like) class LikeAdmin(admin.ModelAdmin): list_display = ['user', 'created_on'] search_fields = ['products__name'] date_hiearchy = 'created_on'	[ 1, 515, 9557, 29889, 21570, 1053, 4113, 13, 3166, 6965, 787, 305, 2603, 1053, 15758, 362, 2392, 13, 13, 3166, 18296, 29889, 9794, 1053, 313, 2940, 29892, 8502, 29892, 10969, 29892, 13987, 29892, 13, 462, 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src/custom_layers.py	iamsoroush/TensorflowCPM	0	148692	import tensorflow.keras as tfk class InstanceNorm(tfk.layers.Layer): """Instance normalization layer. Normalizes the activations of the previous layer at each step, i.e. applies a transformation that maintains the mean activation of each feature map for each instance in batch close to 0 and the standard deviation close to 1. Args: axis: Integer, the axis that should be normalized (typically the features axis). For instance, after a `Conv1D` layer with `data_format="channels_last"`, set `axis=-1` in `InstanceNorm`. Setting `axis=None` will normalize all values in each instance of the batch. Axis 0 is the batch dimension. `axis` cannot be set to 0 to avoid errors. epsilon: Small float added to variance to avoid dividing by zero. Input shape: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a Sequential model. Output shape: Same shape as input. References: - [Instance Normalization: The Missing Ingredient for Fast Stylization]( https://arxiv.org/abs/1607.08022) """ def __init__(self, axis=-1, epsilon=1e-3, mean=0, stddev=1, kwargs): self.supports_masking = True self.axis = axis self.epsilon = epsilon self.mean = mean self.stddev = stddev super(InstanceNorm, self).__init__(kwargs) def build(self, input_shape): ndim = len(input_shape) if self.axis == 0: raise ValueError('Axis cannot be zero') if (self.axis is not None) and (ndim == 2): raise ValueError('Cannot specify axis for rank 1 tensor') self.input_spec = tfk.layers.InputSpec(ndim=ndim) super(InstanceNorm, self).build(input_shape) def call(self, inputs): input_shape = tfk.backend.int_shape(inputs) reduction_axes = list(range(0, len(input_shape))) if self.axis is not None: del reduction_axes[self.axis] del reduction_axes[0] mean = tfk.backend.mean(inputs, reduction_axes, keepdims=True) stddev = tfk.backend.std(inputs, reduction_axes, keepdims=True) + self.epsilon normed = (inputs - 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contextual-multimodal-fusion/create_data.py	ggzhang0071/multimodal-deep-learning	0	146637	<reponame>ggzhang0071/multimodal-deep-learning<gh_stars>0 ## Credits: https://github.com/SenticNet/contextual-utterance-level-multimodal-sentiment-analysis ## ## Authors: <NAME>, <NAME> ## ## Modified for Python 3.5 ## import numpy as np, pandas as pd from collections import defaultdict import pickle from sklearn import preprocessing min_max_scaler = preprocessing.MinMaxScaler() np.random.seed(17) pre_data = np.asarray(pd.read_csv("./data/transcripts.csv" , header=None)) train = pd.read_csv("./data/text_train.csv", header=None) test = pd.read_csv("./data/text_test.csv", header=None) train = np.asarray(train) test = np.asarray(test) train_index = np.asarray(train[:,0], dtype = 'int') test_index = np.asarray(test[:,0], dtype = 'int') def main(name): path = "./data/"+name+"/"+name print(path) train_video_mapping=defaultdict(list) train_video_mapping_index=defaultdict(list) test_video_mapping=defaultdict(list) test_video_mapping_index=defaultdict(list) data_train = np.asarray(pd.read_csv(path+"_train0.csv", header=None)) data_test = np.asarray(pd.read_csv(path+"_test0.csv", header=None)) for i in range(train_index.shape[0]): train_video_mapping[pre_data[train_index[i]][0].rsplit("_",1)[0] ].append(train_index[i]) train_video_mapping_index[pre_data[train_index[i]][0].rsplit("_",1)[0] ].append( int(pre_data[train_index[i]][0].rsplit("_",1)[1]) ) for i in range(test_index.shape[0]): test_video_mapping[pre_data[test_index[i]][0].rsplit("_",1)[0] ].append(test_index[i]) test_video_mapping_index[pre_data[test_index[i]][0].rsplit("_",1)[0] ].append( int(pre_data[test_index[i]][0].rsplit("_",1)[1]) ) train_indices = dict((c, i) for i, c in enumerate(train_index)) test_indices = dict((c, i) for i, c in enumerate(test_index)) max_len = 0 for key,value in train_video_mapping.items(): max_len = max(max_len , len(value)) for key,value in test_video_mapping.items(): max_len = max(max_len, len(value)) pad = np.asarray([0 for i in range(data_train[0][:-1].shape[0])]) print ("Mapping train") train_data_X =[] train_data_Y =[] train_length =[] for key,value in train_video_mapping.items(): lst = np.column_stack((train_video_mapping_index[key],value) ) ind = np.asarray(sorted(lst,key=lambda x: x[0])) lst_X, lst_Y=[],[] ctr=0; for i in range(ind.shape[0]): ctr+=1 #lst_X.append(preprocessing.scale( min_max_scaler.fit_transform(data_train[train_indices[ind[i,1]]][:-1]))) lst_X.append(data_train[train_indices[ind[i,1]]][:-1]) lst_Y.append(data_train[train_indices[ind[i,1]]][-1]) train_length.append(ctr) for i in range(ctr, max_len): lst_X.append(pad) lst_Y.append(0) #dummy label train_data_X.append(lst_X) train_data_Y.append(lst_Y) test_data_X =[] test_data_Y =[] test_length =[] print ("Mapping test") for key,value in test_video_mapping.items(): lst = np.column_stack((test_video_mapping_index[key],value) ) ind = np.asarray(sorted(lst,key=lambda x: x[0])) lst_X, lst_Y=[],[] ctr=0 for i in range(ind.shape[0]): ctr+=1 #lst_X.append(preprocessing.scale( min_max_scaler.transform(data_test[test_indices[ind[i,1]]][:-1]))) lst_X.append(data_test[test_indices[ind[i,1]]][:-1]) lst_Y.append(data_test[test_indices[ind[i,1]]][-1]) test_length.append(ctr) for i in range(ctr, max_len): lst_X.append(pad) lst_Y.append(0) #dummy label test_data_X.append(np.asarray(lst_X)) test_data_Y.append(np.asarray(lst_Y)) train_data_X = np.asarray(train_data_X) test_data_X = np.asarray(test_data_X) print (train_data_X.shape, test_data_X.shape,len(train_length), len(test_length)) print ("Dumping data") with open('./input/'+name+'.pickle', 'wb') as handle: pickle.dump((train_data_X, np.asarray(train_data_Y), test_data_X, np.asarray(test_data_Y), max_len ,train_length, test_length), handle, protocol=pickle.HIGHEST_PROTOCOL) if __name__ == "__main__": names = ['text','audio','video'] for nm in names: main(nm)	[ 1, 529, 276, 1112, 420, 29958, 1505, 29920, 11895, 29900, 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ifaces/management/__init__.py	RossBrunton/RThing	0	164825	<filename>ifaces/management/__init__.py """Management modules for ifaces"""	[ 1, 529, 9507, 29958, 361, 3302, 29914, 21895, 29914, 1649, 2344, 26914, 2272, 13, 15945, 29908, 27107, 10585, 363, 565, 3302, 15945, 29908, 13, 2 ]
tests/test_calc.py	aldencolerain/rxtouchtoggle	0	107868	# test add function def test_example(): assert True	[ 1, 396, 1243, 788, 740, 13, 1753, 1243, 29918, 4773, 7295, 13, 1678, 4974, 5852, 13, 2 ]
tests/test_unit/test_md2po2md/test_md2po2md_cli.py	mondeja/md2po	0	84747	<filename>tests/test_unit/test_md2po2md/test_md2po2md_cli.py """Tests for ``md2po2md`` CLI""" import os import pytest from mdpo.md2po2md.__main__ import run @pytest.mark.parametrize('output_arg', ('-o', '--output')) @pytest.mark.parametrize('langs_arg', ('-l', '--lang')) @pytest.mark.parametrize('all_langs_in_same_arg', (True, False)) @pytest.mark.parametrize( ( 'langs', 'input_paths_glob', 'output', 'input_files_content', 'expected_result', ), ( pytest.param( ['es'], 'README.md', 'locale/{lang}', {'README.md': 'Foo\n\nBar\n'}, { 'locale/es/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/es/README.md': 'Foo\n\nBar\n', }, id='es-locale/{lang}', ), pytest.param( ('es', 'fr'), 'README.md', 'locale/{lang}', {'README.md': 'Foo\n\nBar\n'}, { 'locale/es/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/es/README.md': 'Foo\n\nBar\n', 'locale/fr/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/fr/README.md': 'Foo\n\nBar\n', }, id='README-es,fr-locale/{lang}', ), pytest.param( ('es',), 'README.md', 'locale/', {}, ValueError, id='es-locale/-ValueError', ), pytest.param( ('es',), 'README.md', 'locale/{lang}/{basename}', {'README.md': 'Foo\n\nBar\n'}, { 'locale/es/README.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/es/README': 'Foo\n\nBar\n', }, id='es-locale/{lang}/{basename}', ), pytest.param( ['es'], 'README.md', '{lang}/{basename}', {'README.md': 'Foo\n\nBar\n'}, { 'es/README.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'es/README': 'Foo\n\nBar\n', }, id='es-{lang}/{basename}', ), pytest.param( ['es'], 'README.md', '{lang}', {'README.md': 'Foo\n\nBar\n'}, { 'es/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'es/README.md': 'Foo\n\nBar\n', }, id='es-{lang}', ), pytest.param( ['es'], '[s-m]', '{lang}/{basename}', {}, ValueError, id='invalid-glob-ValueError', ), pytest.param( ['es'], 'foobar', '{lang}/{basename}', {}, FileNotFoundError, id='no-files-matching-input-glob', ), pytest.param( ('es', 'fr', 'de_DE'), 'README.md', 'locale/{lang}', {'README.md': 'Foo\n\nBar\n'}, { 'locale/es/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/es/README.md': 'Foo\n\nBar\n', 'locale/fr/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/fr/README.md': 'Foo\n\nBar\n', 'locale/de_DE/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/de_DE/README.md': 'Foo\n\nBar\n', }, id='README-es,fr-locale/{lang}', ), ), ) def test_md2po2md_arguments( output_arg, langs_arg, all_langs_in_same_arg, langs, input_paths_glob, output, input_files_content, expected_result, # {files: contents} or error tmp_dir, maybe_raises, ): with tmp_dir(input_files_content) as filesdir: # run md2po2md cmd = [ os.path.join(filesdir, input_paths_glob), output_arg, os.path.join(filesdir, output), ] # if all languages are passed in the same `--lang`/`-l` argument if all_langs_in_same_arg: cmd.extend([langs_arg, langs]) else: for lang in langs: cmd.extend([langs_arg, lang]) cmd.append('--no-location') with maybe_raises(expected_result): run(cmd) # Check number of files expected_number_of_files = ( len(expected_result) + len(input_files_content) ) n_files = 0 for root, dirs, files in os.walk(filesdir, topdown=False): n_files += len(files) assert n_files == expected_number_of_files # Check expected content for relpath, content in expected_result.items(): filepath = os.path.join(filesdir, relpath) with open(filepath) as f: assert f.read() == content	[ 1, 529, 9507, 29958, 21150, 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4974, 285, 29889, 949, 580, 1275, 2793, 13, 2 ]
pyslowloris/__main__.py	goasdsdkai/daas	75	129362	""" MIT License Copyright (c) 2017 <NAME> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import argparse import sys from pyslowloris import HostAddress, SlowLorisAttack def _parse_args() -> dict: parser = argparse.ArgumentParser( add_help=True, description="Asynchronous Python implementation of SlowLoris attack" ) parser.add_argument( "-u", "--url", action="store", type=str, required=True, help="Link to a web server (http://google.com) - str" ) # 247 is magic number to prevent OSError: [Errno 24] Too many open files parser.add_argument( "-c", "--connection-count", default=247, action="store", type=int, help="Count of active connections (default value is 247) - int" ) parser.add_argument( "-s", "--silent", action='store_true', help="Ignore all of the errors [pure attack mode] - bool" ) # TODO(mkrivich): add support of this flag # parser.add_argument( # "-v", "--verbose", action='store_false', # help="Produce more logs - bool" # ) if len(sys.argv) == 1: parser.print_help() sys.exit(-1) result = {} args = parser.parse_args() if args.url: try: result["address"] = HostAddress.from_url(args.url) except Exception: parser.print_help() sys.exit(-1) if not (0 < args.connection_count <= 300): parser.print_help() sys.exit(-1) result["connections_count"] = args.connection_count result["silent"] = args.silent return result def _run(target: HostAddress, connections_count: int, silent: bool) -> None: print("Attack info:") print(f"\tTarget: {str(target)}({target.ip_address})") print(f"\tConnection count: {connections_count}") print(f"\tMode (silent): {silent}") loris = SlowLorisAttack(target, connections_count, silent=silent) loris.start() def main(): args = _parse_args() # Sending requests until Ctrl+C is pressed try: _run(args["address"], args["connections_count"], args["silent"]) except KeyboardInterrupt: sys.exit(0) except Exception as ex: print(ex)	[ 1, 9995, 13, 26349, 19245, 13, 13, 11882, 1266, 313, 29883, 29897, 29871, 29906, 29900, 29896, 29955, 529, 5813, 29958, 13, 13, 27293, 338, 1244, 1609, 16896, 29892, 3889, 310, 8323, 29892, 304, 738, 2022, 4017, 292, 263, 3509, 13, 974, 445, 7047, 322, 6942, 5106, 2066, 313, 1552, 376, 6295, 14093, 4968, 304, 5376, 13, 262, 278, 18540, 1728, 24345, 29892, 3704, 1728, 29485, 278, 10462, 13, 517, 671, 29892, 3509, 29892, 6623, 29892, 10366, 29892, 9805, 29892, 1320, 2666, 29892, 269, 803, 1947, 29892, 322, 29914, 272, 19417, 13, 9708, 583, 310, 278, 18540, 29892, 322, 304, 14257, 12407, 304, 6029, 278, 18540, 338, 13, 29888, 595, 3276, 304, 437, 577, 29892, 4967, 304, 278, 1494, 5855, 29901, 13, 13, 1576, 2038, 3509, 1266, 8369, 322, 445, 10751, 8369, 4091, 367, 5134, 297, 599, 13, 9708, 583, 470, 23228, 2011, 1080, 310, 278, 18540, 29889, 13, 13, 28350, 7791, 7818, 12982, 1525, 8519, 13756, 13044, 3352, 376, 3289, 8519, 613, 399, 1806, 8187, 2692, 399, 1718, 29934, 13566, 29979, 8079, 13764, 29979, 476, 22255, 29892, 8528, 15094, 1799, 6323, 13, 29902, 3580, 5265, 3352, 29892, 2672, 6154, 15789, 4214, 350, 2692, 6058, 27848, 3352, 7495, 6093, 399, 1718, 29934, 13566, 29059, 8079, 341, 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project/handler/type/request.py	SnackChain/SnackPi	0	72748	<reponame>SnackChain/SnackPi<filename>project/handler/type/request.py import requests from handler.type.handler import AbstractInstructionHandler from handler.operation.client import OperationClient class RequestInstruction(): def __init__(self, url, method, headers = None, parameters = None, request_source = None, response_source = None): self.http_request = HTTPRequest(url, method, headers, parameters) self.request_source = request_source self.response_source = response_source class HTTPRequest(): def __init__(self, url, method, headers = None, parameters = None): self.url = url self.headers = headers self.parameters = parameters self.method = method def is_get(self): if self.method == 'GET': return True else: return False def is_post(self): if self.method == 'GET': return True else: return False class Network(): def do_request(self, request: HTTPRequest): parameters = {} headers = {} if request.parameters: parameters = request.parameters if request.headers: headers = request.headers if request.is_get(): response = requests.get(request.url) return self.json(response) elif request.is_post(): response = requests.post( request.url, params = parameters, headers = headers ) return self.verify(response) def json(self, response): if response.status_code == requests.codes.ok: return response.json() else: print("Request failed") class RequestHandler(AbstractInstructionHandler): def handle(self, instruction, parameter_provider): if instruction.type == "request": payload = instruction.payload request_instruction = RequestInstruction(**payload) network = Network() json = network.do_request(request_instruction.http_request) if json is None: print("error") return #Get values based on the paths from json response for parameter_source in request_instruction.response_source: value = json for key in parameter_source: value = value[key] parameter_provider.store_value(value) else: super().handle(request)	[ 1, 529, 276, 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asynchronous_qiwi/models/QIWIWallet/master_m/list_qvc.py	LexLuthorReal/asynchronous_qiwi	3	20624	<reponame>LexLuthorReal/asynchronous_qiwi<filename>asynchronous_qiwi/models/QIWIWallet/master_m/list_qvc.py from loguru import logger import datetime from pydantic.fields import ModelField from typing import Optional, List, Union, Any from ....utils.tools.str_datetime import convert from pydantic import BaseModel, Field, validator, ValidationError from ....data_types.QIWIWallet.list_qvc import ReleasedCardStatus, CardType, CardAlias class AmountData(BaseModel): """Object: \"AmountData\"""" amount: float = Field(..., alias="amount") currency: str = Field(..., alias="currency") class Requisites(BaseModel): name: str = Field(..., alias="name") value: str = Field(..., alias="value") class Details(BaseModel): info: str = Field(..., alias="info") description: str = Field(..., alias="description") tariff_link: str = Field(..., alias="tariffLink") offer_link: str = Field(..., alias="offerLink") features: List[Any] = Field(..., alias="features") requisites: List[Union[Requisites]] = Field(..., alias="requisites") class Info(BaseModel): id: int = Field(..., alias="id") name: str = Field(..., alias="name") alias: Union[str, CardAlias] = Field(..., alias="alias") price: AmountData = Field(..., alias="price") period: str = Field(..., alias="period") type: Union[str, CardAlias] = Field(..., alias="type") details: Details = Field(..., alias="details") @validator("alias") def alias_type(cls, alias: Union[str, CardAlias], field: ModelField) -> CardAlias: if isinstance(alias, str): try: alias = CardAlias(alias) except KeyError as e: logger.warning(f"[VALIDATION CONVERT] {field.name.upper()}: " + str(e)) else: return alias elif isinstance(alias, CardAlias): return alias raise ValidationError(model=Info) @validator("type") def card_type_type(cls, card_type: Union[str, CardAlias], field: ModelField) -> CardAlias: if isinstance(card_type, str): try: card_type = CardAlias[card_type] except KeyError as e: logger.warning(f"[VALIDATION CONVERT] {field.name.upper()}: " + str(e)) else: return card_type elif isinstance(card_type, CardAlias): return card_type raise ValidationError(model=Info) class QVX(BaseModel): id: int = Field(..., alias="id") masked_pan: str = Field(..., alias="maskedPan") status: Optional[Union[str, ReleasedCardStatus]] = Field(..., alias="status") card_expire: Optional[Union[str, datetime.datetime]] = Field(..., alias="cardExpire") card_type: Optional[Union[str, CardType]] = Field(..., alias="cardType") card_alias: str = Field(..., alias="cardAlias") card_limit: Optional[str] = Field(..., alias="cardLimit") activated: Optional[Union[str, datetime.datetime]] = Field(..., alias="activated") sms_resended: Optional[Union[str, datetime.datetime]] = Field(..., alias="smsResended") post_number: Optional[str] = Field(..., alias="postNumber") blocked_date: Optional[Union[str, datetime.datetime]] = Field(..., alias="blockedDate") full_pan: Optional[str] = Field(..., alias="fullPan") card_id: int = Field(..., alias="cardId") txn_id: str = Field(..., alias="txnId") card_expire_month: str = Field(..., alias="cardExpireMonth") card_expire_year: str = Field(..., alias="cardExpireYear") @validator('status') def status_types(cls, status: Union[str, ReleasedCardStatus], field: ModelField) -> ReleasedCardStatus: if isinstance(status, str): try: status = ReleasedCardStatus[status] except KeyError as e: logger.warning(f"[VALIDATION CONVERT] {field.name.upper()}: " + str(e)) else: return status elif isinstance(status, ReleasedCardStatus): return status raise ValidationError(model=QVX) @validator('card_expire') def card_expire_datetime(cls, card_expire: Optional[Union[str, datetime.datetime]], field: ModelField) -> Optional[datetime.datetime]: if isinstance(card_expire, str): card_expire = convert(value=card_expire, validator_name=field.name.upper(), alert=False) return card_expire elif isinstance(card_expire, datetime.datetime): return card_expire elif card_expire is None: return card_expire raise ValidationError(model=QVX) @validator('card_type') def card_types(cls, card_type: Union[str, CardType], field: ModelField) -> CardType: if isinstance(card_type, str): try: card_type = CardType[card_type] except KeyError as e: logger.warning(f"[VALIDATION CONVERT] {field.name.upper()}: " + str(e)) else: return card_type elif isinstance(card_type, CardType): return card_type raise ValidationError(model=QVX) @validator('activated') def activated_datetime(cls, activated: Optional[Union[str, datetime.datetime]], field: ModelField) -> Optional[datetime.datetime]: if isinstance(activated, str): activated = convert(value=activated, validator_name=field.name.upper(), alert=False) return activated elif isinstance(activated, datetime.datetime): return activated elif activated is None: return activated raise ValidationError(model=QVX) @validator('sms_resended') def sms_resended_datetime(cls, sms_resended: Optional[Union[str, datetime.datetime]], field: ModelField) -> Optional[datetime.datetime]: if isinstance(sms_resended, str): sms_resended = convert(value=sms_resended, validator_name=field.name.upper(), alert=False) return sms_resended elif isinstance(sms_resended, datetime.datetime): return sms_resended elif sms_resended is None: return sms_resended raise ValidationError(model=QVX) @validator('blocked_date') def blocked_date_datetime(cls, blocked_date: Optional[Union[str, datetime.datetime]], field: ModelField) -> Optional[datetime.datetime]: if isinstance(blocked_date, str): blocked_date = convert(value=blocked_date, validator_name=field.name.upper(), alert=False) return blocked_date elif isinstance(blocked_date, datetime.datetime): return blocked_date elif blocked_date is None: return blocked_date raise ValidationError(model=QVX) class ListCard(BaseModel): qvx: QVX = Field(..., alias="qvx") balance: Optional[AmountData] = Field(..., alias="balance") info: Info = Field(..., alias="info") features: List[Any] = Field(..., 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kg.py	Jie-Yuan/BaiduAI-sdk	0	182771	<filename>kg.py<gh_stars>0 import json import urllib import requests import json import urllib import requests class BaiDuNLP(object): def __init__(self, api_key='<KEY>', secret_key='<KEY>'): self.access_token = None self._get_access_token(api_key, secret_key) def predict(self, text, api_url): """ api_url = 'https://aip.baidubce.com/rpc/2.0/kg/v1/cognitive/entity_annotation?charset=UTF-8&access_token=' api = BaiDuNLP() api.predict('周杰伦', api_url) """ url = api_url + self.access_token # the input is json format input_text = {'data': text} input_text = json.dumps(input_text) r = requests.post(url, data=input_text, headers={'Content-Type': 'application/json'}) return r.json() def _get_access_token(self, api_key, secret_key): host = f'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={api_key}&client_secret={secret_key}' request = urllib.request.Request(host) request.add_header('Content-Type', 'application/json; charset=UTF-8') response = urllib.request.urlopen(request) content = response.read() self.access_token = json.loads(content)['access_token']	[ 1, 529, 9507, 29958, 9415, 29889, 2272, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 5215, 4390, 13, 5215, 3142, 1982, 13, 13, 5215, 7274, 13, 13, 13, 5215, 4390, 13, 5215, 3142, 1982, 13, 13, 5215, 7274, 13, 13, 13, 1990, 350, 1794, 29928, 29884, 29940, 13208, 29898, 3318, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 7882, 29918, 1989, 2433, 29966, 10818, 29958, 742, 7035, 29918, 1989, 2433, 29966, 10818, 16299, 1125, 13, 4706, 1583, 29889, 5943, 29918, 6979, 353, 6213, 13, 4706, 1583, 3032, 657, 29918, 5943, 29918, 6979, 29898, 2754, 29918, 1989, 29892, 7035, 29918, 1989, 29897, 13, 13, 1678, 822, 8500, 29898, 1311, 29892, 1426, 29892, 7882, 29918, 2271, 1125, 13, 4706, 9995, 13, 4706, 7882, 29918, 2271, 353, 525, 991, 597, 29874, 666, 29889, 2291, 333, 431, 346, 29889, 510, 29914, 29878, 6739, 29914, 29906, 29889, 29900, 29914, 9415, 29914, 29894, 29896, 29914, 29883, 3811, 3321, 29914, 10041, 29918, 18317, 29973, 3090, 842, 29922, 10496, 29899, 29947, 29987, 5943, 29918, 6979, 2433, 13, 4706, 7882, 353, 350, 1794, 29928, 29884, 29940, 13208, 580, 13, 4706, 7882, 29889, 27711, 877, 31109, 233, 160, 179, 231, 191, 169, 742, 7882, 29918, 2271, 29897, 13, 4706, 9995, 13, 4706, 3142, 353, 7882, 29918, 2271, 718, 1583, 29889, 5943, 29918, 6979, 13, 308, 13, 4706, 396, 278, 1881, 338, 4390, 3402, 13, 4706, 1881, 29918, 726, 353, 11117, 1272, 2396, 1426, 29913, 13, 4706, 1881, 29918, 726, 353, 4390, 29889, 29881, 17204, 29898, 2080, 29918, 726, 29897, 13, 308, 13, 4706, 364, 353, 7274, 29889, 2490, 29898, 2271, 29892, 848, 29922, 2080, 29918, 726, 29892, 9066, 3790, 29915, 3916, 29899, 1542, 2396, 525, 6214, 29914, 3126, 29915, 1800, 13, 4706, 736, 364, 29889, 3126, 580, 13, 13, 1678, 822, 903, 657, 29918, 5943, 29918, 6979, 29898, 1311, 29892, 7882, 29918, 1989, 29892, 7035, 29918, 1989, 1125, 13, 4706, 3495, 353, 285, 29915, 991, 597, 29874, 666, 29889, 2291, 333, 431, 346, 29889, 510, 29914, 23106, 29914, 29906, 29889, 29900, 29914, 6979, 29973, 629, 424, 29918, 1853, 29922, 4645, 29918, 11944, 9409, 29987, 4645, 29918, 333, 3790, 2754, 29918, 1989, 15704, 4645, 29918, 19024, 3790, 19024, 29918, 1989, 10162, 13, 4706, 2009, 353, 3142, 1982, 29889, 3827, 29889, 3089, 29898, 3069, 29897, 13, 4706, 2009, 29889, 1202, 29918, 6672, 877, 3916, 29899, 1542, 742, 525, 6214, 29914, 3126, 29936, 17425, 29922, 10496, 29899, 29947, 1495, 13, 4706, 2933, 353, 3142, 1982, 29889, 3827, 29889, 332, 417, 2238, 29898, 3827, 29897, 13, 4706, 2793, 353, 2933, 29889, 949, 580, 13, 4706, 1583, 29889, 5943, 29918, 6979, 353, 4390, 29889, 18132, 29898, 3051, 29897, 1839, 5943, 29918, 6979, 2033, 13, 2 ]
flir_lepton_auxiliary/scripts/produce_dataset.py	angetria/flir_lepton_rpi2	15	85046	#!usr/bin/python import sys # The area of interest(pixels) to find the mean temperature x1 = 52 x2 = 54 y1 = 18 y2 = 20 # The area of neutral pixels to find the mean temperature x3 = 66 x4 = 68 y3 = 6 y4 = 8 # Make the dataset from raw pixel flir-lepton images. try: dataset = open("dataset.iemb","w") except IOError: print "dataset file could not be made" sys.exit() # Process each image raw data for all images for i in range(20,51): # The flir-lepton images to be read. fileName = \ "Input your flir-lepton images full path"\ + str(i) + ".iemb" try: data = open(fileName, "r") except IOError: print "No file was found" sys.exit() #counter = 0 signal_val = [] with open(fileName, "r") as file: for line in file: #counter = counter + 1 signal_val.append(line) temp = signal_val[0].split() # To read specific pixel(x,y) must give : gimp gives inverted x,y coords # average = the average temperature of the point of interest # av = the average temperature of a neutral area average = 0 av = 0 if i < 51: for x in range(x1,x2): for y in range(y1,y2): # for raw data images a = y * 80 + x average = average + int(temp[a]) # Find in the other array whats going on in pixel values for x in range(x3,x4): for y in range(y3,y4): b = y * 80 + x av = av + int(temp[b]) average = average / 4 av = av / 4 # If we check second area #else: #for x in range(24,26): #temp = signal_val[x].split() #for y in range(42,44): #a = int(temp[y]) #average = average + a #Find in the other array whats going on in pix values #for x in range(10,12): #temp = signal_val[x].split() #for y in range(65,67): #a = int(temp[y]) #av = av + a #average = average / 4 #av = av / 4 # Make the file with our own standards, for even numbers of rows place # the raw pixel values, for odd the degree's. degree = str(i) + "\n" raw = str(average) + "\n" dataset.write(raw) dataset.write(degree) print "The average of image", i ,"=",average print "The av of image", i ,"=",av data.close() dataset.close()	[ 1, 396, 29991, 4855, 29914, 2109, 29914, 4691, 13, 13, 5215, 10876, 13, 13, 29937, 450, 4038, 310, 4066, 29898, 29886, 861, 1379, 29897, 304, 1284, 278, 2099, 10430, 13, 29916, 29896, 353, 29871, 29945, 29906, 13, 29916, 29906, 353, 29871, 29945, 29946, 13, 29891, 29896, 353, 29871, 29896, 29947, 13, 29891, 29906, 353, 29871, 29906, 29900, 13, 13, 29937, 450, 4038, 310, 21104, 17036, 304, 1284, 278, 2099, 10430, 13, 29916, 29941, 353, 29871, 29953, 29953, 13, 29916, 29946, 353, 29871, 29953, 29947, 13, 29891, 29941, 353, 29871, 29953, 13, 29891, 29946, 353, 29871, 29947, 13, 13, 29937, 8561, 278, 8783, 515, 10650, 15526, 1652, 381, 29899, 280, 12533, 4558, 29889, 13, 2202, 29901, 13, 1678, 8783, 353, 1722, 703, 24713, 29889, 29875, 1590, 3284, 29893, 1159, 13, 19499, 10663, 2392, 29901, 13, 268, 1596, 376, 24713, 934, 1033, 451, 367, 1754, 29908, 13, 1678, 10876, 29889, 13322, 580, 13, 13, 29937, 10554, 1269, 1967, 10650, 848, 363, 599, 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aasaan/utils/datedeux.py	deepakkt/aasaan	0	117147	'''Copyright 2017, Deepak Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' from datetime import date, datetime from functools import partial import re class DateDeux(date): def pydate(self): return date(self.year, self.month, self.day) @classmethod def fromisodate(self, isodatestr): try: return DateDeux((map(int, isodatestr.split('-')))) except: return None @classmethod def frompydate(self, pydate_object): return DateDeux(pydate_object.year, pydate_object.month, pydate_object.day) def monthstart(self): return DateDeux(self.year, self.month, 1) def monthend(self): return DateDeux.fromordinal(DateDeux(self.year, self.month + 1, 1).toordinal() - 1) def yearend(self): return DateDeux(self.year, 12, 31) def yearstart(self): return DateDeux(self.year, 1, 1) def dayname(self): return ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'][self.weekday()] def dayname_short(self): return self.dayname()[:3] def monthname(self): return ['', 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'][self.month] def monthname_short(self): return self.monthname()[:3] def dateformat(self, format): def _format_as_int(number, length): if length < len(str(number)): return str(number)[-length:] format = "%%0%dd" % length return format % number def _format_month(args): return self.monthname_short() def _re_match(matchstring, regex): return re.findall(regex, matchstring)[0] matches = list(map(partial(_re_match, format), ['y+', 'm+', 'd+'])) result = format[:] result = result.replace(matches[0], _format_as_int(self.year, len(matches[0]))) result = result.replace(matches[2], _format_as_int(self.day, len(matches[2]))) _month_func = _format_month if len(matches[1]) == 3 else _format_as_int result = result.replace(matches[1], _month_func(self.month, len(matches[1]))) return result def yearcalendar(self): _start = DateDeux(self.year, 1, 1) _end = DateDeux(self.year, 12, 31) diff = _end.toordinal() - _start.toordinal() + 1 return (_start + x for x in range(0, diff)) def monthcalendar(self): _start = self.monthstart() _end = self.monthend() diff = _end.toordinal() - _start.toordinal() + 1 return (_start + x for x in range(0, diff)) def __add__(self, numdays): return DateDeux.fromordinal(self.toordinal() + numdays) def __sub__(self, numdays): try: return self.toordinal() - numdays.toordinal() except AttributeError: return DateDeux.fromordinal(self.toordinal() - numdays) def __iter__(self): return iter((self.year, self.month, self.day))	[ 1, 14550, 11882, 1266, 29871, 29906, 29900, 29896, 29955, 29892, 21784, 557, 13, 13, 9039, 391, 3224, 322, 671, 297, 2752, 322, 7581, 7190, 29892, 411, 470, 1728, 21733, 29892, 526, 21905, 4944, 393, 278, 1494, 5855, 526, 1539, 29901, 13, 13, 29896, 29889, 4367, 391, 3224, 29879, 310, 2752, 775, 1818, 11551, 278, 2038, 3509, 1266, 8369, 29892, 445, 1051, 310, 5855, 322, 278, 1494, 2313, 433, 4193, 29889, 13, 13, 29906, 29889, 4367, 391, 3224, 29879, 297, 7581, 883, 1818, 18532, 278, 2038, 3509, 1266, 8369, 29892, 445, 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hexrd/ui/color_map_editor.py	HEXRD/hexrdgui	13	199875	import copy from matplotlib import cm import matplotlib.colors import numpy as np import hexrd.ui.constants from hexrd.ui.brightness_contrast_editor import BrightnessContrastEditor from hexrd.ui.hexrd_config import HexrdConfig from hexrd.ui.ui_loader import UiLoader from hexrd.ui.utils import block_signals class ColorMapEditor: def __init__(self, image_object, parent=None): # The image_object can be any object with the following functions: # 1. set_cmap: a function to set the cmap on the image # 2. set_norm: a function to set the norm on the image self.image_object = image_object loader = UiLoader() self.ui = loader.load_file('color_map_editor.ui', parent) self.bounds = (0, 16384) self._data = None self.bc_editor = None self.load_cmaps() self.setup_connections() @property def data(self): return self._data @data.setter def data(self, v): self._data = v self.update_bc_enable_state() if self.bc_editor: self.bc_editor.data = v self.update_bc_editor() def load_cmaps(self): cmaps = sorted(i[:-2] for i in dir(cm) if i.endswith('_r')) self.ui.color_map.addItems(cmaps) # Set the combobox to be the default self.ui.color_map.setCurrentText(hexrd.ui.constants.DEFAULT_CMAP) def setup_connections(self): self.ui.bc_editor_button.pressed.connect(self.bc_editor_button_pressed) self.ui.minimum.valueChanged.connect(self.range_edited) self.ui.maximum.valueChanged.connect(self.range_edited) self.ui.color_map.currentIndexChanged.connect(self.update_cmap) self.ui.reverse.toggled.connect(self.update_cmap) self.ui.show_under.toggled.connect(self.update_cmap) self.ui.show_over.toggled.connect(self.update_cmap) self.ui.log_scale.toggled.connect(self.update_norm) def range_edited(self): self.update_bc_editor() self.update_mins_and_maxes() self.update_norm() def update_bc_enable_state(self): has_data = self.data is not None self.ui.bc_editor_button.setEnabled(has_data) def bc_editor_button_pressed(self): if self.bc_editor: self.bc_editor.ui.reject() bc = self.bc_editor = BrightnessContrastEditor(self.ui) bc.data = self.data bc.edited.connect(self.bc_editor_modified) bc.reset.connect(self.reset_range) bc.ui.finished.connect(self.remove_bc_editor) # Hide overlays while the BC editor is open self._bc_previous_show_overlays = HexrdConfig().show_overlays if self._bc_previous_show_overlays: HexrdConfig().show_overlays = False HexrdConfig().active_material_modified.emit() self.update_bc_editor() self.bc_editor.ui.show() def update_bc_editor(self): if not self.bc_editor: return widgets = (self.ui.minimum, self.ui.maximum) new_range = [x.value() for x in widgets] with block_signals(self.bc_editor): self.bc_editor.ui_range = new_range def remove_bc_editor(self): self.bc_editor = None if self._bc_previous_show_overlays and not HexrdConfig().show_overlays: # Show the overlays again HexrdConfig().show_overlays = True HexrdConfig().active_material_modified.emit() def bc_editor_modified(self): with block_signals(self.ui.minimum, self.ui.maximum): self.ui.minimum.setValue(self.bc_editor.ui_min) self.ui.maximum.setValue(self.bc_editor.ui_max) self.range_edited() def update_mins_and_maxes(self): # We can't do this in PySide2 for some reason: # self.ui.maximum.valueChanged.connect(self.ui.minimum.setMaximum) # self.ui.minimum.valueChanged.connect(self.ui.maximum.setMinimum) self.ui.maximum.setMinimum(self.ui.minimum.value()) self.ui.minimum.setMaximum(self.ui.maximum.value()) def block_updates(self, blocked): self.updates_blocked = blocked def update_bounds(self, data): if hasattr(self, 'updates_blocked') and self.updates_blocked: # We don't want to adjust the bounds return bounds = self.percentile_range(data) self.ui.minimum.setValue(bounds[0]) self.ui.minimum.setToolTip('Min: ' + str(bounds[0])) self.ui.maximum.setValue(bounds[1]) self.ui.maximum.setToolTip('Max: ' + str(bounds[1])) self.bounds = bounds self.data = data @staticmethod def percentile_range(data, low=69.0, high=99.9): if isinstance(data, dict): values = data.values() elif not isinstance(data, (list, tuple)): values = [data] l = min([np.nanpercentile(v, low) for v in values]) h = min([np.nanpercentile(v, high) for v in values]) if h - l < 5: h = l + 5 return l, h def reset_range(self): if hasattr(self, 'updates_blocked') and self.updates_blocked: # We don't want to adjust the range return if self.ui.minimum.maximum() < self.bounds[0]: # Make sure we can actually set the value... self.ui.minimum.setMaximum(self.bounds[0]) self.ui.minimum.setValue(self.bounds[0]) self.ui.maximum.setValue(self.bounds[1]) def update_cmap(self): # Get the Colormap object from the name cmap = cm.get_cmap(self.ui.color_map.currentText()) if self.ui.reverse.isChecked(): cmap = cmap.reversed() # For set_under() and set_over(), we don't want to edit the # original color map, so make a copy cmap = copy.copy(cmap) if self.ui.show_under.isChecked(): cmap.set_under('b') if self.ui.show_over.isChecked(): cmap.set_over('r') 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Domotique/Temperature Fablab/temperature.py	LaFabrickMassy/Projet-Fablab	0	53332	import board import adafruit_ahtx0 from datetime import datetime html_dir = "/var/www/html/temp" data_fname = "temperature.csv" html_fname = "index.html" sensor = adafruit_ahtx0.AHTx0(board.I2C()) temp = sensor.temperature hum = sensor.relative_humidity now = datetime.now() # open data file history = [] with open(html_dir + "/" + data_fname, "r") as f: for l in f.readlines(): fields = l.split(";") dt = datetime.strptime(fields[0], "%d/%m/%y %H:%M") data = [dt, float(fields[1]), float(fields[2])] history.append(data) history.append([now, temp, hum]) t_data = [] h_data = [] dt_data= [] for h in history: t_data.append("{:.1f}".format(h[1])) h_data.append("{:.1f}".format(h[2])) dt_data.append(h[0].strftime("'%Y-%m-%d %H:%m'")) with open(html_dir + "/" + data_fname, "a") as f: dt = now.strftime("%d/%m/%y %H:%M") f.write("{};{};{}\n".format(dt, temp, hum)) head="""<html> <head> <title>Temperature</title> </head> <body>\n""" body1 = """ <div id="plotly-chart"></div> <script type=\"text/javascript\" src=\"./plotly-latest.min.js\"></script> <script type=\"text/javascript\" > var temp = { x: [""" body2="],\n y: [" body3="""], name: 'temperature', type: 'scatter' }; var hum = { x: [""" body4="],\n y: [" body5="""], yaxis: 'y2', name: 'humidity', type: 'scatter' }; var data = [temp, hum]; var layout = { title: 'Double Y Axis Example', yaxis: {title: 'Temperature'}, yaxis2: { title: 'Humidity', overlaying: 'y', side: 'right' } }; Plotly.newPlot('plotly-chart', data, layout); </script> """ tail="</body></html>\n" with open(html_dir + "/" + html_fname, "w") as f: f.write(head) f.write(body1) f.write(",".join(dt_data)) f.write(body2) f.write(",".join(t_data)) f.write(body3) f.write(",".join(dt_data)) f.write(body4) f.write(",".join(h_data)) f.write(body5) f.write(tail)	[ 1, 1053, 7613, 13, 5215, 594, 2142, 9216, 29918, 29874, 400, 29916, 29900, 13, 3166, 12865, 1053, 12865, 13, 13, 1420, 29918, 3972, 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pandas/main.py	monishshah18/python-cp-cheatsheet	140	14032	<gh_stars>100-1000 """ Summarize a column total cases column and total deaths column Country by country data in columns, sum up and match global totals """ import csv import pandas pandas.set_option("display.max_rows", None, "display.max_columns", None) col_list = ["Total Cases", "Country/ Other", "Total Deaths", "# 9/27/2020"] df = pandas.read_csv("covidmilliondead.csv", usecols=col_list, thousands=',') totalCases, totalDeaths = 0,0 for idx, cases,deaths in zip(df["# 9/27/2020"], df["Total Cases"], df["Total Deaths"]): if idx > 0: totalCases += cases if deaths > 0: totalDeaths += deaths for idx, country, cases, deaths in zip(df["# 9/27/2020"], df["Country/ Other"], df["Total Cases"], df["Total Deaths"]): if idx > 0: print("\n",country) print("Cases : ", cases, "/", totalCases, " %", "{:.5%}".format(cases/totalCases)) if deaths > 0: print("Deaths : ", int(deaths), "/", totalDeaths, " %", "{:.5%}".format(deaths/totalDeaths)) print("") print("Total Cases") print(totalCases) print("Total Deaths") print(totalDeaths)	[ 1, 529, 12443, 29918, 303, 1503, 29958, 29896, 29900, 29900, 29899, 29896, 29900, 29900, 29900, 13, 15945, 29908, 13, 11139, 3034, 675, 263, 1897, 3001, 4251, 1897, 322, 3001, 4892, 29879, 1897, 29871, 13, 20779, 491, 4234, 848, 297, 4341, 29892, 2533, 701, 322, 1993, 5534, 2025, 1338, 13, 15945, 29908, 13, 5215, 11799, 29871, 13, 5215, 11701, 13, 13, 15112, 29889, 842, 29918, 3385, 703, 4990, 29889, 3317, 29918, 5727, 613, 6213, 29892, 376, 4990, 29889, 3317, 29918, 13099, 613, 6213, 29897, 13, 1054, 29918, 1761, 353, 6796, 11536, 315, 2129, 613, 376, 20779, 29914, 5901, 613, 376, 11536, 14450, 29879, 613, 12305, 29871, 29929, 29914, 29906, 29955, 29914, 29906, 29900, 29906, 29900, 3108, 13, 2176, 353, 11701, 29889, 949, 29918, 7638, 703, 24542, 333, 19958, 291, 311, 328, 29889, 7638, 613, 671, 22724, 29922, 1054, 29918, 1761, 29892, 17202, 29922, 742, 1495, 13, 13, 7827, 29907, 2129, 29892, 3001, 2772, 493, 29879, 353, 29871, 29900, 29892, 29900, 13, 13, 1454, 22645, 29892, 29871, 4251, 29892, 311, 493, 29879, 297, 14319, 29898, 2176, 3366, 29937, 29871, 29929, 29914, 29906, 29955, 29914, 29906, 29900, 29906, 29900, 12436, 4489, 3366, 11536, 315, 2129, 12436, 4489, 3366, 11536, 14450, 29879, 3108, 1125, 13, 1678, 565, 22645, 1405, 29871, 29900, 29901, 13, 4706, 3001, 29907, 2129, 4619, 4251, 29871, 13, 4706, 565, 4892, 29879, 1405, 29871, 29900, 29901, 13, 9651, 3001, 2772, 493, 29879, 4619, 4892, 29879, 13, 13, 1454, 22645, 29892, 4234, 29892, 4251, 29892, 4892, 29879, 297, 14319, 29898, 2176, 3366, 29937, 29871, 29929, 29914, 29906, 29955, 29914, 29906, 29900, 29906, 29900, 12436, 4489, 3366, 20779, 29914, 5901, 12436, 4489, 3366, 11536, 315, 2129, 12436, 4489, 3366, 11536, 14450, 29879, 3108, 1125, 13, 1678, 565, 22645, 1405, 29871, 29900, 29901, 13, 4706, 1596, 14182, 29876, 613, 13509, 29897, 13, 4706, 1596, 703, 29907, 2129, 584, 9162, 4251, 29892, 5591, 613, 3001, 29907, 2129, 29892, 376, 1273, 613, 376, 25641, 29889, 29945, 29995, 29913, 1642, 4830, 29898, 11436, 29914, 7827, 29907, 2129, 876, 13, 4706, 565, 4892, 29879, 1405, 29871, 29900, 29901, 13, 9651, 1596, 703, 2772, 493, 29879, 584, 9162, 938, 29898, 311, 493, 29879, 511, 5591, 613, 3001, 2772, 493, 29879, 29892, 376, 1273, 613, 376, 25641, 29889, 29945, 29995, 29913, 1642, 4830, 29898, 311, 493, 29879, 29914, 7827, 2772, 493, 29879, 876, 13, 13, 2158, 703, 1159, 13, 2158, 703, 11536, 315, 2129, 1159, 13, 2158, 29898, 7827, 29907, 2129, 29897, 13, 13, 2158, 703, 11536, 14450, 29879, 1159, 13, 2158, 29898, 7827, 2772, 493, 29879, 29897, 2 ]
apps/restaccount/resources.py	solatis/microservices	38	24420	import json import falcon import time import uuid import requests from apps.database import init_db, db_session from apps.models import Account from apps.restaccount.logging import logging logger = logging.getLogger(__name__) from decouple import config ES_HOST = config('EVENTSTORE_HOST', default='eventstore') ES_PORT = config('EVENTSTORE_PORT', default=2113, cast=int) stream_url = 'http://{}:{}/streams/accounts'.format(ES_HOST, ES_PORT) content_header = { 'Content-Type': 'application/vnd.eventstore.events+json' } logger.info('stream_url: {}'.format(stream_url)) def get_account(account_id): return Account.query.get(account_id) class BalanceResource(object): def on_get(self, req, resp, account_id): init_db() doc = db_session.query(Account).get(account_id) db_session.close() if doc is None: raise falcon.HTTPBadRequest('Balance missing', 'Deposit money to start using an account') else: # Create a JSON representation of the resource resp.body = json.dumps(doc.as_dict(), ensure_ascii=False) # The following line can be omitted because 200 is the default # status returned by the framework, but it is included here to # illustrate how this may be overridden as needed. resp.status = falcon.HTTP_200 class DepositResource(object): def on_post(self, req, resp): body = req.stream.read() doc = json.loads(body.decode('utf-8')) logger.info('doc: {}'.format(doc)) payload = [ { "eventId": str(uuid.uuid1()), "eventType": "created-deposit", "data": doc } ] logger.info("payload: {}".format(payload)) r = requests.post(stream_url, data=str(payload), headers=content_header) resp.status = falcon.HTTP_200 class TransferResource(object): def on_post(self, req, resp): body = req.stream.read() doc = json.loads(body.decode('utf-8')) acc = get_account(doc['account_id']) payload = [ { "eventId": str(uuid.uuid1()), "eventType": "created-transfer", "data": doc } ] if acc is None: raise falcon.HTTPBadRequest('Account missing', 'You must deposit into an account before transfering') if acc.balance < doc['amount']: raise falcon.HTTPBadRequest('Insufficient funds', 'Account balance {} less than transfer amount {}'.format(acc.balance, doc['amount'])) else: logger.info("payload: {}".format(payload)) r = requests.post(stream_url, data=str(payload), headers=content_header) resp.status = falcon.HTTP_200	[ 1, 1053, 4390, 13, 5215, 11092, 535, 13, 5215, 931, 13, 5215, 318, 5416, 13, 5215, 7274, 13, 3166, 11446, 29889, 9803, 1053, 2069, 29918, 2585, 29892, 4833, 29918, 7924, 13, 3166, 11446, 29889, 9794, 1053, 16535, 13, 13, 3166, 11446, 29889, 5060, 10149, 29889, 21027, 1053, 12183, 13, 21707, 353, 12183, 29889, 657, 16363, 22168, 978, 1649, 29897, 13, 13, 3166, 1602, 283, 552, 1053, 2295, 13, 13, 2890, 29918, 20832, 353, 2295, 877, 22240, 3919, 1254, 29949, 1525, 29918, 20832, 742, 2322, 2433, 3696, 8899, 1495, 13, 2890, 29918, 15082, 353, 2295, 877, 22240, 3919, 1254, 29949, 1525, 29918, 15082, 742, 2322, 29922, 29906, 29896, 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secondtest/2012/2gauss-seidel.py	JoaoCostaIFG/MNUM	1	1613988	<reponame>JoaoCostaIFG/MNUM #!/usr/bin/env python3 # CHECKED # The answers on the pdf were wrong, except for the first value of x1 # (I believe mine are right because I get the same results when using old, correct. code) # a) n = 4 # matrix order A = [[4.9, -1, -1, 1], [-1, 4.8, 1, -1], [-1, 2, 4.8, -1], [2, -1, -1, 4.8]] b = [1, -1, -1, 0] x0 = [0, 0, 0, 0] x = [0, 0, 0, 0] for k in range(2): for i in range(n): x[i] = b[i] for j in range(i): x[i] -= A[i][j] * x[j] for j in range(i+1, n): x[i] -= A[i][j] * x0[j] x[i] /= A[i][i] # if doing more than 1 iteration, copy method is very important # (see python's soft and hard copies) x0 = x.copy() print(x) # b) # The process converges because the absolute value of each element on the main # diagonal of the matrix ([4.8, 4.8, 4.8, 4.8]) is greater than or equal to (diagonally dominant) # the sum of the absolute values of all other elements in their respective lines: # i=1 4.8 > 3 # i=2 4.8 > 3 # i=3 4.8 > 4 # i=4 4.8 > 4	[ 1, 529, 276, 1112, 420, 29958, 10844, 6241, 29907, 16233, 6545, 29954, 29914, 29924, 13967, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29941, 13, 13, 29937, 23557, 3352, 13, 29937, 450, 6089, 373, 278, 13552, 892, 2743, 29892, 5174, 363, 278, 937, 995, 310, 921, 29896, 13, 29937, 313, 29902, 4658, 7903, 526, 1492, 1363, 306, 679, 278, 1021, 2582, 746, 773, 2030, 29892, 1959, 29889, 775, 29897, 13, 13, 29937, 263, 29897, 13, 29876, 353, 29871, 29946, 29871, 396, 4636, 1797, 13, 29909, 353, 5519, 29946, 29889, 29929, 29892, 448, 29896, 29892, 448, 29896, 29892, 29871, 29896, 1402, 21069, 29896, 29892, 29871, 29946, 29889, 29947, 29892, 29871, 29896, 29892, 448, 29896, 1402, 21069, 29896, 29892, 29871, 29906, 29892, 29871, 29946, 29889, 29947, 29892, 448, 29896, 1402, 518, 29906, 29892, 448, 29896, 29892, 448, 29896, 29892, 29871, 29946, 29889, 29947, 5262, 13, 29890, 353, 518, 29896, 29892, 448, 29896, 29892, 448, 29896, 29892, 29871, 29900, 29962, 13, 13, 29916, 29900, 353, 518, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 29962, 13, 29916, 353, 518, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 29892, 29871, 29900, 29962, 13, 13, 1454, 413, 297, 3464, 29898, 29906, 1125, 13, 1678, 363, 474, 297, 3464, 29898, 29876, 1125, 13, 4706, 921, 29961, 29875, 29962, 353, 289, 29961, 29875, 29962, 13, 4706, 363, 432, 297, 3464, 29898, 29875, 1125, 13, 9651, 921, 29961, 29875, 29962, 22361, 319, 29961, 29875, 3816, 29926, 29962, 334, 921, 29961, 29926, 29962, 13, 13, 4706, 363, 432, 297, 3464, 29898, 29875, 29974, 29896, 29892, 302, 1125, 13, 9651, 921, 29961, 29875, 29962, 22361, 319, 29961, 29875, 3816, 29926, 29962, 334, 921, 29900, 29961, 29926, 29962, 13, 13, 4706, 921, 29961, 29875, 29962, 847, 29922, 319, 29961, 29875, 3816, 29875, 29962, 13, 13, 1678, 396, 565, 2599, 901, 1135, 29871, 29896, 12541, 29892, 3509, 1158, 338, 1407, 4100, 13, 1678, 396, 313, 4149, 3017, 29915, 29879, 4964, 322, 2898, 14591, 29897, 13, 1678, 921, 29900, 353, 921, 29889, 8552, 580, 13, 1678, 1596, 29898, 29916, 29897, 13, 13, 13, 29937, 289, 29897, 13, 29937, 450, 1889, 24144, 1363, 278, 8380, 995, 310, 1269, 1543, 373, 278, 1667, 13, 29937, 19640, 310, 278, 4636, 9310, 29946, 29889, 29947, 29892, 29871, 29946, 29889, 29947, 29892, 29871, 29946, 29889, 29947, 29892, 29871, 29946, 29889, 29947, 2314, 338, 7621, 1135, 470, 5186, 304, 313, 6051, 12841, 635, 28526, 29897, 13, 29937, 278, 2533, 310, 278, 8380, 1819, 310, 599, 916, 3161, 297, 1009, 18067, 3454, 29901, 13, 29937, 474, 29922, 29896, 1678, 29946, 29889, 29947, 1405, 29871, 29941, 13, 29937, 474, 29922, 29906, 1678, 29946, 29889, 29947, 1405, 29871, 29941, 13, 29937, 474, 29922, 29941, 1678, 29946, 29889, 29947, 1405, 29871, 29946, 13, 29937, 474, 29922, 29946, 1678, 29946, 29889, 29947, 1405, 29871, 29946, 13, 2 ]
compile.py	Eleyvie/wreck	1	134519	<reponame>Eleyvie/wreck<filename>compile.py WRECK_VERSION = '1.0.1' # DONE: # new command line parameter: `test`. WRECK will compile the module but won't save anything on disk. Essentially a syntax check. # PLANNED: # local variable lifetime check (paranoia mode) # trace local declaration in all conditional branches # dialogs: dead ends, inaccessible dialogs # QUESTIONABLE: # scripts: detect number of arguments and check all call_script calls # RESCHEDULED FOR 2.0: # change behavior on fatal errors wherever possible: remember, assume, continue compilation, don't export, report # improve globals visibility for modules and plugins # syntax expected datatype check where appropriate # operation parameters expected datatype check # automatically update module.ini when necessary (num_hints parameter for starters) # check for missing/undeclared scenes in SceneObj folder # WILL NOT BE IMPLEMENTED: # check for missing meshes in CommonRes and Resource folder - they're inside brf files, would need brf parser for that import sys if (sys.version_info[0] != 2) or (sys.version_info[1] < 6): exit("\nYou're running Python version {0}.{1}.{2}.\nW.R.E.C.K. requires Python version 2.6.x or 2.7.x to run!\n".format(sys.version_info[0:3])) sys.dont_write_bytecode = True from time import time as gettime from os import makedirs from traceback import extract_tb # Color support if 'bw' in sys.argv: COLORAMA = ('', '', '', '', '', '', '', '') else: try: import colorama colorama.init() COLORAMA = ('\x1b[0m', '\x1b[31m', '\x1b[32m', '\x1b[33m', '\x1b[34m', '\x1b[35m', '\x1b[36m', '\x1b[37m') except: COLORAMA = ('', '', '', '', '', '', '', '') from compiler import write_id_files = "ID_%s.py" # Where the compiler will write new iteration ID-files. show_performance_data = False # Set to true to display compiler performance data by default. export_filename = '%s.txt' # How to name export files (only used for some debugging purposes). WRECK.time_started = gettime() print print '{2}* Warband Refined & Enhanced Compiler Kit (W.R.E.C.K.) version {version!s} {0}'.format(COLORAMA, version = WRECK_VERSION) print 'Please report errors, problems and suggestions at {5}http://lav.lomskih.net/wreck/{0}'.format(COLORAMA) print try: # +----------------------------------------------------------------------------------------------- # / # + # \| print 'Loading module...', try: # Info module and plugins WRECK.current_module = 'info' from module_info import WRECK.destination = export_dir.rstrip('/') globals().update(WRECK.syntax_extensions) globals().update(WRECK.plugin_globals) # Optional modules WRECK.current_module = 'item_modifiers' try: from module_item_modifiers import * except ImportError: #from defaults.module_item_modifiers import * item_modifiers = DEFAULT_ITEM_MODIFIERS WRECK.generate_item_modifiers = False generate_imod_constants_for_backwards_compatibility(item_modifiers) WRECK.current_module = 'ui_strings' try: from module_ui_strings import * except ImportError: ui_strings = [] WRECK.generate_ui_strings = False WRECK.current_module = 'user_hints' try: from module_user_hints import * except ImportError: user_hints = [] WRECK.generate_user_hints = False # Required modules WRECK.current_module = 'skills' from module_skills import * generate_skill_constants_for_backwards_compatibility(skills) WRECK.current_module = 'animations' from module_animations import * WRECK.current_module = 'factions' from module_factions import * WRECK.current_module = 'game_menus' from module_game_menus import * WRECK.current_module = 'info_pages' from module_info_pages import * WRECK.current_module = 'meshes' from module_meshes import * WRECK.current_module = 'mission_templates' from module_mission_templates import * WRECK.current_module = 'tracks' from module_music import * WRECK.current_module = 'particle_systems' from module_particle_systems import * WRECK.current_module = 'postfx_params' from module_postfx import * WRECK.current_module = 'quests' from module_quests import * WRECK.current_module = 'scene_props' from module_scene_props import * WRECK.current_module = 'scenes' from module_scenes import * WRECK.current_module = 'scripts' from module_scripts import * WRECK.current_module = 'simple_triggers' from module_simple_triggers import * WRECK.current_module = 'sounds' from module_sounds import * WRECK.current_module = 'strings' from module_strings import * WRECK.current_module = 'tableaus' from module_tableau_materials import * WRECK.current_module = 'triggers' from module_triggers import * WRECK.current_module = 'items' from module_items import * WRECK.current_module = 'map_icons' from module_map_icons import * WRECK.current_module = 'skins' from module_skins import * WRECK.current_module = 'presentations' from module_presentations import * WRECK.current_module = 'troops' from module_troops import * WRECK.current_module = 'party_templates' from module_party_templates import * WRECK.current_module = 'parties' from module_parties import * WRECK.current_module = 'dialogs' from module_dialogs import * WRECK.current_module = None except Exception, e: print '{1}FAILED.\nMODULE `{module!s}` ERROR:\n{error!s}{0}'.format(COLORAMA, module = WRECK.current_module, error = (e.formatted() if isinstance(e, MSException) else formatted_exception())) if isinstance(e, TypeError) and (('object is not callable' in e.message) or ('indices must be integers' in e.message)): exc_type, exc_value, exc_traceback = sys.exc_info() error_info = extract_tb(exc_traceback)[-1] print '{6} Compiler hint: this error is typically caused by a missing comma.\n Please check that tuples are followed by commas in `{path!s}` above line {line}:\n\n {5}{code!s}{0}'.format(COLORAMA, path = path_split(error_info[0])[1], line = error_info[1], code = error_info[3]) WRECK.time_loaded = gettime() raise MSException() print '{2}DONE.{0}'.format(COLORAMA) WRECK.time_loaded = gettime() # \| # + # \ # +=============================================================================================== # / # + # \| print 'Loading plugins...', try: # Check plugin requirements prereq_errors = [] for plugin, required_by in WRECK.requirements.iteritems(): if plugin not in WRECK.plugins: prereq_errors.append('Plugin %s not imported but required by %s.' % (plugin, ', '.join(required_by))) if prereq_errors: raise MSException('\r\n'.join(prereq_errors)) # Process data injections glob = get_globals() for plugin in WRECK.plugins: for parser in parsers.iterkeys(): if hasattr(glob[plugin], parser): glob[parser].extend(getattr(glob[plugin], parser)) injections = getattr(glob[plugin], 'injection', None) if injections: for inj_name, inj_elements in injections.iteritems(): WRECK.injections.setdefault(inj_name, []).extend(inj_elements) #WRECK.warnings.append('Injection: %d elements for `%s` in `%s`' % (len(inj_elements), inj_name, plugin)) except Exception, e: print '{1}FAILED.\nPLUGIN `{module!s}` ERROR:\n{error!s}{0}'.format(COLORAMA, module = plugin, error = (e.formatted() if isinstance(e, MSException) else formatted_exception())) WRECK.time_plugins = gettime() raise MSException() print '{2}DONE.{0}'.format(COLORAMA) WRECK.time_plugins = gettime() # \| # + # \ # +=============================================================================================== # / # + # \| print 'Checking module syntax...', try: for entity_name, entity_def in parsers.iteritems(): WRECK.current_module = entity_name get_globals()[entity_name] = check_syntax(get_globals()[entity_name], [entity_def['parser']], entity_def.get('uid', 0)) WRECK.current_module = None except Exception, e: print '{1}FAILED.\nMODULE `{module!s}` ERROR:\n{error!s}{0}'.format(COLORAMA, module = entity_name, error = (e.formatted() if isinstance(e, MSException) else formatted_exception())) WRECK.time_syntax = gettime() raise MSException() print '{2}DONE.{0}'.format(COLORAMA) WRECK.time_syntax = gettime() WRECK.anim[7] = animations WRECK.fac[7] = factions WRECK.ip[7] = info_pages WRECK.imod[7] = item_modifiers WRECK.itm[7] = items WRECK.icon[7] = map_icons WRECK.mnu[7] = game_menus WRECK.mesh[7] = meshes WRECK.mt[7] = mission_templates WRECK.track[7] = tracks WRECK.psys[7] = particle_systems WRECK.p[7] = parties WRECK.pt[7] = party_templates WRECK.pfx[7] = postfx_params WRECK.prsnt[7] = presentations WRECK.qst[7] = quests WRECK.spr[7] = scene_props WRECK.scn[7] = scenes WRECK.script[7] = scripts WRECK.skl[7] = skills WRECK.snd[7] = sounds WRECK.s[7] = strings WRECK.tableau[7] = tableaus WRECK.trp[7] = troops # \| # + # \ # +=============================================================================================== # / # + # \| print 'Allocating identifiers...', try: allocate_global_variables() allocate_quick_strings() calculate_identifiers(animations, anim) calculate_identifiers(factions, fac) calculate_identifiers(info_pages, ip) calculate_identifiers(item_modifiers, imod, imodbit) calculate_identifiers(items, itm) calculate_identifiers(map_icons, icon) calculate_identifiers(game_menus, mnu) calculate_identifiers(meshes, mesh) calculate_identifiers(mission_templates, mt) calculate_identifiers(tracks, track) calculate_identifiers(particle_systems, psys) calculate_identifiers(parties, p) calculate_identifiers(party_templates, pt) calculate_identifiers(postfx_params, pfx) calculate_identifiers(presentations, prsnt) calculate_identifiers(quests, qst) calculate_identifiers(scene_props, spr) calculate_identifiers(scenes, scn) calculate_identifiers(scripts, script) calculate_identifiers(skills, skl) calculate_identifiers(sounds, snd) calculate_identifiers(strings, s) calculate_identifiers(tableaus, tableau) calculate_identifiers(troops, trp) undefined = undefined_identifiers() if undefined: raise MSException('undeclared identifiers found in module source:\n %s' % ('\n * '.join(['%s (referenced by \'%s\')' % (name, '\', \''.join(refs)) for name, refs in undefined]))) except Exception, e: print '{1}FAILED.'.format(COLORAMA) if isinstance(e, MSException): print 'MODULE ERROR:\n{error!s}{0}'.format(COLORAMA, error = e.formatted()) else: print 'COMPILER INTERNAL ERROR:\n{error!s}{0}'.format(COLORAMA, error = formatted_exception()) WRECK.time_identifiers = gettime() raise MSException() print '{2}DONE.{0}'.format(COLORAMA) WRECK.time_identifiers = gettime() # \| # + # \ # +=============================================================================================== # / # + # \| print 'Compiling module...', try: stage = 0 # Pre-processing (note that all entity-level injections are already done but script-level injections are not). glob = get_globals() preprocess_entities_internal(glob) stage = 1 for plugin in WRECK.plugins: processor = getattr(glob[plugin], 'preprocess_entities', None) if processor: try: processor(glob) except Exception, e: raise MSException('Error in %r pre-processor script.' % plugin, formatted_exception()) # Compiling... stage = 2 for entity_name, entity_def in parsers.iteritems(): stage = 3 entities = get_globals()[entity_name] stage = 4 for index in xrange(len(entities)): entities[index] = entity_def['processor'](entities[index], index) stage = 5 setattr(WRECK, entity_name, entity_def['aggregator'](entities)) # Post-processing (plugins are NOT allowed to do anything here as we are dealing with already compiled code) stage = 6 postprocess_entities() except Exception, e: print '{1}FAILED.'.format(COLORAMA) if isinstance(e, MSException): if stage == 0: print 'COMPILER PREPROCESSOR ERROR:\n{error!s}{0}'.format(COLORAMA, error = e.formatted()) if stage == 1: print 'PLUGIN {module!s} PREPROCESSOR ERROR:\n{error!s}{0}'.format(COLORAMA, module = plugin, error = e.formatted()) elif stage == 3: print 'MODULE {module!s} ENTITY #{index} COMPILATION ERROR:\n{error!s}{0}'.format(COLORAMA, module = entity_name, index = index, error = e.formatted()) elif stage == 4: print 'MODULE {module!s} AGGREGATOR ERROR:\n{error!s}{0}'.format(COLORAMA, module = entity_name, error = e.formatted()) elif stage == 5: print 'COMPILER POSTPROCESSOR ERROR:\n{error!s}{0}'.format(COLORAMA, error = e.formatted()) else: print 'COMPILER INTERNAL ERROR:\n{error!s}{0}'.format(COLORAMA, error = formatted_exception()) WRECK.time_compile = gettime() raise MSException() print '{2}DONE.{0}'.format(COLORAMA) WRECK.time_compile = gettime() # \| # + # \ # +=============================================================================================== # / # + # \| print 'Exporting module...', export_done = True # Success export = { 'animations': export_filename % 'actions', 'dialogs': export_filename % 'conversation', 'dialog_states': export_filename % 'dialog_states', 'factions': export_filename % 'factions', 'game_menus': export_filename % 'menus', 'info_pages': export_filename % 'info_pages', 'items': export_filename % 'item_kinds1', 'map_icons': export_filename % 'map_icons', 'meshes': export_filename % 'meshes', 'mission_templates': export_filename % 'mission_templates', 'tracks': export_filename % 'music', 'particle_systems': export_filename % 'particle_systems', 'parties': export_filename % 'parties', 'party_templates': export_filename % 'party_templates', 'postfx_params': export_filename % 'postfx', 'presentations': export_filename % 'presentations', 'quests': export_filename % 'quests', 'scene_props': export_filename % 'scene_props', 'scenes': export_filename % 'scenes', 'scripts': export_filename % 'scripts', 'simple_triggers': export_filename % 'simple_triggers', 'skills': export_filename % 'skills', 'skins': export_filename % 'skins', 'sounds': export_filename % 'sounds', 'strings': export_filename % 'strings', 'tableaus': export_filename % 'tableau_materials', 'triggers': export_filename % 'triggers', 'troops': export_filename % 'troops', 'variables': export_filename % 'variables', 'quick_strings': export_filename % 'quick_strings', } if WRECK.generate_item_modifiers: export['item_modifiers'] = 'Data/item_modifiers.txt' if WRECK.generate_ui_strings: export['ui_strings'] = 'Languages/en/ui.csv' if WRECK.generate_user_hints: export['user_hints'] = 'Languages/en/hints.csv' try: if 'test' in sys.argv: raise MSException() for entity_name, filename in export.iteritems(): contents = getattr(WRECK, entity_name) if contents is None: #print 'Module %s has no changes, skipping export.' % entity_name continue #print 'Exporting module %s...' % entity_name filename = path_split(filename.replace('\\', '/')) folder = ('%s/%s' % (WRECK.destination, filename[0])) if filename[0] else WRECK.destination if filename[0] and not(path_exists(folder)): makedirs(folder) with open('%s/%s' % (folder, filename[1]), 'w+b') as f: f.write(contents) except MSException: export_done = False # Skipped. write_id_files = None # Override any value that was there. except Exception, e: print '{1}FAILED.\nCOMPILER INTERNAL ERROR WHILE WRECKING {module!s}:\n{error!s}{0}'.format(COLORAMA, module = entity_name, error = formatted_exception()) WRECK.time_export = gettime() raise MSException() if write_id_files is not None: export = { 'animations': (WRECK.anim, 'anim_'), 'factions': (WRECK.fac, 'fac_'), 'info_pages': (WRECK.ip, 'ip_'), 'items': (WRECK.itm, 'itm_'), 'map_icons': (WRECK.icon, 'icon_'), 'menus': (WRECK.mnu, 'mnu_'), 'meshes': (WRECK.mesh, 'mesh_'), 'mission_templates': (WRECK.mt, 'mt_'), 'music': (WRECK.track, 'track_'), 'particle_systems': (WRECK.psys, 'psys_'), 'parties': (WRECK.p, 'p_'), 'party_templates': (WRECK.pt, 'pt_'), 'postfx_params': (WRECK.pfx, 'pfx_'), 'presentations': (WRECK.prsnt, 'prsnt_'), 'quests': (WRECK.qst, 'qst_'), 'scene_props': (WRECK.spr, 'spr_'), 'scenes': (WRECK.scn, 'scn_'), 'scripts': (WRECK.script, 'script_'), 'skills': (WRECK.skl, 'skl_'), 'sounds': (WRECK.snd, 'snd_'), 'strings': (WRECK.s, 'str_'), 'tableau_materials': (WRECK.tableau, 'tableau_'), 'troops': (WRECK.trp, 'trp_'), } try: for entity_name, (entity, prefix) in export.iteritems(): contents = '\n'.join(['%s%s = %d' % (prefix, ref, index) for ref, index in sorted(map(lambda i:(i[0],int(i[1]&0xFFFFFFFF)), entity[0].iteritems()), lambda x,y:cmp(x[1],y[1]))]) with open(write_id_files % entity_name, 'w+b') as f: f.write(contents) f.write('\n') except Exception, e: print '{1}FAILED.\nCOMPILER INTERNAL ERROR WHILE WRECKING {module!s}:\n{error!s}{0}'.format(COLORAMA, module = write_id_files % entity_name, error = formatted_exception()) WRECK.time_export = gettime() raise MSException() if export_done: print '{2}DONE.{0}'.format(COLORAMA) WRECK.time_export = gettime() else: print '{3}SKIPPED.{0}'.format(COLORAMA) # \| # + # \ # +----------------------------------------------------------------------------------------------- except MSException: WRECK.successful = False print if WRECK.successful: print '{2}COMPILATION SUCCESSFUL.{0}\n'.format(COLORAMA) else: print '{1}COMPILATION FAILED.{0}\n'.format(COLORAMA) error_reporting_level = 3 if 'silent' in sys.argv: error_reporting_level = 0 if ('error' in sys.argv) or ('errors' in sys.argv): error_reporting_level = 1 if ('error' in sys.argv) or ('warnings' in sys.argv): error_reporting_level = 2 if ('notice' in sys.argv) or ('notices' in sys.argv): error_reporting_level = 3 if WRECK.errors and (error_reporting_level > 0): print 'The following errors were generated during compilation:{1}\n '.format(COLORAMA), print '\n '.join(WRECK.errors) print '{0}'.format(COLORAMA) if WRECK.warnings and (error_reporting_level > 1): print 'The following warnings were generated during compilation:{3}\n '.format(COLORAMA), print '\n '.join(WRECK.warnings) print '{0}'.format(COLORAMA) if WRECK.notices and (error_reporting_level > 2): print 'The following notifications were generated during compilation:{6}\n '.format(COLORAMA), print '\n '.join(WRECK.notices) print '{0}'.format(COLORAMA) if show_performance_data and WRECK.time_loaded: print 'Displaying W.R.E.C.K. performance information.' print 'Use {5}show_performance_data = False{0} directive in {5}module_info.py{0} file to disable.'.format(*COLORAMA) print if WRECK.time_loaded: print ' %.03f sec spent to load module data.' % (WRECK.time_loaded - WRECK.time_started) if WRECK.time_plugins: print ' %.03f sec spent to load plugins.' % (WRECK.time_plugins - WRECK.time_loaded) if WRECK.time_syntax: print ' %.03f sec spent to check module syntax.' % (WRECK.time_syntax - WRECK.time_plugins) if WRECK.time_identifiers: print ' %.03f sec spent to allocate identifiers.' % (WRECK.time_identifiers - WRECK.time_syntax) if WRECK.time_compile: print ' %.03f sec spent to compile module.' % (WRECK.time_compile - WRECK.time_identifiers) if WRECK.time_export: print ' %.03f sec spent to export module.' % (WRECK.time_export - WRECK.time_compile) print print ' >>> %.03f sec total time spent. <<<' % (gettime() - WRECK.time_started) print if 'wait' in sys.argv: raw_input('Press Enter to finish>')	[ 1, 529, 276, 1112, 420, 29958, 29923, 2330, 25965, 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mmtbx/bulk_solvent/f_model_all_scales.py	dperl-sol/cctbx_project	155	22809	<reponame>dperl-sol/cctbx_project from __future__ import absolute_import, division, print_function from cctbx.array_family import flex from cctbx import adptbx from mmtbx import bulk_solvent from cctbx.array_family import flex from cctbx import adptbx import mmtbx from libtbx import group_args import mmtbx.arrays import mmtbx.bulk_solvent.scaler from libtbx.test_utils import approx_equal from libtbx.math_utils import ifloor, iceil import mmtbx.f_model import mmtbx.bulk_solvent.bulk_solvent_and_scaling as bss from six.moves import zip, range class run(mmtbx.f_model.manager): """ This is a very specialized routine to perform complex protocols of updating all scales of fmodel, including case of twininng, presence of H and lileky more. Inside it pretends to be fmodel proper (done by dictionary updates before and after - any better ideas of how to do it nicer are welcome!). """ def __init__(self, fmodel, apply_back_trace, remove_outliers, fast, params, refine_hd_scattering, log): ### Must be first thing here self.__dict__.update(fmodel.__dict__) # From this point on: self = fmodel ### russ = self.compute(apply_back_trace = apply_back_trace, remove_outliers = remove_outliers, fast = fast, params = params, refine_hd_scattering = refine_hd_scattering, log = log) ### Must be next to last... fmodel.__dict__.update(self.__dict__) ### ...and this one is last self.russ = russ def compute(self, apply_back_trace, remove_outliers, fast, params, refine_hd_scattering, log): assert [self.arrays.core_twin, self.twin_law].count(None) in [0,2] self.show(prefix = "start", log = log) self.reset_all_scales() self.show(prefix = "re-set all scales", log = log) if(remove_outliers and not self.twinned()): for iii in range(5): self.remove_outliers(use_model = False, log = None) # XXX self.show(prefix = "remove outliers", log = log) result = None if(self.twinned()): for cycle in range(2): if(log is not None): print("cycle %d:"%cycle, file=log) self.update_twin_fraction() self.show(prefix = "update twin fraction", log = log) result = self.update_solvent_and_scale_twin(log = log, refine_hd_scattering = refine_hd_scattering) else: result = self.update_solvent_and_scale_2( fast = fast, params = params, apply_back_trace = apply_back_trace, refine_hd_scattering = refine_hd_scattering, log = log) #XXX if(remove_outliers and not self.twinned()): #XXX self.remove_outliers(use_model = True, log = None) # XXX if(remove_outliers and not self.twinned()): for iii in range(5): self.remove_outliers(use_model = True, log = None) # XXX self.show(prefix = "remove outliers", log = log) return result def reset_all_scales(self): size = self.f_obs().data().size() zero_c = flex.complex_double(size,0) zero_d = flex.double(size,0) one_d = flex.double(size,1) f_part1_twin = self.f_calc_twin() f_part2_twin = self.f_calc_twin() if(f_part1_twin is not None): f_part1_twin = self.f_calc_twin().array(data=zero_c) f_part2_twin = self.f_calc_twin().array(data=zero_c) self.update_core( f_part1 = self.f_calc().array(data=zero_c), f_part2 = self.f_calc().array(data=zero_c), f_part1_twin = f_part1_twin, f_part2_twin = f_part2_twin, k_isotropic = one_d, k_anisotropic = one_d, k_mask = [zero_d]len(self.k_masks())) def show(self, prefix, log, r=None): if(log is None): return if(r is None): r = self.r_all() m = "%24s: r(all,work,free)=%6.4f %6.4f %6.4f n_refl.: %d"%(prefix, r, self.r_work(), self.r_free(), self.f_obs().data().size()) if(not self.twinned()): print(m, file=log) else: print(m+" twin_fraction=%4.2f"%self.twin_fraction, file=log) def need_to_refine_hd_scattering_contribution(self): if(self.xray_structure is None): return False refine_hd_scattering = True hd_selection = self.xray_structure.hd_selection() occ_h_all_zero = self.xray_structure.select( hd_selection).scatterers().extract_occupancies().all_eq(0.0) # riding H if(self.xray_structure.guess_scattering_type_neutron() or hd_selection.count(True)==0 or not occ_h_all_zero): refine_hd_scattering = False return refine_hd_scattering def update_solvent_and_scale_2(self, fast, params, apply_back_trace, refine_hd_scattering, log): if(params is None): params = bss.master_params.extract() if(self.xray_structure is not None): # Figure out Fcalc and Fmask based on presence of H hd_selection = self.xray_structure.hd_selection() xrs_no_h = self.xray_structure.select(~hd_selection) xrs_h = self.xray_structure.select(hd_selection) # Create data container for scalers. If H scattering is refined then it is # assumed that self.f_calc() does not contain H contribution at all. fmodel_kbu = mmtbx.f_model.manager_kbu( f_obs = self.f_obs(), f_calc = self.f_calc(), f_masks = self.f_masks(), ss = self.ss) # Compute k_total and k_mask using one of the two methods (anal or min). # Note: this intentionally ignores previously existing f_part1 and f_part2. # k_sol, b_sol, b_cart, b_adj = [None,]4 if(fast): # analytical assert len(fmodel_kbu.f_masks)==1 result = mmtbx.bulk_solvent.scaler.run_simple( fmodel_kbu = fmodel_kbu, r_free_flags = self.r_free_flags(), bulk_solvent = params.bulk_solvent, aniso_scale = params.anisotropic_scaling, bin_selections = self.bin_selections) r_all_from_scaler = result.r_all() # must be here, before apply_back_trace else: # using minimization: exp solvent and scale model (k_sol,b_sol,b_cart) result = bss.bulk_solvent_and_scales( fmodel_kbu = fmodel_kbu, params = params) k_sol, b_sol, b_cart = result.k_sols(), result.b_sols(), result.b_cart() r_all_from_scaler = result.r_all() # must be here, before apply_back_trace if(apply_back_trace and len(fmodel_kbu.f_masks)==1 and self.xray_structure is not None): o = result.apply_back_trace_of_overall_exp_scale_matrix( xray_structure = self.xray_structure) b_adj = o.b_adj if(not fast): b_sol, b_cart = [o.b_sol], o.b_cart self.update_xray_structure( xray_structure = o.xray_structure, update_f_calc = True) fmodel_kbu = fmodel_kbu.update(f_calc = self.f_calc()) self.show(prefix = "overall B=%s to atoms"%str("%7.2f"%o.b_adj).strip(), log = log) # Update self with new arrays so that H correction knows current R factor. # If no H to account for, then this is the final result. k_masks = result.k_masks() k_anisotropic = result.k_anisotropic() k_isotropic = result.k_isotropic() self.update_core( k_mask = k_masks, k_anisotropic = k_anisotropic, k_isotropic = k_isotropic) self.show(prefix = "bulk-solvent and scaling", log = log) # Consistency check if(not apply_back_trace): assert approx_equal(self.r_all(), r_all_from_scaler) # Add contribution from H (if present and riding). This goes to f_part2. kh, bh = 0, 0 if(refine_hd_scattering and self.need_to_refine_hd_scattering_contribution()): # Obsolete previous contribution f_part2 f_part2 = fmodel_kbu.f_calc.array(data=fmodel_kbu.f_calc.data()0) self.update_core(f_part2 = f_part2) xrs_h = xrs_h.set_occupancies(value=1).set_b_iso(value = 0) f_h = self.compute_f_calc(xray_structure = xrs_h) # Accumulate all mask contributions: Fcalc_atoms+Fbulk_1+...+Fbulk_N data = fmodel_kbu.f_calc.data() for k_mask_, f_mask_ in zip(k_masks, fmodel_kbu.f_masks): data = data + k_mask_f_mask_.data() f_calc_plus_f_bulk_no_scales = fmodel_kbu.f_calc.array(data = data) # Consistency check assert approx_equal(self.f_model().data(), f_calc_plus_f_bulk_no_scales.data()k_isotropick_anisotropic) assert approx_equal(self.f_model_no_scales().data(), f_calc_plus_f_bulk_no_scales.data()) # # Compute contribution from H (F_H) # # Coarse sampling b_mean = flex.mean(xrs_no_h.extract_u_iso_or_u_equiv())adptbx.u_as_b(1.) b_min = int(max(0,b_mean)0.5) b_max = int(b_mean1.5) sc = 1000. kr=[i/sc for i in range(ifloor(0sc), iceil(1.5sc)+1, int(0.1sc))] br=[i/sc for i in range(ifloor(b_minsc), iceil(b_maxsc)+1, int(5.sc))] o = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = fmodel_kbu.f_obs.data(), f_calc = f_calc_plus_f_bulk_no_scales.data(), f_mask = f_h.data(), k_total = k_isotropick_anisotropic, ss = fmodel_kbu.ss, k_sol_range = flex.double(kr), b_sol_range = flex.double(br), r_ref = self.r_work()) if(o.updated()): f_part2 = f_h.array(data = o.k_mask()f_h.data()) kh, bh = o.k_sol(), o.b_sol() self.show(prefix = "add H (%4.2f, %6.2f)"%(kh, bh), log = log, r=o.r()) # Fine sampling k_min = max(0,o.k_sol()-0.1) k_max = o.k_sol()+0.1 b_min = max(0,o.b_sol()-5.) b_max = o.b_sol()+5. kr=[i/sc for i in range(ifloor(k_minsc),iceil(k_maxsc)+1,int(0.01sc))] br=[i/sc for i in range(ifloor(b_minsc),iceil(b_maxsc)+1,int(1.sc))] o = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = fmodel_kbu.f_obs.data(), f_calc = f_calc_plus_f_bulk_no_scales.data(), f_mask = f_h.data(), k_total = k_isotropick_anisotropic, ss = fmodel_kbu.ss, k_sol_range = flex.double(kr), b_sol_range = flex.double(br), r_ref = o.r()) if(o.updated()): f_part2 = f_h.array(data = o.k_mask()f_h.data()) kh, bh = o.k_sol(), o.b_sol() self.show(prefix = "add H (%4.2f, %6.2f)"%(kh, bh), log = log, r=o.r()) # THIS HELPS if fast=true is used, see how it works in reality # if(fast): fmodel_kbu_ = mmtbx.f_model.manager_kbu( f_obs = self.f_obs(), f_calc = f_calc_plus_f_bulk_no_scales, f_masks = [f_part2], ss = self.ss) result = mmtbx.bulk_solvent.scaler.run_simple( fmodel_kbu = fmodel_kbu_, r_free_flags = self.r_free_flags(), bulk_solvent = params.bulk_solvent, aniso_scale = params.anisotropic_scaling, bin_selections = self.bin_selections) f_part2 = f_part2.array(data = result.core.k_mask()f_part2.data()) k_isotropic = result.core.k_isotropicresult.core.k_isotropic_exp k_anisotropic = result.core.k_anisotropic # Update self with final scales self.update_core( k_mask = k_masks, k_anisotropic = k_anisotropic, k_isotropic = k_isotropic, f_part2 = f_part2) # Make sure what came out of scaling matches what self thinks it really is # It must match at least up to 1.e-6. self.show(prefix = "add H (%4.2f, %6.2f)"%(kh, bh), log = log) if(fast): assert approx_equal(result.r_work(), self.r_work(), 1.e-4) else: assert approx_equal(self.r_all(), o.r()), [self.r_all(), o.r()] return group_args( k_sol = k_sol, b_sol = b_sol, b_cart = b_cart, k_h = kh, b_h = bh, b_adj = b_adj) def update_solvent_and_scale_twin(self, refine_hd_scattering, log): if(not self.twinned()): return assert len(self.f_masks()) == 1 # Re-set all scales to unit or zero self.show(prefix = "update scales twin start", log = log) self.reset_all_scales() self.show(prefix = "reset f_part, k_(total,mask)", log = log) f_calc_data = self.f_calc().data() f_calc_data_twin = self.f_calc_twin().data() # Initial trial set sc = 1000. ksr = [i/sc for i in range(ifloor(0sc), iceil(0.6sc)+1, int(0.05sc))] bsr = [i/sc for i in range(ifloor(0sc), iceil(150.sc)+1, int(10.sc))] o_kbu_sol = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = self.f_obs().data(), f_calc_1 = f_calc_data, f_calc_2 = f_calc_data_twin, f_mask_1 = self.arrays.core.f_masks[0].data(), f_mask_2 = self.arrays.core_twin.f_masks[0].data(), ss = self.ss, twin_fraction = self.twin_fraction, k_sol_range = flex.double(ksr), b_sol_range = flex.double(bsr), miller_indices = self.f_obs().indices(), #XXX ??? What about twin-related? unit_cell = self.f_obs().unit_cell(), r_ref = self.r_all()) if(o_kbu_sol.updated()): self.update( k_mask = o_kbu_sol.k_mask(), k_anisotropic = o_kbu_sol.k_anisotropic()) # Second (finer) trial set k_min = max(o_kbu_sol.k_sol()-0.05, 0) k_max = min(o_kbu_sol.k_sol()+0.05, 0.6) ksr = [i/sc for i in range(ifloor(k_minsc), iceil(k_maxsc)+1, int(0.01sc))] b_min = max(o_kbu_sol.b_sol()-10, 0) b_max = min(o_kbu_sol.b_sol()+10, 150) bsr = [i/sc for i in range(ifloor(b_minsc), iceil(b_maxsc)+1, int(1.sc))] o_kbu_sol = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = self.f_obs().data(), f_calc_1 = f_calc_data, f_calc_2 = f_calc_data_twin, f_mask_1 = self.arrays.core.f_masks[0].data(), f_mask_2 = self.arrays.core_twin.f_masks[0].data(), ss = self.ss, twin_fraction = self.twin_fraction, k_sol_range = flex.double(ksr), b_sol_range = flex.double(bsr), miller_indices = self.f_obs().indices(), #XXX ??? What about twin-related? unit_cell = self.f_obs().unit_cell(), r_ref = o_kbu_sol.r()) if(o_kbu_sol.updated()): self.update( k_mask = o_kbu_sol.k_mask(), k_anisotropic = o_kbu_sol.k_anisotropic()) # Disable due to rare failures. Technically they should always match. But # since different routines are used tiny disagreements are possible. # See examples in : /net/anaconda/raid1/afonine/work/bugs/twin_refinement #assert approx_equal(self.r_all(), o_kbu_sol.r(), 1.e-5) ############## # use apply_back_trace in if below if(self.xray_structure is not None): o = mmtbx.bulk_solvent.scaler.tmp( xray_structure = self.xray_structure, k_anisotropic = o_kbu_sol.k_anisotropic(), k_masks = [o_kbu_sol.k_mask()], ss = self.ss) self.update_xray_structure( xray_structure = o.xray_structure, update_f_calc = True) ############# self.update( k_mask = o.k_masks, k_anisotropic = o.k_anisotropic) self.show(prefix = "bulk-solvent and scaling", log = log) # # Add contribution from H (if present and riding). This goes to f_part2. # kh, bh = 0, 0 if(refine_hd_scattering and self.need_to_refine_hd_scattering_contribution()): hd_selection = self.xray_structure.hd_selection() xrs_no_h = self.xray_structure.select(~hd_selection) xrs_h = self.xray_structure.select(hd_selection) # Accumulate all mask contributions: Fcalc_atoms+Fbulk_1+...+Fbulk_N data = self.f_calc().data()+self.f_masks()[0].data()self.k_masks()[0] f_calc_plus_f_bulk_no_scales = self.f_calc().array(data = data) data = self.f_calc_twin().data()+\ self.f_masks_twin()[0].data()self.k_masks_twin()[0] f_calc_plus_f_bulk_no_scales_twin = self.f_calc_twin().array(data = data) # Initial FH contribution xrs_h = xrs_h.set_occupancies(value=1).set_b_iso(value = 0) f_h = self.compute_f_calc(xray_structure = xrs_h) f_h_twin = self.compute_f_calc(xray_structure = xrs_h, miller_array = self.f_calc_twin()) # Coarse sampling b_mean = flex.mean(xrs_no_h.extract_u_iso_or_u_equiv())adptbx.u_as_b(1.) b_min = int(max(0,b_mean)0.5) b_max = int(b_mean1.5) sc = 1000. kr=[i/sc for i in range(ifloor(0sc), iceil(1.5sc)+1, int(0.1sc))] br=[i/sc for i in range(ifloor(b_minsc), iceil(b_maxsc)+1, int(5.sc))] obj = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = self.f_obs().data(), f_calc_1 = f_calc_plus_f_bulk_no_scales.data(), f_calc_2 = f_calc_plus_f_bulk_no_scales_twin.data(), f_mask_1 = f_h.data(), f_mask_2 = f_h_twin.data(), ss = self.ss, twin_fraction = self.twin_fraction, k_sol_range = flex.double(kr), b_sol_range = flex.double(br), miller_indices = self.f_obs().indices(), # XXX What about twin-related? unit_cell = self.f_obs().unit_cell(), r_ref = self.r_work()) if(obj.updated()): f_part2 = f_h.array( data = obj.k_mask()f_h.data()) f_part2_twin = f_h_twin.array(data = obj.k_mask()f_h_twin.data()) kh, bh = obj.k_sol(), obj.b_sol() # Fine sampling k_min = max(0,obj.k_sol()-0.1) k_max = obj.k_sol()+0.1 b_min = max(0,obj.b_sol()-5.) b_max = obj.b_sol()+5. kr=[i/sc for i in range(ifloor(k_minsc),iceil(k_maxsc)+1,int(0.01sc))] br=[i/sc for i in range(ifloor(b_minsc),iceil(b_maxsc)+1,int(5.sc))] obj = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = self.f_obs().data(), f_calc_1 = f_calc_plus_f_bulk_no_scales.data(), f_calc_2 = f_calc_plus_f_bulk_no_scales_twin.data(), f_mask_1 = f_h.data(), f_mask_2 = f_h_twin.data(), ss = self.ss, twin_fraction = self.twin_fraction, k_sol_range = flex.double(kr), b_sol_range = flex.double(br), miller_indices = self.f_obs().indices(), # XXX What about twin-related? unit_cell = self.f_obs().unit_cell(), r_ref = obj.r()) if(obj.updated()): f_part2 = f_h.array( data = obj.k_mask()f_h.data()) f_part2_twin = f_h_twin.array(data = obj.k_mask()f_h_twin.data()) kh, bh = obj.k_sol(), obj.b_sol() self.update_core( f_part2 = f_part2, f_part2_twin = f_part2_twin, k_anisotropic = obj.k_anisotropic()) self.show(prefix = "add H (%4.2f, %6.2f)"%(kh, bh), log = log) b_cart = adptbx.u_as_b(adptbx.u_star_as_u_cart( self.f_obs().unit_cell(), o_kbu_sol.u_star())) return group_args( k_sol = o_kbu_sol.k_sol(), b_sol = o_kbu_sol.b_sol(), b_cart = b_cart, k_h = kh, b_h = bh)	[ 1, 529, 276, 1112, 420, 29958, 29881, 22032, 29899, 2929, 29914, 29883, 312, 29890, 29916, 29918, 4836, 13, 3166, 4770, 29888, 9130, 1649, 1053, 8380, 29918, 5215, 29892, 8542, 29892, 1596, 29918, 2220, 13, 3166, 274, 312, 29890, 29916, 29889, 2378, 29918, 11922, 1053, 8525, 13, 3166, 274, 312, 29890, 29916, 1053, 594, 415, 29890, 29916, 13, 3166, 286, 4378, 29890, 29916, 1053, 21610, 29918, 2929, 794, 13, 3166, 274, 312, 29890, 29916, 29889, 2378, 29918, 11922, 1053, 8525, 13, 3166, 274, 312, 29890, 29916, 1053, 594, 415, 29890, 29916, 13, 5215, 286, 4378, 29890, 29916, 13, 3166, 619, 3116, 29890, 29916, 1053, 2318, 29918, 5085, 13, 5215, 286, 4378, 29890, 29916, 29889, 2378, 29879, 13, 5215, 286, 4378, 29890, 29916, 29889, 8645, 29895, 29918, 2929, 794, 29889, 19529, 261, 13, 3166, 619, 3116, 29890, 29916, 29889, 1688, 29918, 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codeswiftr/app_stats/apps.py	bogdan-veliscu/dev-portfolio-website	0	111728	from django.apps import AppConfig class AppStatsConfig(AppConfig): name = 'app_stats'	[ 1, 515, 9557, 29889, 13371, 1053, 2401, 3991, 13, 13, 13, 1990, 2401, 25060, 3991, 29898, 2052, 3991, 1125, 13, 1678, 1024, 353, 525, 932, 29918, 16202, 29915, 13, 2 ]
fbExceptionMeansSocketShutdown.py	SkyLined/mTCPIPConnection	1	1609623	import socket; def fbExceptionMeansSocketShutdown(oException): return ( (oException.__class__ == socket.error and oException.errno in [ 0x274A # WSAESHUTDOWN ]) );	[ 1, 1053, 9909, 29936, 13, 13, 1753, 285, 29890, 2451, 6816, 550, 11373, 2713, 329, 3204, 29898, 29877, 2451, 1125, 13, 29871, 736, 313, 13, 1678, 313, 29877, 2451, 17255, 1990, 1649, 1275, 9909, 29889, 2704, 322, 288, 2451, 29889, 3127, 1217, 297, 518, 13, 539, 29900, 29916, 29906, 29955, 29946, 29909, 396, 399, 8132, 2890, 29950, 2692, 3970, 16048, 13, 268, 2314, 13, 29871, 3482, 2 ]
apps/cc_cms/admin.py	FreeOurKnowledge/old_platform	11	164027	<filename>apps/cc_cms/admin.py from django_summernote.admin import SummernoteModelAdmin from .models import Page, ColumnsSection, Content, DynamicText class PageAdmin(SummernoteModelAdmin): summernote_fields = '__all__' def register_cms(admin_site): admin_site.register(Page, PageAdmin) admin_site.register(ColumnsSection, PageAdmin) admin_site.register(Content, PageAdmin) admin_site.register(DynamicText, PageAdmin)	[ 1, 529, 9507, 29958, 13371, 29914, 617, 29918, 29883, 1516, 29914, 6406, 29889, 2272, 13, 3166, 9557, 29918, 2083, 29885, 824, 866, 29889, 6406, 1053, 6991, 29885, 824, 866, 3195, 12754, 13, 3166, 869, 9794, 1053, 9305, 29892, 12481, 29879, 13438, 29892, 10576, 29892, 27747, 1626, 13, 13, 13, 1990, 9305, 12754, 29898, 11139, 29885, 824, 866, 3195, 12754, 1125, 13, 1678, 22792, 824, 866, 29918, 9621, 353, 525, 1649, 497, 1649, 29915, 13, 13, 13, 1753, 6036, 29918, 29883, 1516, 29898, 6406, 29918, 2746, 1125, 13, 1678, 4113, 29918, 2746, 29889, 9573, 29898, 5074, 29892, 9305, 12754, 29897, 13, 1678, 4113, 29918, 2746, 29889, 9573, 29898, 14289, 13438, 29892, 9305, 12754, 29897, 13, 1678, 4113, 29918, 2746, 29889, 9573, 29898, 3916, 29892, 9305, 12754, 29897, 13, 1678, 4113, 29918, 2746, 29889, 9573, 29898, 24001, 1626, 29892, 9305, 12754, 29897, 13, 2 ]
pylib/gna/bundles/parameters_yaml_v01.py	gnafit/gna	5	117794	<gh_stars>1-10 """Parameters_yaml v01 bundle Based on: parameters_v03 Implements a set of parameters. New features include: - [v01] read data from yaml file """ from load import ROOT as R from gna.bundle.bundle import * import numpy as np from gna import constructors as C import yaml class parameters_yaml_v01(TransformationBundle): covmat, corrmat = None, None def __init__(self, args, kwargs): self._par_container = [] TransformationBundle.__init__(self, args, *kwargs) self.check_nidx_dim(1, 1, 'major') self.load_data() def load_data(self): if not 'data' in self.cfg: raise Exception('Should provide data file') try: with open(self.cfg.data, 'r') as stream: data = self.data = yaml.load(stream, Loader=yaml.Loader) except: raise Exception('Unable to load input data file: '+self.cfg.data) for name in ('values', 'names', 'mode'): if not name in data: raise Exception('Input file {} misses field {}'.format(self.cfg.data, name)) # if ('correlations' in data or 'uncertainties' in data) and 'covariance' in data: # raise Exception('Conflicting fields in data: correlations/uncertainties and covariance') for field in ('values', 'uncertainties', 'correlations'): #, 'covariance' if field in data: data[field] = np.asanyarray(data[field]) if 'correlations' in data: if self.cfg.get('verbose', 0)>1: print('Correlation matrix') print(self.data['correlations'], ) @staticmethod def _provides(cfg): sepunc = cfg.get('separate_uncertainty', None) if sepunc: return (cfg.parameter, sepunc), () else: return (cfg.parameter,), () def define_variables(self): from gna.parameters import covariance_helpers as ch self._par_container = [] separate_uncertainty = self.cfg.get('separate_uncertainty', False) parname = self.cfg.parameter labelfmt = self.cfg.get('label', '') data = self.data values = data['values'] names = data['names'] unc = data['uncertainties'] mode = data['mode'] if mode=='percent': relunc = unc/100. unc = valuesrelunc elif mode=='relative': relunc = unc unc = valuesrelunc elif mode=='absolute': relunc = unc/values else: raise Exception('Invalid uncertainty mode: '+mode) for it_minor in self.nidx_minor: container = [] for it_major in self.nidx_major: major_values, = it_major.current_values() if not major_values in names: raise Exception('Variable {} is not in the list'.format(major_values)) data_idx = names.index(major_values) it=it_major+it_minor label = it.current_format(labelfmt) if labelfmt else '' if separate_uncertainty: uncpar = self.reqparameter(separate_uncertainty, it, central=1.0, sigma=relunc[data_idx], label=label+' (norm)') par = self.reqparameter(parname, it, central=values[data_idx], fixed=True, label=label) container.append(uncpar) else: par = self.reqparameter(parname, it, central=values[data_idx], sigma=unc[data_idx], label=label) container.append(par) if self.cfg.get("objectize"): trans=par.transformations.value trans.setLabel(label) self.set_output(parname, it, trans.single()) self._par_container.append(container) if 'correlations' in self.data: cormat = self.data['correlations'] self._checkmatrix(cormat, container, is_correlation=True) cormat = self.filter_matrix(cormat, container) if cormat is not None: ch.covariate_pars(container, cormat) # elif 'covariance' in self.cfg: # self._load_covariance_matrix() def build(self): pass def filter_matrix(self, mat, par_container): npars = len(par_container) if npars==1: # No correlations possible return None dim = mat.shape[0] if npars==dim: # no reduction needed return mat # Remove unnecessary rows and columns from the matrix names = [par.name() for par in par_container] for i, name in enumerate(reversed(self.data['names'])): if name in names: continue i=dim-i-1 mat = np.delete(mat, i, 0) mat = np.delete(mat, i, 1) return mat # def _load_covariance_matrix(self): # filename = self.cfg.get('covariance', None) # assert filename # covmat = np.loadtxt(filename) # if self.cfg.get('verbose', 0)>1: # print('Load covariance matrix from %s:'%filename) # print(covmat) # sigma_inv=np.diag(covmat.diagonal()-0.5) # corrmat = np.matmul(np.matmul(sigma_inv, covmat), sigma_inv) # if self.cfg.get('verbose', 0)>1: # print('Compute correlation matrix from:') # print(corrmat) # self._checkmatrix(covmat, is_correlation=False) # self.covmat = covmat # self.corrmat = corrmat # for par, sigma2 in zip(self._par_container, covmat.diagonal()): # par.setSigma(sigma2*0.5) # from gna.parameters import covariance_helpers as ch # ch.covariate_pars(self._par_container, corrmat) def _checkmatrix(self, mat, par_container, is_correlation): if mat.shape[0]!=mat.shape[1]: raise Exception('Non square matrix provided:', mat.shape[0], mat.shape[1]) if len(par_container)>mat.shape[0]: raise Exception('Unable to set correlation to %i parameters with %ix%i matrix'%(len(par_container, mat.shape[0], mat.shape[1]))) mmin, mmax = mat.min(), mat.max() if mmin<-1-1.e-12 or mmax>1.0+1.e-12: raise Exception('Invalid min/max correlation values:', mmin, mmax) diag = mat.diagonal() if is_correlation: ones = diag==1.0 if not ones.all(): raise Exception('There are values !=1 on the 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LeetCode/HashTable/204. Count Primes.py	thehanemperor/LeetCode	0	169713	# EASY # find all multiples less than n # ex Input 6 # arr = [1:True, 2:True ,3:True ,4:True ,5:True ] # start from 2, mark 22, 23, 24 ... False # Time O(N^2) Space O(N) class Solution: def countPrimes(self, n: int) -> int: arr = [1 for _ in range(n)] count = 0 for i in range(2,n): j = 2 while arr[i] and ij < n: arr[i*j] = 0 j += 1 for i in range(2,n): if arr[i]: count+= 1 return count	[ 1, 396, 382, 3289, 29979, 29871, 13, 29937, 1284, 599, 2473, 2701, 3109, 1135, 302, 29871, 13, 29937, 268, 429, 29871, 10567, 29871, 29953, 13, 29937, 632, 3948, 353, 518, 29896, 29901, 5574, 29892, 29871, 29906, 29901, 5574, 1919, 29941, 29901, 5574, 1919, 29946, 29901, 5574, 1919, 29945, 29901, 5574, 4514, 13, 29937, 632, 1369, 515, 29871, 29906, 29892, 2791, 29871, 29906, 29930, 29906, 29892, 29871, 29906, 29930, 29941, 29892, 29871, 29906, 29930, 29946, 2023, 7700, 29871, 13, 13, 29937, 5974, 438, 29898, 29940, 29985, 29906, 29897, 14121, 438, 29898, 29940, 29897, 13, 13, 13, 1990, 24380, 29901, 13, 1678, 822, 2302, 4040, 1355, 29898, 1311, 29892, 302, 29901, 938, 29897, 1599, 938, 29901, 13, 4706, 3948, 353, 518, 29896, 363, 903, 297, 3464, 29898, 29876, 4638, 13, 4706, 2302, 353, 29871, 29900, 13, 4706, 363, 474, 297, 3464, 29898, 29906, 29892, 29876, 1125, 13, 9651, 432, 353, 29871, 29906, 13, 632, 13, 9651, 1550, 3948, 29961, 29875, 29962, 322, 474, 29930, 29926, 529, 302, 29901, 13, 18884, 3948, 29961, 29875, 29930, 29926, 29962, 353, 29871, 29900, 13, 18884, 432, 4619, 29871, 29896, 13, 462, 268, 13, 4706, 363, 474, 297, 3464, 29898, 29906, 29892, 29876, 1125, 13, 9651, 565, 3948, 29961, 29875, 5387, 13, 18884, 2302, 23661, 29871, 29896, 13, 462, 13, 4706, 736, 2302, 13, 2 ]
simulation/model/blocks/order.py	fladdimir/csa-simulation-based-sc-forecast	2	40082	import logging from casymda.blocks.entity import Entity from simpy.core import Environment class Order(Entity): def __init__(self, env: Environment, name: str): super().__init__(env, name) self._time_of_acceptance = -1 self._initial_eta = -1 self._eta = -1 self._ready_at = -1 self._sop_at = -1 self._finished_at = -1 @property def time_of_acceptance(self): return self._time_of_acceptance @time_of_acceptance.setter def time_of_acceptance(self, value): self._time_of_acceptance = value update( self.name, "time_of_acceptance", self._time_of_acceptance, self.env.now, self, ) @property def initial_eta(self): return self._initial_eta @initial_eta.setter def initial_eta(self, value): self._initial_eta = value update(self.name, "initial_eta", self._initial_eta, self.env.now, self) @property def eta(self): return self._eta @eta.setter def eta(self, value): self._eta = value update(self.name, "eta", self._eta, self.env.now, self) @property def ready_at(self): return self._ready_at @ready_at.setter def ready_at(self, value): self._ready_at = value update(self.name, "ready_at", self._ready_at, self.env.now, self) @property def sop_at(self): return self._sop_at @sop_at.setter def sop_at(self, value): self._sop_at = value update(self.name, "sop_at", self._sop_at, self.env.now, self) @property def finished_at(self): return self._finished_at @finished_at.setter def finished_at(self, value): self._finished_at = value update(self.name, "finished_at", self._finished_at, self.env.now, self) def update(name: str, attribute: str, value: float, t: float, order: Order): # (replaced by actual callback during emulation initialization) logging.info( f"order update - name: {name}, attribute: {attribute}, value: {value}, t: {t}" )	[ 1, 1053, 12183, 13, 13, 3166, 3209, 962, 1388, 29889, 1271, 29879, 29889, 10041, 1053, 14945, 13, 3166, 1027, 2272, 29889, 3221, 1053, 16738, 13, 13, 13, 1990, 8170, 29898, 6691, 1125, 13, 1678, 822, 4770, 2344, 12035, 1311, 29892, 8829, 29901, 16738, 29892, 1024, 29901, 851, 1125, 13, 4706, 2428, 2141, 1649, 2344, 12035, 6272, 29892, 1024, 29897, 13, 13, 4706, 1583, 3032, 2230, 29918, 974, 29918, 16044, 749, 353, 448, 29896, 13, 4706, 1583, 3032, 11228, 29918, 1187, 353, 448, 29896, 13, 4706, 1583, 3032, 1187, 353, 448, 29896, 13, 4706, 1583, 3032, 2040, 29918, 271, 353, 448, 29896, 13, 4706, 1583, 3032, 29879, 459, 29918, 271, 353, 448, 29896, 13, 4706, 1583, 3032, 4951, 3276, 29918, 271, 353, 448, 29896, 13, 13, 1678, 732, 6799, 13, 1678, 822, 931, 29918, 974, 29918, 16044, 749, 29898, 1311, 1125, 13, 4706, 736, 1583, 3032, 2230, 29918, 974, 29918, 16044, 749, 13, 13, 1678, 732, 2230, 29918, 974, 29918, 16044, 749, 29889, 842, 357, 13, 1678, 822, 931, 29918, 974, 29918, 16044, 749, 29898, 1311, 29892, 995, 1125, 13, 4706, 1583, 3032, 2230, 29918, 974, 29918, 16044, 749, 353, 995, 13, 4706, 2767, 29898, 13, 9651, 1583, 29889, 978, 29892, 13, 9651, 376, 2230, 29918, 974, 29918, 16044, 749, 613, 13, 9651, 1583, 3032, 2230, 29918, 974, 29918, 16044, 749, 29892, 13, 9651, 1583, 29889, 6272, 29889, 3707, 29892, 13, 9651, 1583, 29892, 13, 4706, 1723, 13, 13, 1678, 732, 6799, 13, 1678, 822, 2847, 29918, 1187, 29898, 1311, 1125, 13, 4706, 736, 1583, 3032, 11228, 29918, 1187, 13, 13, 1678, 732, 11228, 29918, 1187, 29889, 842, 357, 13, 1678, 822, 2847, 29918, 1187, 29898, 1311, 29892, 995, 1125, 13, 4706, 1583, 3032, 11228, 29918, 1187, 353, 995, 13, 4706, 2767, 29898, 1311, 29889, 978, 29892, 376, 11228, 29918, 1187, 613, 1583, 3032, 11228, 29918, 1187, 29892, 1583, 29889, 6272, 29889, 3707, 29892, 1583, 29897, 13, 13, 1678, 732, 6799, 13, 1678, 822, 634, 29874, 29898, 1311, 1125, 13, 4706, 736, 1583, 3032, 1187, 13, 13, 1678, 732, 1187, 29889, 842, 357, 13, 1678, 822, 634, 29874, 29898, 1311, 29892, 995, 1125, 13, 4706, 1583, 3032, 1187, 353, 995, 13, 4706, 2767, 29898, 1311, 29889, 978, 29892, 376, 1187, 613, 1583, 3032, 1187, 29892, 1583, 29889, 6272, 29889, 3707, 29892, 1583, 29897, 13, 13, 1678, 732, 6799, 13, 1678, 822, 7960, 29918, 271, 29898, 1311, 1125, 13, 4706, 736, 1583, 3032, 2040, 29918, 271, 13, 13, 1678, 732, 2040, 29918, 271, 29889, 842, 357, 13, 1678, 822, 7960, 29918, 271, 29898, 1311, 29892, 995, 1125, 13, 4706, 1583, 3032, 2040, 29918, 271, 353, 995, 13, 4706, 2767, 29898, 1311, 29889, 978, 29892, 376, 2040, 29918, 271, 613, 1583, 3032, 2040, 29918, 271, 29892, 1583, 29889, 6272, 29889, 3707, 29892, 1583, 29897, 13, 13, 1678, 732, 6799, 13, 1678, 822, 12103, 29918, 271, 29898, 1311, 1125, 13, 4706, 736, 1583, 3032, 29879, 459, 29918, 271, 13, 13, 1678, 732, 29879, 459, 29918, 271, 29889, 842, 357, 13, 1678, 822, 12103, 29918, 271, 29898, 1311, 29892, 995, 1125, 13, 4706, 1583, 3032, 29879, 459, 29918, 271, 353, 995, 13, 4706, 2767, 29898, 1311, 29889, 978, 29892, 376, 29879, 459, 29918, 271, 613, 1583, 3032, 29879, 459, 29918, 271, 29892, 1583, 29889, 6272, 29889, 3707, 29892, 1583, 29897, 13, 13, 1678, 732, 6799, 13, 1678, 822, 7743, 29918, 271, 29898, 1311, 1125, 13, 4706, 736, 1583, 3032, 4951, 3276, 29918, 271, 13, 13, 1678, 732, 4951, 3276, 29918, 271, 29889, 842, 357, 13, 1678, 822, 7743, 29918, 271, 29898, 1311, 29892, 995, 1125, 13, 4706, 1583, 3032, 4951, 3276, 29918, 271, 353, 995, 13, 4706, 2767, 29898, 1311, 29889, 978, 29892, 376, 4951, 3276, 29918, 271, 613, 1583, 3032, 4951, 3276, 29918, 271, 29892, 1583, 29889, 6272, 29889, 3707, 29892, 1583, 29897, 13, 13, 13, 1753, 2767, 29898, 978, 29901, 851, 29892, 5352, 29901, 851, 29892, 995, 29901, 5785, 29892, 260, 29901, 5785, 29892, 1797, 29901, 8170, 1125, 13, 1678, 396, 313, 3445, 433, 1133, 491, 3935, 6939, 2645, 953, 2785, 17865, 29897, 13, 1678, 12183, 29889, 3888, 29898, 13, 4706, 285, 29908, 2098, 2767, 448, 1024, 29901, 426, 978, 1118, 5352, 29901, 426, 12715, 1118, 995, 29901, 426, 1767, 1118, 260, 29901, 426, 29873, 5038, 13, 1678, 1723, 13, 2 ]
main_a2c.py	WeiChengTseng/DL_final_project	7	143661	<reponame>WeiChengTseng/DL_final_project<filename>main_a2c.py import argparse import torch import torch.optim as optim import os # from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv # from baselines.common.vec_env.vec_frame_stack import VecFrameStack from a2c.models import AtariCNN, A2C, A2CLarge # from a2c.envs import make_env, RenderSubprocVecEnv from a2c.train_multi import train from env_exp import SocTwoEnv parser = argparse.ArgumentParser(description='A2C (Advantage Actor-Critic)') parser.add_argument('--env_path', type=str, default='./env/macos/SoccerTwosFast.app', help='path to the environment binary') parser.add_argument('--render', type=bool, default=False, help='whether to render enviroment') parser.add_argument('--reward_shaping', type=bool, default=False, help='whether to use reward shaping') parser.add_argument('--num-workers', type=int, default=16, help='number of parallel workers') parser.add_argument('--rollout-steps', type=int, default=300, help='steps per rollout') parser.add_argument('--total-steps', type=int, default=int(4e7), help='total number of steps to train for') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--gamma', type=float, default=0.99, help='gamma parameter for GAE') parser.add_argument('--lambd', type=float, default=1.00, help='lambda parameter for GAE') parser.add_argument('--value_coeff', type=float, default=0.5, help='value loss coeffecient') parser.add_argument('--entropy_coeff', type=float, default=0.01, help='entropy loss coeffecient') parser.add_argument('--grad_norm_limit', type=float, default=40., help='gradient norm clipping threshold') args = parser.parse_args() # env_path = './env/macos/SoccerTwosFast.app' env = SocTwoEnv(args.env_path, worker_id=0, train_mode=True, render=args.render) os.environ["CUDA_VISIBLE_DEVICES"]="0" device = torch.device( "cuda:0" if torch.cuda.is_available()else "cpu") print(device) # policy_striker, policy_goalie = A2C(7).to(device), A2C(5).to(device) policy_striker, policy_goalie = A2CLarge(7).to(device), A2CLarge(5).to(device) optim_striker = optim.Adam(policy_striker.parameters(), lr=args.lr) optim_goalie = optim.Adam(policy_goalie.parameters(), lr=args.lr) train(args, policy_striker, policy_goalie, optim_striker, optim_goalie, env, device, args.reward_shaping)	[ 1, 529, 276, 1112, 420, 29958, 4806, 29875, 1451, 996, 29911, 29879, 996, 29914, 19558, 29918, 8394, 29918, 4836, 29966, 9507, 29958, 3396, 29918, 29874, 29906, 29883, 29889, 2272, 13, 5215, 1852, 5510, 13, 5215, 4842, 305, 13, 5215, 4842, 305, 29889, 20640, 408, 5994, 13, 5215, 2897, 13, 13, 29937, 515, 2362, 24210, 29889, 9435, 29889, 2003, 29918, 6272, 29889, 1491, 15439, 29918, 2003, 29918, 6272, 1053, 3323, 15439, 25987, 21745, 13, 29937, 515, 2362, 24210, 29889, 9435, 29889, 2003, 29918, 6272, 29889, 2003, 29918, 2557, 29918, 1429, 1053, 26393, 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code/micropython/robotling_lib/driver/lsm303.py	teuler/robotling2	6	1616475	# ---------------------------------------------------------------------------- # lsm303.py # Class for LSM303 accelerometer/magnetometer driver # # The MIT License (MIT) # Copyright (c) 2018-19 <NAME> # 2018-09-13, v1 # 2018-10-26, bug fix sequence of magnetometer values from I2C call is x,z,y # # Based on the CircuitPython driver: # https://github.com/adafruit/Adafruit_CircuitPython_LSM303 # # The MIT License (MIT) # Copyright (c) 2017 <NAME> for Adafruit Industries # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # ---------------------------------------------------------------------------- import time from micropython import const from robotling_lib.misc.helpers import timed_function try: import struct except ImportError: import ustruct as struct __version__ = "0.1.0.0" CHIP_NAME = "lsm303" CHAN_COUNT = const(1) # pylint: disable=bad-whitespace _ADDRESS_ACCEL = const(0x19) # (0x32 >> 1) // 0011001x _ADDRESS_MAG = const(0x1E) # (0x3C >> 1) // 0011110x _ID = const(0xD4) # (0b11010100) # Accelerometer registers _REG_ACCEL_CTRL_REG1_A = const(0x20) _REG_ACCEL_CTRL_REG2_A = const(0x21) _REG_ACCEL_CTRL_REG3_A = const(0x22) _REG_ACCEL_CTRL_REG4_A = const(0x23) _REG_ACCEL_CTRL_REG5_A = const(0x24) _REG_ACCEL_CTRL_REG6_A = const(0x25) _REG_ACCEL_REFERENCE_A = const(0x26) _REG_ACCEL_STATUS_REG_A = const(0x27) _REG_ACCEL_OUT_X_L_A = const(0x28) _REG_ACCEL_OUT_X_H_A = const(0x29) _REG_ACCEL_OUT_Y_L_A = const(0x2A) _REG_ACCEL_OUT_Y_H_A = const(0x2B) _REG_ACCEL_OUT_Z_L_A = const(0x2C) _REG_ACCEL_OUT_Z_H_A = const(0x2D) _REG_ACCEL_FIFO_CTRL_REG_A = const(0x2E) _REG_ACCEL_FIFO_SRC_REG_A = const(0x2F) _REG_ACCEL_INT1_CFG_A = const(0x30) _REG_ACCEL_INT1_SOURCE_A = const(0x31) _REG_ACCEL_INT1_THS_A = const(0x32) _REG_ACCEL_INT1_DURATION_A = const(0x33) _REG_ACCEL_INT2_CFG_A = const(0x34) _REG_ACCEL_INT2_SOURCE_A = const(0x35) _REG_ACCEL_INT2_THS_A = const(0x36) _REG_ACCEL_INT2_DURATION_A = const(0x37) _REG_ACCEL_CLICK_CFG_A = const(0x38) _REG_ACCEL_CLICK_SRC_A = const(0x39) _REG_ACCEL_CLICK_THS_A = const(0x3A) _REG_ACCEL_TIME_LIMIT_A = const(0x3B) _REG_ACCEL_TIME_LATENCY_A = const(0x3C) _REG_ACCEL_TIME_WINDOW_A = const(0x3D) # Magnetometer registers _REG_MAG_CRA_REG_M = const(0x00) _REG_MAG_CRB_REG_M = const(0x01) _REG_MAG_MR_REG_M = const(0x02) _REG_MAG_OUT_X_H_M = const(0x03) _REG_MAG_OUT_X_L_M = const(0x04) _REG_MAG_OUT_Z_H_M = const(0x05) _REG_MAG_OUT_Z_L_M = const(0x06) _REG_MAG_OUT_Y_H_M = const(0x07) _REG_MAG_OUT_Y_L_M = const(0x08) _REG_MAG_SR_REG_Mg = const(0x09) _REG_MAG_IRA_REG_M = const(0x0A) _REG_MAG_IRB_REG_M = const(0x0B) _REG_MAG_IRC_REG_M = const(0x0C) _REG_MAG_TEMP_OUT_H_M = const(0x31) _REG_MAG_TEMP_OUT_L_M = const(0x32) # Magnetometer gains MAGGAIN_1_3 = const(0x20) # +/- 1.3 MAGGAIN_1_9 = const(0x40) # +/- 1.9 MAGGAIN_2_5 = const(0x60) # +/- 2.5 MAGGAIN_4_0 = const(0x80) # +/- 4.0 MAGGAIN_4_7 = const(0xA0) # +/- 4.7 MAGGAIN_5_6 = const(0xC0) # +/- 5.6 MAGGAIN_8_1 = const(0xE0) # +/- 8.1 # Magentometer rates MAGRATE_0_7 = const(0x00) # 0.75 Hz MAGRATE_1_5 = const(0x01) # 1.5 Hz MAGRATE_3_0 = const(0x62) # 3.0 Hz MAGRATE_7_5 = const(0x03) # 7.5 Hz MAGRATE_15 = const(0x04) # 15 Hz MAGRATE_30 = const(0x05) # 30 Hz MAGRATE_75 = const(0x06) # 75 Hz MAGRATE_220 = const(0x07) # 200 Hz # Conversion constants _LSM303ACCEL_MG_LSB = 16704.0 _GRAVITY_STANDARD = 9.80665 # Earth's gravity in m/s^2 _GAUSS_TO_MICROTESLA = 100.0 # Gauss to micro-Tesla multiplier _GAUSS_TO_NANOTESLA = 100000.0 # Gauss to nano-Tesla multiplier # ---------------------------------------------------------------------------- class LSM303(object): """Driver for LSM303 magnetometer/accelerometer.""" def __init__(self, i2c): """ Requires already initialized I2C bus instance. """ self._i2c = i2c self._isReady = False addrList = self._i2c.deviceAddrList if (_ADDRESS_ACCEL in addrList) and (_ADDRESS_MAG in addrList): # Enable accelerometer and magnetometer self._write_u8(_ADDRESS_ACCEL, _REG_ACCEL_CTRL_REG1_A, 0x27) self._write_u8(_ADDRESS_MAG, _REG_MAG_MR_REG_M, 0x00) # Some initial parameters self._lsm303mag_gauss_lsb_xy = 1100.0 self._lsm303mag_gauss_lsb_z = 980.0 self._mag_gain = MAGGAIN_1_3 self._mag_rate = MAGRATE_0_7 self._isReady = True print("[{0:>12}] {1:35} ({2}): {3}" .format(CHIP_NAME, "Magnetometer/accelerometer", __version__, "ok" if self._isReady else "NOT FOUND")) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #@timed_function @property def raw_accelerometer(self): """ The raw accelerometer sensor values. A 3-tuple of X, Y, Z axis values that are 16-bit signed integers. """ buf = bytearray(6) self._read_bytes(_ADDRESS_ACCEL, _REG_ACCEL_OUT_X_L_A \| 0x80, buf) return struct.unpack_from('<hhh', buf[0:6]) @property def accelerometer(self): """ The processed accelerometer sensor values. A 3-tuple of X, Y, Z axis values in meters per second squared that are signed floats. """ raw_accel_data = self.raw_accelerometer return [n /_LSM303ACCEL_MG_LSB _GRAVITY_STANDARD for n in raw_accel_data] @property def raw_magnetometer(self): """ The raw magnetometer sensor values. A 3-tuple of X, Y, Z axis values that are 16-bit signed integers. """ buf = bytearray(6) self._read_bytes(_ADDRESS_MAG, _REG_MAG_OUT_X_H_M, buf) x, z, y = struct.unpack_from('>hhh', buf[0:6]) return (x, y, z) @property def magnetometer(self): """ The processed magnetometer sensor values. A 3-tuple of X, Y, Z axis values in microteslas that are signed floats. """ mag_x, mag_y, mag_z = self.raw_magnetometer return (mag_x /self._lsm303mag_gauss_lsb_xy _GAUSS_TO_MICROTESLA, mag_y /self._lsm303mag_gauss_lsb_xy _GAUSS_TO_MICROTESLA, mag_z /self._lsm303mag_gauss_lsb_z _GAUSS_TO_MICROTESLA) @property def magnetometer_nT(self): """ The processed magnetometer sensor values. A 3-tuple of X, Y, Z axis values in nanotesla that are signed floats. """ mag_x, mag_y, mag_z = self.raw_magnetometer return (mag_x /self._lsm303mag_gauss_lsb_xy _GAUSS_TO_NANOTESLA, mag_y /self._lsm303mag_gauss_lsb_xy _GAUSS_TO_NANOTESLA, mag_z /self._lsm303mag_gauss_lsb_z *_GAUSS_TO_NANOTESLA) @property def mag_gain(self): """ The magnetometer's gain. """ return self._mag_gain @mag_gain.setter def mag_gain(self, value): assert value in (MAGGAIN_1_3, MAGGAIN_1_9, MAGGAIN_2_5, MAGGAIN_4_0, MAGGAIN_4_7, MAGGAIN_5_6, MAGGAIN_8_1) self._mag_gain = value self._write_u8(_ADDRESS_MAG, _REG_MAG_CRB_REG_M, value) if self._mag_gain == MAGGAIN_1_3: self._lsm303mag_gauss_lsb_xy = 1100.0 self._lsm303mag_gauss_lsb_z = 980.0 elif self._mag_gain == MAGGAIN_1_9: self._lsm303mag_gauss_lsb_xy = 855.0 self._lsm303mag_gauss_lsb_z = 760.0 elif self._mag_gain == MAGGAIN_2_5: self._lsm303mag_gauss_lsb_xy = 670.0 self._lsm303mag_gauss_lsb_z = 600.0 elif self._mag_gain == MAGGAIN_4_0: self._lsm303mag_gauss_lsb_xy = 450.0 self._lsm303mag_gauss_lsb_z = 400.0 elif self._mag_gain == MAGGAIN_4_7: self._lsm303mag_gauss_lsb_xy = 400.0 self._lsm303mag_gauss_lsb_z = 355.0 elif self._mag_gain == MAGGAIN_5_6: self._lsm303mag_gauss_lsb_xy = 330.0 self._lsm303mag_gauss_lsb_z = 295.0 elif self._mag_gain == MAGGAIN_8_1: self._lsm303mag_gauss_lsb_xy = 230.0 self._lsm303mag_gauss_lsb_z = 205.0 @property def mag_rate(self): """ The magnetometer update rate. """ return self._mag_rate @mag_rate.setter def mag_rate(self, value): assert value in (MAGRATE_0_7, MAGRATE_1_5, MAGRATE_3_0, MAGRATE_7_5, MAGRATE_15, MAGRATE_30, MAGRATE_75, MAGRATE_220) self._mag_rate = value reg_m = ((value & 0x07) << 2) & 0xff self._write_u8(_ADDRESS_MAG, _REG_MAG_CRA_REG_M, reg_m) @property def is_ready(self): return self._isReady @property def channel_Count(self): return CHAN_COUNT @property def name(self): return CHIP_NAME # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def _write_u8(self, i2cAddr, regAddr, val): buf = bytearray(2) buf[0] = regAddr & 0xff buf[1] = val & 0xff self._i2c.writeto(i2cAddr, buf) def _read_bytes(self, i2cAddr, regAddr, buf): cmd = bytearray(1) cmd[0] = regAddr & 0xff self._i2c.writeto(i2cAddr, cmd, False) self._i2c.readfrom_into(i2cAddr, buf) # ----------------------------------------------------------------------------	[ 1, 396, 448, 2683, 2683, 2683, 2683, 1378, 5634, 13, 29937, 301, 3844, 29941, 29900, 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29901, 13, 418, 1583, 3032, 3137, 29885, 29941, 29900, 29941, 11082, 29918, 29887, 11214, 29918, 3137, 29890, 29918, 3594, 353, 29871, 29906, 29941, 29900, 29889, 29900, 13, 418, 1583, 3032, 3137, 29885, 29941, 29900, 29941, 11082, 29918, 29887, 11214, 29918, 3137, 29890, 29918, 29920, 29871, 353, 29871, 29906, 29900, 29945, 29889, 29900, 13, 13, 29871, 732, 6799, 13, 29871, 822, 2320, 29918, 10492, 29898, 1311, 1125, 13, 1678, 9995, 450, 10710, 8328, 2767, 6554, 29889, 13, 1678, 9995, 13, 1678, 736, 1583, 3032, 11082, 29918, 10492, 13, 13, 29871, 732, 11082, 29918, 10492, 29889, 842, 357, 13, 29871, 822, 2320, 29918, 10492, 29898, 1311, 29892, 995, 1125, 13, 1678, 4974, 995, 297, 313, 1529, 14345, 3040, 29918, 29900, 29918, 29955, 29892, 341, 10051, 29934, 3040, 29918, 29896, 29918, 29945, 29892, 341, 10051, 29934, 3040, 29918, 29941, 29918, 29900, 29892, 341, 10051, 29934, 3040, 29918, 29955, 29918, 29945, 29892, 13, 462, 268, 341, 10051, 29934, 3040, 29918, 29896, 29945, 29892, 341, 10051, 29934, 3040, 29918, 29941, 29900, 29892, 341, 10051, 29934, 3040, 29918, 29955, 29945, 29892, 341, 10051, 29934, 3040, 29918, 29906, 29906, 29900, 29897, 13, 1678, 1583, 3032, 11082, 29918, 10492, 353, 995, 13, 1678, 1072, 29918, 29885, 353, 5135, 1767, 669, 29871, 29900, 29916, 29900, 29955, 29897, 3532, 29871, 29906, 29897, 669, 29871, 29900, 29916, 600, 13, 1678, 1583, 3032, 3539, 29918, 29884, 29947, 7373, 17744, 26785, 29918, 1529, 29954, 29892, 903, 18166, 29918, 1529, 29954, 29918, 29907, 4717, 29918, 18166, 29918, 29924, 29892, 1072, 29918, 29885, 29897, 13, 13, 29871, 732, 6799, 13, 29871, 822, 338, 29918, 2040, 29898, 1311, 1125, 13, 1678, 736, 1583, 3032, 275, 28181, 13, 13, 29871, 732, 6799, 13, 29871, 822, 8242, 29918, 3981, 29898, 1311, 1125, 13, 1678, 736, 5868, 2190, 29918, 18736, 13, 13, 29871, 732, 6799, 13, 29871, 822, 1024, 29898, 1311, 1125, 13, 1678, 736, 5868, 5690, 29918, 5813, 13, 13, 29871, 396, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 448, 13, 29871, 822, 903, 3539, 29918, 29884, 29947, 29898, 1311, 29892, 474, 29906, 29883, 2528, 29878, 29892, 1072, 2528, 29878, 29892, 659, 1125, 13, 1678, 18392, 1678, 353, 7023, 2378, 29898, 29906, 29897, 13, 1678, 18392, 29961, 29900, 29962, 353, 1072, 2528, 29878, 669, 29871, 29900, 29916, 600, 13, 1678, 18392, 29961, 29896, 29962, 353, 659, 669, 29871, 29900, 29916, 600, 13, 1678, 1583, 3032, 29875, 29906, 29883, 29889, 8231, 10896, 29898, 29875, 29906, 29883, 2528, 29878, 29892, 18392, 29897, 13, 13, 29871, 822, 903, 949, 29918, 13193, 29898, 1311, 29892, 474, 29906, 29883, 2528, 29878, 29892, 1072, 2528, 29878, 29892, 18392, 1125, 13, 1678, 9920, 1678, 353, 7023, 2378, 29898, 29896, 29897, 13, 1678, 9920, 29961, 29900, 29962, 353, 1072, 2528, 29878, 669, 29871, 29900, 29916, 600, 13, 1678, 1583, 3032, 29875, 29906, 29883, 29889, 8231, 10896, 29898, 29875, 29906, 29883, 2528, 29878, 29892, 9920, 29892, 7700, 29897, 13, 1678, 1583, 3032, 29875, 29906, 29883, 29889, 949, 3166, 29918, 8941, 29898, 29875, 29906, 29883, 2528, 29878, 29892, 18392, 29897, 13, 13, 29937, 448, 2683, 2683, 2683, 2683, 1378, 5634, 13, 2 ]
pythonProject/q46.py	Babur171/python	0	154520	string = "blackEyes" s = {string: (string[::2], string[1::2])} print(s)	[ 1, 1347, 353, 376, 8517, 29923, 3582, 29908, 13, 29879, 353, 426, 1807, 29901, 313, 1807, 29961, 1057, 29906, 1402, 1347, 29961, 29896, 1057, 29906, 2314, 29913, 13, 2158, 29898, 29879, 29897, 13, 2 ]
attention_to_gif/visualizer.py	valayDave/attention_to_gif	0	124692	<filename>attention_to_gif/visualizer.py # importing matplot lib import matplotlib.pyplot as plt import numpy as np import torch # importig movie py libraries from moviepy.editor import VideoClip from moviepy.video.io.bindings import mplfig_to_npimage class AttentionVisualizer: """ Creates a GIF of the transition of attention weights across the layers. layer_wise_attention_weights : Tensor : (num_layers,batch_size,num_heads,seq_len_x,seq_len_y) x_label_toks : labels for indices in the sequence. y_label_toks : labels for indices in the sequence. title_message: Message to print in the video/Gif chosen_head : int : a particular to head to visualise attention for. If None: Attention is a summed for all heads at a layer seq_len_x_lim : constrain the size of of head in the x dimension seq_len_y_lim : constrain the size of of head in the y dimension """ def __init__(self,\ layer_wise_attention_weights:torch.Tensor, seq_len_x_lim=None, seq_len_y_lim=None, chosen_item=0, chosen_head=None, x_label_toks=[], y_label_toks=[], fig_size=(10,10), title_message='', ) -> None: #() self.num_layers, self.batch_size , self.num_attention_heads , seq_len_x,seq_len_y = layer_wise_attention_weights.size() # Doing this ensure that it work. self.seq_len_x_lim = seq_len_x_lim self.seq_len_y_lim = seq_len_y_lim self.chosen_item=chosen_item self.layer_wise_attention_weights = layer_wise_attention_weights self.fig, self.ax = None,None self.fig_size=fig_size self.x_label_toks = x_label_toks self.y_label_toks = y_label_toks self.title_message = title_message self.chosen_head = chosen_head # self.fig.colorbar() def get_attention_values(self,layer,chosen_head=None): if chosen_head is not None: conv_arr = self.layer_wise_attention_weights[int(layer)][self.chosen_item][chosen_head].cpu().numpy() else: conv_arr = self.layer_wise_attention_weights[int(layer)][self.chosen_item].sum(dim=0).cpu().numpy() if self.seq_len_x_lim is not None: conv_arr= conv_arr[:,:self.seq_len_x_lim] if self.seq_len_y_lim is not None: conv_arr= conv_arr[:self.seq_len_y_lim] return conv_arr def __call__(self,t): # clear if self.fig is None: self.fig, self.ax = plt.subplots(figsize=self.fig_size) if len(self.ax.images) > 0: self.ax.images[-1].colorbar.remove() self.ax.clear() conv_arr = self.get_attention_values(t,chosen_head=self.chosen_head) cax = self.ax.matshow(conv_arr,origin='lower', cmap='viridis',aspect='auto') # self.y_label_toks self.fig.colorbar(cax,ax=self.ax) if len(self.x_label_toks) > 0: self.ax.set_xticks([i for i in range(len(self.x_label_toks))]) self.ax.set_xticklabels(self.x_label_toks,) if len(self.y_label_toks) > 0: self.ax.set_yticks([len(self.y_label_toks)-i-1 for i in range(len(self.y_label_toks))]) self.ax.set_yticklabels(self.y_label_toks) default_title = f" Attention At Layer : {int(t)} \n" if self.chosen_head is not None: default_title = f" Attention At Layer : {int(t)} And Head : {self.chosen_head}\n" if self.title_message is '': self.ax.set_title(default_title) else: self.ax.set_title(f"{self.title_message}\n {default_title}") return mplfig_to_npimage(self.fig) def save_visualisation(self,viz_name='attention_viz.gif',fps=20): animation = VideoClip(make_frame=self,duration=self.num_layers) animation.write_gif(viz_name,fps=fps) def show_visualisation(self,viz_name='attention_viz.gif',fps = 20, loop = False, autoplay = False): # animation = VideoClip(self, duration = self.num_layers) animation = VideoClip(make_frame=self,duration=self.num_layers) animation.ipython_display(fps =fps ,loop=loop,autoplay=autoplay) def create_single_plot(self, fig_size=(10,10)): fig,axes = plt.subplots(nrows=self.num_layers, ncols=self.num_attention_heads,figsize=fig_size) for lidx,layerax in enumerate(axes): for hidx,headax in enumerate(layerax): conv_arr = self.get_attention_values(lidx,chosen_head=hidx) cax = headax.matshow(conv_arr,origin='lower', cmap='viridis',aspect='auto') fig.colorbar(cax,ax=headax) if len(self.x_label_toks) > 0: headax.set_xticks([i for i in range(len(self.x_label_toks))]) headax.set_xticklabels(self.x_label_toks,) if len(self.y_label_toks) > 0: headax.set_yticks([len(self.y_label_toks)-i-1 for i in range(len(self.y_label_toks))]) headax.set_yticklabels(self.y_label_toks) default_title = f" Attention At Layer : {int(lidx)} And Head : {hidx}\n" headax.set_title(default_title) return fig	[ 1, 529, 9507, 29958, 1131, 2509, 29918, 517, 29918, 18660, 29914, 20119, 3950, 29889, 2272, 13, 29937, 28348, 1775, 5317, 4303, 29871, 13, 5215, 22889, 29889, 2272, 5317, 408, 14770, 29871, 13, 5215, 12655, 408, 7442, 29871, 13, 5215, 4842, 305, 13, 29937, 1053, 335, 14064, 11451, 9562, 29871, 13, 3166, 14064, 2272, 29889, 15204, 1053, 13987, 29907, 3466, 29871, 13, 3166, 14064, 2272, 29889, 9641, 29889, 601, 29889, 5355, 886, 1053, 286, 572, 1003, 29918, 517, 29918, 9302, 3027, 29871, 13, 13, 1990, 6212, 2509, 16227, 3950, 29901, 13, 1678, 9995, 13, 1678, 6760, 1078, 263, 402, 6545, 310, 278, 9558, 310, 8570, 18177, 4822, 278, 15359, 29889, 29871, 13, 1678, 7546, 29918, 3538, 29918, 1131, 2509, 29918, 705, 5861, 584, 323, 6073, 584, 313, 1949, 29918, 29277, 29892, 16175, 29918, 2311, 29892, 1949, 29918, 2813, 29879, 29892, 11762, 29918, 2435, 29918, 29916, 29892, 11762, 29918, 2435, 29918, 29891, 29897, 13, 1678, 921, 29918, 1643, 29918, 517, 2039, 584, 11073, 363, 16285, 297, 278, 5665, 29889, 13, 1678, 343, 29918, 1643, 29918, 517, 2039, 584, 11073, 363, 16285, 297, 278, 5665, 29889, 13, 1678, 3611, 29918, 4906, 29901, 7777, 304, 1596, 297, 278, 4863, 29914, 29954, 361, 13, 1678, 10434, 29918, 2813, 584, 938, 584, 263, 3153, 304, 2343, 304, 7604, 895, 8570, 363, 29889, 29871, 13, 795, 960, 6213, 29901, 6212, 2509, 338, 263, 2533, 2168, 363, 599, 15883, 472, 263, 7546, 13, 1678, 19359, 29918, 2435, 29918, 29916, 29918, 2576, 584, 1040, 6038, 278, 2159, 310, 310, 2343, 297, 278, 921, 9927, 29871, 13, 1678, 19359, 29918, 2435, 29918, 29891, 29918, 2576, 584, 1040, 6038, 278, 2159, 310, 310, 2343, 297, 278, 343, 9927, 29871, 13, 1678, 9995, 13, 1678, 822, 4770, 2344, 12035, 1311, 2053, 13, 18884, 7546, 29918, 3538, 29918, 1131, 2509, 29918, 705, 5861, 29901, 7345, 305, 29889, 29911, 6073, 29892, 13, 18884, 19359, 29918, 2435, 29918, 29916, 29918, 2576, 29922, 8516, 29892, 13, 18884, 19359, 29918, 2435, 29918, 29891, 29918, 2576, 29922, 8516, 29892, 13, 18884, 10434, 29918, 667, 29922, 29900, 29892, 13, 18884, 10434, 29918, 2813, 29922, 8516, 29892, 13, 18884, 921, 29918, 1643, 29918, 517, 2039, 11759, 1402, 13, 18884, 343, 29918, 1643, 29918, 517, 2039, 11759, 1402, 13, 18884, 2537, 29918, 2311, 7607, 29896, 29900, 29892, 29896, 29900, 511, 13, 18884, 3611, 29918, 4906, 2433, 742, 13, 18884, 1723, 1599, 6213, 29901, 13, 4706, 396, 580, 13, 4706, 1583, 29889, 1949, 29918, 29277, 29892, 1583, 29889, 16175, 29918, 2311, 1919, 1583, 29889, 1949, 29918, 1131, 2509, 29918, 2813, 29879, 1919, 19359, 29918, 2435, 29918, 29916, 29892, 11762, 29918, 2435, 29918, 29891, 353, 7546, 29918, 3538, 29918, 1131, 2509, 29918, 705, 5861, 29889, 2311, 580, 13, 4706, 396, 1938, 292, 445, 9801, 393, 372, 664, 29889, 13, 4706, 1583, 29889, 11762, 29918, 2435, 29918, 29916, 29918, 2576, 353, 19359, 29918, 2435, 29918, 29916, 29918, 2576, 13, 4706, 1583, 29889, 11762, 29918, 2435, 29918, 29891, 29918, 2576, 29871, 353, 19359, 29918, 2435, 29918, 29891, 29918, 2576, 13, 4706, 1583, 29889, 305, 7749, 29918, 667, 29922, 305, 7749, 29918, 667, 13, 4706, 1583, 29889, 13148, 29918, 3538, 29918, 1131, 2509, 29918, 705, 5861, 353, 7546, 29918, 3538, 29918, 1131, 2509, 29918, 705, 5861, 13, 4706, 1583, 29889, 1003, 29892, 1583, 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python-is-easy/assignments/snowman/main.py	eDyablo/pirple	0	10767	<filename>python-is-easy/assignments/snowman/main.py<gh_stars>0 ''' Homework assignment for the 'Python is easy' course by Pirple. Written be <NAME>. Snowman(Hangman) game. ''' from os import ( name as os_name, system as system_call, ) from os.path import ( abspath, dirname, join as join_path, ) ''' Screen displays game output ''' class Screen: def clear(self): if os_name == 'nt': system_call('cls') else: system_call('clear') def draw(self, frame): for line in frame: print(line) ''' Input represents game input device ''' class Input: def ask(self, message): answer = '' while answer == '': answer = input(message) return answer ''' Art is a game art which is set of frames that get loaded from a text file. Draws its current frame on a screen. ''' class Art: def __init__(self): self.frames = [] self.current_frame = 0 def load(self, name): frames = [] art_path = join_path(dirname(abspath(__file__)), join_path('arts', name)) with open(art_path, 'r') as art_file: frame_height = int(art_file.readline()) frame = [] line_count = 0 for line in art_file: frame.append(line.strip('\n\r')) line_count += 1 if line_count % frame_height == 0: frames.append(frame) frame = [] self.frames = frames self.current_frame = 0 def draw(self, screen): screen.draw(self.frames[self.current_frame]) def frames_number(self): return len(self.frames) def next_frame(self): self.current_frame = (self.current_frame + 1) % self.frames_number() return self.current_frame ''' Riddle holds secret word and gets solved by guesses ''' class Riddle: def __init__(self, key): self.key = key self.clue = ['_'] * len(key) def length(self): return len(self.key) def range(self): return range(0, self.length()) def guess(self, g): guess_count = 0 for i in self.range(): if g == self.key[i]: guess_count += 1 self.clue[i] = g return guess_count def solved(self): for i in self.range(): if self.clue[i] != self.key[i]: return False return True def unsolved(self): return self.solved() == False def draw(self, screen): screen.draw([' '.join(self.clue)]) ''' Game is a game itself ''' class Game: def __init__(self): self.screen = Screen() self.input = Input() self.art = Art() self.riddle = Riddle('riddle') def play(self): self.start() self.propose_riddle() while self.in_progress(): self.play_round() self.display_result() def start(self): self.art.load('snowman') self.game_over = False def propose_riddle(self): self.riddle = Riddle(self.input.ask('Player 1 pick a word: ').lower()) def in_progress(self): return self.riddle.unsolved() and self.game_over == False def draw_frame(self): self.screen.clear() self.art.draw(self.screen) self.riddle.draw(self.screen) def play_round(self): self.draw_frame() clue = input('Player 2 guess a letter: ').lower() if len(clue) > 0: if 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PythonExercicios/Mundo 2/8_estrutura_de_repeticao_for/ex051.py	GuilhermoCampos/Curso-Python3-curso-em-video	0	159848	print('{}\n {:^25}\n {}\n'.format('='25, '10 TERMOS DE UMA PA', '='25), end=' ') primeiro = int(input('Primeiro Termo: ')) razao = int(input(' Razão: ')) acumulador = 0 for c in range(0, 10): print(primeiro + acumulador, '=> ', end='') acumulador += razao print('ACABOU')	[ 1, 1596, 877, 29912, 1012, 29876, 12365, 29985, 29906, 29945, 1012, 29876, 426, 1012, 29876, 4286, 4830, 877, 2433, 29930, 29906, 29945, 29892, 525, 29896, 29900, 323, 1001, 29924, 3267, 5012, 501, 1529, 17687, 742, 525, 2433, 29930, 29906, 29945, 511, 1095, 2433, 25710, 13, 10080, 3350, 353, 938, 29898, 2080, 877, 4040, 603, 3350, 11814, 29877, 29901, 525, 876, 13, 336, 1362, 29877, 353, 938, 29898, 2080, 877, 24961, 1368, 29901, 525, 876, 13, 562, 398, 352, 3136, 353, 29871, 29900, 13, 1454, 274, 297, 3464, 29898, 29900, 29892, 29871, 29896, 29900, 1125, 13, 1678, 1596, 29898, 10080, 3350, 718, 1274, 398, 352, 3136, 29892, 525, 4261, 13420, 1095, 2433, 1495, 13, 1678, 1274, 398, 352, 3136, 4619, 1153, 1362, 29877, 13, 2158, 877, 2477, 2882, 27269, 1495, 13, 2 ]
tests/unit/test_albums.py	movermeyer/openphoto-python	3	173059	from __future__ import unicode_literals import mock try: import unittest2 as unittest # Python2.6 except ImportError: import unittest import trovebox class TestAlbums(unittest.TestCase): test_host = "test.example.com" test_photos_dict = [{"id": "1a", "tags": ["tag1", "tag2"]}, {"id": "2b", "tags": ["tag3", "tag4"]}] test_albums_dict = [{"cover": {"id": "1a", "tags": ["tag1", "tag2"]}, "id": "1", "name": "Album 1", "photos": [test_photos_dict[0]], "totalRows": 2}, {"cover": {"id": "2b", "tags": ["tag3", "tag4"]}, "id": "2", "name": "Album 2", "photos": [test_photos_dict[1]], "totalRows": 2}] def setUp(self): self.client = trovebox.Trovebox(host=self.test_host) self.test_photos = [trovebox.objects.photo.Photo(self.client, photo) for photo in self.test_photos_dict] self.test_albums = [trovebox.objects.album.Album(self.client, album) for album in self.test_albums_dict] @staticmethod def _return_value(result, message="", code=200): return {"message": message, "code": code, "result": result} class TestAlbumsList(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'get') def test_albums_list(self, mock_get): """Check that the album list is returned correctly""" mock_get.return_value = self._return_value(self.test_albums_dict) result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(len(result), 2) self.assertEqual(result[0].id, "1") self.assertEqual(result[0].name, "Album 1") self.assertEqual(result[1].id, "2") self.assertEqual(result[1].name, "Album 2") @mock.patch.object(trovebox.Trovebox, 'get') def test_empty_result(self, mock_get): """Check that an empty result is transformed into an empty list """ mock_get.return_value = self._return_value("") result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(result, []) @mock.patch.object(trovebox.Trovebox, 'get') def test_zero_rows(self, mock_get): """Check that totalRows=0 is transformed into an empty list """ mock_get.return_value = self._return_value([{"totalRows": 0}]) result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(result, []) @mock.patch.object(trovebox.Trovebox, 'get') def test_albums_list_returns_cover_photos(self, mock_get): """Check that the album list returns cover photo objects""" mock_get.return_value = self._return_value(self.test_albums_dict) result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(len(result), 2) self.assertEqual(result[0].id, "1") self.assertEqual(result[0].name, "Album 1") self.assertEqual(result[0].cover.id, "1a") self.assertEqual(result[0].cover.tags, ["tag1", "tag2"]) self.assertEqual(result[1].id, "2") self.assertEqual(result[1].name, "Album 2") self.assertEqual(result[1].cover.id, "2b") self.assertEqual(result[1].cover.tags, ["tag3", "tag4"]) class TestAlbumUpdateCover(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_cover_update(self, mock_post): """Check that an album cover can be updated""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.cover_update(self.test_albums[0], self.test_photos[0], foo="bar") mock_post.assert_called_with("/album/1/cover/1a/update.json", foo="bar") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_cover_update_id(self, mock_post): """Check that an album cover can be updated using IDs""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.cover_update("1", "1a", foo="bar") mock_post.assert_called_with("/album/1/cover/1a/update.json", foo="bar") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_cover_update(self, mock_post): """Check that an album cover can be updated using the album object directly""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.cover_update(self.test_photos[1], foo="bar") mock_post.assert_called_with("/album/1/cover/2b/update.json", foo="bar") self.assertEqual(album.id, "2") self.assertEqual(album.name, "Album 2") self.assertEqual(album.cover.id, "2b") self.assertEqual(album.cover.tags, ["tag3", "tag4"]) class TestAlbumCreate(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_create(self, mock_post): """Check that an album can be created""" mock_post.return_value = self._return_value(self.test_albums_dict[0]) result = self.client.album.create(name="Test", foo="bar") mock_post.assert_called_with("/album/create.json", name="Test", foo="bar") self.assertEqual(result.id, "1") self.assertEqual(result.name, "Album 1") self.assertEqual(result.cover.id, "1a") self.assertEqual(result.cover.tags, ["tag1", "tag2"]) class TestAlbumDelete(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_delete(self, mock_post): """Check that an album can be deleted""" mock_post.return_value = self._return_value(True) result = self.client.album.delete(self.test_albums[0], foo="bar") mock_post.assert_called_with("/album/1/delete.json", foo="bar") self.assertEqual(result, True) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_delete_id(self, mock_post): """Check that an album can be deleted using its ID""" mock_post.return_value = self._return_value(True) result = self.client.album.delete("1", foo="bar") mock_post.assert_called_with("/album/1/delete.json", foo="bar") self.assertEqual(result, True) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_delete(self, mock_post): """Check that an album can be deleted using the album object directly""" mock_post.return_value = self._return_value(True) album = self.test_albums[0] result = album.delete(foo="bar") mock_post.assert_called_with("/album/1/delete.json", foo="bar") self.assertEqual(result, True) self.assertEqual(album.get_fields(), {}) self.assertEqual(album.id, None) self.assertEqual(album.name, None) class TestAlbumAdd(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add(self, mock_post): """ Check that photos can be added to an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.add(self.test_albums[0], self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_id(self, mock_post): """ Check that photos can be added to an album using IDs """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.add(self.test_albums[0].id, objects=["1a", "2b"], object_type="photo", foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_add(self, mock_post): """ Check that photos can be added to an album using the album object directly """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.add(self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a", "2b"], foo="bar") self.assertEqual(album.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_single(self, mock_post): """ Check that a single photo can be added to an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) self.test_albums[0].add(self.test_photos[0], foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a"], foo="bar") @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_invalid_type(self, _): """ Check that an exception is raised if an invalid object is added to an album. """ with self.assertRaises(AttributeError): self.test_albums[0].add([object()]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_multiple_types(self, _): """ Check that an exception is raised if multiple types are added to an album. """ with self.assertRaises(ValueError): self.test_albums[0].add(self.test_photos+self.test_albums) class TestAlbumRemovePhotos(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove(self, mock_post): """ Check that photos can be removed from an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.remove(self.test_albums[0], self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_id(self, mock_post): """ Check that photos can be removed from an album using IDs """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.remove(self.test_albums[0].id, objects=["1a", "2b"], object_type="photo", foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_remove(self, mock_post): """ Check that photos can be removed from an album using the album object directly """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.remove(self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a", "2b"], foo="bar") self.assertEqual(album.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_single(self, mock_post): """ Check that a single photo can be removed from an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) self.test_albums[0].remove(self.test_photos[0], foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a"], foo="bar") @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_invalid_type(self, _): """ Check that an exception is raised if an invalid object is removed from an album. """ with self.assertRaises(AttributeError): self.test_albums[0].remove([object()]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_multiple_types(self, _): """ Check that an exception is raised if multiple types are removed from an album. """ with self.assertRaises(ValueError): self.test_albums[0].remove(self.test_photos+self.test_albums) class TestAlbumUpdate(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_update(self, mock_post): """Check that an album can be updated""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.update(self.test_albums[0], name="Test") mock_post.assert_called_with("/album/1/update.json", name="Test") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_update_id(self, mock_post): """Check that an album can be updated using its ID""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.update("1", name="Test") mock_post.assert_called_with("/album/1/update.json", name="Test") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_update(self, mock_post): """Check that an album can be updated using the album object directly""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.update(name="Test") mock_post.assert_called_with("/album/1/update.json", name="Test") self.assertEqual(album.id, "2") self.assertEqual(album.name, "Album 2") self.assertEqual(album.cover.id, "2b") self.assertEqual(album.cover.tags, ["tag3", "tag4"]) class TestAlbumView(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'get') def test_album_view(self, mock_get): """Check that an album can be viewed""" mock_get.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.view(self.test_albums[0], includeElements=True) mock_get.assert_called_with("/album/1/view.json", includeElements=True) self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) self.assertEqual(result.photos[0].id, self.test_photos[1].id) @mock.patch.object(trovebox.Trovebox, 'get') def test_album_view_id(self, mock_get): """Check that an album can be viewed using its ID""" mock_get.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.view("1", includeElements=True) mock_get.assert_called_with("/album/1/view.json", includeElements=True) self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) self.assertEqual(result.photos[0].id, self.test_photos[1].id) @mock.patch.object(trovebox.Trovebox, 'get') def test_album_object_view(self, mock_get): """Check that an album can be viewed using the album object directly""" mock_get.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.view(includeElements=True) mock_get.assert_called_with("/album/1/view.json", includeElements=True) self.assertEqual(album.id, "2") self.assertEqual(album.name, "Album 2") self.assertEqual(album.cover.id, "2b") self.assertEqual(album.cover.tags, ["tag3", "tag4"]) self.assertEqual(album.photos[0].id, self.test_photos[1].id) class TestAlbumMisc(TestAlbums): def test_update_fields_with_no_cover(self): """Check that an album object can be updated with no cover""" album = self.test_albums[0] album.cover = None album.photos = None # Check that no exception is raised album._update_fields_with_objects()	[ 1, 515, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 5215, 11187, 13, 2202, 29901, 13, 1678, 1053, 443, 27958, 29906, 408, 443, 27958, 396, 5132, 29906, 29889, 29953, 13, 19499, 16032, 2392, 29901, 13, 1678, 1053, 443, 27958, 13, 13, 5215, 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constants/nodetype.py	vladzaharia/meshnet	1	36020	# Node types TYPE_NODE = b'\x10' TYPE_NODE_NR = b'\x11' # Gateway types TYPE_GATEWAY = b'\x20' TYPE_GATEWAY_TIME = b'\x21' # Special types TYPE_PROVISIONING = b'\xFF'	[ 1, 396, 9071, 4072, 13, 11116, 29918, 6632, 2287, 353, 289, 12764, 29916, 29896, 29900, 29915, 13, 11116, 29918, 6632, 2287, 29918, 16514, 353, 289, 12764, 29916, 29896, 29896, 29915, 13, 13, 29937, 22510, 1582, 4072, 13, 11116, 29918, 29954, 3040, 12982, 29979, 353, 289, 12764, 29916, 29906, 29900, 29915, 13, 11116, 29918, 29954, 3040, 12982, 29979, 29918, 15307, 353, 289, 12764, 29916, 29906, 29896, 29915, 13, 13, 29937, 12630, 4072, 13, 11116, 29918, 8618, 28607, 2725, 4214, 353, 289, 12764, 29916, 4198, 29915, 2 ]
setup.py	adobe/havoc	3	183924	#!/usr/bin/env python from setuptools import setup, find_packages setup(name='havoc', version='0.1.0', description='HAvOC (HAproxy clOud Configuration)', author='TubeMogul Inc.', install_requires=[ 'python-daemon', 'jinja2', 'click', 'boto', 'python-novaclient', 'PyYAML' ], packages=find_packages(), package_dir={'havoc': 'havoc'}, scripts=['bin/havoc'] )	[ 1, 18787, 4855, 29914, 2109, 29914, 6272, 3017, 13, 3166, 731, 21245, 8789, 1053, 6230, 29892, 1284, 29918, 8318, 13, 13, 14669, 29898, 978, 2433, 8708, 542, 742, 13, 4706, 1873, 2433, 29900, 29889, 29896, 29889, 29900, 742, 13, 4706, 6139, 2433, 29950, 12810, 20166, 313, 15715, 14701, 1067, 29949, 566, 20999, 29897, 742, 13, 4706, 4148, 2433, 13425, 29924, 468, 352, 9266, 29889, 742, 13, 4706, 2601, 29918, 276, 339, 2658, 11759, 13, 9651, 525, 4691, 29899, 1388, 9857, 742, 13, 9651, 525, 28789, 1764, 29906, 742, 13, 9651, 525, 3808, 742, 13, 9651, 525, 29890, 3747, 742, 13, 9651, 525, 4691, 29899, 13715, 562, 1593, 742, 13, 9651, 525, 19737, 29979, 23956, 29915, 13, 4706, 21251, 13, 4706, 9741, 29922, 2886, 29918, 8318, 3285, 13, 4706, 3577, 29918, 3972, 3790, 29915, 8708, 542, 2396, 525, 8708, 542, 16675, 13, 4706, 12078, 29922, 1839, 2109, 29914, 8708, 542, 2033, 13, 4706, 1723, 13, 2 ]
Codeforces/452/B.py	zzh8829/CompetitiveProgramming	1	92176	<filename>Codeforces/452/B.py from math import * def d(p1x,p1y,p2x,p2y,p3x,p3y,p4x,p4y): if len(set([(p1x,p1y),(p2x,p2y),(p3x,p3y),(p4x,p4y)])) != 4: return (0,0) dis = sqrt((p2x-p1x)(p2x-p1x) + (p2y-p1y)(p2y-p1y)) + \ sqrt((p3x-p2x)(p3x-p2x) + (p3y-p2y)(p3y-p2y)) + \ sqrt((p4x-p3x)(p4x-p3x) + (p4y-p3y)(p4y-p3y)) return (dis,(p1x,p1y,p2x,p2y,p3x,p3y,p4x,p4y)) def bf(): l = [] for p1x in range(0,n+1): for p1y in range(0,m+1): for p2x in range(0,n+1): for p2y in range(0,m+1): for p3x in range(0,n+1): for p3y in range(0,m+1): for p4x in range(0,n+1): for p4y in range(0,m+1): if len(set([(p1x,p1y),(p2x,p2y),(p3x,p3y),(p4x,p4y)])) == 4: l.append(d(p1x,p1y,p2x,p2y,p3x,p3y,p4x,p4y)) print(list(reversed(sorted(l)))[0]) n,m = list(map(int,input().split())) #bf() if m==0: print("%d %d\n%d %d\n%d %d\n%d %d\n"%(1,0,n,m,0,0,n-1,m)) elif n==0: print("%d %d\n%d %d\n%d %d\n%d %d\n"%(0,1,n,m,0,0,n,m-1)) else: l = [] l.append(d(n-1,m,0,0,n,m,0,1)) l.append(d(n,m-1,0,0,n,m,1,0)) l.append(d(1,0,n,m,0,0,n-1,m)) l.append(d(0,1,n,m,0,0,n,m-1)) l.append(d(0,0,n,m,0,1,n-1,m)) l.append(d(0,0,n,m,1,0,n,m-1)) l.append(d(0,0,n,m,n,0,0,m)) l.append(d(0,0,n,m,0,m,n,0)) a = list(reversed(sorted(l)))[0] #print(a) a = a[1] for i in range(4): print(a[i2],a[i2+1])	[ 1, 529, 9507, 29958, 3399, 1454, 778, 29914, 29946, 29945, 29906, 29914, 29933, 29889, 2272, 13, 3166, 5844, 1053, 334, 30004, 13, 30004, 13, 1753, 270, 29898, 29886, 29896, 29916, 29892, 29886, 29896, 29891, 29892, 29886, 29906, 29916, 29892, 29886, 29906, 29891, 29892, 29886, 29941, 29916, 29892, 29886, 29941, 29891, 29892, 29886, 29946, 29916, 29892, 29886, 29946, 29891, 1125, 30004, 13, 12, 361, 7431, 29898, 842, 4197, 29898, 29886, 29896, 29916, 29892, 29886, 29896, 29891, 21336, 29886, 29906, 29916, 29892, 29886, 29906, 29891, 21336, 29886, 29941, 29916, 29892, 29886, 29941, 29891, 21336, 29886, 29946, 29916, 29892, 29886, 29946, 29891, 4638, 876, 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tica_models.py	DEIB-GECO/TICA_v2	0	1603681	<filename>tica_models.py # This is an auto-generated Django model module. # You'll have to do the following manually to clean this up: # * Rearrange models' order # * Make sure each model has one field with primary_key=True # * Make sure each ForeignKey has `on_delete` set to the desired behavior. # * Remove `managed = False` lines if you wish to allow Django to create, modify, and delete the table # Feel free to rename the models, but don't rename db_table values or field names. from __future__ import unicode_literals from django.db import models class DjangoMigrations(models.Model): app = models.CharField(max_length=255) name = models.CharField(max_length=255) applied = models.DateTimeField() class Meta: managed = False db_table = 'django_migrations' class Hepg2(models.Model): tf1 = models.CharField(primary_key=True, max_length=20) tf2 = models.CharField(max_length=20) distance = models.IntegerField() count_all = models.IntegerField() count_tss = models.IntegerField() cumulative_count_all = models.IntegerField() class Meta: managed = False db_table = 'hepg2' unique_together = (('tf1', 'tf2', 'distance'),) class Hepg2Null(models.Model): tf1 = models.CharField(primary_key=True, max_length=20) tf2 = models.CharField(max_length=20) max_distance = models.IntegerField() average = models.FloatField(blank=True, null=True) median = models.FloatField(blank=True, null=True) mad = models.FloatField(blank=True, null=True) tail_percentage_array = models.TextField(blank=True, null=True) # This field type is a guess. class Meta: managed = False db_table = 'hepg2_null' unique_together = (('tf1', 'tf2', 'max_distance'),) class Hepg2Temp(models.Model): tf1 = models.CharField(max_length=20) tf2 = models.CharField(max_length=20) dist = models.IntegerField() is_tss = models.IntegerField() class Meta: managed = False db_table = 'hepg2_temp'	[ 1, 529, 9507, 29958, 29873, 983, 29918, 9794, 29889, 2272, 13, 29937, 910, 338, 385, 4469, 29899, 13525, 15337, 1904, 3883, 29889, 13, 29937, 887, 29915, 645, 505, 304, 437, 278, 1494, 7522, 304, 5941, 445, 701, 29901, 13, 29937, 259, 334, 390, 799, 3881, 4733, 29915, 1797, 13, 29937, 259, 334, 8561, 1854, 1269, 1904, 756, 697, 1746, 411, 7601, 29918, 1989, 29922, 5574, 13, 29937, 259, 334, 8561, 1854, 1269, 19358, 2558, 756, 421, 265, 29918, 8143, 29952, 731, 304, 278, 7429, 6030, 29889, 13, 29937, 259, 334, 15154, 421, 25240, 353, 7700, 29952, 3454, 565, 366, 6398, 304, 2758, 15337, 304, 1653, 29892, 6623, 29892, 322, 5217, 278, 1591, 13, 29937, 5169, 295, 3889, 304, 19508, 278, 4733, 29892, 541, 1016, 29915, 29873, 19508, 4833, 29918, 2371, 1819, 470, 1746, 2983, 29889, 13, 3166, 4770, 29888, 9130, 1649, 1053, 29104, 29918, 20889, 1338, 13, 13, 3166, 9557, 29889, 2585, 1053, 4733, 13, 13, 13, 1990, 15337, 29924, 4481, 800, 29898, 9794, 29889, 3195, 1125, 13, 1678, 623, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29945, 29945, 29897, 13, 1678, 1024, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29945, 29945, 29897, 13, 1678, 7436, 353, 4733, 29889, 11384, 3073, 580, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 8745, 353, 7700, 13, 4706, 4833, 29918, 2371, 353, 525, 14095, 29918, 26983, 800, 29915, 13, 13, 13, 1990, 940, 4061, 29906, 29898, 9794, 29889, 3195, 1125, 13, 1678, 15886, 29896, 353, 4733, 29889, 27890, 29898, 16072, 29918, 1989, 29922, 5574, 29892, 4236, 29918, 2848, 29922, 29906, 29900, 29897, 13, 1678, 15886, 29906, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29900, 29897, 13, 1678, 5418, 353, 4733, 29889, 7798, 3073, 580, 13, 1678, 2302, 29918, 497, 353, 4733, 29889, 7798, 3073, 580, 13, 1678, 2302, 29918, 29873, 893, 353, 4733, 29889, 7798, 3073, 580, 13, 1678, 13299, 28524, 29918, 2798, 29918, 497, 353, 4733, 29889, 7798, 3073, 580, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 8745, 353, 7700, 13, 4706, 4833, 29918, 2371, 353, 525, 354, 4061, 29906, 29915, 13, 4706, 5412, 29918, 29873, 12966, 353, 313, 877, 13264, 29896, 742, 525, 13264, 29906, 742, 525, 19244, 5477, 29897, 13, 13, 13, 1990, 940, 4061, 29906, 7327, 29898, 9794, 29889, 3195, 1125, 13, 1678, 15886, 29896, 353, 4733, 29889, 27890, 29898, 16072, 29918, 1989, 29922, 5574, 29892, 4236, 29918, 2848, 29922, 29906, 29900, 29897, 13, 1678, 15886, 29906, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29900, 29897, 13, 1678, 4236, 29918, 19244, 353, 4733, 29889, 7798, 3073, 580, 13, 1678, 6588, 353, 4733, 29889, 11031, 3073, 29898, 19465, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 1678, 19194, 353, 4733, 29889, 11031, 3073, 29898, 19465, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 1678, 10395, 353, 4733, 29889, 11031, 3073, 29898, 19465, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 13, 1678, 12464, 29918, 25376, 482, 29918, 2378, 353, 4733, 29889, 15778, 29898, 19465, 29922, 5574, 29892, 1870, 29922, 5574, 29897, 29871, 396, 910, 1746, 1134, 338, 263, 4140, 29889, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 8745, 353, 7700, 13, 4706, 4833, 29918, 2371, 353, 525, 354, 4061, 29906, 29918, 4304, 29915, 13, 4706, 5412, 29918, 29873, 12966, 353, 313, 877, 13264, 29896, 742, 525, 13264, 29906, 742, 525, 3317, 29918, 19244, 5477, 29897, 13, 13, 13, 1990, 940, 4061, 29906, 15637, 29898, 9794, 29889, 3195, 1125, 13, 1678, 15886, 29896, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29900, 29897, 13, 1678, 15886, 29906, 353, 4733, 29889, 27890, 29898, 3317, 29918, 2848, 29922, 29906, 29900, 29897, 13, 1678, 1320, 353, 4733, 29889, 7798, 3073, 580, 13, 1678, 338, 29918, 29873, 893, 353, 4733, 29889, 7798, 3073, 580, 13, 13, 1678, 770, 20553, 29901, 13, 4706, 8745, 353, 7700, 13, 4706, 4833, 29918, 2371, 353, 525, 354, 4061, 29906, 29918, 7382, 29915, 13, 2 ]
Users/models.py	titusnjuguna/FreeDom	1	4604	from django.db import models from django.contrib.auth.models import User from PIL import Image class Profile(models.Model): user = models.ForeignKey(User, on_delete=models.CASCADE) image = models.ImageField(default='default.jpg', upload_to='profile_pic/') def __str__(self): return f'{self.user.username} Profile' def save(self,args,*kwargs): super().save() img = Image(self.prof_pic.path) if img.height > 300 and img.width > 300: output_size = (300,300) img.thumbnail(output_size) img.save(self.prof_pic.path)	[ 1, 515, 9557, 29889, 2585, 1053, 4733, 13, 3166, 9557, 29889, 21570, 29889, 5150, 29889, 9794, 1053, 4911, 13, 3166, 349, 6227, 1053, 7084, 13, 13, 1990, 20802, 29898, 9794, 29889, 3195, 1125, 13, 1678, 1404, 353, 4733, 29889, 27755, 2558, 29898, 2659, 29892, 373, 29918, 8143, 29922, 9794, 29889, 29907, 3289, 5454, 2287, 29897, 13, 1678, 1967, 353, 4733, 29889, 2940, 3073, 29898, 4381, 2433, 4381, 29889, 6173, 742, 13, 462, 795, 6441, 29918, 517, 2433, 10185, 29918, 16447, 29914, 1495, 13, 462, 1669, 13, 13, 1678, 822, 4770, 710, 12035, 1311, 1125, 13, 4706, 736, 285, 29915, 29912, 1311, 29889, 1792, 29889, 6786, 29913, 20802, 29915, 13, 13, 1678, 822, 4078, 29898, 1311, 29892, 29930, 5085, 29892, 1068, 19290, 1125, 13, 4706, 2428, 2141, 7620, 580, 13, 4706, 10153, 353, 7084, 29898, 1311, 29889, 23221, 29918, 16447, 29889, 2084, 29897, 13, 4706, 565, 10153, 29889, 3545, 1405, 29871, 29941, 29900, 29900, 322, 10153, 29889, 2103, 1405, 29871, 29941, 29900, 29900, 29901, 13, 9651, 1962, 29918, 2311, 353, 313, 29941, 29900, 29900, 29892, 29941, 29900, 29900, 29897, 13, 9651, 10153, 29889, 386, 21145, 29898, 4905, 29918, 2311, 29897, 13, 9651, 10153, 29889, 7620, 29898, 1311, 29889, 23221, 29918, 16447, 29889, 2084, 29897, 13, 13, 308, 13, 2 ]
bcipy/acquisition/protocols/lsl/lsl_device.py	theGreenJedi/BciPy	0	74765	<reponame>theGreenJedi/BciPy # pylint: disable=fixme """Defines the driver for the Device for communicating with LabStreamingLayer (LSL).""" import logging import pylsl from bcipy.acquisition.protocols.device import Device log = logging.getLogger(__name__) LSL_TIMESTAMP = 'LSL_timestamp' class Marker(): """Data class which wraps a LSL marker; data pulled from a marker stream is a tuple where the first item is a list of channels and second item is the timestamp. Assumes that marker inlet only has a single channel.""" def __init__(self, data=(None, None)): super(Marker, self).__init__() self.channels, self.timestamp = data @classmethod def empty(cls): """Creates an empty Marker.""" return Marker() def __repr__(self): return f"<value: {self.trg}, timestamp: {self.timestamp}>" @property def is_empty(self): """Test to see if the current marker is empty.""" return self.channels is None or self.timestamp is None @property def trg(self): """Get the trigger.""" # pylint: disable=unsubscriptable-object return self.channels[0] if self.channels else None def inlet_name(inlet) -> str: """Returns the name of a pylsl streamInlet.""" name = '_'.join(inlet.info().name().split()) return name.replace('-', '_') class LslDevice(Device): """Driver for any device streaming data through the LabStreamingLayer lib. Parameters ---------- connection_params : dict parameters used to connect with the server. channels: list, optional list of channel names fs: float, optional sample frequency in (Hz) include_lsl_timestamp: bool, optional if True, appends the LSL_timestamp to each sample. trg_channel_name: str, optional if present, the marker channel with this name is used as the TRG channel in the output and queried for offset; otherwise the last marker_channel added is used. """ # pylint: disable=too-many-instance-attributes,too-many-arguments def __init__(self, connection_params, fs=None, channels=None, include_lsl_timestamp=False, trg_channel_name='BCI_Stimulus_Markers'): super(LslDevice, self).__init__(connection_params, fs, channels) self._appended_channels = [] self.trg_channel_name = trg_channel_name if include_lsl_timestamp: self._appended_channels.append(LSL_TIMESTAMP) self._inlet = None self._marker_inlets = None # There can be 1 current marker for each marker channel. self.current_markers = {} @property def name(self): if 'stream_name' in self._connection_params: return self._connection_params['stream_name'] if self._inlet and self._inlet.info().name(): return self._inlet.info().name() return 'LSL' def connect(self): """Connect to the data source.""" # Streams can be queried by name, type (xdf file format spec), and # other metadata. # NOTE: According to the documentation this is a blocking call that can # only be performed on the main thread in Linux systems. So far testing # seems fine when done in a separate multiprocessing.Process. eeg_streams = pylsl.resolve_stream('type', 'EEG') marker_streams = pylsl.resolve_stream('type', 'Markers') assert eeg_streams, "One or more EEG streams must be present" assert marker_streams, "One or more Marker streams must be present" self._inlet = pylsl.StreamInlet(eeg_streams[0]) self._marker_inlets = [pylsl.StreamInlet(inlet) for inlet in marker_streams] # initialize the current_markers for each marker stream. for inlet in self._marker_inlets: self.current_markers[inlet_name(inlet)] = Marker.empty() def acquisition_init(self): """Initialization step. Reads the channel and data rate information sent by the server and sets the appropriate instance variables. """ assert self._inlet is not None, "Connect call is required." metadata = self._inlet.info() log.debug(metadata.as_xml()) for marker_inlet in self._marker_inlets: log.debug("Streaming from marker inlet: %s", inlet_name(marker_inlet)) info_channels = self._read_channels(metadata) info_fs = metadata.nominal_srate() # If channels are not initially provided, set them from the metadata. # Otherwise, confirm that provided channels match metadata, or meta is # empty. if not self.channels: self.channels = info_channels assert self.channels, "Channels must be provided" else: if info_channels and self.channels != info_channels: raise Exception("Channels read from the device do not match " "the provided parameters") assert len(self.channels) == (metadata.channel_count() + len(self._appended_channels) + len(self._marker_inlets)),\ "Channel count error" if not self.fs: self.fs = info_fs elif self.fs != info_fs: raise Exception("Sample frequency read from device does not match " "the provided parameter") def _trigger_inlet_index(self): """Index of the marker inlet that should be used for the TRG column. If the trg_channel_name parameter is provided, and there is a marker inlet with this name, that is used, otherwise the last marker inlet is used. Returns -1 if there are no marker inlets.""" inlet_names = [inlet_name(inlet) for inlet in self._marker_inlets] if self.trg_channel_name in inlet_names: return inlet_names.index(self.trg_channel_name) return len(inlet_names) - 1 def _read_channels(self, info): """Read channels from the stream metadata if provided and return them as a list. If channels were not specified, returns an empty list. Parameters ---------- info : pylsl.XMLElement Returns ------- list of str """ channels = [] if info.desc().child("channels").empty(): return channels channel = info.desc().child("channels").child("channel") for _ in range(info.channel_count()): channel_name = channel.child_value("label") # If the data stream has a TRG channel, rename it so it doesn't # conflict with the marker channel. if channel_name == 'TRG' and self._marker_inlets: channel_name = "TRG_device_stream" channels.append(channel_name) channel = channel.next_sibling() for appended_channel in self._appended_channels: channels.append(appended_channel) trg_marker_index = self._trigger_inlet_index() for i, inlet in enumerate(self._marker_inlets): col = inlet_name(inlet) if i == trg_marker_index: col = 'TRG' channels.append(col) return channels def read_data(self): """Reads the next packet and returns the sensor data. Returns ------- list with an item for each channel. """ sample, timestamp = self._inlet.pull_sample() # Useful for debugging. if LSL_TIMESTAMP in self._appended_channels: sample.append(timestamp) for marker_inlet in self._marker_inlets: name = inlet_name(marker_inlet) marker = self.current_markers.get(name, Marker().empty()) # Only attempt to retrieve a marker from the inlet if we have # merged the last one with a sample. if marker.is_empty: # A timeout of 0.0 only returns a sample if one is buffered for # immediate pickup. 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applications/reports/process/data_service.py	ebs-integrator/bic-reporting-api	1	153362	import os import pathlib from lxml import etree from abc import ABC from datetime import datetime from xml.etree import ElementTree from applications.reports.process.flow_data_service import FlowDataService class DataService(FlowDataService, ABC): # Credentials username = '' password = '' # Language - The field format is `languageCountry` (ex enUS) # according to ISO 639-1 and ISO 3166-1 language = 'roRO' def __init__(self, company=None, delivery=None): super().__init__() self.company = company self.delivery = delivery def set_credentials(self, username: str, password: str): self.username = username self.password = password def generate(self, kwargs): xml = ElementTree.Element('root') ElementTree.SubElement(xml, 'delivery', {'method': self.delivery}) # Credentials security = ElementTree.SubElement(xml, 'security') username = ElementTree.SubElement(security, 'username') password = ElementTree.SubElement(security, 'password') username.text = self.username password.text = <PASSWORD> # Language language = ElementTree.SubElement(xml, 'language') language.text = self.language request_timestamp = ElementTree.SubElement(xml, 'request_timestamp') request_timestamp.text = datetime.now().strftime('%d-%m-%Y %H:%M:%S') # Forming credit report requests cr_requests = ElementTree.SubElement(xml, 'cr_requests') for request_counter, company in enumerate([self.company]): request_counter = request_counter + 1 parameters: dict = self.entity_parameters(company) # Forming single credit report requests cr_request = ElementTree.SubElement(cr_requests, 'cr_request') cr_id = ElementTree.SubElement(cr_request, 'id') cr_id.text = str(request_counter) customer_type = ElementTree.SubElement(cr_request, 'customer_type') customer_type.text = parameters.get('counterparty_type') include_sivabon = ElementTree.SubElement(cr_request, 'include_sivabon') include_sivabon.text = parameters.get('report_bic') include_fisc = ElementTree.SubElement(cr_request, 'include_fisc') include_fisc.text = parameters.get('report_sfs') include_bns = ElementTree.SubElement(cr_request, 'include_bns') include_bns.text = parameters.get('report_bns') if parameters.get('report_bns') == '1': include_bns_year = ElementTree.SubElement(cr_request, 'bnsYear') include_bns_year.text = parameters.get('report_bns_year') idno_idnp = ElementTree.SubElement(cr_request, 'idno_idnp') idno_idnp.text = parameters.get('fiscal_code') inquiry_purpose = ElementTree.SubElement(cr_request, 'inquiry_purpose') inquiry_purpose.text = parameters.get('request_goal') bic_agreement = ElementTree.SubElement(cr_request, 'credit_application_agreement') bic_agreement.text = parameters.get('bic_agreement') process_indicator = ElementTree.SubElement(cr_request, 'process_indicator') process_indicator.text = parameters.get('bic_agreement') if parameters.get('counterparty_type') == "C": company_name = ElementTree.SubElement(cr_request, 'company_name') company_name.text = parameters.get('company_name') company_short_name = ElementTree.SubElement(cr_request, 'company_short_name') company_short_name.text = parameters.get('company_short_name') last_name = ElementTree.SubElement(cr_request, 'last_name') last_name.text = parameters.get('last_name') first_name = ElementTree.SubElement(cr_request, 'first_name') first_name.text = parameters.get('first_name') father_name = ElementTree.SubElement(cr_request, 'father_name') father_name.text = parameters.get('patronymic') identity_card = ElementTree.SubElement(cr_request, 'identity_card') identity_card.text = parameters.get('passport_series_number') date_of_registration = ElementTree.SubElement(cr_request, 'date_of_registration') date_of_registration.text = parameters.get('date_of_birth') document_number = ElementTree.SubElement(cr_request, 'document_number') document_number.text = parameters.get('document_number') document_date = ElementTree.SubElement(cr_request, 'document_date') document_date.text = parameters.get('document_date') document_type = ElementTree.SubElement(cr_request, 'document_type') document_type.text = parameters.get('document_type') if parameters.get('bic_agreement') == '1': initial_value = ElementTree.SubElement(cr_request, 'initial_value') initial_value.text = parameters.get('credit_amount') document_currency = ElementTree.SubElement(cr_request, 'document_currency') document_currency.text = parameters.get('currency_code') terms_duration = ElementTree.SubElement(cr_request, 'terms_duration') terms_duration.text = parameters.get('credit_term') terms_frequency = ElementTree.SubElement(cr_request, 'terms_frequency') terms_frequency.text = parameters.get('frequency_of_payments') document_insurance = ElementTree.SubElement(cr_request, 'document_insurance') document_insurance.text = parameters.get('type_of_collateral') insurance_value = ElementTree.SubElement(cr_request, 'document_insurance_value') insurance_value.text = parameters.get('collateral_amount') extras_type = ElementTree.SubElement(cr_request, 'extras_type') extras_type.text = parameters.get('report_type') return ElementTree.tostring({ 'element': xml, 'encoding': 'utf-8', 'short_empty_elements': False }).decode() def entity_parameters(self, company=None): if company is None: company = {} date = datetime.now() formats = date.strftime("%d-%m-%Y %H:%M:%S") year = date.strftime("%Y") additional_data = company.get('additional_data', {}) or {} registered_at = additional_data.get('registered_at', None) user = company.get('user', {}) parameters = { "request_date": formats, "counterparty_type": company.get('counterparty_type', 'C') or 'C', "fiscal_code": company.get('idno', ''), "request_goal": company.get('request_goal', 'MDP') or 'MDP', "bic_agreement": company.get('bic_agreement', '1') or '1', "report_bic": company.get('report_bic', '1') or '1', "report_sfs": company.get('report_sfs', '1') or '1', "report_bns": company.get('report_bns', '1') or '1', "report_bns_year": company.get('report_bns_year', year) or year, "company_name": company.get('title', ''), "company_short_name": company.get('title', ''), 'first_name': self.replace_with_ascii(user.get('first_name', 'NA')) or 'NA', 'last_name': self.replace_with_ascii(user.get('last_name', 'NA')) or 'NA', 'patronymic': self.replace_with_ascii(user.get('patronymic', 'NA')) or 'NA', 'passport_series_number': None, 'date_of_birth': None, 'document_number': str(int(date.timestamp())), 'document_date': None, 'date_of_registration': registered_at, 'credit_amount': str(company.get('credit_amount', '0') or '0'), 'currency_code': str(company.get('currency_code', 'MDL') or 'MDL'), 'credit_term': str(company.get('credit_term', '0') or '0'), 'request_type': str(company.get('request_type', 'M') or 'M'), 'document_type': str(company.get('document_type', 'C') or 'C'), 'frequency_of_payments': str(company.get('frequency_of_payments', 'N') or 'N'), 'type_of_collateral': str(company.get('type_of_collateral', 'D') or 'D'), 'collateral_amount': str(company.get('collateral_amount', '0') or '0'), 'report_type': str(company.get('report_type', 'AllSection') or 'AllSection'), } return parameters @staticmethod def upsert_file(path): import os os.makedirs(os.path.dirname(path), exist_ok=True) def html(self, xml: str = '', xslt: str = 'templates/tr.xslt'): xslt_document = os.path.join(pathlib.Path().absolute(), xslt) xslt_document_element = etree.parse(xslt_document) xslt_document_transformer = etree.XSLT(xslt_document_element) name = str(int(datetime.now().timestamp())) xml_document_element = etree.fromstring(xml.encode()) xml_document_transformer = xslt_document_transformer(xml_document_element) html_string = str(xml_document_transformer) name_tag = name html_name = f'storage/companies/reports/html/{name}.html' self.upsert_file(html_name) try: with open(html_name, 'w', encoding="utf-8") as file: file.write(html_string) finally: 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kmd_labs/oracles/listassetchains.py	smk762/kmd_pulp	2	182161	<filename>kmd_labs/oracles/listassetchains.py #!/usr/bin/env python2 import os import json #script_dir = os.getcwd() HOME = os.environ['HOME'] with open(HOME + '/StakedNotary/assetchains.json') as file: assetchains = json.load(file) for chain in assetchains: print(chain['ac_name'])	[ 1, 529, 9507, 29958, 29895, 3487, 29918, 29880, 6897, 29914, 272, 23435, 29914, 1761, 465, 3486, 2708, 29889, 2272, 13, 29937, 14708, 4855, 29914, 2109, 29914, 6272, 3017, 29906, 13, 5215, 2897, 13, 5215, 4390, 13, 13, 29937, 2154, 29918, 3972, 353, 2897, 29889, 657, 29883, 9970, 580, 13, 17353, 353, 2897, 29889, 21813, 1839, 17353, 2033, 13, 2541, 1722, 29898, 17353, 718, 8207, 855, 12535, 3664, 653, 29914, 465, 3486, 2708, 29889, 3126, 1495, 408, 934, 29901, 13, 1678, 24342, 305, 2708, 353, 4390, 29889, 1359, 29898, 1445, 29897, 13, 13, 1454, 9704, 297, 24342, 305, 2708, 29901, 13, 1678, 1596, 29898, 14153, 1839, 562, 29918, 978, 11287, 13, 2 ]
migrations/versions/ddf3cbc60873_.py	MaxHorwood/crude_backend_skeleton	0	63802	"""empty message Revision ID: ddf3cbc60873 Revises: x Create Date: 2020-08-22 17:27:08.449495 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = "<KEY>" down_revision = None branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table( "user", sa.Column("id", sa.Integer(), nullable=False), sa.Column("name", sa.String(length=80), nullable=False), sa.Column("email", sa.String(length=120), nullable=False), sa.PrimaryKeyConstraint("id"), ) op.create_table( "address", sa.Column("id", sa.Integer(), nullable=False), sa.Column("postcode", sa.String(length=80), nullable=False), sa.Column("address", sa.String(length=100), nullable=False), sa.Column("user_id", sa.Integer(), nullable=True), sa.ForeignKeyConstraint( ["user_id"], ["user.id"], ), sa.PrimaryKeyConstraint("id"), ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table("address") op.drop_table("user") # ### end Alembic commands ###	[ 1, 9995, 6310, 2643, 13, 13, 1123, 4924, 3553, 29901, 270, 2176, 29941, 10702, 29883, 29953, 29900, 29947, 29955, 29941, 13, 1123, 1730, 267, 29901, 921, 13, 4391, 4712, 29901, 29871, 29906, 29900, 29906, 29900, 29899, 29900, 29947, 29899, 29906, 29906, 29871, 29896, 29955, 29901, 29906, 29955, 29901, 29900, 29947, 29889, 29946, 29946, 29929, 29946, 29929, 29945, 13, 13, 15945, 29908, 13, 3166, 20712, 29890, 293, 1053, 1015, 13, 5215, 4576, 284, 305, 6764, 408, 872, 13, 13, 13, 29937, 26554, 2893, 14903, 29892, 1304, 491, 319, 2409, 29890, 293, 29889, 13, 276, 4924, 353, 9872, 10818, 11903, 13, 3204, 29918, 276, 4924, 353, 6213, 13, 17519, 29918, 21134, 353, 6213, 13, 2716, 1975, 29918, 265, 353, 6213, 13, 13, 13, 1753, 14955, 7295, 13, 1678, 396, 835, 8260, 4469, 5759, 491, 319, 2409, 29890, 293, 448, 3113, 10365, 29991, 835, 13, 1678, 1015, 29889, 3258, 29918, 2371, 29898, 13, 4706, 376, 1792, 613, 13, 4706, 872, 29889, 4409, 703, 333, 613, 872, 29889, 7798, 3285, 1870, 519, 29922, 8824, 511, 13, 4706, 872, 29889, 4409, 703, 978, 613, 872, 29889, 1231, 29898, 2848, 29922, 29947, 29900, 511, 1870, 519, 29922, 8824, 511, 13, 4706, 872, 29889, 4409, 703, 5269, 613, 872, 29889, 1231, 29898, 2848, 29922, 29896, 29906, 29900, 511, 1870, 519, 29922, 8824, 511, 13, 4706, 872, 29889, 26666, 2558, 21529, 703, 333, 4968, 13, 1678, 1723, 13, 1678, 1015, 29889, 3258, 29918, 2371, 29898, 13, 4706, 376, 7328, 613, 13, 4706, 872, 29889, 4409, 703, 333, 613, 872, 29889, 7798, 3285, 1870, 519, 29922, 8824, 511, 13, 4706, 872, 29889, 4409, 703, 2490, 401, 613, 872, 29889, 1231, 29898, 2848, 29922, 29947, 29900, 511, 1870, 519, 29922, 8824, 511, 13, 4706, 872, 29889, 4409, 703, 7328, 613, 872, 29889, 1231, 29898, 2848, 29922, 29896, 29900, 29900, 511, 1870, 519, 29922, 8824, 511, 13, 4706, 872, 29889, 4409, 703, 1792, 29918, 333, 613, 872, 29889, 7798, 3285, 1870, 519, 29922, 5574, 511, 13, 4706, 872, 29889, 27755, 2558, 21529, 29898, 13, 9651, 6796, 1792, 29918, 333, 12436, 13, 9651, 6796, 1792, 29889, 333, 12436, 13, 4706, 10353, 13, 4706, 872, 29889, 26666, 2558, 21529, 703, 333, 4968, 13, 1678, 1723, 13, 1678, 396, 835, 1095, 319, 2409, 29890, 293, 8260, 835, 13, 13, 13, 1753, 1623, 8228, 7295, 13, 1678, 396, 835, 8260, 4469, 5759, 491, 319, 2409, 29890, 293, 448, 3113, 10365, 29991, 835, 13, 1678, 1015, 29889, 8865, 29918, 2371, 703, 7328, 1159, 13, 1678, 1015, 29889, 8865, 29918, 2371, 703, 1792, 1159, 13, 1678, 396, 835, 1095, 319, 2409, 29890, 293, 8260, 835, 13, 2 ]
fbchat/utils.py	Dainius14/fb-chat-bot-old	2	19916	<reponame>Dainius14/fb-chat-bot-old<filename>fbchat/utils.py import re import json from time import time from random import random USER_AGENTS = [ "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.90 Safari/537.36", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_3) AppleWebKit/601.1.10 (KHTML, like Gecko) Version/8.0.5 Safari/601.1.10", "Mozilla/5.0 (Windows NT 6.3; WOW64; ; NCT50_AAP285C84A1328) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.90 Safari/537.36", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1", "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6" ] def now(): return int(time()1000) def get_json(text): return json.loads(re.sub(r"^[^{]", '', text, 1)) def digit_to_char(digit): if digit < 10: return str(digit) return chr(ord('a') + digit - 10) def str_base(number,base): if number < 0: return '-' + str_base(-number, base) (d, m) = divmod(number, base) if d > 0: return str_base(d, base) + digit_to_char(m) return digit_to_char(m) def generateMessageID(client_id=None): k = now() l = int(random() * 4294967295) return ("<%s:%s-<EMAIL>>" % (k, l, client_id)); def getSignatureID(): return hex(int(random() * 2147483648)) def generateOfflineThreadingID() : ret = now() value = int(random() * 4294967295); string = ("0000000000000000000000" + bin(value))[-22:] msgs = bin(ret) + string return str(int(msgs,2))	[ 1, 529, 276, 1112, 420, 29958, 29928, 475, 2482, 29896, 29946, 29914, 14943, 29899, 13496, 29899, 7451, 29899, 1025, 29966, 9507, 29958, 14943, 13496, 29914, 13239, 29889, 2272, 13, 5215, 337, 13, 5215, 4390, 13, 3166, 931, 1053, 931, 13, 3166, 4036, 1053, 4036, 13, 11889, 29918, 10051, 3919, 29903, 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Formatting_Strings/What_day_is_today?.py

RKiddle/python_reg_expressions

0

164755

<reponame>RKiddle/python_reg_expressions<filename>Formatting_Strings/What_day_is_today?.py # Import datetime from datetime import datetime # Assign date to get_date get_date = datetime.now() # Add named placeholders with format specifiers message = "Good morning. Today is {today:%B %d, %Y}. It's {today:%H:%M} ... time to work!" # Format date print(message.format(today=get_date))

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ripiu/djangocms_aoxomoxoa/admin/options/grid_panel.py

ripiu/djangocms_aoxomoxoa

0

44283

<filename>ripiu/djangocms_aoxomoxoa/admin/options/grid_panel.py from django.contrib import admin from django.utils.translation import ugettext_lazy as _ class GridPanelUniteOptionsAdmin(admin.ModelAdmin): ''' Grid theme ''' fieldsets = ( (_('Strip panel options'), { 'classes': ('collapse',), 'fields': ( 'gridpanel_vertical_scroll', 'gridpanel_grid_align', ( 'gridpanel_padding_border_top', 'gridpanel_padding_border_bottom', ), ( 'gridpanel_padding_border_left', 'gridpanel_padding_border_right', ), 'gridpanel_arrows_skin', ('gridpanel_arrows_align_vert', 'gridpanel_arrows_align_hor',), ( 'gridpanel_arrows_padding_vert', 'gridpanel_arrows_padding_hor', ), 'gridpanel_space_between_arrows', 'gridpanel_arrows_always_on', 'gridpanel_enable_handle', 'gridpanel_handle_align', 'gridpanel_handle_offset', 'gridpanel_handle_skin', 'gridpanel_background_color', 'grid_panes_direction', 'grid_num_cols', 'grid_space_between_cols', 'grid_space_between_rows', 'grid_transition_duration', 'grid_transition_easing', 'grid_carousel', ) }), )

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p_030_039/problem31.py

ericgreveson/projecteuler

0

3235

<gh_stars>0 class CoinArray(list): """ Coin list that is hashable for storage in sets The 8 entries are [1p count, 2p count, 5p count, ... , 200p count] """ def __hash__(self): """ Hash this as a string """ return hash(" ".join([str(i) for i in self])) def main(): """ Entry point """ # Important: sorted smallest to largest coins = [1, 2, 5, 10, 20, 50, 100, 200] coin_index = {coin: index for index, coin in enumerate(coins)} # How many ways are there of making each number from 1 to 200 from these values? # Building up from 1 means we can re-use earlier results # e.g.: # 1p: [{1}] # 2p: [{1,1}, {2}] # 3p: [{1,1,1}, {2,1}] # 4p: [{1,1,1,1}, {2,1,1}, {2,2}] # etc way_sets = [None] for i in range(1, 201): way_set_i = set() # Try using 1 of each coin and then all the ways of the remainder, if > 0 for coin in coins: remainder = i - coin if remainder == 0: # We can make this with exactly this coin alone - but no larger coins coin_count = [0 for i in coins] coin_count[coin_index[coin]] = 1 way_set_i.add(CoinArray(coin_count)) break elif remainder > 0: # We can use this coin and whatever the options for the smaller value are for rem_list in way_sets[remainder]: new_coin_count = [c for c in rem_list] new_coin_count[coin_index[coin]] += 1 way_set_i.add(CoinArray(new_coin_count)) else: # Can't use any bigger coins break way_sets.append(way_set_i) print(f"Number of ways of making £2: {len(way_sets[200])}") return if __name__ == "__main__": main()

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scanpy/tools/paga.py

LuckyMD/scanpy

0

31033

from collections import namedtuple import numpy as np import scipy as sp from scipy.sparse.csgraph import minimum_spanning_tree from .. import logging as logg from ..neighbors import Neighbors from .. import utils from .. import settings def paga( adata, groups='louvain', use_rna_velocity=False, copy=False): """\ Generate cellular maps of differentiation manifolds with complex topologies [Wolf17i]_. Partition-based graph abstraction (PAGA) quantifies the connectivities of partitions of a neighborhood graph of single cells, thereby generating a much simpler abstracted graph whose nodes label the partitions. Together with a random walk-based distance measure, this generates a partial coordinatization of data useful for exploring and explaining its variation. Parameters ---------- adata : :class:`~scanpy.api.AnnData` Annotated data matrix. groups : categorical annotation of observations or 'louvain_groups', optional (default: 'louvain_groups') Criterion to determine the resulting partitions of the single-cell graph. 'louvain_groups' uses the Louvain algorithm and optimizes modularity of the graph. You can also pass your predefined groups by choosing any categorical annotation of observations (`adata.obs`). use_rna_velocity : `bool` (default: `False`) Use RNA velocity to orient edges in the abstracted graph and estimate transitions. copy : `bool`, optional (default: `False`) Copy `adata` before computation and return a copy. Otherwise, perform computation inplace and return `None`. Returns ------- Returns or updates `adata` depending on `copy` with connectivities : np.ndarray (adata.uns['connectivities']) The full adjacency matrix of the abstracted graph, weights correspond to connectivities. confidence : np.ndarray (adata.uns['confidence']) The full adjacency matrix of the abstracted graph, weights correspond to confidence in the presence of an edge. confidence_tree : sc.sparse csr matrix (adata.uns['confidence_tree']) The adjacency matrix of the tree-like subgraph that best explains the topology. """ if 'neighbors' not in adata.uns: raise ValueError( 'You need to run `pp.neighbors` first to compute a neighborhood graph.') adata = adata.copy() if copy else adata utils.sanitize_anndata(adata) logg.info('running partition-based graph abstraction (PAGA)', reset=True) paga = PAGA(adata, groups, use_rna_velocity=use_rna_velocity) paga.compute() # only add if not present if 'paga' not in adata.uns: adata.uns['paga'] = {} if not use_rna_velocity: adata.uns['paga']['connectivities'] = paga.connectivities_coarse adata.uns['paga']['confidence'] = paga.confidence adata.uns['paga']['confidence_tree'] = paga.confidence_tree adata.uns[groups + '_sizes'] = np.array(paga.vc.sizes()) else: adata.uns['paga']['transitions_confidence'] = paga.transitions_confidence adata.uns['paga']['transitions_ttest'] = paga.transitions_ttest adata.uns['paga']['groups'] = groups logg.info(' finished', time=True, end=' ' if settings.verbosity > 2 else '\n') if use_rna_velocity: logg.hint( 'added\n' ' \'paga/transitions_confidence\', confidence adjacency (adata.uns)\n' ' \'paga/transitions_ttest\', confidence subtree (adata.uns)') else: logg.hint( 'added\n' ' \'paga/connectivities\', connectivities adjacency (adata.uns)\n' ' \'paga/confidence\', confidence adjacency (adata.uns)\n' ' \'paga/confidence_tree\', confidence subtree (adata.uns)') return adata if copy else None class PAGA(Neighbors): def __init__(self, adata, groups, use_rna_velocity=False, tree_based_confidence=False): super(PAGA, self).__init__(adata) self._groups = groups self._tree_based_confidence = tree_based_confidence self._use_rna_velocity = use_rna_velocity def compute(self): if self._use_rna_velocity: self.compute_transitions_coarse() else: self.compute_connectivities_coarse() self.compute_confidence() def compute_connectivities_coarse(self): import igraph ones = self.connectivities.copy() # graph where edges carry weight 1 ones.data = np.ones(len(ones.data)) g = utils.get_igraph_from_adjacency(ones) self.vc = igraph.VertexClustering( g, membership=self._adata.obs[self._groups].cat.codes.values) cg = self.vc.cluster_graph(combine_edges='sum') self.connectivities_coarse = utils.get_sparse_from_igraph(cg, weight_attr='weight')/2 def compute_confidence(self): """Translates the connectivities_coarse measure into a confidence measure. """ pseudo_distance = self.connectivities_coarse.copy() pseudo_distance.data = 1./pseudo_distance.data connectivities_coarse_tree = minimum_spanning_tree(pseudo_distance) connectivities_coarse_tree.data = 1./connectivities_coarse_tree.data connectivities_coarse_tree_indices = [ connectivities_coarse_tree[i].nonzero()[1] for i in range(connectivities_coarse_tree.shape[0])] # inter- and intra-cluster based confidence if not self._tree_based_confidence: total_n = self.n_neighbors * np.array(self.vc.sizes()) maximum = self.connectivities_coarse.max() confidence = self.connectivities_coarse.copy() # initializing for i in range(self.connectivities_coarse.shape[0]): for j in range(i+1, self.connectivities_coarse.shape[1]): if self.connectivities_coarse[i, j] > 0: geom_mean = np.sqrt(total_n[i] * total_n[j]) confidence[i, j] = self.connectivities_coarse[i, j] / geom_mean confidence[j, i] = confidence[i, j] # tree-based confidence else: median_connectivities_coarse_tree = np.median(connectivities_coarse_tree.data) confidence = self.connectivities_coarse.copy() confidence.data[self.connectivities_coarse.data >= median_connectivities_coarse_tree] = 1 connectivities_coarse_adjusted = self.connectivities_coarse.copy() connectivities_coarse_adjusted.data -= median_connectivities_coarse_tree connectivities_coarse_adjusted.data = np.exp(connectivities_coarse_adjusted.data) index = self.connectivities_coarse.data < median_connectivities_coarse_tree confidence.data[index] = connectivities_coarse_adjusted.data[index] confidence_tree = self.compute_confidence_tree( confidence, connectivities_coarse_tree_indices) self.confidence = confidence self.confidence_tree = confidence_tree def compute_confidence_tree( self, confidence, connectivities_coarse_tree_indices): confidence_tree = sp.sparse.lil_matrix(confidence.shape, dtype=float) for i, neighbors in enumerate(connectivities_coarse_tree_indices): if len(neighbors) > 0: confidence_tree[i, neighbors] = confidence[i, neighbors] return confidence_tree.tocsr() def compute_transitions_coarse(self): # analogous code using networkx # membership = adata.obs['clusters'].cat.codes.tolist() # partition = defaultdict(list) # for n, p in zip(list(range(len(G))), membership): # partition[p].append(n) # partition = partition.values() # g_abstracted = nx.quotient_graph(g, partition, relabel=True) # for some reason, though, edges aren't oriented in the quotient # graph... import igraph g = utils.get_igraph_from_adjacency( self._adata.uns['velocyto_transitions'], directed=True) vc = igraph.VertexClustering( g, membership=self._adata.obs[self._groups].cat.codes.values) cg_full = vc.cluster_graph(combine_edges=False) g_bool = utils.get_igraph_from_adjacency( self._adata.uns['velocyto_transitions'].astype('bool'), directed=True) vc_bool = igraph.VertexClustering( g_bool, membership=self._adata.obs[self._groups].cat.codes.values) cg_bool = vc_bool.cluster_graph(combine_edges='sum') # collapsed version transitions_coarse = utils.get_sparse_from_igraph(cg_bool, weight_attr='weight') # translate this into a confidence measure # the number of outgoing edges # total_n = np.zeros(len(vc.sizes())) # # (this is not the convention of standard stochastic matrices) # total_outgoing = transitions_coarse.sum(axis=1) # for i in range(len(total_n)): # total_n[i] = vc.subgraph(i).ecount() # total_n[i] += total_outgoing[i, 0] # use the topology based reference, the velocity one might have very small numbers total_n = self.n_neighbors * np.array(vc_bool.sizes()) transitions_ttest = transitions_coarse.copy() transitions_confidence = transitions_coarse.copy() from scipy.stats import ttest_1samp for i in range(transitions_coarse.shape[0]): # no symmetry in transitions_coarse, hence we should not restrict to # upper triangle neighbors = transitions_coarse[i].nonzero()[1] for j in neighbors: forward = cg_full.es.select(_source=i, _target=j)['weight'] backward = cg_full.es.select(_source=j, _target=i)['weight'] # backward direction: add minus sign values = np.array(list(forward) + list(-np.array(backward))) # require some minimal number of observations if len(values) < 5: transitions_ttest[i, j] = 0 transitions_ttest[j, i] = 0 transitions_confidence[i, j] = 0 transitions_confidence[j, i] = 0 continue t, prob = ttest_1samp(values, 0.0) if t > 0: # number of outgoing edges greater than number of ingoing edges # i.e., transition from i to j transitions_ttest[i, j] = -np.log10(max(prob, 1e-10)) transitions_ttest[j, i] = 0 else: transitions_ttest[j, i] = -np.log10(max(prob, 1e-10)) transitions_ttest[i, j] = 0 # geom_mean geom_mean = np.sqrt(total_n[i] * total_n[j]) diff = (len(forward) - len(backward)) / geom_mean if diff > 0: transitions_confidence[i, j] = diff transitions_confidence[j, i] = 0 else: transitions_confidence[j, i] = -diff transitions_confidence[i, j] = 0 transitions_ttest.eliminate_zeros() transitions_confidence.eliminate_zeros() # transpose in order to match convention of stochastic matrices # entry ij means transition from j to i self.transitions_ttest = transitions_ttest.T self.transitions_confidence = transitions_confidence.T def paga_degrees(adata): """Compute the degree of each node in the abstracted graph. Parameters ---------- adata : AnnData Annotated data matrix. Returns ------- degrees : list List of degrees for each node. """ import networkx as nx g = nx.Graph(adata.uns['paga']['confidence']) degrees = [d for _, d in g.degree(weight='weight')] return degrees def paga_expression_entropies(adata): """Compute the median expression entropy for each node-group. Parameters ---------- adata : AnnData Annotated data matrix. Returns ------- entropies : list Entropies of median expressions for each node. """ from scipy.stats import entropy groups_order, groups_masks = utils.select_groups( adata, key=adata.uns['paga']['groups']) entropies = [] for mask in groups_masks: X_mask = adata.X[mask] x_median = np.median(X_mask, axis=0) x_probs = (x_median - np.min(x_median)) / (np.max(x_median) - np.min(x_median)) entropies.append(entropy(x_probs)) return entropies def paga_compare_paths(adata1, adata2, adjacency_key='confidence', adjacency_key2=None): """Compare paths in abstracted graphs in two datasets. Compute the fraction of consistent paths between leafs, a measure for the topological similarity between graphs. By increasing the verbosity to level 4 and 5, the paths that do not agree and the paths that agree are written to the output, respectively. The PAGA "groups key" needs to be the same in both objects. Parameters ---------- adata1, adata2 : AnnData Annotated data matrices to compare. adjacency_key : str Key for indexing the adjacency matrices in `.uns['paga']` to be used in adata1 and adata2. adjacency_key2 : str, None If provided, used for adata2. Returns ------- OrderedTuple with attributes ``n_steps`` (total number of steps in paths) and ``frac_steps`` (fraction of consistent steps), ``n_paths`` and ``frac_paths``. """ import networkx as nx g1 = nx.Graph(adata1.uns['paga'][adjacency_key]) g2 = nx.Graph(adata2.uns['paga'][adjacency_key2 if adjacency_key2 is not None else adjacency_key]) leaf_nodes1 = [str(x) for x in g1.nodes() if g1.degree(x) == 1] logg.msg('leaf nodes in graph 1: {}'.format(leaf_nodes1), v=5, no_indent=True) paga_groups = adata1.uns['paga']['groups'] asso_groups1 = utils.identify_groups(adata1.obs[paga_groups].values, adata2.obs[paga_groups].values) asso_groups2 = utils.identify_groups(adata2.obs[paga_groups].values, adata1.obs[paga_groups].values) orig_names1 = adata1.obs[paga_groups].cat.categories orig_names2 = adata2.obs[paga_groups].cat.categories import itertools n_steps = 0 n_agreeing_steps = 0 n_paths = 0 n_agreeing_paths = 0 # loop over all pairs of leaf nodes in the reference adata1 for (r, s) in itertools.combinations(leaf_nodes1, r=2): r2, s2 = asso_groups1[r][0], asso_groups1[s][0] orig_names = [orig_names1[int(i)] for i in [r, s]] orig_names += [orig_names2[int(i)] for i in [r2, s2]] logg.msg('compare shortest paths between leafs ({}, {}) in graph1 and ({}, {}) in graph2:' .format(*orig_names), v=4, no_indent=True) no_path1 = False try: path1 = [str(x) for x in nx.shortest_path(g1, int(r), int(s))] except nx.NetworkXNoPath: no_path1 = True no_path2 = False try: path2 = [str(x) for x in nx.shortest_path(g2, int(r2), int(s2))] except nx.NetworkXNoPath: no_path2 = True if no_path1 and no_path2: # consistent behavior n_paths += 1 n_agreeing_paths += 1 n_steps += 1 n_agreeing_steps += 1 logg.msg('there are no connecting paths in both graphs', v=5, no_indent=True) continue elif no_path1 or no_path2: # non-consistent result n_paths += 1 n_steps += 1 continue if len(path1) >= len(path2): path_mapped = [asso_groups1[l] for l in path1] path_compare = path2 path_compare_id = 2 path_compare_orig_names = [[orig_names2[int(s)] for s in l] for l in path_compare] path_mapped_orig_names = [[orig_names2[int(s)] for s in l] for l in path_mapped] else: path_mapped = [asso_groups2[l] for l in path2] path_compare = path1 path_compare_id = 1 path_compare_orig_names = [[orig_names1[int(s)] for s in l] for l in path_compare] path_mapped_orig_names = [[orig_names1[int(s)] for s in l] for l in path_mapped] n_agreeing_steps_path = 0 ip_progress = 0 for il, l in enumerate(path_compare[:-1]): for ip, p in enumerate(path_mapped): if ip >= ip_progress and l in p: # check whether we can find the step forward of path_compare in path_mapped if (ip + 1 < len(path_mapped) and path_compare[il + 1] in path_mapped[ip + 1]): # make sure that a step backward leads us to the same value of l # in case we "jumped" logg.msg('found matching step ({} -> {}) at position {} in path{} and position {} in path_mapped' .format(l, path_compare_orig_names[il + 1], il, path_compare_id, ip), v=6) consistent_history = True for iip in range(ip, ip_progress, -1): if l not in path_mapped[iip - 1]: consistent_history = False if consistent_history: # here, we take one step further back (ip_progress - 1); it's implied that this # was ok in the previous step logg.msg(' step(s) backward to position(s) {} in path_mapped are fine, too: valid step' .format(list(range(ip - 1, ip_progress - 2, -1))), v=6) n_agreeing_steps_path += 1 ip_progress = ip + 1 break n_steps_path = len(path_compare) - 1 n_agreeing_steps += n_agreeing_steps_path n_steps += n_steps_path n_paths += 1 if n_agreeing_steps_path == n_steps_path: n_agreeing_paths += 1 # only for the output, use original names path1_orig_names = [orig_names1[int(s)] for s in path1] path2_orig_names = [orig_names2[int(s)] for s in path2] logg.msg(' path1 = {},\n' 'path_mapped = {},\n' ' path2 = {},\n' '-> n_agreeing_steps = {} / n_steps = {}.' .format(path1_orig_names, [list(p) for p in path_mapped_orig_names], path2_orig_names, n_agreeing_steps_path, n_steps_path), v=5, no_indent=True) Result = namedtuple('paga_compare_paths_result', ['frac_steps', 'n_steps', 'frac_paths', 'n_paths']) return Result(frac_steps=n_agreeing_steps/n_steps if n_steps > 0 else np.nan, n_steps=n_steps if n_steps > 0 else np.nan, frac_paths=n_agreeing_paths/n_paths if n_steps > 0 else np.nan, n_paths=n_paths if n_steps > 0 else np.nan)

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wholesomebot/__main__.py

bnb32/wholesome_bot

0

1612892

from wholesomebot.connection import WebSocketClient, IrcSocketClient from wholesomebot.misc import get_logger import asyncio logger = get_logger() ircClient = IrcSocketClient() webClient = WebSocketClient() try: loop = asyncio.new_event_loop() loop = asyncio.get_event_loop() loop.create_task(ircClient.listen_forever()) loop.create_task(webClient.listen_forever()) loop.run_forever() except KeyboardInterrupt: logger.warning("Exiting wholesomebot!") exit()

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test/sample.py

HyechurnJang/umbi

0

94302

# -*- coding: utf-8 -*- ''' Created on 2017. 10. 12. @author: HyechurnJang ''' import json from umbi import UmbiAPI def v(data): print json.dumps(data, indent=2) api = UmbiAPI('85aaa4d3-333e-4212-a7bd-b64300321193') # print 'Domains' # v(api.Domains.Categorization('hecloud.org', 'google.com')) # v(api.Domains.Categories()) # v(api.Domains.Score('hecloud.org', 'google.com')) # v(api.Domains.LatestTags('hecloud.org')) # print '' # print 'Timeline' # v(api.Timeline('domain.com')) # print '' # print 'URL' # v(api.URL.Classifiers('hecloud.org')) # v(api.URL.Info('hecloud.org')) # print '' # print 'Search' # v(api.Search('hecloud.org', '-30days', True)) # print '' # print 'Recommendations' # v(api.Recommendations.Name('hecloud.org')) # print '' # print 'Links' # v(api.Links.Name('hecloud.org')) # print '' # print 'Security' # v(api.Security.Name('hecloud.org')) # print '' # print 'DnsDB' # v(api.DnsDB.Name('hecloud.org')) # v(api.DnsDB.IP('8.8.8.8')) # print '' # print 'BGP' # v(api.BgpRoutes.IP('8.8.8.8')) # v(api.BgpRoutes.ASN(11425)) # print 'WhoIs' # v(api.WhoIs('hecloud.org')) # v(api.WhoIs.Emails('<EMAIL>', '<EMAIL>', limit=2, offset=10, sort='created')) # v(api.WhoIs.Nameservers('ns1.google.com', 'ns2.google.com', limit=2, offset=10, sort='created')) # print '' # print 'IPs' # v(api.IPs.LatestDomains('192.168.3.11')) # print '' # print 'TopMillion' # v(api.TopMillion(limit=100)) # print '' print 'Samples' v(api.Samples('192.168.3.11')) print ''

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third_party/blink/tools/blinkpy/w3c/wpt_uploader.py

chromium/chromium

14,668

175393

# Copyright 2021 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. """Uploads Wpt test results from Chromium to wpt.fyi.""" import argparse import gzip import json import logging import os import requests import tempfile from blinkpy.common.net.rpc import Rpc from blinkpy.common.system.log_utils import configure_logging _log = logging.getLogger(__name__) class WptReportUploader(object): def __init__(self, host): self._host = host self._rpc = Rpc(host) self._dry_run = False configure_logging(logging_level=logging.INFO, include_time=True) def main(self, argv=None): """Pull wpt_report.json from latest CI runs, merge the reports and upload that to wpt.fyi. Returns: A boolean: True if success, False if there were any failures. """ options = self.parse_args(argv) if options.verbose: configure_logging(logging_level=logging.DEBUG, include_time=True) self._dry_run = options.dry_run rv = 0 builders = [("chromium", "ci", "android-webview-pie-x86-wpt-fyi-rel")] for builder in builders: reports = [] _log.info("Uploading report for %s" % builder[2]) build = self.fetch_latest_complete_build(*builder) if build: _log.info("Find latest completed build %d" % build.get("number")) urls = self.fetch_wpt_report_urls(build.get("id")) for url in urls: _log.info("Fetching wpt report from %s" % url) res = self._host.web.request("GET", url) if res.getcode() == 200: body = res.read() reports.append(json.loads(body)) else: _log.error("Failed to fetch wpt report.") merged_report = self.merge_reports(reports) with tempfile.TemporaryDirectory() as tmpdir: path = os.path.join(tmpdir, "reports.json.gz") with gzip.open(path, 'wt', encoding="utf-8") as zipfile: json.dump(merged_report, zipfile) rv = rv | self.upload_report(path) _log.info(" ") return rv def fetch_wpt_report_urls(self, build_id): """Get a list of fetchUrl for wpt-report from given build. This uses the QueryArtifacts rpc format specified in https://source.chromium.org/chromium/infra/infra/+/main:go/src/go.chromium.org/luci/resultdb/proto/v1/resultdb.proto The response is a list of dicts of the following form: { 'artifacts': [ { 'name': 'some name', 'artifactId': 'wpt_reports_dada.json', 'fetchUrl': 'https://something...', 'fetchUrlExpiration': 'some future time', 'sizeBytes': '8472164' }, ... more artifacts ] } Returns a list of URLs for wpt report """ invocation = "invocations/build-%s" % build_id data = { "invocations": [invocation], "predicate": { "followEdges": {"includedInvocations": True} } } url = 'https://results.api.cr.dev/prpc/luci.resultdb.v1.ResultDB/QueryArtifacts' res = self._rpc.luci_rpc(url, data) artifacts = res.get("artifacts") if res else None if not artifacts: return [] rv = [] for artifact in artifacts: if artifact.get("artifactId").startswith("wpt_reports"): rv.append(artifact.get("fetchUrl")) return rv def fetch_latest_complete_build(self, project, bucket, builder_name): """Gets latest successful build from a CI builder. This uses the SearchBuilds rpc format specified in https://cs.chromium.org/chromium/infra/go/src/go.chromium.org/luci/buildbucket/proto/rpc.proto The response is a list of dicts of the following form: { "builds": [ { "id": "8828280326907235505", "builder": { "builder": "android-webview-pie-x86-wpt-fyi-rel" }, "status": "SUCCESS" }, ... more builds } This method returns the latest finished build. """ data = { "predicate": { "builder": { "project": project, "bucket": bucket, "builder": builder_name }, "status": "SUCCESS" }, "fields": "builds.*.builder.builder,builds.*.number,builds.*.status,builds.*.id", "pageSize": 10 } url = 'https://cr-buildbucket.appspot.com/prpc/buildbucket.v2.Builds/SearchBuilds' raw_results_json = self._rpc.luci_rpc(url, data) if 'builds' not in raw_results_json: return None builds = raw_results_json['builds'] return builds[0] if builds else None def upload_report(self, path_to_report): """Upload the wpt report to wpt.fyi The Api is defined at: https://github.com/web-platform-tests/wpt.fyi/tree/main/api#results-creation """ username = "chromium-ci-results-uploader" password = <PASSWORD>" url = "https://staging.wpt.fyi/api/results/upload" session = requests.Session() session.auth = (username, password) with open(path_to_report, 'rb') as fp: files = {'result_file': fp} if self._dry_run: _log.info("Dry run, no report uploaded.") return 0 res = session.post(url=url, files=files) if res.status_code == 200: _log.info("Successfully uploaded wpt report with response: " + res.text) report_id = res.text.split()[1] _log.info("Report uploaded to https://staging.wpt.fyi/api/runs?run_id=%s" % report_id) return 0 else: _log.error("Upload wpt report failed with status code: %d", res.status_code) return 1 def merge_reports(self, reports): if not reports: return {} merged_report = {} merged_report['run_info'] = reports[0]['run_info'] merged_report['time_start'] = reports[0]['time_start'] merged_report['results'] = [] merged_report['time_end'] = reports[0]['time_end'] for report in reports: merged_report['time_start'] = min(merged_report['time_start'], report['time_start']) merged_report['results'].extend(report['results']) merged_report['time_end'] = max(merged_report['time_end'], report['time_end']) return merged_report def parse_args(self, argv): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( '-v', '--verbose', action='store_true', help='log extra details that may be helpful when debugging') parser.add_argument( '--dry-run', action='store_true', help='See what would be done without actually uploading any report.') return parser.parse_args(argv)

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authors/apps/articles/tests/test_tags.py

Tittoh/blog-API

1

73587

import json from django.urls import reverse from rest_framework.test import APITestCase from rest_framework import status from authors.apps.authentication.models import User from authors.apps.authentication.tests.utils import TEST_USER from django.core import mail from authors.apps.authentication.views import VerifyAccount from django.utils.http import urlsafe_base64_encode from django.utils.encoding import force_bytes from authors.apps.authentication.utils import generate_token from rest_framework.test import force_authenticate from rest_framework.test import APIRequestFactory from authors.apps.articles.models import Article user = { "user": { "username": "test", "email": "<EMAIL>", "password": "<PASSWORD>." } } class TestTags(APITestCase): """ This class defines the test suite for the tags of an article. """ def setUp(self): """Define the test client and other test variables.""" self.article1 = { "article": { "title": "How to train your dragon", "description": "Ever wonder how?", "body": "You have to believe", "tagList": ["django-rest", "python"] } } self.bad_tag_list = { "article": { "title": "How to train your dragon", "description": "Ever wonder how?", "body": "You have to believe", "tagList": "django-rest, python" } } self.tag_tuple = { "article": { "title": "How to train your dragon", "description": "Ever wonder how?", "body": "You have to believe", "tagList": ("django-rest, python") } } self.user = { "user": { "username": "test", "email": "<EMAIL>", "password": "<PASSWORD>." } } def login_user(self, user=user): """ login user """ response = self.client.post( reverse("authentication:login"), user, format='json') response.render() user = json.loads(response.content) return user def create_a_user(self, username='test', email='<EMAIL>', password='<PASSWORD>.'): """ Create a test user """ user = User.objects.create_user(username, email, password) user.save() return user def create_article(self): """ Create a test article """ tags = ["django-rest", "python"] user = User.objects.get() article = Article.objects.create( title="How to train your dragon", description="Ever wonder how?", body="You have to believe", tagList=tags, author=user.profile) article.save() return article def verify_user(self, user): """Verify user""" token = generate_token.make_token(user) uid = urlsafe_base64_encode(force_bytes(user.pk)) request = APIRequestFactory().get( reverse("authentication:verify", args=[uid, token])) verify_account = VerifyAccount.as_view() verify_account(request, uidb64=uid, token=token) return user #Test cases def test_tagList_added(self): """Test a tagList is added when an article is created""" user = self.create_a_user() self.verify_user(user) auth_user = self.login_user() user = User.objects.get() response = self.client.post('/api/articles/', self.article1, HTTP_AUTHORIZATION='Bearer ' + auth_user['user']['token'], format='json' ) self.assertIn("django-rest", response.content.decode()), self.assertIn("python", response.content.decode()) def test_tagList_returned(self): """Test api can return a taglist with an article""" user = self.create_a_user() self.verify_user(user) auth_user = self.login_user() self.client.post('/api/articles/', self.article1, HTTP_AUTHORIZATION='Bearer ' + auth_user['user']['token'], format='json' ) response = self.client.get('/api/articles/', format='json' ) self.assertIn("django-rest", response.content.decode()), self.assertIn("python", response.content.decode()) def test_get_tagList(self): """ Test api can get a tagList """ user = self.create_a_user() self.verify_user(user) auth_user = self.login_user() self.client.post('/api/articles/', self.article1, HTTP_AUTHORIZATION='Bearer ' + auth_user['user']['token'], format='json' ) response = self.client.get('/api/tags/', format='json' ) self.assertEqual(response.status_code, status.HTTP_200_OK) def test_tagList_object(self): """ Test a tagList cannot be a string """ user = self.create_a_user() self.verify_user(user) auth_user = self.login_user() response = self.client.post('/api/articles/', self.bad_tag_list, HTTP_AUTHORIZATION='Bearer ' + auth_user['user']['token'], format='json' ) self.assertNotIn("django-rest", response.content.decode()), self.assertNotIn("python", response.content.decode()) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST) def test_tagList_data_structure(self): """ Test a tagList cannot be a tuple """ user = self.create_a_user() self.verify_user(user) auth_user = self.login_user() response = self.client.post('/api/articles/', self.tag_tuple, HTTP_AUTHORIZATION='Bearer ' + auth_user['user']['token'], format='json' ) self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)

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gpytorch/lazy/non_lazy_tensor.py

Sciguymjm/gpytorch

0

192371

#!/usr/bin/env python3 import torch from .. import settings from ..lazy import LazyTensor class NonLazyTensor(LazyTensor): def __init__(self, tsr): """ Not a lazy tensor Args: - tsr (Tensor: matrix) a Tensor """ if not torch.is_tensor(tsr): raise RuntimeError("NonLazyTensor must take a torch.Tensor; got {}".format(tsr.__class__.__name__)) super(NonLazyTensor, self).__init__(tsr) self.tensor = tsr def _matmul(self, rhs): return torch.matmul(self.tensor, rhs) def _t_matmul(self, rhs): return torch.matmul(self.tensor.transpose(-1, -2), rhs) def _quad_form_derivative(self, left_vecs, right_vecs): res = left_vecs.matmul(right_vecs.transpose(-1, -2)) return (res,) def _size(self): return self.tensor.size() def _transpose_nonbatch(self): return NonLazyTensor(self.tensor.transpose(-1, -2)) def _get_indices(self, left_indices, right_indices, *batch_indices): return self.tensor.__getitem__((*batch_indices, left_indices, right_indices)) def diag(self): if self.tensor.ndimension() < 3: return self.tensor.diag() else: row_col_iter = torch.arange(0, self.matrix_shape[-1], dtype=torch.long, device=self.device) return self.tensor[..., row_col_iter, row_col_iter].view(*self.batch_shape, -1) def evaluate(self): return self.tensor def __getitem__(self, index): # Make sure index is a list if not isinstance(index, tuple): index = [index] else: index = list(index) # Handle the ellipsis # Find the index of the ellipsis ellipsis_locs = [index for index, item in enumerate(index) if item is Ellipsis] if settings.debug.on(): if len(ellipsis_locs) > 1: raise RuntimeError( "Cannot have multiple ellipsis in a __getitem__ call. LazyTensor {} " " received index {}.".format(self, index) ) if len(ellipsis_locs) == 1: ellipsis_loc = ellipsis_locs[0] num_to_fill_in = self.ndimension() - (len(index) - 1) index = index[:ellipsis_loc] + [slice(None, None, None)] * num_to_fill_in + index[ellipsis_loc + 1:] # Make the index a tuple again index = tuple(index) # Perform the __getitem__ res = self.tensor[index] # Determine if we should return a LazyTensor or a Tensor if len(index) >= self.ndimension() - 1: row_index = index[self.ndimension() - 2] if isinstance(row_index, int) or torch.is_tensor(row_index): return res if len(index) == self.ndimension(): col_index = index[self.ndimension() - 1] if isinstance(col_index, int) or torch.is_tensor(col_index): return res return NonLazyTensor(res)

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openbatlib/model.py

fastrockstar/openBatLib

4

173858

import numpy as np import numba as nb from dataclasses import dataclass from numba import types from numba.typed import Dict from numba import njit import pandas as pd import time import datetime import csv from openpyxl import load_workbook from pyModbusTCP.client import ModbusClient from pyModbusTCP import utils @dataclass class Data: Ppv: np.array Pbat: np.array Pperi: np.array soc: np.array soc0: int Pbs0: int E: dict class BatModDC(object): """Performance Simulation Class for DC-coupled PV-Battery systems :param parameter: PV battery system parameters :type parameter: dict :param d: array containing parameters :type d: numpy array :param ppv: normalized DC power output of the PV generator :type ppv: numpy array :param pl: AC load power :type pl: numpy array :param Pr: Residual power for battery charging :type Pr: numpy array :param Prpv: AC residual power :type Pr: numpy array :param Ppv: DC power output of the PV generator :type Ppv: numpy array :param ppv2ac: Normalized AC output power of the PV2AC conversion pathway to cover the AC power demand :type ppv2ac: numpy array :param Ppv2ac_out: Target AC output power of the PV2AC conversion pathway :type Ppv2ac_out: numpy array :param dt: time step width in seconds :type dt: integer """ _version = 0.1 def __init__(self, parameter, d, ppv, pl, dt): """Constructor method """ self.parameter = parameter self.d = d self.ppv = ppv self.pl = pl self.dt = dt self.th = False # Start threshold for the recharging of the battery self.spi = float() # Initialization and preallocation self.Real.Pr, self.Real.Prpv, self.Real.Ppv, self.Real.ppv2ac, self.Real.Ppv2ac_out = max_self_consumption(parameter, ppv, pl, pvmod=True) self.Real.Ppv2ac_out0 = 0 self.Real.Ppv2bat_in0 = 0 self.Real.Pbat = np.zeros_like(self.ppv) # DC power of the battery in W self.Real.soc = np.zeros_like(self.ppv) # State of charge of the battery self.Real.soc0 = 0 # State of charge of the battery in the first time step # Input power of the PV2BAT conversion pathway in W self.Real.Ppv2bat_in = np.zeros_like(self.ppv) # Output power of the BAT2AC conversion pathway in W self.Real.Pbat2ac_out = np.zeros_like(self.ppv) self.Real.Pbat2ac_out0 = 0 # AC power of the PV-battery system in W self.Real.Ppvbs = np.zeros_like(self.ppv) # Additional power consumption of other system components (e.g. AC power meter) in W self.Real.Pperi = np.ones(self.ppv.size) * self.parameter['P_PERI_AC'] self.Ideal.Ppv = np.maximum(0, self.ppv) * self.parameter['P_PV'] * 1000 self.Ideal.Pr = self.Ideal.Ppv - self.pl self.Ideal.Pbat = np.zeros_like(self.ppv) self.Ideal.soc = np.zeros_like(self.ppv) self.Ideal.Ppv2bat_in = np.zeros_like(self.ppv) self.Ideal.Ppv2bat_in = np.zeros_like(self.ppv) self.Ideal.Pbat2ac_out = np.zeros_like(self.ppv) self.Ideal.Ppvbs = np.zeros_like(self.ppv) @dataclass class Real(Data): Pr : np.array Prpv : np.array ppv2ac : np.array Ppv2ac_out : np.array Ppv2ac_out0 : int Ppv2bat_in : np.array Pbat2ac_out : np.array Ppvbs : np.array @dataclass class Ideal(Real): def __init__(self): super().__init__() def simulation(self, pvmod=True): """Manages the Performance Simulation Model for AC-coupled PV-Battery Systems """ self.Real.Ppv2ac_out, self.Real.Ppv2bat_in, self.Real.Ppv2bat_in0, self.Real.Pbat2ac_out, self.Real.Pbat2ac_out0, self.Real.Ppvbs, self.Real.Pbat, self.Real.soc, self.Real.soc0 = batmod_dc( self.d, self.dt, self.Real.soc0, self.Real.soc, self.Real.Pr, self.Real.Prpv, self.Real.Ppv, self.Real.Ppv2bat_in0, self.Real.Ppv2bat_in, self.Real.Pbat2ac_out0, self.Real.Pbat2ac_out, self.Real.Ppv2ac_out, self.Real.Ppvbs, self.Real.Pbat) self.Ideal.Pbat, self.Ideal.soc, self.Ideal.soc0 = batmod_dc_ideal(self.d, self.dt, self.Ideal.soc0, self.Ideal.soc, self.Ideal.Pr, self.Ideal.Pbat) # Define missing parameters self.Real.Ppv2ac = self.Real.Ppv2ac_out # AC output power of the PV2AC conversion pathway self.Real.Ppv2bat = self.Real.Ppv2bat_in # DC input power of the PV2BAT conversion pathway self.Ideal.Ppvbs = self.Ideal.Ppv - np.maximum(0, self.Ideal.Pbat) - (np.minimum(0, self.Ideal.Pbat)) # Realized AC power of the PV-battery system self.Ideal.Ppv2ac = self.Ideal.Ppv - np.maximum(0, self.Ideal.Pbat) # AC output power of the PV2AC conversion pathway self.Ideal.Ppv2bat = np.maximum(0, self.Ideal.Pbat) # DC input power of the PV2BAT conversion pathway print() def bat_mod_res(self): """Function to calculate the power flows and energy sums including curtailment of PV power """ self.Real.E = bat_res_mod(self.parameter, self.pl, self.Real.Ppv, self.Real.Pbat, self.dt, self.Real.Ppv2ac, self.Real.Ppv2bat, self.Real.Ppvbs, self.Real.Pperi) self.Ideal.E = bat_res_mod_ideal(self.parameter, self.pl, self.Ideal.Ppv, self.Ideal.Pbat, self.dt, self.Ideal.Ppv2ac, self.Ideal.Ppv2bat, self.Ideal.Ppvbs, self.Ideal.Pperi) def calculate_spi(self): self.spi = calculate_spi(_E_real=self.Real.E, _E_ideal=self.Ideal.E) def get_E(self): """Returns the energy sums of the simulation :return: Energy sums of the simulation in MWh :rtype: dict """ return self.Real.E, self.Ideal.E def get_soc(self): """Returns the state of charge of the battery :return: state of charge of the battery :rtype: numpy array """ return self.soc def get_Pbat(self): """Returns the DC power of the battery in W :return: DC power of the battery in W :rtype: numpy array """ return self.Pbat def get_SPI(self): return self.spi class BatModAC(object): """Performance Simulation Class for AC-coupled PV-Battery systems :param parameter: PV battery system parameters :type parameter: dict :param d: array containing parameters :type d: numpy array :param ppv: normalized DC power output of the PV generator :type ppv: numpy array :param pl: AC load power :type pl: numpy array :param Pr: AC residual power :type Pr: numpy array :param Ppv: DC power output of the PV generator :type Ppv: numpy array :param Ppvs: AC power output of the PV inverter taking into account the conversion losses and maximum output power of the PV inverter :type Ppvs: numpy array :param Pperi: Additional power consumption of other system components (e.g. AC power meter) in W :type Pperi: numpy array :param dt: time step width in seconds :type dt: integer """ _version = '0.1' def __init__(self, parameter, d, ppv, pl, dt): """Constructor method """ self.parameter = parameter self.d = d self.ppv = ppv self.pl = pl self.dt = dt self.spi = float() self.th = False # Start threshold for the recharging of the battery # Initialization and preallocation self.Real.Pr, self.Real.Ppv, self.Real.Ppvs, self.Real.Pperi = max_self_consumption(parameter, ppv, pl, pvmod=True) self.Real.Pbat = np.zeros_like(self.ppv) # DC power of the battery in W self.Real.Pbs = np.zeros_like(self.ppv) # AC power of the battery system in W self.Real.soc = np.zeros_like(self.ppv) # State of charge of the battery self.Real.soc0 = 0 # State of charge of the battery in the first time step self.Real.Pbs0 = 0 # State of the battery storage in the previous time step self.Ideal.Ppv = np.maximum(0, ppv) * parameter['P_PV'] * 1000 self.Ideal.Pr = self.Ideal.Ppv - pl self.Ideal.Pbat = np.zeros_like(self.ppv) self.Ideal.Pbs = np.zeros_like(self.ppv) self.Ideal.Pbs0 = 0 self.Ideal.soc = np.zeros_like(self.ppv) self.Ideal.soc0 = 0 self.Ideal.Ppvs = self.Ideal.Ppv self.Ideal.Pperi = np.zeros_like(self.ppv) @dataclass class Real(Data): Pr : np.array Ppvs : np.array Pbs : np.array @dataclass class Ideal(Real): def __init__(self): super().__init__() def simulation(self): """Manages the Performance Simulation Model for AC-coupled PV-Battery Systems """ self.Real.Pbat, self.Real.Pbs, self.Real.soc, self.Real.soc0, self.Real.Pbs0 = batmod_ac( self.d, self.dt, self.Real.soc0, self.Real.soc, self.Real.Pr, self.Real.Pbs0, self.Real.Pbs, self.Real.Pbat) self.Ideal.Pbs, self.Ideal.Pbat, self.Ideal.soc0, self.Ideal.soc = batmod_ac_ideal( self.d, self.dt, self.Ideal.soc0, self.Ideal.soc, self.Ideal.Pr, self.Ideal.Pbat) def bat_mod_res(self): """Function to calculate the power flows and energy sums including curtailment of PV power """ self.Real.E = bat_res_mod( self.parameter, self.pl, self.Real.Ppv, self.Real.Pbat, self.dt, self.Real.Ppvs, self.Real.Pbs, self.Real.Pperi) self.Ideal.E = bat_res_mod_ideal( self.parameter, self.pl, self.Ideal.Ppv, self.Ideal.Pbat, self.dt, self.Ideal.Ppvs, self.Ideal.Pbs, self.Ideal.Pperi) def calculate_spi(self): self.spi = calculate_spi(_E_real=self.Real.E, _E_ideal=self.Ideal.E) def get_E(self): """Returns the energy sums of the simulation :return: Energy sums of the simulation in MWh :rtype: dict """ return self.Real.E, self.Ideal.E def get_soc(self): """Returns the state of charge of the battery :return: state of charge of the battery :rtype: numpy array """ return self.soc def get_Pbat(self): """Returns the DC power of the battery in W :return: DC power of the battery in W :rtype: numpy array """ return self.Pbat def get_Pbs(self): """Returns the AC power of the battery system in W :return: AC power of the battery system in W :rtype: numpy array """ return self.Pbs def get_SPI(self): return self.spi class BatModPV(object): """Performance Simulation Class for PV-coupled PV-Battery systems :param parameter: PV battery system parameters :type parameter: dict :param d: array containing parameters :type d: numpy array :param ppv: normalized DC power output of the PV generator :type ppv: numpy array :param pl: AC load power :type pl: numpy array :param Pac: Power demand on the AC side :type Pac: numpy array :param Ppv: DC power output of the PV generator :type Ppv: numpy array :param Pperi: Additional power consumption of other system components (e.g. AC power meter) in W :type Pperi: numpy array :param dt: time step width in seconds :type dt: integer """ _version = '0.1' def __init__(self, parameter, d, ppv, pl, Pac, Ppv, Pperi, dt): """Constructor method """ self.parameter = parameter self.d = d self.ppv = ppv self.pl = pl self.Pac = Pac self.Ppv = Ppv self.Pperi = Pperi self.dt = dt # Initialization and preallocation self.Pbat = np.zeros_like(self.ppv) # DC power of the battery in W self.soc = np.zeros_like(self.ppv) # State of charge of the battery # Output power of the PV2AC conversion pathway in W self.Ppv2ac_out = np.zeros_like(self.ppv) # Input power of the PV2BAT conversion pathway in W self.Ppv2bat_in = np.zeros_like(self.ppv) self.Ppv2bat_in0 = 0 # Output power of the BAT2PV conversion pathway in W self.Pbat2pv_out = np.zeros_like(self.ppv) self.Pbat2pv_out0 = 0 # AC power of the PV-battery system in W self.Ppvbs = np.zeros_like(self.ppv) self.simulation() self.bat_mod_res() def simulation(self, pvmod=True): """Manages the Performance Simulation Model for AC-coupled PV-Battery Systems """ self.th = 0 # Start threshold for the recharging of the battery self.soc0 = 0 # Initial state of charge of the battery in the first time step # Simulation of the battery system #start = time.process_time() self.soc, self.soc0, self.Ppv, self.Ppvbs, self.Pbat, self.Ppv2ac_out, self.Pbat2pv_out, self.Ppv2bat_in = batmod_pv(self.d, self.dt, self.soc0, self.soc, self.Ppv, self.Pac, self.Ppv2bat_in0, self.Ppv2bat_in, self.Ppv2ac_out, self.Pbat2pv_out0, self.Pbat2pv_out, self.Ppvbs, self.Pbat) #print(time.process_time()-start) # Define missing parameters self.Ppv2ac = self.Ppv2ac_out # AC output power of the PV2AC conversion pathway self.Ppv2bat = self.Ppv2bat_in # DC input power of the PV2BAT conversion pathway def bat_mod_res(self): """Function to calculate the power flows and energy sums including curtailment of PV power """ self.E = bat_res_mod(self.parameter, self.pl, self.Ppv, self.Pbat, self.dt, self.Ppv2ac, self.Ppv2bat, self.Ppvbs, self.Pperi) def get_E(self): """Returns the energy sums of the simulation :return: Energy sums of the simulation in MWh :rtype: dict """ return self.E def get_soc(self): """Returns the state of charge of the battery :return: state of charge of the battery :rtype: numpy array """ return self.soc def get_Pbat(self): """Returns the DC power of the battery in W :return: DC power of the battery in W :rtype: numpy array """ return self.Pbat class ModBus(object): """Establishes connection to a battery system via ModBus protocol :param host: IP address of the host :type host: string :param port: Server port of the host :type port: integer :param unit_id: Unit-ID of the host :type unit_id: integer """ def __init__(self, host, port, unit_id, input_vals, dt, fname): """Constructor method """ self.host = host self.port = port self.unit_id = unit_id self.dt = dt self.input_vals = input_vals self.fname = fname self.open_connection() self.create_csv_file() self.start_loop() def open_connection(self): """Opens the connection to the host """ # Open ModBus connection try: self.c = ModbusClient(host=self.host, port=self.port, unit_id=self.unit_id, auto_open=True, auto_close=True) except ValueError: print("Error with host: {}, port: {} or unit-ID: {} params".format( self.host, self.port, self.unit_id)) def start_loop(self): """Starts the writing and reading process """ # Transform the array to fit the 1 minute time duration #self.set_vals = np.repeat(self.input_vals, self.dt * 60) i = 0 idx = pd.date_range(start=datetime.datetime.now(), periods=(self.input_vals.size), freq='S') while i < len(idx): if datetime.datetime.now().second == idx[i].second: # Set chrging value self.set_val = int(self.input_vals[i]) if self.set_val < 0: # Write negative value to battery charge power (AC) setpoint register self.c.write_single_register(1024, self.set_val & 0xFFFF) # Log writing time self.set_time = datetime.datetime.now() else: # Write positive value to battery charge power (AC) setpoint to register self.c.write_single_register(1024, self.set_val) # Log writing time self.set_time = datetime.datetime.now() try: # Read total AC power value from register _P_ac = self.c.read_holding_registers(172, 2) self.read_time_P_ac = datetime.datetime.now() except: print('Could not read register 172!') try: # Read actual battery charge/discharge power value from register _P_bat = self.c.read_holding_registers(582, 1) self.read_time_P_bat = datetime.datetime.now() except: print('Could not read register 582!') # Load content of two registers into a single float value zregs = utils.word_list_to_long(_P_ac, big_endian=False) # Decode and store float value of the AC-power self.P_ac = utils.decode_ieee(*zregs) # Store the DC charging power self.P_bat = np.int16(*_P_bat) # Read actual soc self.soc0 = self.read_soc(210) try: # Save the values to a csv file self.save_to_csv() except: print('Could not save to csv!') i += 1 def read_soc(self, reg): """Reads the state of charge of the battery """ # Load the actual state fo charge of the battery regs = self.c.read_holding_registers(reg, 2) # Load content of two registers into a single float value zregs = utils.word_list_to_long(regs, big_endian=False) return utils.decode_ieee(*zregs) def create_csv_file(self): """Creates a csv file from set and read values """ # Create a new csv-file with open(self.fname, 'w') as f: writer = csv.writer(f, dialect='excel') writer.writerow(['set_time', 'read_time_P_ac', 'read_time_P_bat', 'soc', 'set_value', 'P_ac', 'P_bat']) def save_to_csv(self): """Saves the set and read values to s csv file """ # Save the read values to a csv file with open(self.fname, "a") as f: wr = csv.writer(f, dialect='excel') wr.writerow([self.set_time, self.read_time_P_ac, self.read_time_P_bat, self.soc0, self.set_val, self.P_ac, self.P_bat]) def max_self_consumption(parameter, ppv, pl, pvmod=True, ideal=False): """Function for maximizing self consumption :param parameter: PV battery system parameters :type parameter: dict :param ppv: normalized DC power output of the PV generator :type ppv: numpy array :param pl: AC load power :type pl: numpy array """ # Maximize self consumption for AC-coupled systems if parameter['Top'] == 'AC': # DC power output of the PV generator if pvmod: # ppv: Normalized DC power output of the PV generator in kW/kWp if ideal: Ppv = np.maximum(0, ppv ) * parameter['P_PV'] * 1000 else: Ppv = np.minimum(ppv * parameter['P_PV'], parameter['P_PV2AC_in']) * 1000 else: # ppv: DC power output of the PV generator in W if ideal: Ppv = np.maximum(0, ppv) else: Ppv = np.minimum(ppv, parameter['P_PV2AC_in'] * 1000) # Normalized input power of the PV inverter ppvinvin = Ppv / parameter['P_PV2AC_in'] / 1000 # AC power output of the PV inverter taking into account the conversion losses and maximum # output power of the PV inverter Ppvs = np.minimum(np.maximum(0, Ppv-(parameter['PV2AC_a_in'] * ppvinvin * ppvinvin + parameter['PV2AC_b_in'] * ppvinvin + parameter['PV2AC_c_in'])), parameter['P_PV2AC_out'] * 1000) # 3.2 Residual power # Additional power consumption of other system components (e.g. AC power meter) in W Pperi = np.ones_like(ppv) * parameter['P_PERI_AC'] # Adding the standby consumption of the PV inverter in times without any AC power output of the PV system # to the additional power consumption Pperi[Ppvs == 0] += parameter['P_PVINV_AC'] # Residual power if ideal: Pr = Ppv - pl else: Pr = Ppvs - pl - Pperi return Pr, Ppv, Ppvs, Pperi # Maximize self consumption for DC-coupled systems elif parameter['Top'] == 'DC': # Initialization and preallocation Ppv2ac_in_ac = np.zeros_like(ppv) Ppv = np.empty_like(ppv) # DC power output of the PV generator if pvmod: # ppv: Normalized DC power output of the PV generator in kW/kWp Ppv = ppv * parameter['P_PV'] * 1000 else: Ppv = ppv # DC power output of the PV generator taking into account the maximum # DC input power of the PV2AC conversion pathway Ppv = np.minimum(Ppv, parameter['P_PV2AC_in'] * 1000) # Residual power # Power demand on the AC side Pac = pl + parameter['P_PERI_AC'] # Normalized AC output power of the PV2AC conversion pathway to cover the AC # power demand ppv2ac = np.minimum( Pac, parameter['P_PV2AC_out'] * 1000) / parameter['P_PV2AC_out'] / 1000 # Target DC input power of the PV2AC conversion pathway Ppv2ac_in_ac = np.minimum(Pac, parameter['P_PV2AC_out'] * 1000) + ( parameter['PV2AC_a_out'] * ppv2ac**2 + parameter['PV2AC_b_out'] * ppv2ac + parameter['PV2AC_c_out']) # Normalized DC input power of the PV2AC conversion pathway TODO 1 ppv2ac = Ppv / parameter['P_PV2AC_in'] / 1000 # Target AC output power of the PV2AC conversion pathway Ppv2ac_out = np.maximum( 0, Ppv - (parameter['PV2AC_a_in'] * ppv2ac**2 + parameter['PV2AC_b_in'] * ppv2ac + parameter['PV2AC_c_in'])) # Residual power for battery charging Prpv = Ppv - Ppv2ac_in_ac # Residual power for battery discharging Pr = Ppv2ac_out - Pac return Pr, Prpv, Ppv, ppv2ac, Ppv2ac_out # Maximize self consumption for PV-coupled systems elif parameter['Top'] == 'PV': # Preallocation # Pbat = np.zeros_like(ppv) # DC power of the battery in W # soc = np.zeros_like(ppv) # State of charge of the battery # Ppv2ac_out = np.zeros_like(ppv) # Output power of the PV2AC conversion pathway in W # Ppv2bat_in = np.zeros_like(ppv) # Input power of the PV2BAT conversion pathway in W # Pbat2pv_out = np.zeros_like(ppv) # Output power of the BAT2PV conversion pathway in W # Ppvbs = np.zeros_like(ppv) # AC power of the PV-battery system in W Ppv = np.empty_like(ppv) # DC power output of the PV generator # Additional power consumption of other system components (e.g. AC power meter) in W Pperi = np.ones_like(ppv) * parameter['P_PERI_AC'] # dt = 1 # Time increment in s # th = 0 # Start threshold for the recharging of the battery # soc0 = 0 # State of charge of the battery in the first time step # DC power output of the PV generator if pvmod: # ppv: Normalized DC power output of the PV generator in kW/kWp Ppv = ppv * parameter['P_PV'] * 1000 else: # ppv: DC power output of the PV generator in W Ppv = ppv # Power demand on the AC side Pac = pl + Pperi return Pac, Ppv, Pperi @nb.jit(nopython=True) def batmod_ac(d, _dt, _soc0, _soc, _Pr, _Pbs0, _Pbs, _Pbat): """Performance Simulation function for AC-coupled battery systems :param d: array containing parameters :type d: numpy array :param dt: time step width :type dt: integer :param soc0: state of charge in the previous time step :type soc0: float :param Pr: residual power :type Pr: numpy array :param Pbs0: AC-power of the battery system in the previous time step :type Pbs0: float :param Pbs: AC-power of the battery syste :type Pbs: numpy array :param Pbat: DC-power oof the battery :type Pbat: numpy array """ # Loading of particular variables _E_BAT = d[0] _eta_BAT = d[1] _t_CONSTANT = d[2] _P_SYS_SOC0_DC = d[3] _P_SYS_SOC0_AC = d[4] _P_SYS_SOC1_DC = d[5] _P_SYS_SOC1_AC = d[6] _AC2BAT_a_in = d[7] _AC2BAT_b_in = d[8] _AC2BAT_c_in = d[9] _BAT2AC_a_out = d[10] _BAT2AC_b_out = d[11] _BAT2AC_c_out = d[12] _P_AC2BAT_DEV = d[13] _P_BAT2AC_DEV = d[14] _P_BAT2AC_out = d[15] _P_AC2BAT_in = d[16] _t_DEAD = int(round(d[17])) _SOC_h = d[18] _P_AC2BAT_min = _AC2BAT_c_in _P_BAT2AC_min = _BAT2AC_c_out # Correction factor to avoid over charge and discharge the battery corr = 0.1 # Initialization of particular variables _tde = _t_CONSTANT > 0 # Binary variable to activate the first-order time delay element # Factor of the first-order time delay element _ftde = 1 - np.exp(-_dt / _t_CONSTANT) # First time step with regard to the dead time of the system control _tstart = np.maximum(2, 1 + _t_DEAD) _tend = int(_Pr.size) _th = 0 # Capacity of the battery, conversion from kWh to Wh _E_BAT *= 1000 # Effiency of the battery in percent _eta_BAT /= 100 # Check if the dead or settling time can be ignored and set flags accordingly if _dt >= (3 * _t_CONSTANT) or _tend == 1: _tstart = 1 T_DEAD = False else: T_DEAD = True if _dt >= _t_DEAD + 3 * _t_CONSTANT: SETTLING = False else: SETTLING = True for t in range(_tstart - 1, _tend): # Energy content of the battery in the previous time step E_b0 = _soc0 * _E_BAT # Calculate the AC power of the battery system from the residual power # with regard to the dead time of the system control if T_DEAD: P_bs = _Pr[t - _t_DEAD] else: P_bs = _Pr[t] # Check if the battery holds enough unused capacity for charging or discharging # Estimated amount of energy in Wh that is supplied to or discharged from the storage unit. E_bs_est = P_bs * _dt / 3600 # Reduce P_bs to avoid over charging of the battery if E_bs_est > 0 and E_bs_est > (_E_BAT - E_b0): P_bs = (_E_BAT - E_b0) * 3600 / _dt # When discharging take the correction factor into account elif E_bs_est < 0 and np.abs(E_bs_est) > (E_b0): P_bs = (E_b0 * 3600 / _dt) * (1-corr) # Adjust the AC power of the battery system due to the stationary # deviations taking the minimum charging and discharging power into # account if P_bs > _P_AC2BAT_min: P_bs = np.maximum(_P_AC2BAT_min, P_bs + _P_AC2BAT_DEV) elif P_bs < -_P_BAT2AC_min: P_bs = np.minimum(-_P_BAT2AC_min, P_bs - _P_BAT2AC_DEV) else: P_bs = 0 # Limit the AC power of the battery system to the rated power of the # battery converter P_bs = np.maximum(-_P_BAT2AC_out * 1000, np.minimum(_P_AC2BAT_in * 1000, P_bs)) # Adjust the AC power of the battery system due to the settling time # (modeled by a first-order time delay element) Hier hat der Schritt vorher eine Null? # Muss der vorherige Wert mit übergeben werden? if SETTLING: if t > 0: P_bs = _tde * _Pbs[t-1] + _tde * (P_bs - _Pbs[t-1]) * _ftde + P_bs * (not _tde) else: P_bs = _tde * _Pbs0 + _tde * (P_bs - _Pbs0) * _ftde + P_bs * (not _tde) # Decision if the battery should be charged or discharged if P_bs > 0 and _soc0 < 1 - _th * (1 - _SOC_h): # The last term th*(1-SOC_h) avoids the alternation between # charging and standby mode due to the DC power consumption of the # battery converter when the battery is fully charged. The battery # will not be recharged until the SOC falls below the SOC-threshold # (SOC_h) for recharging from PV. # Normalized AC power of the battery system p_bs = P_bs / _P_AC2BAT_in / 1000 # DC power of the battery affected by the AC2BAT conversion losses # of the battery converter P_bat = np.maximum( 0, P_bs - (_AC2BAT_a_in * p_bs * p_bs + _AC2BAT_b_in * p_bs + _AC2BAT_c_in)) elif P_bs < 0 and _soc0 > 0: # Normalized AC power of the battery system p_bs = np.abs(P_bs / _P_BAT2AC_out / 1000) # DC power of the battery affected by the BAT2AC conversion losses # of the battery converter P_bat = P_bs - (_BAT2AC_a_out * p_bs * p_bs + _BAT2AC_b_out * p_bs + _BAT2AC_c_out) else: # Neither charging nor discharging of the battery # Set the DC power of the battery to zero P_bat = 0 # Decision if the standby mode is active if P_bat == 0 and _soc0 <= 0: # Standby mode in discharged state # DC and AC power consumption of the battery converter P_bat = -np.maximum(0, _P_SYS_SOC0_DC) P_bs = _P_SYS_SOC0_AC elif P_bat == 0 and _soc0 > 0: # Standby mode in fully charged state # DC and AC power consumption of the battery converter P_bat = -np.maximum(0, _P_SYS_SOC1_DC) P_bs = _P_SYS_SOC1_AC # Transfer the realized AC power of the battery system and # the DC power of the battery _Pbs0 = P_bs _Pbs[t] = P_bs _Pbat[t] = P_bat # Change the energy content of the battery from Ws to Wh conversion if P_bat > 0: E_b = E_b0 + P_bat * np.sqrt(_eta_BAT) * _dt / 3600 elif P_bat < 0: E_b = E_b0 + P_bat / np.sqrt(_eta_BAT) * _dt / 3600 else: E_b = E_b0 # Calculate the state of charge of the battery _soc0 = E_b / (_E_BAT) _soc[t] = _soc0 # Adjust the hysteresis threshold to avoid alternation # between charging and standby mode due to the DC power # consumption of the battery converter. if _th and _soc[t] > _SOC_h or _soc[t] > 1: _th = True else: _th = False return _Pbat, _Pbs, _soc, _soc0, _Pbs0 @nb.jit(nopython=True) def batmod_ac_ideal(d, _dt, _soc0, _soc, _Pr, _Pbat): _E_BAT = d[0] for t in range(_Pr.size): # Energy content of the battery in the previous time step E_b0 = _soc0 * _E_BAT * 1000 # Calculate the DC power of the battery from the residual power P_bat = _Pr[t] # Decision if the battery should be charged or discharged if P_bat > 0 and _soc0 < 1: # Battery charging E_b = E_b0 + P_bat * _dt / 3600 # Change the energy content of the battery elif P_bat < 0 and _soc0 > 0: # Battery discharging # Change the energy content of the battery E_b = E_b0 + P_bat * _dt / 3600 else: # Neither charging nor discharging of the battery # Set the DC power of the battery to zero P_bat = 0 # No change in the energy content of the battery E_b = E_b0 # Transfer the realized DC power of the battery _Pbat[t] = P_bat # Calculate the state of charge of the battery _soc0 = E_b / (_E_BAT * 1000) _soc[t] = _soc0 # Define missing parameters _Pbs = _Pbat # Realized AC power of the battery system return _Pbs, _Pbat, _soc0, _soc @nb.jit(nopython=True) def batmod_dc(d, _dt, _soc0, _soc, _Pr, _Prpv, _Ppv, _Ppv2bat_in0, _Ppv2bat_in, _Pbat2ac_out0, _Pbat2ac_out, _Ppv2ac_out, _Ppvbs, _Pbat): """Performance simulation function for DC-coupled battery systems :param d: array containing parameters :type d: numpy array :param dt: time step width :type dt: integer :param soc0: state of charge in the previous time step :type soc0: float :param Pr: residual power :type Pr: numpy array :param Prpv: residual power of the PV-system :type Prpv: numpy array :param Ppv: PV-power :type Ppv: numpy array :param Ppv2bat_in0: AC input power of the battery system in the previous time step :type Ppv2bat_in0: float :param Ppv2bat_in: AC input power of the battery system :type Ppv2bat_in: numpy array :param Pbat2ac_out0: AC output power of the battery system in the previous time step :type Pbat2ac_out0: float :param Pbat2ac_out: AC output power of the battery system :type Pbat2ac_out: numpy array :param Ppv2ac_out0: AC output power of the PV inverter in the previous time step :type Ppv2ac_out0: float :param Ppv2ac_out: AC output power of the PV inverter :type Ppv2ac_out: numpy array :param Ppvbs: AC power from the PV system to the battery system :type Ppvbs: numpy array :param Pbat: DC power of the battery :type Pbat: float """ _E_BAT = d[0] _P_PV2AC_in = d[1] _P_PV2AC_out = d[2] _P_PV2BAT_in = d[3] _P_BAT2AC_out = d[4] _PV2AC_a_in = d[5] _PV2AC_b_in = d[6] _PV2AC_c_in = d[7] _PV2BAT_a_in = d[8] _PV2BAT_b_in = d[9] _BAT2AC_a_out = d[10] _BAT2AC_b_out = d[11] _BAT2AC_c_out = d[12] _eta_BAT = d[13] _SOC_h = d[14] _P_PV2BAT_DEV = d[15] _P_BAT2AC_DEV = d[16] _t_DEAD = int(round(d[17])) _t_CONSTANT = d[18] _P_SYS_SOC1_DC = d[19] _P_SYS_SOC0_AC = d[20] _P_SYS_SOC0_DC = d[21] _P_PV2AC_min = _PV2AC_c_in # Capacity of the battery, conversion from kWh to Wh _E_BAT *= 1000 # Effiency of the battery in percent _eta_BAT /= 100 # Initialization of particular variables # _P_PV2AC_min = _parameter['PV2AC_c_in'] # Minimum input power of the PV2AC conversion pathway _tde = _t_CONSTANT > 0 # Binary variable to activate the first-order time delay element # Factor of the first-order time delay element _ftde = 1 - np.exp(-_dt / _t_CONSTANT) # First time step with regard to the dead time of the system control _tstart = np.maximum(2, 1 + _t_DEAD) _tend = int(_Pr.size) _th = 0 corr = 0.1 # Check if the dead or settling time can be ignored and set flags accordingly if _dt >= (3 * _t_CONSTANT) or _tend == 1: _tstart = 1 T_DEAD = False else: T_DEAD = True if _dt >= _t_DEAD + 3 * _t_CONSTANT: SETTLING = False else: SETTLING = True for t in range(_tstart - 1, _tend): # Energy content of the battery in the previous time step E_b0 = _soc0 * _E_BAT # Residual power with regard to the dead time of the system control if T_DEAD: P_rpv = _Prpv[t - _t_DEAD] P_r = _Pr[t - _t_DEAD] else: P_rpv = _Prpv[t] P_r = _Pr[t] # Check if the battery holds enough unused capacity for charging or discharging # Estimated amount of energy that is supplied to or discharged from the storage unit. E_bs_rpv = P_rpv * _dt / 3600 E_bs_r = P_r * _dt / 3600 # Reduce P_bs to avoid over charging of the battery if E_bs_rpv > 0 and E_bs_rpv > (_E_BAT - E_b0): P_rpv = (_E_BAT - E_b0) * 3600 / _dt # When discharging take the correction factor into account elif E_bs_r < 0 and np.abs(E_bs_r) > (E_b0): P_r = ((E_b0) * 3600 / _dt) * (1-corr) # Decision if the battery should be charged or discharged if P_rpv > 0 and _soc0 < 1 - _th * (1 - _SOC_h): ''' The last term th*(1-SOC_h) avoids the alternation between charging and standby mode due to the DC power consumption of the battery converter when the battery is fully charged. The battery will not be recharged until the SOC falls below the SOC-threshold (SOC_h) for recharging from PV. ''' # Charging power P_pv2bat_in = P_rpv # Adjust the charging power due to the stationary deviations P_pv2bat_in = np.maximum(0, P_pv2bat_in + _P_PV2BAT_DEV) # Limit the charging power to the maximum charging power P_pv2bat_in = np.minimum(P_pv2bat_in, _P_PV2BAT_in * 1000) # Adjust the charging power due to the settling time # (modeled by a first-order time delay element) if SETTLING: if t > 0: P_pv2bat_in = _tde * _Ppv2bat_in[(t-1)] + _tde * ( P_pv2bat_in - _Ppv2bat_in[(t-1)]) * _ftde + P_pv2bat_in * (not _tde) else: P_pv2bat_in = _tde * _Ppv2bat_in0 + _tde * \ (P_pv2bat_in - _Ppv2bat_in0) * \ _ftde + P_pv2bat_in * (not _tde) # Limit the charging power to the current power output of the PV generator P_pv2bat_in = np.minimum(P_pv2bat_in, _Ppv[t]) # Normalized charging power ppv2bat = P_pv2bat_in / _P_PV2BAT_in / 1000 # DC power of the battery affected by the PV2BAT conversion losses # (the idle losses of the PV2BAT conversion pathway are not taken # into account) P_bat = np.maximum( 0, P_pv2bat_in - (_PV2BAT_a_in * ppv2bat**2 + _PV2BAT_b_in * ppv2bat)) # Realized DC input power of the PV2AC conversion pathway P_pv2ac_in = _Ppv[t] - P_pv2bat_in # Normalized DC input power of the PV2AC conversion pathway _ppv2ac = P_pv2ac_in / _P_PV2AC_in / 1000 # Realized AC power of the PV-battery system P_pv2ac_out = np.maximum( 0, P_pv2ac_in - (_PV2AC_a_in * _ppv2ac**2 + _PV2AC_b_in * _ppv2ac + _PV2AC_c_in)) P_pvbs = P_pv2ac_out # Transfer the final values _Ppv2ac_out[t] = P_pv2ac_out _Ppv2bat_in0 = P_pv2bat_in _Ppv2bat_in[t] = P_pv2bat_in elif P_rpv < 0 and _soc0 > 0: # Discharging power P_bat2ac_out = P_r * -1 # Adjust the discharging power due to the stationary deviations P_bat2ac_out = np.maximum(0, P_bat2ac_out + _P_BAT2AC_DEV) # Adjust the discharging power to the maximum discharging power P_bat2ac_out = np.minimum(P_bat2ac_out, _P_BAT2AC_out * 1000) # Adjust the discharging power due to the settling time # (modeled by a first-order time delay element) if SETTLING: if t > 0: P_bat2ac_out = _tde * _Pbat2ac_out[t-1] + _tde * ( P_bat2ac_out - _Pbat2ac_out[t-1]) * _ftde + P_bat2ac_out * (not _tde) else: P_bat2ac_out = _tde * _Pbat2ac_out0 + _tde * \ (P_bat2ac_out - _Pbat2ac_out0) * \ _ftde + P_bat2ac_out * (not _tde) # Limit the discharging power to the maximum AC power output of the PV-battery system P_bat2ac_out = np.minimum( _P_PV2AC_out * 1000 - _Ppv2ac_out[t], P_bat2ac_out) # Normalized discharging power ppv2bat = P_bat2ac_out / _P_BAT2AC_out / 1000 # DC power of the battery affected by the BAT2AC conversion losses # (if the idle losses of the PV2AC conversion pathway are covered by # the PV generator, the idle losses of the BAT2AC conversion pathway # are not taken into account) if _Ppv[t] > _P_PV2AC_min: P_bat = -1 * (P_bat2ac_out + (_BAT2AC_a_out * ppv2bat**2 + _BAT2AC_b_out * ppv2bat)) else: P_bat = -1 * (P_bat2ac_out + (_BAT2AC_a_out * ppv2bat ** 2 + _BAT2AC_b_out * ppv2bat + _BAT2AC_c_out)) + _Ppv[t] # Realized AC power of the PV-battery system P_pvbs = _Ppv2ac_out[t] + P_bat2ac_out # Transfer the final values _Pbat2ac_out0 = P_bat2ac_out _Pbat2ac_out[t] = P_bat2ac_out else: # Neither charging nor discharging of the battery # Set the DC power of the battery to zero P_bat = 0 # Realized AC power of the PV-battery system P_pvbs = _Ppv2ac_out[t] # Decision if the standby mode is active if P_bat == 0 and P_pvbs == 0 and _soc0 <= 0: # Standby mode in discharged state # DC and AC power consumption of the PV-battery inverter P_bat = -np.maximum(0, _P_SYS_SOC0_DC) P_pvbs = -_P_SYS_SOC0_AC elif P_bat == 0 and P_pvbs > 0 and _soc0 > 0: # Standby mode in fully charged state # DC power consumption of the PV-battery inverter P_bat = -np.maximum(0, _P_SYS_SOC1_DC) # Transfer the realized AC power of the PV-battery system and the DC power of the battery _Ppvbs[t] = P_pvbs _Pbat[t] = P_bat # Change the energy content of the battery Wx to Wh conversion if P_bat > 0: E_b = E_b0 + P_bat * np.sqrt(_eta_BAT) * _dt / 3600 elif P_bat < 0: E_b = E_b0 + P_bat / np.sqrt(_eta_BAT) * _dt / 3600 else: E_b = E_b0 # Calculate the state of charge of the battery _soc0 = E_b / _E_BAT _soc[t] = _soc0 # Adjust the hysteresis threshold to avoid alternation between charging # and standby mode due to the DC power consumption of the # PV-battery inverter if _th and _soc[t] > _SOC_h or _soc[t] > 1: _th = True else: _th = False return _Ppv2ac_out, _Ppv2bat_in, _Ppv2bat_in0, _Pbat2ac_out, _Pbat2ac_out0, _Ppvbs, _Pbat, _soc, _soc0 @nb.jit(nopython=True) def batmod_dc_ideal(d, _dt, _soc0, _soc, _Pr, _Pbat): _E_BAT = d[0] for t in range(_Pr.size): # Energy content of the battery in the previous time step E_b0 = _soc0 * _E_BAT * 1000 P_bat = _Pr[t] if P_bat > 0 and _soc0 < 1: # Battery charging # Change the energy content of the battery E_b = E_b0 + P_bat * _dt / 3600 elif P_bat < 0 and _soc0 > 0: # Battery discharging # Change the energy content of the battery E_b = E_b0 + P_bat * _dt / 3600 else: # Neither charging nor discharging of the battery P_bat = 0 E_b = E_b0 _Pbat[t] = P_bat _soc0 = E_b / (_E_BAT * 1000) _soc[t] = _soc0 return _Pbat, _soc, _soc0 @nb.jit(nopython=True) def batmod_pv(d, _dt, _soc0, _soc, _Ppv, _Pac, _Ppv2bat_in0, _Ppv2bat_in, _Ppv2ac_out, _Pbat2pv_out0, _Pbat2pv_out, _Ppvbs, _Pbat): """Performance simulation function for PV-coupled battery systems :param d: array containing parameters :type d: numpy array :param dt: time step width :type dt: integer :param soc0: state of charge of the battery in the previous time step :type soc0: float :param soc: state of charge of the battery :type soc: numpy array :param Pr: residual power :type Pr: numpy array :param Ppv: PV-power :type Ppv: numpy array :param Pac: AC output power of the PV inverter :type Pac: numpy array :param Ppv2bat_in: AC input power of the battery system :type Ppv2bat_in: numpy array :param Ppv2bat_in0: AC input power of the battery system in the previous time step :type Ppv2bat_in0: float :param Pbat2pv_out0: AC output power of the battery system in the previous time step :type Pbat2pv_out0: float :param Pbat2pv_out: AC output power of the battery system :type Pbat2pv_out: numpy array :param Ppvbs: AC power from the PV system to the battery system :type Ppvbs: numpy array :param Pbat: DC power of the battery :type Pbat: float """ # Initialization of particular variables _E_BAT = d[0] _P_PV2AC_in = d[1] _P_PV2AC_out = d[2] _P_PV2BAT_in = d[3] _P_BAT2PV_out = d[4] _PV2AC_a_in = d[5] _PV2AC_b_in = d[6] _PV2AC_c_in = d[7] _PV2BAT_a_in = d[8] _PV2BAT_b_in = d[9] _PV2BAT_c_in = d[10] _PV2AC_a_out = d[11] _PV2AC_b_out = d[12] _PV2AC_c_out = d[13] _BAT2PV_a_out = d[14] _BAT2PV_b_out = d[15] _BAT2PV_c_out = d[16] _eta_BAT = d[17] _SOC_h = d[18] _P_PV2BAT_DEV = d[19] _P_BAT2AC_DEV = d[20] _P_SYS_SOC1_DC = d[21] _P_SYS_SOC0_AC = d[22] _P_SYS_SOC0_DC = d[23] _t_DEAD = int(round(d[24])) _t_CONSTANT = d[25] # Correction factor to avoid over charge and discharge the battery corr = 0.1 _P_PV2BAT_min = _PV2BAT_c_in # Minimum DC charging power _P_BAT2PV_min = _BAT2PV_c_out # Minimum DC discharging power # Initialization of particular variables _tde = _t_CONSTANT > 0 # Binary variable to activate the first-order time delay element # Factor of the first-order time delay element _ftde = 1 - np.exp(-_dt / _t_CONSTANT) # First time step with regard to the dead time of the system control _tstart = np.maximum(2, 1 + _t_DEAD) _tend = int(_Ppv.size) _th = 0 _E_BAT *= 1000 # Conversion from W to kW _eta_BAT /= 100 # Check if the dead or settling time can be ignored and set flags accordingly if _dt >= (3 * _t_CONSTANT) or _tend == 1: _tstart = 1 T_DEAD = False else: T_DEAD = True if _dt >= _t_DEAD + 3 * _t_CONSTANT: SETTLING = False else: SETTLING = True for t in range(_tstart - 1, _tend): # Energy content of the battery in the previous time step E_b0 = _soc0 * _E_BAT # Target AC output power of the PV-battery system to cover the AC power demand if T_DEAD: P_pvbs = np.minimum(_Pac[t - _t_DEAD], _P_PV2AC_out * 1000) else: P_pvbs = np.minimum(_Pac[t], _P_PV2AC_out * 1000) # Normalized AC output power of the PV2AC conversion pathway ppv2ac = P_pvbs / _P_PV2AC_out / 1000 # Target DC input power of the PV2AC conversion pathway P_pv2ac_in = P_pvbs + (_PV2AC_a_out * ppv2ac ** 2 + _PV2AC_b_out * ppv2ac + _PV2AC_c_out) # Residual power if T_DEAD: P_rpv = _Ppv[t - _t_DEAD] - P_pv2ac_in else: P_rpv = _Ppv[t] - P_pv2ac_in # Check if the battery holds enough unused capacity for charging or discharging # Estimated amount of energy that is supplied to or discharged from the storage unit. E_bs_rpv = P_rpv * _dt / 3600 # Reduce P_bs to avoid over charging of the battery if E_bs_rpv > 0 and E_bs_rpv > (_E_BAT - E_b0): P_rpv = ((_E_BAT - E_b0) * 3600) / _dt # When charging take the correction factor into account elif E_bs_rpv < 0 and np.abs(E_bs_rpv) > (E_b0): P_rpv = ((E_b0) * 3600 / _dt) * (1-corr) # Decision if the battery should be charged or discharged if P_rpv > _P_PV2BAT_min and _soc0 < 1 - _th * (1 - _SOC_h): ''' The last term th*(1-SOC_h) avoids the alternation between charging and standby mode due to the DC power consumption of the battery converter when the battery is fully charged. The battery will not be recharged until the SOC falls below the SOC-threshold (SOC_h) for recharging from PV. ''' # Charging power P_pv2bat_in = P_rpv # Adjust the charging power due to stationary deviations P_pv2bat_in = np.maximum(0, P_pv2bat_in + _P_PV2BAT_DEV) # Limit the charging power to the maximum charging power P_pv2bat_in = np.minimum(P_pv2bat_in, _P_PV2BAT_in * 1000) # Adjust the charging power due to the settling time # (modeled by a first-order time delay element) if SETTLING: if t > 0: P_pv2bat_in = _tde * _Ppv2bat_in[t-1] + _tde * ( P_pv2bat_in - _Ppv2bat_in[t-1]) * _ftde + P_pv2bat_in * (not _tde) else: P_pv2bat_in = _tde * _Ppv2bat_in0 + _tde * \ (P_pv2bat_in - _Ppv2bat_in0) * \ _ftde + P_pv2bat_in * (not _tde) # Limit the charging power to the current power output of the PV generator P_pv2bat_in = np.minimum(P_pv2bat_in, _Ppv[t]) # Normalized charging power ppv2bat = P_pv2bat_in / _P_PV2BAT_in / 1000 # DC power of the battery P_bat = np.maximum(0, P_pv2bat_in - (_PV2BAT_a_in * ppv2bat**2 + _PV2BAT_b_in * ppv2bat + _PV2BAT_c_in)) # Realized DC input power of the PV2AC conversion pathway P_pv2ac_in = _Ppv[t] - P_pv2bat_in # Limit the DC input power of the PV2AC conversion pathway P_pv2ac_in = np.minimum(P_pv2ac_in, _P_PV2AC_in * 1000) # Recalculate Ppv(t) with limited PV2AC input power _Ppv[t] = P_pv2ac_in + P_pv2bat_in # Normalized DC input power of the PV2AC conversion pathway ppv2ac = P_pv2ac_in / _P_PV2AC_in / 1000 # Realized AC power of the PV-battery system P_pv2ac_out = np.maximum( 0, P_pv2ac_in - (_PV2AC_a_in * ppv2ac**2 + _PV2AC_b_in * ppv2ac + _PV2AC_c_in)) P_pvbs = P_pv2ac_out # Transfer the final values _Ppv2ac_out[t] = P_pv2ac_out _Ppv2bat_in0 = P_pv2bat_in _Ppv2bat_in[t] = P_pv2bat_in elif P_rpv < -_P_BAT2PV_min and _soc0 > 0: # Target discharging power of the battery P_bat2pv_out = np.abs(P_rpv) # Adjust the discharging power due to the stationary deviations P_bat2pv_out = np.maximum(0, P_bat2pv_out + _P_BAT2AC_DEV) # Adjust the discharging power to the maximum discharging power P_bat2pv_out = np.minimum(P_bat2pv_out, _P_BAT2PV_out * 1000) # Adjust the discharging power due to the settling time # (modeled by a first-order time delay element) if SETTLING: if t > 0: P_bat2pv_out = _tde * _Pbat2pv_out[t-1] + _tde * (P_bat2pv_out - _Pbat2pv_out[t-1]) * _ftde + P_bat2pv_out * (not _tde) else: P_bat2pv_out = _tde * _Pbat2pv_out0 + _tde * (P_bat2pv_out - _Pbat2pv_out0) * _ftde + P_bat2pv_out * (not _tde) # Recalculate Ppv(t) with limited PV2AC input power _Ppv[t] = np.minimum(_P_PV2AC_in * 1000, _Ppv[t]) # Limit the discharging power to the maximum AC power output of the PV-battery system P_bat2pv_out = np.minimum(_P_PV2AC_in * 1000 - _Ppv[t], P_bat2pv_out) # Normalized discharging power pbat2pv = P_bat2pv_out / _P_BAT2PV_out / 1000 # DC power of the battery affected by the BAT2PV conversion losses P_bat = -1*(P_bat2pv_out+(_BAT2PV_a_out * pbat2pv**2 + _BAT2PV_b_out * pbat2pv + _BAT2PV_c_out)) # Realized DC input power of the PV2AC conversion pathway P_pv2ac_in = _Ppv[t] + P_bat2pv_out # Normalized DC input power of the PV2AC conversion pathway ppv2ac = P_pv2ac_in / _P_PV2AC_in / 1000 # AC power of the PV-battery system P_pvbs = np.maximum(0, P_pv2ac_in-(_PV2AC_a_in * ppv2ac**2 + _PV2AC_b_in * ppv2ac + _PV2AC_c_in)) P_pv2ac_out = P_pvbs # Transfer the final values _Ppv2ac_out[t] = P_pv2ac_out _Pbat2pv_out0 = P_bat2pv_out _Pbat2pv_out[t] = P_bat2pv_out else: # Neither charging nor discharging of the battery # Set the DC power of the battery to zero P_bat = 0 # Limit the power output of the PV generator to the maximum input power # of the PV inverter _Ppv[t] = np.minimum(_Ppv[t], _P_PV2AC_in * 1000) # Normalized DC input power of the PV2AC conversion pathway ppv2ac = _Ppv[t] / _P_PV2AC_in / 1000 # Realized AC power of the PV-battery system P_pvbs = np.maximum(0, _Ppv[t] - (_PV2AC_a_in * ppv2ac**2 + _PV2AC_b_in * ppv2ac + _PV2AC_c_in)) # Transfer the final values _Ppv2ac_out[t] = P_pvbs # Decision if the standby mode is active if P_bat == 0 and _soc0 <= 0: # Standby mode in discharged state # DC power consumption of the battery converter P_bat = -np.maximum(0, _P_SYS_SOC0_DC) if P_pvbs == 0: P_pvbs = -_P_SYS_SOC0_AC elif P_bat == 0 and P_pvbs > 0 and _soc0 > 0: # Standby mode in fully charged state # DC power consumption of the battery converter P_bat = -np.maximum(0, _P_SYS_SOC1_DC) # Transfer the realized AC power of the battery system and # the DC power of the battery _Ppvbs[t] = P_pvbs _Pbat[t] = P_bat # Change the energy content of the battery Wx to Wh conversio if P_bat > 0: E_b = E_b0 + P_bat * np.sqrt(_eta_BAT) * _dt / 3600 elif P_bat < 0: E_b = E_b0 + P_bat / np.sqrt(_eta_BAT) * _dt / 3600 else: E_b = E_b0 # Calculate the state of charge of the battery _soc0 = E_b / (_E_BAT) _soc[t] = _soc0 # Adjust the hysteresis threshold to avoid alternation # between charging and standby mode due to the DC power # consumption of the battery converter. if _th and _soc[t] > _SOC_h or _soc[t] > 1: _th = True else: _th = False return _soc, _soc0, _Ppv, _Ppvbs, _Pbat, _Ppv2ac_out, _Pbat2pv_out, _Ppv2bat_in def bat_res_mod(_parameter, _Pl, _Ppv, _Pbat, _dt, *args): """Function for calculating energy sums :param _parameter: parameter of the system :type _parameter: dict :param _Pl: load power :type _Pl: numpy array :param _Ppv: output power of the PV generator :type _Ppv: numpy array :param _Pbat: DC power of the battery :type _Pbat: numpy array :param _dt: time step width :type _dt: integer :return: energy sums :rtype: dict """ _E = dict() if _parameter['Top'] == 'AC': # AC-coupled systems _Ppvs = args[0] # AC output power of the PV system _Pbs = args[1] # AC power of the battery system # Additional power consumption of the other system components _Pperi = args[2] elif _parameter['Top'] == 'DC' or _parameter['Top'] == 'PV': # DC- and PV-coupled systems _Ppv2ac = args[0] # AC output power of the PV2AC conversion pathway _Ppv2bat_in = args[1] # Input power of the PV2BAT conversion pathway _Ppvbs = args[2] # AC power of the PV-battery system # Additional power consumption of the other system components _Pperi = args[3] _Ppv2ac_in = _Ppv - _Ppv2bat_in # Input power of the PV2AC conversion pathway # Total load including the power consumption of the other system components _Plt = _Pl + _Pperi # DC input power of the battery (charged) _Pbatin = np.maximum(0, _Pbat) # DC output power of the battery (discharged) _Pbatout = np.minimum(0, _Pbat) # Maximum PV feed-in power _P_ac2g_max = _parameter['p_ac2g_max'] * _parameter['P_PV'] * 1000 if _parameter['Top'] == 'AC': # AC-coupled systems # Residual power without curtailment _Pr = _Ppvs - _Plt # AC input power of the battery system _Pac2bs = np.maximum(0, _Pbs) # AC output power of the battery system _Pbs2ac = np.minimum(0, _Pbs) # Negative residual power (residual load demand) _Prn = np.minimum(0, _Pr) # Positive residual power (surplus PV power) _Prp = np.maximum(0, _Pr) # Direct use of PV power by the load _Ppvs2l = np.minimum(_Ppvs, _Plt) # PV charging power _Ppvs2bs = np.minimum(_Prp, _Pac2bs) # Grid charging power _Pg2bs = np.maximum(_Pac2bs - _Prp, 0) # Grid supply power of the load _Pg2l = np.minimum(_Prn - _Pbs2ac, 0) # Battery supply power of the load _Pbs2l = np.maximum(_Prn, _Pbs2ac) # Battery feed-in power _Pbs2g = np.minimum(_Pbs2ac - _Prn, 0) # PV feed-in power including curtailment _Ppvs2g = np.minimum(np.maximum(_Prp - _Pac2bs, 0), _P_ac2g_max) # Power demand from the grid _Pg2ac = _Pg2l - _Pg2bs # Feed-in power to the grid _Pac2g = _Ppvs2g - _Pbs2g # Grid power _Pg = _Pac2g + _Pg2ac # Curtailed PV power (AC output power) _Pct = np.maximum(_Prp - _Pac2bs, 0) - _Ppvs2g # AC output power of the PV system including curtailment _Ppvs = _Ppvs - _Pct # Residual power including curtailment _Pr = _Ppvs - _Plt # Index for PV curtailment _idx = np.where(_Pct > 0)[0] for i in range(len(_idx)): _tct = _idx[i] # Normalized output power of the PV inverter _ppvinvout = _Ppvs[_tct] / _parameter['P_PV2AC_out'] / 1000 # DC output power of the PV generator taking into account the # conversion and curtailment losses _Ppv[_tct] = _Ppvs[_tct] + (_parameter['PV2AC_a_out'] * _ppvinvout ** 2 + _parameter['PV2AC_b_out'] * _ppvinvout + _parameter['PV2AC_c_out']) elif _parameter['Top'] == 'DC' or _parameter['Top'] == 'PV': # DC- and PV-coupled systems # Grid power demand of the PV-battery system _Pg2pvbs = np.minimum(0, _Ppvbs) # AC input power of the PV-battery system _Pac2pvbs = _Pg2pvbs # AC output power of the PV-battery system _Ppvbs2ac = np.maximum(0, _Ppvbs) # Load supply power by the PV-battery system _Ppvbs2l = np.minimum(_Plt, _Ppvbs2ac) # Load supply power by the grid _Pg2l = _Plt - _Ppvbs2l # Direct use of PV power by the load _Ppv2l = np.minimum(_Plt, _Ppv2ac) # PV feed-in power including curtailment _Ppv2g = np.minimum(_Ppv2ac - _Ppv2l, _P_ac2g_max) # Curtailed PV power (AC output power) _Pct = _Ppv2ac - _Ppv2l - _Ppv2g if np.sum(_Pct) > 0: # Power of the PV-battery system including curtailment _Ppvbs = _Ppvbs - _Pct # AC output power of the PV-battery system including curtailment _Ppvbs2ac = np.maximum(0, _Ppvbs) # AC output power of the PV2AC conversion pathway including curtailment _Ppv2ac = _Ppv2ac - _Pct # Index for PV curtailment _idx = np.where(_Pct > 0)[0] for i in range(len(_idx)): _tct = _idx[i] # Specific AC output power of the PV2AC conversion pathway _ppv2ac = _Ppv2ac[_tct] / _parameter['P_PV2AC_out'] / 1000 # DC input power of the PV2AC conversion pathway including curtailment _Ppv2ac_in[_tct] = _Ppv2ac[_tct] + (_parameter['PV2AC_a_out'] * _ppv2ac ** 2 + _parameter['PV2AC_b_out'] * _ppv2ac + _parameter['PV2AC_c_out']) # DC output power of the PV generator including curtailment _Ppv = _Ppv2ac_in + _Ppv2bat_in # Grid power including curtailment _Pg = _Ppvbs-_Plt # Feed-in power to the grid including curtailment _Pac2g = np.maximum(0, _Pg) # Power demand from the grid _Pg2ac = np.minimum(0, _Pg) # Energy sums in MWH # Electrical demand including the energy consumption of the other system components _E['El'] = np.sum(np.abs(_Plt)) * _dt / 3.6e9 # DC output of the PV generator including curtailment _E['Epv'] = np.sum(np.abs(_Ppv)) * _dt / 3.6e9 # DC input of the battery (charged) _E['Ebatin'] = np.sum(np.abs(_Pbatin)) * _dt / 3.6e9 # DC output of the battery (discharged) _E['Ebatout'] = np.sum(np.abs(_Pbatout)) * _dt / 3.6e9 # Grid feed-in _E['Eac2g'] = np.sum(np.abs(_Pac2g)) * _dt / 3.6e9 # Grid demand _E['Eg2ac'] = np.sum(np.abs(_Pg2ac)) * _dt / 3.6e9 # Load supply by the grid _E['Eg2l'] = np.sum(np.abs(_Pg2l)) * _dt / 3.6e9 # Demand of the other system components _E['Eperi'] = np.sum(np.abs(_Pperi)) * _dt / 3.6e9 # Curtailed PV energy _E['Ect'] = np.sum(np.abs(_Pct)) * _dt / 3.6e9 if _parameter['Top'] == 'AC': # AC-coupled systems # AC output of the PV system including curtailment _E['Epvs'] = np.sum(np.abs(_Ppvs)) * _dt / 3.6e9 # AC input of the battery system _E['Eac2bs'] = np.sum(np.abs(_Pac2bs)) * _dt / 3.6e9 # AC output of the battery system _E['Ebs2ac'] = np.sum(np.abs(_Pbs2ac)) * _dt / 3.6e9 # Direct use of PV energy _E['Epvs2l'] = np.sum(np.abs(_Ppvs2l)) * _dt / 3.6e9 # PV charging _E['Epvs2bs'] = np.sum(np.abs(_Ppvs2bs)) * _dt / 3.6e9 # Grid charging _E['Eg2bs'] = np.sum(np.abs(_Pg2bs)) * _dt / 3.6e9 # PV feed-in _E['Epvs2g'] = np.sum(np.abs(_Ppvs2g)) * _dt / 3.6e9 # Load supply by the battery system _E['Ebs2l'] = np.sum(np.abs(_Pbs2l)) * _dt / 3.6e9 # Battery feed-in _E['Ebs2g'] = np.sum(np.abs(_Pbs2g)) * _dt / 3.6e9 elif _parameter['Top'] == 'DC' or _parameter['Top'] == 'PV': # DC- and PV-coupled systems # Grid demand of the PV-battery system _E['Eg2pvbs'] = np.sum(np.abs(_Pg2pvbs)) * _dt / 3.6e9 # AC input of the PV-battery system _E['Eac2pvbs'] = np.sum(np.abs(_Pac2pvbs)) * _dt / 3.6e9 # AC output of the PV-battery system _E['Epvbs2ac'] = np.sum(np.abs(_Ppvbs2ac)) * _dt / 3.6e9 # Load supply by the PV-battery system _E['Epvbs2l'] = np.sum(np.abs(_Ppvbs2l)) * _dt / 3.6e9 return _E def bat_res_mod_ideal(_parameter, _Pl, _Ppv, _Pbat, _dt, *args): E = dict() # Dictionary to store energy sums if _parameter['Top'] == 'AC': Ppvs = args[0] # AC output power of the PV system Pbs = args[1] # AC power of the battery system Pperi = args[2] # Additional power consumption of the other system components elif _parameter['Top'] == 'DC': Ppv2ac = args[0] Ppv2bat_in = args[1] Ppvbs = args[2] Pperi = args[3] Ppv2ac_in = _Ppv - Ppv2bat_in # Additional power consumption of the other system components Pperi = np.zeros_like(_Ppv) # Total load including the power consumption of the other system components Plt = _Pl # DC input power of the battery (charged) Pbatin = np.maximum(0, _Pbat) # DC output power of the battery (discharged) Pbatout = np.minimum(0, _Pbat) if _parameter['Top'] == 'AC': # Grid power Pg = Ppvs - _Pl - Pbs # Residual power Pr = Ppvs - Plt # AC input power of the battery system Pac2bs = np.maximum(0, Pbs) # AC output power of the battery system Pbs2ac = np.minimum(0, Pbs) # Negative residual power (residual load demand) Prn = np.minimum(0, Pr) # Positive residual power (surplus PV power) Prp = np.maximum(0, Pr) # Direct use of PV power by the load Ppvs2l = np.minimum(Ppvs, Plt) # PV charging power Ppvs2bs=np.minimum(Prp, Pac2bs) # Grid charging power Pg2bs=np.maximum(Pac2bs - Prp, 0) # Grid supply power of the load Pg2l=np.minimum(Prn - Pbs2ac, 0) # Battery supply power of the load Pbs2l=np.maximum(Prn, Pbs2ac) # Battery feed-in power Pbs2g=np.minimum(Pbs2ac - Prn, 0) # PV feed-in power Ppvs2g=np.maximum(Prp - Pac2bs, 0) elif _parameter['Top'] == 'DC': # Grid power Pg = Ppvbs - _Pl # Grid power demand of the PV-battery system Pg2pvbs = np.minimum(0, Ppvbs) # AC input power of the PV-battery system Pac2pvbs = Pg2pvbs # AC output power of the PV-battery system Ppvbs2ac = np.maximum(0, Ppvbs) # Load supply power by the PV-battery system Ppvbs2l = np.minimum(_Pl, Ppvbs2ac) # Load supply power by the grid Pg2l = (Plt - Ppvbs2l) # Curtailed PV power (AC output power) Pct = np.zeros_like(_Ppv) # Power demand from the grid Pg2ac = np.minimum(0, Pg) # Feed-in power to the grid Pac2g=np.maximum(0, Pg) # Energy sums # Electrical demand including the energy consumption of the other system components E['El'] = np.sum(np.abs(Plt)) / 3.6e9 # DC output of the PV generator including curtailment E['Epv'] = np.sum(np.abs(_Ppv)) / 3.6e9 # DC input of the battery (charged) E['Ebatin'] = np.sum(np.abs(Pbatin)) / 3.6e9 # DC output of the battery (discharged) E['Ebatout'] = np.sum(np.abs(Pbatout)) / 3.6e9 # Grid feed-in E['Eac2g'] = np.sum(np.abs(Pac2g)) / 3.6e9 # Grid demand E['Eg2ac'] = np.sum(np.abs(Pg2ac)) / 3.6e9 # Load supply by the grid E['Eg2l'] = np.sum(np.abs(Pg2l)) / 3.6e9 # Demand of the other system components E['Eperi'] = np.sum(np.abs(Pperi)) / 3.6e9 # Curtailed PV energy E['Ect'] = np.sum(np.abs(Pct)) / 3.6e9 if _parameter['Top'] == 'AC': # AC output of the PV system including curtailment E['Epvs']=np.sum(np.abs(Ppvs)) / 3.6e9 # AC input of the battery system E['Eac2bs']=np.sum(np.abs(Pac2bs)) / 3.6e9 # AC output of the battery system E['Ebs2ac']=np.sum(np.abs(Pbs2ac)) / 3.6e9 # Direct use of PV energy E['Epvs2l']=np.sum(np.abs(Ppvs2l)) / 3.6e9 # PV charging E['Epvs2bs']=np.sum(np.abs(Ppvs2bs)) / 3.6e9 # Grid charging E['Eg2bs']=np.sum(np.abs(Pg2bs)) / 3.6e9 # PV feed-in E['Epvs2g']=np.sum(np.abs(Ppvs2g)) / 3.6e9 # Load supply by the battery system E['Ebs2l']=np.sum(np.abs(Pbs2l)) / 3.6e9 # Battery feed-in E['Ebs2g']=np.sum(np.abs(Pbs2g)) / 3.6e9 elif _parameter['Top'] == 'DC': # Grid demand of the PV-battery system E['Eg2pvbs'] = np.sum(np.abs(Pg2pvbs)) / 3.6e9 # AC input of the PV-battery system E['Eac2pvbs'] = np.sum(np.abs(Pac2pvbs)) / 3.6e9 # AC output of the PV-battery system E['Epvbs2ac'] = np.sum(np.abs(Ppvbs2ac)) / 3.6e9 # Load supply by the PV-battery system E['Epvbs2l'] = np.sum(np.abs(Ppvbs2l)) / 3.6e9 return E def load_parameter(fname, col_name): """Loads system parameter from excel file :param fname: Path to the excel file :type fname: string :param col_name: Column to read data from :type col_name: string :return: Dictionary holding parameters from the Excel sheet :rtype: dict """ wb = load_workbook(fname, data_only=True) ws = wb['Data'] # Load Data sheet of excel file # read keys and values from Excel sheet keys = (c.value for c in ws['E'][1:]) values = (c.value if c.value != 'ns' else None for c in ws[col_name][1:]) parameter = dict(zip(keys, values)) # deletes entries where key is None del parameter[None] # Assign specific parameters parameter['P_PV2AC_out_PVINV'] = ws[col_name][15].value parameter['P_PV2AC_out'] = ws[col_name][24].value parameter['P_AC2BAT_in_DCC'] = ws[col_name][25].value parameter['P_AC2BAT_in'] = ws[col_name][26].value parameter['P_BAT2AC_out'] = ws[col_name][27].value parameter['P_BAT2AC_out_DCC'] = ws[col_name][28].value # Set refrence case values to boolean if parameter['ref_1'] == 'yes': parameter['ref_1'] = True elif parameter['ref_1'] == 'no': parameter['ref_1'] = False if parameter['ref_2'] == 'yes': parameter['ref_2'] = True elif parameter['ref_2'] == 'no': parameter['ref_2'] = False # Specific parameters of DC-coupled systems if parameter['Top'] == 'DC': parameter['P_AC2BAT_in'] = parameter['P_AC2BAT_in_DCC'] # Nominal charging power (AC) in kW parameter['P_BAT2AC_out'] = parameter['P_BAT2AC_out_DCC'] # Specific parameters of PV inverters and AC-coupled systems if parameter['Top'] == 'PVINV' or parameter['Top'] == 'AC' and parameter['P_PV2AC_out_PVINV'] is not None: parameter['P_PV2AC_out'] = parameter['P_PV2AC_out_PVINV'] # Specific parameters of PV-coupled systems if parameter['Top'] == 'PV': parameter['P_BAT2PV_in'] = parameter['P_BAT2AC_in'] parameter['P_BAT2AC_out'] = parameter['P_BAT2AC_out_DCC'] # replace 'ns', 'o' and 'c' entries to None for key, value in parameter.items(): if value == 'ns' or value == 'o' or value == 'c' or value == ' ': parameter[key] = None # Convert to kW convert_to_kw = ['P_PV2AC_in', 'P_PV2AC_out_PVINV','P_PV2AC_out','P_AC2BAT_in_DCC','P_AC2BAT_in','P_BAT2AC_out', 'P_BAT2AC_out_DCC','P_PV2BAT_in','P_BAT2PV_out','P_PV2BAT_out','P_BAT2AC_in'] for par in convert_to_kw: if parameter[par]: parameter[par] /= 1000 return parameter def eta2abc(parameter): """Function to calculate the parameters of the power loss functions (quadratic equations) from the path efficiencies :param parameter: Holds parameters of the system :type parameter: dict :return: Dictionary holding parameters from the Excel sheet :rtype: dict """ # PV2AC conversion pathway TODO if parameter['Top'] == 'DC' or parameter['Top'] == 'PVINV' or parameter['Top'] == 'PV' and parameter['P_PV2AC_out'] is not None or parameter['Top'] == 'AC' and parameter['P_PV2AC_out'] is not None: # Create variables for the sampling points and corresponding efficiencies TODO p_pv2ac = np.fromiter((value for key, value in parameter.items() if 'p_PV2AC_' in key and value is not None), float) eta_pv2ac = np.fromiter((value / 100 for key, value in parameter.items() if 'eta_PV2AC_' in key and value is not None), float) # Absolute input and output power in W p_pv2ac_out = parameter['P_PV2AC_out'] * p_pv2ac * 1000 p_pv2ac_in = p_pv2ac_out / eta_pv2ac # Absolute power loss in W P_l_pv2ac_in = (1 - eta_pv2ac) * p_pv2ac_in P_l_pv2ac_out = (1 / eta_pv2ac - 1) * p_pv2ac_out # Polynomial curve fitting parameters of the power loss functions in W # Based on input power p = np.polyfit(p_pv2ac_in / parameter['P_PV2AC_in'] / 1000, P_l_pv2ac_in, 2) parameter['PV2AC_a_in'] = p[0] parameter['PV2AC_b_in'] = p[1] parameter['PV2AC_c_in'] = p[2] # Based on output power p = np.polyfit(p_pv2ac, P_l_pv2ac_out, 2) parameter['PV2AC_a_out'] = p[0] parameter['PV2AC_b_out'] = p[1] parameter['PV2AC_c_out'] = p[2] # PV2BAT conversion pathway if parameter['Top'] == 'DC' or parameter['Top'] == 'PV': # Create variables for the sampling points and corresponding efficiencies p_pv2bat = np.array([value for key, value in parameter.items() if 'p_PV2BAT_' in key]) eta_pv2bat = np.array([value / 100 for key, value in parameter.items() if 'eta_PV2BAT_' in key]) # Create missing variables # Nominal input power of the PV2BAT conversion pathway of DC-coupled systems if parameter['P_PV2BAT_in'] is None: parameter['P_PV2BAT_in'] = parameter['P_PV2BAT_out'] / (parameter['eta_PV2BAT_100'] / 100) # Absolute input and output power in W p_pv2bat_out = parameter['P_PV2BAT_out'] * p_pv2bat * 1000 p_pv2bat_in = p_pv2bat_out / eta_pv2bat # Absolute power loss in W P_l_pv2bat_in = (1 - eta_pv2bat) * p_pv2bat_in P_l_pv2bat_out = (1 / eta_pv2bat - 1) * p_pv2bat_out # Polynomial curve fitting parameters of the power loss functions in W # Based on input power p = np.polyfit(p_pv2bat_in / parameter['P_PV2BAT_in'] / 1000, P_l_pv2bat_in, 2) parameter['PV2BAT_a_in'] = p[0] parameter['PV2BAT_b_in'] = p[1] parameter['PV2BAT_c_in'] = p[2] # Based on output power p = np.polyfit(p_pv2bat, P_l_pv2bat_out, 2) parameter['PV2BAT_a_out'] = p[0] parameter['PV2BAT_b_out'] = p[1] parameter['PV2BAT_c_out'] = p[2] # AC2BAT conversion pathway if parameter['Top'] == 'AC' or parameter['Top'] == 'DC' and parameter['P_AC2BAT_in'] is not None: # Create variables for the sampling points and corresponding efficiencies TODO p_ac2bat = np.fromiter((value for key, value in parameter.items() if 'p_AC2BAT_' in key), float) eta_ac2bat = np.fromiter((value / 100 for key, value in parameter.items() if 'eta_AC2BAT_' in key), float) # Absolute input and output power in W p_ac2bat_out = parameter['P_PV2BAT_out'] * p_ac2bat * 1000 p_ac2bat_in = p_ac2bat_out / eta_ac2bat # Absolute power loss in W P_l_ac2bat_in = (1 - eta_ac2bat) * p_ac2bat_in P_l_ac2bat_out = (1 / eta_ac2bat - 1) * p_ac2bat_out # Polynomial curve fitting parameters of the power loss functions in W # Based on input power p = np.polyfit(p_ac2bat_in / parameter['P_AC2BAT_in'] / 1000, P_l_ac2bat_in, 2) parameter['AC2BAT_a_in'] = p[0] parameter['AC2BAT_b_in'] = p[1] parameter['AC2BAT_c_in'] = p[2] # Based on output power p = np.polyfit(p_ac2bat, P_l_ac2bat_out, 2) parameter['AC2BAT_a_out'] = p[0] parameter['AC2BAT_b_out'] = p[1] parameter['AC2BAT_c_out'] = p[2] # BAT2AC conversion pathway if parameter['Top'] =='AC' or parameter['Top'] =='DC' or parameter['Top'] =='PV' and parameter['P_BAT2AC_out'] is not None: # Create variables for the sampling points and corresponding efficiencies TODO p_bat2ac = np.fromiter((value for key, value in parameter.items() if 'p_BAT2AC_' in key), float) eta_bat2ac = np.fromiter((value / 100 for key, value in parameter.items() if 'eta_BAT2AC_' in key), float) # Absolute input and output power in W p_bat2ac_out = parameter['P_BAT2AC_out'] * p_bat2ac * 1000 p_bat2ac_in = p_bat2ac_out / eta_bat2ac # Absolute power loss in W P_l_bat2ac_in = (1 - eta_bat2ac) * p_bat2ac_in P_l_bat2ac_out = (1 / eta_bat2ac - 1) * p_bat2ac_out # Polynomial curve fitting parameters of the power loss functions in W # Based on input power p = np.polyfit(p_bat2ac_in / parameter['P_BAT2AC_in'] / 1000, P_l_bat2ac_in, 2) parameter['BAT2AC_a_in'] = p[0] parameter['BAT2AC_b_in'] = p[1] parameter['BAT2AC_c_in'] = p[2] # Based on output power p = np.polyfit(p_bat2ac, P_l_bat2ac_out, 2) parameter['BAT2AC_a_out'] = p[0] parameter['BAT2AC_b_out'] = p[1] parameter['BAT2AC_c_out'] = p[2] # BAT2PV conversion pathway if parameter['Top'] =='PV': # Create variables for the sampling points and corresponding efficiencies TODO p_bat2pv = np.fromiter((value for key, value in parameter.items() if 'p_BAT2PV_' in key), float) eta_bat2pv = np.fromiter((value / 100 for key, value in parameter.items() if 'eta_BAT2PV_' in key), float) # Absolute input and output power in W p_bat2pv_out = parameter['P_BAT2PV_out'] * p_bat2pv * 1000 p_bat2pv_in = p_bat2pv_out / eta_bat2pv # Absolute power loss in W P_l_bat2pv_in = (1 - eta_bat2pv) * p_bat2pv_in P_l_bat2pv_out = (1 / eta_bat2pv - 1) * p_bat2pv_out # Polynomial curve fitting parameters of the power loss functions in W # Based on input power TODO p = np.polyfit(p_bat2pv_in / parameter['P_BAT2AC_in'] / 1000, P_l_bat2pv_in, 2) parameter['BAT2PV_a_in'] = p[0] parameter['BAT2PV_b_in'] = p[1] parameter['BAT2PV_c_in'] = p[2] # Based on output power p = np.polyfit(p_bat2pv, P_l_bat2pv_out, 2) parameter['BAT2PV_a_out'] = p[0] parameter['BAT2PV_b_out'] = p[1] parameter['BAT2PV_c_out'] = p[2] # Additional parameters # Mean battery capacity in kWh try: parameter['E_BAT'] = (parameter['E_BAT_usable'] / parameter['eta_BAT'] * 100 + parameter['E_BAT_usable']) / 2 except: parameter['E_BAT'] = None # Mean stationary deviation of the charging power in W try: parameter['P_PV2BAT_DEV'] = parameter['P_PV2BAT_DEV_IMPORT'] - parameter['P_PV2BAT_DEV_EXPORT'] except: parameter['P_PV2BAT_DEV'] = None if parameter['Top'] == 'AC': parameter['P_AC2BAT_DEV'] = parameter['P_PV2BAT_DEV'] # Mean stationary deviation of the discharging power in W try: parameter['P_BAT2AC_DEV'] = parameter['P_BAT2AC_DEV_EXPORT'] - parameter['P_BAT2AC_DEV_IMPORT'] except: parameter['P_BAT2AC_DEV'] = None # Time constant for the first-order time delay element in s try: parameter['t_CONSTANT'] = (parameter['t_SETTLING'] - round(parameter['t_DEAD'])) / 3 except: parameter['t_CONSTANT'] = None # Hysteresis threshold for the recharging of the battery parameter['SOC_h'] = 0.98 # Feed-in power limit in kW/kWp parameter['p_ac2g_max'] = 0.7 return parameter def load_ref_case(fname, name): """Loads PV power or Load from the reference cases :param fname: Path to mat file :type fname: string :param name: Identifier for PV Power or Load :type name: string :return: Returns PV power or load from the reference case :rtype: numpy array """ with open(fname, 'rb') as f: a = np.load(f) data = a[name] return data def resample_data_frame(df): """Function for resampling data frames :param df: data frame :type df: pandas data frame :return: data frame :rtype: pandas data frame """ df_rs = df.resample('15min').mean() return df_rs def transform_dict_to_array(parameter): """Function for transforming a dict to an numpy array :param parameter: dict of system parameters :type parameter: dict :return: array of system parameters :rtype: numpy array """ if parameter['Top'] == 'AC': d = np.array(parameter['E_BAT']) # 0 d = np.append(d, parameter['eta_BAT']) # 1 d = np.append(d, parameter['t_CONSTANT']) # 2 d = np.append(d, parameter['P_SYS_SOC0_DC']) # 3 d = np.append(d, parameter['P_SYS_SOC0_AC']) # 4 d = np.append(d, parameter['P_SYS_SOC1_DC']) # 5 d = np.append(d, parameter['P_SYS_SOC1_AC']) # 6 d = np.append(d, parameter['AC2BAT_a_in']) # 7 d = np.append(d, parameter['AC2BAT_b_in']) # 8 d = np.append(d, parameter['AC2BAT_c_in']) # 9 d = np.append(d, parameter['BAT2AC_a_out']) # 10 d = np.append(d, parameter['BAT2AC_b_out']) # 11 d = np.append(d, parameter['BAT2AC_c_out']) # 12 d = np.append(d, parameter['P_AC2BAT_DEV']) # 13 d = np.append(d, parameter['P_BAT2AC_DEV']) # 14 d = np.append(d, parameter['P_BAT2AC_out']) # 15 d = np.append(d, parameter['P_AC2BAT_in']) # 16 d = np.append(d, parameter['t_DEAD']) # 17 d = np.append(d, parameter['SOC_h']) # 18 if parameter['Top'] == 'DC': d = np.array(parameter['E_BAT']) # 1 d = np.append(d, parameter['P_PV2AC_in']) # 2 d = np.append(d, parameter['P_PV2AC_out']) # 3 d = np.append(d, parameter['P_PV2BAT_in']) # 4 d = np.append(d, parameter['P_BAT2AC_out']) # 5 d = np.append(d, parameter['PV2AC_a_in']) # 6 d = np.append(d, parameter['PV2AC_b_in']) # 7 d = np.append(d, parameter['PV2AC_c_in']) # 8 d = np.append(d, parameter['PV2BAT_a_in']) # 9 d = np.append(d, parameter['PV2BAT_b_in']) # 10 d = np.append(d, parameter['BAT2AC_a_out']) # 11 d = np.append(d, parameter['BAT2AC_b_out']) # 12 d = np.append(d, parameter['BAT2AC_c_out']) # 13 d = np.append(d, parameter['eta_BAT']) # 14 d = np.append(d, parameter['SOC_h']) # 15 d = np.append(d, parameter['P_PV2BAT_DEV']) # 16 d = np.append(d, parameter['P_BAT2AC_DEV']) # 17 d = np.append(d, parameter['t_DEAD']) # 18 d = np.append(d, parameter['t_CONSTANT']) # 19 d = np.append(d, parameter['P_SYS_SOC1_DC']) # 20 d = np.append(d, parameter['P_SYS_SOC0_AC']) # 21 d = np.append(d, parameter['P_SYS_SOC0_DC']) # 22 if parameter['Top'] == 'PV': d = np.array(parameter['E_BAT']) d = np.append(d, parameter['P_PV2AC_in']) d = np.append(d, parameter['P_PV2AC_out']) d = np.append(d, parameter['P_PV2BAT_in']) d = np.append(d, parameter['P_BAT2PV_out']) d = np.append(d, parameter['PV2AC_a_in']) d = np.append(d, parameter['PV2AC_b_in']) d = np.append(d, parameter['PV2AC_c_in']) d = np.append(d, parameter['PV2BAT_a_in']) d = np.append(d, parameter['PV2BAT_b_in']) d = np.append(d, parameter['PV2BAT_c_in']) d = np.append(d, parameter['PV2AC_a_out']) d = np.append(d, parameter['PV2AC_b_out']) d = np.append(d, parameter['PV2AC_c_out']) d = np.append(d, parameter['BAT2PV_a_out']) d = np.append(d, parameter['BAT2PV_b_out']) d = np.append(d, parameter['BAT2PV_c_out']) d = np.append(d, parameter['eta_BAT']) d = np.append(d, parameter['SOC_h']) d = np.append(d, parameter['P_PV2BAT_DEV']) d = np.append(d, parameter['P_BAT2AC_DEV']) d = np.append(d, parameter['P_SYS_SOC1_DC']) d = np.append(d, parameter['P_SYS_SOC0_AC']) d = np.append(d, parameter['P_SYS_SOC0_DC']) d = np.append(d, parameter['t_DEAD']) d = np.append(d, parameter['t_CONSTANT']) return d def calculate_spi(_E_real, _E_ideal): # SPI calculation for the reference case: # Grid electricity price in Euro/kWh pg2ac = 0.30 # Grid feed-in tariff in Euro/kWh pac2g = 0.12 # Grid electricity costs without a PV-battery system in Euro/a Cref = _E_ideal['El'] * pg2ac * 1000 # Net grid electricity costs with the lossless PV-battery system in Euro/a Cideal = _E_ideal['Eg2ac'] * pg2ac * 1000 - _E_ideal['Eac2g'] * pac2g * 1000 # Net grid electricity costs with the real PV-battery system in Euro/a Creal = _E_real['Eg2ac'] * pg2ac * 1000 - _E_real['Eac2g'] * pac2g * 1000 # Reduction of the net grid electricity costs by the lossless PV-battery system in Euro/a dCideal = Cref - Cideal # Reduction of the net grid electricity costs by the real PV-battery system in Euro/a dCreal = Cref - Creal # System Performance Index (SPI) spi = dCreal / dCideal return spi

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tests/test_operations.py

HumanCellAtlas/data-store

46

154788

#!/usr/bin/env python # coding: utf-8 import io import os import sys import uuid import json import argparse import unittest import string import random import copy import datetime import tempfile from collections import namedtuple from unittest import mock from boto3.s3.transfer import TransferConfig from botocore.exceptions import ClientError from cloud_blobstore import BlobNotFoundError pkg_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..')) # noqa sys.path.insert(0, pkg_root) # noqa from tests import skip_on_travis from tests.infra import testmode from dss.operations import DSSOperationsCommandDispatch from dss.operations.util import map_bucket_results from dss.operations import checkout, storage, sync, secrets, lambda_params, iam, events from dss.operations.lambda_params import get_deployed_lambdas, fix_ssm_variable_prefix from dss.operations.iam import IAMSEPARATOR from dss.operations.secrets import SecretsChecker from dss.logging import configure_test_logging from dss.config import BucketConfig, Config, Replica, override_bucket_config from dss.storage.hcablobstore import FileMetadata, compose_blob_key from dss.util.version import datetime_to_version_format from tests import CaptureStdout, SwapStdin from tests.test_bundle import TestBundleApiMixin from tests.infra import get_env, DSSUploadMixin, TestAuthMixin, DSSAssertMixin from tests.infra.server import ThreadedLocalServer def setUpModule(): configure_test_logging() def random_alphanumeric_string(N=10): return ''.join(random.choices(string.ascii_uppercase + string.digits, k=N)) @testmode.standalone class TestOperations(unittest.TestCase): def test_dispatch(self): with self.subTest("dispatch without mutually exclusive arguments"): self._test_dispatch() with self.subTest("dispatch with mutually exclusive arguments"): self._test_dispatch(mutually_exclusive=True) with self.subTest("dispatch with action overrides"): self._test_dispatch(action_overrides=True) def _test_dispatch(self, mutually_exclusive=None, action_overrides=False): dispatch = DSSOperationsCommandDispatch() target = dispatch.target( "my_target", arguments={ "foo": dict(default="george", type=int), "--argument-a": None, "--argument-b": dict(default="bar"), }, mutually_exclusive=(["--argument-a", "--argument-b"] if mutually_exclusive else None) ) if action_overrides: @target.action("my_action", arguments={"foo": None, "--bar": dict(default="bars")}) def my_action(argv, args): self.assertEqual(args.argument_b, "LSDKFJ") self.assertEqual(args.foo, "24") self.assertEqual(args.bar, "bars") else: @target.action("my_action") def my_action(argv, args): self.assertEqual(args.argument_b, "LSDKFJ") self.assertEqual(args.foo, 24) dispatch(["my_target", "my_action", "24", "--argument-b", "LSDKFJ"]) def test_map_bucket(self): with override_bucket_config(BucketConfig.TEST_FIXTURE): for replica in Replica: with self.subTest(replica=replica): handle = Config.get_blobstore_handle(replica) count_list = 0 for key in handle.list(replica.bucket, prefix="bundles/"): count_list += 1 def counter(keys): count = 0 for key in keys: count += 1 return count total = 0 for count in map_bucket_results(counter, handle, replica.bucket, "bundles/", 2): total += count self.assertGreater(count_list, 0) self.assertEqual(count_list, total) def test_repair_blob_metadata(self): uploader = {Replica.aws: self._put_s3_file, Replica.gcp: self._put_gs_file} with override_bucket_config(BucketConfig.TEST): for replica in Replica: handle = Config.get_blobstore_handle(replica) key = str(<KEY>()) file_metadata = { FileMetadata.SHA256: "foo", FileMetadata.SHA1: "foo", FileMetadata.S3_ETAG: "foo", FileMetadata.CRC32C: "foo", FileMetadata.CONTENT_TYPE: "foo" } blob_key = compose_blob_key(file_metadata) uploader[replica](key, json.dumps(file_metadata).encode("utf-8"), "application/json") uploader[replica](blob_key, b"123", "bar") args = argparse.Namespace(keys=[key], entity_type="files", job_id="", replica=replica.name) with self.subTest("Blob content type repaired", replica=replica): storage.repair_file_blob_metadata([], args).process_key(key) self.assertEqual(handle.get_content_type(replica.bucket, blob_key), file_metadata[FileMetadata.CONTENT_TYPE]) with self.subTest("Should handle arbitrary exceptions", replica=replica): with mock.patch("dss.operations.storage.StorageOperationHandler.log_error") as log_error: with mock.patch("dss.config.Config.get_native_handle") as thrower: thrower.side_effect = Exception() storage.repair_file_blob_metadata([], args).process_key(key) log_error.assert_called() self.assertEqual(log_error.call_args[0][0], "Exception") with self.subTest("Should handle missing file metadata", replica=replica): with mock.patch("dss.operations.storage.StorageOperationHandler.log_warning") as log_warning: storage.repair_file_blob_metadata([], args).process_key("wrong key") self.assertEqual(log_warning.call_args[0][0], "BlobNotFoundError") with self.subTest("Should handle missing blob", replica=replica): with mock.patch("dss.operations.storage.StorageOperationHandler.log_warning") as log_warning: file_metadata[FileMetadata.SHA256] = "wrong" uploader[replica](key, json.dumps(file_metadata).encode("utf-8"), "application/json") storage.repair_file_blob_metadata([], args).process_key(key) self.assertEqual(log_warning.call_args[0][0], "BlobNotFoundError") with self.subTest("Should handle corrupt file metadata", replica=replica): with mock.patch("dss.operations.storage.StorageOperationHandler.log_warning") as log_warning: uploader[replica](key, b"this is not json", "application/json") storage.repair_file_blob_metadata([], args).process_key(key) self.assertEqual(log_warning.call_args[0][0], "JSONDecodeError") def test_bundle_reference_list(self): class MockHandler: mock_file_data = {"uuid": "987", "version": "987", "sha256": "256k", "sha1": "1thing", "s3-etag": "s34me", "crc32c": "wthisthis"} mock_bundle_metadata = {"files": [mock_file_data]} mock_bundle_key = 'bundles/123.456' handle = mock.Mock() def get(self, bucket, key): return json.dumps(self.mock_bundle_metadata) for replica in Replica: with self.subTest("Test Bundle Reference"): with override_bucket_config(BucketConfig.TEST): with mock.patch("dss.operations.storage.Config") as mock_handle: mock_handle.get_blobstore_handle = mock.MagicMock(return_value=MockHandler()) args = argparse.Namespace(keys=[MockHandler.mock_bundle_key], replica=replica.name, entity_type='bundles', job_id="") res = storage.build_reference_list([], args).process_key(MockHandler.mock_bundle_key) self.assertIn(MockHandler.mock_bundle_key, res) self.assertIn(f'files/{MockHandler.mock_file_data["uuid"]}.' f'{MockHandler.mock_file_data["version"]}', res) self.assertIn(compose_blob_key(MockHandler.mock_file_data), res) def test_update_content_type(self): TestCase = namedtuple("TestCase", "replica upload size update initial_content_type expected_content_type") with override_bucket_config(BucketConfig.TEST): key = f"operations/{uuid.uuid4()}" large_size = 64 * 1024 * 1024 + 1 tests = [ TestCase(Replica.aws, self._put_s3_file, 1, storage.update_aws_content_type, "a", "b"), TestCase(Replica.aws, self._put_s3_file, large_size, storage.update_aws_content_type, "a", "b"), TestCase(Replica.gcp, self._put_gs_file, 1, storage.update_gcp_content_type, "a", "b"), ] for test in tests: data = os.urandom(test.size) with self.subTest(test.replica.name): handle = Config.get_blobstore_handle(test.replica) native_handle = Config.get_native_handle(test.replica) test.upload(key, data, test.initial_content_type) old_checksum = handle.get_cloud_checksum(test.replica.bucket, key) test.update(native_handle, test.replica.bucket, key, test.expected_content_type) self.assertEqual(test.expected_content_type, handle.get_content_type(test.replica.bucket, key)) self.assertEqual(handle.get(test.replica.bucket, key), data) self.assertEqual(old_checksum, handle.get_cloud_checksum(test.replica.bucket, key)) def test_verify_blob_replication(self): key = "blobs/alsdjflaskjdf" from_handle = mock.Mock() to_handle = mock.Mock() from_handle.get_size = mock.Mock(return_value=10) to_handle.get_size = mock.Mock(return_value=10) with self.subTest("no replication error"): res = sync.verify_blob_replication(from_handle, to_handle, "", "", key) self.assertEqual(res, list()) with self.subTest("Unequal size blobs reports error"): to_handle.get_size = mock.Mock(return_value=11) res = sync.verify_blob_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("mismatch", res[0].anomaly) with self.subTest("Missing target blob reports error"): to_handle.get_size.side_effect = BlobNotFoundError res = sync.verify_blob_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("missing", res[0].anomaly) def test_verify_file_replication(self): key = "blobs/alsdjflaskjdf" from_handle = mock.Mock() to_handle = mock.Mock() file_metadata = json.dumps({'sha256': "", 'sha1': "", 's3-etag': "", 'crc32c': ""}) from_handle.get = mock.Mock(return_value=file_metadata) to_handle.get = mock.Mock(return_value=file_metadata) with self.subTest("no replication error"): with mock.patch("dss.operations.sync.verify_blob_replication") as vbr: vbr.return_value = list() res = sync.verify_file_replication(from_handle, to_handle, "", "", key) self.assertEqual(res, list()) with self.subTest("Unequal file metadata"): to_handle.get.return_value = "{}" res = sync.verify_file_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("mismatch", res[0].anomaly) with self.subTest("Missing file metadata"): to_handle.get.side_effect = BlobNotFoundError res = sync.verify_file_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("missing", res[0].anomaly) def test_verify_bundle_replication(self): key = "blobs/alsdjflaskjdf" from_handle = mock.Mock() to_handle = mock.Mock() bundle_metadata = json.dumps({ "creator_uid": 8008, "files": [{"uuid": None, "version": None}] }) from_handle.get = mock.Mock(return_value=bundle_metadata) to_handle.get = mock.Mock(return_value=bundle_metadata) with mock.patch("dss.operations.sync.verify_file_replication") as vfr: with self.subTest("replication ok"): vfr.return_value = list() res = sync.verify_bundle_replication(from_handle, to_handle, "", "", key) self.assertEqual(res, []) with self.subTest("replication problem"): vfr.return_value = [sync.ReplicationAnomaly(key="", anomaly="")] res = sync.verify_bundle_replication(from_handle, to_handle, "", "", key) self.assertEqual(res, vfr.return_value) with self.subTest("Unequal bundle metadata"): to_handle.get.return_value = "{}" res = sync.verify_bundle_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("mismatch", res[0].anomaly) with self.subTest("Missing destination bundle metadata"): to_handle.get.side_effect = BlobNotFoundError res = sync.verify_bundle_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("missing on target", res[0].anomaly) with self.subTest("Missing source bundle metadata"): from_handle.get.side_effect = BlobNotFoundError res = sync.verify_bundle_replication(from_handle, to_handle, "", "", key) self.assertEqual(res[0].key, key) self.assertIn("missing on source", res[0].anomaly) def _put_s3_file(self, key, data, content_type="blah", part_size=None): s3 = Config.get_native_handle(Replica.aws) with io.BytesIO(data) as fh: s3.upload_fileobj(Bucket=Replica.aws.bucket, Key=key, Fileobj=fh, ExtraArgs=dict(ContentType=content_type), Config=TransferConfig(multipart_chunksize=64 * 1024 * 1024)) def _put_gs_file(self, key, data, content_type="blah"): gs = Config.get_native_handle(Replica.gcp) gs_bucket = gs.bucket(Replica.gcp.bucket) gs_blob = gs_bucket.blob(key, chunk_size=1 * 1024 * 1024) with io.BytesIO(data) as fh: gs_blob.upload_from_file(fh, content_type="application/octet-stream") def test_iam_aws_list_policies(self): def _get_aws_list_policies_kwargs(**kwargs): # Set default kwarg values, then set any user-specified kwargs custom_kwargs = dict( cloud_provider="aws", group_by=None, output=None, force=False, include_managed=False, exclude_headers=False, quiet=True ) for kw, val in kwargs.items(): custom_kwargs[kw] = val return custom_kwargs def _get_fake_policy_document(): """Utility function to get a fake policy document for mocking the AWS API""" return { "Version": "2000-01-01", "Statement": [ { "Effect": "Allow", "Action": ["fakeservice:*"], "Resource": [ "arn:aws:fakeservice:us-east-1:861229788715:foo:bar*", "arn:aws:fakeservice:us-east-1:861229788715:foo:bar/baz*", ], } ], } with self.subTest("List AWS policies"): with mock.patch("dss.operations.iam.iam_client") as iam_client: # calling list_policies() will call list_aws_policies() # which will call extract_aws_policies() # which will call get_paginator("list_policies") # which will call paginate() to ask for each page, # and ask for ["Policies"] for the page items, # and ["PolicyName"] for the items class MockPaginator(object): def paginate(self, *args, **kwargs): # Return a mock page from the mock paginator return [{"Policies": [{"PolicyName": "fake-policy"}]}] # Plain call to list_policies iam_client.get_paginator.return_value = MockPaginator() with CaptureStdout() as output: kwargs = _get_aws_list_policies_kwargs() iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn("fake-policy", output) # Check write to output file temp_prefix = "dss-test-operations-iam-aws-list-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) iam_client.get_paginator.return_value = MockPaginator() kwargs = _get_aws_list_policies_kwargs(output=fname, force=True) iam.list_policies([], argparse.Namespace(**kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn("fake-policy", output) # Define utility functions and classes to help test the --group-by flags def _get_detail_lists(asset_type): """Utility function to return a fake user detail list for mocking AWS API""" if asset_type not in ['users', 'groups', 'roles']: raise RuntimeError("Error: invalid asset type given, cannot mock AWS API") user_detail_list = [ { "UserName": "fake-user-1", "UserId": random_alphanumeric_string(N=21).upper(), "AttachedManagedPolicies": [], "UserPolicyList": [ { "PolicyName": "fake-policy-attached-to-fake-user-1", "PolicyDocument": _get_fake_policy_document(), } ], } ] group_detail_list = [ { "GroupName": "fake-group-1", "GroupId": random_alphanumeric_string(N=21).upper(), "AttachedManagedPolicies": [], "GroupPolicyList": [ { "PolicyName": "fake-policy-attached-to-fake-group-1", "PolicyDocument": _get_fake_policy_document(), } ], } ] role_detail_list = [ { "RoleName": "fake-role-1", "RoleId": random_alphanumeric_string(N=21).upper(), "AttachedManagedPolicies": [], "RolePolicyList": [ { "PolicyName": "fake-policy-attached-to-fake-role-1", "PolicyDocument": _get_fake_policy_document(), } ], } ] return { "GroupDetailList": group_detail_list if asset_type == "groups" else [], "RoleDetailList": role_detail_list if asset_type == "roles" else [], "UserDetailList": user_detail_list if asset_type == "users" else [], } class MockPaginator_UserPolicies(object): def paginate(self, *args, **kwargs): yield _get_detail_lists("users") class MockPaginator_GroupPolicies(object): def paginate(self, *args, **kwargs): yield _get_detail_lists("groups") class MockPaginator_RolePolicies(object): def paginate(self, *args, **kwargs): yield _get_detail_lists("roles") with self.subTest("List AWS policies grouped by user"): with mock.patch("dss.operations.iam.iam_client") as iam_client: # this will call list_aws_user_policies() # which will call list_aws_policies_grouped() # which will call get_paginator("get_account_authorization_details") # (this is what we mock) # then it calls paginate() to ask for each page, # which we mock in the mock classes above. iam_client.get_paginator.return_value = MockPaginator_UserPolicies() # Plain call to list_policies with CaptureStdout() as output: kwargs = _get_aws_list_policies_kwargs(group_by="users") iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn(IAMSEPARATOR.join(["fake-user-1", "fake-policy-attached-to-fake-user-1"]), output) # Check write to output file temp_prefix = "dss-test-operations-iam-aws-list-users-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) iam_client.get_paginator.return_value = MockPaginator_UserPolicies() kwargs = _get_aws_list_policies_kwargs(group_by="users", output=fname, force=True) iam.list_policies([], argparse.Namespace(**kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn( IAMSEPARATOR.join(["fake-user-1", "fake-policy-attached-to-fake-user-1"]), output ) with self.subTest("List AWS policies grouped by user"): with mock.patch("dss.operations.iam.iam_client") as iam_client: # calls list_aws_group_policies # then list_aws_policies_grouped # then get_paginator("get_account_authorization_details") # (this is what we mock) iam_client.get_paginator.return_value = MockPaginator_GroupPolicies() # Plain call to list_policies with CaptureStdout() as output: kwargs = _get_aws_list_policies_kwargs(group_by="groups") iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn( IAMSEPARATOR.join(["fake-group-1", "fake-policy-attached-to-fake-group-1"]), output ) # Check write to output file temp_prefix = "dss-test-operations-iam-aws-list-groups-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) iam_client.get_paginator.return_value = MockPaginator_GroupPolicies() kwargs = _get_aws_list_policies_kwargs(group_by="groups", output=fname, force=True) iam.list_policies([], argparse.Namespace(**kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn( IAMSEPARATOR.join(["fake-group-1", "fake-policy-attached-to-fake-group-1"]), output ) with self.subTest("List AWS policies grouped by role"): with mock.patch("dss.operations.iam.iam_client") as iam_client: # calls list_aws_group_policies # then list_aws_policies_grouped # then get_paginator("get_account_authorization_details") # (this is what we mock) iam_client.get_paginator.return_value = MockPaginator_RolePolicies() # Plain call to list_policies with CaptureStdout() as output: kwargs = _get_aws_list_policies_kwargs(group_by="roles") iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn( IAMSEPARATOR.join(["fake-role-1", "fake-policy-attached-to-fake-role-1"]), output ) # Check write to output file temp_prefix = "dss-test-operations-iam-aws-list-roles-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) iam_client.get_paginator.return_value = MockPaginator_RolePolicies() kwargs = _get_aws_list_policies_kwargs(group_by="roles", output=fname, force=True) iam.list_policies([], argparse.Namespace(**kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn( IAMSEPARATOR.join(["fake-role-1", "fake-policy-attached-to-fake-role-1"]), output ) # Make sure we can't overwrite without --force with self.assertRaises(RuntimeError): kwargs = _get_aws_list_policies_kwargs(group_by="roles", output=fname, force=False) iam.list_policies([], argparse.Namespace(**kwargs)) # Test error-handling and exceptions last with self.subTest("Test exceptions and error-handling for AWS IAM functions in dss-ops"): with self.assertRaises(RuntimeError): kwargs = _get_aws_list_policies_kwargs(cloud_provider="invalid-cloud-provider") iam.list_policies([], argparse.Namespace(**kwargs)) with self.assertRaises(RuntimeError): kwargs = _get_aws_list_policies_kwargs(group_by="another-invalid-choice") iam.list_policies([], argparse.Namespace(**kwargs)) def test_iam_fus_list_policies(self): def _get_fus_list_policies_kwargs(**kwargs): # Set default kwargs values, then set user-specified kwargs custom_kwargs = dict( cloud_provider="fusillade", group_by=None, output=None, force=False, exclude_headers=False, include_managed=False, quiet=True ) for kw, val in kwargs.items(): custom_kwargs[kw] = val return custom_kwargs with self.subTest("Fusillade client"): with mock.patch("dss.operations.iam.DCPServiceAccountManager") as SAM, \ mock.patch("dss.operations.iam.requests") as req: # Mock the service account manager so it won't hit the fusillade server class FakeServiceAcctMgr(object): def get_authorization_header(self, *args, **kwargs): return {} SAM.from_secrets_manager = mock.MagicMock(return_value=FakeServiceAcctMgr()) # Create fake API response (one page) class FakeResponse(object): def __init__(self): self.headers = {} def raise_for_status(self, *args, **kwargs): pass def json(self, *args, **kwargs): return {"key": "value"} # Test call_api() req.get = mock.MagicMock(return_value=FakeResponse()) client = iam.FusilladeClient("testing") result = client.call_api("/foobar", "key") self.assertEqual(result, "value") # Mock paginated responses with and without Link header class FakePaginatedResponse(object): def __init__(self): self.headers = {} def raise_for_status(self, *args, **kwargs): pass def json(self, *args, **kwargs): return {"key": ["values", "values"]} class FakePaginatedResponseWithLink(FakePaginatedResponse): def __init__(self): self.headers = {"Link": "<https://api.github.com/user/repos?page=3&per_page=100>;"} # Test paginate() req.get = mock.MagicMock(side_effect=[FakePaginatedResponseWithLink(), FakePaginatedResponse()]) result = client.paginate("/foobar", "key") self.assertEqual(result, ["values"] * 4) def _wrap_policy(policy_doc): """Wrap a policy doc the way Fusillade stores/returns them""" return {"IAMPolicy": policy_doc} def _repatch_fus_client(fus_client): """ Re-patch a mock Fusillade client with the proper responses for no --group-by flag or for the --group-by users flag. """ # When we call list_policies(), which calls list_fus_user_policies(), # it calls the paginate() method to get a list of all users, # then the paginate() method twice for each user (once for groups, once for roles), side_effects = [ [ "<EMAIL>", "<EMAIL>" ], ["fake-group"], ["fake-role"], ["fake-group-2"], ["fake-role-2"] ] fus_client().paginate = mock.MagicMock(side_effect=side_effects) # Once we have called the paginate() methods, # we call the call_api() method to get IAM policies attached to roles and groups policy_docs = [ '{"Id": "fake-group-policy"}', '{"Id": "fake-role-policy"}', '{"Id": "fake-group-2-policy"}', '{"Id": "fake-role-2-policy"}', ] fus_client().call_api = mock.MagicMock(side_effect=[_wrap_policy(doc) for doc in policy_docs]) with self.subTest("List Fusillade policies"): with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: # Note: Need to call _repatch_fus_client() before each test # Plain call to list_fus_policies with CaptureStdout() as output: _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs() iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn("fake-group-policy", output) self.assertIn("fake-role-policy", output) self.assertIn("fake-group-2-policy", output) self.assertIn("fake-role-2-policy", output) # Check exclude headers with CaptureStdout() as output: _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs(exclude_headers=True) iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn("fake-group-policy", output) self.assertIn("fake-role-policy", output) self.assertIn("fake-group-2-policy", output) self.assertIn("fake-role-2-policy", output) # Check write to output file temp_prefix = "dss-test-operations-iam-fus-list-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs(output=fname, force=True) iam.list_policies([], argparse.Namespace(**kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn("fake-group-policy", output) self.assertIn("fake-role-policy", output) self.assertIn("fake-group-2-policy", output) self.assertIn("fake-role-2-policy", output) with self.subTest("List Fusillade policies grouped by users"): with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: # List fusillade policies grouped by user with CaptureStdout() as output: _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="users") iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-2-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-2-policy" ]), output) # Check exclude headers with CaptureStdout() as output: _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="users", exclude_headers=True) iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-2-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-2-policy" ]), output) # Check write to output file temp_prefix = "dss-test-operations-iam-fus-list-users-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) _repatch_fus_client(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="users", output=fname, force=True) iam.list_policies([], argparse.Namespace(**kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-group-2-policy" ]), output) self.assertIn(IAMSEPARATOR.join([ "<EMAIL>", "fake-role-2-policy" ]), output) with self.subTest("List Fusillade policies grouped by groups"): # We can't use _repatch_fus_client() to repatch, # since grouping by groups makes different function calls def _repatch_fus_client_groups(fus_client): """Re-patch a mock Fusillade client with the proper responses for using the --group-by groups flag""" # When we call list_policies(), which calls list_fus_group_policies(), # it calls paginate() to get all groups, # then calls paginate() to get roles for each group responses = [["fake-group", "fake-group-2"], ["fake-role"], ["fake-role-2"]] fus_client().paginate = mock.MagicMock(side_effect=responses) # For each role, list_fus_group_policies() calls get_fus_role_attached_policies(), # which calls call_api() on each role and returns a corresponding policy document # @chmreid TODO: should this be calling get policy on each group, too? (inline policies) policy_docs = ['{"Id": "fake-role-policy"}', '{"Id": "fake-role-2-policy"}'] fus_client().call_api = mock.MagicMock(side_effect=[_wrap_policy(doc) for doc in policy_docs]) with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: # List fusillade policies grouped by groups with CaptureStdout() as output: _repatch_fus_client_groups(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="groups") iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn(IAMSEPARATOR.join(["fake-group", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-group-2", "fake-role-2-policy"]), output) # Check exclude headers with CaptureStdout() as output: _repatch_fus_client_groups(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="groups", exclude_headers=True) iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn(IAMSEPARATOR.join(["fake-group", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-group-2", "fake-role-2-policy"]), output) # Check write to output file temp_prefix = "dss-test-operations-iam-fus-list-groups-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) _repatch_fus_client_groups(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="groups", output=fname, force=True) iam.list_policies([], argparse.Namespace(**kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn(IAMSEPARATOR.join(["fake-group", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-group-2", "fake-role-2-policy"]), output) with self.subTest("List Fusillade policies grouped by roles"): # repatch the fusillade client for calling a list of policies grouped by roles def _repatch_fus_client_roles(fus_client): """Re-patch a mock Fusillade client with the proper responses for using the --group-by roles flag""" # When we call list_policies, which calls list_fus_role_policies(), # it calls paginate() to get the list of all roles, side_effects = [["fake-role", "fake-role-2"]] fus_client().paginate = mock.MagicMock(side_effect=side_effects) # list_fus_role_policies then calls get_fus_role_attached_policies() # to get a list of policies attached to the role, # which calls call_api() for each role returned by the paginate command policy_docs = ['{"Id": "fake-role-policy"}', '{"Id": "fake-role-2-policy"}'] fus_client().call_api = mock.MagicMock(side_effect=[_wrap_policy(doc) for doc in policy_docs]) with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: # List fusillade policies grouped by roles with CaptureStdout() as output: _repatch_fus_client_roles(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="roles") iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn(IAMSEPARATOR.join(["fake-role", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-role-2", "fake-role-2-policy"]), output) # Check exclude headers with CaptureStdout() as output: _repatch_fus_client_roles(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="roles", exclude_headers=True) iam.list_policies([], argparse.Namespace(**kwargs)) self.assertIn(IAMSEPARATOR.join(["fake-role", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-role-2", "fake-role-2-policy"]), output) # Check write to output file temp_prefix = "dss-test-operations-iam-list-roles-temp-output" f, fname = tempfile.mkstemp(prefix=temp_prefix) _repatch_fus_client_roles(fus_client) kwargs = _get_fus_list_policies_kwargs(group_by="roles", output=fname, force=True) iam.list_policies([], argparse.Namespace(**kwargs)) with open(fname, "r") as f: output = f.read() self.assertIn(IAMSEPARATOR.join(["fake-role", "fake-role-policy"]), output) self.assertIn(IAMSEPARATOR.join(["fake-role-2", "fake-role-2-policy"]), output) def test_iam_aws_list_assets(self): def _get_aws_list_assets_kwargs(**kwargs): # Set default kwargs values, then set user-specified kwargs custom_kwargs = dict( cloud_provider="aws", output=None, force=False, exclude_headers=False, ) for kw, val in kwargs.items(): custom_kwargs[kw] = val return custom_kwargs with self.subTest("AWS list users"): with mock.patch("dss.operations.iam.iam_client") as iam_client: class MockPaginator_Users(object): def paginate(self, *args, **kwargs): return [{"Users": [ {"UserName": "<EMAIL>"}, {"UserName": "<EMAIL>"} ]}] iam_client.get_paginator.return_value = MockPaginator_Users() with CaptureStdout() as output: kwargs = _get_aws_list_assets_kwargs() iam.list_users([], argparse.Namespace(**kwargs)) self.assertIn("<EMAIL>", output) self.assertIn("<EMAIL>", output) with self.subTest("AWS list groups"): with mock.patch("dss.operations.iam.iam_client") as iam_client: class MockPaginator_Groups(object): def paginate(self, *args, **kwargs): return [{"Groups": [{"GroupName": "fake-group-1"}, {"GroupName": "fake-group-2"}]}] iam_client.get_paginator.return_value = MockPaginator_Groups() with CaptureStdout() as output: kwargs = _get_aws_list_assets_kwargs() iam.list_groups([], argparse.Namespace(**kwargs)) self.assertIn("fake-group-1", output) self.assertIn("fake-group-2", output) with self.subTest("AWS list roles"): with mock.patch("dss.operations.iam.iam_client") as iam_client: class MockPaginator_Roles(object): def paginate(self, *args, **kwargs): return [{"Roles": [{"RoleName": "fake-role-1"}, {"RoleName": "fake-role-2"}]}] iam_client.get_paginator.return_value = MockPaginator_Roles() with CaptureStdout() as output: kwargs = _get_aws_list_assets_kwargs() iam.list_roles([], argparse.Namespace(**kwargs)) self.assertIn("fake-role-1", output) self.assertIn("fake-role-2", output) def test_iam_fus_list_assets(self): def _get_fus_list_assets_kwargs(**kwargs): # Set default kwargs values, then set user-specified kwargs custom_kwargs = dict( cloud_provider="fusillade", output=None, force=False, exclude_headers=False, ) for kw, val in kwargs.items(): custom_kwargs[kw] = val return custom_kwargs with self.subTest("Fusillade list users"): with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: side_effects = [[ "<EMAIL>", "<EMAIL>" ]] fus_client().paginate = mock.MagicMock(side_effect=side_effects) kwargs = _get_fus_list_assets_kwargs() with CaptureStdout() as output: iam.list_users([], argparse.Namespace(**kwargs)) self.assertIn("<EMAIL>", output) self.assertIn("<EMAIL>", output) with self.subTest("Fusillade list groups"): with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: side_effects = [["fake-group-1", "fake-group-2"]] fus_client().paginate = mock.MagicMock(side_effect=side_effects) kwargs = _get_fus_list_assets_kwargs() with CaptureStdout() as output: iam.list_groups([], argparse.Namespace(**kwargs)) self.assertIn("fake-group-1", output) self.assertIn("fake-group-2", output) with self.subTest("Fusillade list roles"): with mock.patch("dss.operations.iam.FusilladeClient") as fus_client: side_effects = [["fake-role-1", "fake-role-2"]] fus_client().paginate = mock.MagicMock(side_effect=side_effects) kwargs = _get_fus_list_assets_kwargs() with CaptureStdout() as output: iam.list_roles([], argparse.Namespace(**kwargs)) self.assertIn("fake-role-1", output) self.assertIn("fake-role-2", output) def test_secrets_crud(self): # CRUD (create read update delete) test procedure: # - create new secret # - list secrets and verify new secret shows up # - get secret value and verify it is correct # - update secret value # - get secret value and verify it is correct # - delete secret which_stage = os.environ["DSS_DEPLOYMENT_STAGE"] which_store = os.environ["DSS_SECRETS_STORE"] secret_name = random_alphanumeric_string() testvar_name = f"{which_store}/{which_stage}/{secret_name}" testvar_value = "Hello world!" testvar_value2 = "Goodbye world!" unusedvar_name = f"{which_store}/{which_stage}/admin_user_emails" with self.subTest("Create a new secret"): # Monkeypatch the secrets manager with mock.patch("dss.operations.secrets.sm_client") as sm: # Creating a new variable will first call get, which will not find it sm.get_secret_value = mock.MagicMock(return_value=None, side_effect=ClientError({}, None)) # Next we will use the create secret command sm.create_secret = mock.MagicMock(return_value=None) # Create initial secret value: # Dry run first with SwapStdin(testvar_value): secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=True, infile=None, quiet=True, force=True ), ) # Provide secret via stdin with SwapStdin(testvar_value): secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=False, infile=None, quiet=True, force=True ), ) # Provide secret via infile with tempfile.NamedTemporaryFile(prefix='dss-test-operations-new-secret-temp-input', mode='w') as f: f.write(testvar_value) secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=False, infile=f.name, force=True, quiet=True ), ) # Check error-catching with non-existent infile mf = 'this-file-is-not-here' with self.assertRaises(RuntimeError): secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=False, infile=mf, force=True, quiet=True ), ) with self.subTest("List secrets"): with mock.patch("dss.operations.secrets.sm_client") as sm: # Listing secrets requires creating a paginator first, # so mock what the paginator returns class MockPaginator(object): def paginate(self): # Return a mock page from the mock paginator return [{"SecretList": [{"Name": testvar_name}, {"Name": unusedvar_name}]}] sm.get_paginator.return_value = MockPaginator() # Non-JSON output first with CaptureStdout() as output: secrets.list_secrets([], argparse.Namespace(json=False)) self.assertIn(testvar_name, output) # JSON output with CaptureStdout() as output: secrets.list_secrets([], argparse.Namespace(json=True)) all_secrets_output = json.loads("\n".join(output)) self.assertIn(testvar_name, all_secrets_output) with self.subTest("Get secret value"): with mock.patch("dss.operations.secrets.sm_client") as sm: # Requesting the variable will try to get secret value and succeed sm.get_secret_value.return_value = {"SecretString": testvar_value} # Now run get secret value in JSON mode and non-JSON mode # and verify variable name/value is in both. # New output file with tempfile.NamedTemporaryFile(prefix='dss-test-operations-get-secret-temp-output', mode='w') as f: # Try to overwrite outfile without --force with self.assertRaises(RuntimeError): secrets.get_secret( [], argparse.Namespace(secret_name=testvar_name, outfile=f.name, force=False) ) # Overwrite outfile with --force secrets.get_secret( [], argparse.Namespace(secret_name=testvar_name, outfile=f.name, force=True) ) with open(f.name, 'r') as fr: file_contents = fr.read() self.assertIn(testvar_value, file_contents) # Output secret to stdout with CaptureStdout() as output: secrets.get_secret( [], argparse.Namespace(secret_name=testvar_name, outfile=None, force=False) ) self.assertIn(testvar_value, "\n".join(output)) with self.subTest("Update existing secret"): with mock.patch("dss.operations.secrets.sm_client") as sm: # Updating the variable will try to get secret value and succeed sm.get_secret_value = mock.MagicMock(return_value={"SecretString": testvar_value}) # Next we will call the update secret command sm.update_secret = mock.MagicMock(return_value=None) # Update secret: # Dry run first with SwapStdin(testvar_value2): secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=True, infile=None, force=True, quiet=True ), ) # Use stdin with SwapStdin(testvar_value2): secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=False, infile=None, force=True, quiet=True ), ) # Use input file with tempfile.NamedTemporaryFile(prefix='dss-test-operations-update-secret-temp-input', mode='w') as f: f.write(testvar_value2) secrets.set_secret( [], argparse.Namespace( secret_name=testvar_name, dry_run=False, infile=f.name, force=True, quiet=True ), ) with self.subTest("Delete secret"): with mock.patch("dss.operations.secrets.sm_client") as sm: # Deleting the variable will try to get secret value and succeed sm.get_secret_value = mock.MagicMock(return_value={"SecretString": testvar_value}) sm.delete_secret = mock.MagicMock(return_value=None) # Delete secret # Dry run first secrets.del_secret( [], argparse.Namespace(secret_name=testvar_name, force=True, dry_run=True, quiet=True) ) # Real thing secrets.del_secret( [], argparse.Namespace(secret_name=testvar_name, force=True, dry_run=False, quiet=True) ) def test_ssmparams_utilities(self): prefix = f"/{os.environ['DSS_PARAMETER_STORE']}/{os.environ['DSS_DEPLOYMENT_STAGE']}" gold_var = f"{prefix}/dummy_variable" var = "dummy_variable" new_var = fix_ssm_variable_prefix(var) self.assertEqual(new_var, gold_var) var = "/dummy_variable" new_var = fix_ssm_variable_prefix(var) self.assertEqual(new_var, gold_var) var = f"{prefix}/dummy_variable" new_var = fix_ssm_variable_prefix(var) self.assertEqual(new_var, gold_var) var = f"{prefix}/dummy_variable/" new_var = fix_ssm_variable_prefix(var) self.assertEqual(new_var, gold_var) def test_ssmparams_crud(self): # CRUD (create read update delete) test for setting environment variables in SSM param store testvar_name = random_alphanumeric_string() testvar_value = "Hello world!" # Assemble environment to return old_env = {"DUMMY_VARIABLE": "dummy_value"} new_env = dict(**old_env) new_env[testvar_name] = testvar_value ssm_new_env = self._wrap_ssm_env(new_env) with self.subTest("Print the SSM environment"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm: # listing params will call ssm.get_parameter to get the entire environment ssm.get_parameter = mock.MagicMock(return_value=ssm_new_env) # Now call our params.py module. Output var=value on each line. with CaptureStdout() as output: lambda_params.ssm_environment([], argparse.Namespace(json=False)) self.assertIn(f"{testvar_name}={testvar_value}", output) def test_lambdaparams_crud(self): # CRUD (create read update delete) test for setting lambda function environment variables testvar_name = random_alphanumeric_string() testvar_value = "Hello world!" testvar_value2 = "Goodbye world!" # Assemble an old and new environment to return old_env = {"DUMMY_VARIABLE": "dummy_value"} new_env = dict(**old_env) new_env[testvar_name] = testvar_value ssm_old_env = self._wrap_ssm_env(old_env) ssm_new_env = self._wrap_ssm_env(new_env) lam_old_env = self._wrap_lambda_env(old_env) lam_new_env = self._wrap_lambda_env(new_env) with self.subTest("Create a new lambda parameter"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm, \ mock.patch("dss.operations.lambda_params.lambda_client") as lam: # If this is not a dry run, lambda_set in params.py # will update the SSM first, so we mock those first. # Before we have set the new test variable for the # first time, we will see the old environment. ssm.put_parameter = mock.MagicMock(return_value=None) ssm.get_parameter = mock.MagicMock(return_value=ssm_old_env) # The lambda_set func in params.py will update lambdas, # so we mock the calls that those will make too. lam.get_function = mock.MagicMock(return_value=None) lam.get_function_configuration = mock.MagicMock(return_value=lam_old_env) lam.update_function_configuration = mock.MagicMock(return_value=None) with SwapStdin(testvar_value): lambda_params.lambda_set( [], argparse.Namespace(name=testvar_name, dry_run=True, quiet=True) ) with SwapStdin(testvar_value): lambda_params.lambda_set( [], argparse.Namespace(name=testvar_name, dry_run=False, quiet=True) ) with self.subTest("List lambda parameters"): with mock.patch("dss.operations.lambda_params.lambda_client") as lam: # The lambda_list func in params.py calls get_deployed_lambas, which calls lam.get_function() # using daemon folder names (this function is called only to ensure no exception is thrown) lam.get_function = mock.MagicMock(return_value=None) # Next we call get_deployed_lambda_environment(), which calls lam.get_function_configuration # (this returns the mocked new env vars json) lam.get_function_configuration = mock.MagicMock(return_value=lam_new_env) # Used to specify a lambda by name stage = os.environ["DSS_DEPLOYMENT_STAGE"] # Non-JSON fmt with CaptureStdout() as output: lambda_params.lambda_list([], argparse.Namespace(json=False)) # Check that all deployed lambdas are present for lambda_name in lambda_params.get_deployed_lambdas(quiet=True): self.assertIn(f"{lambda_name}", output) # JSON fmt with CaptureStdout() as output: lambda_params.lambda_list([], argparse.Namespace(json=True)) # Check that all deployed lambdas are present all_lams_output = json.loads("\n".join(output)) for lambda_name in lambda_params.get_deployed_lambdas(quiet=True): self.assertIn(lambda_name, all_lams_output) with self.subTest("Get environments of each lambda function"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm, \ mock.patch("dss.operations.lambda_params.lambda_client") as lam: # lambda_environment() function in dss/operations/lambda_params.py calls get_deployed_lambdas() # (which only does local operations) # then it calls get_deployed_lambda_environment() on every lambda, # which calls lambda_client.get_function() (only called to ensure no exception is thrown) lam.get_function = mock.MagicMock(return_value=None) # then calls lambda_client.get_function_configuration() lam.get_function_configuration = mock.MagicMock(return_value=lam_new_env) # TODO: reduce copypasta # Non-JSON, no lambda name specified with CaptureStdout() as output: lambda_params.lambda_environment([], argparse.Namespace(lambda_name=None, json=False)) # Check that all deployed lambdas are present output = "\n".join(output) for lambda_name in lambda_params.get_deployed_lambdas(quiet=True): self.assertIn(lambda_name, output) # Non-JSON, lambda name specified with CaptureStdout() as output: lambda_params.lambda_environment([], argparse.Namespace(lambda_name=f"dss-{stage}", json=False)) output = "\n".join(output) self.assertIn(f"dss-{stage}", output) # JSON, no lambda name specified with CaptureStdout() as output: lambda_params.lambda_environment([], argparse.Namespace(lambda_name=None, json=True)) # Check that all deployed lambdas are present all_lams_output = json.loads("\n".join(output)) for lambda_name in lambda_params.get_deployed_lambdas(quiet=True): self.assertIn(lambda_name, all_lams_output) # JSON, lambda name specified with CaptureStdout() as output: lambda_params.lambda_environment([], argparse.Namespace(lambda_name=f"dss-{stage}", json=True)) all_lams_output = json.loads("\n".join(output)) self.assertIn(f"dss-{stage}", all_lams_output) with self.subTest("Update (set) existing lambda parameters"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm, \ mock.patch("dss.operations.lambda_params.lambda_client") as lam: # Mock the same way we did for create new param above. # First we mock the SSM param store ssm.get_parameter = mock.MagicMock(return_value=ssm_new_env) ssm.put_parameter = mock.MagicMock(return_value=None) # Next we mock the lambda client lam.get_function = mock.MagicMock(return_value=None) lam.get_function_configuration = mock.MagicMock(return_value=lam_new_env) lam.update_function_configuration = mock.MagicMock(return_value=None) # Dry run then real (mocked) thing with SwapStdin(testvar_value2): lambda_params.lambda_set( [], argparse.Namespace(name=testvar_name, dry_run=True, quiet=True) ) with SwapStdin(testvar_value2): lambda_params.lambda_set( [], argparse.Namespace(name=testvar_name, dry_run=False, quiet=True) ) with self.subTest("Update lambda environment stored in SSM store under $DSS_DEPLOYMENT_STAGE/environment"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm, \ mock.patch("dss.operations.lambda_params.lambda_client") as lam, \ mock.patch("dss.operations.lambda_params.es_client") as es, \ mock.patch("dss.operations.lambda_params.sm_client") as sm, \ mock.patch("dss.operations.lambda_params.set_ssm_environment") as set_ssm: # If we call lambda_update in dss/operations/lambda_params.py, # it calls get_local_lambda_environment() # (local operations only) # lambda_update() then calls set_ssm_environment(), # which we mocked above into set_ssm set_ssm = mock.MagicMock(return_value=None) # noqa ssm.put_parameter = mock.MagicMock(return_value=None) # get_elasticsearch_endpoint() calls es.describe_elasticsearch_domain() es_endpoint_secret = { "DomainStatus": { "Endpoint": "this-invalid-es-endpoint-value-comes-from-dss-test-operations" } } es.describe_elasticsearch_domain = mock.MagicMock( return_value=es_endpoint_secret ) # get_admin_emails() calls sm.get_secret_value() several times: # - google service acct secret (json string) # - admin email secret # use side_effect when returning multiple values google_service_acct_secret = json.dumps( {"client_email": "this-invalid-email-comes-from-dss-test-operations"} ) admin_email_secret = "this-invalid-email-list-comes-from-dss-test-operations" # Finally, we call set_ssm_environment # which calls ssm.put_parameter() # (mocked above). # If we also update deployed lambdas: # get_deployed_lambdas() -> lam_client.get_function() # get_deployed_lambda_environment() -> lam_client.get_function_configuration() # set_deployed_lambda_environment() -> lam_client.update_function_configuration() lam.get_function = mock.MagicMock(return_value=None) lam.get_function_configuration = mock.MagicMock(return_value=lam_new_env) lam.update_function_configuration = mock.MagicMock(return_value=None) # The function sm.get_secret_value() must return things in the right order # Re-mock it before each call email_side_effect = [ self._wrap_secret(google_service_acct_secret), self._wrap_secret(admin_email_secret), ] # Dry run, then real (mocked) thing sm.get_secret_value = mock.MagicMock(side_effect=email_side_effect) lambda_params.lambda_update( [], argparse.Namespace(update_deployed=False, dry_run=True, force=True, quiet=True) ) sm.get_secret_value = mock.MagicMock(side_effect=email_side_effect) lambda_params.lambda_update( [], argparse.Namespace(update_deployed=False, dry_run=False, force=True, quiet=True) ) sm.get_secret_value = mock.MagicMock(side_effect=email_side_effect) lambda_params.lambda_update( [], argparse.Namespace(update_deployed=True, dry_run=False, force=True, quiet=True) ) with self.subTest("Unset lambda parameters"): with mock.patch("dss.operations.lambda_params.ssm_client") as ssm, \ mock.patch("dss.operations.lambda_params.lambda_client") as lam: # If this is not a dry run, lambda_set in params.py # will update the SSM first, so we mock those first. # Before we have set the new test variable for the # first time, we will see the old environment. ssm.put_parameter = mock.MagicMock(return_value=None) # use deepcopy here to prevent delete operation from being permanent ssm.get_parameter = mock.MagicMock(return_value=copy.deepcopy(ssm_new_env)) # The lambda_set func in params.py will update lambdas, # so we mock the calls that those will make too. lam.get_function = mock.MagicMock(return_value=None) # use side effect here, and copy the environment for each lambda, so that deletes won't be permanent lam.get_function_configuration = mock.MagicMock( side_effect=[copy.deepcopy(lam_new_env) for j in get_deployed_lambdas()] ) lam.update_function_configuration = mock.MagicMock(return_value=None) lambda_params.lambda_unset([], argparse.Namespace(name=testvar_name, dry_run=True, quiet=True)) lambda_params.lambda_unset([], argparse.Namespace(name=testvar_name, dry_run=False, quiet=True)) def test_events_operations_journal(self): with self.subTest("Should forward to lambda when `starting_journal_id` is `None`"): self._test_events_operations_journal(None, 0, 2) with self.subTest("Should execute journaling when `starting_journal_id` is not `None`"): self._test_events_operations_journal("blah", 1, 0) def _test_events_operations_journal(self, starting_journal_id: str, expected_journal_flashflood_calls: int, expected_sqs_messenger_calls: int): sqs_messenger = mock.MagicMock() with mock.patch("dss.operations.events.SQSMessenger", return_value=sqs_messenger), \ mock.patch("dss.operations.events.list_new_flashflood_journals"), \ mock.patch("dss.operations.events.journal_flashflood") as journal_flashflood, \ mock.patch("dss.operations.events.monitor_logs"): args = argparse.Namespace(prefix="pfx", number_of_events=5, starting_journal_id=starting_journal_id, job_id=None) events.journal([], args) self.assertEqual(expected_journal_flashflood_calls, len(journal_flashflood.mock_calls)) self.assertEqual(expected_sqs_messenger_calls, len(sqs_messenger.mock_calls)) def _wrap_ssm_env(self, e): """ Package up the SSM environment the way AWS returns it. :param dict e: the dict containing the environment to package up and send to SSM store at $DSS_DEPLOYMENT_STAGE/environment. """ # Value should be serialized JSON ssm_e = {"Parameter": {"Name": "environment", "Value": json.dumps(e)}} return ssm_e def _wrap_lambda_env(self, e): """ Package up the lambda environment (a.k.a. function configuration) the way AWS returns it. :param dict e: the dict containing the lambda function's environment variables """ # Value should be a dict (NOT a string) lam_e = {"Environment": {"Variables": e}} return lam_e def _wrap_secret(self, val): """ Package up the secret the way AWS returns it. """ return {"SecretString": val} @testmode.integration class TestOperationsIntegration(TestBundleApiMixin): @classmethod def setUpClass(cls): cls.app = ThreadedLocalServer() cls.app.start() @classmethod def tearDownClass(cls): cls.app.shutdown() def setUp(self): Config.set_config(BucketConfig.TEST) self.s3_test_bucket = get_env("DSS_S3_BUCKET_TEST") self.gs_test_bucket = get_env("DSS_GS_BUCKET_TEST") self.s3_test_fixtures_bucket = get_env("DSS_S3_BUCKET_TEST_FIXTURES") self.gs_test_fixtures_bucket = get_env("DSS_GS_BUCKET_TEST_FIXTURES") def test_checkout_operations(self): with override_bucket_config(BucketConfig.TEST): for replica, fixture_bucket in [(Replica['aws'], self.s3_test_fixtures_bucket), (Replica['gcp'], self.gs_test_fixtures_bucket)]: bundle, bundle_uuid = self._create_bundle(replica, fixture_bucket) args = argparse.Namespace(replica=replica.name, keys=[f'bundles/{bundle_uuid}.{bundle["version"]}']) checkout_status = checkout.Verify([], args).process_keys() for key in args.keys: self.assertIn(key, checkout_status) checkout.Remove([], args).process_keys() checkout_status = checkout.Verify([], args).process_keys() for key in args.keys: for file in checkout_status[key]: self.assertIs(False, file['bundle_checkout']) self.assertIs(False, file['blob_checkout']) checkout.Add([], args).process_keys() checkout_status = checkout.Verify([], args).process_keys() for key in args.keys: for file in checkout_status[key]: self.assertIs(True, file['bundle_checkout']) self.assertIs(True, file['blob_checkout']) self.delete_bundle(replica, bundle_uuid) def _create_bundle(self, replica: Replica, fixtures_bucket: str): schema = replica.storage_schema bundle_uuid = str(uuid.uuid4()) file_uuid = str(uuid.uuid4()) resp_obj = self.upload_file_wait( f"{schema}://{fixtures_bucket}/test_good_source_data/0", replica, file_uuid, bundle_uuid=bundle_uuid, ) file_version = resp_obj.json['version'] bundle_version = datetime_to_version_format(datetime.datetime.utcnow()) resp_obj = self.put_bundle(replica, bundle_uuid, [(file_uuid, file_version, "LICENSE")], bundle_version) return resp_obj.json, bundle_uuid @testmode.integration class TestSecretsChecker(unittest.TestCase): """Test the SecretsChecker class defined in dss/operations/secrets.py""" @skip_on_travis def test_check_secrets(self): """Check that the current stage's secrets conform to expected values""" secrets.check_secrets([], argparse.Namespace()) @skip_on_travis def test_custom_stage_secrets(self): """ The SecretsChecker should not test stages that are not in the list: dev, staging, integration, prod. """ s = SecretsChecker(stage='somenonsensenamelikeprod') s.run() @skip_on_travis def test_invalid_secrets(self): """Check that a ValueError is raised when an unqualified email is stored in a secret.""" s = SecretsChecker(stage='dev') # Override the email field obtained from terraform s.email = ['nonsense'] with self.assertRaises(ValueError): s.run() if __name__ == '__main__': unittest.main()

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tests/test_handlers_item_builder.py

MacHu-GWU/afwf_aws_tools-project

6

59329

<reponame>MacHu-GWU/afwf_aws_tools-project<filename>tests/test_handlers_item_builder.py # -*- coding: utf-8 -*- import pytest from workflow.workflow3 import Workflow3 from aws_tools.handlers.item_builder import item_builders class TestItemBuilders: wf = Workflow3() def setup_method(self): self.wf = Workflow3() def test_select_aws_profile_as_default(self): item_builders.select_aws_profile_as_default( wf=self.wf, aws_profile_list=["a", "b", "c"], set_default_aws_profile_handler_id="handler_id" ) def test_select_aws_profile_for_mfa(self): item_builders.select_aws_profile_for_mfa( wf=self.wf, aws_profile_list=["a", "b", "c"], execute_mfa_auth_handler_id="handler_id" ) def test_set_aws_profile_as_aws_tools_default(self): item_builders.set_aws_profile_as_aws_tools_default( wf=self.wf, aws_profile_list=["a", "b", "c"], set_aws_profile_as_aws_tools_default_handler_id="handler_id" ) if __name__ == "__main__": import os basename = os.path.basename(__file__) pytest.main([basename, "-s", "--tb=native"])

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kirppuauth/urls.py

Glowieh/kirppu

0

156190

<filename>kirppuauth/urls.py from django.conf.urls import patterns, include, url urlpatterns = patterns('kirppuauth.views', url(r'^addclerk$', 'register_clerk', name='kirppu_register_clerk'), )

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praw_memories/prawdebug.py

elnuno/praw_memories

1

50951

"""Debugging facilities for PRAW-based projects.""" import datetime import json import logging import time import praw import prawcore

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Intro_to_Recommendation_Systems/api.py

NunoEdgarGFlowHub/sciblog_support

668

174503

import cherrypy from paste.translogger import TransLogger from flask import Flask, request, abort, jsonify, make_response import json import os from utils import decode_string from DeepRecommender.reco_encoder.data import input_layer_api, input_layer from DeepRecommender.reco_encoder.model import model import torch from torch.autograd import Variable from parameters import * # app app = Flask(__name__) BAD_REQUEST = 400 STATUS_OK = 200 NOT_FOUND = 404 SERVER_ERROR = 500 @app.errorhandler(BAD_REQUEST) def bad_request(error): return make_response(jsonify({'error': 'Bad request'}), BAD_REQUEST) @app.errorhandler(NOT_FOUND) def not_found(error): return make_response(jsonify({'error': 'Not found'}), NOT_FOUND) @app.errorhandler(SERVER_ERROR) def server_error(error): return make_response(jsonify({'error': 'Server Internal Error'}), SERVER_ERROR) def run_server(): # Enable WSGI access logging via Paste app_logged = TransLogger(app) # Mount the WSGI callable object (app) on the root directory cherrypy.tree.graft(app_logged, '/') # Set the configuration of the web server cherrypy.config.update({ 'engine.autoreload_on': True, 'log.screen': True, 'log.access_file': 'access.log', 'log.error_file': "cherrypy.log", 'server.socket_port': 5000, 'server.socket_host': '0.0.0.0', 'server.thread_pool': 50, # 10 is default }) # Start the CherryPy WSGI web server cherrypy.engine.start() cherrypy.engine.block() def load_model_weights(model_architecture, weights_path): if os.path.isfile(weights_path): cherrypy.log("CHERRYPYLOG Loading model from: {}".format(weights_path)) model_architecture.load_state_dict(torch.load(weights_path)) else: raise ValueError("Path not found {}".format(weights_path)) def load_recommender(vector_dim, hidden, activation, dropout, weights_path): rencoder_api = model.AutoEncoder(layer_sizes=[vector_dim] + [int(l) for l in hidden.split(',')], nl_type=activation, is_constrained=False, dp_drop_prob=dropout, last_layer_activations=False) load_model_weights(rencoder_api, weights_path) rencoder_api.eval() rencoder_api = rencoder_api.cuda() return rencoder_api def load_train_data(data_dir): params = dict() params['batch_size'] = 1 params['data_dir'] = data_dir params['major'] = 'users' params['itemIdInd'] = 1 params['userIdInd'] = 0 cherrypy.log("CHERRYPYLOG Loading training data") data_layer = input_layer.UserItemRecDataProvider(params=params) return data_layer def manage_query(dict_query, data_layer): params = dict() params['batch_size'] = 1 params['data_dict'] = dict_query params['major'] = 'users' params['itemIdInd'] = 1 params['userIdInd'] = 0 data_api = input_layer_api.UserItemRecDataProviderAPI(params=params, user_id_map=data_layer.userIdMap, item_id_map=data_layer.itemIdMap) cherrypy.log("CHERRYPYLOG Input data: {}".format(data_api.data)) data_api.src_data = data_layer.data return data_api def evaluate_model(rencoder_api, data_api): result = dict() for i, ((out, src), major_ind) in enumerate(data_api.iterate_one_epoch_eval(for_inf=True)): inputs = Variable(src.cuda().to_dense()) targets_np = out.to_dense().numpy()[0, :] non_zeros = targets_np.nonzero()[0].tolist() outputs = rencoder_api(inputs).cpu().data.numpy()[0, :] for ind in non_zeros: result[ind] = outputs[ind] return result @app.route("/") def index(): return "Yeah, yeah, I highly recommend it" @app.route("/recommend", methods=['POST']) def recommend(): if not request.is_json: abort(BAD_REQUEST) dict_query = request.get_json() dict_query = dict((decode_string(k), decode_string(v)) for k, v in dict_query.items()) data_api = manage_query(dict_query, data_layer) result = evaluate_model(rencoder_api, data_api) cherrypy.log("CHERRYPYLOG Result: {}".format(result)) result = dict((str(k), str(v)) for k,v in result.items()) return make_response(jsonify(result), STATUS_OK) #Load data and model as global variables data_layer = load_train_data(TRAIN) rencoder_api = load_recommender(data_layer.vector_dim, HIDDEN, ACTIVATION, DROPOUT, MODEL_PATH) if __name__ == "__main__": run_server()

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downstream/tinypersons/tests/test_onnx/__init__.py

bwconrad/solo-learn

326

174461

<filename>downstream/tinypersons/tests/test_onnx/__init__.py from .utils import ort_validate __all__ = ['ort_validate']

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access_restricted/tests/test_fields_get.py

trojikman/access-addons

0

91915

from odoo.tests.common import TransactionCase, tagged from odoo.addons.base.models.res_users import name_selection_groups @tagged("at_install", "post_install") class TestFieldsGet(TransactionCase): def test_base(self): demo_user = self.env.ref("base.user_demo") group_erp_manager = self.env.ref("base.group_erp_manager") group_system = self.env.ref("base.group_system") demo_user.write({"groups_id": [(3, group_system.id)]}) group_system.write({"users": [(3, demo_user.id)]}) demo_user.write({"groups_id": [(4, group_erp_manager.id)]}) view_users_form = self.env.ref("base.view_users_form") res = ( self.env["res.users"] .with_user(demo_user) .with_context({"uid": demo_user.id}) .load_views([[view_users_form.id, "form"]]) ) sel_groups = name_selection_groups([group_erp_manager.id]) res = self.env["res.users"].with_user(demo_user).fields_get() self.assertTrue(res.get(sel_groups)) demo_user.write({"groups_id": [(4, group_system.id)]}) sel_groups = name_selection_groups([group_erp_manager.id, group_system.id]) res = self.env["res.users"].with_user(demo_user).fields_get() self.assertTrue(res.get(sel_groups))

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tests/tools/test_rm.py

Vikash-Kothary/aem-cmd-python

2

147959

# coding: utf-8 from mock import patch from httmock import urlmatch, HTTMock from nose.tools import eq_ from acmd import tool_repo, Server from test_utils.compat import StringIO @urlmatch(netloc='localhost:4502', method='DELETE') def service_rm(url, request): return { 'content': '', 'status_code': 204 } @patch('sys.stdout', new_callable=StringIO) @patch('sys.stderr', new_callable=StringIO) def test_rm(stderr, stdout): with HTTMock(service_rm): tool = tool_repo.get_tool('rm') server = Server('localhost') status = tool.execute(server, ['rm', '/content/path/node']) eq_(0, status) eq_('/content/path/node\n', stdout.getvalue()) @patch('sys.stdout', new_callable=StringIO) @patch('sys.stderr', new_callable=StringIO) @patch('sys.stdin', new=StringIO('/content/path/node\n')) def test_rm_stdin(stderr, stdout): with HTTMock(service_rm): tool = tool_repo.get_tool('rm') server = Server('localhost') status = tool.execute(server, ['rm']) eq_(0, status) eq_('/content/path/node\n', stdout.getvalue())

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data_visualizer.py

austinmdillow/natural-selection-simulator

0

94648

from environment import Environment from species import Species import matplotlib.pyplot as plt import matplotlib matplotlib.use('TkAgg') # <-- THIS MAKES IT FAST! class DataVisualizer(): def __init__(self, env): self.environments = [env] plt.ion() plt.show() print("Init of Data Visualizer complete") def plotPopulations(self): plt.clf() plt.plot(self.environments[0].population_log[Species.Rabbit]) #plt.draw() plt.pause(0.0001)

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lk_lambdex/traceback_.py

likianta/lk-lambdex

0

67171

from textwrap import dedent from textwrap import indent def format_exception( direct_caller_frame, lambdex_definition_place, traceback_obj, source_code_block, ) -> str: # caller: see `.main.lambdex.<vars:code>.<code_template>.<except_clause>`. def _show_place(filename, lineno): return '{}:{}'.format(filename, lineno) # pycharm console font color: https://blog.csdn.net/mouday/article/details # /79152994 return dedent(''' An error is raised when executing lambda function within lk-lambdex. Here's the detailed infomation: Lambdex definition start: {lambdex_definition_place_head} Lambdex definition body: {lambdex_definition_place_body} \033[37m{source_lines}\033[0m -> value: \033[36m{value}\033[0m -> error: \033[31m{error_info}!\033[0m Direct caller: {direct_caller_place} ''').format( lambdex_definition_place_head=_show_place( lambdex_definition_place[0], lambdex_definition_place[1], ), lambdex_definition_place_body=_show_place( lambdex_definition_place[0], lambdex_definition_place[1] + traceback_obj.tb_lineno - 6, ), source_lines=_traceback_source_lines( source_code_block, lineno=traceback_obj.tb_lineno - 6, indentation=12 ), value={k: v for k, v in traceback_obj.tb_frame.f_locals.items() if k not in ('e', 'sys', 'somefunc')}, error_info=_pretty_error( text=traceback_obj.tb_frame.f_locals['e'].__repr__(), indentation=8 + 10 # (8 + 10): 8 is the indentation of '-> error', 10 is the string # length of '-> error: ', (8 + 10) is made for aligning the # succeeding lines to look more pretty. ), direct_caller_place=_show_place( direct_caller_frame.f_code.co_filename, direct_caller_frame.f_lineno ), ).strip() def _traceback_source_lines( source_code_block, lineno, charno=-1, indentation=0, extend_lineno_range=True, lineno_range=3 ) -> str: if extend_lineno_range: temp = [] while lineno >= 0 and len(temp) < lineno_range: line = source_code_block[lineno] if line.strip(): temp.append(line) lineno -= 1 source_lines = dedent('\n'.join(reversed(temp))) else: source_lines = source_code_block[lineno].strip() if charno == -1: return indent(source_lines, ' ' * indentation).lstrip() else: return indent(source_lines, ' ' * indentation).lstrip() + '\n' \ + ' ' * indentation + ' ' * charno + '^' def _pretty_error(text: str, indentation: int): if '\\n' not in text: return text lines = text.split('\\n') return '\n'.join( ( lines[0], indent(dedent('\n'.join(lines[1:])), ' ' * indentation) ) )

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potok/tabular/utils.py

ksetherg/Chereda

1

90531

<filename>potok/tabular/utils.py import scipy.stats as sst import numpy as np import pandas as pd from potok.tabular import TabularData class SyntheticData: def __init__(self, problem='regression', pdf_train=sst.norm(loc=-1, scale=2), pdf_test=sst.norm(loc=1, scale=3), seed=None): assert problem in ['regression', 'classification'] self.problem = problem self.pdf_train = pdf_train self.pdf_test = pdf_test self.seed = seed def _create_sample_(self, pdf, size): x = pdf.rvs(size=size, random_state=self.seed) if self.problem == 'regression': y = 2*x + 1 if self.problem == 'classification': y = (x > np.mean(x)).astype(int) df = pd.DataFrame({'X': x, 'Target': y}, index=list(range(size))) return df def create_train(self, size=10): df_train = self._create_sample_(self.pdf_train, size) return TabularData(df_train, target=['Target']) def create_test(self, size=10): data_test = self._create_sample_(self.pdf_test, size) return TabularData(data_test, target=['Target'])

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idfield/fields.py

lis-space/django-idfield

0

55106

<gh_stars>0 from django.db import models import random class IDField(models.Field): def __init__(self, alphabet=None, readable=False, *args, **kwargs): self.max_length = kwargs.get('max_length', 10) self.readable = readable self.alphabet = alphabet if not self.alphabet: if self.readable: self.alphabet = "ABCDEFGHJKMNPQRSTUVWXYZ23456789" else: self.alphabet = "abcdefghijklmnopqrstuvwxyz0123456789" super(IDField, self).__init__(*args, **kwargs) def db_type(self, connection): return 'char({})'.format(self.max_length) def pre_save(self, model_instance, add): value = super(IDField, self).pre_save(model_instance, add) if not value: value = '' for i in range(0, self.max_length): value += random.choice(self.alphabet) setattr(model_instance, self.attname, value) return value def formfield(self, **kwargs): return super(IDField, self).formfield(**kwargs) def deconstruct(self): return super(IDField, self).deconstruct()

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docs/streammarker_connected_sensor/streammarker_connected_sensor_reader.py

urlgrey/streammarker-collector

0

96035

#!/usr/bin/python import os import httplib import serial import re import json ser = serial.Serial('/dev/ttyAMA0', 9600) headers = {"Content-type": "application/json", "Accept": "application/json"} if 'STREAMMARKER_API_KEY' in os.environ: headers["X-API-KEY"] = os.environ['STREAMMARKER_API_KEY'] r_unwanted = re.compile("[\n\r]") while True: try: msg = ser.readline() msg = r_unwanted.sub("", msg) readings = msg.split(',') body = json.dumps({"timestamp": 0,"relay_id": "3AC91DEC-B0DD-4DB5-B56D-7682B7C9B28C","status": "ok","sensors": [{"id":"31E541A7-815A-4527-88FC-CEA39808FCAC","readings": [{"timestamp": 0,"measurements": [{"name":"soil_moisture_1","value": float(readings[0]),"unit":"VWC"}, {"name":"soil_temperature_1","value": float(readings[1]),"unit":"Celsius"}]}]}]}) print body except KeyboardInterrupt: break except: # catch-all e = sys.exc_info()[0] print(e) ser.close()

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examples/04_sweep_wind_directions.py

ElieKadoche/floris

0

12814

# Copyright 2022 NREL # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of # the License at http://www.apache.org/licenses/LICENSE-2.0 # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. # See https://floris.readthedocs.io for documentation import matplotlib.pyplot as plt import numpy as np from floris.tools import FlorisInterface from floris.tools.visualization import visualize_cut_plane """ 04_sweep_wind_directions This example demonstrates vectorization of wind direction. A vector of wind directions is passed to the intialize function and the powers of the two simulated turbines is computed for all wind directions in one call The power of both turbines for each wind direction is then plotted """ # Instantiate FLORIS using either the GCH or CC model fi = FlorisInterface("inputs/gch.yaml") # GCH model matched to the default "legacy_gauss" of V2 # fi = FlorisInterface("inputs/cc.yaml") # New CumulativeCurl model # Define a two turbine farm D = 126. layout_x = np.array([0, D*6]) layout_y = [0, 0] fi.reinitialize(layout = [layout_x, layout_y]) # Sweep wind speeds but keep wind direction fixed wd_array = np.arange(250,291,1.) fi.reinitialize(wind_directions=wd_array) # Define a matrix of yaw angles to be all 0 # Note that yaw angles is now specified as a matrix whose dimesions are # wd/ws/turbine num_wd = len(wd_array) # Number of wind directions num_ws = 1 # Number of wind speeds num_turbine = len(layout_x) # Number of turbines yaw_angles = np.zeros((num_wd, num_ws, num_turbine)) # Calculate fi.calculate_wake(yaw_angles=yaw_angles) # Collect the turbine powers turbine_powers = fi.get_turbine_powers() / 1E3 # In kW # Pull out the power values per turbine pow_t0 = turbine_powers[:,:,0].flatten() pow_t1 = turbine_powers[:,:,1].flatten() # Plot fig, ax = plt.subplots() ax.plot(wd_array,pow_t0,color='k',label='Upstream Turbine') ax.plot(wd_array,pow_t1,color='r',label='Downstream Turbine') ax.grid(True) ax.legend() ax.set_xlabel('Wind Direction (deg)') ax.set_ylabel('Power (kW)') plt.show()

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learnMongo/mongoInsert.py

huobingli/pyAlgorithm

0

24764

import pymongo from bson.objectid import ObjectId # mongo 增加 def main(): client = pymongo.MongoClient(host='172.16.17.32', port=27017) db = client.test collection = db.students # 插入一条数据 student = { 'id': '20170101', 'name': 'Kevin', 'age': 20, 'gender': 'male' } # 每条数据其实都有一个 _id 属性来唯一标识。 # 如果没有显式指明该属性，MongoDB 会自动产生一个 ObjectId 类型的 _id 属性。 # insert() 方法会在执行后返回_id 值。 result = collection.insert(student) print(result) # 插入多条数据 student1 = { 'id': '20170101', 'name': 'Jordan', 'age': 20, 'gender': 'male' } student2 = { 'id': '20170202', 'name': 'Mike', 'age': 20, 'gender': 'male' } # 这里用列表方式传入 # 返回结果也是列表方式 result = collection.insert([student1, student2]) print(result) # pymongo 官方不推荐使用insert # 推荐使用 insert_one 和 insert_many 插入一条或者多条记录 student = { 'id': '20170101', 'name': 'Jordan', 'age': 20, 'gender': 'male' } result = collection.insert_one(student) print(result) print(result.inserted_id) student1 = { 'id': '20170101', 'name': 'Jordan', 'age': 20, 'gender': 'male' } student2 = { 'id': '20170202', 'name': 'Mike', 'age': 20, 'gender': 'male' } result = collection.insert_many([student1, student2]) print(result) print(result.inserted_ids) if __name__ == '__main__': main()

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cpgan_data.py

basilevh/object-discovery-cp-gan

14

18657

# <NAME>, March 2020 # Common code for PyTorch implementation of Copy-Pasting GAN import copy import itertools import matplotlib.pyplot as plt import numpy as np import os, platform, time import torch import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.transforms as transforms from PIL import Image, ImageDraw from torch.utils.data import Dataset from tqdm import tqdm def read_image_robust(img_path, monochromatic=False): ''' Returns an image that meets conditions along with a success flag, in order to avoid crashing. ''' try: # image = plt.imread(img_path).copy() image = np.array(Image.open(img_path)).copy() # always uint8 success = True if np.any(np.array(image.strides) < 0): success = False # still negative stride elif not(monochromatic) and (image.ndim != 3 or image.shape[2] != 3): success = False # not RGB elif monochromatic: # width, height = image.shape[1], image.shape[0] # image = np.broadcast_to(x[:, :, np.newaxis], (height, width, 3)) image = image[:, :, np.newaxis] # one channel <=> only one ground truth except IOError: # Probably corrupt file image = None success = False return image, success def paint_squares(image, noisy=False, channels=10): ''' Paints one or more squares at random locations to create an artificial foreground image. Generates multiple associated ground truth masks; one per object. ''' width, height = image.shape[1], image.shape[0] image = image.copy() # do not overwrite background object_count = np.random.randint(1, 5) # [1, 4] inclusive masks = np.zeros((height, width, channels), dtype=np.uint8) for i in range(object_count): sq_w, sq_h = 9, 9 x1 = np.random.randint(0, width - sq_w + 1) y1 = np.random.randint(0, height - sq_h + 1) x2 = x1 + sq_w y2 = y1 + sq_h masks[y1:y2, x1:x2, i] = 255 if not(noisy): # Pick one fixed (not necessarily saturated) color for the whole square clr = np.random.randint(0, 256, 3) image[y1:y2, x1:x2] = clr else: # Pick a random fully saturated (extremal) color for every pixel image[y1:y2, x1:x2] = np.random.choice([0, 255], (sq_h, sq_w, 3)) return image, masks, object_count def create_random_gfake_mask(width, height): ''' See Appendix D. ''' x0, y0 = np.random.rand(2) * 0.8 + 0.1 num_verts = np.random.randint(4, 7) # TODO possible improvement: allow up to more vertices? # TODO possible improvement: encourage convex (currently many "sharp" objects) radii = np.random.rand(num_verts) * 0.4 + 0.1 # radii = np.random.rand(num_verts) * 0.8 + 0.2 # TODO: not very clear from paper angles = np.sort(np.random.rand(num_verts)) * 2.0 * np.pi poly_polar = list(zip(radii, angles)) poly_cart = [(int(width * (x0 + r * np.cos(a)) / 1), int(height * (y0 + r * np.sin(a)) / 1)) for (r, a) in poly_polar] # poly_cart = [(x1, y1), (x2, y2), ...] img = Image.new('L', (width, height), 0) ImageDraw.Draw(img).polygon(poly_cart, outline=1, fill=255) mask = np.array(img, dtype='uint8') assert(mask.shape == (height, width)) return mask def apply_border_zero(masks): ndim = len(masks.shape) if ndim == 2: masks[0, :] = 0 masks[-1, :] = 0 masks[:, 0] = 0 masks[:, -1] = 0 elif ndim == 3: masks[:, 0, :] = 0 masks[:, -1, :] = 0 masks[:, :, 0] = 0 masks[:, :, -1] = 0 elif ndim == 4: masks[:, :, 0, :] = 0 masks[:, :, -1, :] = 0 masks[:, :, :, 0] = 0 masks[:, :, :, -1] = 0 else: raise Exception('Mask has too many dimensions') return masks def copy_paste(fores, masks, backs, border_zero=True): # TODO possible improvement: poisson blending # if hard_thres > 0: # used_masks = (masks > hard_thres).float() # force binary # else: used_masks = masks.clone() # Border zeroing implemented in April 2020 if border_zero: used_masks = apply_border_zero(used_masks) return used_masks * fores + (1.0 - used_masks) * backs class MyCopyPasteDataset(Dataset): ''' Custom dataset class with foreground, background, and optional mask folders as image sources. Only one object may appear per image, since the object count is not kept track of. Returns irrelevant foreground anti-shortcuts as well. Enforces color (RGB) images. ''' def __init__(self, fore_dir, back_dir, mask_dir=None, rand_horz_flip=True, post_resize=-1, center_crop=False): self.fore_dir = fore_dir self.back_dir = back_dir self.rand_horz_flip = rand_horz_flip if post_resize <= 0: self.post_tf = transforms.ToTensor() # converts [0, 255] to [0.0, 1.0] elif center_crop: # Resize + square center crop self.post_tf = transforms.Compose([ transforms.ToPILImage(), transforms.Resize(post_resize), transforms.CenterCrop(post_resize), transforms.ToTensor() ]) else: # Resize both dimensions, possibly distorting the images self.post_tf = transforms.Compose([ transforms.ToPILImage(), transforms.Resize((post_resize, post_resize)), transforms.ToTensor() ]) self.has_masks = (mask_dir is not None) # Load all file paths; file names must be the same across all 2 or 3 given directories # self.all_fore_files = [] # self.all_mask_files = [] # self.all_back_files = [] # for fn in os.listdir(fore_dir): # fore_fp = os.path.join(fore_dir, fn) # if os.path.isfile(fore_fp): # back_fp = os.path.join(back_dir, fn) # assert(os.path.isfile(back_fp)) # self.all_fore_files.append(fore_fp) # self.all_back_files.append(back_fp) # if self.has_masks: # mask_fp = os.path.join(mask_dir, fn) # assert(os.path.isfile(mask_fp)) # self.all_mask_files.append(mask_fp) # Load all file paths; file names must be the same across foreground and segmentation masks self.all_fore_files = [] self.all_mask_files = [] self.all_back_files = [] for fn in os.listdir(fore_dir): fore_fp = os.path.join(fore_dir, fn) self.all_fore_files.append(fore_fp) if self.has_masks: mask_fp_jpg = os.path.join(mask_dir, fn[:-4] + '.jpg') mask_fp_png = os.path.join(mask_dir, fn[:-4] + '.png') if os.path.isfile(mask_fp_jpg): self.all_mask_files.append(mask_fp_jpg) elif os.path.isfile(mask_fp_png): self.all_mask_files.append(mask_fp_png) else: raise Exception('No matching mask file found for ' + fore_fp) for fn in os.listdir(back_dir): back_fp = os.path.join(back_dir, fn) self.all_back_files.append(back_fp) self.fore_count = len(self.all_fore_files) self.back_count = len(self.all_back_files) print('Image file count: ' + str(self.fore_count) + ' foreground, ' + str(self.back_count) + ' background, has masks: ' + str(self.has_masks)) def __len__(self): return self.fore_count def __getitem__(self, idx): # Force randomness (especially if num_workers > 0) np.random.seed(idx + int((time.time() * 654321) % 123456)) # Read random pair of images from file system success = False while not(success): file_idx = np.random.choice(self.fore_count) fp = self.all_fore_files[file_idx] fore, success = read_image_robust(fp) if not(success): continue if self.has_masks: fp = self.all_mask_files[file_idx] mask, success = read_image_robust(fp, monochromatic=True) assert(success) # must match fore # mask = ((mask > 0) * 255.0).astype('uint8') # convert soft masks to hard else: mask = None # Read random background image success = False while not(success): file_idx2 = np.random.choice(self.back_count) fp = self.all_back_files[file_idx2] back, success = read_image_robust(fp) # Read irrelevant foreground image success = False while not(success): file_idx3 = np.random.choice(self.fore_count) if file_idx3 == file_idx: continue # try again, cannot pick same image fp = self.all_fore_files[file_idx3] irrel, success = read_image_robust(fp) # Transform foregrounds (+ masks) and backgrounds # NOTE: identical random choices must be made for some images if self.rand_horz_flip: if np.random.rand() < 0.5: fore = fore[:, ::-1, :].copy() if self.has_masks: mask = mask[:, ::-1, :].copy() if np.random.rand() < 0.5: irrel = irrel[:, ::-1, :].copy() if np.random.rand() < 0.5: back = back[:, ::-1, :].copy() fore = self.post_tf(fore) irrel = self.post_tf(irrel) back = self.post_tf(back) if self.has_masks: mask = self.post_tf(mask) # Verify sizes assert(fore.shape[1:] == irrel.shape[1:]) assert(fore.shape[1:] == back.shape[1:]) if self.has_masks: assert(fore.shape[1:] == mask.shape[1:]) # Create grounded fake mask and composite width, height = fore.shape[2], fore.shape[1] # fore is (C, H, W) gfake_mask = self.post_tf(create_random_gfake_mask(width, height)) comp_gfake = copy_paste(fore, gfake_mask, back) # Construct dictionary; object count is unknown result = {'fore': fore, 'back': back, 'irrel': irrel, 'object_cnt': 1, 'gfake_mask': gfake_mask, 'comp_gfake': comp_gfake} if self.has_masks: result['mask'] = mask # don't set None, otherwise crash return result class MySquaresDataset(Dataset): ''' Custom dataset class with just a collection of background images as source. One or more artificial objects are painted to create a foreground, keeping track of object count. Returns irrelevant foreground anti-shortcuts as well. Enforces color (RGB) images. ''' def __init__(self, back_dir, rand_horz_flip=True, noisy=False, max_objects=10): self.back_dir = back_dir self.rand_horz_flip = rand_horz_flip self.post_tf = transforms.ToTensor() # converts [0, 255] to [0.0, 1.0] self.noisy = noisy self.max_objects = max_objects # Load all file paths; file names must be the same across all 2 or 3 given directories self.all_back_files = [] for fn in os.listdir(back_dir): back_fp = os.path.join(back_dir, fn) self.all_back_files.append(back_fp) self.file_count = len(self.all_back_files) print('Image file count: ' + str(self.file_count) + ', noisy: ' + str(self.noisy) + ', max objects: ' + str(self.max_objects)) def __len__(self): return self.file_count def __getitem__(self, idx): # Read a random triplet (relevant + background + irrelevant) of non-overlapping backgrounds from file system success = False while not(success): file_idx = np.random.choice(self.file_count) fp = self.all_back_files[file_idx] fore, success = read_image_robust(fp) success = False while not(success): file_idx2 = np.random.choice(self.file_count) if file_idx2 == file_idx: continue # try again, cannot pick same image fp = self.all_back_files[file_idx2] back, success = read_image_robust(fp) success = False while not(success): file_idx3 = np.random.choice(self.file_count) if file_idx3 == file_idx or file_idx3 == file_idx2: continue # try again, cannot pick same image fp = self.all_back_files[file_idx3] irrel, success = read_image_robust(fp) # Create corresponding foregrounds and masks; leave actual background unchanged fore, masks, object_cnt = paint_squares(fore, noisy=self.noisy, channels=self.max_objects) irrel, _, _ = paint_squares(irrel, noisy=self.noisy, channels=self.max_objects) # Transform foregrounds (+ masks) and backgrounds # NOTE: identical random choices must be made for some images if self.rand_horz_flip: if np.random.rand() < 0.5: fore = fore[:, ::-1, :].copy() masks = masks[:, ::-1, :].copy() if np.random.rand() < 0.5: irrel = irrel[:, ::-1, :].copy() if np.random.rand() < 0.5: back = back[:, ::-1, :].copy() fore = self.post_tf(fore) masks = self.post_tf(masks) irrel = self.post_tf(irrel) back = self.post_tf(back) # Create grounded fake mask and composite width, height = fore.shape[2], fore.shape[1] # fore is (C, H, W) gfake_mask = self.post_tf(create_random_gfake_mask(width, height)) comp_gfake = copy_paste(fore, gfake_mask, back) # Construct dictionary result = {'fore': fore, 'back': back, 'irrel': irrel, 'mask': masks, 'object_cnt': object_cnt, 'gfake_mask': gfake_mask, 'comp_gfake': comp_gfake} return result

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dpx2ffv1/test.py

DerouineauNicolas/dpx_to_ffv1

0

1605438

<reponame>DerouineauNicolas/dpx_to_ffv1<filename>dpx2ffv1/test.py<gh_stars>0 from unittest import TestCase from dpx2ffv1.dpx2ffv1 import dpx2ffv1 class TestJoke(TestCase): def test_main_function(self): out = dpx2ffv1('./test/', 'out.mkv', 24) assert(out == 0) if __name__ == '__main__': unittest.main()

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RSA_keys.py

davarbri/jpegForensics

0

59093

<filename>RSA_keys.py # !/usr/bin/python3 # RSA_keys.py # <NAME> # January 2022 # usage: python RSA_keys.py <bitslenght> # -------------------------------------------- # The script generates a public and private # RSA key pair of the desired length. # Only 1024, 2048 and 4096 bits are possible # -------------------------------------------- import sys from Crypto.PublicKey import RSA def main(): # possible key length in bits valid_length_keys = [1024,2048,4096] try: length = int(sys.argv[1]) if (length in valid_length_keys): generate_key(length) else: print("That was no valid length") except ValueError: print("Oops! That was no valid input. Try again...") def generate_key(length): print("[] Generating", length, "bits RSA key pair...") key = RSA.generate(length) # Private key private_key = key.export_key() try: file_out = open("private.pem", "wb") file_out.write(private_key) print(" [+] Private keyring file written...") except IOError: msg = "Unable to create Private Key File, private.pem, on disk" print(msg) finally: file_out.close() # Public key public_key = key.publickey().export_key() try: file_out = open("public.pem", "wb") file_out.write(public_key) print(" [+] Public keyring file written...") except IOError: msg = "Unable to create Public Key File, public.pem, on disk" print(msg) finally: file_out.close() if __name__ == "__main__": if len(sys.argv) != 2: print("usage: python RSA_keys.py <bitslenght>") else: main()

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test/test_vlan_group.py

nrfta/python-netbox-client

0

16388

<filename>test/test_vlan_group.py # coding: utf-8 """ NetBox API API to access NetBox # noqa: E501 OpenAPI spec version: 2.8 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import netbox_client from netbox_client.models.vlan_group import VLANGroup # noqa: E501 from netbox_client.rest import ApiException class TestVLANGroup(unittest.TestCase): """VLANGroup unit test stubs""" def setUp(self): pass def tearDown(self): pass def testVLANGroup(self): """Test VLANGroup""" # FIXME: construct object with mandatory attributes with example values # model = netbox_client.models.vlan_group.VLANGroup() # noqa: E501 pass if __name__ == '__main__': unittest.main()

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Linked List/ListNode.py

paulpatault/Algorithmic

0

154208

<gh_stars>0 class ListNode(object): def __init__(self, x): self.val = x self.next = None def __repr__(self): node = self output = "" while node != None: output += str(node.val) output += " " node = node.next return output

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project/repository/user.py

tobiasaditya/fastapi-blog

0

21890

from typing import List from fastapi import APIRouter from fastapi.params import Depends from fastapi import HTTPException, status from sqlalchemy.orm.session import Session from project import schema, models, database, hashing router = APIRouter( prefix="/user", tags=['Users'] ) @router.post('/new') def create_user(request:schema.User, db:Session = Depends(database.get_db)): hashed_pass = hashing.get_password_hash(request.password) new_user = models.User(name = request.name,username = request.username, password = hashed_pass) db.add(new_user) db.commit() db.refresh(new_user) return request @router.get('/find', response_model= List[schema.showUser]) def show_user_all(db:Session=Depends(database.get_db)): all_users = db.query(models.User).all() return all_users @router.get('/find/{id}',response_model= schema.showUser) def show_user_id(id:int, db:Session = Depends(database.get_db)): selected_project = db.query(models.User).filter(models.User.id == id).first() if not selected_project: raise HTTPException(status_code=status.HTTP_404_NOT_FOUND,detail=f"User {id} not found.") return selected_project # @router.put('/{id}') # def update_project_id(id:int,request:schema.Project,db:Session = Depends(database.get_db)): # #Search for projects' id # selected_project = db.query(models.Project).filter(models.Project.id == id) # if not selected_project.first(): # raise HTTPException(status_code=status.HTTP_404_NOT_FOUND,detail=f"Project {id} not found.") # selected_project.update(dict(request)) # return {'status':f'project {id} updated'} # @router.delete('/{id}') # def delete_project_id(id:int,db:Session = Depends(database.get_db)): # selected_project = db.query(models.Project).filter(models.Project.id == id).first() # if not selected_project: # raise HTTPException(status_code=status.HTTP_404_NOT_FOUND,detail=f"Project {id} not found.") # db.delete(selected_project) # db.commit() # return {'status':f'delete project_id {id} successful'}

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test_mersenne.py

Crulzor/algorithms-python-intro-ex

0

26134

<reponame>Crulzor/algorithms-python-intro-ex<filename>test_mersenne.py from mersenne import generatePotentialMP, isPrime def test_generatePotentialMP(): assert(generatePotentialMP(2) == 3) assert(generatePotentialMP(1) ==1) def test_isPrime(): assert(isPrime(7)) assert(isPrime(2)) assert(isPrime(3)) assert(isPrime(4) == False)

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tests/test_files/kwonly_arg_annotated.py

misteraverin/flake8-annotations-coverage

29

159131

<reponame>misteraverin/flake8-annotations-coverage def foo(): pass def bar(*args, kwonly_arg: str = None): pass

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setup.py

NoamNol/gematriapy

1

96771

<reponame>NoamNol/gematriapy import setuptools import gematriapy with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() setuptools.setup( name="gematriapy", version=gematriapy.__version__, author="<NAME>", author_email="<EMAIL>", description=gematriapy.__doc__, long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/NoamNol/gematriapy", packages=['gematriapy'], license="MIT", classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", 'Development Status :: 4 - Beta', ], keywords="hebrew gematria gematriapy", python_requires='>=3.7', )

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upcfcardsearch/c106.py

ProfessorSean/Kasutamaiza

0

192069

<filename>upcfcardsearch/c106.py import discord from discord.ext import commands from discord.utils import get class c106(commands.Cog, name="c106"): def __init__(self, bot: commands.Bot): self.bot = bot @commands.command(name='Golden_Statue_in_the_Sky', aliases=['c106']) async def example_embed(self, ctx): embed = discord.Embed(title='Golden Statue in the Sky', color=0xA086B7) embed.set_thumbnail(url='https://www.duelingbook.com/images/custom-pics/2300000/2321563.jpg') embed.add_field(name='Status (Archetype)', value='Casual:3/Tournament:3', inline=True) embed.add_field(name='Type (Attribute)', value='Rock/Fusion (LIGHT)', inline=False) embed.add_field(name='Level (ATK/DEF)', value='8 (1000/3500)', inline=False) embed.add_field(name='Monster Effect', value='1 Fairy monster + 1 Rock monster', inline=False) embed.set_footer(text='Set Code: ANCF') await ctx.send(embed=embed) def setup(bot: commands.Bot): bot.add_cog(c106(bot))

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backoffice/views.py

chervaliery/tv_show_fetcher

0

1600272

from django.http import HttpResponse from models import * from utils import fetch_show, get_show # Create your views here. def home(request): return HttpResponse("Bonjour monde!")

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tnia/viewing/napari_helper.py

True-North-Intelligent-Algorithms/tnia-python

3

104456

import napari def show_image(im, title, viewer=None, label=False): """ convenience helper function to show image in Napari Args: im (numpy array): image to show title (string): title of image viewer (napari instance, optional): pre-existing Napari label (bool, optional): True if a label Returns: [napari instance]: a Napari instance """ if viewer==None: viewer = napari.Viewer() if label==False: viewer.add_image(im,name=title) else: viewer.add_labels(im,name=title) return viewer def show_image_and_label(im, label, title, viewer=None): """[summary] Args: im (numpy array): image to show label (numpy integer array): labels to show title (string): title of image viewer (napari instance, optional): pre-existing Napari Returns: [napari instance]: a Napari instance """ viewer = show_image(im, title, viewer, False) show_image(label, title+' label', viewer, True) return viewer

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time_calculator.py

yashvarshney003/Time-Calculator-Freecodecamp

0

192439

<filename>time_calculator.py def add_time(start, duration, day=None): time, period = start.split() initial_period = period hr_start, min_start = time.split(':') hr_duration, min_duration = duration.split(':') min_new = int(min_start) + int(min_duration) hr_new = int(hr_start) + int(hr_duration) periods_later = 0 days_later = 0 DAYS_OF_WEEK = [ "Saturday", "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday" ] if min_new > 59: min_new -= 60 hr_new += 1 hr_new_period = hr_new while hr_new > 12: hr_new -= 12 while hr_new_period > 11: hr_new_period -= 12 period = "PM" if period == "AM" else "AM" periods_later += 1 if periods_later % 2 != 0: if initial_period == "PM": periods_later += 1 else: periods_later -= 1 days_later = periods_later / 2 new_time = f"{hr_new}:{str(min_new).zfill(2)} {period}" if day: day_index = DAYS_OF_WEEK.index(day.title()) new_day_index = int((day_index + days_later) % 7) new_time += f", {DAYS_OF_WEEK[new_day_index]}" if days_later == 1: new_time += " (next day)" if days_later > 1: new_time += f" ({int(days_later)} days later)" return new_time

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boto-python-workshop/01_list_objects.py

shy950521/ecs-samples

28

186798

<reponame>shy950521/ecs-samples<gh_stars>10-100 ''' Same stuff as 00_getting_started. If you've pasted all this into your python interpreter already, you can skip past this. ''' from boto.s3.connection import S3Connection, OrdinaryCallingFormat from boto.s3.key import Key from boto.s3.prefix import Prefix host = "object.ecstestdrive.com" port = 443 secure = port == 9021 or port == 443 access_key_id = 'user1' secret_key = '' debug_level = 2 calling_format = OrdinaryCallingFormat() s3 = S3Connection(aws_access_key_id=access_key_id, aws_secret_access_key=secret_key, is_secure=secure, port=port, host=host, debug=debug_level, calling_format=calling_format) bucket_name = "mybucket" bucket = s3.create_bucket(bucket_name) ''' Create a bunch of keys to test with. ''' key_names = ["20151102/account-12345/bill.xml", "20151102/account-12345/bill.pdf", "20151102/account-12345/bill.html", "20151102/account-55555/bill.xml", "20151102/account-55555/bill.pdf", "20151102/account-55555/bill.html", "20151102/account-77777/bill.xml", "20151102/account-77777/bill.pdf", "20151102/account-77777/bill.html", "20151103/account-11111/bill.xml", "20151103/account-11111/bill.pdf", "20151103/account-11111/bill.html", "20151103/account-11122/bill.xml", "20151103/account-11122/bill.pdf", "20151103/account-11122/bill.html", "20151103/account-11133/bill.xml", "20151103/account-11133/bill.pdf", "20151103/account-11133/bill.html", "20141103/account-11111/bill.xml", "20141103/account-11111/bill.pdf", "20141103/account-11111/bill.html", "20141103/account-11122/bill.xml", "20141103/account-11122/bill.pdf", "20141103/account-11122/bill.html", "20141103/account-11133/bill.xml", "20141103/account-11133/bill.pdf", "20141103/account-11133/bill.html"] for key_name in key_names: key = bucket.new_key(key_name) key.set_contents_from_string("data for key %s" % key_name, {"content-type": "text/plain"}) def bucket_list(): """ Start a loop to experiment with bucket listing. """ running = True while running: prefix = raw_input("Prefix: ") delimiter = raw_input("Delimiter: ") marker = raw_input("Marker: ") results = bucket.list(prefix, delimiter, marker) print "%30s %10s %s" % ("LastModified", "Size", "Key") print "------------------------------ ---------- ------------------------------------------" for result in results: if type(result) is Key: print "%30s %10d %s" % (result.last_modified, result.size, result.name) if type(result) is Prefix: print "%30s %10s %s" % ("", "(PREFIX)", result.name) again = raw_input("Again? (y/n) ") running = again == "y" bucket_list()

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auxiliary/auxiliary.py

amageh/respy-tut

0

76491

<reponame>amageh/respy-tut """This file contains auxiliary function that we use throughout the respy tutorial.""" import matplotlib.pyplot as plt import pandas as pd def plot_fishing_grounds(df): """Plot choice shares for observables example.""" fig, ax = plt.subplots(1, 2, figsize=(10, 4)) for i, observable in enumerate(["rich", "poor"]): df.query("Fishing_Grounds == @observable").groupby( "Period" ).Choice.value_counts(normalize=True,).unstack().plot.bar( width=0.4, stacked=True, rot=0, legend=False, ax=ax[i] ) ax[i].set_title("Fishing grounds: " + observable, pad=10) ax[i].xaxis.label.set_visible(False) plt.legend(loc="lower center", bbox_to_anchor=(-0.15, -0.3), ncol=2) plt.suptitle("Robinson's choices by period", y=1.05) plt.show() def plot_choice_shares(df, friday=False): """Plot choice shares.""" if friday: color = ["C0", "C2", "C1"] choices = 3 else: color = ["C0", "C1"] choices = 2 fig, ax = plt.subplots() df.groupby("Period").Choice.value_counts(normalize=True).unstack().plot.bar( stacked=True, ax=ax, color=color ) plt.xticks(rotation="horizontal") plt.legend(loc="lower center", bbox_to_anchor=(0.5, -0.275), ncol=choices) plt.show() plt.close() def plot_choice_prob_and_exp_level(df): """Plot choices and experience levels.""" fig, axs = plt.subplots(1, 2, figsize=(10, 4)) ( df.groupby("Period") .Choice.value_counts(normalize=True) .unstack() .plot.bar(ax=axs[0], stacked=True, rot=0, title="Choice Probabilities") ) ( df.groupby("Period") .Experience_Fishing.value_counts(normalize=True) .unstack() .plot.bar( ax=axs[1], stacked=True, rot=0, title="Share of Experience Level per Period", cmap="Blues", ) ) axs[0].legend(loc="lower center", bbox_to_anchor=(0.5, -0.4), ncol=2) axs[1].legend(loc="right", bbox_to_anchor=(1.3, 0.5), ncol=1, title="Experience") plt.show() def plot_choice_prob(df): """Plot choices.""" fig, ax = plt.subplots() df.groupby("Period").Choice.value_counts(normalize=True).unstack().plot.bar( stacked=True, ax=ax ) plt.xticks(rotation="horizontal") plt.legend(loc="lower center", bbox_to_anchor=(0.5, -0.275), ncol=2) plt.show() plt.close() def plot_choices_kw(example="1994"): """Plot models from Keane and Wolpin (1994,1997,2000).""" if example == "1994": fig, ax = plt.subplots(1, 3, figsize=(15, 5)) models = ["kw_94_one", "kw_94_two", "kw_94_three"] if example == "1997": fig, ax = plt.subplots(1, 2, figsize=(15, 5)) models = ["kw_97_basic", "kw_97_extended"] for i, model in enumerate(models): df = pd.read_pickle(f"data/choices_{model}.pkl") df.plot( alpha=0.8, stacked=True, ax=ax[i], legend=False, kind="area", rot=0, title=f"Example model: {model}", linewidth=0, ) df.plot(stacked=True, ax=ax[i], legend=False, color="white", linewidth=1, rot=0) ax[i].margins(x=0) ax[i].spines["right"].set_visible(False) ax[i].spines["top"].set_visible(False) handles, labels = plt.gca().get_legend_handles_labels() by_label = dict(zip(labels, handles)) if len(ax) % 2 == 1: move_left = -0.7 else: move_left = -0.2 plt.legend( by_label.values(), by_label.keys(), loc="lower center", ncol=len(df.columns), bbox_to_anchor=(move_left, -0.25), fontsize=12, ) plt.show() def plot_kw_97_comparison(): """Plot comparison of KW 97 models and observed data.""" models = ["kw_97_obs", "kw_97_basic", "kw_97_extended"] colors = ["tab:blue", "tab:orange", "tab:green", "tab:red", "tab:purple"] data = {} for model in models: df = pd.read_pickle(f"data/choices_{model}.pkl") data[model] = df fig, ax = plt.subplots(2, 3, figsize=(15, 9)) ax = ax.ravel() for i, (choice, color) in enumerate(zip(df.columns, colors)): ax[i].plot( data["kw_97_obs"][choice], linestyle="dashed", color="k", label="Observed" ) ax[i].plot( data["kw_97_basic"][choice], linestyle="dotted", alpha=0.8, color=color, label="kw_97_basic", ) ax[i].plot( data["kw_97_extended"][choice], color=color, alpha=0.8, label="kw_97_extended", ) ax[i].set_title(choice.capitalize().replace("_", " ")) ax[i].set_xlabel("Period") ax[4].legend( loc="lower center", ncol=5, bbox_to_anchor=(-0.3, -0.3), fontsize=12, ) fig.delaxes(ax[5]) plt.show()

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monitor/rotation.py

UWCubeSat/bairing

0

116388

import serial import struct import time import numpy as np from ahrs.filters import Madgwick from cube import CubeRenderer sync = b'\xEF\xBE\xAD\xDE' def reset(ser): ser.write(sync) ser.write(b'\x01\x00') ser.write(b'\x00') def begin(ser): ser.write(sync) ser.write(b'\x03\x00') ser.write(b'\x10') ser.write(b'\x32\0x00') # 50 ms period def receive(ser): recognized = False pattern = [ 0, 0, 0, 0 ] while not recognized: pattern.append(ser.read(1)) # sync pattern pattern.pop(0) recognized = True for i in range(0, 4): recognized = recognized and (pattern[i] == sync[i:i+1]) rcvd = ser.read(2) len = struct.unpack('<H', rcvd)[0] # packet length cmd = ser.read(1) data = ser.read(len - 1) return [cmd, data] def main(): connecting = True while connecting: try: ser = serial.Serial('COM4') connecting = False except serial.serialutil.SerialException: print("Connection to COM4 timed out, retrying...") reset(ser) print("resetting") time.sleep(3) begin(ser) # q_last = np.array([0.7071, -0.7071, 0.0, 0.0]) q_last = np.array([1., 0., 0., 0.]) madgwick = Madgwick(Dt = 0.05, q0 = q_last) renderer = CubeRenderer() while True: [cmd, data] = receive(ser) if cmd == b'\x10': raw = struct.unpack('<fffffffff', data) # 3 float vectors, 9 floats mag = raw[0:3] acc = raw[3:6] gyro = raw[6:9] # q_last = madgwick.updateMARG(q_last, gyr=gyro, acc=acc, mag=mag) q_last = madgwick.updateIMU(q_last, gyr=gyro, acc=acc) print(q_last) renderer.render(q_last) if __name__ == '__main__': main()

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src/train.py

saikrishnaklu/Kaggle_Categorical_Feature_Encoding-Challenge_II

1

103442

from __future__ import print_function import os from sklearn import preprocessing from sklearn import ensemble from sklearn.metrics import accuracy_score,roc_auc_score import pandas as pd import joblib import numpy as np #import dispatcher folds={ 0:[1,2,3,4], 1:[0,2,3,4], 2:[1,0,3,4], 3:[1,2,0,4], 4:[1,2,3,0], } TRAINING="input/train_folds.csv" TEST="input/test.csv" FOLD=2 MODEL="randomforest" MODEL2="logisticregression" """ TRAINING=os.getenv(['TRAINING']) TEST=os.getenv(['TEST']) FOLD=os.getenv(['FOLD']) MODEL=os.getenv(['MODEL']) """ MODELS={ "randomforest":ensemble.RandomForestClassifier(n_estimators=200,n_jobs=-1,verbose=2), "extratreesclassfier":ensemble.ExtraTreesClassifier(n_estimators=200,n_jobs=-1,verbose=2) } if __name__=="__main__": df_train_main=pd.read_csv(TRAINING) df_test=pd.read_csv(TEST) df_train=df_train_main[df_train_main.kfold.isin(folds.get(FOLD))].reset_index(drop=True) df_val=df_train[df_train_main.kfold==0].reset_index(drop=True) df_y_train=df_train.target.values df_y_val=df_val.target.values df_train=df_train.drop(['id','target','kfold'],axis=1) df_val=df_val.drop(['id','target','kfold'],axis=1) df_val=df_val[df_train.columns] label_encoders={} for c in df_train.columns: label_encoder=preprocessing.LabelEncoder() label_encoder.fit(df_train[c].values.tolist()+df_val[c].values.tolist()+df_test[c].values.tolist()) onehot_encoder=preprocessing.OneHotEncoder() df_train.loc[:,c]=label_encoder.transform(df_train[c].values.tolist()) df_val.loc[:,c]=label_encoder.transform(df_val[c].values.tolist()) label_encoders[c]=label_encoder model=MODELS[MODEL] model.fit(df_train,df_y_train) pred=model.predict_proba(df_val)[:,1] pred2=model.predict(df_val) print(roc_auc_score(df_y_val,pred)) print(accuracy_score(df_y_val,pred2)) joblib.dump(label_encoders,f"models/{MODEL}_{FOLD}_label_encoder.pkl") joblib.dump(model,f"models/{MODEL}_{FOLD}.pkl") joblib.dump(df_train.columns,f"models/{MODEL}_{FOLD}_columns.pkl")

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notebooks/__code/_utilities/array.py

mabrahamdevops/python_notebooks

0

128582

<reponame>mabrahamdevops/python_notebooks import numpy as np def exclude_y_value_when_error_is_nan(axis, error_axis): axis_cleaned = [] error_axis_cleaned = [] for _x, _error in zip(axis, error_axis): if (_x == "None") or (_error == "None") or (_x is None) or (_error is None): axis_cleaned.append(np.NaN) error_axis_cleaned.append(np.NaN) else: axis_cleaned.append(np.float(_x)) error_axis_cleaned.append(np.float(_error)) return axis_cleaned, error_axis_cleaned def check_size(x_axis=None, y_axis=None): size_x = len(x_axis) size_y = len(y_axis) min_len = np.min([size_x, size_y]) return x_axis[:min_len], y_axis[:min_len] def get_n_random_int_of_max_value_m(n=1, max=10): list_random = np.random.random(n) * max list_random_int = [np.int(_value) for _value in list_random] return list_random_int def reject_outliers(array=None): if len(array) < 3: return array new_array = [_value for _value in array if (not (_value == np.min(array))) and (not (_value == np.max(array)))] return new_array def reject_n_outliers(array=None, n=1): set_array = list(array) set_array.sort() return set_array[n: -n] def find_nearest_index(array, value): idx = (np.abs(np.array(array) - value)).argmin() return idx def get_closest_index(array=None, value=None): idx = (np.abs(np.array(array) - value)).argmin() return idx def get_nearest_index(array=None, value=None): return get_closest_index(array=array, value=value)

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chapter_3/ex_3-10.py

akshaymoharir/PythonCrashCourse

0

62899

<reponame>akshaymoharir/PythonCrashCourse<filename>chapter_3/ex_3-10.py ## Python Crash Course # Exercise 3.10: Every Function: # Think of something you could store in a list. # For example, you could make a list of mountains, rivers, countries, cities, languages, or any- thing else you’d like. # Write a program that creates a list containing these items and then uses each function introduced in this chapter at least once . def main(): print("Skipping this exercise since this is redundant exercise to use previously used functions again.") if __name__ == '__main__': main()

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fred_crawler.py

Tyromancer/economics.py

0

46646

# Usage: # Find and download fred series data # then find the specific data on yyyy-mm-dd import requests # used for downloading text files from FRED import os # used for removing temporary text files # Usage : download series data in the form of text file and extract data on given date # param: # series: the series number # year : target year. Format: 4 digit YYYY # month : target month. Format: 2 digit MM # day : target day. Format: 2 digit DD def fred_crawler(series, year, month, day): r = requests.get('https://fred.stlouisfed.org/data/' + series + '.txt') fname = series + 'txt' with open(fname, 'wb') as f: f.write(r.content) with open(fname, 'r', encoding='utf-8') as f: lines = f.readlines() data_file = dict() for line in lines: if not line[0].isdigit(): continue else: temp = line.rstrip().split() data_file[temp[0]] = temp[1] if len(month) == 1: month = '0' + mm if len(day) == 1: day = '0' + day date = year + '-' + month + '-' + day # print('The result is: {}'.format(data_file[date])) print("{} {}: {}".format(series.upper(), date, data_file[date])) os.remove(fname) if __name__ == "__main__": query = input('Please enter the series ID ==> ').upper() yyyy = input('Year == > ') mm = input('Month ==> ') dd = input('Day ==> ') fred_crawler(query, yyyy, mm, dd)

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pyscf/nao/test/test_0003_na2_nao.py

robert-anderson/pyscf

3

6101

<filename>pyscf/nao/test/test_0003_na2_nao.py # Copyright 2014-2018 The PySCF Developers. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest from pyscf.nao.m_siesta_utils import get_siesta_command, get_pseudo class KnowValues(unittest.TestCase): def test_siesta2sv_df(self): import subprocess import os siesta_fdf = """ xml.write .true. PAO.EnergyShift 100 meV %block ChemicalSpeciesLabel 1 11 Na %endblock ChemicalSpeciesLabel NumberOfAtoms 2 NumberOfSpecies 1 %block AtomicCoordinatesAndAtomicSpecies 0.77573521 0.00000000 0.00000000 1 -0.77573521 0.00000000 0.00000000 1 %endblock AtomicCoordinatesAndAtomicSpecies MD.NumCGsteps 0 COOP.Write .true. WriteDenchar .true. """ label = 'siesta' fi = open(label+'.fdf', 'w') print(siesta_fdf, file=fi) fi.close() for sp in ['Na']: try: os.remove(sp+'.psf') except : pass try: pppath = get_pseudo(sp) except: print('get_pseudo( '+sp+' ) is not working--> skip siesta run' ) return os.symlink(pppath, sp+'.psf') errorcode = subprocess.call(get_siesta_command(label), shell=True) if errorcode: raise RuntimeError('siesta returned an error: {0}'.format(errorcode)) # run test system_vars from pyscf.nao import mf sv = mf(label=label) self.assertEqual(sv.norbs, 10) self.assertTrue( sv.diag_check() ) self.assertTrue( sv.overlap_check()) if __name__ == "__main__": unittest.main()

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scripts/add_probs.py

sharmaeklavya2/ekuOJ

10

109871

""" This module has functions which syncs problems data in database with data in OJ_data """ import os FILE_PATH = os.path.abspath(__file__) BASE_DIR = os.path.dirname(os.path.dirname(FILE_PATH)) import sys if BASE_DIR not in sys.path: sys.path.append(BASE_DIR) import json def add_prob(prob_path, contest=None): pcode = os.path.basename(prob_path) if contest==None: ccode = os.path.basename(os.path.dirname(prob_path)) contest = Contest.objects.get_or_create(ccode=ccode)[0] (problem, created) = Problem.objects.get_or_create(pcode=pcode, contest=contest) title_path = os.path.join(prob_path, "title.txt") data_path = os.path.join(prob_path, "data.json") try: problem.title = open(title_path).read().strip() except FileNotFoundError: pass try: prob_data = json.load(open(data_path)) problem.source_lim = prob_data.get("source_lim", None) problem.time_lim_s = prob_data.get("time_lim_s", None) problem.mem_lim_k = prob_data.get("mem_lim_k", None) problem.output_lim_k = prob_data.get("output_lim_k", None) except FileNotFoundError: pass except ValueError as e: print("The following error was encountered while reading", os.path.join(ccode, pcode, "data.json")+".", "The file has been ignored.") print(e) problem.save() return created def add_contest(contest_path): # create contest object in db ccode = os.path.basename(contest_path) (contest, created) = Contest.objects.get_or_create(ccode=ccode) title_path = os.path.join(contest_path, "title.txt") try: contest.title = open(title_path).read().strip() except FileNotFoundError: pass contest.save() # add problems to db pcodes = os.listdir(contest_path) for pcode in pcodes: prob_path = os.path.join(contest_path, pcode) add_prob(prob_path, contest) return created FILE_NAME = os.path.basename(FILE_PATH) if __name__=="__main__": USAGE_STR = "usage: python3 " + FILE_NAME + " <contest_codes>" else: USAGE_STR = "No contests specified" CONTESTS_DIR_PATH = os.path.join(BASE_DIR, "OJ_data", "contests") import sys def main(*args): if len(args)==0: print(USAGE_STR, file=sys.stderr) else: for ccode in args: contest_path = os.path.join(CONTESTS_DIR_PATH, ccode) created = add_contest(contest_path) if created: print("Added contest", ccode) else: print("Updated contest", ccode) if __name__=="__main__": # set up django os.environ.setdefault("DJANGO_SETTINGS_MODULE", "project_conf.settings") print("Setting up Django ...", flush=True, end='') import django django.setup() print(" done") from main.models import Problem, Contest if __name__=="__main__": main(*(sys.argv[1:]))

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hydrobox/geostat/variogram.py

VForWaTer/hydrobox

4

183855

<filename>hydrobox/geostat/variogram.py import numpy as np import skgstat as skg import gstools as gs from hydrobox.geostat import typing from hydrobox.plotting import plot_function_loader def variogram( coordinates: np.ndarray, values: np.ndarray, estimator: typing.Estimator = 'matheron', model: typing.Model = 'spherical', dist_func: typing.DistFunc = 'euclidean', bin_func: typing.BinFunc = 'even', fit_method: typing.FitMethod = 'trf', fit_sigma: typing.FitSigma = None, use_nugget: bool = False, maxlag: typing.Maxlag = None, n_lags: typing.Union[int, None] = 10, return_type: typing.Literal['object', 'describe', 'plot', 'distance_difference', 'location_trend', 'scattergram'] = 'object', **kwargs ) -> skg.Variogram: """ Uses scikit-gstat to estimate a Variogram. Refer to the :class:`Variogram <skgstat.Variogram>` class to learn about the different Parameters. Parameters ---------- return_type : str Specify how the Variogram instance should be returned. Object will return the actual instance. 'describe' is the dictionary output generated by the class, which is serializable. All other options return the different plot types Returns ------- variogram : skgstat.Variogram If the return type is `'object'` plot : plotly.Figure, matplotlib.pyplot.Figure If the return type is one of the plots description : dict If the return type is `'describe'` """ # create the variogram v = skg.Variogram( coordinates=coordinates, values=values, estimator=estimator, model=model, dist_func=dist_func, bin_func=bin_func, fit_method=fit_method, fit_sigma=fit_sigma, use_nugget=use_nugget, maxlag=maxlag, n_lags=n_lags, **kwargs ) if return_type == 'object': return v elif return_type == 'describe': return v.describe(short=False, flat=False) # otherwise create a plot pfunc = plot_function_loader('variogram') fig = pfunc( func_args=dict( variogram=v, plot_type=return_type ), plot_args=kwargs ) return fig

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seeds/plugins/action/StopOnConvergence.py

briandconnelly/seeds

11

146974

<reponame>briandconnelly/seeds # -*- coding: utf-8 -*- """ Stop the experiment when the number of existing Cell types in the population drops below a threshold. This allows experiments to end earlier than the configured number if epochs have passed or experiments to not specify the total number of epochs to observe. This is accomplished by calling the Experiment's end method. """ __author__ = "<NAME> <<EMAIL>>" __credits__ = "<NAME>" from seeds.Action import * from seeds.Plugin import * from seeds.SEEDSError import * class StopOnConvergence(Action, Plugin): """ Stop the experiment when the number of exisiting cell types in the population drops below the configured threshold. Configuration is done in the [StopOnConvergence] section Configuration Options: epoch_start The epoch at which to start executing (default: 0) epoch_end The epoch at which to stop executing (default: end of experiment) frequency The frequency (epochs) at which to execute (default: 1) priority The priority of this action. Actions with higher priority get run first. (default: 0) threshold The number of cell types at or below which to stop the experiment (default: 0) Configuration Example: [StopOnConvergence] threshold = 2 epoch_start = 0 epoch_end = 100 frequency = 1 priority = 0 """ __name__ = "StopOnConvergence" __version__ = (1,0) __author__ = "<NAME> <<EMAIL>>" __credits__ = "<NAME>" __description__ = "Stop the experiment when the number of existiing cell types in the population falls below the given threshold" __type__ = 4 __requirements__ = [] def __init__(self, experiment, label=None): """Initialize the StopOnConvergence Action""" super(StopOnConvergence, self).__init__(experiment, name="StopOnConvergence", label=label) self.threshold = self.experiment.config.getint(self.config_section, 'threshold', 0) self.epoch_start = self.experiment.config.getint(self.config_section, 'epoch_start', 0) self.epoch_end = self.experiment.config.getint(self.config_section, 'epoch_end', default=self.experiment.config.getint('Experiment', 'epochs', default=-1)) self.frequency = self.experiment.config.getint(self.config_section, 'frequency', 1) self.priority = self.experiment.config.getint(self.config_section, 'priority', 0) if self.threshold < 0: raise ConfigurationError("StopOnConvergence: threshold value must be at least 0") def update(self): """Execute the Action""" if self.skip_update(): return converged = True num_active_types = 0 for tcount in self.experiment.data['population']['type_count']: if tcount > 0: num_active_types += 1 if num_active_types >= self.threshold: converged = False if converged: self.experiment.end()

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examples/chanlun/feature/03/ex_setup_commands.py

garywangiam02/vnpy

0

67069

# coding=utf-8 from pytdx.parser.base import BaseParser from pytdx.helper import get_datetime, get_volume, get_price from collections import OrderedDict import struct class ExSetupCmd1(BaseParser): def setup(self): # self.send_pkg = bytearray.fromhex("01 01 48 65 00 01 52 00 52 00 54 24 1f 32 c6 e5" # "d5 3d fb 41 1f 32 c6 e5 d5 3d fb 41 1f 32 c6 e5" # "d5 3d fb 41 1f 32 c6 e5 d5 3d fb 41 1f 32 c6 e5" # "d5 3d fb 41 1f 32 c6 e5 d5 3d fb 41 1f 32 c6 e5" # "d5 3d fb 41 1f 32 c6 e5 d5 3d fb 41 cc e1 6d ff" # "d5 ba 3f b8 cb c5 7a 05 4f 77 48 ea") self.send_pkg = bytearray.fromhex("01 01 48 65 00 01 52 00 52 00 54 24" "FC F0 0E 92 F3 C8 37 83 1F 32 C6 E5 D5 3D FB 41 CD 9C" "F2 07 FC D0 3C F6 F2 F7 A4 77 47 83 1D 59 9D CC 1F 91" "D5 55 82 DC 09 07 EE 29 DD FE 4C 28 1F 32 C6 E5 D5 3D" "FB 41 1F 32 C6 E5 D5 3D FB 41 F3 43 87 E6 68 A9 2A A3" "70 11 E4 9C D2 6E B0 1A") def parseResponse(self, body_buf): pass

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asyncupbankapi/models/__init__.py

unchartedshark/async-up-bank-api

1

95562

<gh_stars>1-10 """Typed python client for interacting with Up's banking API.""" from asyncupbankapi.models.accounts import Account, Accounts from asyncupbankapi.models.categories import Category, Categories from asyncupbankapi.models.tags import Tags from asyncupbankapi.models.transactions import Transaction, Transactions from asyncupbankapi.models.utility import Ping from asyncupbankapi.models.webhooks import Webhook, WebhookEvent, WebhookLogs, Webhooks

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hikari/api/special_endpoints.py

patchwork-systems/hikari

0

195915

<reponame>patchwork-systems/hikari # -*- coding: utf-8 -*- # cython: language_level=3 # Copyright (c) 2020 Nekokatt # Copyright (c) 2021-present davfsa # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """Special additional endpoints used by the REST API.""" from __future__ import annotations __all__: typing.List[str] = [ "ActionRowBuilder", "ButtonBuilder", "CommandBuilder", "SlashCommandBuilder", "ContextMenuCommandBuilder", "ComponentBuilder", "TypingIndicator", "GuildBuilder", "InteractionAutocompleteBuilder", "InteractionDeferredBuilder", "InteractionResponseBuilder", "InteractionMessageBuilder", "InteractiveButtonBuilder", "LinkButtonBuilder", "SelectMenuBuilder", "SelectOptionBuilder", ] import abc import typing from hikari import undefined if typing.TYPE_CHECKING: import types from hikari import channels from hikari import colors from hikari import commands from hikari import embeds as embeds_ from hikari import emojis from hikari import files from hikari import guilds from hikari import messages from hikari import permissions as permissions_ from hikari import snowflakes from hikari import users from hikari import voices from hikari.api import entity_factory as entity_factory_ from hikari.interactions import base_interactions from hikari.internal import data_binding from hikari.internal import time _T = typing.TypeVar("_T") _SelectMenuBuilderT = typing.TypeVar("_SelectMenuBuilderT", bound="SelectMenuBuilder[typing.Any]") _ContainerT = typing.TypeVar("_ContainerT") class TypingIndicator(abc.ABC): """Result type of `hikari.api.rest.RESTClient.trigger_typing`. This is an object that can either be awaited like a coroutine to trigger the typing indicator once, or an async context manager to keep triggering the typing indicator repeatedly until the context finishes. !!! note This is a helper class that is used by `hikari.api.rest.RESTClient`. You should only ever need to use instances of this class that are produced by that API. """ __slots__: typing.Sequence[str] = () @abc.abstractmethod def __await__(self) -> typing.Generator[None, typing.Any, None]: ... @abc.abstractmethod async def __aenter__(self) -> None: ... @abc.abstractmethod async def __aexit__( self, exception_type: typing.Type[BaseException], exception: BaseException, exception_traceback: types.TracebackType, ) -> None: ... class GuildBuilder(abc.ABC): """Result type of `hikari.api.rest.RESTClient.guild_builder`. This is used to create a guild in a tidy way using the HTTP API, since the logic behind creating a guild on an API level is somewhat confusing and detailed. !!! note This is a helper class that is used by `hikari.api.rest.RESTClient`. You should only ever need to use instances of this class that are produced by that API, thus, any details about the constructor are omitted from the following examples for brevity. Examples -------- Creating an empty guild. ```py guild = await rest.guild_builder("My Server!").create() ``` Creating a guild with an icon ```py from hikari.files import WebResourceStream guild_builder = rest.guild_builder("My Server!") guild_builder.icon = WebResourceStream("cat.png", "http://...") guild = await guild_builder.create() ``` Adding roles to your guild. ```py from hikari.permissions import Permissions guild_builder = rest.guild_builder("My Server!") everyone_role_id = guild_builder.add_role("@everyone") admin_role_id = guild_builder.add_role("Admins", permissions=Permissions.ADMINISTRATOR) await guild_builder.create() ``` !!! warning The first role must always be the `@everyone` role. !!! note If you call `add_role`, the default roles provided by discord will be created. This also applies to the `add_` functions for text channels/voice channels/categories. !!! note Functions that return a `hikari.snowflakes.Snowflake` do **not** provide the final ID that the object will have once the API call is made. The returned IDs are only able to be used to re-reference particular objects while building the guild format. This is provided to allow creation of channels within categories, and to provide permission overwrites. Adding a text channel to your guild. ```py guild_builder = rest.guild_builder("My Server!") category_id = guild_builder.add_category("My safe place") channel_id = guild_builder.add_text_channel("general", parent_id=category_id) await guild_builder.create() ``` """ __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def name(self) -> str: """Name of the guild to create. Returns ------- builtins.str The guild name. """ @property @abc.abstractmethod def default_message_notifications(self) -> undefined.UndefinedOr[guilds.GuildMessageNotificationsLevel]: """Default message notification level that can be overwritten. If not overridden, this will use the Discord default level. Returns ------- hikari.undefined.UndefinedOr[hikari.guilds.GuildMessageNotificationsLevel] The default message notification level, if overwritten. """ # noqa: D401 - Imperative mood @default_message_notifications.setter def default_message_notifications( self, default_message_notifications: undefined.UndefinedOr[guilds.GuildMessageNotificationsLevel], / ) -> None: raise NotImplementedError @property @abc.abstractmethod def explicit_content_filter_level(self) -> undefined.UndefinedOr[guilds.GuildExplicitContentFilterLevel]: """Explicit content filter level that can be overwritten. If not overridden, this will use the Discord default level. Returns ------- hikari.undefined.UndefinedOr[hikari.guilds.GuildExplicitContentFilterLevel] The explicit content filter level, if overwritten. """ @explicit_content_filter_level.setter def explicit_content_filter_level( self, explicit_content_filter_level: undefined.UndefinedOr[guilds.GuildExplicitContentFilterLevel], / ) -> None: raise NotImplementedError @property @abc.abstractmethod def icon(self) -> undefined.UndefinedOr[files.Resourceish]: """Guild icon to use that can be overwritten. If not overridden, the guild will not have an icon. Returns ------- hikari.undefined.UndefinedOr[hikari.files.Resourceish] The guild icon to use, if overwritten. """ @icon.setter def icon(self, icon: undefined.UndefinedOr[files.Resourceish], /) -> None: raise NotImplementedError @property @abc.abstractmethod def verification_level(self) -> undefined.UndefinedOr[typing.Union[guilds.GuildVerificationLevel, int]]: """Verification level required to join the guild that can be overwritten. If not overridden, the guild will use the default verification level for Returns ------- hikari.undefined.UndefinedOr[typing.Union[hikari.guilds.GuildVerificationLevel, builtins.int]] The verification level required to join the guild, if overwritten. """ @verification_level.setter def verification_level( self, verification_level: undefined.UndefinedOr[typing.Union[guilds.GuildVerificationLevel, int]], / ) -> None: raise NotImplementedError @abc.abstractmethod async def create(self) -> guilds.RESTGuild: """Send the request to Discord to create the guild. The application user will be added to this guild as soon as it is created. All IDs that were provided when building this guild will become invalid and will be replaced with real IDs. Returns ------- hikari.guilds.RESTGuild The created guild. Raises ------ hikari.errors.BadRequestError If any values set in the guild builder are invalid. hikari.errors.UnauthorizedError If you are unauthorized to make the request (invalid/missing token). hikari.errors.ForbiddenError If you are already in 10 guilds. hikari.errors.InternalServerError If an internal error occurs on Discord while handling the request. """ @abc.abstractmethod def add_role( self, name: str, /, *, permissions: undefined.UndefinedOr[permissions_.Permissions] = undefined.UNDEFINED, color: undefined.UndefinedOr[colors.Colorish] = undefined.UNDEFINED, colour: undefined.UndefinedOr[colors.Colorish] = undefined.UNDEFINED, hoist: undefined.UndefinedOr[bool] = undefined.UNDEFINED, mentionable: undefined.UndefinedOr[bool] = undefined.UNDEFINED, position: undefined.UndefinedOr[int] = undefined.UNDEFINED, ) -> snowflakes.Snowflake: """Create a role. !!! warning The first role you create must always be the `@everyone` role. Parameters ---------- name : builtins.str The role's name. Other Parameters ---------------- permissions : hikari.undefined.UndefinedOr[hikari.permissions.Permissions] If provided, the permissions for the role. color : hikari.undefined.UndefinedOr[hikari.colors.Colorish] If provided, the role's color. colour : hikari.undefined.UndefinedOr[hikari.colors.Colorish] An alias for `color`. hoist : hikari.undefined.UndefinedOr[builtins.bool] If provided, whether to hoist the role. mentionable : hikari.undefined.UndefinedOr[builtins.bool] If provided, whether to make the role mentionable. reason : hikari.undefined.UndefinedOr[builtins.str] If provided, the reason that will be recorded in the audit logs. Maximum of 512 characters. Returns ------- hikari.snowflakes.Snowflake The dummy ID for this role that can be used temporarily to refer to this object while designing the guild layout. When the guild is created, this will be replaced with a different ID. Raises ------ builtins.ValueError If you are defining the first role, but did not name it `@everyone`. builtins.TypeError If you specify both `color` and `colour` together or if you try to specify `color`, `colour`, `hoisted`, `mentionable` or `position` for the `@everyone` role. """ @abc.abstractmethod def add_category( self, name: str, /, *, position: undefined.UndefinedOr[int] = undefined.UNDEFINED, permission_overwrites: undefined.UndefinedOr[ typing.Collection[channels.PermissionOverwrite] ] = undefined.UNDEFINED, ) -> snowflakes.Snowflake: """Create a category channel. Parameters ---------- name : builtins.str The channels name. Must be between 2 and 1000 characters. Other Parameters ---------------- position : hikari.undefined.UndefinedOr[builtins.int] If provided, the position of the category. permission_overwrites : hikari.undefined.UndefinedOr[typing.Sequence[hikari.channels.PermissionOverwrite]] If provided, the permission overwrites for the category. Returns ------- hikari.snowflakes.Snowflake The dummy ID for this channel that can be used temporarily to refer to this object while designing the guild layout. When the guild is created, this will be replaced with a different ID. """ # noqa: E501 - Line too long @abc.abstractmethod def add_text_channel( self, name: str, /, *, parent_id: undefined.UndefinedOr[snowflakes.Snowflake] = undefined.UNDEFINED, topic: undefined.UndefinedOr[str] = undefined.UNDEFINED, rate_limit_per_user: undefined.UndefinedOr[time.Intervalish] = undefined.UNDEFINED, position: undefined.UndefinedOr[int] = undefined.UNDEFINED, permission_overwrites: undefined.UndefinedOr[ typing.Collection[channels.PermissionOverwrite] ] = undefined.UNDEFINED, nsfw: undefined.UndefinedOr[bool] = undefined.UNDEFINED, ) -> snowflakes.Snowflake: """Create a text channel. Parameters ---------- name : builtins.str The channels name. Must be between 2 and 1000 characters. Other Parameters ---------------- position : hikari.undefined.UndefinedOr[builtins.int] If provided, the position of the channel (relative to the category, if any). topic : hikari.undefined.UndefinedOr[builtins.str] If provided, the channels topic. Maximum 1024 characters. nsfw : hikari.undefined.UndefinedOr[builtins.bool] If provided, whether to mark the channel as NSFW. rate_limit_per_user : hikari.undefined.UndefinedOr[builtins.int] If provided, the amount of seconds a user has to wait before being able to send another message in the channel. Maximum 21600 seconds. permission_overwrites : hikari.undefined.UndefinedOr[typing.Sequence[hikari.channels.PermissionOverwrite]] If provided, the permission overwrites for the channel. category : hikari.undefined.UndefinedOr[hikari.snowflakes.SnowflakeishOr[hikari.channels.GuildCategory]] The category to create the channel under. This may be the object or the ID of an existing category. Returns ------- hikari.snowflakes.Snowflake The dummy ID for this channel that can be used temporarily to refer to this object while designing the guild layout. When the guild is created, this will be replaced with a different ID. """ # noqa: E501 - Line too long @abc.abstractmethod def add_voice_channel( self, name: str, /, *, parent_id: undefined.UndefinedOr[snowflakes.Snowflake] = undefined.UNDEFINED, bitrate: undefined.UndefinedOr[int] = undefined.UNDEFINED, video_quality_mode: undefined.UndefinedOr[typing.Union[channels.VideoQualityMode, int]] = undefined.UNDEFINED, position: undefined.UndefinedOr[int] = undefined.UNDEFINED, permission_overwrites: undefined.UndefinedOr[ typing.Collection[channels.PermissionOverwrite] ] = undefined.UNDEFINED, region: undefined.UndefinedNoneOr[typing.Union[voices.VoiceRegion, str]], user_limit: undefined.UndefinedOr[int] = undefined.UNDEFINED, ) -> snowflakes.Snowflake: """Create a voice channel. Parameters ---------- name : builtins.str The channels name. Must be between 2 and 1000 characters. Other Parameters ---------------- position : hikari.undefined.UndefinedOr[builtins.int] If provided, the position of the channel (relative to the category, if any). user_limit : hikari.undefined.UndefinedOr[builtins.int] If provided, the maximum users in the channel at once. Must be between 0 and 99 with 0 meaning no limit. bitrate : hikari.undefined.UndefinedOr[builtins.int] If provided, the bitrate for the channel. Must be between 8000 and 96000 or 8000 and 128000 for VIP servers. video_quality_mode: hikari.undefined.UndefinedOr[typing.Union[hikari.channels.VideoQualityMode, builtins.int]] If provided, the new video quality mode for the channel. permission_overwrites : hikari.undefined.UndefinedOr[typing.Sequence[hikari.channels.PermissionOverwrite]] If provided, the permission overwrites for the channel. region : hikari.undefined.UndefinedOr[typing.Union[hikari.voices.VoiceRegion, builtins.str]] If provided, the voice region to for this channel. Passing `builtins.None` here will set it to "auto" mode where the used region will be decided based on the first person who connects to it when it's empty. category : hikari.undefined.UndefinedOr[hikari.snowflakes.SnowflakeishOr[hikari.channels.GuildCategory]] The category to create the channel under. This may be the object or the ID of an existing category. Returns ------- hikari.snowflakes.Snowflake The dummy ID for this channel that can be used temporarily to refer to this object while designing the guild layout. When the guild is created, this will be replaced with a different ID. """ # noqa: E501 - Line too long @abc.abstractmethod def add_stage_channel( self, name: str, /, *, parent_id: undefined.UndefinedOr[snowflakes.Snowflake] = undefined.UNDEFINED, bitrate: undefined.UndefinedOr[int] = undefined.UNDEFINED, position: undefined.UndefinedOr[int] = undefined.UNDEFINED, permission_overwrites: undefined.UndefinedOr[ typing.Collection[channels.PermissionOverwrite] ] = undefined.UNDEFINED, region: undefined.UndefinedNoneOr[typing.Union[voices.VoiceRegion, str]], user_limit: undefined.UndefinedOr[int] = undefined.UNDEFINED, ) -> snowflakes.Snowflake: """Create a stage channel. Parameters ---------- name : builtins.str The channels name. Must be between 2 and 1000 characters. Other Parameters ---------------- position : hikari.undefined.UndefinedOr[builtins.int] If provided, the position of the channel (relative to the category, if any). user_limit : hikari.undefined.UndefinedOr[builtins.int] If provided, the maximum users in the channel at once. Must be between 0 and 99 with 0 meaning no limit. bitrate : hikari.undefined.UndefinedOr[builtins.int] If provided, the bitrate for the channel. Must be between 8000 and 96000 or 8000 and 128000 for VIP servers. permission_overwrites : hikari.undefined.UndefinedOr[typing.Sequence[hikari.channels.PermissionOverwrite]] If provided, the permission overwrites for the channel. region : hikari.undefined.UndefinedOr[typing.Union[hikari.voices.VoiceRegion, builtins.str]] If provided, the voice region to for this channel. Passing `builtins.None` here will set it to "auto" mode where the used region will be decided based on the first person who connects to it when it's empty. category : hikari.undefined.UndefinedOr[hikari.snowflakes.SnowflakeishOr[hikari.channels.GuildCategory]] The category to create the channel under. This may be the object or the ID of an existing category. Returns ------- hikari.snowflakes.Snowflake The dummy ID for this channel that can be used temporarily to refer to this object while designing the guild layout. When the guild is created, this will be replaced with a different ID. """ class InteractionResponseBuilder(abc.ABC): """Base class for all interaction response builders used in the interaction server.""" @property @abc.abstractmethod def type(self) -> typing.Union[int, base_interactions.ResponseType]: """Return the type of this response. Returns ------- typing.Union[builtins.int, hikari.interactions.base_interactions.ResponseType] The type of response this is. """ @abc.abstractmethod def build(self, entity_factory: entity_factory_.EntityFactory, /) -> data_binding.JSONObject: """Build a JSON object from this builder. Parameters ---------- entity_factory : hikari.api.entity_factory.EntityFactory The entity factory to use to serialize entities within this builder. Returns ------- hikari.internal.data_binding.JSONObject The built json object representation of this builder. """ class InteractionDeferredBuilder(InteractionResponseBuilder, abc.ABC): """Interface of a deferred message interaction response builder.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def type(self) -> base_interactions.DeferredResponseTypesT: """Return the type of this response. Returns ------- hikari.interactions.base_interactions.DeferredResponseTypesT The type of response this is. """ @property @abc.abstractmethod def flags(self) -> typing.Union[undefined.UndefinedType, int, messages.MessageFlag]: """Message flags this response should have. !!! note As of writing the only message flag which can be set here is `hikari.messages.MessageFlag.EPHEMERAL`. Returns ------- typing.Union[hikari.undefined.UndefinedType, builtins.int, hikari.messages.MessageFlag] The message flags this response should have if set else `hikari.undefined.UNDEFINED`. """ @abc.abstractmethod def set_flags(self: _T, flags: typing.Union[undefined.UndefinedType, int, messages.MessageFlag], /) -> _T: """Set message flags for this response. !!! note As of writing, the only message flag which can be set is EPHEMERAL. Parameters ---------- flags : typing.Union[hikari.undefined.UndefinedType, builtins.int, hikari.messages.MessageFlag] The message flags to set for this response. Returns ------- InteractionMessageBuilder Object of this builder. """ class InteractionAutocompleteBuilder(InteractionResponseBuilder, abc.ABC): """Interface of an autocomplete interaction response builder.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def choices(self) -> typing.Sequence[commands.CommandChoice]: """Return autocomplete choices.""" @abc.abstractmethod def set_choices(self: _T, choices: typing.Sequence[commands.CommandChoice], /) -> _T: """Set autocomplete choices. Returns ------- InteractionAutocompleteBuilder Object of this builder. """ class InteractionMessageBuilder(InteractionResponseBuilder, abc.ABC): """Interface of an interaction message response builder used within REST servers. This can be returned by the listener registered to `hikari.api.interaction_server.InteractionServer` as a response to the interaction create. """ __slots__: typing.Sequence[str] = () # Required fields @property @abc.abstractmethod def type(self) -> base_interactions.MessageResponseTypesT: """Return the type of this response. Returns ------- hikari.interactions.base_interactions.MessageResponseTypesT The type of response this is. """ # Extendable fields @property @abc.abstractmethod def components(self) -> typing.Sequence[ComponentBuilder]: """Sequence of up to 5 component builders to send in this response.""" @property @abc.abstractmethod def embeds(self) -> typing.Sequence[embeds_.Embed]: """Sequence of up to 10 of the embeds included in this response. Returns ------- typing.Sequence[hikari.embeds.Embed] A sequence of up to 10 of the embeds included in this response. """ # Settable fields @property @abc.abstractmethod def content(self) -> undefined.UndefinedOr[str]: """Response's message content. Returns ------- hikari.undefined.UndefinedOr[builtins.str] The response's message content, if set. """ @property @abc.abstractmethod def flags(self) -> typing.Union[undefined.UndefinedType, int, messages.MessageFlag]: """Message flags this response should have. !!! note As of writing the only message flag which can be set here is `hikari.messages.MessageFlag.EPHEMERAL`. Returns ------- typing.Union[hikari.undefined.UndefinedType, builtins.int, hikari.messages.MessageFlag] The message flags this response should have if set else `hikari.undefined.UNDEFINED`. """ @property @abc.abstractmethod def is_tts(self) -> undefined.UndefinedOr[bool]: """Whether this response's content should be treated as text-to-speech. Returns ------- builtins.bool Whether this response's content should be treated as text-to-speech. If left as `hikari.undefined.UNDEFINED` then this will be disabled. """ @property @abc.abstractmethod def mentions_everyone(self) -> undefined.UndefinedOr[bool]: """Whether @everyone and @here mentions should be enabled for this response. Returns ------- hikari.undefined.UndefinedOr[builtins.bool] Whether @everyone mentions should be enabled for this response. If left as `hikari.undefined.UNDEFINED` then they will be disabled. """ @property @abc.abstractmethod def role_mentions( self, ) -> undefined.UndefinedOr[typing.Union[snowflakes.SnowflakeishSequence[guilds.PartialRole], bool]]: """Whether and what role mentions should be enabled for this response. Returns ------- hikari.undefined.UndefinedOr[typing.Union[hikari.snowflakes.SnowflakeishSequence[hikari.users.PartialUser], builtins.bool]] Either a sequence of object/IDs of the roles mentions should be enabled for, `builtins.False` or `hikari.undefined.UNDEFINED` to disallow any role mentions or `True` to allow all role mentions. """ # noqa: E501 - Line too long @property @abc.abstractmethod def user_mentions( self, ) -> undefined.UndefinedOr[typing.Union[snowflakes.SnowflakeishSequence[users.PartialUser], bool]]: """Whether and what user mentions should be enabled for this response. Returns ------- hikari.undefined.UndefinedOr[typing.Union[hikari.snowflakes.SnowflakeishSequence[hikari.users.PartialUser], builtins.bool]] Either a sequence of object/IDs of the users mentions should be enabled for, `builtins.False` or `hikari.undefined.UNDEFINED` to disallow any user mentions or `True` to allow all user mentions. """ # noqa: E501 - Line too long @abc.abstractmethod def add_component(self: _T, component: ComponentBuilder, /) -> _T: """Add a component to this response. Parameters ---------- component : ComponentBuilder The component builder to add to this response. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def add_embed(self: _T, embed: embeds_.Embed, /) -> _T: """Add an embed to this response. Parameters ---------- embed : hikari.embeds.Embed Object of the embed to add to this response. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def set_content(self: _T, content: undefined.UndefinedOr[str], /) -> _T: """Set the response's message content. Parameters ---------- content : hikari.undefined.UndefinedOr[builtins.str] The message content to set for this response. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def set_flags(self: _T, flags: typing.Union[undefined.UndefinedType, int, messages.MessageFlag], /) -> _T: """Set message flags for this response. !!! note As of writing, the only message flag which can be set is EPHEMERAL. Parameters ---------- flags : typing.Union[hikari.undefined.UndefinedType, builtins.int, hikari.messages.MessageFlag] The message flags to set for this response. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def set_tts(self: _T, tts: undefined.UndefinedOr[bool], /) -> _T: """Set whether this response should trigger text-to-speech processing. Parameters ---------- tts : Whether this response should trigger text-to-speech processing. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def set_mentions_everyone(self: _T, mentions: undefined.UndefinedOr[bool] = undefined.UNDEFINED, /) -> _T: """Set whether this response should be able to mention @everyone/@here. Parameters ---------- mentions : hikari.undefined.UndefinedOr[builtins.bool] Whether this response should be able to mention @everyone/@here. Returns ------- InteractionMessageBuilder Object of this builder. """ @abc.abstractmethod def set_role_mentions( self: _T, mentions: undefined.UndefinedOr[ typing.Union[snowflakes.SnowflakeishSequence[guilds.PartialRole], bool] ] = undefined.UNDEFINED, /, ) -> _T: """Set whether and what role mentions should be possible for this response. Parameters ---------- mentions : hikari.undefined.UndefinedOr[typing.Union[hikari.snowflakes.SnowflakeishSequence[hikari.users.PartialUser], builtins.bool]] Either a sequence of object/IDs of the roles mentions should be enabled for, `builtins.False` or `hikari.undefined.UNDEFINED` to disallow any role mentions or `True` to allow all role mentions. Returns ------- InteractionMessageBuilder Object of this builder. """ # noqa: E501 - Line too long @abc.abstractmethod def set_user_mentions( self: _T, mentions: undefined.UndefinedOr[ typing.Union[snowflakes.SnowflakeishSequence[users.PartialUser], bool] ] = undefined.UNDEFINED, /, ) -> _T: """Set whether and what user mentions should be possible for this response. Parameters ---------- mentions: hikari.undefined.UndefinedOr[typing.Union[hikari.snowflakes.SnowflakeishSequence[hikari.users.PartialUser], builtins.bool]] Either a sequence of object/IDs of the users mentions should be enabled for, `builtins.False` or `hikari.undefined.UNDEFINED` to disallow any user mentions or `True` to allow all user mentions. Returns ------- InteractionMessageBuilder Object of this builder. """ # noqa: E501 - Line too long class CommandBuilder(abc.ABC): """Interface of a command builder used when bulk creating commands over REST.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def name(self) -> str: r"""Name to set for this command. !!! warning This should match the regex `^[\w-]{1,32}$` in Unicode mode and must be lowercase. Returns ------- builtins.str The name to set for this command. """ @property @abc.abstractmethod def type(self) -> undefined.UndefinedOr[commands.CommandType]: """Return the type of this command. Returns ------- hikari.commands.CommandType The type of this command. """ @property @abc.abstractmethod def id(self) -> undefined.UndefinedOr[snowflakes.Snowflake]: """ID of this command. Returns ------- hikari.undefined.UndefinedOr[hikari.snowflakes.Snowflake] The ID of this command if set. """ @property @abc.abstractmethod def default_permission(self) -> undefined.UndefinedOr[bool]: """Whether the command should be enabled by default (without any permissions). Defaults to `builtins.bool`. Returns ------- undefined.UndefinedOr[builtins.bool] Whether the command should be enabled by default (without any permissions). """ @abc.abstractmethod def set_id(self: _T, id_: undefined.UndefinedOr[snowflakes.Snowflakeish], /) -> _T: """Set the ID of this command. Parameters ---------- id_ : hikari.undefined.UndefinedOr[hikari.snowflakes.Snowflake] The ID to set for this command. Returns ------- CommandBuilder Object of this command builder. """ @abc.abstractmethod def set_default_permission(self: _T, state: undefined.UndefinedOr[bool], /) -> _T: """Whether this command should be enabled by default (without any permissions). Parameters ---------- state : hikari.undefined.UndefinedOr[builtins.bool] Whether this command should be enabled by default. Returns ------- CommandBuilder Object of this command builder for chained calls. """ @abc.abstractmethod def build(self, entity_factory: entity_factory_.EntityFactory, /) -> data_binding.JSONObject: """Build a JSON object from this builder. Parameters ---------- entity_factory : hikari.api.entity_factory.EntityFactory The entity factory to use to serialize entities within this builder. Returns ------- hikari.internal.data_binding.JSONObject The built json object representation of this builder. """ class SlashCommandBuilder(CommandBuilder): """SlashCommandBuilder.""" @property @abc.abstractmethod def description(self) -> str: """Return the description to set for this command. !!! warning This should be inclusively between 1-100 characters in length. Returns ------- builtins.str The description to set for this command. """ @property @abc.abstractmethod def options(self) -> typing.Sequence[commands.CommandOption]: """Sequence of up to 25 of the options set for this command. Returns ------- typing.Sequence[hikari.commands.CommandOption] A sequence of up to 25 of the options set for this command. """ @abc.abstractmethod def add_option(self: _T, option: commands.CommandOption) -> _T: """Add an option to this command. !!! note A command can have up to 25 options. Parameters ---------- option : hikari.commands.CommandOption The option to add to this command. Returns ------- CommandBuilder Object of this command builder. """ class ContextMenuCommandBuilder(CommandBuilder): """ContextMenuCommandBuilder.""" class ComponentBuilder(abc.ABC): """Base class for all component builder classes.""" __slots__: typing.Sequence[str] = () @abc.abstractmethod def build(self) -> data_binding.JSONObject: """Build a JSON object from this builder. Returns ------- hikari.internal.data_binding.JSONObject The built json object representation of this builder. """ class ButtonBuilder(ComponentBuilder, abc.ABC, typing.Generic[_ContainerT]): """Builder class for a message button component.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def style(self) -> typing.Union[messages.ButtonStyle, int]: """Button's style. Returns ------- typing.Union[builtins.int, hikari.messages.ButtonStyle] The button's style. """ @property @abc.abstractmethod def emoji(self) -> typing.Union[snowflakes.Snowflakeish, emojis.Emoji, str, undefined.UndefinedType]: """Emoji which should appear on this button. Returns ------- typing.Union[hikari.snowflakes.Snowflakeish, hikari.emojis.Emoji, builtins.str, hikari.undefined.UndefinedType] Object or ID or raw string of the emoji which should be displayed on this button if set. """ @property @abc.abstractmethod def label(self) -> undefined.UndefinedOr[str]: """Text label which should appear on this button. !!! note The text label to that should appear on this button. This may be up to 80 characters long. Returns ------- hikari.undefined.UndefinedOr[builtins.str] Text label which should appear on this button. """ @property @abc.abstractmethod def is_disabled(self) -> bool: """Whether the button should be marked as disabled. !!! note Defaults to `builtins.False`. Returns ------- builtins.bool Whether the button should be marked as disabled. """ @abc.abstractmethod def set_emoji( self: _T, emoji: typing.Union[snowflakes.Snowflakeish, emojis.Emoji, str, undefined.UndefinedType], / ) -> _T: """Set the emoji to display on this button. Parameters ---------- emoji : typing.Union[hikari.snowflakes.Snowflakeish, hikari.emojis.Emoji, builtins.str, hikari.undefined.UndefinedType] Object, ID or raw string of the emoji which should be displayed on this button. Returns ------- ButtonBuilder The builder object to enable chained calls. """ # noqa E501 - Line too long @abc.abstractmethod def set_label(self: _T, label: undefined.UndefinedOr[str], /) -> _T: """Set the text label which should be displayed on this button. Parameters ---------- label : hikari.undefined.UndefinedOr[builtins.str] The text label to show on this button. This may be up to 80 characters long. Returns ------- ButtonBuilder The builder object to enable chained calls. """ @abc.abstractmethod def set_is_disabled(self: _T, state: bool, /) -> _T: """Set whether this button should be disabled. Parameters ---------- state : bool Whether this button should be disabled. Returns ------- ButtonBuilder The builder object to enable chained calls. """ @abc.abstractmethod def add_to_container(self) -> _ContainerT: """Add this button to the container component it belongs to. This is used as the finalising call during chained calls. Returns ------- _ContainerT The container component that owns this button. """ class LinkButtonBuilder(ButtonBuilder[_ContainerT], abc.ABC): """Builder interface for link buttons.""" @property @abc.abstractmethod def url(self) -> str: """Url this button should link to when pressed. Returns ------- builtins.str Url this button should link to when pressed. """ class InteractiveButtonBuilder(ButtonBuilder[_ContainerT], abc.ABC): """Builder interface for interactive buttons.""" @property @abc.abstractmethod def custom_id(self) -> str: """Developer set custom ID used for identifying interactions with this button. Returns ------- builtins.str Developer set custom ID used for identifying interactions with this button. """ class SelectOptionBuilder(ComponentBuilder, abc.ABC, typing.Generic[_SelectMenuBuilderT]): """Builder class for select menu options.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def label(self) -> str: """User-facing name of the option, max 100 characters. Returns ------- builtins.str User-facing name of the option. """ @property @abc.abstractmethod def value(self) -> str: """Developer-defined value of the option, max 100 characters. Returns ------- builtins.str Developer-defined value of the option. """ @property @abc.abstractmethod def description(self) -> undefined.UndefinedOr[str]: """Return the description of the option, max 100 characters. Returns ------- hikari.undefined.UndefinedOr[builtins.str] Description of the option, if set. """ @property @abc.abstractmethod def emoji(self) -> typing.Union[snowflakes.Snowflakeish, emojis.Emoji, str, undefined.UndefinedType]: """Emoji which should appear on this option. Returns ------- typing.Union[hikari.snowflakes.Snowflakeish, hikari.emojis.Emoji, builtins.str, hikari.undefined.UndefinedType] Object or ID or raw string of the emoji which should be displayed on this option if set. """ @property @abc.abstractmethod def is_default(self) -> bool: """Whether this option should be marked as selected by default. Defaults to `builtins.False`. Returns ------- builtins.bool Whether this option should be marked as selected by default. """ @abc.abstractmethod def set_description(self: _T, value: undefined.UndefinedOr[str], /) -> _T: """Set the option's description. Parameters ---------- value : hikari.undefined.UndefinedOr[builtins.str] Description to set for this option. This can be up to 100 characters long. Returns ------- SelectOptionBuilder The builder object to enable chained calls. """ @abc.abstractmethod def set_emoji( self: _T, emoji: typing.Union[snowflakes.Snowflakeish, emojis.Emoji, str, undefined.UndefinedType], / ) -> _T: """Set the emoji to display on this option. Parameters ---------- emoji : typing.Union[hikari.snowflakes.Snowflakeish, hikari.emojis.Emoji, builtins.str, hikari.undefined.UndefinedType] Object, ID or raw string of the emoji which should be displayed on this option. Returns ------- SelectOptionBuilder The builder object to enable chained calls. """ # noqa E501 - Line too long @abc.abstractmethod def set_is_default(self: _T, state: bool, /) -> _T: """Set whether this option should be selected by default. Parameters ---------- state : builtins.bool Whether this option should be selected by default. Returns ------- SelectOptionBuilder The builder object to enable chained calls. """ @abc.abstractmethod def add_to_menu(self) -> _SelectMenuBuilderT: """Add this option to the menu component it belongs to. This is used as the finalising call during chained calls. Returns ------- _SelectMenuBuilderT The menu component that owns this button. """ class SelectMenuBuilder(ComponentBuilder, abc.ABC, typing.Generic[_ContainerT]): """Builder class for select menu options.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def custom_id(self) -> str: """Developer set custom ID used for identifying interactions with this menu. Returns ------- builtins.str Developer set custom ID used for identifying interactions with this menu. """ @property @abc.abstractmethod def is_disabled(self) -> bool: """Whether the select menu should be marked as disabled. !!! note Defaults to `builtins.False`. Returns ------- builtins.bool Whether the select menu should be marked as disabled. """ @property @abc.abstractmethod def options(self: _SelectMenuBuilderT) -> typing.Sequence[SelectOptionBuilder[_SelectMenuBuilderT]]: """Sequence of the options set for this select menu. Returns ------- typing.Sequence[SelectOptionBuilder[Self]] Sequence of the options set for this select menu. """ @property @abc.abstractmethod def placeholder(self) -> undefined.UndefinedOr[str]: """Return the placeholder text to display when no options are selected. Returns ------- hikari.undefined.UndefinedOr[builtins.str] Placeholder text to display when no options are selected, if defined. """ @property @abc.abstractmethod def min_values(self) -> int: """Minimum number of options which must be chosen. Defaults to 1. Must be less than or equal to `SelectMenuBuilder.max_values` and greater than or equal to 0. Returns ------- builtins.str Minimum number of options which must be chosen. """ @property @abc.abstractmethod def max_values(self) -> int: """Maximum number of options which can be chosen. Defaults to 1. Must be greater than or equal to `SelectMenuBuilder.min_values` and less than or equal to 25. Returns ------- builtins.str Maximum number of options which can be chosen. """ @abc.abstractmethod def add_option(self: _SelectMenuBuilderT, label: str, value: str, /) -> SelectOptionBuilder[_SelectMenuBuilderT]: """Add an option to this menu. Parameters ---------- label : builtins.str The user-facing name of this option, max 100 characters. value : builtins.str The developer defined value of this option, max 100 characters. Returns ------- SelectOptionBuilder[Self] Option builder object. This should be finalised by calling `SelectOptionBuilder.add_to_menu`. """ @abc.abstractmethod def set_is_disabled(self: _T, state: bool, /) -> _T: """Set whether this option is disabled. Defaults to `builtins.False`. Parameters ---------- state : builtins.bool Whether this option is disabled. Returns ------- SelectMenuBuilder The builder object to enable chained calls. """ @abc.abstractmethod def set_placeholder(self: _T, value: undefined.UndefinedOr[str], /) -> _T: """Set place-holder text to be shown when no option is selected. Parameters ---------- value : hikari.undefined.UndefinedOr[builtins.str] Place-holder text to be displayed when no option is selected. Max 100 characters. Returns ------- SelectMenuBuilder The builder object to enable chained calls. """ @abc.abstractmethod def set_min_values(self: _T, value: int, /) -> _T: """Set the minimum amount of options which need to be selected for this menu. !!! note This defaults to 1 if not set and must be greater than or equal to 0 and less than or equal to `SelectMenuBuilder.max_values`. Parameters ---------- value : builtins.int The minimum amount of options which need to be selected for this menu. Returns ------- SelectMenuBuilder The builder object to enable chained calls. """ @abc.abstractmethod def set_max_values(self: _T, value: int, /) -> _T: """Set the maximum amount of options which can be selected for this menu. !!! note This defaults to 1 if not set and must be less than or equal to 25 and greater than or equal to `SelectMenuBuilder.min_values`. Parameters ---------- value : builtins.int The maximum amount of options which can selected for this menu. Returns ------- SelectMenuBuilder The builder object to enable chained calls. """ @abc.abstractmethod def add_to_container(self) -> _ContainerT: """Finalise this builder by adding it to its parent container component. Returns ------- _ContainerT The parent container component builder. """ class ActionRowBuilder(ComponentBuilder, abc.ABC): """Builder class for action row components.""" __slots__: typing.Sequence[str] = () @property @abc.abstractmethod def components(self) -> typing.Sequence[ComponentBuilder]: """Sequence of the component builders registered within this action row. Returns ------- typing.Sequence[ComponentBuilder] Sequence of the component builders registered within this action row. """ @abc.abstractmethod def add_component( self: _T, component: ComponentBuilder, /, ) -> _T: """Add a component to this action row builder. !!! warning It is generally better to use `ActionRowBuilder.add_button` and `ActionRowBuilder.add_select_menu` to add your component to the builder. Those methods utilize this one. Parameters ---------- component : ComponentBuilder The component builder to add to the action row. Returns ------- ActionRowBuilder The builder object to enable chained calls. """ @typing.overload @abc.abstractmethod def add_button( self: _T, style: messages.InteractiveButtonTypesT, custom_id: str, / ) -> InteractiveButtonBuilder[_T]: ... @typing.overload @abc.abstractmethod def add_button(self: _T, style: typing.Literal[messages.ButtonStyle.LINK, 5], url: str, /) -> LinkButtonBuilder[_T]: ... @typing.overload @abc.abstractmethod def add_button( self: _T, style: typing.Union[int, messages.ButtonStyle], url_or_custom_id: str, / ) -> typing.Union[LinkButtonBuilder[_T], InteractiveButtonBuilder[_T]]: ... @abc.abstractmethod def add_button( self: _T, style: typing.Union[int, messages.ButtonStyle], url_or_custom_id: str, / ) -> typing.Union[LinkButtonBuilder[_T], InteractiveButtonBuilder[_T]]: """Add a button component to this action row builder. Parameters ---------- style : typing.Union[builtins.int, hikari.messages.ButtonStyle] The button's style. url_or_custom_id : builtins.str For interactive button styles this is a developer-defined custom identifier used to identify which button triggered component interactions. For Link button styles this is the URL the link button should redirect to. Returns ------- typing.Union[LinkButtonBuilder[Self], InteractiveButtonBuilder[Self]] Button builder object. `ButtonBuilder.add_to_container` should be called to finalise the button. """ @abc.abstractmethod def add_select_menu(self: _T, custom_id: str, /) -> SelectMenuBuilder[_T]: """Add a select menu component to this action row builder. Parameters ---------- custom_id : builtins.str A developer-defined custom identifier used to identify which menu triggered component interactions. Returns ------- SelectMenuBuilder[Self] Select menu builder object. `SelectMenuBuilder.add_to_container` should be called to finalise the button. """

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demo/mislabelled_data_demo.py

bottydim/dginn

0

1615850

<reponame>bottydim/dginn<gh_stars>0 if __name__ == '__main__': import tensorflow as tf tf.compat.v1.enable_eager_execution() from aggregator_utils import get_count_aggregators, uncertainty_pred, compute_dg from core import * from dataset_utils import filter_dataset from mnist_loader import load_mnist, get_mnist_model """ Things to try: - Dependency graph computation approach (dg_relevance) - Aggregation approach + similarity to aggregator - Number of aggregator samples - Depth of the model """ def main(): # Load dataset train_x, train_y, test_x, test_y = load_mnist() # Filter out subset of classes selected_classes = [0, 1, 2, 3] train_x, train_y = filter_dataset((train_x, train_y), selected_classes) test_x, test_y = filter_dataset((test_x, test_y), selected_classes) # Create model model = get_mnist_model(train_x, train_y) # Obtain subset of incorrectly labelled training points preds = np.argmax(model.predict(train_x), axis=1) # @Dima union of array & Iterable got boolean? incorrects = np.nonzero(preds != train_y)[0] incorrect_samples, incorrect_labels = train_x[incorrects], train_y[incorrects] incorrect_samples, incorrect_labels = incorrect_samples[:100], incorrect_labels[:100] # Select random set of samples n_mislabelled = 100 indices = np.random.choice(train_x.shape[0], n_mislabelled, replace=False) random_x, random_y = train_x[indices], train_y[indices] # Combine two sets of samples combined_x, combined_y = np.concatenate((incorrect_samples, random_x), axis=0), \ np.concatenate((incorrect_labels, random_y), axis=0) print("# samples: ", combined_x.shape[0]) # Get sample aggregators aggregators = get_count_aggregators(train_x, train_y, model, 100) sim_scores = {} agg_labels = {} compute_fx = Activations_Computer(model=model, agg_data_points=True) for i, sample in enumerate(combined_x): sample = np.expand_dims(sample, axis=0) dg = compute_dg(sample,compute_fx) pred_label, sim_score = uncertainty_pred(dg, aggregators) agg_labels[i] = False sim_scores[i] = sim_score y_pred = np.argmax(model.predict(sample), axis=1) # TODO: remove after testing # Temporary printing information if pred_label == combined_y[i]: agg_labels[i] = True print("correct sample ", i) if pred_label != y_pred: print("disagreement ", i, " predicted: ", pred_label, " model: ", y_pred) # Sort samples by similarity sorted_keys = sorted(sim_scores, key=sim_scores.get) print(sorted_keys) labels = [agg_labels[i] for i in sorted_keys] scores = [sim_scores[i] for i in sorted_keys] print(labels) print(scores) # Count how many mislabelled samples are in the second half of the sorted keys # (i.e. have high similarity) n_incorrect = len(incorrect_labels) mislabelled = sum([1 for key in sorted_keys[n_incorrect:] if key >= n_incorrect]) print("Mislabelled: ", mislabelled) return sorted_keys if __name__ == '__main__': main() """ Observations: 1) High certainty corresponds to higher chance of a correct prediction 2) Mislabelled samples have higher certainty than misclassified ones (though difference is not pronounced enough) 3) Sometimes, uncertainty gives correct estimate, whilst the model does not """

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1596, 703, 15728, 4559, 9162, 474, 29897, 13, 13, 9651, 565, 4450, 29918, 1643, 2804, 343, 29918, 11965, 29901, 13, 18884, 1596, 703, 2218, 351, 276, 882, 9162, 474, 29892, 376, 25383, 29901, 9162, 4450, 29918, 1643, 29892, 376, 1904, 29901, 9162, 343, 29918, 11965, 29897, 13, 13, 13, 1678, 396, 20025, 11916, 491, 29501, 13, 1678, 12705, 29918, 8149, 353, 12705, 29898, 3601, 29918, 1557, 2361, 29892, 1820, 29922, 3601, 29918, 1557, 2361, 29889, 657, 29897, 13, 1678, 1596, 29898, 24582, 29918, 8149, 29897, 13, 13, 1678, 11073, 353, 518, 16170, 29918, 21134, 29961, 29875, 29962, 363, 474, 297, 12705, 29918, 8149, 29962, 13, 1678, 19435, 353, 518, 3601, 29918, 1557, 2361, 29961, 29875, 29962, 363, 474, 297, 12705, 29918, 8149, 29962, 13, 1678, 1596, 29898, 21134, 29897, 13, 1678, 1596, 29898, 1557, 2361, 29897, 13, 13, 13, 1678, 396, 3917, 920, 1784, 3984, 1643, 839, 11916, 526, 297, 278, 1473, 4203, 310, 278, 12705, 6611, 13, 1678, 396, 313, 29875, 29889, 29872, 29889, 505, 1880, 29501, 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tools/pycocoEval.py

Manthata/hrnet

80

171187

import matplotlib.pyplot as plt from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval import numpy as np import skimage.io as io import ipdb;pdb=ipdb.set_trace from collections import OrderedDict # markdown format output def _print_name_value(name_value, full_arch_name): names = name_value.keys() values = name_value.values() num_values = len(name_value) print( '| Arch ' + ' '.join(['| {}'.format(name) for name in names]) + ' |' ) print('|---' * (num_values+1) + '|') if len(full_arch_name) > 15: full_arch_name = full_arch_name[:8] + '...' print( '| ' + full_arch_name + ' ' + ' '.join(['| {:.3f}'.format(value) for value in values]) + ' |' ) gt_anns = 'data/coco/annotations/person_keypoints_val2017.json' # dt_anns = '/home/xyliu/2D_pose/deep-high-resolution-net.pytorch/person_keypoints.json' dt_anns = '/home/xyliu/2D_pose/simple-pose-estimation/person_keypoints.json' annType = 'keypoints' cocoGt=COCO(gt_anns) cocoDt=cocoGt.loadRes(dt_anns) cocoEval = COCOeval(cocoGt,cocoDt,annType) cocoEval.evaluate() cocoEval.accumulate() cocoEval.summarize() stats_names = ['AP', 'Ap .5', 'AP .75', 'AP (M)', 'AP (L)', 'AR', 'AR .5', 'AR .75', 'AR (M)', 'AR (L)'] info_str = [] for ind, name in enumerate(stats_names): info_str.append((name, cocoEval.stats[ind])) name_values = OrderedDict(info_str) model_name = 'openpose' if isinstance(name_values, list): for name_value in name_values: _print_name_value(name_value, model_name) else: _print_name_value(name_values, model_name)

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image_classification/token_labeling/tlt/models/lvvit.py

AK391/UniFormer

367

89498

<reponame>AK391/UniFormer<filename>image_classification/token_labeling/tlt/models/lvvit.py import torch import torch.nn as nn from timm.models.helpers import load_pretrained from timm.models.registry import register_model from timm.models.layers import trunc_normal_ from timm.models.resnet import resnet26d, resnet50d, resnet101d import numpy as np from .layers import * def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, 'crop_pct': .9, 'interpolation': 'bicubic', 'mean': (0.485, 0.456, 0.406), 'std': (0.229, 0.224, 0.225), 'classifier': 'head', **kwargs } default_cfgs = { 'LV_ViT_Tiny': _cfg(), 'LV_ViT': _cfg(), 'LV_ViT_Medium': _cfg(crop_pct=1.0), 'LV_ViT_Large': _cfg(crop_pct=1.0), } def get_block(block_type, **kargs): if block_type=='mha': # multi-head attention block return MHABlock(**kargs) elif block_type=='ffn': # feed forward block return FFNBlock(**kargs) elif block_type=='tr': # transformer block return Block(**kargs) def rand_bbox(size, lam): W = size[2] H = size[3] cut_rat = np.sqrt(1. - lam) cut_w = np.int(W * cut_rat) cut_h = np.int(H * cut_rat) # uniform cx = np.random.randint(W) cy = np.random.randint(H) bbx1 = np.clip(cx - cut_w // 2, 0, W) bby1 = np.clip(cy - cut_h // 2, 0, H) bbx2 = np.clip(cx + cut_w // 2, 0, W) bby2 = np.clip(cy + cut_h // 2, 0, H) return bbx1, bby1, bbx2, bby2 def get_dpr(drop_path_rate,depth,drop_path_decay='linear'): if drop_path_decay=='linear': # linear dpr decay dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule elif drop_path_decay=='fix': # use fixed dpr dpr= [drop_path_rate]*depth else: # use predefined drop_path_rate list assert len(drop_path_rate)==depth dpr=drop_path_rate return dpr class LV_ViT(nn.Module): """ Vision Transformer with tricks Arguements: p_emb: different conv based position embedding (default: 4 layer conv) skip_lam: residual scalar for skip connection (default: 1.0) order: which order of layers will be used (default: None, will override depth if given) mix_token: use mix token augmentation for batch of tokens (default: False) return_dense: whether to return feature of all tokens with an additional aux_head (default: False) """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., drop_path_decay='linear', hybrid_backbone=None, norm_layer=nn.LayerNorm, p_emb='4_2', head_dim = None, skip_lam = 1.0,order=None, mix_token=False, return_dense=False): super().__init__() self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.output_dim = embed_dim if num_classes==0 else num_classes if hybrid_backbone is not None: self.patch_embed = HybridEmbed( hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim) else: if p_emb=='4_2': patch_embed_fn = PatchEmbed4_2 elif p_emb=='4_2_128': patch_embed_fn = PatchEmbed4_2_128 else: patch_embed_fn = PatchEmbedNaive self.patch_embed = patch_embed_fn(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) self.pos_drop = nn.Dropout(p=drop_rate) if order is None: dpr=get_dpr(drop_path_rate, depth, drop_path_decay) self.blocks = nn.ModuleList([ Block( dim=embed_dim, num_heads=num_heads, head_dim=head_dim, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, skip_lam=skip_lam) for i in range(depth)]) else: # use given order to sequentially generate modules dpr=get_dpr(drop_path_rate, len(order), drop_path_decay) self.blocks = nn.ModuleList([ get_block(order[i], dim=embed_dim, num_heads=num_heads, head_dim=head_dim, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, skip_lam=skip_lam) for i in range(len(order))]) self.norm = norm_layer(embed_dim) self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() self.return_dense=return_dense self.mix_token=mix_token if return_dense: self.aux_head=nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() if mix_token: self.beta = 1.0 assert return_dense, "always return all features when mixtoken is enabled" trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, GroupLinear): trunc_normal_(m.group_weight, std=.02) if isinstance(m, GroupLinear) and m.group_bias is not None: nn.init.constant_(m.group_bias, 0) elif isinstance(m, nn.LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token'} def get_classifier(self): return self.head def reset_classifier(self, num_classes, global_pool=''): self.num_classes = num_classes self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() def forward_embeddings(self,x): x = self.patch_embed(x) return x def forward_tokens(self, x): B = x.shape[0] cls_tokens = self.cls_token.expand(B, -1, -1) x = torch.cat((cls_tokens, x), dim=1) x = x + self.pos_embed x = self.pos_drop(x) for blk in self.blocks: x = blk(x) x = self.norm(x) return x def forward_features(self,x): # simple forward to obtain feature map (without mixtoken) x = self.forward_embeddings(x) x = x.flatten(2).transpose(1, 2) x = self.forward_tokens(x) return x def forward(self, x): x = self.forward_embeddings(x) # token level mixtoken augmentation if self.mix_token and self.training: lam = np.random.beta(self.beta, self.beta) patch_h, patch_w = x.shape[2],x.shape[3] bbx1, bby1, bbx2, bby2 = rand_bbox(x.size(), lam) temp_x = x.clone() temp_x[:, :, bbx1:bbx2, bby1:bby2] = x.flip(0)[:, :, bbx1:bbx2, bby1:bby2] x = temp_x else: bbx1, bby1, bbx2, bby2 = 0,0,0,0 x = x.flatten(2).transpose(1, 2) x = self.forward_tokens(x) x_cls = self.head(x[:,0]) if self.return_dense: x_aux = self.aux_head(x[:,1:]) if not self.training: return x_cls+0.5*x_aux.max(1)[0] # recover the mixed part if self.mix_token and self.training: x_aux = x_aux.reshape(x_aux.shape[0],patch_h, patch_w,x_aux.shape[-1]) temp_x = x_aux.clone() temp_x[:, bbx1:bbx2, bby1:bby2, :] = x_aux.flip(0)[:, bbx1:bbx2, bby1:bby2, :] x_aux = temp_x x_aux = x_aux.reshape(x_aux.shape[0],patch_h*patch_w,x_aux.shape[-1]) return x_cls, x_aux, (bbx1, bby1, bbx2, bby2) return x_cls @register_model def vit(pretrained=False, **kwargs): model = LV_ViT(patch_size=16, embed_dim=384, depth=16, num_heads=6, mlp_ratio=3., p_emb=1, **kwargs) model.default_cfg = default_cfgs['LV_ViT'] return model @register_model def lvvit(pretrained=False, **kwargs): model = LV_ViT(patch_size=16, embed_dim=384, depth=16, num_heads=6, mlp_ratio=3., p_emb='4_2',skip_lam=2., **kwargs) model.default_cfg = default_cfgs['LV_ViT'] return model @register_model def lvvit_s(pretrained=False, **kwargs): model = LV_ViT(patch_size=16, embed_dim=384, depth=16, num_heads=6, mlp_ratio=3., p_emb='4_2',skip_lam=2., return_dense=True,mix_token=True, **kwargs) model.default_cfg = default_cfgs['LV_ViT'] return model @register_model def lvvit_m(pretrained=False, **kwargs): model = LV_ViT(patch_size=16, embed_dim=512, depth=20, num_heads=8, mlp_ratio=3., p_emb='4_2',skip_lam=2., return_dense=True,mix_token=True, **kwargs) model.default_cfg = default_cfgs['LV_ViT_Medium'] return model @register_model def lvvit_l(pretrained=False, **kwargs): order = ['tr']*24 # this will override depth, can also be set as None model = LV_ViT(patch_size=16, embed_dim=768,depth=24, num_heads=12, mlp_ratio=3., p_emb='4_2_128',skip_lam=3., return_dense=True,mix_token=True, order=order, **kwargs) model.default_cfg = default_cfgs['LV_ViT_Large'] return model

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gputools/denoise/denoise_filter2.py

VolkerH/gputools

0

119149

<reponame>VolkerH/gputools<gh_stars>0 """ A collection of some denoising algorithms in 2d MW, 2014 """ from __future__ import print_function, unicode_literals, absolute_import, division import numpy as np import subprocess import logging logger = logging.getLogger(__name__) def bilateral(data, fSize, sigma, sigma_x = 10., dev= None): """bilateral filter """ if dev is None: dev = imgtools.__DEFAULT_OPENCL_DEVICE__ if dev is None: raise ValueError("no OpenCLDevice found...") dtype = data.dtype.type dtypes_kernels = {np.float32:"run2d_float", np.uint16:"run2d_short"} if not dtype in dtypes_kernels: logger.error("data type %s not supported yet, casting to float:"%dtype,list(dtypes_kernels.keys())) return img = dev.createImage_like(data) buf = dev.createBuffer(data.size,dtype = dtype) dev.writeImage(img,data) proc = OCLProcessor(dev,absPath("kernels/bilateral.cl")) proc.runKernel(dtypes_kernels[dtype],img.shape,None,img,buf, np.int32(img.shape[0]),np.int32(img.shape[1]), np.int32(fSize),np.float32(sigma_x),np.float32(sigma)) return dev.readBuffer(buf,dtype=dtype).reshape(data.shape) def test_bilateral(): from imgtools import test_images, calcPSNR data = test_images.lena() data = 100.*data/np.amax(data) y = data + np.random.normal(0,20,data.shape) y = y.astype(np.float32) outs = [calcPSNR(data,bilateral(y,2,sigs)) for sigs in np.linspace(1.,200,20)] return outs # def bilateralBuffer(clDev, data, fSize, sigmaX, sigmaP): # clProc = OCLProcessor(clDev,absPath("kernels/bilateral.cl")) # clBufIn = clDev.createBuffer(data.size, mem_flag = cl.mem_flags.READ_ONLY) # clBufOut = clDev.createBuffer(data.size) # clDev.writeBuffer(clBufIn,data.astype(np.uint16)) # clProc.runKernel("run2dBuf",data.shape[::-1],None,clBufIn,clBufOut, # np.int32(data.shape[1]),np.int32(data.shape[0]), # np.int32(fSize),np.float32(sigmaX),np.float32(sigmaP)) # return clDev.readBuffer(clBufOut).reshape(data.shape) # def bilateralAdapt(clDev, data, sensor, fSize, sigmaX, fac): # clProc = OCLProcessor(clDev, absPath("kernels/bilateralAdapt.cl")) # clImg = clDev.createImage(data.shape[::-1]) # clImgSensor = clDev.createImage(sensor.shape[::-1]) # clBuf = clDev.createBuffer(data.size) # clDev.writeImage(clImg,data.astype(np.uint16)) # clDev.writeImage(clImgSensor,sensor.astype(np.uint16)) # clProc.runKernel("run2d",clImg.shape,None,clImg,clImgSensor,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]), # np.int32(fSize),np.float32(sigmaX),np.float32(fac)) # return clDev.readBuffer(clBuf).reshape(data.shape) # def nlMeansShared(clDev, data, fSize, bSize, sigma): # locSize = 16 # clProc = OCLProcessor(clDev,absPath("kernels/nlmeans.cl"),options=["-D FS=%d -D BS=%d -D GS=%d"%(fSize,bSize,locSize)]) # clImg = clDev.createImage(data.shape[::-1]) # clBuf = clDev.createBuffer(data.size) # clDev.writeImage(clImg,data.astype(np.uint16)) # clProc.runKernel("run2d_SHARED",clImg.shape,(locSize,locSize),clImg,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]),np.float32(sigma)) # return clDev.readBuffer(clBuf).reshape(data.shape) def nlm(data, fSize, bSize, sigma, dev = None, proc = None): if dev is None: dev = imgtools.__DEFAULT_OPENCL_DEVICE__ if dev is None: raise ValueError("no OpenCLDevice found...") dtype = data.dtype.type dtypes_kernels = {np.float32:"run2d_float", np.uint16:"run2d_short"} if not dtype in dtypes_kernels: logger.error("data type %s not supported yet, please convert to:"%dtype,list(dtypes_kernels.keys())) return img = dev.createImage_like(data) buf = dev.createBuffer(data.size,dtype = dtype) dev.writeImage(img,data) if proc is None: proc = OCLProcessor(dev,absPath("kernels/nlmeans.cl")) proc.runKernel(dtypes_kernels[dtype],img.shape,None,img,buf, np.int32(img.shape[0]),np.int32(img.shape[1]), np.int32(fSize), np.int32(bSize),np.float32(sigma)).wait() return dev.readBuffer(buf,dtype=dtype).reshape(data.shape) def nlm_fast(data,FS,BS,sigma,dev = None, proc = None): """for noise level (and FS,BS = 2,3) of sigma_0, choose sigma = 1.5*sigma_0 """ if dev is None: dev = imgtools.__DEFAULT_OPENCL_DEVICE__ if dev is None: raise ValueError("no OpenCLDevice found...") if proc is None: proc = OCLProcessor(dev,absPath("kernels/nlm_fast.cl"),options="-D FS=%i -D BS=%i"%(FS,BS)) img = dev.createImage_like(data) distImg = dev.createImage_like(data) distImg = dev.createImage_like(data, mem_flags = "READ_WRITE") tmpImg = dev.createImage_like(data, mem_flags = "READ_WRITE") tmpImg2 = dev.createImage_like(data, mem_flags = "READ_WRITE") accBuf = dev.createBuffer(data.size, mem_flags = cl.mem_flags.READ_WRITE, dtype = np.float32) weightBuf = dev.createBuffer(data.size, mem_flags = cl.mem_flags.READ_WRITE, dtype = np.float32) dev.writeImage(img,data); dev.writeBuffer(weightBuf,np.zeros_like(data,dtype=np.float32)); for dx in range(BS+1): for dy in range(-BS,BS+1): proc.runKernel("dist",img.shape,None,img,tmpImg,np.int32(dx),np.int32(dy)) proc.runKernel("convolve",img.shape,None,tmpImg,tmpImg2,np.int32(1)) proc.runKernel("convolve",img.shape,None,tmpImg2,distImg,np.int32(2)) proc.runKernel("computePlus",img.shape,None,img,distImg,accBuf,weightBuf, np.int32(img.shape[0]),np.int32(img.shape[1]), np.int32(dx),np.int32(dy),np.float32(sigma)) if any([dx,dy]): proc.runKernel("computeMinus",img.shape,None,img,distImg,accBuf,weightBuf, np.int32(img.shape[0]),np.int32(img.shape[1]), np.int32(dx),np.int32(dy),np.float32(sigma)) acc = dev.readBuffer(accBuf,dtype=np.float32).reshape(data.shape) weights = dev.readBuffer(weightBuf,dtype=np.float32).reshape(data.shape) return acc/weights def test_nlm(): from imgtools import test_images, calcPSNR data = test_images.lena() data = 100.*data/np.amax(data) y = data + np.random.normal(0,20,data.shape) y = y.astype(np.float32) out = nlm(y,2,3,25.) sigs = np.linspace(2,70,10) outs = [calcPSNR(data,nlm(y,2,3,s)) for s in sigs] print("nlm: sig_max = %s" %sigs[np.argmax(outs)]) return outs def test_nlm_fast(): from imgtools import test_images, calcPSNR data = test_images.lena() data = 100.*data/np.amax(data) y = data + np.random.normal(0,20,data.shape) y = y.astype(np.float32) out = nlm_fast(y,2,3,5.) sigs = np.linspace(3,70,30) outs = [calcPSNR(data,nlm_fast(y,2,3,s)) for s in sigs] bests = [] for s0 in np.linspace(2,20,10): y = data + np.random.normal(0,s0,data.shape) y = y.astype(np.float32) ind=np.argmax([calcPSNR(data,nlm_fast(y,2,3,s)) for s in sigs]) print(ind) bests.append([s0,sigs[ind]]) print("nlm fast: sig_max = %s" %sigs[np.argmax(outs)]) return bests # def nlMeansBuffer(clDev, data, fSize, bSize, sigma): # clProc = OCLProcessor(clDev,absPath("kernels/nlmeans.cl")) # clBufIn = clDev.createBuffer(data.size, mem_flag = cl.mem_flags.READ_ONLY) # clBufOut = clDev.createBuffer(data.size) # clDev.writeBuffer(clBufIn,data.astype(np.uint16)) # clProc.runKernel("run2dBuf",(data.size,1),None,clBufIn,clBufOut, # np.int32(data.shape[1]),np.int32(data.shape[0]), # np.int32(fSize), np.int32(bSize),np.float32(sigma)) # return clDev.readBuffer(clBufOut).reshape(data.shape) # def nlMeansPrefetch(clDev, data, fSize, bSize, sigma): # clProc = OCLProcessor(clDev,absPath("kernels/nlmeans.cl"),options=["-D FS=%d -D BS=%d"%(fSize,bSize)]) # clImg = clDev.createImage(data.shape[::-1]) # clBuf = clDev.createBuffer(data.size) # clDev.writeImage(clImg,data.astype(np.uint16)) # clProc.runKernel("run2d_FIXED",clImg.shape,None,clImg,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]),np.float32(sigma)) # return clDev.readBuffer(clBuf).reshape(data.shape) # def nlMeansProjected(clDev, data, fSize, bSize, sigma, proc = None): # clImg = clDev.createImage(data.shape[::-1]) # patchImg = clDev.createImage(data.shape[::-1], mem_flags = cl.mem_flags.READ_WRITE, channel_order = cl.channel_order.RGBA, channel_type=cl.channel_type.FLOAT) # clBuf = clDev.createBuffer(data.size) # if proc ==None: # proc = OCLProcessor(clDev,absPath("kernels/patch_kernel.cl")) # clDev.writeImage(clImg,data.astype(np.uint16)) # proc.runKernel("project4",clImg.shape,None,clImg,patchImg, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]),np.int32(fSize)) # proc.runKernel("nlm2dProject",clImg.shape,None,clImg,patchImg,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]), # np.int32(fSize), np.int32(bSize),np.float32(sigma)) # return clDev.readBuffer(clBuf).reshape(data.shape) # def nlMeansProjected2(clDev, data, fSize, bSize, sigma, proc = None): # clImg = clDev.createImage(data.shape[::-1]) # patchImg = clDev.createImage(data.shape[::-1], mem_flags = cl.mem_flags.READ_WRITE, channel_order = cl.channel_order.RGBA, channel_type=cl.channel_type.FLOAT) # clBuf = clDev.createBuffer(data.size) # if proc ==None: # proc = OCLProcessor(clDev,absPath("kernels/nlmeans_projected.cl")) # clDev.writeImage(clImg,data.astype(np.uint16)) # from time import time # t = time() # proc.runKernel("project4",clImg.shape,None,clImg,patchImg, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]),np.int32(fSize)) # proc.runKernel("nlm2dProject",clImg.shape,None,clImg,patchImg,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]), # np.int32(fSize), np.int32(bSize),np.float32(sigma)) # out= clDev.readBuffer(clBuf).reshape(data.shape) # print time()-t ,"second" # return out # def nlMeansProjectedSensor(clDev, data, sensor, fSize, bSize, sigma, proc = None): # clImg = clDev.createImage(data.shape[::-1]) # patchImg = clDev.createImage(data.shape[::-1], mem_flags = cl.mem_flags.READ_WRITE, channel_order = cl.channel_order.RGBA, channel_type=cl.channel_type.FLOAT) # sensorImg = clDev.createImage(data.shape[::-1], mem_flags = cl.mem_flags.READ_WRITE, channel_order = cl.channel_order.R, channel_type=cl.channel_type.FLOAT) # clBuf = clDev.createBuffer(data.size) # if proc ==None: # proc = OCLProcessor(clDev,absPath("kernels/patch_kernel.cl")) # clDev.writeImage(clImg,data.astype(np.uint16)) # clDev.writeImage(sensorImg,sensor.astype(np.float32)) # proc.runKernel("project4",clImg.shape,None,clImg,patchImg, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]),np.int32(fSize)) # proc.runKernel("nlm2dProjectSensor",clImg.shape,None,clImg,patchImg,sensorImg,clBuf, # np.int32(clImg.shape[0]),np.int32(clImg.shape[1]), # np.int32(fSize), np.int32(bSize),np.float32(sigma)) # return clDev.readBuffer(clBuf).reshape(data.shape) def bm3d(data,sigma): from scipy.misc import imsave, imread # meanData = np.mean(data) fName = "0123456789_TMP.png" imsave(fName,data) subprocess.call([os.path.join(os.path.dirname(__file__), "cxx_code/bm3d/bm3d"),fName,str(sigma),fName], stdout=subprocess.PIPE) out = imread(fName) return out # return out*(1.*meanData/np.mean(out)) def dct_denoising(data,sigma): from scipy.misc import imsave, imread meanData = np.mean(data) fName = "0123456789_TMP.png" imsave(fName,data) subprocess.call([os.path.join(os.path.dirname(__file__), "cxx_code/dct/demo_DCTdenoising"),fName,str(sigma),fName]) out = imread(fName) return out*(1.*meanData/np.mean(out)) def roundUp(n,k): #rounds to the next number divisible by k return np.int(np.ceil(1.*n/k)*k) def roundDown(n,k): #rounds to the next number divisible by k return np.int(np.floor(1.*n/k)*k) def dct_8x8(data,sigma,dev = None, proc=None): if dev is None: dev = imgtools.__DEFAULT_OPENCL_DEVICE__ if dev is None: raise ValueError("no OpenCLDevice found...") if not proc: proc = OCLProcessor(dev,absPath("kernels/dct_8x8.cl")) src = dev.createImage(data.shape[::-1],channel_type = cl.channel_type.FLOAT,mem_flags = cl.mem_flags.READ_ONLY) acc = dev.createBuffer(roundUp(data.shape[0],8)*roundUp(data.shape[1],8),dtype=np.float32) dst = dev.createImage(data.shape[::-1],channel_type = cl.channel_type.FLOAT,mem_flags = cl.mem_flags.WRITE_ONLY) dev.writeImage(src,data.astype(np.float32)) astride = roundDown(dst.width,8) for j in range(8): for i in range(8): proc.runKernel("dct_denoise_8x8_r",(roundDown(src.width-i,8),roundDown(src.height-j,8)),(8,8), src,acc, np.float32(sigma),np.int32(i),np.int32(j),np.int32(astride),np.int32(i==0 and j==0)) proc.runKernel("dct_denoise_normalise_r",(roundDown(src.width,8),roundDown(src.height,8)),(8,8), acc, dst,np.int32(astride)) out = dev.readBuffer(acc,dtype=np.float32) return np.maximum(0,dev.readImage(dst,dtype=np.float32)).astype(data.dtype) def test_dct(): from imgtools import test_images, calcPSNR data = test_images.lena() data = 100.*data/np.amax(data) y = data + np.random.normal(0,20,data.shape) y = y.astype(np.float32) out = dct_8x8(y,60) outs = [calcPSNR(data,dct_8x8(y,s)) for s in np.linspace(10,60,20)] return out def test_filter(): from functools import partial import time data = np.random.uniform(0,1,[2**8,2**8]).astype(np.float32) print("running on image shape : {} \n\n".format(data.shape)) fs = { "bilateral":lambda:bilateral(data,3,10,10), "nlMeans":lambda:nlm_fast(data,3,7,10), # "nlMeans S":lambda:nlMeansShared(clDev,data,3,7,10), # "nlMeans P":lambda:nlMeansProjected(clDev,data,3,7,10), "bm3d ":lambda:bm3d(data,10), "dct ":lambda:dct_denoising(data,10), "dct_8x8 ":lambda:dct_8x8(data,10), # "nlMeans prefetched":lambda:nlMeansPrefetch(clDev,data,3,5,10), #"nlMeans test":lambda:nlMeansTest(clDev,data,3,5,10), } t = time.time() for f in fs.keys: fs[f]() print("%s \t: \t %.2f s"%(f,time.time()-t)) t = time.time() if __name__ == '__main__': test_filter() # out1 = test_bilateral() # out = test_nlm_fast() # out3 = test_nlm() # out4 = test_dct() # test_filter() # from SpimUtils import test_images # data0 = test_images.lena() # y = np.maximum(0,1.*data0+np.random.normal(0,20,data0.shape)).astype(np.uint16) # dev = OCLDevice() # out = nlMeansProjected2(dev,y,3,4,35)

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tests/strategies/base.py

lycantropos/clipping

4

47439

from fractions import Fraction from functools import partial from hypothesis import strategies MAX_NUMBER = 10 ** 10 MIN_NUMBER = -MAX_NUMBER coordinates_strategies_factories = { float: partial(strategies.floats, allow_nan=False, allow_infinity=False), Fraction: partial(strategies.fractions, max_denominator=MAX_NUMBER), int: strategies.integers, } coordinates_strategies = strategies.sampled_from( [factory(MIN_NUMBER, MAX_NUMBER) for factory in coordinates_strategies_factories.values()])

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speech_to_text_azure.py

jaykasundra2/Speech_To_Text

0

180496

# Convert the differnt audio formats into .wav format import subprocess, os os.chdir(r'D:\STT\src') # audio file formats are converted using ffmpeg tool # set the path where the executable (.exe) file is located # download ffmpeg build from https://ffmpeg.zeranoe.com/builds/ and place it in your project folder # ffmpeg source code is available at https://www.ffmpeg.org/download.html FFMPEG_PATH = r'D:\STT\ffmpeg\bin\ffmpeg' import glob # Below is the folder structure for audio files. Multiple speakers each having individual folder of his/her recordings # recordings # - speaker 1 # - speaker 2 # - ... # set the path where audio files are located folder_path = r'..\data\recordings' for speaker_folder in glob.glob(folder_path + "/*"): speaker = speaker_folder.split("\\")[-1] print(speaker) for file in glob.glob(speaker_folder + "/*"): file_name = file.split("\\")[-1].split(".")[0] # output file name output_file_name = folder_path + '\\' + speaker + '_' + file_name + '.wav' print(output_file_name) # ffmpeg command to convert the audio file format command = [FFMPEG_PATH, '-i', file,output_file_name] subprocess.call(command) ################### All files converted to .wav fomrat ######################### ################### Generate transcript from this audio files ################## import time import azure.cognitiveservices.speech as speechsdk # configure API keys and service region speech_key, service_region = "53f0b034226a41c18e8ab5639604b74f", "centralindia" # configure speechadk object speech_config = speechsdk.SpeechConfig(subscription=speech_key, region=service_region) # create an empty output output = "" def combine_text(evt): """ this function will combine the transcript that is generated by multiple utterances/sentences from a audio file""" global output output = output + evt.result.text print(evt.result.text) def speech_recognize_continuous_from_file(): """performs continuous speech recognition with input from an audio file""" done = False def stop_cb(evt): """callback that stops continuous recognition upon receiving an event `evt`""" speech_recognizer.stop_continuous_recognition() nonlocal done done = True speech_recognizer.recognized.connect(lambda evt: combine_text(evt)) speech_recognizer.session_stopped.connect(stop_cb) speech_recognizer.canceled.connect(stop_cb) # Start continuous speech recognition speech_recognizer.start_continuous_recognition() while not done: time.sleep(.5) # get a list of files that have the extension wav filelist = filter(lambda f: f.split('.')[-1] in ['wav'], os.listdir(folder_path)) filelist = sorted(filelist) # generate transcript for each file for file in filelist: audio_config = speechsdk.audio.AudioConfig(filename=folder_path+"\\"+file) speech_recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config, audio_config=audio_config) speech_recognize_continuous_from_file() # save the transcript to a text file f= open(r'..\data\recordings\\'+file.split(".")[0] +".txt","w+") f.write(output) f.close() # reset the output to blank for next file output = ""

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shop/admin.py

Zadigo/mycommerce

0

162772

from django.contrib import admin from jsonschema import ValidationError from shop.models import (Image, Like, Product, Video, Wishlist) import random from shop.utils import create_product_slug @admin.register(Product) class ProductAdmin(admin.ModelAdmin): list_display = ['name', 'color', 'category', 'unit_price', 'active'] filter_horizontal = ['images'] list_filter = ['active'] date_hiearchy = 'created_on' search_fields = ['name', 'slug'] fieldsets = [ ['General', {'fields': ['name', 'sku']}], ['Variant', {'fields': ['color', 'category']}], ['Media', {'fields': ['images', 'video']}], ['Pricing', {'fields': ['unit_price', 'sale_value', 'sale_price', 'on_sale']}], ['Other', {'fields': ['display_new', 'active', 'slug']}] ] actions = ['activate', 'deactivate', 'download_csv', 'copy_products', 'create_default_sizes'] def activate(self, request, queryset): queryset.update(active=True) def deactivate(self, request, queryset): queryset.update(active=False) def download_csv(self, request, queryset): queryset = None def copy_products(self, request, queryset): new_products = [] for product in queryset: new_name = f"{product.name}{random.randrange(1, 999)}" slug = create_product_slug(new_name, product.color) new_products.append(Product(name=new_name, color=product.color, category=product.category, unit_price=product.unit_price, slug=slug)) Product.objects.bulk_create(new_products) def create_default_sizes(self, request, queryset): """For selected products, create three basic sizes XS, S and M which are generally the average size for clothes""" sizes = ['XS', 'S', 'M'] for product in queryset: for size in sizes: try: product.size_set.create(product=product, name=size, availability=False) except ValidationError: # If the size already exists for a given # product, just fail silently pass self.message_user(request, f'Created default sizes for {len(queryset)} products') @admin.register(Image) class ImageAdmin(admin.ModelAdmin): list_display = ['name', 'original', 'created_on', 'is_main_image'] date_hiearchy = 'created_on' search_fields = ['name', 'original'] list_filter = ['is_main_image'] @admin.register(Video) class VideoAdmin(admin.ModelAdmin): list_display = ['name', 'content'] search_fields = ['name'] # @admin.register(AdditionalVariant) # class AdditionalVariantAdmin(admin.ModelAdmin): # list_display = ['reference', 'in_stock', 'active'] # list_filter = ['active', 'in_stock'] @admin.register(Wishlist) class WishlistAdmin(admin.ModelAdmin): list_display = ['user', 'name', 'created_on'] date_hiearchy = 'created_on' @admin.register(Like) class LikeAdmin(admin.ModelAdmin): list_display = ['user', 'created_on'] search_fields = ['products__name'] date_hiearchy = 'created_on'

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src/custom_layers.py

iamsoroush/TensorflowCPM

0

148692

import tensorflow.keras as tfk class InstanceNorm(tfk.layers.Layer): """Instance normalization layer. Normalizes the activations of the previous layer at each step, i.e. applies a transformation that maintains the mean activation of each feature map for each instance in batch close to 0 and the standard deviation close to 1. Args: axis: Integer, the axis that should be normalized (typically the features axis). For instance, after a `Conv1D` layer with `data_format="channels_last"`, set `axis=-1` in `InstanceNorm`. Setting `axis=None` will normalize all values in each instance of the batch. Axis 0 is the batch dimension. `axis` cannot be set to 0 to avoid errors. epsilon: Small float added to variance to avoid dividing by zero. Input shape: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a Sequential model. Output shape: Same shape as input. References: - [Instance Normalization: The Missing Ingredient for Fast Stylization]( https://arxiv.org/abs/1607.08022) """ def __init__(self, axis=-1, epsilon=1e-3, mean=0, stddev=1, **kwargs): self.supports_masking = True self.axis = axis self.epsilon = epsilon self.mean = mean self.stddev = stddev super(InstanceNorm, self).__init__(**kwargs) def build(self, input_shape): ndim = len(input_shape) if self.axis == 0: raise ValueError('Axis cannot be zero') if (self.axis is not None) and (ndim == 2): raise ValueError('Cannot specify axis for rank 1 tensor') self.input_spec = tfk.layers.InputSpec(ndim=ndim) super(InstanceNorm, self).build(input_shape) def call(self, inputs): input_shape = tfk.backend.int_shape(inputs) reduction_axes = list(range(0, len(input_shape))) if self.axis is not None: del reduction_axes[self.axis] del reduction_axes[0] mean = tfk.backend.mean(inputs, reduction_axes, keepdims=True) stddev = tfk.backend.std(inputs, reduction_axes, keepdims=True) + self.epsilon normed = (inputs - mean + self.mean) / stddev * self.stddev return normed def get_config(self): config = { 'axis': self.axis, 'epsilon': self.epsilon, } base_config = super(InstanceNorm, self).get_config() return dict(list(base_config.items()) + list(config.items()))

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contextual-multimodal-fusion/create_data.py

ggzhang0071/multimodal-deep-learning

0

146637

<reponame>ggzhang0071/multimodal-deep-learning<gh_stars>0 ## Credits: https://github.com/SenticNet/contextual-utterance-level-multimodal-sentiment-analysis ## ## Authors: <NAME>, <NAME> ## ## Modified for Python 3.5 ## import numpy as np, pandas as pd from collections import defaultdict import pickle from sklearn import preprocessing min_max_scaler = preprocessing.MinMaxScaler() np.random.seed(17) pre_data = np.asarray(pd.read_csv("./data/transcripts.csv" , header=None)) train = pd.read_csv("./data/text_train.csv", header=None) test = pd.read_csv("./data/text_test.csv", header=None) train = np.asarray(train) test = np.asarray(test) train_index = np.asarray(train[:,0], dtype = 'int') test_index = np.asarray(test[:,0], dtype = 'int') def main(name): path = "./data/"+name+"/"+name print(path) train_video_mapping=defaultdict(list) train_video_mapping_index=defaultdict(list) test_video_mapping=defaultdict(list) test_video_mapping_index=defaultdict(list) data_train = np.asarray(pd.read_csv(path+"_train0.csv", header=None)) data_test = np.asarray(pd.read_csv(path+"_test0.csv", header=None)) for i in range(train_index.shape[0]): train_video_mapping[pre_data[train_index[i]][0].rsplit("_",1)[0] ].append(train_index[i]) train_video_mapping_index[pre_data[train_index[i]][0].rsplit("_",1)[0] ].append( int(pre_data[train_index[i]][0].rsplit("_",1)[1]) ) for i in range(test_index.shape[0]): test_video_mapping[pre_data[test_index[i]][0].rsplit("_",1)[0] ].append(test_index[i]) test_video_mapping_index[pre_data[test_index[i]][0].rsplit("_",1)[0] ].append( int(pre_data[test_index[i]][0].rsplit("_",1)[1]) ) train_indices = dict((c, i) for i, c in enumerate(train_index)) test_indices = dict((c, i) for i, c in enumerate(test_index)) max_len = 0 for key,value in train_video_mapping.items(): max_len = max(max_len , len(value)) for key,value in test_video_mapping.items(): max_len = max(max_len, len(value)) pad = np.asarray([0 for i in range(data_train[0][:-1].shape[0])]) print ("Mapping train") train_data_X =[] train_data_Y =[] train_length =[] for key,value in train_video_mapping.items(): lst = np.column_stack((train_video_mapping_index[key],value) ) ind = np.asarray(sorted(lst,key=lambda x: x[0])) lst_X, lst_Y=[],[] ctr=0; for i in range(ind.shape[0]): ctr+=1 #lst_X.append(preprocessing.scale( min_max_scaler.fit_transform(data_train[train_indices[ind[i,1]]][:-1]))) lst_X.append(data_train[train_indices[ind[i,1]]][:-1]) lst_Y.append(data_train[train_indices[ind[i,1]]][-1]) train_length.append(ctr) for i in range(ctr, max_len): lst_X.append(pad) lst_Y.append(0) #dummy label train_data_X.append(lst_X) train_data_Y.append(lst_Y) test_data_X =[] test_data_Y =[] test_length =[] print ("Mapping test") for key,value in test_video_mapping.items(): lst = np.column_stack((test_video_mapping_index[key],value) ) ind = np.asarray(sorted(lst,key=lambda x: x[0])) lst_X, lst_Y=[],[] ctr=0 for i in range(ind.shape[0]): ctr+=1 #lst_X.append(preprocessing.scale( min_max_scaler.transform(data_test[test_indices[ind[i,1]]][:-1]))) lst_X.append(data_test[test_indices[ind[i,1]]][:-1]) lst_Y.append(data_test[test_indices[ind[i,1]]][-1]) test_length.append(ctr) for i in range(ctr, max_len): lst_X.append(pad) lst_Y.append(0) #dummy label test_data_X.append(np.asarray(lst_X)) test_data_Y.append(np.asarray(lst_Y)) train_data_X = np.asarray(train_data_X) test_data_X = np.asarray(test_data_X) print (train_data_X.shape, test_data_X.shape,len(train_length), len(test_length)) print ("Dumping data") with open('./input/'+name+'.pickle', 'wb') as handle: pickle.dump((train_data_X, np.asarray(train_data_Y), test_data_X, np.asarray(test_data_Y), max_len ,train_length, test_length), handle, protocol=pickle.HIGHEST_PROTOCOL) if __name__ == "__main__": names = ['text','audio','video'] for nm in names: main(nm)

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ifaces/management/__init__.py

RossBrunton/RThing

0

164825

<filename>ifaces/management/__init__.py """Management modules for ifaces"""

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tests/test_calc.py

aldencolerain/rxtouchtoggle

0

107868

# test add function def test_example(): assert True

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tests/test_unit/test_md2po2md/test_md2po2md_cli.py

mondeja/md2po

0

84747

<filename>tests/test_unit/test_md2po2md/test_md2po2md_cli.py """Tests for ``md2po2md`` CLI""" import os import pytest from mdpo.md2po2md.__main__ import run @pytest.mark.parametrize('output_arg', ('-o', '--output')) @pytest.mark.parametrize('langs_arg', ('-l', '--lang')) @pytest.mark.parametrize('all_langs_in_same_arg', (True, False)) @pytest.mark.parametrize( ( 'langs', 'input_paths_glob', 'output', 'input_files_content', 'expected_result', ), ( pytest.param( ['es'], 'README.md', 'locale/{lang}', {'README.md': 'Foo\n\nBar\n'}, { 'locale/es/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/es/README.md': 'Foo\n\nBar\n', }, id='es-locale/{lang}', ), pytest.param( ('es', 'fr'), 'README.md', 'locale/{lang}', {'README.md': 'Foo\n\nBar\n'}, { 'locale/es/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/es/README.md': 'Foo\n\nBar\n', 'locale/fr/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/fr/README.md': 'Foo\n\nBar\n', }, id='README-es,fr-locale/{lang}', ), pytest.param( ('es',), 'README.md', 'locale/', {}, ValueError, id='es-locale/-ValueError', ), pytest.param( ('es',), 'README.md', 'locale/{lang}/{basename}', {'README.md': 'Foo\n\nBar\n'}, { 'locale/es/README.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/es/README': 'Foo\n\nBar\n', }, id='es-locale/{lang}/{basename}', ), pytest.param( ['es'], 'README.md', '{lang}/{basename}', {'README.md': 'Foo\n\nBar\n'}, { 'es/README.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'es/README': 'Foo\n\nBar\n', }, id='es-{lang}/{basename}', ), pytest.param( ['es'], 'README.md', '{lang}', {'README.md': 'Foo\n\nBar\n'}, { 'es/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'es/README.md': 'Foo\n\nBar\n', }, id='es-{lang}', ), pytest.param( ['es'], '[s-m]', '{lang}/{basename}', {}, ValueError, id='invalid-glob-ValueError', ), pytest.param( ['es'], 'foobar*', '{lang}/{basename}', {}, FileNotFoundError, id='no-files-matching-input-glob', ), pytest.param( ('es', 'fr', 'de_DE'), 'README.md', 'locale/{lang}', {'README.md': 'Foo\n\nBar\n'}, { 'locale/es/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/es/README.md': 'Foo\n\nBar\n', 'locale/fr/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/fr/README.md': 'Foo\n\nBar\n', 'locale/de_DE/README.md.po': ( '#\nmsgid ""\nmsgstr ""\n\nmsgid "Foo"\nmsgstr ""\n\n' 'msgid "Bar"\nmsgstr ""\n' ), 'locale/de_DE/README.md': 'Foo\n\nBar\n', }, id='README-es,fr-locale/{lang}', ), ), ) def test_md2po2md_arguments( output_arg, langs_arg, all_langs_in_same_arg, langs, input_paths_glob, output, input_files_content, expected_result, # {files: contents} or error tmp_dir, maybe_raises, ): with tmp_dir(input_files_content) as filesdir: # run md2po2md cmd = [ os.path.join(filesdir, input_paths_glob), output_arg, os.path.join(filesdir, output), ] # if all languages are passed in the same `--lang`/`-l` argument if all_langs_in_same_arg: cmd.extend([langs_arg, *langs]) else: for lang in langs: cmd.extend([langs_arg, lang]) cmd.append('--no-location') with maybe_raises(expected_result): run(cmd) # Check number of files expected_number_of_files = ( len(expected_result) + len(input_files_content) ) n_files = 0 for root, dirs, files in os.walk(filesdir, topdown=False): n_files += len(files) assert n_files == expected_number_of_files # Check expected content for relpath, content in expected_result.items(): filepath = os.path.join(filesdir, relpath) with open(filepath) as f: assert f.read() == content

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pyslowloris/__main__.py

goasdsdkai/daas

75

129362

""" MIT License Copyright (c) 2017 <NAME> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ import argparse import sys from pyslowloris import HostAddress, SlowLorisAttack def _parse_args() -> dict: parser = argparse.ArgumentParser( add_help=True, description="Asynchronous Python implementation of SlowLoris attack" ) parser.add_argument( "-u", "--url", action="store", type=str, required=True, help="Link to a web server (http://google.com) - str" ) # 247 is magic number to prevent OSError: [Errno 24] Too many open files parser.add_argument( "-c", "--connection-count", default=247, action="store", type=int, help="Count of active connections (default value is 247) - int" ) parser.add_argument( "-s", "--silent", action='store_true', help="Ignore all of the errors [pure attack mode] - bool" ) # TODO(mkrivich): add support of this flag # parser.add_argument( # "-v", "--verbose", action='store_false', # help="Produce more logs - bool" # ) if len(sys.argv) == 1: parser.print_help() sys.exit(-1) result = {} args = parser.parse_args() if args.url: try: result["address"] = HostAddress.from_url(args.url) except Exception: parser.print_help() sys.exit(-1) if not (0 < args.connection_count <= 300): parser.print_help() sys.exit(-1) result["connections_count"] = args.connection_count result["silent"] = args.silent return result def _run(target: HostAddress, connections_count: int, silent: bool) -> None: print("Attack info:") print(f"\tTarget: {str(target)}({target.ip_address})") print(f"\tConnection count: {connections_count}") print(f"\tMode (silent): {silent}") loris = SlowLorisAttack(target, connections_count, silent=silent) loris.start() def main(): args = _parse_args() # Sending requests until Ctrl+C is pressed try: _run(args["address"], args["connections_count"], args["silent"]) except KeyboardInterrupt: sys.exit(0) except Exception as ex: print(ex)

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project/handler/type/request.py

SnackChain/SnackPi

0

72748

<reponame>SnackChain/SnackPi<filename>project/handler/type/request.py import requests from handler.type.handler import AbstractInstructionHandler from handler.operation.client import OperationClient class RequestInstruction(): def __init__(self, url, method, headers = None, parameters = None, request_source = None, response_source = None): self.http_request = HTTPRequest(url, method, headers, parameters) self.request_source = request_source self.response_source = response_source class HTTPRequest(): def __init__(self, url, method, headers = None, parameters = None): self.url = url self.headers = headers self.parameters = parameters self.method = method def is_get(self): if self.method == 'GET': return True else: return False def is_post(self): if self.method == 'GET': return True else: return False class Network(): def do_request(self, request: HTTPRequest): parameters = {} headers = {} if request.parameters: parameters = request.parameters if request.headers: headers = request.headers if request.is_get(): response = requests.get(request.url) return self.json(response) elif request.is_post(): response = requests.post( request.url, params = parameters, headers = headers ) return self.verify(response) def json(self, response): if response.status_code == requests.codes.ok: return response.json() else: print("Request failed") class RequestHandler(AbstractInstructionHandler): def handle(self, instruction, parameter_provider): if instruction.type == "request": payload = instruction.payload request_instruction = RequestInstruction(**payload) network = Network() json = network.do_request(request_instruction.http_request) if json is None: print("error") return #Get values based on the paths from json response for parameter_source in request_instruction.response_source: value = json for key in parameter_source: value = value[key] parameter_provider.store_value(value) else: super().handle(request)

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asynchronous_qiwi/models/QIWIWallet/master_m/list_qvc.py

LexLuthorReal/asynchronous_qiwi

3

20624

<reponame>LexLuthorReal/asynchronous_qiwi<filename>asynchronous_qiwi/models/QIWIWallet/master_m/list_qvc.py from loguru import logger import datetime from pydantic.fields import ModelField from typing import Optional, List, Union, Any from ....utils.tools.str_datetime import convert from pydantic import BaseModel, Field, validator, ValidationError from ....data_types.QIWIWallet.list_qvc import ReleasedCardStatus, CardType, CardAlias class AmountData(BaseModel): """Object: \"AmountData\"""" amount: float = Field(..., alias="amount") currency: str = Field(..., alias="currency") class Requisites(BaseModel): name: str = Field(..., alias="name") value: str = Field(..., alias="value") class Details(BaseModel): info: str = Field(..., alias="info") description: str = Field(..., alias="description") tariff_link: str = Field(..., alias="tariffLink") offer_link: str = Field(..., alias="offerLink") features: List[Any] = Field(..., alias="features") requisites: List[Union[Requisites]] = Field(..., alias="requisites") class Info(BaseModel): id: int = Field(..., alias="id") name: str = Field(..., alias="name") alias: Union[str, CardAlias] = Field(..., alias="alias") price: AmountData = Field(..., alias="price") period: str = Field(..., alias="period") type: Union[str, CardAlias] = Field(..., alias="type") details: Details = Field(..., alias="details") @validator("alias") def alias_type(cls, alias: Union[str, CardAlias], field: ModelField) -> CardAlias: if isinstance(alias, str): try: alias = CardAlias(alias) except KeyError as e: logger.warning(f"[VALIDATION CONVERT] {field.name.upper()}: " + str(e)) else: return alias elif isinstance(alias, CardAlias): return alias raise ValidationError(model=Info) @validator("type") def card_type_type(cls, card_type: Union[str, CardAlias], field: ModelField) -> CardAlias: if isinstance(card_type, str): try: card_type = CardAlias[card_type] except KeyError as e: logger.warning(f"[VALIDATION CONVERT] {field.name.upper()}: " + str(e)) else: return card_type elif isinstance(card_type, CardAlias): return card_type raise ValidationError(model=Info) class QVX(BaseModel): id: int = Field(..., alias="id") masked_pan: str = Field(..., alias="maskedPan") status: Optional[Union[str, ReleasedCardStatus]] = Field(..., alias="status") card_expire: Optional[Union[str, datetime.datetime]] = Field(..., alias="cardExpire") card_type: Optional[Union[str, CardType]] = Field(..., alias="cardType") card_alias: str = Field(..., alias="cardAlias") card_limit: Optional[str] = Field(..., alias="cardLimit") activated: Optional[Union[str, datetime.datetime]] = Field(..., alias="activated") sms_resended: Optional[Union[str, datetime.datetime]] = Field(..., alias="smsResended") post_number: Optional[str] = Field(..., alias="postNumber") blocked_date: Optional[Union[str, datetime.datetime]] = Field(..., alias="blockedDate") full_pan: Optional[str] = Field(..., alias="fullPan") card_id: int = Field(..., alias="cardId") txn_id: str = Field(..., alias="txnId") card_expire_month: str = Field(..., alias="cardExpireMonth") card_expire_year: str = Field(..., alias="cardExpireYear") @validator('status') def status_types(cls, status: Union[str, ReleasedCardStatus], field: ModelField) -> ReleasedCardStatus: if isinstance(status, str): try: status = ReleasedCardStatus[status] except KeyError as e: logger.warning(f"[VALIDATION CONVERT] {field.name.upper()}: " + str(e)) else: return status elif isinstance(status, ReleasedCardStatus): return status raise ValidationError(model=QVX) @validator('card_expire') def card_expire_datetime(cls, card_expire: Optional[Union[str, datetime.datetime]], field: ModelField) -> Optional[datetime.datetime]: if isinstance(card_expire, str): card_expire = convert(value=card_expire, validator_name=field.name.upper(), alert=False) return card_expire elif isinstance(card_expire, datetime.datetime): return card_expire elif card_expire is None: return card_expire raise ValidationError(model=QVX) @validator('card_type') def card_types(cls, card_type: Union[str, CardType], field: ModelField) -> CardType: if isinstance(card_type, str): try: card_type = CardType[card_type] except KeyError as e: logger.warning(f"[VALIDATION CONVERT] {field.name.upper()}: " + str(e)) else: return card_type elif isinstance(card_type, CardType): return card_type raise ValidationError(model=QVX) @validator('activated') def activated_datetime(cls, activated: Optional[Union[str, datetime.datetime]], field: ModelField) -> Optional[datetime.datetime]: if isinstance(activated, str): activated = convert(value=activated, validator_name=field.name.upper(), alert=False) return activated elif isinstance(activated, datetime.datetime): return activated elif activated is None: return activated raise ValidationError(model=QVX) @validator('sms_resended') def sms_resended_datetime(cls, sms_resended: Optional[Union[str, datetime.datetime]], field: ModelField) -> Optional[datetime.datetime]: if isinstance(sms_resended, str): sms_resended = convert(value=sms_resended, validator_name=field.name.upper(), alert=False) return sms_resended elif isinstance(sms_resended, datetime.datetime): return sms_resended elif sms_resended is None: return sms_resended raise ValidationError(model=QVX) @validator('blocked_date') def blocked_date_datetime(cls, blocked_date: Optional[Union[str, datetime.datetime]], field: ModelField) -> Optional[datetime.datetime]: if isinstance(blocked_date, str): blocked_date = convert(value=blocked_date, validator_name=field.name.upper(), alert=False) return blocked_date elif isinstance(blocked_date, datetime.datetime): return blocked_date elif blocked_date is None: return blocked_date raise ValidationError(model=QVX) class ListCard(BaseModel): qvx: QVX = Field(..., alias="qvx") balance: Optional[AmountData] = Field(..., alias="balance") info: Info = Field(..., alias="info") features: List[Any] = Field(..., alias="features") class ListCardMaster(BaseModel): data: List[Union[ListCard]] = Field(..., alias="data")

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kg.py

Jie-Yuan/BaiduAI-sdk

0

182771

<filename>kg.py<gh_stars>0 import json import urllib import requests import json import urllib import requests class BaiDuNLP(object): def __init__(self, api_key='<KEY>', secret_key='<KEY>'): self.access_token = None self._get_access_token(api_key, secret_key) def predict(self, text, api_url): """ api_url = 'https://aip.baidubce.com/rpc/2.0/kg/v1/cognitive/entity_annotation?charset=UTF-8&access_token=' api = BaiDuNLP() api.predict('周杰伦', api_url) """ url = api_url + self.access_token # the input is json format input_text = {'data': text} input_text = json.dumps(input_text) r = requests.post(url, data=input_text, headers={'Content-Type': 'application/json'}) return r.json() def _get_access_token(self, api_key, secret_key): host = f'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={api_key}&client_secret={secret_key}' request = urllib.request.Request(host) request.add_header('Content-Type', 'application/json; charset=UTF-8') response = urllib.request.urlopen(request) content = response.read() self.access_token = json.loads(content)['access_token']

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flir_lepton_auxiliary/scripts/produce_dataset.py

angetria/flir_lepton_rpi2

15

85046

#!usr/bin/python import sys # The area of interest(pixels) to find the mean temperature x1 = 52 x2 = 54 y1 = 18 y2 = 20 # The area of neutral pixels to find the mean temperature x3 = 66 x4 = 68 y3 = 6 y4 = 8 # Make the dataset from raw pixel flir-lepton images. try: dataset = open("dataset.iemb","w") except IOError: print "dataset file could not be made" sys.exit() # Process each image raw data for all images for i in range(20,51): # The flir-lepton images to be read. fileName = \ "Input your flir-lepton images full path"\ + str(i) + ".iemb" try: data = open(fileName, "r") except IOError: print "No file was found" sys.exit() #counter = 0 signal_val = [] with open(fileName, "r") as file: for line in file: #counter = counter + 1 signal_val.append(line) temp = signal_val[0].split() # To read specific pixel(x,y) must give : gimp gives inverted x,y coords # average = the average temperature of the point of interest # av = the average temperature of a neutral area average = 0 av = 0 if i < 51: for x in range(x1,x2): for y in range(y1,y2): # for raw data images a = y * 80 + x average = average + int(temp[a]) # Find in the other array whats going on in pixel values for x in range(x3,x4): for y in range(y3,y4): b = y * 80 + x av = av + int(temp[b]) average = average / 4 av = av / 4 # If we check second area #else: #for x in range(24,26): #temp = signal_val[x].split() #for y in range(42,44): #a = int(temp[y]) #average = average + a #Find in the other array whats going on in pix values #for x in range(10,12): #temp = signal_val[x].split() #for y in range(65,67): #a = int(temp[y]) #av = av + a #average = average / 4 #av = av / 4 # Make the file with our own standards, for even numbers of rows place # the raw pixel values, for odd the degree's. degree = str(i) + "\n" raw = str(average) + "\n" dataset.write(raw) dataset.write(degree) print "The average of image", i ,"=",average print "The av of image", i ,"=",av data.close() dataset.close()

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aasaan/utils/datedeux.py

deepakkt/aasaan

0

117147

'''Copyright 2017, Deepak Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ''' from datetime import date, datetime from functools import partial import re class DateDeux(date): def pydate(self): return date(self.year, self.month, self.day) @classmethod def fromisodate(self, isodatestr): try: return DateDeux(*(map(int, isodatestr.split('-')))) except: return None @classmethod def frompydate(self, pydate_object): return DateDeux(pydate_object.year, pydate_object.month, pydate_object.day) def monthstart(self): return DateDeux(self.year, self.month, 1) def monthend(self): return DateDeux.fromordinal(DateDeux(self.year, self.month + 1, 1).toordinal() - 1) def yearend(self): return DateDeux(self.year, 12, 31) def yearstart(self): return DateDeux(self.year, 1, 1) def dayname(self): return ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'][self.weekday()] def dayname_short(self): return self.dayname()[:3] def monthname(self): return ['', 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'][self.month] def monthname_short(self): return self.monthname()[:3] def dateformat(self, format): def _format_as_int(number, length): if length < len(str(number)): return str(number)[-length:] format = "%%0%dd" % length return format % number def _format_month(*args): return self.monthname_short() def _re_match(matchstring, regex): return re.findall(regex, matchstring)[0] matches = list(map(partial(_re_match, format), ['y+', 'm+', 'd+'])) result = format[:] result = result.replace(matches[0], _format_as_int(self.year, len(matches[0]))) result = result.replace(matches[2], _format_as_int(self.day, len(matches[2]))) _month_func = _format_month if len(matches[1]) == 3 else _format_as_int result = result.replace(matches[1], _month_func(self.month, len(matches[1]))) return result def yearcalendar(self): _start = DateDeux(self.year, 1, 1) _end = DateDeux(self.year, 12, 31) diff = _end.toordinal() - _start.toordinal() + 1 return (_start + x for x in range(0, diff)) def monthcalendar(self): _start = self.monthstart() _end = self.monthend() diff = _end.toordinal() - _start.toordinal() + 1 return (_start + x for x in range(0, diff)) def __add__(self, numdays): return DateDeux.fromordinal(self.toordinal() + numdays) def __sub__(self, numdays): try: return self.toordinal() - numdays.toordinal() except AttributeError: return DateDeux.fromordinal(self.toordinal() - numdays) def __iter__(self): return iter((self.year, self.month, self.day))

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hexrd/ui/color_map_editor.py

HEXRD/hexrdgui

13

199875

import copy from matplotlib import cm import matplotlib.colors import numpy as np import hexrd.ui.constants from hexrd.ui.brightness_contrast_editor import BrightnessContrastEditor from hexrd.ui.hexrd_config import HexrdConfig from hexrd.ui.ui_loader import UiLoader from hexrd.ui.utils import block_signals class ColorMapEditor: def __init__(self, image_object, parent=None): # The image_object can be any object with the following functions: # 1. set_cmap: a function to set the cmap on the image # 2. set_norm: a function to set the norm on the image self.image_object = image_object loader = UiLoader() self.ui = loader.load_file('color_map_editor.ui', parent) self.bounds = (0, 16384) self._data = None self.bc_editor = None self.load_cmaps() self.setup_connections() @property def data(self): return self._data @data.setter def data(self, v): self._data = v self.update_bc_enable_state() if self.bc_editor: self.bc_editor.data = v self.update_bc_editor() def load_cmaps(self): cmaps = sorted(i[:-2] for i in dir(cm) if i.endswith('_r')) self.ui.color_map.addItems(cmaps) # Set the combobox to be the default self.ui.color_map.setCurrentText(hexrd.ui.constants.DEFAULT_CMAP) def setup_connections(self): self.ui.bc_editor_button.pressed.connect(self.bc_editor_button_pressed) self.ui.minimum.valueChanged.connect(self.range_edited) self.ui.maximum.valueChanged.connect(self.range_edited) self.ui.color_map.currentIndexChanged.connect(self.update_cmap) self.ui.reverse.toggled.connect(self.update_cmap) self.ui.show_under.toggled.connect(self.update_cmap) self.ui.show_over.toggled.connect(self.update_cmap) self.ui.log_scale.toggled.connect(self.update_norm) def range_edited(self): self.update_bc_editor() self.update_mins_and_maxes() self.update_norm() def update_bc_enable_state(self): has_data = self.data is not None self.ui.bc_editor_button.setEnabled(has_data) def bc_editor_button_pressed(self): if self.bc_editor: self.bc_editor.ui.reject() bc = self.bc_editor = BrightnessContrastEditor(self.ui) bc.data = self.data bc.edited.connect(self.bc_editor_modified) bc.reset.connect(self.reset_range) bc.ui.finished.connect(self.remove_bc_editor) # Hide overlays while the BC editor is open self._bc_previous_show_overlays = HexrdConfig().show_overlays if self._bc_previous_show_overlays: HexrdConfig().show_overlays = False HexrdConfig().active_material_modified.emit() self.update_bc_editor() self.bc_editor.ui.show() def update_bc_editor(self): if not self.bc_editor: return widgets = (self.ui.minimum, self.ui.maximum) new_range = [x.value() for x in widgets] with block_signals(self.bc_editor): self.bc_editor.ui_range = new_range def remove_bc_editor(self): self.bc_editor = None if self._bc_previous_show_overlays and not HexrdConfig().show_overlays: # Show the overlays again HexrdConfig().show_overlays = True HexrdConfig().active_material_modified.emit() def bc_editor_modified(self): with block_signals(self.ui.minimum, self.ui.maximum): self.ui.minimum.setValue(self.bc_editor.ui_min) self.ui.maximum.setValue(self.bc_editor.ui_max) self.range_edited() def update_mins_and_maxes(self): # We can't do this in PySide2 for some reason: # self.ui.maximum.valueChanged.connect(self.ui.minimum.setMaximum) # self.ui.minimum.valueChanged.connect(self.ui.maximum.setMinimum) self.ui.maximum.setMinimum(self.ui.minimum.value()) self.ui.minimum.setMaximum(self.ui.maximum.value()) def block_updates(self, blocked): self.updates_blocked = blocked def update_bounds(self, data): if hasattr(self, 'updates_blocked') and self.updates_blocked: # We don't want to adjust the bounds return bounds = self.percentile_range(data) self.ui.minimum.setValue(bounds[0]) self.ui.minimum.setToolTip('Min: ' + str(bounds[0])) self.ui.maximum.setValue(bounds[1]) self.ui.maximum.setToolTip('Max: ' + str(bounds[1])) self.bounds = bounds self.data = data @staticmethod def percentile_range(data, low=69.0, high=99.9): if isinstance(data, dict): values = data.values() elif not isinstance(data, (list, tuple)): values = [data] l = min([np.nanpercentile(v, low) for v in values]) h = min([np.nanpercentile(v, high) for v in values]) if h - l < 5: h = l + 5 return l, h def reset_range(self): if hasattr(self, 'updates_blocked') and self.updates_blocked: # We don't want to adjust the range return if self.ui.minimum.maximum() < self.bounds[0]: # Make sure we can actually set the value... self.ui.minimum.setMaximum(self.bounds[0]) self.ui.minimum.setValue(self.bounds[0]) self.ui.maximum.setValue(self.bounds[1]) def update_cmap(self): # Get the Colormap object from the name cmap = cm.get_cmap(self.ui.color_map.currentText()) if self.ui.reverse.isChecked(): cmap = cmap.reversed() # For set_under() and set_over(), we don't want to edit the # original color map, so make a copy cmap = copy.copy(cmap) if self.ui.show_under.isChecked(): cmap.set_under('b') if self.ui.show_over.isChecked(): cmap.set_over('r') self.image_object.set_cmap(cmap) def update_norm(self): min = self.ui.minimum.value() max = self.ui.maximum.value() if self.ui.log_scale.isChecked(): # The min cannot be 0 here, or this will raise an exception # For some reason, if it is less than 1.e-7, for some datasets, # matplotlib will round it to 0, and then raise an exception. # Thus, keep it at 1.e-7 for now. min = 1.e-7 if min < 1.e-7 else min norm = matplotlib.colors.LogNorm(vmin=min, vmax=max) else: norm = matplotlib.colors.Normalize(vmin=min, vmax=max) self.image_object.set_norm(norm)

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Domotique/Temperature Fablab/temperature.py

LaFabrickMassy/Projet-Fablab

0

53332

import board import adafruit_ahtx0 from datetime import datetime html_dir = "/var/www/html/temp" data_fname = "temperature.csv" html_fname = "index.html" sensor = adafruit_ahtx0.AHTx0(board.I2C()) temp = sensor.temperature hum = sensor.relative_humidity now = datetime.now() # open data file history = [] with open(html_dir + "/" + data_fname, "r") as f: for l in f.readlines(): fields = l.split(";") dt = datetime.strptime(fields[0], "%d/%m/%y %H:%M") data = [dt, float(fields[1]), float(fields[2])] history.append(data) history.append([now, temp, hum]) t_data = [] h_data = [] dt_data= [] for h in history: t_data.append("{:.1f}".format(h[1])) h_data.append("{:.1f}".format(h[2])) dt_data.append(h[0].strftime("'%Y-%m-%d %H:%m'")) with open(html_dir + "/" + data_fname, "a") as f: dt = now.strftime("%d/%m/%y %H:%M") f.write("{};{};{}\n".format(dt, temp, hum)) head="""<html> <head> <title>Temperature</title> </head> <body>\n""" body1 = """ <div id="plotly-chart"></div> <script type=\"text/javascript\" src=\"./plotly-latest.min.js\"></script> <script type=\"text/javascript\" > var temp = { x: [""" body2="],\n y: [" body3="""], name: 'temperature', type: 'scatter' }; var hum = { x: [""" body4="],\n y: [" body5="""], yaxis: 'y2', name: 'humidity', type: 'scatter' }; var data = [temp, hum]; var layout = { title: 'Double Y Axis Example', yaxis: {title: 'Temperature'}, yaxis2: { title: 'Humidity', overlaying: 'y', side: 'right' } }; Plotly.newPlot('plotly-chart', data, layout); </script> """ tail="</body></html>\n" with open(html_dir + "/" + html_fname, "w") as f: f.write(head) f.write(body1) f.write(",".join(dt_data)) f.write(body2) f.write(",".join(t_data)) f.write(body3) f.write(",".join(dt_data)) f.write(body4) f.write(",".join(h_data)) f.write(body5) f.write(tail)

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pandas/main.py

monishshah18/python-cp-cheatsheet

140

14032

<gh_stars>100-1000 """ Summarize a column total cases column and total deaths column Country by country data in columns, sum up and match global totals """ import csv import pandas pandas.set_option("display.max_rows", None, "display.max_columns", None) col_list = ["Total Cases", "Country/ Other", "Total Deaths", "# 9/27/2020"] df = pandas.read_csv("covidmilliondead.csv", usecols=col_list, thousands=',') totalCases, totalDeaths = 0,0 for idx, cases,deaths in zip(df["# 9/27/2020"], df["Total Cases"], df["Total Deaths"]): if idx > 0: totalCases += cases if deaths > 0: totalDeaths += deaths for idx, country, cases, deaths in zip(df["# 9/27/2020"], df["Country/ Other"], df["Total Cases"], df["Total Deaths"]): if idx > 0: print("\n",country) print("Cases : ", cases, "/", totalCases, " %", "{:.5%}".format(cases/totalCases)) if deaths > 0: print("Deaths : ", int(deaths), "/", totalDeaths, " %", "{:.5%}".format(deaths/totalDeaths)) print("") print("Total Cases") print(totalCases) print("Total Deaths") print(totalDeaths)

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apps/restaccount/resources.py

solatis/microservices

38

24420

import json import falcon import time import uuid import requests from apps.database import init_db, db_session from apps.models import Account from apps.restaccount.logging import logging logger = logging.getLogger(__name__) from decouple import config ES_HOST = config('EVENTSTORE_HOST', default='eventstore') ES_PORT = config('EVENTSTORE_PORT', default=2113, cast=int) stream_url = 'http://{}:{}/streams/accounts'.format(ES_HOST, ES_PORT) content_header = { 'Content-Type': 'application/vnd.eventstore.events+json' } logger.info('stream_url: {}'.format(stream_url)) def get_account(account_id): return Account.query.get(account_id) class BalanceResource(object): def on_get(self, req, resp, account_id): init_db() doc = db_session.query(Account).get(account_id) db_session.close() if doc is None: raise falcon.HTTPBadRequest('Balance missing', 'Deposit money to start using an account') else: # Create a JSON representation of the resource resp.body = json.dumps(doc.as_dict(), ensure_ascii=False) # The following line can be omitted because 200 is the default # status returned by the framework, but it is included here to # illustrate how this may be overridden as needed. resp.status = falcon.HTTP_200 class DepositResource(object): def on_post(self, req, resp): body = req.stream.read() doc = json.loads(body.decode('utf-8')) logger.info('doc: {}'.format(doc)) payload = [ { "eventId": str(uuid.uuid1()), "eventType": "created-deposit", "data": doc } ] logger.info("payload: {}".format(payload)) r = requests.post(stream_url, data=str(payload), headers=content_header) resp.status = falcon.HTTP_200 class TransferResource(object): def on_post(self, req, resp): body = req.stream.read() doc = json.loads(body.decode('utf-8')) acc = get_account(doc['account_id']) payload = [ { "eventId": str(uuid.uuid1()), "eventType": "created-transfer", "data": doc } ] if acc is None: raise falcon.HTTPBadRequest('Account missing', 'You must deposit into an account before transfering') if acc.balance < doc['amount']: raise falcon.HTTPBadRequest('Insufficient funds', 'Account balance {} less than transfer amount {}'.format(acc.balance, doc['amount'])) else: logger.info("payload: {}".format(payload)) r = requests.post(stream_url, data=str(payload), headers=content_header) resp.status = falcon.HTTP_200

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secondtest/2012/2gauss-seidel.py

JoaoCostaIFG/MNUM

1

1613988

<reponame>JoaoCostaIFG/MNUM #!/usr/bin/env python3 # CHECKED # The answers on the pdf were wrong, except for the first value of x1 # (I believe mine are right because I get the same results when using old, correct. code) # a) n = 4 # matrix order A = [[4.9, -1, -1, 1], [-1, 4.8, 1, -1], [-1, 2, 4.8, -1], [2, -1, -1, 4.8]] b = [1, -1, -1, 0] x0 = [0, 0, 0, 0] x = [0, 0, 0, 0] for k in range(2): for i in range(n): x[i] = b[i] for j in range(i): x[i] -= A[i][j] * x[j] for j in range(i+1, n): x[i] -= A[i][j] * x0[j] x[i] /= A[i][i] # if doing more than 1 iteration, copy method is very important # (see python's soft and hard copies) x0 = x.copy() print(x) # b) # The process converges because the absolute value of each element on the main # diagonal of the matrix ([4.8, 4.8, 4.8, 4.8]) is greater than or equal to (diagonally dominant) # the sum of the absolute values of all other elements in their respective lines: # i=1 4.8 > 3 # i=2 4.8 > 3 # i=3 4.8 > 4 # i=4 4.8 > 4

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compile.py

Eleyvie/wreck

1

134519

<reponame>Eleyvie/wreck<filename>compile.py WRECK_VERSION = '1.0.1' # DONE: # new command line parameter: `test`. WRECK will compile the module but won't save anything on disk. Essentially a syntax check. # PLANNED: # local variable lifetime check (paranoia mode) # trace local declaration in all conditional branches # dialogs: dead ends, inaccessible dialogs # QUESTIONABLE: # scripts: detect number of arguments and check all call_script calls # RESCHEDULED FOR 2.0: # change behavior on fatal errors wherever possible: remember, assume, continue compilation, don't export, report # improve globals visibility for modules and plugins # syntax expected datatype check where appropriate # operation parameters expected datatype check # automatically update module.ini when necessary (num_hints parameter for starters) # check for missing/undeclared scenes in SceneObj folder # WILL NOT BE IMPLEMENTED: # check for missing meshes in CommonRes and Resource folder - they're inside brf files, would need brf parser for that import sys if (sys.version_info[0] != 2) or (sys.version_info[1] < 6): exit("\nYou're running Python version {0}.{1}.{2}.\nW.R.E.C.K. requires Python version 2.6.x or 2.7.x to run!\n".format(*sys.version_info[0:3])) sys.dont_write_bytecode = True from time import time as gettime from os import makedirs from traceback import extract_tb # Color support if 'bw' in sys.argv: COLORAMA = ('', '', '', '', '', '', '', '') else: try: import colorama colorama.init() COLORAMA = ('\x1b[0m', '\x1b[31m', '\x1b[32m', '\x1b[33m', '\x1b[34m', '\x1b[35m', '\x1b[36m', '\x1b[37m') except: COLORAMA = ('', '', '', '', '', '', '', '') from compiler import * write_id_files = "ID_%s.py" # Where the compiler will write new iteration ID-files. show_performance_data = False # Set to true to display compiler performance data by default. export_filename = '%s.txt' # How to name export files (only used for some debugging purposes). WRECK.time_started = gettime() print print '{2}*** Warband Refined & Enhanced Compiler Kit (W.R.E.C.K.) version {version!s} ***{0}'.format(*COLORAMA, version = WRECK_VERSION) print 'Please report errors, problems and suggestions at {5}http://lav.lomskih.net/wreck/{0}'.format(*COLORAMA) print try: # +----------------------------------------------------------------------------------------------- # / # + # | print 'Loading module...', try: # Info module and plugins WRECK.current_module = 'info' from module_info import * WRECK.destination = export_dir.rstrip('/') globals().update(WRECK.syntax_extensions) globals().update(WRECK.plugin_globals) # Optional modules WRECK.current_module = 'item_modifiers' try: from module_item_modifiers import * except ImportError: #from defaults.module_item_modifiers import * item_modifiers = DEFAULT_ITEM_MODIFIERS WRECK.generate_item_modifiers = False generate_imod_constants_for_backwards_compatibility(item_modifiers) WRECK.current_module = 'ui_strings' try: from module_ui_strings import * except ImportError: ui_strings = [] WRECK.generate_ui_strings = False WRECK.current_module = 'user_hints' try: from module_user_hints import * except ImportError: user_hints = [] WRECK.generate_user_hints = False # Required modules WRECK.current_module = 'skills' from module_skills import * generate_skill_constants_for_backwards_compatibility(skills) WRECK.current_module = 'animations' from module_animations import * WRECK.current_module = 'factions' from module_factions import * WRECK.current_module = 'game_menus' from module_game_menus import * WRECK.current_module = 'info_pages' from module_info_pages import * WRECK.current_module = 'meshes' from module_meshes import * WRECK.current_module = 'mission_templates' from module_mission_templates import * WRECK.current_module = 'tracks' from module_music import * WRECK.current_module = 'particle_systems' from module_particle_systems import * WRECK.current_module = 'postfx_params' from module_postfx import * WRECK.current_module = 'quests' from module_quests import * WRECK.current_module = 'scene_props' from module_scene_props import * WRECK.current_module = 'scenes' from module_scenes import * WRECK.current_module = 'scripts' from module_scripts import * WRECK.current_module = 'simple_triggers' from module_simple_triggers import * WRECK.current_module = 'sounds' from module_sounds import * WRECK.current_module = 'strings' from module_strings import * WRECK.current_module = 'tableaus' from module_tableau_materials import * WRECK.current_module = 'triggers' from module_triggers import * WRECK.current_module = 'items' from module_items import * WRECK.current_module = 'map_icons' from module_map_icons import * WRECK.current_module = 'skins' from module_skins import * WRECK.current_module = 'presentations' from module_presentations import * WRECK.current_module = 'troops' from module_troops import * WRECK.current_module = 'party_templates' from module_party_templates import * WRECK.current_module = 'parties' from module_parties import * WRECK.current_module = 'dialogs' from module_dialogs import * WRECK.current_module = None except Exception, e: print '{1}FAILED.\nMODULE `{module!s}` ERROR:\n{error!s}{0}'.format(*COLORAMA, module = WRECK.current_module, error = (e.formatted() if isinstance(e, MSException) else formatted_exception())) if isinstance(e, TypeError) and (('object is not callable' in e.message) or ('indices must be integers' in e.message)): exc_type, exc_value, exc_traceback = sys.exc_info() error_info = extract_tb(exc_traceback)[-1] print '{6} Compiler hint: this error is typically caused by a missing comma.\n Please check that tuples are followed by commas in `{path!s}` above line {line}:\n\n {5}{code!s}{0}'.format(*COLORAMA, path = path_split(error_info[0])[1], line = error_info[1], code = error_info[3]) WRECK.time_loaded = gettime() raise MSException() print '{2}DONE.{0}'.format(*COLORAMA) WRECK.time_loaded = gettime() # | # + # \ # +=============================================================================================== # / # + # | print 'Loading plugins...', try: # Check plugin requirements prereq_errors = [] for plugin, required_by in WRECK.requirements.iteritems(): if plugin not in WRECK.plugins: prereq_errors.append('Plugin %s not imported but required by %s.' % (plugin, ', '.join(required_by))) if prereq_errors: raise MSException('\r\n'.join(prereq_errors)) # Process data injections glob = get_globals() for plugin in WRECK.plugins: for parser in parsers.iterkeys(): if hasattr(glob[plugin], parser): glob[parser].extend(getattr(glob[plugin], parser)) injections = getattr(glob[plugin], 'injection', None) if injections: for inj_name, inj_elements in injections.iteritems(): WRECK.injections.setdefault(inj_name, []).extend(inj_elements) #WRECK.warnings.append('Injection: %d elements for `%s` in `%s`' % (len(inj_elements), inj_name, plugin)) except Exception, e: print '{1}FAILED.\nPLUGIN `{module!s}` ERROR:\n{error!s}{0}'.format(*COLORAMA, module = plugin, error = (e.formatted() if isinstance(e, MSException) else formatted_exception())) WRECK.time_plugins = gettime() raise MSException() print '{2}DONE.{0}'.format(*COLORAMA) WRECK.time_plugins = gettime() # | # + # \ # +=============================================================================================== # / # + # | print 'Checking module syntax...', try: for entity_name, entity_def in parsers.iteritems(): WRECK.current_module = entity_name get_globals()[entity_name] = check_syntax(get_globals()[entity_name], [entity_def['parser']], entity_def.get('uid', 0)) WRECK.current_module = None except Exception, e: print '{1}FAILED.\nMODULE `{module!s}` ERROR:\n{error!s}{0}'.format(*COLORAMA, module = entity_name, error = (e.formatted() if isinstance(e, MSException) else formatted_exception())) WRECK.time_syntax = gettime() raise MSException() print '{2}DONE.{0}'.format(*COLORAMA) WRECK.time_syntax = gettime() WRECK.anim[7] = animations WRECK.fac[7] = factions WRECK.ip[7] = info_pages WRECK.imod[7] = item_modifiers WRECK.itm[7] = items WRECK.icon[7] = map_icons WRECK.mnu[7] = game_menus WRECK.mesh[7] = meshes WRECK.mt[7] = mission_templates WRECK.track[7] = tracks WRECK.psys[7] = particle_systems WRECK.p[7] = parties WRECK.pt[7] = party_templates WRECK.pfx[7] = postfx_params WRECK.prsnt[7] = presentations WRECK.qst[7] = quests WRECK.spr[7] = scene_props WRECK.scn[7] = scenes WRECK.script[7] = scripts WRECK.skl[7] = skills WRECK.snd[7] = sounds WRECK.s[7] = strings WRECK.tableau[7] = tableaus WRECK.trp[7] = troops # | # + # \ # +=============================================================================================== # / # + # | print 'Allocating identifiers...', try: allocate_global_variables() allocate_quick_strings() calculate_identifiers(animations, anim) calculate_identifiers(factions, fac) calculate_identifiers(info_pages, ip) calculate_identifiers(item_modifiers, imod, imodbit) calculate_identifiers(items, itm) calculate_identifiers(map_icons, icon) calculate_identifiers(game_menus, mnu) calculate_identifiers(meshes, mesh) calculate_identifiers(mission_templates, mt) calculate_identifiers(tracks, track) calculate_identifiers(particle_systems, psys) calculate_identifiers(parties, p) calculate_identifiers(party_templates, pt) calculate_identifiers(postfx_params, pfx) calculate_identifiers(presentations, prsnt) calculate_identifiers(quests, qst) calculate_identifiers(scene_props, spr) calculate_identifiers(scenes, scn) calculate_identifiers(scripts, script) calculate_identifiers(skills, skl) calculate_identifiers(sounds, snd) calculate_identifiers(strings, s) calculate_identifiers(tableaus, tableau) calculate_identifiers(troops, trp) undefined = undefined_identifiers() if undefined: raise MSException('undeclared identifiers found in module source:\n * %s' % ('\n * '.join(['%s (referenced by \'%s\')' % (name, '\', \''.join(refs)) for name, refs in undefined]))) except Exception, e: print '{1}FAILED.'.format(*COLORAMA) if isinstance(e, MSException): print 'MODULE ERROR:\n{error!s}{0}'.format(*COLORAMA, error = e.formatted()) else: print 'COMPILER INTERNAL ERROR:\n{error!s}{0}'.format(*COLORAMA, error = formatted_exception()) WRECK.time_identifiers = gettime() raise MSException() print '{2}DONE.{0}'.format(*COLORAMA) WRECK.time_identifiers = gettime() # | # + # \ # +=============================================================================================== # / # + # | print 'Compiling module...', try: stage = 0 # Pre-processing (note that all entity-level injections are already done but script-level injections are not). glob = get_globals() preprocess_entities_internal(glob) stage = 1 for plugin in WRECK.plugins: processor = getattr(glob[plugin], 'preprocess_entities', None) if processor: try: processor(glob) except Exception, e: raise MSException('Error in %r pre-processor script.' % plugin, formatted_exception()) # Compiling... stage = 2 for entity_name, entity_def in parsers.iteritems(): stage = 3 entities = get_globals()[entity_name] stage = 4 for index in xrange(len(entities)): entities[index] = entity_def['processor'](entities[index], index) stage = 5 setattr(WRECK, entity_name, entity_def['aggregator'](entities)) # Post-processing (plugins are NOT allowed to do anything here as we are dealing with already compiled code) stage = 6 postprocess_entities() except Exception, e: print '{1}FAILED.'.format(*COLORAMA) if isinstance(e, MSException): if stage == 0: print 'COMPILER PREPROCESSOR ERROR:\n{error!s}{0}'.format(*COLORAMA, error = e.formatted()) if stage == 1: print 'PLUGIN {module!s} PREPROCESSOR ERROR:\n{error!s}{0}'.format(*COLORAMA, module = plugin, error = e.formatted()) elif stage == 3: print 'MODULE {module!s} ENTITY #{index} COMPILATION ERROR:\n{error!s}{0}'.format(*COLORAMA, module = entity_name, index = index, error = e.formatted()) elif stage == 4: print 'MODULE {module!s} AGGREGATOR ERROR:\n{error!s}{0}'.format(*COLORAMA, module = entity_name, error = e.formatted()) elif stage == 5: print 'COMPILER POSTPROCESSOR ERROR:\n{error!s}{0}'.format(*COLORAMA, error = e.formatted()) else: print 'COMPILER INTERNAL ERROR:\n{error!s}{0}'.format(*COLORAMA, error = formatted_exception()) WRECK.time_compile = gettime() raise MSException() print '{2}DONE.{0}'.format(*COLORAMA) WRECK.time_compile = gettime() # | # + # \ # +=============================================================================================== # / # + # | print 'Exporting module...', export_done = True # Success export = { 'animations': export_filename % 'actions', 'dialogs': export_filename % 'conversation', 'dialog_states': export_filename % 'dialog_states', 'factions': export_filename % 'factions', 'game_menus': export_filename % 'menus', 'info_pages': export_filename % 'info_pages', 'items': export_filename % 'item_kinds1', 'map_icons': export_filename % 'map_icons', 'meshes': export_filename % 'meshes', 'mission_templates': export_filename % 'mission_templates', 'tracks': export_filename % 'music', 'particle_systems': export_filename % 'particle_systems', 'parties': export_filename % 'parties', 'party_templates': export_filename % 'party_templates', 'postfx_params': export_filename % 'postfx', 'presentations': export_filename % 'presentations', 'quests': export_filename % 'quests', 'scene_props': export_filename % 'scene_props', 'scenes': export_filename % 'scenes', 'scripts': export_filename % 'scripts', 'simple_triggers': export_filename % 'simple_triggers', 'skills': export_filename % 'skills', 'skins': export_filename % 'skins', 'sounds': export_filename % 'sounds', 'strings': export_filename % 'strings', 'tableaus': export_filename % 'tableau_materials', 'triggers': export_filename % 'triggers', 'troops': export_filename % 'troops', 'variables': export_filename % 'variables', 'quick_strings': export_filename % 'quick_strings', } if WRECK.generate_item_modifiers: export['item_modifiers'] = 'Data/item_modifiers.txt' if WRECK.generate_ui_strings: export['ui_strings'] = 'Languages/en/ui.csv' if WRECK.generate_user_hints: export['user_hints'] = 'Languages/en/hints.csv' try: if 'test' in sys.argv: raise MSException() for entity_name, filename in export.iteritems(): contents = getattr(WRECK, entity_name) if contents is None: #print 'Module %s has no changes, skipping export.' % entity_name continue #print 'Exporting module %s...' % entity_name filename = path_split(filename.replace('\\', '/')) folder = ('%s/%s' % (WRECK.destination, filename[0])) if filename[0] else WRECK.destination if filename[0] and not(path_exists(folder)): makedirs(folder) with open('%s/%s' % (folder, filename[1]), 'w+b') as f: f.write(contents) except MSException: export_done = False # Skipped. write_id_files = None # Override any value that was there. except Exception, e: print '{1}FAILED.\nCOMPILER INTERNAL ERROR WHILE WRECKING {module!s}:\n{error!s}{0}'.format(*COLORAMA, module = entity_name, error = formatted_exception()) WRECK.time_export = gettime() raise MSException() if write_id_files is not None: export = { 'animations': (WRECK.anim, 'anim_'), 'factions': (WRECK.fac, 'fac_'), 'info_pages': (WRECK.ip, 'ip_'), 'items': (WRECK.itm, 'itm_'), 'map_icons': (WRECK.icon, 'icon_'), 'menus': (WRECK.mnu, 'mnu_'), 'meshes': (WRECK.mesh, 'mesh_'), 'mission_templates': (WRECK.mt, 'mt_'), 'music': (WRECK.track, 'track_'), 'particle_systems': (WRECK.psys, 'psys_'), 'parties': (WRECK.p, 'p_'), 'party_templates': (WRECK.pt, 'pt_'), 'postfx_params': (WRECK.pfx, 'pfx_'), 'presentations': (WRECK.prsnt, 'prsnt_'), 'quests': (WRECK.qst, 'qst_'), 'scene_props': (WRECK.spr, 'spr_'), 'scenes': (WRECK.scn, 'scn_'), 'scripts': (WRECK.script, 'script_'), 'skills': (WRECK.skl, 'skl_'), 'sounds': (WRECK.snd, 'snd_'), 'strings': (WRECK.s, 'str_'), 'tableau_materials': (WRECK.tableau, 'tableau_'), 'troops': (WRECK.trp, 'trp_'), } try: for entity_name, (entity, prefix) in export.iteritems(): contents = '\n'.join(['%s%s = %d' % (prefix, ref, index) for ref, index in sorted(map(lambda i:(i[0],int(i[1]&0xFFFFFFFF)), entity[0].iteritems()), lambda x,y:cmp(x[1],y[1]))]) with open(write_id_files % entity_name, 'w+b') as f: f.write(contents) f.write('\n') except Exception, e: print '{1}FAILED.\nCOMPILER INTERNAL ERROR WHILE WRECKING {module!s}:\n{error!s}{0}'.format(*COLORAMA, module = write_id_files % entity_name, error = formatted_exception()) WRECK.time_export = gettime() raise MSException() if export_done: print '{2}DONE.{0}'.format(*COLORAMA) WRECK.time_export = gettime() else: print '{3}SKIPPED.{0}'.format(*COLORAMA) # | # + # \ # +----------------------------------------------------------------------------------------------- except MSException: WRECK.successful = False print if WRECK.successful: print '{2}COMPILATION SUCCESSFUL.{0}\n'.format(*COLORAMA) else: print '{1}COMPILATION FAILED.{0}\n'.format(*COLORAMA) error_reporting_level = 3 if 'silent' in sys.argv: error_reporting_level = 0 if ('error' in sys.argv) or ('errors' in sys.argv): error_reporting_level = 1 if ('error' in sys.argv) or ('warnings' in sys.argv): error_reporting_level = 2 if ('notice' in sys.argv) or ('notices' in sys.argv): error_reporting_level = 3 if WRECK.errors and (error_reporting_level > 0): print 'The following errors were generated during compilation:{1}\n '.format(*COLORAMA), print '\n '.join(WRECK.errors) print '{0}'.format(*COLORAMA) if WRECK.warnings and (error_reporting_level > 1): print 'The following warnings were generated during compilation:{3}\n '.format(*COLORAMA), print '\n '.join(WRECK.warnings) print '{0}'.format(*COLORAMA) if WRECK.notices and (error_reporting_level > 2): print 'The following notifications were generated during compilation:{6}\n '.format(*COLORAMA), print '\n '.join(WRECK.notices) print '{0}'.format(*COLORAMA) if show_performance_data and WRECK.time_loaded: print 'Displaying W.R.E.C.K. performance information.' print 'Use {5}show_performance_data = False{0} directive in {5}module_info.py{0} file to disable.'.format(*COLORAMA) print if WRECK.time_loaded: print ' %.03f sec spent to load module data.' % (WRECK.time_loaded - WRECK.time_started) if WRECK.time_plugins: print ' %.03f sec spent to load plugins.' % (WRECK.time_plugins - WRECK.time_loaded) if WRECK.time_syntax: print ' %.03f sec spent to check module syntax.' % (WRECK.time_syntax - WRECK.time_plugins) if WRECK.time_identifiers: print ' %.03f sec spent to allocate identifiers.' % (WRECK.time_identifiers - WRECK.time_syntax) if WRECK.time_compile: print ' %.03f sec spent to compile module.' % (WRECK.time_compile - WRECK.time_identifiers) if WRECK.time_export: print ' %.03f sec spent to export module.' % (WRECK.time_export - WRECK.time_compile) print print ' >>> %.03f sec total time spent. <<<' % (gettime() - WRECK.time_started) print if 'wait' in sys.argv: raw_input('Press Enter to finish>')

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mmtbx/bulk_solvent/f_model_all_scales.py

dperl-sol/cctbx_project

155

22809

<reponame>dperl-sol/cctbx_project from __future__ import absolute_import, division, print_function from cctbx.array_family import flex from cctbx import adptbx from mmtbx import bulk_solvent from cctbx.array_family import flex from cctbx import adptbx import mmtbx from libtbx import group_args import mmtbx.arrays import mmtbx.bulk_solvent.scaler from libtbx.test_utils import approx_equal from libtbx.math_utils import ifloor, iceil import mmtbx.f_model import mmtbx.bulk_solvent.bulk_solvent_and_scaling as bss from six.moves import zip, range class run(mmtbx.f_model.manager): """ This is a very specialized routine to perform complex protocols of updating all scales of fmodel, including case of twininng, presence of H and lileky more. Inside it pretends to be fmodel proper (done by dictionary updates before and after - any better ideas of how to do it nicer are welcome!). """ def __init__(self, fmodel, apply_back_trace, remove_outliers, fast, params, refine_hd_scattering, log): ### Must be first thing here self.__dict__.update(fmodel.__dict__) # From this point on: self = fmodel ### russ = self.compute(apply_back_trace = apply_back_trace, remove_outliers = remove_outliers, fast = fast, params = params, refine_hd_scattering = refine_hd_scattering, log = log) ### Must be next to last... fmodel.__dict__.update(self.__dict__) ### ...and this one is last self.russ = russ def compute(self, apply_back_trace, remove_outliers, fast, params, refine_hd_scattering, log): assert [self.arrays.core_twin, self.twin_law].count(None) in [0,2] self.show(prefix = "start", log = log) self.reset_all_scales() self.show(prefix = "re-set all scales", log = log) if(remove_outliers and not self.twinned()): for iii in range(5): self.remove_outliers(use_model = False, log = None) # XXX self.show(prefix = "remove outliers", log = log) result = None if(self.twinned()): for cycle in range(2): if(log is not None): print("cycle %d:"%cycle, file=log) self.update_twin_fraction() self.show(prefix = "update twin fraction", log = log) result = self.update_solvent_and_scale_twin(log = log, refine_hd_scattering = refine_hd_scattering) else: result = self.update_solvent_and_scale_2( fast = fast, params = params, apply_back_trace = apply_back_trace, refine_hd_scattering = refine_hd_scattering, log = log) #XXX if(remove_outliers and not self.twinned()): #XXX self.remove_outliers(use_model = True, log = None) # XXX if(remove_outliers and not self.twinned()): for iii in range(5): self.remove_outliers(use_model = True, log = None) # XXX self.show(prefix = "remove outliers", log = log) return result def reset_all_scales(self): size = self.f_obs().data().size() zero_c = flex.complex_double(size,0) zero_d = flex.double(size,0) one_d = flex.double(size,1) f_part1_twin = self.f_calc_twin() f_part2_twin = self.f_calc_twin() if(f_part1_twin is not None): f_part1_twin = self.f_calc_twin().array(data=zero_c) f_part2_twin = self.f_calc_twin().array(data=zero_c) self.update_core( f_part1 = self.f_calc().array(data=zero_c), f_part2 = self.f_calc().array(data=zero_c), f_part1_twin = f_part1_twin, f_part2_twin = f_part2_twin, k_isotropic = one_d, k_anisotropic = one_d, k_mask = [zero_d]*len(self.k_masks())) def show(self, prefix, log, r=None): if(log is None): return if(r is None): r = self.r_all() m = "%24s: r(all,work,free)=%6.4f %6.4f %6.4f n_refl.: %d"%(prefix, r, self.r_work(), self.r_free(), self.f_obs().data().size()) if(not self.twinned()): print(m, file=log) else: print(m+" twin_fraction=%4.2f"%self.twin_fraction, file=log) def need_to_refine_hd_scattering_contribution(self): if(self.xray_structure is None): return False refine_hd_scattering = True hd_selection = self.xray_structure.hd_selection() occ_h_all_zero = self.xray_structure.select( hd_selection).scatterers().extract_occupancies().all_eq(0.0) # riding H if(self.xray_structure.guess_scattering_type_neutron() or hd_selection.count(True)==0 or not occ_h_all_zero): refine_hd_scattering = False return refine_hd_scattering def update_solvent_and_scale_2(self, fast, params, apply_back_trace, refine_hd_scattering, log): if(params is None): params = bss.master_params.extract() if(self.xray_structure is not None): # Figure out Fcalc and Fmask based on presence of H hd_selection = self.xray_structure.hd_selection() xrs_no_h = self.xray_structure.select(~hd_selection) xrs_h = self.xray_structure.select(hd_selection) # Create data container for scalers. If H scattering is refined then it is # assumed that self.f_calc() does not contain H contribution at all. fmodel_kbu = mmtbx.f_model.manager_kbu( f_obs = self.f_obs(), f_calc = self.f_calc(), f_masks = self.f_masks(), ss = self.ss) # Compute k_total and k_mask using one of the two methods (anal or min). # Note: this intentionally ignores previously existing f_part1 and f_part2. # k_sol, b_sol, b_cart, b_adj = [None,]*4 if(fast): # analytical assert len(fmodel_kbu.f_masks)==1 result = mmtbx.bulk_solvent.scaler.run_simple( fmodel_kbu = fmodel_kbu, r_free_flags = self.r_free_flags(), bulk_solvent = params.bulk_solvent, aniso_scale = params.anisotropic_scaling, bin_selections = self.bin_selections) r_all_from_scaler = result.r_all() # must be here, before apply_back_trace else: # using minimization: exp solvent and scale model (k_sol,b_sol,b_cart) result = bss.bulk_solvent_and_scales( fmodel_kbu = fmodel_kbu, params = params) k_sol, b_sol, b_cart = result.k_sols(), result.b_sols(), result.b_cart() r_all_from_scaler = result.r_all() # must be here, before apply_back_trace if(apply_back_trace and len(fmodel_kbu.f_masks)==1 and self.xray_structure is not None): o = result.apply_back_trace_of_overall_exp_scale_matrix( xray_structure = self.xray_structure) b_adj = o.b_adj if(not fast): b_sol, b_cart = [o.b_sol], o.b_cart self.update_xray_structure( xray_structure = o.xray_structure, update_f_calc = True) fmodel_kbu = fmodel_kbu.update(f_calc = self.f_calc()) self.show(prefix = "overall B=%s to atoms"%str("%7.2f"%o.b_adj).strip(), log = log) # Update self with new arrays so that H correction knows current R factor. # If no H to account for, then this is the final result. k_masks = result.k_masks() k_anisotropic = result.k_anisotropic() k_isotropic = result.k_isotropic() self.update_core( k_mask = k_masks, k_anisotropic = k_anisotropic, k_isotropic = k_isotropic) self.show(prefix = "bulk-solvent and scaling", log = log) # Consistency check if(not apply_back_trace): assert approx_equal(self.r_all(), r_all_from_scaler) # Add contribution from H (if present and riding). This goes to f_part2. kh, bh = 0, 0 if(refine_hd_scattering and self.need_to_refine_hd_scattering_contribution()): # Obsolete previous contribution f_part2 f_part2 = fmodel_kbu.f_calc.array(data=fmodel_kbu.f_calc.data()*0) self.update_core(f_part2 = f_part2) xrs_h = xrs_h.set_occupancies(value=1).set_b_iso(value = 0) f_h = self.compute_f_calc(xray_structure = xrs_h) # Accumulate all mask contributions: Fcalc_atoms+Fbulk_1+...+Fbulk_N data = fmodel_kbu.f_calc.data() for k_mask_, f_mask_ in zip(k_masks, fmodel_kbu.f_masks): data = data + k_mask_*f_mask_.data() f_calc_plus_f_bulk_no_scales = fmodel_kbu.f_calc.array(data = data) # Consistency check assert approx_equal(self.f_model().data(), f_calc_plus_f_bulk_no_scales.data()*k_isotropic*k_anisotropic) assert approx_equal(self.f_model_no_scales().data(), f_calc_plus_f_bulk_no_scales.data()) # # Compute contribution from H (F_H) # # Coarse sampling b_mean = flex.mean(xrs_no_h.extract_u_iso_or_u_equiv())*adptbx.u_as_b(1.) b_min = int(max(0,b_mean)*0.5) b_max = int(b_mean*1.5) sc = 1000. kr=[i/sc for i in range(ifloor(0*sc), iceil(1.5*sc)+1, int(0.1*sc))] br=[i/sc for i in range(ifloor(b_min*sc), iceil(b_max*sc)+1, int(5.*sc))] o = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = fmodel_kbu.f_obs.data(), f_calc = f_calc_plus_f_bulk_no_scales.data(), f_mask = f_h.data(), k_total = k_isotropic*k_anisotropic, ss = fmodel_kbu.ss, k_sol_range = flex.double(kr), b_sol_range = flex.double(br), r_ref = self.r_work()) if(o.updated()): f_part2 = f_h.array(data = o.k_mask()*f_h.data()) kh, bh = o.k_sol(), o.b_sol() self.show(prefix = "add H (%4.2f, %6.2f)"%(kh, bh), log = log, r=o.r()) # Fine sampling k_min = max(0,o.k_sol()-0.1) k_max = o.k_sol()+0.1 b_min = max(0,o.b_sol()-5.) b_max = o.b_sol()+5. kr=[i/sc for i in range(ifloor(k_min*sc),iceil(k_max*sc)+1,int(0.01*sc))] br=[i/sc for i in range(ifloor(b_min*sc),iceil(b_max*sc)+1,int(1.*sc))] o = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = fmodel_kbu.f_obs.data(), f_calc = f_calc_plus_f_bulk_no_scales.data(), f_mask = f_h.data(), k_total = k_isotropic*k_anisotropic, ss = fmodel_kbu.ss, k_sol_range = flex.double(kr), b_sol_range = flex.double(br), r_ref = o.r()) if(o.updated()): f_part2 = f_h.array(data = o.k_mask()*f_h.data()) kh, bh = o.k_sol(), o.b_sol() self.show(prefix = "add H (%4.2f, %6.2f)"%(kh, bh), log = log, r=o.r()) # THIS HELPS if fast=true is used, see how it works in reality # if(fast): fmodel_kbu_ = mmtbx.f_model.manager_kbu( f_obs = self.f_obs(), f_calc = f_calc_plus_f_bulk_no_scales, f_masks = [f_part2], ss = self.ss) result = mmtbx.bulk_solvent.scaler.run_simple( fmodel_kbu = fmodel_kbu_, r_free_flags = self.r_free_flags(), bulk_solvent = params.bulk_solvent, aniso_scale = params.anisotropic_scaling, bin_selections = self.bin_selections) f_part2 = f_part2.array(data = result.core.k_mask()*f_part2.data()) k_isotropic = result.core.k_isotropic*result.core.k_isotropic_exp k_anisotropic = result.core.k_anisotropic # Update self with final scales self.update_core( k_mask = k_masks, k_anisotropic = k_anisotropic, k_isotropic = k_isotropic, f_part2 = f_part2) # Make sure what came out of scaling matches what self thinks it really is # It must match at least up to 1.e-6. self.show(prefix = "add H (%4.2f, %6.2f)"%(kh, bh), log = log) if(fast): assert approx_equal(result.r_work(), self.r_work(), 1.e-4) else: assert approx_equal(self.r_all(), o.r()), [self.r_all(), o.r()] return group_args( k_sol = k_sol, b_sol = b_sol, b_cart = b_cart, k_h = kh, b_h = bh, b_adj = b_adj) def update_solvent_and_scale_twin(self, refine_hd_scattering, log): if(not self.twinned()): return assert len(self.f_masks()) == 1 # Re-set all scales to unit or zero self.show(prefix = "update scales twin start", log = log) self.reset_all_scales() self.show(prefix = "reset f_part, k_(total,mask)", log = log) f_calc_data = self.f_calc().data() f_calc_data_twin = self.f_calc_twin().data() # Initial trial set sc = 1000. ksr = [i/sc for i in range(ifloor(0*sc), iceil(0.6*sc)+1, int(0.05*sc))] bsr = [i/sc for i in range(ifloor(0*sc), iceil(150.*sc)+1, int(10.*sc))] o_kbu_sol = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = self.f_obs().data(), f_calc_1 = f_calc_data, f_calc_2 = f_calc_data_twin, f_mask_1 = self.arrays.core.f_masks[0].data(), f_mask_2 = self.arrays.core_twin.f_masks[0].data(), ss = self.ss, twin_fraction = self.twin_fraction, k_sol_range = flex.double(ksr), b_sol_range = flex.double(bsr), miller_indices = self.f_obs().indices(), #XXX ??? What about twin-related? unit_cell = self.f_obs().unit_cell(), r_ref = self.r_all()) if(o_kbu_sol.updated()): self.update( k_mask = o_kbu_sol.k_mask(), k_anisotropic = o_kbu_sol.k_anisotropic()) # Second (finer) trial set k_min = max(o_kbu_sol.k_sol()-0.05, 0) k_max = min(o_kbu_sol.k_sol()+0.05, 0.6) ksr = [i/sc for i in range(ifloor(k_min*sc), iceil(k_max*sc)+1, int(0.01*sc))] b_min = max(o_kbu_sol.b_sol()-10, 0) b_max = min(o_kbu_sol.b_sol()+10, 150) bsr = [i/sc for i in range(ifloor(b_min*sc), iceil(b_max*sc)+1, int(1.*sc))] o_kbu_sol = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = self.f_obs().data(), f_calc_1 = f_calc_data, f_calc_2 = f_calc_data_twin, f_mask_1 = self.arrays.core.f_masks[0].data(), f_mask_2 = self.arrays.core_twin.f_masks[0].data(), ss = self.ss, twin_fraction = self.twin_fraction, k_sol_range = flex.double(ksr), b_sol_range = flex.double(bsr), miller_indices = self.f_obs().indices(), #XXX ??? What about twin-related? unit_cell = self.f_obs().unit_cell(), r_ref = o_kbu_sol.r()) if(o_kbu_sol.updated()): self.update( k_mask = o_kbu_sol.k_mask(), k_anisotropic = o_kbu_sol.k_anisotropic()) # Disable due to rare failures. Technically they should always match. But # since different routines are used tiny disagreements are possible. # See examples in : /net/anaconda/raid1/afonine/work/bugs/twin_refinement #assert approx_equal(self.r_all(), o_kbu_sol.r(), 1.e-5) ############## # use apply_back_trace in if below if(self.xray_structure is not None): o = mmtbx.bulk_solvent.scaler.tmp( xray_structure = self.xray_structure, k_anisotropic = o_kbu_sol.k_anisotropic(), k_masks = [o_kbu_sol.k_mask()], ss = self.ss) self.update_xray_structure( xray_structure = o.xray_structure, update_f_calc = True) ############# self.update( k_mask = o.k_masks, k_anisotropic = o.k_anisotropic) self.show(prefix = "bulk-solvent and scaling", log = log) # # Add contribution from H (if present and riding). This goes to f_part2. # kh, bh = 0, 0 if(refine_hd_scattering and self.need_to_refine_hd_scattering_contribution()): hd_selection = self.xray_structure.hd_selection() xrs_no_h = self.xray_structure.select(~hd_selection) xrs_h = self.xray_structure.select(hd_selection) # Accumulate all mask contributions: Fcalc_atoms+Fbulk_1+...+Fbulk_N data = self.f_calc().data()+self.f_masks()[0].data()*self.k_masks()[0] f_calc_plus_f_bulk_no_scales = self.f_calc().array(data = data) data = self.f_calc_twin().data()+\ self.f_masks_twin()[0].data()*self.k_masks_twin()[0] f_calc_plus_f_bulk_no_scales_twin = self.f_calc_twin().array(data = data) # Initial FH contribution xrs_h = xrs_h.set_occupancies(value=1).set_b_iso(value = 0) f_h = self.compute_f_calc(xray_structure = xrs_h) f_h_twin = self.compute_f_calc(xray_structure = xrs_h, miller_array = self.f_calc_twin()) # Coarse sampling b_mean = flex.mean(xrs_no_h.extract_u_iso_or_u_equiv())*adptbx.u_as_b(1.) b_min = int(max(0,b_mean)*0.5) b_max = int(b_mean*1.5) sc = 1000. kr=[i/sc for i in range(ifloor(0*sc), iceil(1.5*sc)+1, int(0.1*sc))] br=[i/sc for i in range(ifloor(b_min*sc), iceil(b_max*sc)+1, int(5.*sc))] obj = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = self.f_obs().data(), f_calc_1 = f_calc_plus_f_bulk_no_scales.data(), f_calc_2 = f_calc_plus_f_bulk_no_scales_twin.data(), f_mask_1 = f_h.data(), f_mask_2 = f_h_twin.data(), ss = self.ss, twin_fraction = self.twin_fraction, k_sol_range = flex.double(kr), b_sol_range = flex.double(br), miller_indices = self.f_obs().indices(), # XXX What about twin-related? unit_cell = self.f_obs().unit_cell(), r_ref = self.r_work()) if(obj.updated()): f_part2 = f_h.array( data = obj.k_mask()*f_h.data()) f_part2_twin = f_h_twin.array(data = obj.k_mask()*f_h_twin.data()) kh, bh = obj.k_sol(), obj.b_sol() # Fine sampling k_min = max(0,obj.k_sol()-0.1) k_max = obj.k_sol()+0.1 b_min = max(0,obj.b_sol()-5.) b_max = obj.b_sol()+5. kr=[i/sc for i in range(ifloor(k_min*sc),iceil(k_max*sc)+1,int(0.01*sc))] br=[i/sc for i in range(ifloor(b_min*sc),iceil(b_max*sc)+1,int(5.*sc))] obj = bulk_solvent.k_sol_b_sol_k_anisotropic_scaler_twin( f_obs = self.f_obs().data(), f_calc_1 = f_calc_plus_f_bulk_no_scales.data(), f_calc_2 = f_calc_plus_f_bulk_no_scales_twin.data(), f_mask_1 = f_h.data(), f_mask_2 = f_h_twin.data(), ss = self.ss, twin_fraction = self.twin_fraction, k_sol_range = flex.double(kr), b_sol_range = flex.double(br), miller_indices = self.f_obs().indices(), # XXX What about twin-related? unit_cell = self.f_obs().unit_cell(), r_ref = obj.r()) if(obj.updated()): f_part2 = f_h.array( data = obj.k_mask()*f_h.data()) f_part2_twin = f_h_twin.array(data = obj.k_mask()*f_h_twin.data()) kh, bh = obj.k_sol(), obj.b_sol() self.update_core( f_part2 = f_part2, f_part2_twin = f_part2_twin, k_anisotropic = obj.k_anisotropic()) self.show(prefix = "add H (%4.2f, %6.2f)"%(kh, bh), log = log) b_cart = adptbx.u_as_b(adptbx.u_star_as_u_cart( self.f_obs().unit_cell(), o_kbu_sol.u_star())) return group_args( k_sol = o_kbu_sol.k_sol(), b_sol = o_kbu_sol.b_sol(), b_cart = b_cart, k_h = kh, b_h = bh)

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codeswiftr/app_stats/apps.py

bogdan-veliscu/dev-portfolio-website

0

111728

from django.apps import AppConfig class AppStatsConfig(AppConfig): name = 'app_stats'

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fbExceptionMeansSocketShutdown.py

SkyLined/mTCPIPConnection

1

1609623

import socket; def fbExceptionMeansSocketShutdown(oException): return ( (oException.__class__ == socket.error and oException.errno in [ 0x274A # WSAESHUTDOWN ]) );

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apps/cc_cms/admin.py

FreeOurKnowledge/old_platform

11

164027

<filename>apps/cc_cms/admin.py from django_summernote.admin import SummernoteModelAdmin from .models import Page, ColumnsSection, Content, DynamicText class PageAdmin(SummernoteModelAdmin): summernote_fields = '__all__' def register_cms(admin_site): admin_site.register(Page, PageAdmin) admin_site.register(ColumnsSection, PageAdmin) admin_site.register(Content, PageAdmin) admin_site.register(DynamicText, PageAdmin)

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pylib/gna/bundles/parameters_yaml_v01.py

gnafit/gna

5

117794

<gh_stars>1-10 """Parameters_yaml v01 bundle Based on: parameters_v03 Implements a set of parameters. New features include: - [v01] read data from yaml file """ from load import ROOT as R from gna.bundle.bundle import * import numpy as np from gna import constructors as C import yaml class parameters_yaml_v01(TransformationBundle): covmat, corrmat = None, None def __init__(self, *args, **kwargs): self._par_container = [] TransformationBundle.__init__(self, *args, **kwargs) self.check_nidx_dim(1, 1, 'major') self.load_data() def load_data(self): if not 'data' in self.cfg: raise Exception('Should provide data file') try: with open(self.cfg.data, 'r') as stream: data = self.data = yaml.load(stream, Loader=yaml.Loader) except: raise Exception('Unable to load input data file: '+self.cfg.data) for name in ('values', 'names', 'mode'): if not name in data: raise Exception('Input file {} misses field {}'.format(self.cfg.data, name)) # if ('correlations' in data or 'uncertainties' in data) and 'covariance' in data: # raise Exception('Conflicting fields in data: correlations/uncertainties and covariance') for field in ('values', 'uncertainties', 'correlations'): #, 'covariance' if field in data: data[field] = np.asanyarray(data[field]) if 'correlations' in data: if self.cfg.get('verbose', 0)>1: print('Correlation matrix') print(self.data['correlations'], ) @staticmethod def _provides(cfg): sepunc = cfg.get('separate_uncertainty', None) if sepunc: return (cfg.parameter, sepunc), () else: return (cfg.parameter,), () def define_variables(self): from gna.parameters import covariance_helpers as ch self._par_container = [] separate_uncertainty = self.cfg.get('separate_uncertainty', False) parname = self.cfg.parameter labelfmt = self.cfg.get('label', '') data = self.data values = data['values'] names = data['names'] unc = data['uncertainties'] mode = data['mode'] if mode=='percent': relunc = unc/100. unc = values*relunc elif mode=='relative': relunc = unc unc = values*relunc elif mode=='absolute': relunc = unc/values else: raise Exception('Invalid uncertainty mode: '+mode) for it_minor in self.nidx_minor: container = [] for it_major in self.nidx_major: major_values, = it_major.current_values() if not major_values in names: raise Exception('Variable {} is not in the list'.format(major_values)) data_idx = names.index(major_values) it=it_major+it_minor label = it.current_format(labelfmt) if labelfmt else '' if separate_uncertainty: uncpar = self.reqparameter(separate_uncertainty, it, central=1.0, sigma=relunc[data_idx], label=label+' (norm)') par = self.reqparameter(parname, it, central=values[data_idx], fixed=True, label=label) container.append(uncpar) else: par = self.reqparameter(parname, it, central=values[data_idx], sigma=unc[data_idx], label=label) container.append(par) if self.cfg.get("objectize"): trans=par.transformations.value trans.setLabel(label) self.set_output(parname, it, trans.single()) self._par_container.append(container) if 'correlations' in self.data: cormat = self.data['correlations'] self._checkmatrix(cormat, container, is_correlation=True) cormat = self.filter_matrix(cormat, container) if cormat is not None: ch.covariate_pars(container, cormat) # elif 'covariance' in self.cfg: # self._load_covariance_matrix() def build(self): pass def filter_matrix(self, mat, par_container): npars = len(par_container) if npars==1: # No correlations possible return None dim = mat.shape[0] if npars==dim: # no reduction needed return mat # Remove unnecessary rows and columns from the matrix names = [par.name() for par in par_container] for i, name in enumerate(reversed(self.data['names'])): if name in names: continue i=dim-i-1 mat = np.delete(mat, i, 0) mat = np.delete(mat, i, 1) return mat # def _load_covariance_matrix(self): # filename = self.cfg.get('covariance', None) # assert filename # covmat = np.loadtxt(filename) # if self.cfg.get('verbose', 0)>1: # print('Load covariance matrix from %s:'%filename) # print(covmat) # sigma_inv=np.diag(covmat.diagonal()**-0.5) # corrmat = np.matmul(np.matmul(sigma_inv, covmat), sigma_inv) # if self.cfg.get('verbose', 0)>1: # print('Compute correlation matrix from:') # print(corrmat) # self._checkmatrix(covmat, is_correlation=False) # self.covmat = covmat # self.corrmat = corrmat # for par, sigma2 in zip(self._par_container, covmat.diagonal()): # par.setSigma(sigma2**0.5) # from gna.parameters import covariance_helpers as ch # ch.covariate_pars(self._par_container, corrmat) def _checkmatrix(self, mat, par_container, is_correlation): if mat.shape[0]!=mat.shape[1]: raise Exception('Non square matrix provided:', mat.shape[0], mat.shape[1]) if len(par_container)>mat.shape[0]: raise Exception('Unable to set correlation to %i parameters with %ix%i matrix'%(len(par_container, mat.shape[0], mat.shape[1]))) mmin, mmax = mat.min(), mat.max() if mmin<-1-1.e-12 or mmax>1.0+1.e-12: raise Exception('Invalid min/max correlation values:', mmin, mmax) diag = mat.diagonal() if is_correlation: ones = diag==1.0 if not ones.all(): raise Exception('There are values !=1 on the diagonal (d-1): ', diag[~ones]-1.0) else: if (diag<0).any(): raise Exception('Covariance matrix contains values below zero')

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LeetCode/HashTable/204. Count Primes.py

thehanemperor/LeetCode

0

169713

# EASY # find all multiples less than n # ex Input 6 # arr = [1:True, 2:True ,3:True ,4:True ,5:True ] # start from 2, mark 2*2, 2*3, 2*4 ... False # Time O(N^2) Space O(N) class Solution: def countPrimes(self, n: int) -> int: arr = [1 for _ in range(n)] count = 0 for i in range(2,n): j = 2 while arr[i] and i*j < n: arr[i*j] = 0 j += 1 for i in range(2,n): if arr[i]: count+= 1 return count

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simulation/model/blocks/order.py

fladdimir/csa-simulation-based-sc-forecast

2

40082

import logging from casymda.blocks.entity import Entity from simpy.core import Environment class Order(Entity): def __init__(self, env: Environment, name: str): super().__init__(env, name) self._time_of_acceptance = -1 self._initial_eta = -1 self._eta = -1 self._ready_at = -1 self._sop_at = -1 self._finished_at = -1 @property def time_of_acceptance(self): return self._time_of_acceptance @time_of_acceptance.setter def time_of_acceptance(self, value): self._time_of_acceptance = value update( self.name, "time_of_acceptance", self._time_of_acceptance, self.env.now, self, ) @property def initial_eta(self): return self._initial_eta @initial_eta.setter def initial_eta(self, value): self._initial_eta = value update(self.name, "initial_eta", self._initial_eta, self.env.now, self) @property def eta(self): return self._eta @eta.setter def eta(self, value): self._eta = value update(self.name, "eta", self._eta, self.env.now, self) @property def ready_at(self): return self._ready_at @ready_at.setter def ready_at(self, value): self._ready_at = value update(self.name, "ready_at", self._ready_at, self.env.now, self) @property def sop_at(self): return self._sop_at @sop_at.setter def sop_at(self, value): self._sop_at = value update(self.name, "sop_at", self._sop_at, self.env.now, self) @property def finished_at(self): return self._finished_at @finished_at.setter def finished_at(self, value): self._finished_at = value update(self.name, "finished_at", self._finished_at, self.env.now, self) def update(name: str, attribute: str, value: float, t: float, order: Order): # (replaced by actual callback during emulation initialization) logging.info( f"order update - name: {name}, attribute: {attribute}, value: {value}, t: {t}" )

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main_a2c.py

WeiChengTseng/DL_final_project

7

143661

<reponame>WeiChengTseng/DL_final_project<filename>main_a2c.py import argparse import torch import torch.optim as optim import os # from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv # from baselines.common.vec_env.vec_frame_stack import VecFrameStack from a2c.models import AtariCNN, A2C, A2CLarge # from a2c.envs import make_env, RenderSubprocVecEnv from a2c.train_multi import train from env_exp import SocTwoEnv parser = argparse.ArgumentParser(description='A2C (Advantage Actor-Critic)') parser.add_argument('--env_path', type=str, default='./env/macos/SoccerTwosFast.app', help='path to the environment binary') parser.add_argument('--render', type=bool, default=False, help='whether to render enviroment') parser.add_argument('--reward_shaping', type=bool, default=False, help='whether to use reward shaping') parser.add_argument('--num-workers', type=int, default=16, help='number of parallel workers') parser.add_argument('--rollout-steps', type=int, default=300, help='steps per rollout') parser.add_argument('--total-steps', type=int, default=int(4e7), help='total number of steps to train for') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--gamma', type=float, default=0.99, help='gamma parameter for GAE') parser.add_argument('--lambd', type=float, default=1.00, help='lambda parameter for GAE') parser.add_argument('--value_coeff', type=float, default=0.5, help='value loss coeffecient') parser.add_argument('--entropy_coeff', type=float, default=0.01, help='entropy loss coeffecient') parser.add_argument('--grad_norm_limit', type=float, default=40., help='gradient norm clipping threshold') args = parser.parse_args() # env_path = './env/macos/SoccerTwosFast.app' env = SocTwoEnv(args.env_path, worker_id=0, train_mode=True, render=args.render) os.environ["CUDA_VISIBLE_DEVICES"]="0" device = torch.device( "cuda:0" if torch.cuda.is_available()else "cpu") print(device) # policy_striker, policy_goalie = A2C(7).to(device), A2C(5).to(device) policy_striker, policy_goalie = A2CLarge(7).to(device), A2CLarge(5).to(device) optim_striker = optim.Adam(policy_striker.parameters(), lr=args.lr) optim_goalie = optim.Adam(policy_goalie.parameters(), lr=args.lr) train(args, policy_striker, policy_goalie, optim_striker, optim_goalie, env, device, args.reward_shaping)

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code/micropython/robotling_lib/driver/lsm303.py

teuler/robotling2

6

1616475

# ---------------------------------------------------------------------------- # lsm303.py # Class for LSM303 accelerometer/magnetometer driver # # The MIT License (MIT) # Copyright (c) 2018-19 <NAME> # 2018-09-13, v1 # 2018-10-26, bug fix sequence of magnetometer values from I2C call is x,z,y # # Based on the CircuitPython driver: # https://github.com/adafruit/Adafruit_CircuitPython_LSM303 # # The MIT License (MIT) # Copyright (c) 2017 <NAME> for Adafruit Industries # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # ---------------------------------------------------------------------------- import time from micropython import const from robotling_lib.misc.helpers import timed_function try: import struct except ImportError: import ustruct as struct __version__ = "0.1.0.0" CHIP_NAME = "lsm303" CHAN_COUNT = const(1) # pylint: disable=bad-whitespace _ADDRESS_ACCEL = const(0x19) # (0x32 >> 1) // 0011001x _ADDRESS_MAG = const(0x1E) # (0x3C >> 1) // 0011110x _ID = const(0xD4) # (0b11010100) # Accelerometer registers _REG_ACCEL_CTRL_REG1_A = const(0x20) _REG_ACCEL_CTRL_REG2_A = const(0x21) _REG_ACCEL_CTRL_REG3_A = const(0x22) _REG_ACCEL_CTRL_REG4_A = const(0x23) _REG_ACCEL_CTRL_REG5_A = const(0x24) _REG_ACCEL_CTRL_REG6_A = const(0x25) _REG_ACCEL_REFERENCE_A = const(0x26) _REG_ACCEL_STATUS_REG_A = const(0x27) _REG_ACCEL_OUT_X_L_A = const(0x28) _REG_ACCEL_OUT_X_H_A = const(0x29) _REG_ACCEL_OUT_Y_L_A = const(0x2A) _REG_ACCEL_OUT_Y_H_A = const(0x2B) _REG_ACCEL_OUT_Z_L_A = const(0x2C) _REG_ACCEL_OUT_Z_H_A = const(0x2D) _REG_ACCEL_FIFO_CTRL_REG_A = const(0x2E) _REG_ACCEL_FIFO_SRC_REG_A = const(0x2F) _REG_ACCEL_INT1_CFG_A = const(0x30) _REG_ACCEL_INT1_SOURCE_A = const(0x31) _REG_ACCEL_INT1_THS_A = const(0x32) _REG_ACCEL_INT1_DURATION_A = const(0x33) _REG_ACCEL_INT2_CFG_A = const(0x34) _REG_ACCEL_INT2_SOURCE_A = const(0x35) _REG_ACCEL_INT2_THS_A = const(0x36) _REG_ACCEL_INT2_DURATION_A = const(0x37) _REG_ACCEL_CLICK_CFG_A = const(0x38) _REG_ACCEL_CLICK_SRC_A = const(0x39) _REG_ACCEL_CLICK_THS_A = const(0x3A) _REG_ACCEL_TIME_LIMIT_A = const(0x3B) _REG_ACCEL_TIME_LATENCY_A = const(0x3C) _REG_ACCEL_TIME_WINDOW_A = const(0x3D) # Magnetometer registers _REG_MAG_CRA_REG_M = const(0x00) _REG_MAG_CRB_REG_M = const(0x01) _REG_MAG_MR_REG_M = const(0x02) _REG_MAG_OUT_X_H_M = const(0x03) _REG_MAG_OUT_X_L_M = const(0x04) _REG_MAG_OUT_Z_H_M = const(0x05) _REG_MAG_OUT_Z_L_M = const(0x06) _REG_MAG_OUT_Y_H_M = const(0x07) _REG_MAG_OUT_Y_L_M = const(0x08) _REG_MAG_SR_REG_Mg = const(0x09) _REG_MAG_IRA_REG_M = const(0x0A) _REG_MAG_IRB_REG_M = const(0x0B) _REG_MAG_IRC_REG_M = const(0x0C) _REG_MAG_TEMP_OUT_H_M = const(0x31) _REG_MAG_TEMP_OUT_L_M = const(0x32) # Magnetometer gains MAGGAIN_1_3 = const(0x20) # +/- 1.3 MAGGAIN_1_9 = const(0x40) # +/- 1.9 MAGGAIN_2_5 = const(0x60) # +/- 2.5 MAGGAIN_4_0 = const(0x80) # +/- 4.0 MAGGAIN_4_7 = const(0xA0) # +/- 4.7 MAGGAIN_5_6 = const(0xC0) # +/- 5.6 MAGGAIN_8_1 = const(0xE0) # +/- 8.1 # Magentometer rates MAGRATE_0_7 = const(0x00) # 0.75 Hz MAGRATE_1_5 = const(0x01) # 1.5 Hz MAGRATE_3_0 = const(0x62) # 3.0 Hz MAGRATE_7_5 = const(0x03) # 7.5 Hz MAGRATE_15 = const(0x04) # 15 Hz MAGRATE_30 = const(0x05) # 30 Hz MAGRATE_75 = const(0x06) # 75 Hz MAGRATE_220 = const(0x07) # 200 Hz # Conversion constants _LSM303ACCEL_MG_LSB = 16704.0 _GRAVITY_STANDARD = 9.80665 # Earth's gravity in m/s^2 _GAUSS_TO_MICROTESLA = 100.0 # Gauss to micro-Tesla multiplier _GAUSS_TO_NANOTESLA = 100000.0 # Gauss to nano-Tesla multiplier # ---------------------------------------------------------------------------- class LSM303(object): """Driver for LSM303 magnetometer/accelerometer.""" def __init__(self, i2c): """ Requires already initialized I2C bus instance. """ self._i2c = i2c self._isReady = False addrList = self._i2c.deviceAddrList if (_ADDRESS_ACCEL in addrList) and (_ADDRESS_MAG in addrList): # Enable accelerometer and magnetometer self._write_u8(_ADDRESS_ACCEL, _REG_ACCEL_CTRL_REG1_A, 0x27) self._write_u8(_ADDRESS_MAG, _REG_MAG_MR_REG_M, 0x00) # Some initial parameters self._lsm303mag_gauss_lsb_xy = 1100.0 self._lsm303mag_gauss_lsb_z = 980.0 self._mag_gain = MAGGAIN_1_3 self._mag_rate = MAGRATE_0_7 self._isReady = True print("[{0:>12}] {1:35} ({2}): {3}" .format(CHIP_NAME, "Magnetometer/accelerometer", __version__, "ok" if self._isReady else "NOT FOUND")) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #@timed_function @property def raw_accelerometer(self): """ The raw accelerometer sensor values. A 3-tuple of X, Y, Z axis values that are 16-bit signed integers. """ buf = bytearray(6) self._read_bytes(_ADDRESS_ACCEL, _REG_ACCEL_OUT_X_L_A | 0x80, buf) return struct.unpack_from('<hhh', buf[0:6]) @property def accelerometer(self): """ The processed accelerometer sensor values. A 3-tuple of X, Y, Z axis values in meters per second squared that are signed floats. """ raw_accel_data = self.raw_accelerometer return [n /_LSM303ACCEL_MG_LSB *_GRAVITY_STANDARD for n in raw_accel_data] @property def raw_magnetometer(self): """ The raw magnetometer sensor values. A 3-tuple of X, Y, Z axis values that are 16-bit signed integers. """ buf = bytearray(6) self._read_bytes(_ADDRESS_MAG, _REG_MAG_OUT_X_H_M, buf) x, z, y = struct.unpack_from('>hhh', buf[0:6]) return (x, y, z) @property def magnetometer(self): """ The processed magnetometer sensor values. A 3-tuple of X, Y, Z axis values in microteslas that are signed floats. """ mag_x, mag_y, mag_z = self.raw_magnetometer return (mag_x /self._lsm303mag_gauss_lsb_xy *_GAUSS_TO_MICROTESLA, mag_y /self._lsm303mag_gauss_lsb_xy *_GAUSS_TO_MICROTESLA, mag_z /self._lsm303mag_gauss_lsb_z *_GAUSS_TO_MICROTESLA) @property def magnetometer_nT(self): """ The processed magnetometer sensor values. A 3-tuple of X, Y, Z axis values in nanotesla that are signed floats. """ mag_x, mag_y, mag_z = self.raw_magnetometer return (mag_x /self._lsm303mag_gauss_lsb_xy *_GAUSS_TO_NANOTESLA, mag_y /self._lsm303mag_gauss_lsb_xy *_GAUSS_TO_NANOTESLA, mag_z /self._lsm303mag_gauss_lsb_z *_GAUSS_TO_NANOTESLA) @property def mag_gain(self): """ The magnetometer's gain. """ return self._mag_gain @mag_gain.setter def mag_gain(self, value): assert value in (MAGGAIN_1_3, MAGGAIN_1_9, MAGGAIN_2_5, MAGGAIN_4_0, MAGGAIN_4_7, MAGGAIN_5_6, MAGGAIN_8_1) self._mag_gain = value self._write_u8(_ADDRESS_MAG, _REG_MAG_CRB_REG_M, value) if self._mag_gain == MAGGAIN_1_3: self._lsm303mag_gauss_lsb_xy = 1100.0 self._lsm303mag_gauss_lsb_z = 980.0 elif self._mag_gain == MAGGAIN_1_9: self._lsm303mag_gauss_lsb_xy = 855.0 self._lsm303mag_gauss_lsb_z = 760.0 elif self._mag_gain == MAGGAIN_2_5: self._lsm303mag_gauss_lsb_xy = 670.0 self._lsm303mag_gauss_lsb_z = 600.0 elif self._mag_gain == MAGGAIN_4_0: self._lsm303mag_gauss_lsb_xy = 450.0 self._lsm303mag_gauss_lsb_z = 400.0 elif self._mag_gain == MAGGAIN_4_7: self._lsm303mag_gauss_lsb_xy = 400.0 self._lsm303mag_gauss_lsb_z = 355.0 elif self._mag_gain == MAGGAIN_5_6: self._lsm303mag_gauss_lsb_xy = 330.0 self._lsm303mag_gauss_lsb_z = 295.0 elif self._mag_gain == MAGGAIN_8_1: self._lsm303mag_gauss_lsb_xy = 230.0 self._lsm303mag_gauss_lsb_z = 205.0 @property def mag_rate(self): """ The magnetometer update rate. """ return self._mag_rate @mag_rate.setter def mag_rate(self, value): assert value in (MAGRATE_0_7, MAGRATE_1_5, MAGRATE_3_0, MAGRATE_7_5, MAGRATE_15, MAGRATE_30, MAGRATE_75, MAGRATE_220) self._mag_rate = value reg_m = ((value & 0x07) << 2) & 0xff self._write_u8(_ADDRESS_MAG, _REG_MAG_CRA_REG_M, reg_m) @property def is_ready(self): return self._isReady @property def channel_Count(self): return CHAN_COUNT @property def name(self): return CHIP_NAME # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def _write_u8(self, i2cAddr, regAddr, val): buf = bytearray(2) buf[0] = regAddr & 0xff buf[1] = val & 0xff self._i2c.writeto(i2cAddr, buf) def _read_bytes(self, i2cAddr, regAddr, buf): cmd = bytearray(1) cmd[0] = regAddr & 0xff self._i2c.writeto(i2cAddr, cmd, False) self._i2c.readfrom_into(i2cAddr, buf) # ----------------------------------------------------------------------------

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pythonProject/q46.py

Babur171/python

0

154520

string = "blackEyes" s = {string: (string[::2], string[1::2])} print(s)

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attention_to_gif/visualizer.py

valayDave/attention_to_gif

0

124692

<filename>attention_to_gif/visualizer.py # importing matplot lib import matplotlib.pyplot as plt import numpy as np import torch # importig movie py libraries from moviepy.editor import VideoClip from moviepy.video.io.bindings import mplfig_to_npimage class AttentionVisualizer: """ Creates a GIF of the transition of attention weights across the layers. layer_wise_attention_weights : Tensor : (num_layers,batch_size,num_heads,seq_len_x,seq_len_y) x_label_toks : labels for indices in the sequence. y_label_toks : labels for indices in the sequence. title_message: Message to print in the video/Gif chosen_head : int : a particular to head to visualise attention for. If None: Attention is a summed for all heads at a layer seq_len_x_lim : constrain the size of of head in the x dimension seq_len_y_lim : constrain the size of of head in the y dimension """ def __init__(self,\ layer_wise_attention_weights:torch.Tensor, seq_len_x_lim=None, seq_len_y_lim=None, chosen_item=0, chosen_head=None, x_label_toks=[], y_label_toks=[], fig_size=(10,10), title_message='', ) -> None: #() self.num_layers, self.batch_size , self.num_attention_heads , seq_len_x,seq_len_y = layer_wise_attention_weights.size() # Doing this ensure that it work. self.seq_len_x_lim = seq_len_x_lim self.seq_len_y_lim = seq_len_y_lim self.chosen_item=chosen_item self.layer_wise_attention_weights = layer_wise_attention_weights self.fig, self.ax = None,None self.fig_size=fig_size self.x_label_toks = x_label_toks self.y_label_toks = y_label_toks self.title_message = title_message self.chosen_head = chosen_head # self.fig.colorbar() def get_attention_values(self,layer,chosen_head=None): if chosen_head is not None: conv_arr = self.layer_wise_attention_weights[int(layer)][self.chosen_item][chosen_head].cpu().numpy() else: conv_arr = self.layer_wise_attention_weights[int(layer)][self.chosen_item].sum(dim=0).cpu().numpy() if self.seq_len_x_lim is not None: conv_arr= conv_arr[:,:self.seq_len_x_lim] if self.seq_len_y_lim is not None: conv_arr= conv_arr[:self.seq_len_y_lim] return conv_arr def __call__(self,t): # clear if self.fig is None: self.fig, self.ax = plt.subplots(figsize=self.fig_size) if len(self.ax.images) > 0: self.ax.images[-1].colorbar.remove() self.ax.clear() conv_arr = self.get_attention_values(t,chosen_head=self.chosen_head) cax = self.ax.matshow(conv_arr,origin='lower', cmap='viridis',aspect='auto') # self.y_label_toks self.fig.colorbar(cax,ax=self.ax) if len(self.x_label_toks) > 0: self.ax.set_xticks([i for i in range(len(self.x_label_toks))]) self.ax.set_xticklabels(self.x_label_toks,) if len(self.y_label_toks) > 0: self.ax.set_yticks([len(self.y_label_toks)-i-1 for i in range(len(self.y_label_toks))]) self.ax.set_yticklabels(self.y_label_toks) default_title = f" Attention At Layer : {int(t)} \n" if self.chosen_head is not None: default_title = f" Attention At Layer : {int(t)} And Head : {self.chosen_head}\n" if self.title_message is '': self.ax.set_title(default_title) else: self.ax.set_title(f"{self.title_message}\n {default_title}") return mplfig_to_npimage(self.fig) def save_visualisation(self,viz_name='attention_viz.gif',fps=20): animation = VideoClip(make_frame=self,duration=self.num_layers) animation.write_gif(viz_name,fps=fps) def show_visualisation(self,viz_name='attention_viz.gif',fps = 20, loop = False, autoplay = False): # animation = VideoClip(self, duration = self.num_layers) animation = VideoClip(make_frame=self,duration=self.num_layers) animation.ipython_display(fps =fps ,loop=loop,autoplay=autoplay) def create_single_plot(self, fig_size=(10,10)): fig,axes = plt.subplots(nrows=self.num_layers, ncols=self.num_attention_heads,figsize=fig_size) for lidx,layerax in enumerate(axes): for hidx,headax in enumerate(layerax): conv_arr = self.get_attention_values(lidx,chosen_head=hidx) cax = headax.matshow(conv_arr,origin='lower', cmap='viridis',aspect='auto') fig.colorbar(cax,ax=headax) if len(self.x_label_toks) > 0: headax.set_xticks([i for i in range(len(self.x_label_toks))]) headax.set_xticklabels(self.x_label_toks,) if len(self.y_label_toks) > 0: headax.set_yticks([len(self.y_label_toks)-i-1 for i in range(len(self.y_label_toks))]) headax.set_yticklabels(self.y_label_toks) default_title = f" Attention At Layer : {int(lidx)} And Head : {hidx}\n" headax.set_title(default_title) return fig

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python-is-easy/assignments/snowman/main.py

eDyablo/pirple

0

10767

<filename>python-is-easy/assignments/snowman/main.py<gh_stars>0 ''' Homework assignment for the 'Python is easy' course by Pirple. Written be <NAME>. Snowman(Hangman) game. ''' from os import ( name as os_name, system as system_call, ) from os.path import ( abspath, dirname, join as join_path, ) ''' Screen displays game output ''' class Screen: def clear(self): if os_name == 'nt': system_call('cls') else: system_call('clear') def draw(self, frame): for line in frame: print(line) ''' Input represents game input device ''' class Input: def ask(self, message): answer = '' while answer == '': answer = input(message) return answer ''' Art is a game art which is set of frames that get loaded from a text file. Draws its current frame on a screen. ''' class Art: def __init__(self): self.frames = [] self.current_frame = 0 def load(self, name): frames = [] art_path = join_path(dirname(abspath(__file__)), join_path('arts', name)) with open(art_path, 'r') as art_file: frame_height = int(art_file.readline()) frame = [] line_count = 0 for line in art_file: frame.append(line.strip('\n\r')) line_count += 1 if line_count % frame_height == 0: frames.append(frame) frame = [] self.frames = frames self.current_frame = 0 def draw(self, screen): screen.draw(self.frames[self.current_frame]) def frames_number(self): return len(self.frames) def next_frame(self): self.current_frame = (self.current_frame + 1) % self.frames_number() return self.current_frame ''' Riddle holds secret word and gets solved by guesses ''' class Riddle: def __init__(self, key): self.key = key self.clue = ['_'] * len(key) def length(self): return len(self.key) def range(self): return range(0, self.length()) def guess(self, g): guess_count = 0 for i in self.range(): if g == self.key[i]: guess_count += 1 self.clue[i] = g return guess_count def solved(self): for i in self.range(): if self.clue[i] != self.key[i]: return False return True def unsolved(self): return self.solved() == False def draw(self, screen): screen.draw([' '.join(self.clue)]) ''' Game is a game itself ''' class Game: def __init__(self): self.screen = Screen() self.input = Input() self.art = Art() self.riddle = Riddle('riddle') def play(self): self.start() self.propose_riddle() while self.in_progress(): self.play_round() self.display_result() def start(self): self.art.load('snowman') self.game_over = False def propose_riddle(self): self.riddle = Riddle(self.input.ask('Player 1 pick a word: ').lower()) def in_progress(self): return self.riddle.unsolved() and self.game_over == False def draw_frame(self): self.screen.clear() self.art.draw(self.screen) self.riddle.draw(self.screen) def play_round(self): self.draw_frame() clue = input('Player 2 guess a letter: ').lower() if len(clue) > 0: if clue[0] == '.': self.stop() elif self.riddle.guess(clue[0]) == 0: self.art.next_frame() if self.art.current_frame == self.art.frames_number() - 1: self.stop() def stop(self): self.game_over = True def display_result(self): self.draw_frame() if self.game_over: self.screen.draw(['Player 2 lost']) else: self.screen.draw(['Player 2 wins']) Game().play()

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PythonExercicios/Mundo 2/8_estrutura_de_repeticao_for/ex051.py

GuilhermoCampos/Curso-Python3-curso-em-video

0

159848

print('{}\n {:^25}\n {}\n'.format('='*25, '10 TERMOS DE UMA PA', '='*25), end=' ') primeiro = int(input('Primeiro Termo: ')) razao = int(input(' Razão: ')) acumulador = 0 for c in range(0, 10): print(primeiro + acumulador, '=> ', end='') acumulador += razao print('ACABOU')

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tests/unit/test_albums.py

movermeyer/openphoto-python

3

173059

from __future__ import unicode_literals import mock try: import unittest2 as unittest # Python2.6 except ImportError: import unittest import trovebox class TestAlbums(unittest.TestCase): test_host = "test.example.com" test_photos_dict = [{"id": "1a", "tags": ["tag1", "tag2"]}, {"id": "2b", "tags": ["tag3", "tag4"]}] test_albums_dict = [{"cover": {"id": "1a", "tags": ["tag1", "tag2"]}, "id": "1", "name": "Album 1", "photos": [test_photos_dict[0]], "totalRows": 2}, {"cover": {"id": "2b", "tags": ["tag3", "tag4"]}, "id": "2", "name": "Album 2", "photos": [test_photos_dict[1]], "totalRows": 2}] def setUp(self): self.client = trovebox.Trovebox(host=self.test_host) self.test_photos = [trovebox.objects.photo.Photo(self.client, photo) for photo in self.test_photos_dict] self.test_albums = [trovebox.objects.album.Album(self.client, album) for album in self.test_albums_dict] @staticmethod def _return_value(result, message="", code=200): return {"message": message, "code": code, "result": result} class TestAlbumsList(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'get') def test_albums_list(self, mock_get): """Check that the album list is returned correctly""" mock_get.return_value = self._return_value(self.test_albums_dict) result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(len(result), 2) self.assertEqual(result[0].id, "1") self.assertEqual(result[0].name, "Album 1") self.assertEqual(result[1].id, "2") self.assertEqual(result[1].name, "Album 2") @mock.patch.object(trovebox.Trovebox, 'get') def test_empty_result(self, mock_get): """Check that an empty result is transformed into an empty list """ mock_get.return_value = self._return_value("") result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(result, []) @mock.patch.object(trovebox.Trovebox, 'get') def test_zero_rows(self, mock_get): """Check that totalRows=0 is transformed into an empty list """ mock_get.return_value = self._return_value([{"totalRows": 0}]) result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(result, []) @mock.patch.object(trovebox.Trovebox, 'get') def test_albums_list_returns_cover_photos(self, mock_get): """Check that the album list returns cover photo objects""" mock_get.return_value = self._return_value(self.test_albums_dict) result = self.client.albums.list(foo="bar") mock_get.assert_called_with("/albums/list.json", foo="bar") self.assertEqual(len(result), 2) self.assertEqual(result[0].id, "1") self.assertEqual(result[0].name, "Album 1") self.assertEqual(result[0].cover.id, "1a") self.assertEqual(result[0].cover.tags, ["tag1", "tag2"]) self.assertEqual(result[1].id, "2") self.assertEqual(result[1].name, "Album 2") self.assertEqual(result[1].cover.id, "2b") self.assertEqual(result[1].cover.tags, ["tag3", "tag4"]) class TestAlbumUpdateCover(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_cover_update(self, mock_post): """Check that an album cover can be updated""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.cover_update(self.test_albums[0], self.test_photos[0], foo="bar") mock_post.assert_called_with("/album/1/cover/1a/update.json", foo="bar") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_cover_update_id(self, mock_post): """Check that an album cover can be updated using IDs""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.cover_update("1", "1a", foo="bar") mock_post.assert_called_with("/album/1/cover/1a/update.json", foo="bar") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_cover_update(self, mock_post): """Check that an album cover can be updated using the album object directly""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.cover_update(self.test_photos[1], foo="bar") mock_post.assert_called_with("/album/1/cover/2b/update.json", foo="bar") self.assertEqual(album.id, "2") self.assertEqual(album.name, "Album 2") self.assertEqual(album.cover.id, "2b") self.assertEqual(album.cover.tags, ["tag3", "tag4"]) class TestAlbumCreate(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_create(self, mock_post): """Check that an album can be created""" mock_post.return_value = self._return_value(self.test_albums_dict[0]) result = self.client.album.create(name="Test", foo="bar") mock_post.assert_called_with("/album/create.json", name="Test", foo="bar") self.assertEqual(result.id, "1") self.assertEqual(result.name, "Album 1") self.assertEqual(result.cover.id, "1a") self.assertEqual(result.cover.tags, ["tag1", "tag2"]) class TestAlbumDelete(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_delete(self, mock_post): """Check that an album can be deleted""" mock_post.return_value = self._return_value(True) result = self.client.album.delete(self.test_albums[0], foo="bar") mock_post.assert_called_with("/album/1/delete.json", foo="bar") self.assertEqual(result, True) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_delete_id(self, mock_post): """Check that an album can be deleted using its ID""" mock_post.return_value = self._return_value(True) result = self.client.album.delete("1", foo="bar") mock_post.assert_called_with("/album/1/delete.json", foo="bar") self.assertEqual(result, True) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_delete(self, mock_post): """Check that an album can be deleted using the album object directly""" mock_post.return_value = self._return_value(True) album = self.test_albums[0] result = album.delete(foo="bar") mock_post.assert_called_with("/album/1/delete.json", foo="bar") self.assertEqual(result, True) self.assertEqual(album.get_fields(), {}) self.assertEqual(album.id, None) self.assertEqual(album.name, None) class TestAlbumAdd(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add(self, mock_post): """ Check that photos can be added to an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.add(self.test_albums[0], self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_id(self, mock_post): """ Check that photos can be added to an album using IDs """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.add(self.test_albums[0].id, objects=["1a", "2b"], object_type="photo", foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_add(self, mock_post): """ Check that photos can be added to an album using the album object directly """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.add(self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a", "2b"], foo="bar") self.assertEqual(album.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_single(self, mock_post): """ Check that a single photo can be added to an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) self.test_albums[0].add(self.test_photos[0], foo="bar") mock_post.assert_called_with("/album/1/photo/add.json", ids=["1a"], foo="bar") @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_invalid_type(self, _): """ Check that an exception is raised if an invalid object is added to an album. """ with self.assertRaises(AttributeError): self.test_albums[0].add([object()]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_add_multiple_types(self, _): """ Check that an exception is raised if multiple types are added to an album. """ with self.assertRaises(ValueError): self.test_albums[0].add(self.test_photos+self.test_albums) class TestAlbumRemovePhotos(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove(self, mock_post): """ Check that photos can be removed from an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.remove(self.test_albums[0], self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_id(self, mock_post): """ Check that photos can be removed from an album using IDs """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.remove(self.test_albums[0].id, objects=["1a", "2b"], object_type="photo", foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a", "2b"], foo="bar") self.assertEqual(result.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_remove(self, mock_post): """ Check that photos can be removed from an album using the album object directly """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.remove(self.test_photos, foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a", "2b"], foo="bar") self.assertEqual(album.id, self.test_albums[1].id) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_single(self, mock_post): """ Check that a single photo can be removed from an album """ mock_post.return_value = self._return_value(self.test_albums_dict[1]) self.test_albums[0].remove(self.test_photos[0], foo="bar") mock_post.assert_called_with("/album/1/photo/remove.json", ids=["1a"], foo="bar") @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_invalid_type(self, _): """ Check that an exception is raised if an invalid object is removed from an album. """ with self.assertRaises(AttributeError): self.test_albums[0].remove([object()]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_remove_multiple_types(self, _): """ Check that an exception is raised if multiple types are removed from an album. """ with self.assertRaises(ValueError): self.test_albums[0].remove(self.test_photos+self.test_albums) class TestAlbumUpdate(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'post') def test_album_update(self, mock_post): """Check that an album can be updated""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.update(self.test_albums[0], name="Test") mock_post.assert_called_with("/album/1/update.json", name="Test") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_update_id(self, mock_post): """Check that an album can be updated using its ID""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.update("1", name="Test") mock_post.assert_called_with("/album/1/update.json", name="Test") self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) @mock.patch.object(trovebox.Trovebox, 'post') def test_album_object_update(self, mock_post): """Check that an album can be updated using the album object directly""" mock_post.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.update(name="Test") mock_post.assert_called_with("/album/1/update.json", name="Test") self.assertEqual(album.id, "2") self.assertEqual(album.name, "Album 2") self.assertEqual(album.cover.id, "2b") self.assertEqual(album.cover.tags, ["tag3", "tag4"]) class TestAlbumView(TestAlbums): @mock.patch.object(trovebox.Trovebox, 'get') def test_album_view(self, mock_get): """Check that an album can be viewed""" mock_get.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.view(self.test_albums[0], includeElements=True) mock_get.assert_called_with("/album/1/view.json", includeElements=True) self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) self.assertEqual(result.photos[0].id, self.test_photos[1].id) @mock.patch.object(trovebox.Trovebox, 'get') def test_album_view_id(self, mock_get): """Check that an album can be viewed using its ID""" mock_get.return_value = self._return_value(self.test_albums_dict[1]) result = self.client.album.view("1", includeElements=True) mock_get.assert_called_with("/album/1/view.json", includeElements=True) self.assertEqual(result.id, "2") self.assertEqual(result.name, "Album 2") self.assertEqual(result.cover.id, "2b") self.assertEqual(result.cover.tags, ["tag3", "tag4"]) self.assertEqual(result.photos[0].id, self.test_photos[1].id) @mock.patch.object(trovebox.Trovebox, 'get') def test_album_object_view(self, mock_get): """Check that an album can be viewed using the album object directly""" mock_get.return_value = self._return_value(self.test_albums_dict[1]) album = self.test_albums[0] album.view(includeElements=True) mock_get.assert_called_with("/album/1/view.json", includeElements=True) self.assertEqual(album.id, "2") self.assertEqual(album.name, "Album 2") self.assertEqual(album.cover.id, "2b") self.assertEqual(album.cover.tags, ["tag3", "tag4"]) self.assertEqual(album.photos[0].id, self.test_photos[1].id) class TestAlbumMisc(TestAlbums): def test_update_fields_with_no_cover(self): """Check that an album object can be updated with no cover""" album = self.test_albums[0] album.cover = None album.photos = None # Check that no exception is raised album._update_fields_with_objects()

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3682, 338, 10425, 13, 4706, 3769, 3032, 5504, 29918, 9621, 29918, 2541, 29918, 12650, 580, 13, 2 ]

constants/nodetype.py

vladzaharia/meshnet

1

36020

# Node types TYPE_NODE = b'\x10' TYPE_NODE_NR = b'\x11' # Gateway types TYPE_GATEWAY = b'\x20' TYPE_GATEWAY_TIME = b'\x21' # Special types TYPE_PROVISIONING = b'\xFF'

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setup.py

adobe/havoc

3

183924

#!/usr/bin/env python from setuptools import setup, find_packages setup(name='havoc', version='0.1.0', description='HAvOC (HAproxy clOud Configuration)', author='TubeMogul Inc.', install_requires=[ 'python-daemon', 'jinja2', 'click', 'boto', 'python-novaclient', 'PyYAML' ], packages=find_packages(), package_dir={'havoc': 'havoc'}, scripts=['bin/havoc'] )

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Codeforces/452/B.py

zzh8829/CompetitiveProgramming

1

92176

<filename>Codeforces/452/B.py from math import * def d(p1x,p1y,p2x,p2y,p3x,p3y,p4x,p4y): if len(set([(p1x,p1y),(p2x,p2y),(p3x,p3y),(p4x,p4y)])) != 4: return (0,0) dis = sqrt((p2x-p1x)*(p2x-p1x) + (p2y-p1y)*(p2y-p1y)) + \ sqrt((p3x-p2x)*(p3x-p2x) + (p3y-p2y)*(p3y-p2y)) + \ sqrt((p4x-p3x)*(p4x-p3x) + (p4y-p3y)*(p4y-p3y)) return (dis,(p1x,p1y,p2x,p2y,p3x,p3y,p4x,p4y)) def bf(): l = [] for p1x in range(0,n+1): for p1y in range(0,m+1): for p2x in range(0,n+1): for p2y in range(0,m+1): for p3x in range(0,n+1): for p3y in range(0,m+1): for p4x in range(0,n+1): for p4y in range(0,m+1): if len(set([(p1x,p1y),(p2x,p2y),(p3x,p3y),(p4x,p4y)])) == 4: l.append(d(p1x,p1y,p2x,p2y,p3x,p3y,p4x,p4y)) print(list(reversed(sorted(l)))[0]) n,m = list(map(int,input().split())) #bf() if m==0: print("%d %d\n%d %d\n%d %d\n%d %d\n"%(1,0,n,m,0,0,n-1,m)) elif n==0: print("%d %d\n%d %d\n%d %d\n%d %d\n"%(0,1,n,m,0,0,n,m-1)) else: l = [] l.append(d(n-1,m,0,0,n,m,0,1)) l.append(d(n,m-1,0,0,n,m,1,0)) l.append(d(1,0,n,m,0,0,n-1,m)) l.append(d(0,1,n,m,0,0,n,m-1)) l.append(d(0,0,n,m,0,1,n-1,m)) l.append(d(0,0,n,m,1,0,n,m-1)) l.append(d(0,0,n,m,n,0,0,m)) l.append(d(0,0,n,m,0,m,n,0)) a = list(reversed(sorted(l)))[0] #print(a) a = a[1] for i in range(4): print(a[i*2],a[i*2+1])

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tica_models.py

DEIB-GECO/TICA_v2

0

1603681

<filename>tica_models.py # This is an auto-generated Django model module. # You'll have to do the following manually to clean this up: # * Rearrange models' order # * Make sure each model has one field with primary_key=True # * Make sure each ForeignKey has `on_delete` set to the desired behavior. # * Remove `managed = False` lines if you wish to allow Django to create, modify, and delete the table # Feel free to rename the models, but don't rename db_table values or field names. from __future__ import unicode_literals from django.db import models class DjangoMigrations(models.Model): app = models.CharField(max_length=255) name = models.CharField(max_length=255) applied = models.DateTimeField() class Meta: managed = False db_table = 'django_migrations' class Hepg2(models.Model): tf1 = models.CharField(primary_key=True, max_length=20) tf2 = models.CharField(max_length=20) distance = models.IntegerField() count_all = models.IntegerField() count_tss = models.IntegerField() cumulative_count_all = models.IntegerField() class Meta: managed = False db_table = 'hepg2' unique_together = (('tf1', 'tf2', 'distance'),) class Hepg2Null(models.Model): tf1 = models.CharField(primary_key=True, max_length=20) tf2 = models.CharField(max_length=20) max_distance = models.IntegerField() average = models.FloatField(blank=True, null=True) median = models.FloatField(blank=True, null=True) mad = models.FloatField(blank=True, null=True) tail_percentage_array = models.TextField(blank=True, null=True) # This field type is a guess. class Meta: managed = False db_table = 'hepg2_null' unique_together = (('tf1', 'tf2', 'max_distance'),) class Hepg2Temp(models.Model): tf1 = models.CharField(max_length=20) tf2 = models.CharField(max_length=20) dist = models.IntegerField() is_tss = models.IntegerField() class Meta: managed = False db_table = 'hepg2_temp'

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Users/models.py

titusnjuguna/FreeDom

1

4604

from django.db import models from django.contrib.auth.models import User from PIL import Image class Profile(models.Model): user = models.ForeignKey(User, on_delete=models.CASCADE) image = models.ImageField(default='default.jpg', upload_to='profile_pic/') def __str__(self): return f'{self.user.username} Profile' def save(self,*args,**kwargs): super().save() img = Image(self.prof_pic.path) if img.height > 300 and img.width > 300: output_size = (300,300) img.thumbnail(output_size) img.save(self.prof_pic.path)

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bcipy/acquisition/protocols/lsl/lsl_device.py

theGreenJedi/BciPy

0

74765

<reponame>theGreenJedi/BciPy # pylint: disable=fixme """Defines the driver for the Device for communicating with LabStreamingLayer (LSL).""" import logging import pylsl from bcipy.acquisition.protocols.device import Device log = logging.getLogger(__name__) LSL_TIMESTAMP = 'LSL_timestamp' class Marker(): """Data class which wraps a LSL marker; data pulled from a marker stream is a tuple where the first item is a list of channels and second item is the timestamp. Assumes that marker inlet only has a single channel.""" def __init__(self, data=(None, None)): super(Marker, self).__init__() self.channels, self.timestamp = data @classmethod def empty(cls): """Creates an empty Marker.""" return Marker() def __repr__(self): return f"<value: {self.trg}, timestamp: {self.timestamp}>" @property def is_empty(self): """Test to see if the current marker is empty.""" return self.channels is None or self.timestamp is None @property def trg(self): """Get the trigger.""" # pylint: disable=unsubscriptable-object return self.channels[0] if self.channels else None def inlet_name(inlet) -> str: """Returns the name of a pylsl streamInlet.""" name = '_'.join(inlet.info().name().split()) return name.replace('-', '_') class LslDevice(Device): """Driver for any device streaming data through the LabStreamingLayer lib. Parameters ---------- connection_params : dict parameters used to connect with the server. channels: list, optional list of channel names fs: float, optional sample frequency in (Hz) include_lsl_timestamp: bool, optional if True, appends the LSL_timestamp to each sample. trg_channel_name: str, optional if present, the marker channel with this name is used as the TRG channel in the output and queried for offset; otherwise the last marker_channel added is used. """ # pylint: disable=too-many-instance-attributes,too-many-arguments def __init__(self, connection_params, fs=None, channels=None, include_lsl_timestamp=False, trg_channel_name='BCI_Stimulus_Markers'): super(LslDevice, self).__init__(connection_params, fs, channels) self._appended_channels = [] self.trg_channel_name = trg_channel_name if include_lsl_timestamp: self._appended_channels.append(LSL_TIMESTAMP) self._inlet = None self._marker_inlets = None # There can be 1 current marker for each marker channel. self.current_markers = {} @property def name(self): if 'stream_name' in self._connection_params: return self._connection_params['stream_name'] if self._inlet and self._inlet.info().name(): return self._inlet.info().name() return 'LSL' def connect(self): """Connect to the data source.""" # Streams can be queried by name, type (xdf file format spec), and # other metadata. # NOTE: According to the documentation this is a blocking call that can # only be performed on the main thread in Linux systems. So far testing # seems fine when done in a separate multiprocessing.Process. eeg_streams = pylsl.resolve_stream('type', 'EEG') marker_streams = pylsl.resolve_stream('type', 'Markers') assert eeg_streams, "One or more EEG streams must be present" assert marker_streams, "One or more Marker streams must be present" self._inlet = pylsl.StreamInlet(eeg_streams[0]) self._marker_inlets = [pylsl.StreamInlet(inlet) for inlet in marker_streams] # initialize the current_markers for each marker stream. for inlet in self._marker_inlets: self.current_markers[inlet_name(inlet)] = Marker.empty() def acquisition_init(self): """Initialization step. Reads the channel and data rate information sent by the server and sets the appropriate instance variables. """ assert self._inlet is not None, "Connect call is required." metadata = self._inlet.info() log.debug(metadata.as_xml()) for marker_inlet in self._marker_inlets: log.debug("Streaming from marker inlet: %s", inlet_name(marker_inlet)) info_channels = self._read_channels(metadata) info_fs = metadata.nominal_srate() # If channels are not initially provided, set them from the metadata. # Otherwise, confirm that provided channels match metadata, or meta is # empty. if not self.channels: self.channels = info_channels assert self.channels, "Channels must be provided" else: if info_channels and self.channels != info_channels: raise Exception("Channels read from the device do not match " "the provided parameters") assert len(self.channels) == (metadata.channel_count() + len(self._appended_channels) + len(self._marker_inlets)),\ "Channel count error" if not self.fs: self.fs = info_fs elif self.fs != info_fs: raise Exception("Sample frequency read from device does not match " "the provided parameter") def _trigger_inlet_index(self): """Index of the marker inlet that should be used for the TRG column. If the trg_channel_name parameter is provided, and there is a marker inlet with this name, that is used, otherwise the last marker inlet is used. Returns -1 if there are no marker inlets.""" inlet_names = [inlet_name(inlet) for inlet in self._marker_inlets] if self.trg_channel_name in inlet_names: return inlet_names.index(self.trg_channel_name) return len(inlet_names) - 1 def _read_channels(self, info): """Read channels from the stream metadata if provided and return them as a list. If channels were not specified, returns an empty list. Parameters ---------- info : pylsl.XMLElement Returns ------- list of str """ channels = [] if info.desc().child("channels").empty(): return channels channel = info.desc().child("channels").child("channel") for _ in range(info.channel_count()): channel_name = channel.child_value("label") # If the data stream has a TRG channel, rename it so it doesn't # conflict with the marker channel. if channel_name == 'TRG' and self._marker_inlets: channel_name = "TRG_device_stream" channels.append(channel_name) channel = channel.next_sibling() for appended_channel in self._appended_channels: channels.append(appended_channel) trg_marker_index = self._trigger_inlet_index() for i, inlet in enumerate(self._marker_inlets): col = inlet_name(inlet) if i == trg_marker_index: col = 'TRG' channels.append(col) return channels def read_data(self): """Reads the next packet and returns the sensor data. Returns ------- list with an item for each channel. """ sample, timestamp = self._inlet.pull_sample() # Useful for debugging. if LSL_TIMESTAMP in self._appended_channels: sample.append(timestamp) for marker_inlet in self._marker_inlets: name = inlet_name(marker_inlet) marker = self.current_markers.get(name, Marker().empty()) # Only attempt to retrieve a marker from the inlet if we have # merged the last one with a sample. if marker.is_empty: # A timeout of 0.0 only returns a sample if one is buffered for # immediate pickup. Without a timeout, this is a blocking call. marker_data = marker_inlet.pull_sample(timeout=0.0) marker = Marker(marker_data) self.current_markers[name] = marker if not marker.is_empty: log.debug( "Read marker %s from %s; current sample time: %s", marker, name, timestamp) trg = "0" if not marker.is_empty and timestamp >= marker.timestamp: trg = marker.trg log.debug(("Appending %s marker %s to sample at time %s; ", "time diff: %s"), name, marker, timestamp, timestamp - marker.timestamp) self.current_markers[name] = Marker.empty() # clear current # Add marker field to sample sample.append(trg) return sample

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applications/reports/process/data_service.py

ebs-integrator/bic-reporting-api

1

153362

import os import pathlib from lxml import etree from abc import ABC from datetime import datetime from xml.etree import ElementTree from applications.reports.process.flow_data_service import FlowDataService class DataService(FlowDataService, ABC): # Credentials username = '' password = '' # Language - The field format is `languageCountry` (ex enUS) # according to ISO 639-1 and ISO 3166-1 language = 'roRO' def __init__(self, company=None, delivery=None): super().__init__() self.company = company self.delivery = delivery def set_credentials(self, username: str, password: str): self.username = username self.password = password def generate(self, **kwargs): xml = ElementTree.Element('root') ElementTree.SubElement(xml, 'delivery', {'method': self.delivery}) # Credentials security = ElementTree.SubElement(xml, 'security') username = ElementTree.SubElement(security, 'username') password = ElementTree.SubElement(security, 'password') username.text = self.username password.text = <PASSWORD> # Language language = ElementTree.SubElement(xml, 'language') language.text = self.language request_timestamp = ElementTree.SubElement(xml, 'request_timestamp') request_timestamp.text = datetime.now().strftime('%d-%m-%Y %H:%M:%S') # Forming credit report requests cr_requests = ElementTree.SubElement(xml, 'cr_requests') for request_counter, company in enumerate([self.company]): request_counter = request_counter + 1 parameters: dict = self.entity_parameters(company) # Forming single credit report requests cr_request = ElementTree.SubElement(cr_requests, 'cr_request') cr_id = ElementTree.SubElement(cr_request, 'id') cr_id.text = str(request_counter) customer_type = ElementTree.SubElement(cr_request, 'customer_type') customer_type.text = parameters.get('counterparty_type') include_sivabon = ElementTree.SubElement(cr_request, 'include_sivabon') include_sivabon.text = parameters.get('report_bic') include_fisc = ElementTree.SubElement(cr_request, 'include_fisc') include_fisc.text = parameters.get('report_sfs') include_bns = ElementTree.SubElement(cr_request, 'include_bns') include_bns.text = parameters.get('report_bns') if parameters.get('report_bns') == '1': include_bns_year = ElementTree.SubElement(cr_request, 'bnsYear') include_bns_year.text = parameters.get('report_bns_year') idno_idnp = ElementTree.SubElement(cr_request, 'idno_idnp') idno_idnp.text = parameters.get('fiscal_code') inquiry_purpose = ElementTree.SubElement(cr_request, 'inquiry_purpose') inquiry_purpose.text = parameters.get('request_goal') bic_agreement = ElementTree.SubElement(cr_request, 'credit_application_agreement') bic_agreement.text = parameters.get('bic_agreement') process_indicator = ElementTree.SubElement(cr_request, 'process_indicator') process_indicator.text = parameters.get('bic_agreement') if parameters.get('counterparty_type') == "C": company_name = ElementTree.SubElement(cr_request, 'company_name') company_name.text = parameters.get('company_name') company_short_name = ElementTree.SubElement(cr_request, 'company_short_name') company_short_name.text = parameters.get('company_short_name') last_name = ElementTree.SubElement(cr_request, 'last_name') last_name.text = parameters.get('last_name') first_name = ElementTree.SubElement(cr_request, 'first_name') first_name.text = parameters.get('first_name') father_name = ElementTree.SubElement(cr_request, 'father_name') father_name.text = parameters.get('patronymic') identity_card = ElementTree.SubElement(cr_request, 'identity_card') identity_card.text = parameters.get('passport_series_number') date_of_registration = ElementTree.SubElement(cr_request, 'date_of_registration') date_of_registration.text = parameters.get('date_of_birth') document_number = ElementTree.SubElement(cr_request, 'document_number') document_number.text = parameters.get('document_number') document_date = ElementTree.SubElement(cr_request, 'document_date') document_date.text = parameters.get('document_date') document_type = ElementTree.SubElement(cr_request, 'document_type') document_type.text = parameters.get('document_type') if parameters.get('bic_agreement') == '1': initial_value = ElementTree.SubElement(cr_request, 'initial_value') initial_value.text = parameters.get('credit_amount') document_currency = ElementTree.SubElement(cr_request, 'document_currency') document_currency.text = parameters.get('currency_code') terms_duration = ElementTree.SubElement(cr_request, 'terms_duration') terms_duration.text = parameters.get('credit_term') terms_frequency = ElementTree.SubElement(cr_request, 'terms_frequency') terms_frequency.text = parameters.get('frequency_of_payments') document_insurance = ElementTree.SubElement(cr_request, 'document_insurance') document_insurance.text = parameters.get('type_of_collateral') insurance_value = ElementTree.SubElement(cr_request, 'document_insurance_value') insurance_value.text = parameters.get('collateral_amount') extras_type = ElementTree.SubElement(cr_request, 'extras_type') extras_type.text = parameters.get('report_type') return ElementTree.tostring(**{ 'element': xml, 'encoding': 'utf-8', 'short_empty_elements': False }).decode() def entity_parameters(self, company=None): if company is None: company = {} date = datetime.now() formats = date.strftime("%d-%m-%Y %H:%M:%S") year = date.strftime("%Y") additional_data = company.get('additional_data', {}) or {} registered_at = additional_data.get('registered_at', None) user = company.get('user', {}) parameters = { "request_date": formats, "counterparty_type": company.get('counterparty_type', 'C') or 'C', "fiscal_code": company.get('idno', ''), "request_goal": company.get('request_goal', 'MDP') or 'MDP', "bic_agreement": company.get('bic_agreement', '1') or '1', "report_bic": company.get('report_bic', '1') or '1', "report_sfs": company.get('report_sfs', '1') or '1', "report_bns": company.get('report_bns', '1') or '1', "report_bns_year": company.get('report_bns_year', year) or year, "company_name": company.get('title', ''), "company_short_name": company.get('title', ''), 'first_name': self.replace_with_ascii(user.get('first_name', 'NA')) or 'NA', 'last_name': self.replace_with_ascii(user.get('last_name', 'NA')) or 'NA', 'patronymic': self.replace_with_ascii(user.get('patronymic', 'NA')) or 'NA', 'passport_series_number': None, 'date_of_birth': None, 'document_number': str(int(date.timestamp())), 'document_date': None, 'date_of_registration': registered_at, 'credit_amount': str(company.get('credit_amount', '0') or '0'), 'currency_code': str(company.get('currency_code', 'MDL') or 'MDL'), 'credit_term': str(company.get('credit_term', '0') or '0'), 'request_type': str(company.get('request_type', 'M') or 'M'), 'document_type': str(company.get('document_type', 'C') or 'C'), 'frequency_of_payments': str(company.get('frequency_of_payments', 'N') or 'N'), 'type_of_collateral': str(company.get('type_of_collateral', 'D') or 'D'), 'collateral_amount': str(company.get('collateral_amount', '0') or '0'), 'report_type': str(company.get('report_type', 'AllSection') or 'AllSection'), } return parameters @staticmethod def upsert_file(path): import os os.makedirs(os.path.dirname(path), exist_ok=True) def html(self, xml: str = '', xslt: str = 'templates/tr.xslt'): xslt_document = os.path.join(pathlib.Path().absolute(), xslt) xslt_document_element = etree.parse(xslt_document) xslt_document_transformer = etree.XSLT(xslt_document_element) name = str(int(datetime.now().timestamp())) xml_document_element = etree.fromstring(xml.encode()) xml_document_transformer = xslt_document_transformer(xml_document_element) html_string = str(xml_document_transformer) name_tag = name html_name = f'storage/companies/reports/html/{name}.html' self.upsert_file(html_name) try: with open(html_name, 'w', encoding="utf-8") as file: file.write(html_string) finally: file.close() return file, html_string, html_name, name_tag

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kmd_labs/oracles/listassetchains.py

smk762/kmd_pulp

2

182161

<filename>kmd_labs/oracles/listassetchains.py #!/usr/bin/env python2 import os import json #script_dir = os.getcwd() HOME = os.environ['HOME'] with open(HOME + '/StakedNotary/assetchains.json') as file: assetchains = json.load(file) for chain in assetchains: print(chain['ac_name'])

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migrations/versions/ddf3cbc60873_.py

MaxHorwood/crude_backend_skeleton

0

63802

"""empty message Revision ID: ddf3cbc60873 Revises: x Create Date: 2020-08-22 17:27:08.449495 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = "<KEY>" down_revision = None branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table( "user", sa.Column("id", sa.Integer(), nullable=False), sa.Column("name", sa.String(length=80), nullable=False), sa.Column("email", sa.String(length=120), nullable=False), sa.PrimaryKeyConstraint("id"), ) op.create_table( "address", sa.Column("id", sa.Integer(), nullable=False), sa.Column("postcode", sa.String(length=80), nullable=False), sa.Column("address", sa.String(length=100), nullable=False), sa.Column("user_id", sa.Integer(), nullable=True), sa.ForeignKeyConstraint( ["user_id"], ["user.id"], ), sa.PrimaryKeyConstraint("id"), ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table("address") op.drop_table("user") # ### end Alembic commands ###

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fbchat/utils.py

Dainius14/fb-chat-bot-old

2

19916

<reponame>Dainius14/fb-chat-bot-old<filename>fbchat/utils.py import re import json from time import time from random import random USER_AGENTS = [ "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.90 Safari/537.36", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_3) AppleWebKit/601.1.10 (KHTML, like Gecko) Version/8.0.5 Safari/601.1.10", "Mozilla/5.0 (Windows NT 6.3; WOW64; ; NCT50_AAP285C84A1328) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.90 Safari/537.36", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1", "Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11", "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6" ] def now(): return int(time()*1000) def get_json(text): return json.loads(re.sub(r"^[^{]*", '', text, 1)) def digit_to_char(digit): if digit < 10: return str(digit) return chr(ord('a') + digit - 10) def str_base(number,base): if number < 0: return '-' + str_base(-number, base) (d, m) = divmod(number, base) if d > 0: return str_base(d, base) + digit_to_char(m) return digit_to_char(m) def generateMessageID(client_id=None): k = now() l = int(random() * 4294967295) return ("<%s:%s-<EMAIL>>" % (k, l, client_id)); def getSignatureID(): return hex(int(random() * 2147483648)) def generateOfflineThreadingID() : ret = now() value = int(random() * 4294967295); string = ("0000000000000000000000" + bin(value))[-22:] msgs = bin(ret) + string return str(int(msgs,2))

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