Datasets:
paulo037
/
starcoder

Dataset card Data Studio Files Community
1
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
strips/utils.py
cyclone923/pyplanners
0
117539
from .ff import ff_fn, plan_cost, first_goals from misc.numerical import INF from misc.functions import in_add from planner.progression.best_first import a_star_search, best_first_search, deferred_best_first_search def h_0(state, goal, operators): return 0 def h_naive(state, goal, operators): return sum(1 for literal in goal.conditions if literal not in state) def h_blind(state, goal, operators): return min(operator.cost for operator in ha_applicable(state, goal, operators)) ########################################################################### def filter_axioms(operators): return filter(lambda o: not o.is_axiom(), operators) def ha_all(state, goal, operators): return operators def ha_applicable(state, goal, operators): return [operator for operator in operators if operator(state) is not None] def ha_sorted(state, goal, operators): return sorted(ha_applicable(state, goal, operators), key=lambda o: o.cost) def ha_combine(state, goal, operators, *helpful_actions): seen_operators = set() for ha in helpful_actions: ha_operators = [] for operator in ha(state, goal, operators): if not in_add(seen_operators, operator): ha_operators.append(operator) yield ha_operators ########################################################################### def combine(heuristic, helpful_actions): return lambda s, g, o: (heuristic(s, g, o), helpful_actions(s, g, o)) #default_successors = combine(h_add, ha_applicable) default_successors = ff_fn(plan_cost, first_goals, op=sum) ########################################################################### def single_generator(initial, goal, operators, successors): def generator(vertex): yield successors(vertex.state, goal, operators) return generator #return = lambda v: (yield successors(v.state, goal, operators)) def filter_axioms_generator(goal, operators, axioms, successors): def generator(vertex): #heuristic, helpful_actions = successors(vertex.state, goal, operators + axioms) heuristic, helpful_actions = successors(vertex.derived_state, goal, operators + axioms) #yield heuristic, helpful_actions #yield heuristic, operators yield heuristic, filter_axioms(helpful_actions) # NOTE - the first_actions should be anything applicable in derived_state #yield heuristic, filter(lambda op: op not in axioms, helpful_actions) return generator, axioms default_generator = lambda i, g, o: single_generator(i, g, o, default_successors) ########################################################################### def weighted(w): if w == INF: return lambda v: v.h_cost return lambda v: (v.cost + w*v.h_cost) uniform = weighted(0) astar = weighted(1) greedy = weighted(INF) ########################################################################### # TODO: # stack=True vs False can matter quite a bit #default_search = lambda initial, goal, generator: a_star_search(initial, goal, generator, astar, stack=True) default_search = lambda initial, goal, generator: best_first_search(initial, goal, generator, greedy, stack=False) #default_search = lambda initial, goal, generator: deferred_best_first_search(initial, goal, generator, greedy, stack=False) def default_plan(initial, goal, operators): return default_search(initial, goal, default_generator(initial, goal, operators)) def default_derived_plan(initial, goal, operators, axioms): #return default_search(initial, goal, (lambda v: iter([default_successors(v.state, goal, operators + axioms)]), axioms)) return default_search(initial, goal, filter_axioms_generator(goal, operators, axioms, default_successors))
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zero_padding.py
dcxSt/kaggle_mais
0
110574
#! /home/steve/anaconda3/bin/python3.7 import numpy as np train_images = np.load("train_images.npy") padded_train_images = [] print("padding train images") for i in train_images: entry=[] entry.append(np.zeros(32)) entry.append(np.zeros(32)) for j in i: entry.append([0,0]+list(j)+[0,0]) entry.append(np.zeros(32)) entry.append(np.zeros(32)) padded_train_images.append(entry) print("saving padded train images") np.save("padded_train_images.npy", np.array(padded_train_images)) test_images = np.load("test_images.npy") padded_test_images = [] print("padding test images") for i in test_images: entry=[] entry.append(np.zeros(32)) entry.append(np.zeros(32)) for j in i: entry.append([0,0]+list(j)+[0,0]) entry.append(np.zeros(32)) entry.append(np.zeros(32)) padded_test_images.append(entry) print("saving padded test images") np.save("padded_test_images.npy", np.array(padded_test_images))
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medium/1780-check-if-number-is-a-sum-of-powers-of-three.py
wanglongjiang/leetcode
2
184392
''' 判断一个数字是否可以表示成三的幂的和 给你一个整数 n ，如果你可以将 n 表示成若干个不同的三的幂之和，请你返回 true ，否则请返回 false 。 对于一个整数 y ，如果存在整数 x 满足 y == 3x ，我们称这个整数 y 是三的幂。 提示： 1 <= n <= 107 ''' ''' 思路：数学 对于一个整数x，如果是3的幂的和，说明x=y*3 or x= y*3+1 也就是x是3的整数倍或整数倍+1 不对，是3个不同的幂之和如果是21，3^2+3^2+3^1 TODO '''
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test_tscribe.py
craigmayhew/aws_transcribe_to_docx
0
135911
<reponame>craigmayhew/aws_transcribe_to_docx import tscribe import os def test_multiple_speakers(): """ Test output exists with multiple speaker input # GIVEN a sample file containing multiple speakers # WHEN calling tscribe.write(...) # THEN produce the .docx without errors """ # Setup input_file = "sample_multiple.json" output_file = "sample_multiple.docx" assert os.access(input_file, os.F_OK), "Input file not found" # Function tscribe.write(input_file) assert os.access(output_file, os.F_OK), "Output file not found" # Teardown os.remove(output_file) os.remove("chart.png") def test_multiple_speakers_with_save_as(): """ Test output exists with multiple speaker input, and save_as defined # GIVEN a sample file containing multiple speakers, and an output filename # WHEN calling tscribe.write(...) # THEN produce the .docx, named correctly, without errors """ # Setup input_file = "sample_multiple.json" output_file = "test_sample.docx" assert os.access(input_file, os.F_OK), "Input file not found" # Function tscribe.write(input_file, save_as=output_file) assert os.access(output_file, os.F_OK), "Output file not found" # Teardown os.remove(output_file) os.remove("chart.png") def test_multiple_speakers_with_save_as_with_tmp_dir(): """ Test output exists with multiple speaker input, and save_as defined, and tmp_dir defined # GIVEN a sample file containing multiple speakers, and an output filename, and a writable tmp directory # WHEN calling tscribe.write(...) # THEN produce the .docx, with a chart, named correctly, without errors """ # Setup input_file = "sample_multiple.json" output_file = "test_sample.docx" tmp_dir = "/tmp/" assert os.access(input_file, os.F_OK), "Input file not found" # Function tscribe.write(input_file, save_as=output_file, tmp_dir=tmp_dir) assert os.access(tmp_dir+"chart.png", os.F_OK), "Chart file not found" assert os.access(output_file, os.F_OK), "Output file not found" # Teardown os.remove(output_file) os.remove(tmp_dir+"chart.png") def test_single_speaker(): """ Test output exists with single speaker input # GIVEN a sample file containing single speaker # WHEN calling tscribe.write(...) # THEN produce the .docx without errors """ # Setup input_file = "sample_single.json" output_file = "sample_single.docx" assert os.access(input_file, os.F_OK), "Input file not found" # Function tscribe.write(input_file) assert os.access(output_file, os.F_OK), "Output file not found" # Teardown os.remove(output_file) os.remove("chart.png") def test_single_speaker_with_save_as(): """ Test output exists with single speaker input, and save_as defined # GIVEN a sample file containing single speaker, and an output filename # WHEN calling tscribe.write(...) # THEN produce the .docx, named correctly, without errors """ # Setup input_file = "sample_single.json" output_file = "test_sample.docx" assert os.access(input_file, os.F_OK), "Input file not found" # Function tscribe.write(input_file, save_as=output_file) assert os.access(output_file, os.F_OK), "Output file not found" # Teardown os.remove(output_file) os.remove("chart.png") def test_single_speaker_with_save_as_with_tmp_dir(): """ Test output exists with single speaker input, and save_as defined, and tmp_dir defined # GIVEN a sample file containing single speaker, and an output filename, and a writable tmp directory # WHEN calling tscribe.write(...) # THEN produce the .docx, with a chart, named correctly, without errors """ # Setup input_file = "sample_single.json" output_file = "test_sample.docx" tmp_dir = "/tmp/" assert os.access(input_file, os.F_OK), "Input file not found" # Function tscribe.write(input_file, save_as=output_file, tmp_dir=tmp_dir) assert os.access(tmp_dir+"chart.png", os.F_OK), "Chart file not found" assert os.access(output_file, os.F_OK), "Output file not found" # Teardown os.remove(output_file) os.remove(tmp_dir+"chart.png")
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src/badd/__init__.py
wksmirnowa/badd
1
145161
<filename>src/badd/__init__.py<gh_stars>1-10 from .ad import AdDetector from .toxic import ToxicDetector from .obscene import ObsceneDetector from .file_loader import FileLoader
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simpleml/classifiers/decisiontree.py
get9/ml-test
1
1602078
<gh_stars>1-10 import sys import numpy as np from .baseclassifier import BaseClassifier # Calculate gini impurity of vector {f} def gini(f): return 1.0 - np.sum(np.square(f)) # Score the split given by {splitval} on {dim} according to gini impurity def score_split(splitval, dim, left, right): N = len(left) + len(right) fleft = np.bincount(left[:, 2].astype(np.int)) + np.finfo(float).eps fleft /= np.sum(fleft) fright = np.bincount(right[:, 2].astype(np.int)) + np.finfo(float).eps fright /= np.sum(fright) return (len(left) * gini(fleft) + len(right) * gini(fright)) / N # Splits dataset {d} according to {splitval} on {dim} def split(splitval, dim, d): assert dim < len(d[0]) - 1 return d[d[:, dim] < splitval], d[d[:, dim] >= splitval] # A node in the decision tree class DTNode: def __init__(self, val, left=None, right=None, feature_idx=-1): self.val = val self.left = left self.right = right self.feature_idx = feature_idx def __str__(self): return "val = {}; feature_idx = {}".format(self.val, self.feature_idx) # Main decision tree class class DecisionTree(BaseClassifier): def __init__(self, depth=3, nsplits=100): super().__init__() self.t = None self.depth = depth self.nsplits = nsplits # Main entry point for recursive call that builds the tree def train(self, dataset): self.t = self.train_internal(dataset, self.depth, list(range(len(dataset[0]) - 1))) # Recursive training method. Stops when no more dimensions to condition def train_internal(self, dataset, depth, dims): # Base case: we're at a leaf, so return whichever label is more common labelcount = np.bincount(dataset[:, 2].astype(np.int)) if depth == 0 or labelcount.max() == len(dataset): ret = DTNode(np.argmax(labelcount)) assert ret.val == 0 or ret.val == 1 return ret best_split_val = None best_split_dim = None best_left = np.array([]) best_right = np.array([]) best_score = 1.0 # For each dimension, check every possible split and see if it's best # When we find the best split after going through all dimensions and # all possible splits, remove that dimension from further consideration for i in range(len(dims)): col = dataset[:, i] splits = np.linspace(np.min(col), np.max(col), self.nsplits) for j in range(len(splits)): left, right = split(splits[j], dims[i], dataset) score = score_split(splits[j], dims[i], left, right) if score < best_score: best_score = score best_split_val = splits[j] best_split_dim = dims[i] best_left = left best_right = right # Recurse assert score != 1.0 left_node = self.train_internal(best_left, depth-1, dims) right_node = self.train_internal(best_right, depth-1, dims) return DTNode(best_split_val, left=left_node, right=right_node, feature_idx=best_split_dim) def print_tree(self): self.print_tree_r(self.t) def print_tree_r(self, node, indent=''): if not node: return print(indent + str(node)) self.print_tree_r(node.left, indent+' ') self.print_tree_r(node.right, indent+' ') def predict(self, points): predicted_l = np.empty((len(points)), dtype=np.int) for i in range(len(points)): predicted_l[i] = self.predict_single(points[i]) return predicted_l def predict_single(self, point): assert self.t != None currnode = self.t while currnode.feature_idx > -1: if point[currnode.feature_idx] <= currnode.val: currnode = currnode.left else: currnode = currnode.right return currnode.val def error(self, predicted, actual): return np.count_nonzero(np.abs(predicted - actual)) / len(predicted)
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code/custom_utils.py
dataspider/momo
4
63097
import functools import json import re from collections import Counter import matplotlib.pyplot as plt import networkx as nx import numpy as np import pandas as pd import seaborn as sns from statics import STRUCTURE_TYPES sns.set_style("whitegrid") plt.rcParams["figure.figsize"] = (18, 12) plt.rcParams["font.size"] = 12 np.random.seed(1234) def get_jaccard(structure1, structure2, structure_type): if structure_type == "clique": nodes1 = set(structure1["nodes"]) nodes2 = set(structure2["nodes"]) overlap = nodes1.intersection(nodes2) union = nodes1.union(nodes2) return len(overlap) / len(union) if structure_type in ["biclique", "starclique"]: left1, left2 = set(structure1["left_nodes"]), set(structure2["left_nodes"]) right1, right2 = set(structure1["right_nodes"]), set(structure2["right_nodes"]) left_overlap = left1.intersection(left2) left_union = left1.union(left2) right_overlap = right1.intersection(right2) right_union = right1.union(right2) return ( len(left_overlap) / len(left_union) + len(right_overlap) / len(right_union) ) / 2 if structure_type == "star": hub1, hub2 = {structure1["hub"]}, {structure2["hub"]} spokes1, spokes2 = set(structure1["spokes"]), set(structure2["spokes"]) hub_overlap = hub1.intersection(hub2) hub_union = hub1.union(hub2) spoke_overlap = spokes1.intersection(spokes2) spoke_union = spokes1.union(spokes2) return ( len(hub_overlap) / len(hub_union) + len(spoke_overlap) / len(spoke_union) ) / 2 raise Exception(f"Unknown structure type: {structure_type}!") def get_dataset_color(dataset): if dataset.startswith("ors") or dataset.startswith("asb"): return "dodgerblue" elif dataset.startswith("orp") or dataset.startswith("asp"): return "lightskyblue" elif dataset.startswith("usl") or dataset.startswith("lus"): return "r" elif dataset.startswith("del") or dataset.startswith("lde"): return "darkorange" elif dataset.startswith("clg"): return "purple" elif dataset.startswith("csi"): return "magenta" elif "bio$_{\mathcal{A}}" in dataset: return "green" elif dataset.startswith("bio\n") or dataset.startswith("bio"): return "g" elif dataset.startswith("bag") or dataset.startswith("rba"): return "gray" elif dataset.startswith("erg") or dataset.startswith("rer"): return "darkgray" else: raise Exception(dataset) def load_json(file): """ load a json file as a dictionary """ with open(file) as f: model_json = json.load(f) return model_json def load_log(file): """ load a log file as a list of log file lines """ with open(file) as f: model_log = f.read().split("\n") return model_log def create_df(model_json): """ convert the model json computed by julia into a pd.DataFrame """ tuples = list( zip( model_json["macro_structures"], model_json["macro_structure_description_lengths"], model_json["description_lengths_over_time"], ) ) df = pd.DataFrame( tuples, columns=["structure", "structure_cost", "description_length"] ) df["n_edges_total"] = [ x.get("n_edges_total", model_json["m"]) for x in df.structure ] df["n_nodes_total"] = [ x.get("n_nodes_total", model_json["n"]) for x in df.structure ] df["structure_type"] = [x.get("structure_type") for x in df.structure] df["structure_shape"] = [ get_node_marker(x) if x in STRUCTURE_TYPES else "X" for x in df.structure_type ] df["structure_color"] = [ get_node_color(x) if x in STRUCTURE_TYPES else "k" for x in df.structure_type ] return df def create_progression_plot(df, save_path=None): """ position of structure in the sequence on x, description length after adding structure on y, color signaling structure type, size signalling number of edges """ scattertuples = list( zip( df.index - 1, df.description_length / df.description_length.max(), df.n_edges_total, df.structure_color, df.structure_shape, ) ) for t in reversed(scattertuples[1:]): plt.scatter(t[0], t[1], s=t[2] if t[3] != "k" else 10, c=t[3], marker="o") plt.xticks(range(0, len(scattertuples[1:]) + 1, 2)) plt.xlim(-1, len(scattertuples[1:]) + 1) plt.xlabel("Selected structure") plt.ylabel("Total description length after structure selected") plt.title(save_path) plt.tight_layout() if save_path is not None: plt.savefig(save_path) plt.close() def create_size_plot(model_json, x_granularity, y_granularity, save_path=None): """ number of nodes on x, number of edges on y, color signaling structure type """ structure_types, n_nodes, n_edges = list( zip( *( [ (s["structure_type"], s.get("n_nodes_total", 0), s["n_edges_total"]) for s in model_json["macro_structures"] ] ) ) ) plt.scatter( n_nodes[2:], n_edges[2:], c=list(map(get_node_color, structure_types[2:])), ) plt.xlabel("Number of Nodes") plt.xticks(range(0, max(n_nodes[2:]) + x_granularity, x_granularity)) plt.yticks(range(0, max(n_edges[2:]) + y_granularity, y_granularity)) plt.ylim(0, max(n_edges[2:]) + y_granularity) plt.ylabel("Number of Edges") plt.title(save_path) plt.tight_layout() if save_path is not None: plt.savefig(save_path) plt.close() def get_structures_added(model_json): """ return list of dicts, with each dict a structure added in the model building process (i.e., generic structures are excluded) """ return model_json["macro_structures"][2:] def get_node_sets(structures_added): """ return a list of lists, with each inner list holding the nodes of a structure """ return [_get_nodes(structure) for structure in structures_added] def _get_nodes(structure): """ helper for get_node_sets """ if structure["structure_type"] in ["biclique", "starclique"]: return structure["left_nodes"] + structure["right_nodes"] elif structure["structure_type"] == "clique": return structure["nodes"] elif structure["structure_type"] == "star": return [structure["hub"]] + structure["spokes"] else: raise Exception(f"Unknown structure type {structure['structure_type']}!") def get_structure_dfs(structures_added, node_sets): """ return two pd.DataFrame objects encoding the node overlap between structures: abs_df (# nodes in the overlap), rel_df (jaccard similarity) """ abs_df = pd.DataFrame( index=range(len(structures_added)), columns=range(len(structures_added)), data=np.nan, ) rel_df = pd.DataFrame( index=range(len(structures_added)), columns=range(len(structures_added)), data=np.nan, ) for idx in range(0, len(node_sets) - 1): for idx2 in range(idx + 1, len(node_sets)): abs_df.at[idx, idx2] = len( set(node_sets[idx]).intersection(set(node_sets[idx2])) ) abs_df.at[idx2, idx] = abs_df.at[idx, idx2] rel_df.at[idx, idx2] = len( set(node_sets[idx]).intersection(set(node_sets[idx2])) ) / len(set(node_sets[idx]).union(set(node_sets[idx2]))) rel_df.at[idx2, idx] = rel_df.at[idx, idx2] return abs_df, rel_df def _get_n_nodes_covered(node_sets): """ helper for get_fraction_nodes_covered """ return len(set(functools.reduce(lambda x, y: x + y, node_sets, []))) def get_fraction_nodes_covered(node_sets, model_json): return _get_n_nodes_covered(node_sets) / model_json["n"] def plot_overlap_heatmap(df, save_path=None): """ structures added to model on x and y, similarity as per df as color, default colormap, robust=False """ sns.heatmap(df, square=True) if save_path is not None: plt.savefig(save_path) plt.close() def create_rooted_bfs_tree(df, layout=False): G = nx.Graph(df.fillna(0)) maxst = nx.tree.maximum_spanning_tree(G) artificial_root = G.number_of_nodes() ccs = list(nx.connected_components(G)) for c in ccs: component_subgraph = maxst.subgraph(c) component_root = max(nx.degree(component_subgraph), key=lambda tup: tup[-1])[ 0 ] # node with max unweighted degree maxst.add_edge(artificial_root, component_root, weight=np.finfo(float).eps) tree = nx.traversal.bfs_tree(maxst, artificial_root) for e in tree.edges(): tree.edges[e]["weight"] = maxst.edges[e]["weight"] if layout: pos = nx.layout.kamada_kawai_layout(maxst, weight=None) return tree, pos else: return tree def add_tree_layout(G, root, node_sep, level_sep): for node in G.nodes(): G.nodes[node]["y"] = -level_sep * nx.dijkstra_path_length( G, root, node, weight=None ) base = 0 for node in nx.dfs_postorder_nodes(G, root): succ = sorted(list(G.successors(node)), reverse=True) if len(succ) < 1: G.nodes[node]["x"] = base + node_sep base += node_sep else: xmin = min([G.nodes[node]["x"] for node in succ]) xmax = max([G.nodes[node]["x"] for node in succ]) G.nodes[node]["x"] = xmin + (xmax - xmin) / 2 for node in G.nodes: G.nodes[node]["x"] = -G.nodes[node]["x"] return G def add_color(G, df): for node in G.nodes(): G.nodes[node]["color"] = ( df.at[node + 2, "structure_color"] if node != len(df) - 2 else "k" ) return G def plot_tree(G, df, save_path=None): G = add_color(G, df) _, ax = plt.subplots(1, 1, figsize=(12, 12)) for node in G.nodes(): x = G.nodes[node]["x"] y = G.nodes[node]["y"] color = G.nodes[node]["color"] for succ in G.successors(node): ax.plot( [x, G.nodes[succ]["x"]], [y, G.nodes[succ]["y"]], "-k", linewidth=max(G.edges[node, succ]["weight"] * 10, 1), zorder=1, alpha=1, ) ax.scatter( x, y, color=color, s=df.at[node + 2, "n_nodes_total"] * 6 if node != len(df) - 2 else 300, marker=df.at[node + 2, "structure_shape"] if node != len(df) - 2 else "X", zorder=2, alpha=1, ) # if node != len(df) - 2: # ax.annotate(node + 1, (x, y), fontsize=10, ha="center", va="center") plt.tick_params(left=False, labelleft=False, bottom=False, labelbottom=False) plt.axis("off") plt.tight_layout() if save_path is not None: plt.savefig(save_path, transparent=True, bbox_inches="tight") plt.close() def plot_structure_tree(tree, layout, df, save_path=None): """ plot structure tree in basic kamada kawai layout; structure identifiers in order of structure addition and color corresponding to structure type (artificial root node black) """ nx.draw_networkx_edges(tree, pos=layout) for node, (x, y) in layout.items(): plt.scatter( x, y, color=df.at[node + 2, "structure_color"] if node != len(df) - 2 else "k", s=df.at[node + 2, "n_nodes_total"] * 6 if node != len(df) - 2 else 100, marker=df.at[node + 2, "structure_shape"] if node != len(df) - 2 else "X", zorder=2, alpha=0.8, ) labels = {idx: idx + 1 for idx in tree.nodes()} nx.draw_networkx_labels(tree, pos=layout, labels=labels) plt.axis("off") if save_path is not None: plt.savefig(save_path) plt.close() def write_plots_for_model_json( json_path, save_base, x_granularity_size, y_granularity_size, ): """ end-to-end plot generation for json file at given json_path """ print(f"Starting {json_path}...") model_json = load_json(json_path) save_base = save_base.split("_size")[0] df = create_df(model_json) df.to_csv(re.sub("figure", "structures", save_base) + ".csv", index=False) structures_added = get_structures_added(model_json) node_sets = get_node_sets(structures_added) try: abs_df, rel_df = get_structure_dfs(structures_added, node_sets) rel_df.to_csv(re.sub("figure", "structure_overlap_matrix", save_base) + ".csv") tree, layout = create_rooted_bfs_tree(rel_df, layout=True) plot_tree( add_tree_layout(tree, tree.number_of_nodes() - 1, 10, 10), df, re.sub("figure", "tree-hierarchical", save_base) + ".pdf", ) plot_structure_tree( tree, layout, df, re.sub("figure", "tree-kamada", save_base) + ".pdf" ) G = create_overlap_quotient_graph(structures_added, abs_df, model_json["n"]) plot_overlap_quotient_graph( G, df, model_json["n"], re.sub("figure", "overlap-quotient", save_base) + ".pdf", ) G = create_structure_quotient_graph(node_sets, save_base) plot_structure_quotient_graph( G, node_sets, structures_added, save_path=re.sub("figure", "structure-quotient", save_base) + ".pdf", ) except: print( f"Error for overlap dataframes or graph plots: {json_path} - moving on..." ) try: create_progression_plot( df, re.sub("figure", "progress", save_base) + ".pdf", ) except: print(f"Error for progression plot: {json_path} - moving on...") try: create_size_plot( model_json, x_granularity_size, y_granularity_size, re.sub("figure", "sizes", save_base) + ".pdf", ) except: print(f"Error for size plot: {json_path} - moving on...") def get_edgelist_separator(edgelist_path): with open(edgelist_path) as f: for line in f: if not line.startswith("#"): if "\t" in line: return "\t" elif "," in line: return "," elif " " in line: return " " else: raise def create_structure_quotient_graph(nodes, save_base): nodemap_path = ( re.sub("figure-", "", re.sub("graphics/", "results/", save_base)) + "-nodemap.csv" ) nodemap = pd.read_csv(nodemap_path) edgelist_path = ( re.sub("figure-", "", re.sub("graphics/", "data/", save_base)) + ".txt" ) edges = pd.read_csv( edgelist_path, sep=get_edgelist_separator(edgelist_path), comment="#", header=None, usecols=[0, 1], ).rename({0: "u", 1: "v"}, axis=1) new_edges = edges.merge(nodemap, left_on="u", right_on="original_id").merge( nodemap, left_on="v", right_on="original_id", suffixes=("_u", "_v") )[["julia_id_u", "julia_id_v"]] assert len(edges) == len(new_edges) nodes_to_structures = get_nodes_to_structures(nodes) G = nx.MultiGraph() G.add_nodes_from(range(1, len(nodes) + 1)) for u, v in zip(new_edges.julia_id_u, new_edges.julia_id_v): u_structures = nodes_to_structures.get(u, []) v_structures = nodes_to_structures.get(v, []) if ( u_structures and v_structures and not set(u_structures).intersection(v_structures) ): for us in u_structures: for vs in v_structures: G.add_edge(us, vs) wG = nx.Graph() wG.add_nodes_from(G.nodes()) wG.add_weighted_edges_from([(*k, v) for k, v in dict(Counter(G.edges())).items()]) return wG def get_nodes_to_structures(nodes): nodes_to_structures = {} for idx, nodeset in enumerate(nodes, start=1): for node in nodeset: nodes_to_structures[node] = nodes_to_structures.get(node, []) + [idx] return nodes_to_structures def get_node_color(node_type): if node_type == "star": return "orange" elif node_type == "clique": return "dodgerblue" elif node_type == "biclique": return "#BE271A" # red3 elif node_type == "starclique": return "orchid" else: raise def get_node_marker(node_type): if node_type == "star": return "^" elif node_type == "clique": return "o" elif node_type == "biclique": return "s" elif node_type == "starclique": return "d" else: raise def plot_structure_quotient_graph(wG, nodes, structures, save_path=None): pos = nx.layout.fruchterman_reingold_layout(wG, k=2.5, seed=0) _ = plt.figure(figsize=(12, 12)) nx.draw_networkx_edges( wG, pos=pos, edgelist=wG.edges(), width=[w / 100 for u, v, w in wG.edges(data="weight")], ) for node in wG.nodes(): plt.scatter( *pos[node], s=len(nodes[node - 1]) * 5, c=get_node_color(structures[node - 1]["structure_type"]), marker=get_node_marker(structures[node - 1]["structure_type"]), ) nx.draw_networkx_labels(wG, pos, zorder=100) plt.axis("off") plt.tight_layout() if save_path is not None: plt.savefig(save_path) plt.close() def create_overlap_quotient_graph(structures_added, abs_df, n_total): G = nx.Graph() for idx, structure in enumerate(structures_added, start=1): G.add_node( idx, **{**structure, "n_relative": structure["n_nodes_total"] / n_total} ) for i in range(len(abs_df)): for j in range(i + 1, len(abs_df)): edge_weight = abs_df.at[i, j] / n_total if edge_weight > 0: G.add_edge(i + 1, j + 1, weight=edge_weight) return G def plot_overlap_quotient_graph(G, df, n_total, save_path=None): np.random.seed(1234) pos = nx.layout.fruchterman_reingold_layout(G) _, ax = plt.subplots(1, 1, figsize=(12, 12)) for x, y, w in G.edges(data="weight"): if w * n_total > 1: ax.plot( [pos[x][0], pos[y][0]], [pos[x][1], pos[y][1]], "-k", linewidth=w * n_total / 100, zorder=-10, alpha=0.5, ) for node in G.nodes(data=True): ax.scatter( *pos[node[0]], s=5.0 * node[1]["n_nodes_total"], c=df.at[node[0] + 1, "structure_color"], marker=df.at[node[0] + 1, "structure_shape"], zorder=1, ) nx.draw_networkx_labels(G, pos, zorder=100) plt.axis("off") plt.tight_layout() if save_path is not None: plt.savefig(save_path, transparent=True) plt.close()
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Exercicios-mundo-2/desafio044.py
talitadeoa/CEV-Exercicios-Python
0
115342
#Um gerenciador de pagementos simples preco = float(input('Qual o valor da compra? ')) pagamento = int(input("""Qual será a forma de pagamento? Digite 0 para pagamento à vista no dinheiro ou cheque Digite 1 para pagamento à vista no cartão Digite 2 para pagamento parcelado em até 2x Digite 3 para pagamento parcelado em 3x ou mais """)) if pagamento == 0: pagamento = 'pagamento à vista em dinheiro ou cheque' saldo = preco - (preco*10/100) print("""Você escolheu a opção {} Você irá pagar {}""".format(pagamento,saldo)) elif pagamento == 1: pagamento = 'pagamento à vista no cartão' saldo = preco - (preco*5/100) print("""Você escolheu a opção {} Você irá pagar {}""".format(pagamento,saldo)) elif pagamento == 2: pagamento = 'pagamento parcelado em até 2x' saldo = preco print("""Você escolheu a opção {} Você irá pagar {}""".format(pagamento,saldo)) elif pagamento == 3: pagamento = 'pagamento parcelado em 3x ou mais' saldo = preco + (preco*20/100) print("""Você escolheu a opção {} Você irá pagar {}""".format(pagamento,saldo))
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3Sum15.py
Bit64L/LeetCode-Python-
0
114876
class Solution(object): def threeSum(self, nums): """ :type nums: List[int] :rtype: List[List[int]] """ nums.sort() n = len(nums) ans = [] for i in range(0, n-2): if i > 0 and nums[i] == nums[i-1]: continue j,k = i + 1, n - 1 while j < k: s = nums[i] + nums[j] + nums[k] if s > 0: k -= 1 elif s < 0: j += 1 else: ans.append([nums[i], nums[j], nums[k]]) while j<k and nums[j] == nums[j+1]: j += 1 while j<k and nums[k] == nums[k-1]: k -= 1 j, k = j+1, k-1 return ans solution = Solution() print(solution.threeSum( [-1, 0, 1, 2, -1, -4]))
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TaskManager/profiles/views.py
Z0ltek/TaskManager_v2
0
129182
<filename>TaskManager/profiles/views.py from django.contrib.auth import login as auth_login from django.http import Http404 from django.shortcuts import render, redirect, get_object_or_404 from django.views.generic import TemplateView from django.contrib.auth import logout from django.contrib.auth.models import User from .forms import SignUpForm from .forms import CreateTaskForm from .forms import CreateSubtaskForm from .models import Project as ProjectModel, Task as TaskModel, Subtask, Status def HomeView(request): return render(request, 'home.html', {}) def projectview(request): proj = ProjectModel.objects.filter(owner=request.user) return render(request, 'projects.html', {'proj': proj}) def signup(request): if request.method == 'POST': form = SignUpForm(request.POST) if form.is_valid(): user = form.save() auth_login(request, user) return redirect('home') else: form = SignUpForm() return render(request, 'signup.html', {'form': form}) def LogoutView(request): logout(request) def project_tasks(request, id): project = get_object_or_404(ProjectModel, id=id) tasks = project.tasks.all().order_by('status') return render(request, 'tasks.html', {'project': project, 'tasks': tasks}) def task_view(request, id, task_id): project = get_object_or_404(ProjectModel, id=id) task = get_object_or_404(TaskModel, id=task_id) subtasks = task.subtasks.all().order_by('status') if request.method == 'POST': status_id = request.POST.get('status_id') task.status = status_id task.save() return redirect('project_tasks', id=project.id) return render(request, 'subtasks.html', {'project': project, 'task': task, 'subtasks': subtasks, 'status': Status.STATUS_CHOISE}) def view_subtask(request, id, task_id, subtask_id): project = get_object_or_404(ProjectModel, id=id) task = get_object_or_404(TaskModel, id=task_id) subtask = get_object_or_404(Subtask, id=subtask_id) if request.method == 'POST': status_id = request.POST.get('status_id') subtask.status = status_id subtask.save() return redirect('task_view', id=id, task_id=task.id) return render(request, 'view_subtasks.html', {'project': project, 'task': task, 'subtask': subtask, 'status': Status.STATUS_CHOISE}) def new_task(request, id): project = get_object_or_404(ProjectModel, id=id) user = User.objects.first() if request.method == 'POST': form = CreateTaskForm(request.POST) if form.is_valid(): task = form.save(commit=False) task.project = project task.created_by = user task.status = 2 task.save() return redirect('project_tasks', id=project.id) else: form = CreateTaskForm() return render(request, 'new_task.html', {'form': form, 'project': project}) def new_subtask(request, id, task_id): project = get_object_or_404(ProjectModel, id=id) task = get_object_or_404(TaskModel, id=task_id) user = User.objects.first() if request.method == 'POST': form = CreateSubtaskForm(request.POST) if form.is_valid(): subtask = form.save(commit=False) subtask.task = task subtask.created_by = user subtask.status = 2 subtask.save() return redirect('task_view', id=id, task_id=task.id) else: form = CreateSubtaskForm() return render(request, 'new_subtask.html', {'form': form, 'task': task, 'project': project})
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rest_framework_swagger/compat.py
kaitlin/django-rest-swagger
0
1615886
<filename>rest_framework_swagger/compat.py import platform if platform.python_version_tuple() < ('2', '7'): import ordereddict OrderedDict = ordereddict.OrderedDict else: import collections OrderedDict = collections.OrderedDict if platform.python_version_tuple() < ('3', '0'): from HTMLParser import HTMLParser class MLStripper(HTMLParser): def __init__(self): self.reset() self.fed = [] def handle_data(self, d): self.fed.append(d) def get_data(self): return ''.join(self.fed) else: from html.parser import HTMLParser class MLStripper(HTMLParser): def __init__(self): self.reset() self.strict = False self.convert_charrefs = True self.fed = [] def handle_data(self, d): self.fed.append(d) def get_data(self): return ''.join(self.fed) def strip_tags(html): s = MLStripper() s.feed(html) return s.get_data() try: from django.utils.module_loading import import_string except ImportError: def import_string(dotted_path): from django.utils.importlib import import_module from django.core.exceptions import ImproperlyConfigured module, attr = dotted_path.rsplit('.', 1) try: mod = import_module(module) except ImportError as e: raise ImproperlyConfigured('Error importing module %s: "%s"' % (module, e)) try: view = getattr(mod, attr) except AttributeError: raise ImproperlyConfigured('Module "%s" does not define a "%s".' % (module, attr)) return view
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Packs/Netskope/Integrations/NetskopeAPIv1/NetskopeAPIv1.py
jrauen/content
2
91008
<gh_stars>1-10 # type: ignore from copy import deepcopy from typing import Any, Dict, List, Optional, Tuple from urllib.parse import urljoin import urllib3 import demistomock as demisto from CommonServerPython import * from CommonServerUserPython import * # disable insecure warnings urllib3.disable_warnings() DEFAULT_PAGE = 1 DEFAULT_LIMIT = 50 DEFAULT_MAX_FETCH = DEFAULT_LIMIT DEFAULT_EVENTS_FETCH = DEFAULT_LIMIT DEFAULT_EVENT_TYPE = 'application' DEFAULT_FIRST_FETCH = '7 days' MAX_LIMIT = 100 MAX_FETCH = 200 MAX_EVENTS_FETCH = 200 TIME_PERIOD_MAPPING = { 'Last 60 Minutes': 3600, 'Last 24 Hours': 86400, 'Last 7 Days': 604800, 'Last 30 Days': 2592000, 'Last 60 Days': 5184000, 'Last 90 Days': 7776000 } class Client(BaseClient): """ Client for Netskope RESTful API. Args: base_url (str): The base URL of Netskope. token (str): The token to authenticate against Netskope API. use_ssl (bool): Specifies whether to verify the SSL certificate or not. use_proxy (bool): Specifies if to use XSOAR proxy settings. """ def __init__(self, base_url: str, token: str, use_ssl: bool, use_proxy: bool): super().__init__(urljoin(base_url, '/api/v1/'), verify=use_ssl, proxy=use_proxy) self._session.params['token'] = token def list_events_request(self, query: Optional[str] = None, event_type: Optional[str] = None, timeperiod: Optional[int] = None, start_time: Optional[int] = None, end_time: Optional[int] = None, insertion_start_time: Optional[int] = None, insertion_end_time: Optional[int] = None, limit: Optional[int] = None, skip: Optional[int] = None, unsorted: Optional[bool] = None) -> Dict[str, Any]: """ Get events extracted from SaaS traffic and or logs. Args: query (Optional[str]): Free query to filter the events. event_type (Optional[str]): Select events by their type. timeperiod (Optional[int]): Get all events from a certain time period. start_time (Optional[int]): Restrict events to those that have timestamps greater than the provided timestamp. end_time (Optional[int]): Restrict events to those that have timestamps less than or equal to the provided timestamp. insertion_start_time (Optional[int]): Restrict events to those that were inserted to the system after the provided timestamp. insertion_end_time (Optional[int]): Restrict events to those that were inserted to the system before the provided timestamp. limit (Optional[int]): The maximum amount of events to retrieve (up to 10000 events). skip (Optional[int]): The skip number of the events to retrieve (minimum is 1). unsorted (Optional[bool]): If true, the returned data will not be sorted (useful for improved performance). Returns: Dict[str, Any]: Netskope events. """ body = remove_empty_elements({ 'query': query, 'type': event_type, 'timeperiod': timeperiod, 'starttime': start_time, 'endtime': end_time, 'insertionstarttime': insertion_start_time, 'insertionendtime': insertion_end_time, 'limit': limit, 'skip': skip, 'unsorted': unsorted }) return self._http_request(method='POST', url_suffix='events', json_data=body) def list_alerts_request(self, query: Optional[str] = None, alert_type: Optional[str] = None, acked: Optional[bool] = None, timeperiod: Optional[int] = None, start_time: Optional[int] = None, end_time: Optional[int] = None, insertion_start_time: Optional[int] = None, insertion_end_time: Optional[int] = None, limit: Optional[int] = None, skip: Optional[int] = None, unsorted: Optional[bool] = None) -> Dict[str, Any]: """ Get alerts generated by Netskope, including policy, DLP, and watch list alerts. Args: query (Optional[str]): Free query to filter the alerts. alert_type (Optional[str]): Select alerts by their type. acked (Optional[bool]): Whether to retrieve acknowledged alerts or not. timeperiod (Optional[int]): Get alerts from certain time period. start_time (Optional[int]): Restrict alerts to those that have timestamps greater than the provided timestamp. end_time (Optional[int]): Restrict alerts to those that have timestamps less than or equal to the provided timestamp. insertion_start_time (Optional[int]): Restrict alerts which have been inserted into the system after the provided timestamp. insertion_end_time (Optional[int]): Restrict alerts which have been inserted into the system before the provided timestamp. limit (Optional[int]): The maximum number of alerts to return (up to 10000). skip (Optional[int]): The skip number of the alerts to retrieve (minimum is 1). unsorted (Optional[bool]): If true, the returned data will not be sorted (useful for improved performance). Returns: Dict[str, Any]: Netskope alerts. """ body = remove_empty_elements({ 'query': query, 'alert_type': alert_type, 'acked': acked, 'timeperiod': timeperiod, 'starttime': start_time, 'endtime': end_time, 'insertionstarttime': insertion_start_time, 'insertionendtime': insertion_end_time, 'limit': limit, 'skip': skip, 'unsorted': unsorted }) return self._http_request(method='POST', url_suffix='alerts', json_data=body) def list_quarantined_files_request(self, start_time: Optional[int] = None, end_time: Optional[int] = None, limit: Optional[int] = None, skip: Optional[int] = None) -> Dict[str, Any]: """ List all quarantined files. Args: start_time (Optional[int]): Get files last modified within a certain time period. end_time (Optional[int]): Get files last modified within a certain time period. limit (Optional[int]): The maximum amount of clients to retrieve (up to 10000). skip (Optional[int]): The skip number of the clients to retrieve (minimum is 1). Returns: Dict[str, Any]: Netskope quarantine files. """ body = remove_empty_elements({ 'starttime': start_time, 'endtime': end_time, 'limit': limit, 'skip': skip, 'op': 'get-files' }) return self._http_request(method='POST', url_suffix='quarantine', json_data=body) def get_quarantined_file_request(self, quarantine_profile_id: str, file_id: str) -> bytes: """ Download a quarantined file. Args: quarantine_profile_id (str): The ID of quarantine profile. file_id (str): The ID of the quarantined file. Returns: bytes: The quarantined file content. """ body = { 'quarantine_profile_id': quarantine_profile_id, 'file_id': file_id, 'op': 'download-url' } return self._http_request(method='POST', url_suffix='quarantine', json_data=body, resp_type='content') def update_quarantined_file_request(self, quarantine_profile_id: str, file_id: str, action: str) -> None: """ Take an action on a quarantined file. Args: quarantine_profile_id (str): The profile id of the quarantined file. file_id (str): The id of the quarantined file. action (str): Action to be performed on a quarantined. """ body = { 'quarantine_profile_id': quarantine_profile_id, 'file_id': file_id, 'action': action, 'op': 'take-action' } self._http_request(method='POST', url_suffix='quarantine', json_data=body, resp_type='text') def update_url_list_request(self, name: str, urls: List[str]) -> None: """ Update the URL List with the values provided. Args: name (str): Name of an existing URL List shown in the Netskope UI on the URL List skip. urls (List[str]): The content of the URL list. """ body = {'name': name, 'list': ','.join(urls)} self._http_request(method='POST', url_suffix='updateUrlList', json_data=body) def update_file_hash_list_request(self, name: str, hashes: List[str]) -> None: """ Update file hash list with the values provided. Args: name (str): Name of an existing file hash list shown in the Netskope UI on the file hash list skip. hashes (str): List of file hashes (md5 or sha256). """ body = {'name': name, 'list': ','.join(hashes)} return self._http_request(method='POST', url_suffix='updateFileHashList', json_data=body) def list_clients_request(self, query: Optional[str] = None, limit: Optional[int] = None, skip: Optional[int] = None) -> Dict[str, Any]: """ Get information about the Netskope clients. Args: query (Optional[str]): Free query on the clients, based on the client fields. limit (Optional[int]): The maximum amount of clients to retrieve (up to 10000). skip (Optional[int]): The skip number of the clients to retrieve (minimum is 1). Returns: Dict[str, Any]: The clients information. """ body = remove_empty_elements({'query': query, 'limit': limit, 'skip': skip}) return self._http_request(method='POST', url_suffix='clients', params=body) def _http_request(self, *args, **kwargs): response = super()._http_request(*args, **kwargs) if isinstance(response, dict) and 'errors' in response: errors = '\n'.join(response['errors']) raise DemistoException(f'Invalid API call: {errors}', res=response) return response def arg_to_boolean(arg: Optional[str]) -> Optional[bool]: """ Converts an XSOAR argument to a Python boolean or None. Args: arg (Optional[str]): The argument to convert. Returns: Optional[bool]: A boolean if arg can be converted, or None if arg is None. """ if arg is None: return None return argToBoolean(arg) def arg_to_seconds_timestamp(arg: Optional[str]) -> Optional[int]: """ Converts an XSOAR date string argument to a timestamp in seconds. Args: arg (Optional[str]): The argument to convert. Returns: Optional[int]: A timestamp if arg can be converted, or None if arg is None. """ if arg is None: return None return date_to_seconds_timestamp(arg_to_datetime(arg)) def date_to_seconds_timestamp(date_str_or_dt: Union[str, datetime]) -> int: """ Converts date string or datetime object to a timestamp in seconds. Args: date_str_or_dt (Union[str, datetime]): The datestring or datetime. Returns: int: The timestamp in seconds. """ return date_to_timestamp(date_str_or_dt) // 1000 def validate_time_arguments(start_time: Optional[int] = None, end_time: Optional[int] = None, insertion_start_time: Optional[int] = None, insertion_end_time: Optional[int] = None, timeperiod: Optional[int] = None) -> None: """ Validates time arguments from the user. The user must provide one of the following: - start_time and end_time. - insertion_start_time and insertion_end_time. - timeperiod. Args: start_time (Optional[int], optional): The start time to fetch from the API. end_time (Optional[int], optional): The end time to fetch from the API. insertion_start_time (Optional[int], optional): The insertion start time to fetch from the API. insertion_end_time (Optional[int], optional): The insertion end time to fetch from the API. timeperiod (Optional[str], optional): The timeperiod to fetch from the API. Raises: DemistoException: The user did not provide valid timestamp. """ combination = (all((start_time, end_time)), all( (insertion_start_time, insertion_end_time)), bool(timeperiod)) if not any(combination): raise DemistoException('Missing time arguments. Please provide start_time and end_time, ' 'or insertion_start_time and or insertion_end_time or timeperiod.') if combination.count(True) > 1: raise DemistoException( 'Invalid time arguments. Please provide only start_time and end_time, ' 'or insertion_start_time and or insertion_end_time or timeperiod. ' 'You must not combine between the mentioned options.') def validate_fetch_params(max_fetch: int, max_events_fetch: int, fetch_events: bool, first_fetch: str, event_types: List[str]) -> None: """ Validates the parameters for fetch incident command. Args: max_fetch: (int): The maximum number of incidents for one fetch. max_events_fetch (int) The maximum number of events per incident for one fetch. fetch_events (bool): Whether or not fetch events when fetching incident. first_fetch: (str): First fetch time in words. """ if first_fetch: arg_to_datetime(first_fetch) # verify that it is a date. if max_fetch > MAX_FETCH: return_error(f'The Maximum number of incidents per fetch should not exceed {MAX_FETCH}.') if fetch_events and max_events_fetch > MAX_EVENTS_FETCH: return_error( f'The Maximum number of events for each incident per fetch should not exceed {MAX_EVENTS_FETCH}.' ) if not isinstance(event_types, list): return_error('The fetched event types must be a list.') def get_pagination_readable_message(header: str, page: int, limit: int) -> str: return f'{header}\n Current page size: {limit}\n Showing page {page} out of others that may exist.' def get_pagination_arguments(args: Dict[str, Any]) -> Tuple[int, int, int]: """ Gets and validates pagination arguments for client (skip and limit). Args: args (Dict[str, Any]): The command arguments (page and limit). Returns: Tuple[int, int]: The page, calculated skip and limit after validation. """ page = arg_to_number(args.get('page', DEFAULT_PAGE)) limit = arg_to_number(args.get('limit', DEFAULT_LIMIT)) if page < 1: raise DemistoException('Page argument must be greater than 1') if not 1 <= limit <= MAX_LIMIT: raise DemistoException(f'Limit argument must be between 1 to {MAX_LIMIT}') return page, (page - 1) * limit, limit def list_events_command(client: Client, args: Dict[str, str]) -> CommandResults: """ Get events extracted from SaaS traffic and or logs. Args: client (client): The Netskope client. args (Dict[str, Any]): Command arguments from XSOAR. Returns: CommandResults: Command results with raw response, outputs and readable outputs. """ query = args.get('query') event_type = args['event_type'] timeperiod = TIME_PERIOD_MAPPING.get(args.get('timeperiod')) start_time = arg_to_seconds_timestamp(args.get('start_time')) end_time = arg_to_seconds_timestamp(args.get('end_time')) insertion_start_time = arg_to_seconds_timestamp(args.get('insertion_start_time')) insertion_end_time = arg_to_seconds_timestamp(args.get('insertion_end_time')) page, skip, limit = get_pagination_arguments(args) unsorted = arg_to_boolean(args.get('unsorted')) validate_time_arguments(start_time=start_time, end_time=end_time, timeperiod=timeperiod, insertion_start_time=insertion_start_time, insertion_end_time=insertion_end_time) response = client.list_events_request(query=query, event_type=event_type, timeperiod=timeperiod, start_time=start_time, end_time=end_time, insertion_start_time=insertion_start_time, insertion_end_time=insertion_end_time, limit=limit, skip=skip, unsorted=unsorted) outputs = deepcopy(response['data']) for event in outputs: event['event_id'] = event['_id'] event['timestamp'] = timestamp_to_datestring(event['timestamp'] * 1000) readable_output = tableToMarkdown( get_pagination_readable_message('Events List:', page=page, limit=limit), outputs, removeNull=True, headers=['event_id', 'timestamp', 'type', 'access_method', 'app', 'traffic_type'], headerTransform=string_to_table_header) return CommandResults(outputs_prefix='Netskope.Event', outputs_key_field='event_id', outputs=outputs, readable_output=readable_output, raw_response=response) def list_alerts_command(client: Client, args: Dict[str, str]) -> CommandResults: """ Get alerts generated by Netskope, including policy, DLP, and watch list alerts. Args: client (client): The Netskope client. args (Dict[str, Any]): Command arguments from XSOAR. Returns: CommandResults: Command results with raw response, outputs and readable outputs. """ query = args.get('query') alert_type = args.get('alert_type') acked = arg_to_boolean(args.get('acked')) timeperiod = TIME_PERIOD_MAPPING.get(args.get('timeperiod')) start_time = arg_to_seconds_timestamp(args.get('start_time')) end_time = arg_to_seconds_timestamp(args.get('end_time')) insertion_start_time = arg_to_seconds_timestamp(args.get('insertion_start_time')) insertion_end_time = arg_to_seconds_timestamp(args.get('insertion_end_time')) page, skip, limit = get_pagination_arguments(args) unsorted = arg_to_boolean(args.get('unsorted')) validate_time_arguments(start_time=start_time, end_time=end_time, timeperiod=timeperiod, insertion_start_time=insertion_start_time, insertion_end_time=insertion_end_time) response = client.list_alerts_request(query=query, alert_type=alert_type, acked=acked, timeperiod=timeperiod, start_time=start_time, end_time=end_time, insertion_start_time=insertion_start_time, insertion_end_time=insertion_end_time, limit=limit, skip=skip, unsorted=unsorted) outputs = deepcopy(response['data']) for alert in outputs: alert['alert_id'] = alert['_id'] alert['timestamp'] = timestamp_to_datestring(alert['timestamp'] * 1000) readable_output = tableToMarkdown( get_pagination_readable_message('Alerts List:', page=page, limit=limit), outputs, removeNull=True, headers=['alert_id', 'alert_name', 'alert_type', 'timestamp', 'action'], headerTransform=string_to_table_header) return CommandResults(outputs_prefix='Netskope.Alert', outputs_key_field='alert_id', outputs=outputs, readable_output=readable_output, raw_response=response) def list_quarantined_files_command(client: Client, args: Dict[str, str]) -> CommandResults: """ List all quarantined files. Args: client (client): The Netskope client. args (Dict[str, Any]): Command arguments from XSOAR. Returns: CommandResults: Command results with raw response, outputs and readable outputs. """ start_time = arg_to_seconds_timestamp(args.get('start_time')) end_time = arg_to_seconds_timestamp(args.get('end_time')) page, skip, limit = get_pagination_arguments(args) response = client.list_quarantined_files_request(start_time=start_time, end_time=end_time, limit=limit, skip=skip) outputs = dict_safe_get(response, ['data', 'quarantined']) for output in outputs: for file_output in output['files']: file_output['quarantine_profile_id'] = output['quarantine_profile_id'] file_output['quarantine_profile_name'] = output['quarantine_profile_name'] outputs = sum((output['files'] for output in outputs), []) readable_header = get_pagination_readable_message('Quarantined Files List:', page=page, limit=limit) readable_output = tableToMarkdown(readable_header, outputs, removeNull=True, headers=[ 'quarantine_profile_id', 'quarantine_profile_name', 'file_id', 'original_file_name', 'policy' ], headerTransform=string_to_table_header) return CommandResults(outputs_prefix='Netskope.Quarantine', outputs_key_field='file_id', outputs=outputs, readable_output=readable_output, raw_response=response) def get_quarantined_file_command(client: Client, args: Dict[str, str]) -> CommandResults: """ Download a quarantined file. Args: client (client): The Netskope client. args (Dict[str, Any]): Command arguments from XSOAR. Returns: CommandResults: Command results with raw response, outputs and readable outputs. """ quarantine_profile_id = args['quarantine_profile_id'] file_id = args['file_id'] response = client.get_quarantined_file_request(quarantine_profile_id=quarantine_profile_id, file_id=file_id) return fileResult(filename=f'{file_id}.zip', data=response, file_type=EntryType.FILE) def update_quarantined_file_command(client: Client, args: Dict[str, str]) -> CommandResults: """ Take an action on a quarantined file. Args: client (client): The Netskope client. args (Dict[str, Any]): Command arguments from XSOAR. Returns: CommandResults: Command results with raw response, outputs and readable outputs. """ quarantine_profile_id = args['quarantine_profile_id'] file_id = args['file_id'] action = args['action'] client.update_quarantined_file_request(quarantine_profile_id=quarantine_profile_id, file_id=file_id, action=action) readable_output = f'## The file {file_id} was successfully {action}ed!' return CommandResults(readable_output=readable_output) def update_url_list_command(client: Client, args: Dict[str, str]) -> CommandResults: """ Update the URL List with the values provided. Args: client (client): The Netskope client. args (Dict[str, Any]): Command arguments from XSOAR. Returns: CommandResults: Command results with raw response, outputs and readable outputs. """ name = args['name'] urls = argToList(args['urls']) client.update_url_list_request(name=name, urls=urls) outputs = {'name': name, 'URL': urls} readable_output = f'URL List {name}:\n{", ".join(urls)}' return CommandResults(outputs_prefix='Netskope.URLList', outputs_key_field='name', outputs=outputs, readable_output=readable_output) def update_file_hash_list_command(client: Client, args: Dict[str, str]) -> CommandResults: """ Update file hash list with the values provided. Args: client (client): The Netskope client. args (Dict[str, Any]): Command arguments from XSOAR. Returns: CommandResults: Command results with raw response, outputs and readable outputs. """ name = args.get('name') hashes = argToList(args.get('hash')) client.update_file_hash_list_request(name=name, hashes=hashes) outputs = {'name': name, 'hash': hashes} readable_output = f'Hash List {name}:\n{", ".join(hashes)}' return CommandResults(outputs_prefix='Netskope.FileHashList', outputs_key_field='name', outputs=outputs, readable_output=readable_output) def list_clients_command(client: Client, args: Dict[str, str]) -> CommandResults: """ Get information about the Netskope clients. Args: client (client): The Netskope client. args (Dict[str, Any]): Command arguments from XSOAR. Returns: CommandResults: Command results with raw response, outputs and readable outputs. """ query = args.get('query') page, skip, limit = get_pagination_arguments(args) response = client.list_clients_request(query=query, limit=limit, skip=skip) outputs = [client['attributes'] for client in response['data']] for output in outputs: output['client_id'] = output['_id'] readable_header = get_pagination_readable_message('Clients List:', page=page, limit=limit) readable_output = tableToMarkdown( readable_header, outputs, removeNull=True, headers=['client_id', 'client_version', 'device_id', 'user_added_time'], headerTransform=string_to_table_header) return CommandResults(outputs_prefix='Netskope.Client', outputs_key_field='client_id', outputs=outputs, readable_output=readable_output, raw_response=response) def list_host_associated_user_command(client: Client, args: Dict[str, str]) -> CommandResults: """ List all users of certain host by its hostname. Args: client (client): The Netskope client. args (Dict[str, Any]): Command arguments from XSOAR. Returns: CommandResults: Command results with raw response, outputs and readable outputs. """ hostname = args['hostname'] page, skip, limit = get_pagination_arguments(args) response = client.list_clients_request(query=f'host_info.hostname eq {hostname}', limit=limit, skip=skip) outputs = sum((client['attributes'].get('users') for client in response['data']), []) for output in outputs: output['user_id'] = output['_id'] readable_header = get_pagination_readable_message(f'Users Associated With {hostname}:', page=page, limit=limit) readable_output = tableToMarkdown(readable_header, outputs, removeNull=True, headers=['user_id', 'username', 'user_source'], headerTransform=string_to_table_header) return CommandResults(outputs_prefix='Netskope.User', outputs_key_field='user_id', outputs=outputs, readable_output=readable_output, raw_response=response) def list_user_associated_host_command(client: Client, args: Dict[str, str]) -> CommandResults: """ List all hosts related to a certain username. Args: client (client): The Netskope client. args (Dict[str, Any]): Command arguments from XSOAR. Returns: CommandResults: Command results with raw response, outputs and readable outputs. """ username = args['username'] page, skip, limit = get_pagination_arguments(args) response = client.list_clients_request(query=f'username eq {username}', limit=limit, skip=skip) outputs = [] for client in response['data']: attributes = client['attributes'] agent_status = dict_safe_get(attributes, ['last_event', 'status']) outputs.append({'agent_status': agent_status, **attributes['host_info']}) readable_header = get_pagination_readable_message(f'Hosts Associated With {username}:', page=page, limit=limit) readable_output = tableToMarkdown(readable_header, outputs, removeNull=True, headers=['hostname', 'os_version', 'agent_status'], headerTransform=string_to_table_header) return CommandResults(outputs_prefix='Netskope.Host', outputs_key_field='nsdeviceuid', outputs=outputs, readable_output=readable_output, raw_response=response) def test_module(client: Client, max_fetch: int, first_fetch: str, fetch_events: bool, max_events_fetch: int, event_types: List[str]) -> str: """ Validates all integration parameters, and tests connection to Netskope instance. """ validate_fetch_params(max_fetch, max_events_fetch, fetch_events, first_fetch, event_types) client.list_alerts_request(limit=1, skip=0, start_time=date_to_seconds_timestamp(datetime.now()), end_time=date_to_seconds_timestamp(datetime.now())) return 'ok' def fetch_multiple_type_events(client: Client, max_fetch: int, start_time: int, event_types: List[str], query: Optional[str]) -> List[Dict[str, Any]]: """ Fetches events from multiple types. The function makes an API call for each type, since the API requires specifying the event type. Args: client (Client): The Netskope client. max_fetch (int): The maximum amount of events to fetch for each type. start_time (int): The time to fetch the events from. event_types (List[str]): The event types to fetch as incidents. query (Optional[str]): Query for filtering the events. Returns: List[Dict[str, Any]]: The fetched events. """ events = [] if event_types: max_fetch = max_fetch // len(event_types) for event_type in event_types: new_events = client.list_events_request(start_time=start_time, end_time=date_to_seconds_timestamp(datetime.now()), limit=max_fetch, unsorted=False, event_type=event_type, query=query)['data'] for event in new_events: event['event_id'] = event['_id'] event['incident_type'] = event_type events.extend(new_events) return events def fetch_incidents(client: Client, max_fetch: int, first_fetch: str, fetch_events: bool, max_events_fetch: int, event_types: List[str], alerts_query: Optional[str], events_query: Optional[str]) -> None: """ Fetches alerts and events as incidents. Args: client (Client): The Netskope client. max_fetch (int): Maximum number of incidents to fetch. first_fetch (str): The timestamp to fetch the incidents from. max_events_fetch (int): Maximum number of events to fetch. event_types (List[str]): The type of events to fetch. alerts_query (Optional[str]): Query for filtering the fetched alerts. events_query (Optional[str]): Query for filtering the fetched events. """ validate_fetch_params(max_fetch, max_events_fetch, fetch_events, first_fetch, event_types) last_run = demisto.getLastRun() or {} first_fetch = arg_to_seconds_timestamp(first_fetch) last_alert_time = last_run.get('last_alert_time') or first_fetch alerts = client.list_alerts_request(start_time=last_alert_time, end_time=date_to_seconds_timestamp(datetime.now()), limit=max_fetch, query=alerts_query, unsorted=False)['data'] last_event_time = last_run.get('last_event_time') or first_fetch if fetch_events: events = fetch_multiple_type_events(client, max_fetch=max_events_fetch, start_time=last_event_time, event_types=event_types, query=events_query) else: events = [] incidents = [] for alert in alerts: alert['incident_type'] = alert['alert_type'] incidents.append({ 'name': alert['alert_name'], 'occurred': timestamp_to_datestring(alert['timestamp']), 'rawJSON': json.dumps(alert) }) for event in events: incidents.append({ 'name': event['event_id'], 'occurred': timestamp_to_datestring(event['timestamp']), 'rawJSON': json.dumps(event) }) # The alerts and events are sorted in descending order. # Also, we increment the timestamp in one second to avoid duplicates. demisto.setLastRun({ 'last_alert_time': alerts[0]['timestamp'] + 1 if alerts else last_alert_time, 'last_event_time': events[0]['timestamp'] + 1 if events else last_event_time }) demisto.incidents(incidents) def main(): params = demisto.params() url = params['url'] token = params['<PASSWORD>']['password'] use_ssl = not params.get('insecure', False) use_proxy = params.get('proxy', False) max_fetch = arg_to_number(params.get('max_fetch', DEFAULT_MAX_FETCH)) first_fetch = params.get('first_fetch', DEFAULT_FIRST_FETCH) fetch_events = argToBoolean(params.get('fetch_events', False)) event_types = argToList(params.get('fetch_event_types', DEFAULT_EVENT_TYPE)) max_events_fetch = arg_to_number(params.get('max_events_fetch', DEFAULT_EVENTS_FETCH)) client = Client(url, token, use_ssl, use_proxy) commands = { 'netskope-event-list': list_events_command, 'netskope-alert-list': list_alerts_command, 'netskope-quarantined-file-list': list_quarantined_files_command, 'netskope-quarantined-file-get': get_quarantined_file_command, 'netskope-quarantined-file-update': update_quarantined_file_command, 'netskope-url-list-update': update_url_list_command, 'netskope-file-hash-list-update': update_file_hash_list_command, 'netskope-client-list': list_clients_command, 'netskope-host-associated-user-list': list_host_associated_user_command, 'netskope-user-associated-host-list': list_user_associated_host_command, } try: command = demisto.command() if command == 'test-module': return_results( test_module(client, max_fetch=max_fetch, first_fetch=first_fetch, fetch_events=fetch_events, max_events_fetch=max_events_fetch, event_types=event_types)) elif command == 'fetch-incidents': fetch_incidents(client, max_fetch=max_fetch, first_fetch=first_fetch, fetch_events=fetch_events, max_events_fetch=max_events_fetch, event_types=event_types, alerts_query=demisto.params().get('alert_query'), events_query=demisto.params().get('events_query')) elif command in commands: return_results(commands[command](client, demisto.args())) else: raise NotImplementedError(f'The command {command} does not exist!') except Exception as e: demisto.error(traceback.format_exc()) return_error(f'Failed to execute {demisto.command()} command.\nError:\n{e}') if __name__ in ('__main__', '__builtin__', 'builtins'): main()
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UQ/UQtutorial.py
ISR3D/ISR3D
0
39100
import subprocess lib_list = ['numpy','ymmsl','sobol_seq','csv','seaborn','zenodo_get'] for lib_name in lib_list: try: import lib_name except ImportError: if lib_name == 'csv': print(lib_name,' Module not installed') subprocess.run(['pip','install','python-csv']) else: print(lib_name,' Module not installed') subprocess.run(['pip','install','%s'%lib_name]) import numpy as np import ymmsl import sobol_seq import csv import os import seaborn as sns import zenodo_get # Transform the normalized sample matrix to ranges of uncertain parameters def dim_transform(sobol_vector,uncertain_list): dim = len(uncertain_list) for num_dim in range(dim): para_max = uncertain_list[num_dim].get('max') para_min = uncertain_list[num_dim].get('min') sobol_vector[:,num_dim] = para_min + (para_max-para_min)*sobol_vector[:,num_dim] return sobol_vector #################################################################################### ##### Sample generation and UQ campaign creation (including instances folder)####### #################################################################################### # Note: # This is used to generate UQ samples for only four biological parameters: # 1) Endothelium endpoint 2)smc max stran 3)balloon extension 4) Fenestration probability # Naming of Folder and files for samples # Level 0: UQtest (UQ campaign name) # Level 1: UQtest/A (sample matrix of sobol sequence) # Level 2: UQtest/A/A_X where X vary from 1 -> N (N: number of samples) # Level 3: UQtest/A/A_X/input.ymmsl ### Main function # Number of samples for UQ # Note that ISR3D is a computationally intensive application. # Running 128 instances would need some cluster resources # You can start with a small number, 16 for instances. NumSample = 128 # Template path to the ymmsl file (relative path from ISR3D/Result/UQtest/ to ISR3D/UQ/template/input_stage4.ymmsl) input_path = '../../UQ/template/' input_ymmsl_filename = 'input_stage4.ymmsl' # Output directory for UQ campagin folder and name output_path = './' experiment_name = 'UQtest' # Read in the data of template ymmsl file with open(input_path+input_ymmsl_filename,'r') as f: ymmsl_data = ymmsl.load(f) # Take out the unchanged model part and need-for-change settings part for ymmsl model model = ymmsl_data.model settings = ymmsl_data.settings # Set uncertain parameters and its ranges as a list ymmsl_uncertain_parameters = [ { 'name': 'smc.endo_endpoint', 'min': 10.0, 'max': 20.0 }, { 'name': 'smc.balloon_extension', 'min': 0.5, 'max': 1.5 }, { 'name': 'smc.smc_max_strain', 'min': 1.2, 'max': 1.8 }, { 'name': 'smc.fenestration_probability', 'min': 0.0,# Calculate the lumen volume from (lumen_area_of_each_slice*depth_of_slice) 'max': 0.1 }] # Count the total uncertain input dimensions (here 4 parameters) num_uncer_para = len(ymmsl_uncertain_parameters) print('Number of uncertain parameter: '+str(num_uncer_para)) # Generate sobel sequence range (0,1), save the file and transform to (min,max) A = sobol_seq.i4_sobol_generate(num_uncer_para,NumSample) A = dim_transform(A,ymmsl_uncertain_parameters) np.savetxt("A.csv",A) # Create corresponding directory and folders try: os.mkdir(output_path+experiment_name) except OSError: print ("Creation of the directory %s failed" % output_path+experiment_name) else: print ("Successfully created the directory %s" % output_path+experiment_name) # A: Replace the corresponding value within the dict and output the file os.mkdir(output_path+experiment_name+'/A') checklist = ['A'] for n in range(NumSample): sample_path = output_path+experiment_name+'/A'+'/A_'+str(n) os.mkdir(sample_path) # Generate file for ymmsl num_para = 0 for para in ymmsl_uncertain_parameters: settings[para.get('name')] = float(A[n,num_para]) num_para = num_para + 1 config = ymmsl.Configuration(model, settings, None, None) with open(sample_path+'/input_stage4.ymmsl', 'w') as f: ymmsl.save(config, f) print('ymmsl input for each UQ instance has been generated') #################################################################################### ##### Run shell script to broadcast other input files to each sample folder######### #################################################################################### import subprocess # Download Other input files from Zenodo print('Start to download other input files for ISR3D from Zenodo') subprocess.run(['wget https://zenodo.org/record/4603912/files/stage3.test_vessel.dat'],shell = True) subprocess.run(['wget https://zenodo.org/record/4603912/files/stage3.test_vessel_nb.dat'],shell = True) subprocess.run(['wget https://zenodo.org/record/4603912/files/test_vessel_centerline.csv'],shell = True) print('Start to broadcast the input to each UQ instance directory') # Template path to the ymmsl file (relative path from ISR3D/Result/UQtest/ to ISR3D/UQ/function/BCastStage3.sh) pass_arg = str(NumSample) subprocess.run(['bash','../../UQ/function/BCastStage3.sh', '%s'%pass_arg]) print('Sample generation done')
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example/django_example/polls/tests.py
dmsimard/dynaconf
0
24926
from django.conf import settings from django.test import TestCase # Create your tests here. class SettingsTest(TestCase): def test_settings(self): self.assertEqual(settings.SERVER, 'prodserver.com') self.assertEqual( settings.STATIC_URL, '/changed/in/settings.toml/by/dynaconf/') self.assertEqual(settings.USERNAME, 'admin_user_from_env') self.assertEqual(settings.PASSWORD, '<PASSWORD>') self.assertEqual(settings.get('PASSWORD'), '<PASSWORD>') self.assertEqual(settings.FOO, 'It overrides every other env') with settings.using_env('development'): self.assertEqual(settings.SERVER, 'devserver.com') self.assertEqual(settings.PASSWORD, False) self.assertEqual(settings.USERNAME, 'admin_user_from_env') self.assertEqual(settings.FOO, 'It overrides every other env') self.assertEqual(settings.SERVER, 'prodserver.com') self.assertEqual(settings.PASSWORD, '<PASSWORD>') self.assertEqual(settings.USERNAME, 'admin_user_from_env') self.assertEqual(settings.FOO, 'It overrides every other env') with settings.using_env('staging'): self.assertEqual(settings.SERVER, 'stagingserver.com') self.assertEqual(settings.PASSWORD, False) self.assertEqual(settings.USERNAME, 'admin_user_from_env') self.assertEqual(settings.FOO, 'It overrides every other env') self.assertEqual(settings.SERVER, 'prodserver.com') self.assertEqual(settings.PASSWORD, '<PASSWORD>') self.assertEqual(settings.USERNAME, 'admin_user_from_env') self.assertEqual(settings.FOO, 'It overrides every other env') with settings.using_env('customenv'): self.assertEqual(settings.SERVER, 'customserver.com') self.assertEqual(settings.PASSWORD, False) self.assertEqual(settings.USERNAME, 'admin_user_from_env') self.assertEqual(settings.FOO, 'It overrides every other env') self.assertEqual(settings.SERVER, 'prodserver.com') self.assertEqual(settings.PASSWORD, '<PASSWORD>') self.assertEqual(settings.USERNAME, 'admin_user_from_env') self.assertEqual(settings.FOO, 'It overrides every other env')
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app/workers/main.py
brotskydotcom/public-services.py
0
114657
# MIT License # # Copyright (c) 2020 <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. # MIT License # # # 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 asyncio import os from random import uniform from typing import List, ClassVar from .csv_transfer import process_csv_lists from .webhook_transfer import process_webhook_lists from ..base import prinl, log_error from ..db import ItemListStore from ..utils import MapContext async def worker(item_type: str): """ The main worker loop. This is meant to be run as a task, either in the web process or a separate worker process. It only processes items of the given type, so if you want to process both types in one worker you will need to run one of these tasks for each type of worker. You have to do MapContext and ItemListStore initialization and teardown around your call to this function. """ if item_type not in ("webhook", "csv"): raise ValueError(f"Worker item type ({item_type}) must be 'webhook' or 'csv'") try: while True: if item_type == "webhook": await process_webhook_lists() else: await process_csv_lists() prinl(f"Waiting for new {item_type} items to arrive...") key = await ItemListStore.select_from_channel(item_type) if key: prinl(f"New incoming {item_type} item list: {key}") else: # this happens on shutdown, so we do a silent exit break # minimize conflict between multiple workers with random stagger await asyncio.sleep(uniform(0.1, 0.9)) except asyncio.CancelledError: prinl(f"Cancelled: {item_type} worker.") raise async def app(item_types: List[str]): """ The main worker app, run as the only task in a process. Spawns tasks for each worker type and waits for them. """ MapContext.initialize() await ItemListStore.initialize() try: await EmbeddedWorkers.start(item_types) await EmbeddedWorkers.run() except asyncio.CancelledError: raise except: log_error(f"Exception in worker") await EmbeddedWorkers.stop() finally: await ItemListStore.finalize() MapContext.finalize() class EmbeddedWorkers: """ A way of using _workers as tasks, rather than as a top-level process, so they can be embedded in a web server or other async process. """ _item_types: ClassVar[List[str]] = [] _workers: ClassVar[List[asyncio.Task]] = [] @classmethod async def _cancel_workers(cls): """ Forcibly cancel any running worker tasks. """ for item_type, task in zip(cls._item_types, cls._workers): if task.done(): continue try: task.cancel() await task except asyncio.CancelledError: pass except: log_error(f"Failure: {item_type} worker") @classmethod async def _main(cls): """ Run _workers as sub-tasks. We assume all the initialization, teardown, and error handling is done by our embedding process or the worker itself. """ try: for item_type in cls._item_types: prinl(f"Starting {item_type} worker.") cls._workers.append(asyncio.create_task(worker(item_type))) except asyncio.CancelledError: await cls._cancel_workers() raise except: log_error(f"Exception in worker manager") await cls._cancel_workers() raise @classmethod async def start(cls, item_types: List[str] = None): """ Starts embedded worker tasks for each of the given item types. If not item types are given, we check the OS environment variable EMBEDDED_WEBHOOK_TYPES and, if it's non-empty, we treat it as a list of item types separated by ':' No workers are started if the resulting list of item types is empty. """ if cls._workers: raise NotImplementedError("Embedded workers are already started") if not item_types: if types := os.getenv("EMBEDDED_WORKER_TYPES", ""): item_types = types.split(":") if not item_types: return cls._item_types = item_types cls._workers = [] await cls._main() @classmethod async def run(cls, ignore_exceptions=False): """ Wait for currently running workers to complete. If ignore_exceptions is specified, an exception in one of the workers will not be raised to the caller. """ await asyncio.gather(*cls._workers, return_exceptions=ignore_exceptions) @classmethod async def stop(cls): """ Shuts down any running workers. """ if cls._workers: await cls._cancel_workers() cls._workers = []
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eth_tester/normalization/common.py
PabloLefort/eth-tester
215
12080
from cytoolz.functoolz import ( curry, ) from eth_utils import ( to_dict, to_tuple, ) @curry @to_dict def normalize_dict(value, normalizers): for key, item in value.items(): normalizer = normalizers[key] yield key, normalizer(item) @curry @to_tuple def normalize_array(value, normalizer): """ This is just `map` but it's nice to have it return a consisten type (tuple). """ for item in value: yield normalizer(item) @curry def normalize_if(value, conditional_fn, normalizer): if conditional_fn(value): return normalizer(value) else: return value
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bubble_sort.py
rogueleaderr/import_madness
2
1614496
import time __author__ = 'rogueleaderr' def bubble_sort(list_to_sort): start_time = time.time() while True: list_len = len(list_to_sort) max_index = (list_len - 1) swaps = 0 for i in range(list_len): j = i + 1 if j > max_index: break if list_to_sort[i] > list_to_sort[j]: list_to_sort[j], list_to_sort[i] = list_to_sort[i], list_to_sort[j] swaps += 1 if swaps == 0: break finished_time = time.time() print("Sort took {}".format(finished_time - start_time)) return list_to_sort
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DQM/L1TMonitorClient/python/L1TOccupancyClient_cff.py
ckamtsikis/cmssw
852
1605203
import FWCore.ParameterSet.Config as cms from DQM.L1TMonitorClient.L1TOccupancyClient_cfi import *
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portfolio/gui/tabresults/righttable.py
timeerr/portfolio
0
8509
<filename>portfolio/gui/tabresults/righttable.py #!/usr/bin/python3 from datetime import datetime from PyQt5.QtWidgets import QTableWidgetItem, QTableWidget, QAbstractItemView, QMenu, QMessageBox from PyQt5.QtGui import QCursor from PyQt5.QtCore import Qt, pyqtSignal, QObject from portfolio.db.fdbhandler import results, strategies, balances def updatingdata(func): """ Decorator to flag self.updatingdata_flag whenever a function that edits data without user intervention is being run """ def wrapper(self, *args, **kwargs): self.updatingdata_flag = True func(self, *args, **kwargs) self.updatingdata_flag = False return wrapper class RightTable(QTableWidget): """ Table dynamically showing results """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # Custom Menu self.setContextMenuPolicy(Qt.CustomContextMenu) self.customContextMenuRequested.connect(self.showMenu) # A signal that will be emited whenever a line is removed self.lineremoved = LineRemoved() # UI Tweaks self.verticalHeader().hide() self.setSortingEnabled(True) self.setHorizontalHeaderLabels( ["id", self.tr("Date"), self.tr("Account"), self.tr("Strategy"), self.tr("Amount")]) # When edited, change the data on the database too self.cellChanged.connect(self.changeCellOnDatabase) # A flag to prevent changeCellOnDatabase execution when needed self.updatingdata_flag = True # Initialization: show all transactions self.setData(datetime(1980, 1, 1), datetime.today(), "All", "All") @updatingdata def setData(self, startdate, enddate, strategy, account): """ Asks the database for results data within certain parameters, then shows that data on the table """ # Clear table self.clear() self.setHorizontalHeaderLabels( ["id", self.tr("Date"), self.tr("Account"), self.tr("Strategy"), self.tr("Amount"), self.tr("Description")]) # Get desired data from db results_to_show = results.get_results_from_query( start_date=startdate, end_date=enddate, strategy=strategy, account=account) # If the data is empty, we are done if len(results_to_show) == 0: self.setRowCount(0) return # Resize table self.setRowCount(len(results_to_show)) self.setColumnCount(len(results_to_show[0])) # Change content for rownum, row in enumerate(results_to_show): for colnum, data in enumerate(row): item = QTableWidgetItem() # Item that will be inserted if colnum == 0: # Ids can't be editable item.setFlags(Qt.ItemIsSelectable) elif colnum == 1: # Change format to display date better data = datetime.fromtimestamp(data).strftime("%d-%m-%Y") # Data is now formatted, we can write it on table item.setData(0, data) self.setItem(rownum, colnum, item) def showMenu(self, event): """ Custom Menu to show when an item is right-clicked Options: - Remove Line: removes line from table and database """ menu = QMenu() # Actions remove_action = menu.addAction(self.tr("Remove Line")) # Getting action selected by user action = menu.exec_(QCursor.pos()) # Act accordingly if action == remove_action: self.removeSelection() self.lineremoved.lineRemoved.emit() @updatingdata def removeSelection(self): """ Removes the entire row of every selected item, and then does the same on the databse """ # Getting selected indexes, and their corresponding ids # from the database selected_indexes_table, selected_ids = [], [] for index in self.selectedIndexes(): index = index.row() # Row number if index not in selected_indexes_table: # Avoid duplicates selected_indexes_table.append(index) selected_ids.append(int(self.item(index, 0).text())) # Removing the rows from the table and the database for index, id_db in zip(selected_indexes_table, selected_ids): results.delete_result(id_db) self.removeRow(index) print("Removed rows with ids on db : ", selected_ids, "\n & ids on table: ", selected_indexes_table) def changeCellOnDatabase(self, row, column): """ When a Table Item is edited by the user, we want to check if it fits the type and edit it on the database too """ if self.updatingdata_flag is True: return # The data is being modified internally (not by the user) # so no errors assumed new_item = self.item(row, column) new_item_data = new_item.text() database_entry_id = self.item(row, 0).text() previous_amount = results.getResultAmountById( database_entry_id) # Useful for balance adjustments later columnselected_name = self.horizontalHeaderItem(column).text() # Depending on from which column the item is, we check the data # proposed differently # Check which part of the transaction has been edited, and accting accordingly # -------------- id -------------------- if columnselected_name == self.tr("Id"): # Ids can't be edited error_mssg = QMessageBox() error_mssg.setIcon(QMessageBox.Warning) error_mssg.setText(self.tr("Ids can't be edited")) error_mssg.exec_() # -------------- Date -------------------- elif columnselected_name == self.tr("Date"): # The new text has to be a date try: new_date = datetime.strptime(new_item_data, "%d-%m-%Y") results.update_result( database_entry_id, new_date=new_date.timestamp()) except ValueError: error_mssg = QMessageBox() error_mssg.setIcon(QMessageBox.Warning) error_mssg.setText( self.tr("Has to be a date in format dd-mm-yyyy")) error_mssg.exec_() # Reset date to previous one previous_date_timestamp = results.get_result_date_by_id( database_entry_id) previous_date_text = datetime.fromtimestamp( previous_date_timestamp).strftime("%d-%m-%Y") self.updatingdata_flag = True new_item.setData(0, previous_date_text) self.updatingdata_flag = False # -------------- Account -------------------- elif columnselected_name == self.tr("Account"): # The account has to be an existing one all_accounts = [a[0] for a in balances.get_all_accounts()] previous_account = results.get_result_account_by_id( database_entry_id) if new_item_data not in all_accounts: error_mssg = QMessageBox() error_mssg.setIcon(QMessageBox.Warning) error_mssg.setText( self.tr("The account has to be an existing one. \nAdd it first manually")) error_mssg.exec_() # Reset strategy to previous one self.updatingdata_flag = True new_item.setData(0, previous_account) self.updatingdata_flag = False else: # The data is good # Change the result on the results table on the db results.update_result( database_entry_id, new_account=new_item_data) # Update the balance of the two accounts involved, # according to the result amount balances.update_balances_with_new_result( previous_account, - previous_amount) balances.update_balances_with_new_result( new_item_data, previous_amount) # -------------- Strategy -------------------- elif columnselected_name == self.tr("Strategy"): # The strategy has to be an existing one previous_strategy = results.get_result_strategy_by_id( database_entry_id) all_strategies = [s[0] for s in strategies.get_all_strategies()] if new_item_data not in all_strategies: error_mssg = QMessageBox() error_mssg.setIcon(QMessageBox.Warning) error_mssg.setText( self.tr("The strategy has to be an existing one. \nAdd it first manually")) error_mssg.exec_() # Reset strategy to previous one self.updatingdata_flag = True new_item.setData(0, previous_strategy) self.updatingdata_flag = False else: # The data is good # Change the result on the results table of the db results.updateResult( database_entry_id, newstrategy=new_item_data) # Update the pnl of the two strategies involved, # according to the result amount strategies.update_strategies_with_new_result( previous_strategy, - previous_amount) strategies.update_strategies_with_new_result( new_item_data, previous_amount) # -------------- Amount -------------------- elif columnselected_name == self.tr("Amount"): # The amount has to be an integer try: new_item_data = int(new_item_data) # Change the result on the results table of the db results.update_result( database_entry_id, new_amount=new_item_data) # Update the balances and strategies with the difference # between the old and the new result diff_betweeen_results = new_item_data - previous_amount account_involved = results.get_result_account_by_id( database_entry_id) strategy_involved = results.get_result_strategy_by_id( database_entry_id) balances.update_balances_with_new_result( account_involved, diff_betweeen_results) strategies.update_strategies_with_new_result( strategy_involved, diff_betweeen_results) except Exception: error_mssg = QMessageBox() error_mssg.setIcon(QMessageBox.Warning) error_mssg.setText( self.tr("Has to be an integer")) error_mssg.exec_() # Reset to previous amount previous_amount = results.get_result_amount_by_id( database_entry_id) self.updatingdata_flag = True new_item.setData(0, previous_amount) self.updatingdata_flag = False # -------------- Description -------------------- elif columnselected_name == self.tr("Description"): # A description can be any data. So no checks results.update_result( database_entry_id, new_description=new_item_data) class LineRemoved(QObject): lineRemoved = pyqtSignal()
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util1.py
Daim-Akram/chat_app-Socket
0
118510
''' This file contains basic utility functions that you can use. ''' import binascii MAX_NUM_CLIENTS = 10 TIME_OUT = 0.5 # 500ms NUM_OF_RETRANSMISSIONS = 3 CHUNK_SIZE = 1400 # 1400 Bytes def validate_checksum(message): ''' Validates Checksum of a message and returns true/false ''' try: msg, checksum = message.rsplit('|', 1) msg += '|' return generate_checksum(msg.encode()) == checksum except BaseException: return False def generate_checksum(message): ''' Returns Checksum of the given message ''' return str(binascii.crc32(message) & 0xffffffff) def make_packet(msg_type="data", seqno=0, msg=""): ''' This will add the header to your message. The formats is `<message_type> <sequence_number> <body> <checksum>` msg_type can be data, ack, end, start seqno is a packet sequence number (integer) msg is the actual message string ''' body = "%s|%d|%s|" % (msg_type, seqno, msg) checksum = generate_checksum(body.encode()) packet = "%s%s" % (body, checksum) return packet def parse_packet(message): ''' This function will parse the packet in the same way it was made in the above function. ''' pieces = message.split('|') msg_type, seqno = pieces[0:2] checksum = pieces[-1] data = '|'.join(pieces[2:-1]) return msg_type, seqno, data, checksum def make_message(msg_type, msg_format, message=None): ''' This function can be used to format your message according to any one of the formats described in the documentation. msg_type defines type like join, disconnect etc. msg_format is either 1,2,3 or 4 msg is remaining. ''' if msg_format == 2: msg_len = 0 return "%s %d" % (msg_type, msg_len) if msg_format in [1, 3, 4]: msg_len = len(message) return "%s %d %s" % (msg_type, msg_len, message) return ""
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solutionbox/structured_data/mltoolbox/_structured_data/preprocess/local_preprocess.py
freyrsae/pydatalab
198
18258
<gh_stars>100-1000 # Copyright 2017 Google Inc. 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 absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import argparse import collections import json import os import six import sys from tensorflow.python.lib.io import file_io SCHEMA_FILE = 'schema.json' NUMERICAL_ANALYSIS_FILE = 'stats.json' CATEGORICAL_ANALYSIS_FILE = 'vocab_%s.csv' def parse_arguments(argv): """Parse command line arguments. Args: argv: list of command line arguments, includeing programe name. Returns: An argparse Namespace object. """ parser = argparse.ArgumentParser( description='Runs Preprocessing on structured CSV data.') parser.add_argument('--input-file-pattern', type=str, required=True, help='Input CSV file names. May contain a file pattern') parser.add_argument('--output-dir', type=str, required=True, help='Google Cloud Storage which to place outputs.') parser.add_argument('--schema-file', type=str, required=True, help=('BigQuery json schema file')) args = parser.parse_args(args=argv[1:]) # Make sure the output folder exists if local folder. file_io.recursive_create_dir(args.output_dir) return args def run_numerical_categorical_analysis(args, schema_list): """Makes the numerical and categorical analysis files. Args: args: the command line args schema_list: python object of the schema json file. Raises: ValueError: if schema contains unknown column types. """ header = [column['name'] for column in schema_list] input_files = file_io.get_matching_files(args.input_file_pattern) # Check the schema is valid for col_schema in schema_list: col_type = col_schema['type'].lower() if col_type != 'string' and col_type != 'integer' and col_type != 'float': raise ValueError('Schema contains an unsupported type %s.' % col_type) # initialize the results def _init_numerical_results(): return {'min': float('inf'), 'max': float('-inf'), 'count': 0, 'sum': 0.0} numerical_results = collections.defaultdict(_init_numerical_results) categorical_results = collections.defaultdict(set) # for each file, update the numerical stats from that file, and update the set # of unique labels. for input_file in input_files: with file_io.FileIO(input_file, 'r') as f: for line in f: parsed_line = dict(zip(header, line.strip().split(','))) for col_schema in schema_list: col_name = col_schema['name'] col_type = col_schema['type'] if col_type.lower() == 'string': categorical_results[col_name].update([parsed_line[col_name]]) else: # numerical column. # if empty, skip if not parsed_line[col_name].strip(): continue numerical_results[col_name]['min'] = ( min(numerical_results[col_name]['min'], float(parsed_line[col_name]))) numerical_results[col_name]['max'] = ( max(numerical_results[col_name]['max'], float(parsed_line[col_name]))) numerical_results[col_name]['count'] += 1 numerical_results[col_name]['sum'] += float(parsed_line[col_name]) # Update numerical_results to just have min/min/mean for col_schema in schema_list: if col_schema['type'].lower() != 'string': col_name = col_schema['name'] mean = numerical_results[col_name]['sum'] / numerical_results[col_name]['count'] del numerical_results[col_name]['sum'] del numerical_results[col_name]['count'] numerical_results[col_name]['mean'] = mean # Write the numerical_results to a json file. file_io.write_string_to_file( os.path.join(args.output_dir, NUMERICAL_ANALYSIS_FILE), json.dumps(numerical_results, indent=2, separators=(',', ': '))) # Write the vocab files. Each label is on its own line. for name, unique_labels in six.iteritems(categorical_results): labels = '\n'.join(list(unique_labels)) file_io.write_string_to_file( os.path.join(args.output_dir, CATEGORICAL_ANALYSIS_FILE % name), labels) def run_analysis(args): """Builds an analysis files for training.""" # Read the schema and input feature types schema_list = json.loads( file_io.read_file_to_string(args.schema_file)) run_numerical_categorical_analysis(args, schema_list) # Also save a copy of the schema in the output folder. file_io.copy(args.schema_file, os.path.join(args.output_dir, SCHEMA_FILE), overwrite=True) def main(argv=None): args = parse_arguments(sys.argv if argv is None else argv) run_analysis(args) if __name__ == '__main__': main()
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3cw.py
lisoleg/pybancor
0
58518
import numpy as np import matplotlib.pyplot as plt import random s0=4.96*10**8 mtop=5.04*10**8 oneday=24*60*60 oneyear=365 t=[i for i in range(1,oneyear*30)] bprice0=6.4669 bprice=bprice0 bnum0=1000000 b0=1.0*bnum0 cw0=0.065 cw1=0.075 cw2=0.08 cw3=0.1 realmine=0 p0list=[] b0list=[] s0list=[] p1list=[] b1list=[] s1list=[] p2list=[] b2list=[] s2list=[] p3list=[] b3list=[] s3list=[] b=b0 p0=b0/(cw0*s0) percentforbancor=0.08 dotpersec=2 s=1.0*s0 newprice=p=p0 sp=s0*p0 allmine=0 for i in t: l=i/(365*4+1) mineperday=(1.0*dotpersec/(2**l))*oneday if allmine+mineperday>mtop: realmine=mtop-allmine else: realmine=mineperday allmine+=realmine s+=realmine newprice=b/(cw0*s) b+=realmine*percentforbancor*newprice newprice=b/(cw0*s) b0list.append(b/10**6) s0list.append(s/10**6) p0list.append(newprice*10**3) b=b0 s=s0 realmine=allmine=0 for i in t: l=i/(365*4+1) mineperhalfday=(1.0*dotpersec/(2**l))*oneday if allmine+mineperhalfday>mtop: realmine=mtop-allmine else: realmine=mineperhalfday allmine+=realmine s+=realmine newprice=b/(cw1*s) b+=realmine*percentforbancor*newprice newprice=b/(cw1*s) b1list.append(b/10**6) s1list.append(s/10**6) p1list.append(newprice*10**3) b=b0 s=s0 realmine=allmine=0 for i in t: l=i/(365*4+1) mineperhalfday=(1.0*dotpersec/(2**l))*oneday if allmine+mineperhalfday>mtop: realmine=mtop-allmine else: realmine=mineperhalfday allmine+=realmine s+=realmine newprice=b/(cw2*s) b+=realmine*percentforbancor*newprice newprice=b/(cw2*s) b2list.append(b/10**6) s2list.append(s/10**6) p2list.append(newprice*10**3) b=b0 s=s0 realmine=allmine=0 for i in t: l=i/(365*4+1) mineperhalfday=(1.0*dotpersec/(2**l))*oneday if allmine+mineperhalfday>mtop: realmine=mtop-allmine else: realmine=mineperhalfday allmine+=realmine s+=realmine newprice=b/(cw3*s) b+=realmine*percentforbancor*newprice newprice=b/(cw3*s) b3list.append(b/10**6) s3list.append(s/10**6) p3list.append(newprice*10**3) sp=plt.subplot(311) sp.plot(t,np.array(s0list),color="black") sp.plot(t,np.array(s1list),color="red") sp.plot(t,np.array(s2list),color="green") sp.plot(t,np.array(s3list),color="grey") bp=plt.subplot(312) bp.plot(t,np.array(b0list),color="black") bp.plot(t,np.array(b1list),color="red") bp.plot(t,np.array(b2list),color="green") bp.plot(t,np.array(b3list),color="grey") pp=plt.subplot(313) pp.plot(t,np.array(p0list),color="black") pp.plot(t,np.array(p1list),color="red") pp.plot(t,np.array(p2list),color="green") pp.plot(t,np.array(p3list),color="grey") plt.legend() plt.rcParams['font.sans-serif']=['AR PL UKai CN'] pp.set_title('Price-Day') pp.set_ylabel("10^-3EOS") sp.set_title("Supply-Day cw b="+str(cw0)+"/r"+str(cw1)+"/g"+str(cw2)+"/y"+str(cw3)+" rate="+str(percentforbancor)) sp.set_ylabel("mDOT") bp.set_title("Reserve-Day") bp.set_ylabel("mEOS") plt.tight_layout() plt.show()
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hackerrank/contests/30_days_of_code/operators-class-instance.py
spradeepv/dive-into-python
0
111808
""" Problem Statement Welcome to Day 4! Check out a video review of logical operations here, or just jump right into the problem. Note: This task is focused on Object Oriented concepts, so it is only enabled in a few languages. You will create a class Person and write a constructor that takes an integer, initial_Age. In this constructor, you should check that the initial_Age is not negative because we can't have people with negative ages. If the initial_Age is negative, set the instance's age equal to zero then print "This person is not valid, setting age to 0." without the quotations.. Inside of this class, you will also create an instance variable called age and if initial_Age is not negative, then you will set age to equal the value of initial_Age. In addition, you will write an instance method, amIOld(), that prints whether people are old or not to the console. In amIOld(), do the following things: If the age of the Person instance is less than 13, then print "You are young." If the age of the Person instance is equal or greater than 13, but less 18, print "You are a teenager." Otherwise, print "You are old." In addition, create an instance function called yearPasses() that increases the age of the person instance by one. Much of the structure of the code is given to you below, but in the future, you will write this. The code that will create instances of your Person class is in the main function. You may not understand it all yet, but take a look just to see what's going on. Do not change any of the variable names or remove any of the code given. Input Format First line contains T, number of test cases. Each test case contains an integer age, representing age of the person. Constraints 1<=T<=4 -5<=age<=30 Output Format The code that will test your methods is already in the editor. All you have to do is edit the methods given to you in the editor so that they perform correctly as stated above. If your methods are implemented correctly, each testcase will print out either two or three lines. If the age is less than zero, then your program should print out: This person is not valid, setting age to 0. You are young. You are young. If the age is equal or greater than 0, then your program should print out two lines. The first line that the program prints out should be the output of amIOld() on the current age. Then, three years pass via yearPasses() and the second line the program prints should be the output of amIOld() after the time has passed. Sample Input 4 -1 10 16 18 Sample Output This person is not valid, setting age to 0. You are young. You are young. You are young. You are a teenager. You are a teenager. You are old. You are old. You are old. Explanation For the first testcase, the age is less than 0 so we set the age to 0.Three years pass and the age is 3. So we print out: This person is not valid, setting age to 0. You are young. You are young. For the second testcase, the age is 10, which is considered young according to our program. Three years pass and the age is 13. 13 is considered a 'teenager' age so we print out: You are young. You are a teenager. For the third testcase, the age is 16, which is the age of a teenager. Three years pass and the age is 19. 19 is considered an 'old' age according to our program so we print out: You are a teenager. You are old. For the last testcase, the age is 18, which is considered an old age according to our program. Three years pass and the age is 21. 21 is still considered old so we print out: You are old. You are old. """ class Person: def __init__(self, initial_Age): # Add some more code to run some checks on initial_Age if initial_Age < 0: print "This person is not valid, setting age to 0." self.initial_age = 0 self.age = initial_Age def amIOld(self): # Do some computations in here and print out the correct statement to # the console if self.age < 13: print "You are young." elif self.age >= 13 and self.age < 18: print "You are a teenager." else: print "You are old." def yearPasses(self): # Increment the age of the person in here self.age += 1 T = int(raw_input()) for i in range(0, T): age = int(raw_input()) p = Person(age) p.amIOld() for j in range(0, 3): p.yearPasses() p.amIOld() print ""
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pyrobolearn/dynamics/nn.py
Pandinosaurus/pyrobolearn
2
167349
<reponame>Pandinosaurus/pyrobolearn #!/usr/bin/env python # -*- coding: utf-8 -*- """Provides dynamic transition neural network approximators For instance, a dynamic network is a function represented by a neural network that maps a state-action to the next state. """ from pyrobolearn.approximators import NNApproximator, MLPApproximator from pyrobolearn.dynamics.dynamic import ParametrizedDynamicModel __author__ = "<NAME>" __copyright__ = "Copyright 2018, PyRoboLearn" __credits__ = ["<NAME>"] __license__ = "GNU GPLv3" __version__ = "1.0.0" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Development" class NNDynamicModel(ParametrizedDynamicModel): r"""Neural Network Dynamic Model Dynamic model using neural networks. Pros: Cons: requires lot of samples, overfitting,... """ def __init__(self, state, action, model, next_state=None, distributions=None, preprocessors=None, postprocessors=None): """ Initialize the NN dynamic model. Args: state (State): state inputs. action (Action): action inputs. next_state (State, None): state outputs. If None, it will take the state inputs as the outputs. model (NNApproximator, NN): neural network model. distributions (torch.distributions.Distribution): distribution to use to sample the next state. If None, it will be deterministic. preprocessors (Processor, list of Processor, None): pre-processors to be applied to the given input postprocessors (Processor, list of Processor, None): post-processors to be applied to the output """ if model is None: raise TypeError("Expecting the model to be a neural network and not None.") elif not isinstance(model, NNApproximator): if next_state is None: next_state = state model = NNApproximator(inputs=[state, action], outputs=next_state, model=model, preprocessors=preprocessors, postprocessors=postprocessors) super(NNDynamicModel, self).__init__(state, action, model=model, next_state=next_state, distributions=distributions) class MLPDynamicModel(NNDynamicModel): r"""MLP Dynamic Model """ def __init__(self, state, action, next_state=None, hidden_units=(), activation='linear', last_activation=None, dropout=None, distributions=None, preprocessors=None, postprocessors=None): """ Initialize the multi-layer perceptron model. Args: state (State): state inputs. action (Action): action inputs. next_state (State, None): state outputs. If None, it will take the state inputs as the outputs. hidden_units (tuple, list of int): number of hidden units in each layer activation (str): activation function to apply on each layer last_activation (str, None): activation function to apply on the last layer dropout (None, float): dropout probability distributions (torch.distributions.Distribution): distribution to use to sample the next state. If None, it will be deterministic. preprocessors (Processor, list of Processor, None): pre-processors to be applied to the given input postprocessors (Processor, list of Processor, None): post-processors to be applied to the output """ if next_state is None: next_state = state model = MLPApproximator(inputs=[state, action], outputs=next_state, hidden_units=hidden_units, activation=activation, last_activation=last_activation, dropout=dropout) super(MLPDynamicModel, self).__init__(state, action, model=model, next_state=next_state, distributions=distributions, preprocessors=preprocessors, postprocessors=postprocessors)
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FortiJson/rpcrequest.py
talbiston/fortijson-rpc
0
72869
<reponame>talbiston/fortijson-rpc import json from collections import OrderedDict import requests def sort_request(request): """ Sort a JSON-RPC request dict. This has no effect other than making the request nicer to read. >>> json.dumps(sort_request( ... {'id': 2, 'params': [2, 3], 'method': 'add', 'jsonrpc': '2.0'})) '{"jsonrpc": "2.0", "method": "add", "params": [2, 3], "id": 2}' Args: request: JSON-RPC request in dict format. """ sort_order = ["jsonrpc", "method", "params", "id", "session", "verbose"] return OrderedDict(sorted(request.items(), key=lambda k: sort_order.index(k[0]))) def fix_keys(kwargs): # get keys to change change_keys = [] if kwargs: for k in kwargs.keys(): if '_' in k: change_keys.append(k) #change keys for i in change_keys: left, right = i.split('_') if left == 'meta': kwargs[f'{left} {right}'] = kwargs.pop(i) else: kwargs[f'{left}-{right}'] = kwargs.pop(i) return kwargs class JsonRpc(dict): # type: ignore def __init__(self, method: str, *args, **kwargs): super().__init__(jsonrpc="2.0", method=method) # Add the params to self. kwargs = fix_keys(kwargs) session = kwargs.pop('session', None) verbose = kwargs.pop('verbose', None) base_list = [] if args and kwargs: # The 'params' can be *EITHER* "by-position" (a list) or "by-name" (a dict). # Therefore, in this case it violates the JSON-RPC 2.0 specification. # However, it provides the same behavior as the previous version of # jsonrpcclient to keep compatibility. # TODO: consider to raise a warning. print("Args: ",args) print("Kwargs:", kwargs) params_list = list(args) params_list.append(kwargs) self.update(params=params_list) self.update(session=session) self.update(verbose=verbose) elif args: self.update(params=list(args)) self.update(session=session) self.update(verbose=verbose) elif kwargs: base_list.append(kwargs) self.update(params=base_list) self.update(session=session) self.update(verbose=verbose) def __json__(self): return sort_request(self) class HTTPclient: def __init__(self, baseUrl, timeOut=20): self.baseUrl = baseUrl self.timeout = timeOut def send(self, json): response = requests.post(self.baseUrl, json=json, timeout=self.timeout, verify=False) return response if __name__ == '__main__': jrpc = JsonRpc('get', url='https://adc.com', session="123456789qwewerwert") print(jrpc)
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liqian/crawl_dblp.py
doge-search/webdoge
0
73232
<gh_stars>0 #!/usr/bin/python #coding=utf-8 import requests from lxml import etree from collections import namedtuple import sys #f_handler = open('out.log', 'w') #sys.stdout = f_handler DBLP_BASE_URL = 'http://dblp.uni-trier.de/' DBLP_AUTHOR_SEARCH_URL = DBLP_BASE_URL + 'search/author' DBLP_PERSON_URL = DBLP_BASE_URL + 'pers/xk/{urlpt}' DBLP_PUBLICATION_URL = DBLP_BASE_URL + 'rec/bibtex/{key}.xml' class LazyAPIData(object): def __init__(self, lazy_attrs): self.lazy_attrs = set(lazy_attrs) self.data = None def __getattr__(self, key): if key in self.lazy_attrs: if self.data is None: self.load_data() return self.data[key] raise AttributeError, key def load_data(self): pass class Author(LazyAPIData): """ Represents a DBLP author. All data but the author's key is lazily loaded. Fields that aren't provided by the underlying XML are None. Attributes: name - the author's primary name record publications - a list of lazy-loaded Publications results by this author homepages - a list of author homepage URLs homonyms - a list of author aliases """ def __init__(self, urlpt): self.urlpt = urlpt self.xml = None super(Author, self).__init__(['name','publications','homepages', 'homonyms']) def load_data(self): resp = requests.get(DBLP_PERSON_URL.format(urlpt=self.urlpt)) # TODO error handling xml = resp.content self.xml = xml root = etree.fromstring(xml) data = { 'name':root.attrib['name'], 'publications':[Publication(k) for k in root.xpath('/dblpperson/dblpkey[not(@type)]/text()')], 'homepages':root.xpath( '/dblpperson/dblpkey[@type="person record"]/text()'), 'homonyms':root.xpath('/dblpperson/homonym/text()') } self.data = data def first_or_none(seq): try: return next(iter(seq)) except StopIteration: pass Publisher = namedtuple('Publisher', ['name', 'href']) Series = namedtuple('Series', ['text','href']) Citation = namedtuple('Citation', ['reference','label']) class Publication(LazyAPIData): """ Represents a DBLP publication- eg, article, inproceedings, etc. All data but the key is lazily loaded. Fields that aren't provided by the underlying XML are None. Attributes: type - the publication type, eg "article", "inproceedings", "proceedings", "incollection", "book", "phdthesis", "mastersthessis" sub_type - further type information, if provided- eg, "encyclopedia entry", "informal publication", "survey" title - the title of the work authors - a list of author names journal - the journal the work was published in, if applicable volume - the volume, if applicable number - the number, if applicable chapter - the chapter, if this work is part of a book or otherwise applicable pages - the page numbers of the work, if applicable isbn - the ISBN for works that have them ee - an ee URL crossref - a crossrel relative URL publisher - the publisher, returned as a (name, href) named tuple citations - a list of (text, label) named tuples representing cited works series - a (text, href) named tuple describing the containing series, if applicable """ def __init__(self, key): self.key = key self.xml = None super(Publication, self).__init__( ['type', 'sub_type', 'mdate', 'authors', 'editors', 'title', 'year', 'month', 'journal', 'volume', 'number', 'chapter', 'pages', 'ee', 'isbn', 'url', 'booktitle', 'crossref', 'publisher', 'school', 'citations', 'series']) def load_data(self): resp = requests.get(DBLP_PUBLICATION_URL.format(key=self.key)) xml = resp.content self.xml = xml root = etree.fromstring(xml) publication = first_or_none(root.xpath('/dblp/*[1]')) if publication is None: raise ValueError data = { 'type':publication.tag, 'sub_type':publication.attrib.get('publtype', None), 'mdate':publication.attrib.get('mdate', None), 'authors':publication.xpath('author/text()'), 'editors':publication.xpath('editor/text()'), 'title':first_or_none(publication.xpath('title/text()')), 'year':int(first_or_none(publication.xpath('year/text()'))), 'month':first_or_none(publication.xpath('month/text()')), 'journal':first_or_none(publication.xpath('journal/text()')), 'volume':first_or_none(publication.xpath('volume/text()')), 'number':first_or_none(publication.xpath('number/text()')), 'chapter':first_or_none(publication.xpath('chapter/text()')), 'pages':first_or_none(publication.xpath('pages/text()')), 'ee':first_or_none(publication.xpath('ee/text()')), 'isbn':first_or_none(publication.xpath('isbn/text()')), 'url':first_or_none(publication.xpath('url/text()')), 'booktitle':first_or_none(publication.xpath('booktitle/text()')), 'crossref':first_or_none(publication.xpath('crossref/text()')), 'publisher':first_or_none(publication.xpath('publisher/text()')), 'school':first_or_none(publication.xpath('school/text()')), 'citations':[Citation(c.text, c.attrib.get('label',None)) for c in publication.xpath('cite') if c.text != '...'], 'series':first_or_none(Series(s.text, s.attrib.get('href', None)) for s in publication.xpath('series')) } self.data = data def dblp_search(author_str): resp = requests.get(DBLP_AUTHOR_SEARCH_URL, params={'xauthor':author_str}) #TODO error handling root = etree.fromstring(resp.content) return [Author(urlpt) for urlpt in root.xpath('/authors/author/@urlpt')] import urllib2 import HTMLParser import xml.dom.minidom as minidom from htmlentitydefs import entitydefs try: import xml.etree.cElementTree as ET except ImportError: import xml.etree.ElementTree as ET import glob import os reload(sys) sys.setdefaultencoding('utf-8') schools = [#'brown', 'Caltech', 'columbia', 'duke', 'harvard', 'JHU', 'northwestern', 'NYU', 'OSU', 'PSU', 'purdue', 'rice', 'UCI', 'UCLA', 'UCSD', 'UMASS', 'UMD', 'umich', 'UMN', 'UNC', 'upenn', 'USC', 'virginia', 'WISC', 'yale'] def search(ini_name): name = ini_name.strip() nname = name.split(',') if len(nname) > 1: name = nname[1] + ' ' + nname[0] authors = dblp_search(' ' + name + ' ') author_num = len(authors) if not authors: print "not found: " + name.encode('utf-8') + '\n' sys.stdout.flush() return (ini_name, -1) else: if author_num > 10: print "multiple: " + str(author_num) + ' ' + name.encode('utf-8') + '\n' sys.stdout.flush() cnt = 0 index = 0 if author_num > 1: for indx in range(author_num): if authors[indx].name == name: #print "match!" + name break index += 1 if index == len(authors): print "not found in the list!" + name + ' compare with ' + authors[0].name.encode('utf-8') + ' use default 0...' + '\n' sys.stdout.flush() index = 0 publications = authors[index].publications profname = authors[index].name for pub in publications: auth_len = len(pub.authors) pub_authors = [] for i in range(auth_len): pub_authors.append(pub.authors[i]) try: idx = pub_authors.index(profname) except ValueError: split_name = profname.split(' ') #profname = split_name[0] + ' ' + split_name[1][0] + '. ' + split_name[-1] #change middle name idx = 0 for i in range(auth_len): pub_split = pub_authors[i].split(' ') if pub_split[0] == split_name[0] and pub_split[-1] == split_name[-1]: break idx += 1 if idx == auth_len: print pub.title.encode('utf-8') + '\m' sys.stdout.flush() #print pub.authors idx = -1 if idx >= 0: cnt += 1.0 / (idx + 1) return (ini_name, cnt) if __name__ == "__main__": for school in schools: print "=== start crawling school:" + school + '\n' sys.stdout.flush() filename = school + '/' + school + '.xml' if not os.path.isfile(filename): print "cannot find: " + filename + '\n' sys.stdout.flush() continue tree = ET.ElementTree(file = filename) fout_xml = file(school+'/'+school+'_sort.xml', 'w') doc = minidom.Document() institution = doc.createElement("institution") doc.appendChild(institution) for prof in tree.getroot(): for info in prof: if info.tag == 'name': name, cnt = search(info.text) print school + ': ' + name.encode('utf-8') + ' with score: ' + "%.4f" % cnt + '\n' sys.stdout.flush() professor = doc.createElement("professor") namenode = doc.createElement("name") namenode.appendChild(doc.createTextNode(name)) professor.appendChild(namenode) papernode = doc.createElement("papers") papernode.appendChild(doc.createTextNode(str(cnt))) professor.appendChild(papernode) institution.appendChild(professor) doc.writexml(fout_xml, "\t", "\t", "\n") fout_xml.close() print "=== finished crawling: " + school + '\n' sys.stdout.flush()
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flashnrf.py
struempelix/flashnrf
0
187726
<filename>flashnrf.py """ This file contains example code meant to be used in order to test the pynrfjprog API and Hex. If multiple devices are connected, pop-up will appear. Sample program: program_hex.py Requires nrf51-DK or nrf52-DK for visual confirmation (LEDs). Run from command line: python program_hex.py or if imported as "from pynrfjprog import examples" examples.program_hex.run() Program flow: 0. API is opened and checked to see if correct family type is used 1. Memory is erased 2. test_program_path is parsed and written to memory 3. Device is reset and application is run """ from __future__ import division from __future__ import print_function from builtins import int import sys import time import logging from watchdog.observers import Observer from watchdog.events import PatternMatchingEventHandler # Import pynrfjprog API module and HEX parser module from pynrfjprog import API, Hex import os # Used to create path to .hex file class myHandler(PatternMatchingEventHandler): patterns = ["*.hex"] ignore_patterns = ["_OTA"] def process(self, event): print (event.dest_path, event.event_type) def on_moved(self, event): self.process(event) print('# pynrfjprog program hex example started... ') device_family = API.DeviceFamily.NRF51 # Start out with nrf51, will be checked and changed if needed # Init API with NRF51, open, connect, then check if NRF51 is correct print('# Opening API with device %s, checking if correct ' % device_family) api = API.API(device_family) # Initializing API with correct NRF51 family type (will be checked later if correct) api.open() # Open the dll with the set family type api.connect_to_emu_without_snr() # Connect to emulator, it multiple are connected - pop up will appear # Check if family used was correct or else change try: device_version = api.read_device_version() except API.APIError as e: if e.err_code == API.NrfjprogdllErr.WRONG_FAMILY_FOR_DEVICE: device_family = API.DeviceFamily.NRF52 print('# Closing API and re-opening with device %s ' % device_family) api.close() # Close API so that correct family can be used to open # Re-Init API, open, connect, and erase device api = API.API(device_family) # Initializing API with correct family type [API.DeviceFamily.NRF51 or ...NRF52] api.open() # Open the dll with the set family type api.connect_to_emu_without_snr() # Connect to emulator, it multiple are connected - pop up will appear# change else: raise e print('# Erasing all... ') api.erase_all() # Erase memory of device # Find path to test hex file #module_dir, module_file = os.path.split(__file__) #hex_file_path = os.path.join(os.path.abspath(module_dir), device_family.name + '_dk_blinky.hex') # Parse hex, program to device print('# Parsing hex file into segments ') program = Hex.Hex(event.dest_path) # Parse .hex file into segments print('# Writing %s to device ' % event.dest_path) for segment in program: api.write(segment.address, segment.data, True) # Reset device, run api.sys_reset() # Reset device api.go() # Run application print('# Application running ') # Close API api.close() # Close the dll print('# done... ') os.system('say "done"') def run(): args = sys.argv[1:] observer = Observer() observer.schedule(myHandler(), path=args[0] if args else '.') observer.start() try: while True: time.sleep(1) except KeyboardInterrupt: observer.stop() observer.join() if __name__ == '__main__': run()
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ontquery/utils.py
tmsincomb/ontquery
1
106656
<gh_stars>1-10 import logging from functools import wraps red = '\x1b[31m{}\x1b[0m' def makeSimpleLogger(name, level=logging.INFO): logger = logging.getLogger(name) logger.setLevel(level) ch = logging.StreamHandler() # FileHander goes to disk fmt = ('[%(asctime)s] - %(levelname)8s - ' '%(name)14s - ' '%(filename)16s:%(lineno)-4d - ' '%(message)s') formatter = logging.Formatter(fmt) ch.setFormatter(formatter) logger.addHandler(ch) return logger log = makeSimpleLogger('ontquery') __logged = set() def _already_logged(thing): case = thing in __logged if not case: __logged.add(thing) return case def subclasses(start, done=None): if done is None: done = set() for sc in start.__subclasses__(): if sc is not None and sc not in done: done.add(sc) yield sc yield from subclasses(sc, done) class SubClassCompare: def __init__(self, cls): self.cls = cls def __lt__(self, other, *, forgt=False): if self.cls is None: # i.e. None is the least derived class return False elif other.cls is None: return True elif issubclass(other.cls, self.cls): # if you are a subclass of the other, you are greater # fewer parent classes rank lower return True elif issubclass(self.cls, other.cls): return False else: # NOTE useful in some cases # but not useful in the case where you just want # the parent class to be above the child but not # above other classes ls, lo = len(self.cls.mro()), len(other.cls.mro()) return ls < lo def __gt__(self, other): lt = self.__lt__(other, forgt=True) return not lt #return False if lt is None else not lt def __eq__(self, other): return self.cls is other.cls or not self < other and not self > other def __repr__(self): return f'SubClassCompare({self.cls!r})' def bunch(pairs): """ pairs -> dict """ out = {} for k, v in pairs: if k not in out: out[k] = [] out[k].append(v) return out def cullNone(**kwargs): return {k:v for k, v in kwargs.items() if v is not None} def one_or_many(arg): return tuple() if not arg else ((arg,) if isinstance(arg, str) else arg) def mimicArgs(function_to_mimic): def decorator(function): @wraps(function_to_mimic) def wrapper(*args, **kwargs): return function(*args, **kwargs) return wrapper return decorator class Graph(): """ I can be pickled! And I can be loaded from a pickle dumped from a graph loaded via rdflib. """ def __init__(self, triples=tuple()): self.store = triples def add(triple): self.store += triple def subjects(self, predicate, object): # this method by iteself is sufficient to build a keyword based query interface via query(predicate='object') for s, p, o in self.store: if (predicate is None or predicate == p) and (object == None or object == o): yield s def predicates(self, subject, object): for s, p, o in self.store: if (subject is None or subject == s) and (object == None or object == o): yield p def predicate_objects(subject): # this is sufficient to let OntTerm work as desired for s, p, o in self.store: if subject == None or subject == s: yield p, o class QueryResult: """ Encapsulate query results and allow for clear and clean documentation of how a particular service maps their result terminology onto the ontquery keyword api. """ @classmethod def new_from_instrumented(cls, instrumented): return type(cls.__name__, (cls,), dict(_instrumented=instrumented)) def __init__(self, query_args, iri=None, curie=None, label=None, labels=tuple(), abbrev=None, # TODO acronym=None, # TODO definition=None, synonyms=tuple(), deprecated=None, prefix=None, category=None, predicates=None, # FIXME dict type=None, types=tuple(), _graph=None, _blob=None, # FIXME unify graph/blob under progenitor ? source=None, ): self.__query_args = query_args # for debug self.__dict = {} for k, v in dict(iri=iri, curie=curie, label=label, labels=labels, definition=definition, synonyms=synonyms, deprecated=deprecated, predicates=predicates, type=type, types=types, _graph=_graph, _blob=_blob, source=source).items(): # this must return the empty values for all keys # so that users don't have to worry about hasattring # to make sure they aren't about to step into a typeless void setattr(self, k, v) self.__dict[k] = v #self.__dict__[k] = v @property def OntTerm(self): # FIXME naming XXXX deprecate this if self.iri is None: raise BaseException(f'I can\'t believe you\'ve done this! {self!r}') return self._instrumented._from_query_result(self) def asTerm(self): return self.OntTerm @property def hasOntTerm(self): # FIXME naming # run against _OntTerm to prevent recursion return hasattr(self, '_instrumented') def keys(self): yield from self.__dict.keys() def values(self): yield from self.__dict.values() def items(self): yield from self.__dict.items() def __iter__(self): yield from self.__dict def __getitem__(self, key): try: return self.__dict[key] except KeyError as e: self.__missing__(key, e) def __contains__(self, key): return key in self.__dict def __missing__(self, key, e=None): raise KeyError(f'{key} {type(key)}') from e def __setitem__(self, key, value): raise ValueError('Cannot set results of a query.') def __repr__(self): return f'{self.__class__.__name__}({self.__dict!r})'
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Python code/isPrime.py
jlwgong/hangman
0
101687
# This function tells a user whether or not a number is prime def isPrime(number): # this will tell us if the number is prime, set to True automatically # We will set to False if the number is divisible by any number less than it number_is_prime = True # loop over all numbers less than the input number for i in range(2, number): # calculate the remainder remainder = number % i # if the remainder is 0, then the number is not prime by definition! if remainder == 0: number_is_prime = False # return result to the user return number_is_prime
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ramjet/analysis/viewer/preloader.py
golmschenk/ramjet
3
133160
""" Code to load view entities in the background so they show up quickly when displayed. """ import pandas as pd import asyncio from asyncio import Task from collections import deque import warnings from pathlib import Path from typing import Union, Deque from ramjet.analysis.viewer.view_entity import ViewEntity from ramjet.data_interface.tess_data_interface import NoDataProductsFoundException class Preloader: """ A class to load view entities in the background so they show up quickly when displayed. """ minimum_preloaded = 25 maximum_preloaded = 50 def __init__(self): self.current_view_entity: Union[None, ViewEntity] = None self.next_view_entity_deque: Deque[ViewEntity] = deque(maxlen=self.maximum_preloaded) self.previous_view_entity_deque: Deque[ViewEntity] = deque(maxlen=self.maximum_preloaded) self.identifier_data_frame: Union[pd.DataFrame, None] = None self.running_loading_task: Union[Task, None] = None async def load_view_entity_at_index_as_current(self, index: int): """ Loads the view entity at the passed index as the current view entity. :param index: The index in the path list to load. """ await self.cancel_loading_task() self.current_view_entity = await ViewEntity.from_identifier_data_frame_row( self.identifier_data_frame.iloc[index]) await self.reset_deques() async def load_surrounding_view_entities(self): """ Loads the next and previous view entities relative to the current view entity. """ await self.load_next_view_entities() await self.load_previous_view_entities() async def load_next_view_entities(self): """ Preload the next view entities. """ if len(self.next_view_entity_deque) > 0: last_index = self.next_view_entity_deque[-1].index else: last_index = self.current_view_entity.index while (len(self.next_view_entity_deque) < self.minimum_preloaded and last_index != self.identifier_data_frame.shape[0] - 1): last_index += 1 try: last_view_entity = await ViewEntity.from_identifier_data_frame_row( self.identifier_data_frame.iloc[last_index]) except NoDataProductsFoundException: warnings.warn(f'No light curve found for identifier {self.identifier_data_frame.iloc[last_index]}.') continue self.next_view_entity_deque.append(last_view_entity) async def load_previous_view_entities(self): """ Preload the previous view entities. """ if len(self.previous_view_entity_deque) > 0: first_index = self.previous_view_entity_deque[0].index else: first_index = self.current_view_entity.index while (len(self.previous_view_entity_deque) < self.minimum_preloaded and first_index != 0): first_index -= 1 try: first_view_entity = await ViewEntity.from_identifier_data_frame_row( self.identifier_data_frame.iloc[first_index]) except NoDataProductsFoundException: warnings.warn(f'No light curve found for identifier {self.identifier_data_frame.iloc[first_index]}.') continue self.previous_view_entity_deque.appendleft(first_view_entity) async def increment(self) -> ViewEntity: """ Increments to the next view entity, and calls loading as necessary. :return: The new current view entity. """ self.previous_view_entity_deque.append(self.current_view_entity) if len(self.next_view_entity_deque) == 0 and ( self.running_loading_task is not None and not self.running_loading_task.done()): await self.running_loading_task self.current_view_entity = self.next_view_entity_deque.popleft() await self.refresh_surrounding_light_curve_loading() return self.current_view_entity async def decrement(self) -> ViewEntity: """ Decrements to the previous view entity, and calls loading as necessary. :return: The new current view entity. """ self.next_view_entity_deque.appendleft(self.current_view_entity) while len(self.previous_view_entity_deque) == 0 and ( self.running_loading_task is not None and not self.running_loading_task.done()): await self.running_loading_task self.current_view_entity = self.previous_view_entity_deque.pop() await self.refresh_surrounding_light_curve_loading() return self.current_view_entity async def refresh_surrounding_light_curve_loading(self): """ Cancels the existing loading task and starts a new one. """ await self.cancel_loading_task() self.running_loading_task = asyncio.create_task(self.load_surrounding_view_entities()) async def cancel_loading_task(self): """ Cancels an existing loading task if it exists. """ if self.running_loading_task is not None: self.running_loading_task.cancel() try: await self.running_loading_task except asyncio.CancelledError: pass async def reset_deques(self): """ Cancels any loading tasks, clears the deques, and starts the loading task. """ await self.cancel_loading_task() self.previous_view_entity_deque = deque(maxlen=self.maximum_preloaded) self.next_view_entity_deque = deque(maxlen=self.maximum_preloaded) self.running_loading_task = asyncio.create_task(self.load_surrounding_view_entities()) @classmethod async def from_csv_path(cls, csv_path: Path, starting_index: int = 0): """ Create a preloader from a CSV of light curve identifiers. :param csv_path: A path to a CSV containing light curve identifier information. :param starting_index: The starting index to preload around. :return: The preloader. """ preloader = cls() preloader.identifier_data_frame = pd.read_csv(csv_path) await preloader.load_view_entity_at_index_as_current(starting_index) return preloader
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qa327_test/backend/test_r1_post.py
winterNebs/HyperTextAssassins
2
152751
import pytest from seleniumbase import BaseCase from qa327.models import db, User from qa327_test.conftest import base_url from unittest.mock import patch from werkzeug.security import generate_password_hash, check_password_hash test_user = User( email='<EMAIL>', name='testuser', password=generate_password_hash('<PASSWORD>$') ) testuser1 = User(email='<EMAIL>', name='testuser', password=generate_password_hash('<PASSWORD>$') ) testuser2 = User( email='<EMAIL>', name='testuser', password=generate_password_hash('<PASSWORD>$') ) testuser3 = User( email="this'isactuallyv{<EMAIL>", name='testuser', password=generate_password_hash('<PASSWORD>$') ) testuser4 = User(email='<EMAIL>', name='testuser', password=generate_password_hash("<PASSWORD>") ) testuser5 = User(email='<EMAIL>', name='testuser', password=generate_password_hash('<PASSWORD>') ) class R1TestPost(BaseCase): # Test form post @patch('qa327.backend.get_user', return_value=test_user) def test_r1_post_1(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter user name and password self.type("#email", "<EMAIL>") self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") self.open(base_url) # validate that we are logged in (ie we can see the welcome header # and our name) self.assert_element("#welcome-header") self.assert_text("Hi testuser !") # test both password username empty def test_r1_post_2_1(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # try submit login without password or name self.click("input[type='submit']") # should be format is incorrect self.assert_element("#message") self.assert_text("Email/password format is incorrect.") # test password empty @patch('qa327.backend.get_user', return_value=test_user) def test_r1_post_2_2(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter user name self.type("#email", "<EMAIL>") # try submit form self.click("input[type='submit']") # should be email format is incorrect self.assert_element("#message") self.assert_text("Email/password format is incorrect.") # test no email def test_r1_post_2_3(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter password but no email self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") # should be email format is incorrect self.assert_element("#message") self.assert_text("Email/password format is incorrect.") # test invalid email formats def test_r1_post_3_1(self, *_): for i in ["<EMAIL>", "<EMAIL>"]: # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", i) # enter password self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") # should be email format is incorrect self.assert_element("#message") self.assert_text("Email/password format is incorrect.") # test invalid email characters def test_r1_post_3_2(self, *_): for i in ["test\"(test,:;<>[\\]<EMAIL>", "test\"test <EMAIL>", "t<EMAIL>"]: # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", i) # enter password self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") # should be email format is incorrect self.assert_element("#message") self.assert_text("Email/password format is incorrect.") # test valid emails @patch('qa327.backend.get_user', return_value=testuser1) def test_r1_post_3_3_1(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", '<EMAIL>') # enter password self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") self.open(base_url) # validate that we are logged in (ie we can see the welcome header # and our name) self.assert_element("#welcome-header") self.assert_text("Hi testuser !") # test valid email @patch('qa327.backend.get_user', return_value=testuser2) def test_r1_post_3_3_2(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", '<EMAIL>') # enter password self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") self.open(base_url) # validate that we are logged in (ie we can see the welcome header # and our name) self.assert_element("#welcome-header") self.assert_text("Hi testuser !") # test valid email @patch('qa327.backend.get_user', return_value=testuser3) def test_r1_post_3_3_3(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", "this'isactuallyv{[email protected]") # enter password self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") self.open(base_url) # validate that we are logged in (ie we can see the welcome header # and our name) self.assert_element("#welcome-header") self.assert_text("Hi testuser !") # test email that is too long def test_r1_post_3_4(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", "12345678<EMAIL>2345678<EMAIL>2345678<EMAIL>5678<EMAIL>2345678<EMAIL>8<EMAIL>" ) # enter password self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") # should be format is incorrect self.assert_element("#message") self.assert_text("Email/password format is incorrect.") # test invalid email formats def test_r1_post_3_5(self, *_): # for the following emails for i in ["test..<EMAIL>", "<EMAIL>.<EMAIL>",".<EMAIL>", "<EMAIL>"]: # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", i) # enter password self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") # should be format is incorrect self.assert_element("#message") self.assert_text("Email/password format is incorrect.") # password too short def test_r1_post_4_1(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", "<EMAIL>") # enter password self.type("#password", "<PASSWORD>!") # try submit form self.click("input[type='submit']") # should be format is incorrect self.assert_element("#message") self.assert_text("Email/password format is incorrect.") # password missing special character def test_r1_post_4_2(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", "<EMAIL>") # enter password self.type("#password", "<PASSWORD>") # try submit form self.click("input[type='submit']") # should be format is incorrect self.assert_element("#message") self.assert_text("Email/password format is incorrect.") # password missing lowercase def test_r1_post_4_3(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", "<EMAIL>") # enter password self.type("#password", "<PASSWORD>!!!!") # try submit form self.click("input[type='submit']") # should be format is incorrect self.assert_element("#message") self.assert_text("Email/password format is incorrect.") # password missing uppercase def test_r1_post_4_4(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", "<EMAIL>") # enter password self.type("#password", "<PASSWORD>!!!!") # try submit form self.click("input[type='submit']") # should be format is incorrect self.assert_element("#message") self.assert_text("Email/password format is incorrect.") # test that valid passwords work @patch('qa327.backend.get_user', return_value=testuser4) def test_r1_post_4_6_1(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", '<EMAIL>') # enter password self.type("#password", "<PASSWORD>") # try submit form self.click("input[type='submit']") self.open(base_url) # validate that we are logged in (ie we can see the welcome header # and our name) self.assert_element("#welcome-header") self.assert_text("Hi testuser !") # test that valid passwords work @patch('qa327.backend.get_user', return_value=testuser5) def test_r1_post_4_6_2(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", "<EMAIL>") # enter password self.type("#password", "<PASSWORD>") # try submit form self.click("input[type='submit']") self.open(base_url) # validate that we are logged in (ie we can see the welcome header # and our name) self.assert_element("#welcome-header") self.assert_text("Hi testuser !") # test that redirected to / with valid email pw @patch('qa327.backend.get_user', return_value=test_user) def test_r1_post_6(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter user name and password self.type("#email", "<EMAIL>") self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") # check that we are on / now self.assertEqual(self.get_current_url(), base_url+'/') # incorrect passwoord @patch('qa327.backend.get_user', return_value=test_user) def test_r1_post_7_1(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", "<EMAIL>") # enter password but no name self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") # should be incorrect combo self.assert_element("#message") self.assert_text("Email/password combination incorrect") # incorrect email @patch('qa327.backend.get_user', return_value=test_user) def test_r1_post_7_2(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", "<EMAIL>") # enter password self.type("#password", "<PASSWORD>$") # try submit form self.click("input[type='submit']") # should be incorrect combo self.assert_element("#message") self.assert_text("Email/password combination incorrect") # incorrect email and password @patch('qa327.backend.get_user', return_value=test_user) def test_r1_post_7_3(self, *_): # logout if logged in self.open(base_url + '/logout') # open login page self.open(base_url +'/login') # enter email self.type("#email", "<EMAIL>") # enter password self.type("#password", "<PASSWORD>!") # try submit form self.click("input[type='submit']") # should be incorrect combo self.assert_element("#message") self.assert_text("Email/password combination incorrect")
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src/generator/AutoRest.Python.Azure.Tests/AcceptanceTests/xms_request_clientid_tests.py
yugangw-msft/AutoRest
3
88681
# -------------------------------------------------------------------------- # # Copyright (c) Microsoft Corporation. All rights reserved. # # The MIT License (MIT) # # 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 unittest import subprocess import sys import isodate import tempfile import json from uuid import uuid4 from datetime import date, datetime, timedelta import os from os.path import dirname, pardir, join, realpath cwd = dirname(realpath(__file__)) log_level = int(os.environ.get('PythonLogLevel', 30)) tests = realpath(join(cwd, pardir, "Expected", "AcceptanceTests")) sys.path.append(join(tests, "AzureSpecials")) from msrest.serialization import Deserializer from msrest.exceptions import DeserializationError from msrest.authentication import BasicTokenAuthentication from msrestazure.azure_exceptions import CloudError, CloudErrorData from autorestazurespecialparameterstestclient import AutoRestAzureSpecialParametersTestClient from autorestazurespecialparameterstestclient import models class XmsRequestClientIdTests(unittest.TestCase): def test_xms_request_client_id(self): validSubscription = '1234-5678-9012-3456' validClientId = '9C4D50EE-2D56-4CD3-8152-34347DC9F2B0' cred = BasicTokenAuthentication({"access_token":123}) client = AutoRestAzureSpecialParametersTestClient(cred, validSubscription, base_url="http://localhost:3000") custom_headers = {"x-ms-client-request-id": validClientId } result1 = client.xms_client_request_id.get(custom_headers = custom_headers, raw=True) #TODO: should we put the x-ms-request-id into response header of swagger spec? self.assertEqual("123", result1.response.headers.get("x-ms-request-id")) result2 = client.xms_client_request_id.param_get(validClientId, raw=True) self.assertEqual("123", result2.response.headers.get("x-ms-request-id")) def test_custom_named_request_id(self): validSubscription = '1234-5678-9012-3456' expectedRequestId = '9C4D50EE-2D56-4CD3-8152-34347DC9F2B0' cred = BasicTokenAuthentication({"access_token":123}) client = AutoRestAzureSpecialParametersTestClient(cred, validSubscription, base_url="http://localhost:3000") response = client.header.custom_named_request_id(expectedRequestId, raw=True) self.assertEqual("123", response.response.headers.get("foo-request-id")) def test_custom_named_request_id_param_grouping(self): validSubscription = '1234-5678-9012-3456' expectedRequestId = '9C4D50EE-2D56-4CD3-8152-34347DC9F2B0' cred = BasicTokenAuthentication({"access_token":123}) client = AutoRestAzureSpecialParametersTestClient(cred, validSubscription, base_url="http://localhost:3000") group = models.HeaderCustomNamedRequestIdParamGroupingParameters(foo_client_request_id=expectedRequestId) response = client.header.custom_named_request_id_param_grouping(group, raw=True) self.assertEqual("123", response.response.headers.get("foo-request-id")) def test_client_request_id_in_exception(self): validSubscription = '1234-5678-9012-3456' expectedRequestId = '9C4D50EE-2D56-4CD3-8152-34347DC9F2B0' cred = BasicTokenAuthentication({"access_token":123}) client = AutoRestAzureSpecialParametersTestClient(cred, validSubscription, base_url="http://localhost:3000") try: client.xms_client_request_id.get() self.fail("CloudError wasn't raised as expected") except CloudError as err: self.assertEqual("123", err.request_id) def test_xms_request_client_id_in_client(self): validSubscription = '1234-5678-9012-3456' expectedRequestId = '9C4D50EE-2D56-4CD3-8152-34347DC9F2B0' cred = BasicTokenAuthentication({"access_token":123}) client = AutoRestAzureSpecialParametersTestClient(cred, validSubscription, base_url="http://localhost:3000") client.config.generate_client_request_id = False client.xms_client_request_id.get() if __name__ == '__main__': unittest.main()
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intern/views/alliance.py
mrcrgl/gge-storage
0
1612443
<reponame>mrcrgl/gge-storage<filename>intern/views/alliance.py from __future__ import unicode_literals from django.views.generic import (ListView, DetailView, View) from django.shortcuts import render_to_response, RequestContext, Http404, HttpResponseRedirect from django.core.urlresolvers import reverse from gge_proxy_manager.models import Alliance, Kingdom, Castle # from django.db.models import Q from .mixins import GameFilterMixin, game_queryset class AllianceListView(GameFilterMixin, ListView): model = Alliance template_name = "alliance/list.html" paginate_by = 25 def get_queryset(self): queryset = game_queryset(self) #super(GameFilterMixin, self).get_queryset() query = self.request.GET.get("q", None) if not query: return queryset for word in query.split(): queryset = queryset.filter(name__icontains=word) return queryset class AllianceDetailView(DetailView): model = Alliance template_name = "alliance/detail.html" template_name_field = "alliance" class AllianceMapNoKdView(View, GameFilterMixin): def get(self, request, pk): kingdom = Kingdom.objects.filter(game=self.get_game()).order_by('kid').first() return HttpResponseRedirect(reverse("intern:alliance_neighborhood", kwargs={"pk": pk, "kingdom_id": kingdom.pk})) class AllianceMapView(View, GameFilterMixin): x_start = 0 x_stop = 1400 y_start = 0 y_stop = 1400 def get_kingdoms(self): return Kingdom.objects.filter(game=self.get_game()).order_by('kid') def kingdom_or_404(self, kingdom_id): try: return Kingdom.objects.get(pk=kingdom_id) except Kingdom.DoesNotExist: raise Http404 def alliance_or_404(self, alliance_id): try: return Alliance.objects.get(pk=alliance_id) except Alliance.DoesNotExist: raise Http404 def get(self, request, pk, kingdom_id): alliance = self.alliance_or_404(pk) kingdom = self.kingdom_or_404(kingdom_id) kingdoms = self.get_kingdoms() castles = Castle.objects.filter(kingdom=kingdom, player__alliance=alliance) # , type__in=Castle.TYPE_WITH_WARRIORS ruler_steps = [i for i in range(self.x_start, self.x_stop, 100)] return render_to_response( "alliance/neighborhood.html", { "kingdom_list": kingdoms, "kingdom": kingdom, "alliance": alliance, "castle_list": castles, "x_start": self.x_start, "x_stop": self.x_stop, "y_start": self.y_start, "y_stop": self.y_stop, "ruler_steps": ruler_steps, }, context_instance=RequestContext(request) )
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src/dispatch/tag/recommender.py
homebysix/dispatch
0
86441
""" .. module: dispatch.tag.recommender :platform: Unix :copyright: (c) 2019 by Netflix Inc., see AUTHORS for more :license: Apache, see LICENSE for more details. """ import logging from typing import List, Any from collections import defaultdict import tempfile import pandas as pd from pandas.core.frame import DataFrame from dispatch.database import SessionLocal from dispatch.tag import service as tag_service log = logging.getLogger(__name__) def save_model(dataframe: DataFrame, model_name: str): """Saves a correlation dataframe to disk.""" file_name = f"{tempfile.gettempdir()}/{model_name}.pkl" dataframe.to_pickle(file_name) def load_model(model_name: str): """Loads a correlation dataframe from disk.""" file_name = f"{tempfile.gettempdir()}/{model_name}.pkl" return pd.read_pickle(file_name) def correlation(df, tag_a, tag_b): """Determine the probability of correlation/association.""" # Find all rows where a AND b == True a_and_b = df[(df[tag_a]) & (df[tag_b])] # Find all rows where a == True AND b != True a_not_b = df[(df[tag_a]) & ~(df[tag_b])] # Find all rows where b == True AND a != True b_not_a = df[(df[tag_b]) & ~(df[tag_a])] # Calculate the number of positive and possible outcomes using the shape attribute possible_outcomes = ( a_and_b.shape[0] + a_not_b.shape[0] + b_not_a.shape[0] ) # shape[0] returns the number of rows positive_outcomes = a_and_b.shape[0] # Calculate the final correlation coefficient r = positive_outcomes / possible_outcomes return r def correlate_with_every_tag(df, tag_a): """Create correlations between every tag.""" unique_tags = list(df.columns) # Loop through every tag and store the correlation in a list correlation_list = [] for tag_b in unique_tags: correlation_list.append(correlation(df, tag_a, tag_b)) return correlation_list def get_unique_tags(items: List[Any]): """Get unique tags.""" unique_tags = {} for i in items: for t in i.tags: unique_tags[t.id] = t.id return unique_tags def create_correlation_dataframe(dataframe): """Create the correlation dataframe based on the boolean dataframe.""" unique_tags = list(dataframe.columns) correlation_matrix_dict = {} for tag_a in unique_tags: correlation_matrix_dict[tag_a] = correlate_with_every_tag(dataframe, tag_a) correlated_dataframe = pd.DataFrame(correlation_matrix_dict) correlated_dataframe["index"] = unique_tags return correlated_dataframe.set_index("index") def create_boolean_dataframe(items: List[Any]): """Create a boolean dataframe with tag and item data.""" unique_tags = get_unique_tags(items) boolean_df = pd.DataFrame(columns=unique_tags.values()) data_dict = defaultdict(list) for col in boolean_df: for i in items: tag_ids = [t.id for t in i.tags] data_dict[col].append(col in tag_ids) for col in boolean_df: boolean_df[col] = data_dict[col] return boolean_df def find_correlations(dataframe, tag): """Find all correlations for the given tag.""" # Setup empty list correlations = [] columns = [] # Loop through all column at the row with the tag as its index for i, corr in enumerate(dataframe.loc[tag, :]): # Find the column col = dataframe.columns[i] # Append the correlation to the list correlations.append(corr) columns.append(col) # Create a df out of the lists results_df = pd.DataFrame({"tag": columns, "correlation": correlations}) return results_df def find_highest_correlations(correlated_dataframe, recommendations): """Find the correlations with the highest relevancy.""" # Sort the input df corr_df_sorted = correlated_dataframe.sort_values(by=["correlation"], ascending=False) # Extract the relevant correlations corr_df_sliced = corr_df_sorted.iloc[1 : recommendations + 1] # noqa return corr_df_sliced def get_recommendations(db_session: SessionLocal, tag_ids: List[str], model_name: str, recommendations: int = 5): """Get recommendations based on current tag.""" try: correlation_dataframe = load_model(model_name) except FileNotFoundError: log.warning(f"No model file found. ModelName: {model_name}") return [] recommendations_dataframe = pd.DataFrame() for tag_id in tag_ids: correlated_dataframe = find_correlations(correlation_dataframe, tag_id) recommendations_dataframe = pd.concat( [ recommendations_dataframe, find_highest_correlations(correlated_dataframe, recommendations), ], ignore_index=True, ) # convert back to tag objects tags = [] for t in recommendations_dataframe["tag"][:recommendations]: tags.append(tag_service.get(db_session=db_session, tag_id=int(t))) log.debug(f"Making tag recommendation. RecommendedTags: {','.join([t.name for t in tags])} ModelName: {model_name}") return tags def build_model(items: List[Any], model_name: str): """Builds the correlation dataframe for items.""" boolean_dataframe = create_boolean_dataframe(items) correlation_dataframe = create_correlation_dataframe(boolean_dataframe) save_model(correlation_dataframe, model_name)
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examples/get_data_advanced.py
dark-vex/sysdig-sdk-python
45
84301
#!/usr/bin/env python # # This script shows an advanced Sysdig Monitor data request that leverages # filtering and segmentation. # # The request returns the last 10 minutes of CPU utilization for the 5 # busiest containers inside the given host, with 1 minute data granularity # import json import sys from sdcclient import SdcClient # # Parse arguments # if len(sys.argv) != 3: print(('usage: %s <sysdig-token> <hostname>' % sys.argv[0])) print('You can find your token at https://app.sysdigcloud.com/#/settings/user') sys.exit(1) sdc_token = sys.argv[1] hostname = sys.argv[2] # # Instantiate the SDC client # sdclient = SdcClient(sdc_token) # # Prepare the metrics list. # metrics = [ # The first metric we request is the container name. This is a segmentation # metric, and you can tell by the fact that we don't specify any aggregation # criteria. This entry tells Sysdig Monitor that we want to see the CPU # utilization for each container separately. {"id": "container.name"}, # The second metric we request is the CPU. We aggregate it as an average. {"id": "cpu.used.percent", "aggregations": { "time": "avg", "group": "avg" } } ] # # Prepare the filter # filter = "host.hostName = '%s'" % hostname # # Paging (from and to included; by default you get from=0 to=9) # Here we'll get the top 5. # paging = {"from": 0, "to": 4} # # Fire the query. # ok, res = sdclient.get_data(metrics=metrics, # List of metrics to query start_ts=-600, # Start of query span is 600 seconds ago end_ts=0, # End the query span now sampling_s=60, # 1 data point per minute filter=filter, # The filter specifying the target host paging=paging, # Paging to limit to just the 5 most busy datasource_type='container') # The source for our metrics is the container # # Show the result! # print((json.dumps(res, sort_keys=True, indent=4))) if not ok: sys.exit(1)
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Python/lc_497_random_point_nonoverlapping_rectangles.py
cmattey/leetcode_problems
6
151020
# Time: init: O(N), pick: O(logN) # Space: O(N) class Solution: import random def __init__(self, rects: List[List[int]]): self.rects = rects rect_areas = [] for rect in rects: rect_areas.append(self.get_area(rect)) self.sum_areas = sum(rect_areas) self.cum_sum = [rect_areas[0]] for i in range(1,len(rect_areas)): self.cum_sum.append(self.cum_sum[i-1]+rect_areas[i]) def get_area(self,rect): x1,y1,x2,y2 = rect return abs(y2-y1+1)*abs(x2-x1+1) def pick(self) -> List[int]: rand_num = random.randint(0,self.sum_areas) rect_index = -1 # for index,sum in enumerate(self.cum_sum): # Can do binary search here to reduce to O(logN) # if sum>=rand_num: # rect_index = index # break low = 0 high = len(self.cum_sum)-1 while low<=high: mid = (low+high)//2 if self.cum_sum[mid]==rand_num: rect_index = mid break elif self.cum_sum[mid]>rand_num: high = mid-1 else: low = mid+1 if rect_index==-1: rect_index = low rect = self.rects[rect_index] min_x = min(rect[0],rect[2]) max_x = max(rect[0],rect[2]) min_y = min(rect[1],rect[3]) max_y = max(rect[1],rect[3]) return [random.randint(min_x,max_x),random.randint(min_y,max_y)] # Your Solution object will be instantiated and called as such: # obj = Solution(rects) # param_1 = obj.pick()
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log/slack_sender.py
SmashKs/BarBarian
0
2888
<gh_stars>0 from slackclient import SlackClient from external import SLACK_API_KEY class SlackBot: API_CHAT_MSG = 'chat.postMessage' BOT_NAME = 'News Bot' DEFAULT_CHANNEL = 'news_notification' def __new__(cls, *p, **k): if '_the_instance' not in cls.__dict__: cls._the_instance = object.__new__(cls) return cls._the_instance def __init__(self): self.__slack_client = SlackClient(SLACK_API_KEY) def send_msg_to(self, text='', channel=DEFAULT_CHANNEL): self.__slack_client.api_call(SlackBot.API_CHAT_MSG, username=SlackBot.BOT_NAME, channel=channel, text=text) def send_formatted_msg_to(self, text='', channel=DEFAULT_CHANNEL): self.__slack_client.api_call(SlackBot.API_CHAT_MSG, username=SlackBot.BOT_NAME, mrkdwn=True, channel=channel, text=text) if __name__ == '__main__': SlackBot().send_msg_to('hello world!!')
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scripts/mlp/wholebody/croccodyl.py
JasonChmn/multicontact-locomotion-planning
0
133067
<filename>scripts/mlp/wholebody/croccodyl.py import mlp.config as cfg def generateWholeBodyMotion(cs,fullBody=None,viewer=None): raise NotImplemented("TODO")
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impy/tests/test_new_interface.py
kotania/impy
6
70127
<filename>impy/tests/test_new_interface.py import sys import os import numpy as np root_dir = os.path.abspath(os.path.dirname(__file__) + "/..") sys.path.append(root_dir) sys.path.append(os.path.join(root_dir,'../DPMJET-III-gitlab')) from impy.definitions import * from impy.constants import * from impy.kinematics import EventKinematics from impy.common import impy_config, pdata # AF: This is what the user interaction has to yield. # It is the typical expected configuration that one # wants to run (read pp-mode at energies not exceeding # 7 TeV). If you want cosmic ray energies, this should # be rather p-N at 10 EeV and lab frame (not yet defined). event_kinematics = EventKinematics( ecm=7 * TeV, p1pdg=-211, # nuc1_prop=(12,6), nuc2_prop=(12, 6)) impy_config["user_frame"] = 'laboratory' generator = make_generator_instance(interaction_model_by_tag['DPMJETIII171']) generator.init_generator(event_kinematics) # import IPython # IPython.embed() # This for event in generator.event_generator(event_kinematics, 10): event.filter_final_state() # print 'px', event.px # print 'py', event.py # print 'pz', event.pz # print 'en', event.en print('p_ids', event.p_id) print('impact param', event.impact_parameter) # import IPython # IPython.embed() # print event.impact_parameter, event.n_wounded_A, event.n_wounded_B#, event.n_NN_interactions
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src/modules/print.py
wuttinanhi/dumb-lang
0
95812
from typing import Dict from src.token import Token from src.modules.content import ContentParser from src.variable_storage import VariableStorage class PrintModule: @staticmethod def execute(token: Token, variable_storage: VariableStorage): # seperate text by space content = token.line.script.split(" ", 1) # parse content and print parsed = ContentParser.parse(content[1], variable_storage) # output to screen print(parsed)
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10.py
tlgs/aoc-2021
1
157874
"""Day 10: Syntax Scoring""" import sys def syntax_error_score(lines): pairs = {")": "(", "]": "[", "}": "{", ">": "<"} points = {"(": 3, "[": 57, "{": 1197, "<": 25137} total = 0 for line in lines: stack = [] for c in line: if c in points: stack.append(c) elif pairs[c] == stack[-1]: stack.pop() else: total += points[pairs[c]] break return total def middle_autocomplete_score(lines): pairs = {")": "(", "]": "[", "}": "{", ">": "<"} points = {"(": 1, "[": 2, "{": 3, "<": 4} scores = [] for line in lines: stack = [] for c in line: if c in points: stack.append(c) elif pairs[c] == stack[-1]: stack.pop() else: break else: total = 0 while stack: total = total * 5 + points[stack.pop()] scores.append(total) scores.sort() return scores[len(scores) // 2] class Test: example = [ "[({(<(())[]>[[{[]{<()<>>", "[(()[<>])]({[<{<<[]>>(", "{([(<{}[<>[]}>{[]{[(<()>", "(((({<>}<{<{<>}{[]{[]{}", "[[<[([]))<([[{}[[()]]]", "[{[{({}]{}}([{[{{{}}([]", "{<[[]]>}<{[{[{[]{()[[[]", "[<(<(<(<{}))><([]([]()", "<{([([[(<>()){}]>(<<{{", "<{([{{}}[<[[[<>{}]]]>[]]", ] def test_one(self): assert syntax_error_score(self.example) == 26397 def test_two(self): assert middle_autocomplete_score(self.example) == 288957 def main(): puzzle = [line.rstrip() for line in sys.stdin] print("part 1:", syntax_error_score(puzzle)) print("part 2:", middle_autocomplete_score(puzzle)) if __name__ == "__main__": main()
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donkeycar/parts/angle_adjust.py
hironorinaka99/donkeycar
0
31211
class angle_adjustclass(): #ステアリングの切れ角を調整する def __init__(self): self.angle_adjust = 1.0 return def angleincrease(self): self.angle_adjust = round(min(2.0, self.angle_adjust + 0.05), 2) print("In angle_adjust increase",self.angle_adjust) def angledecrease(self): self.angle_adjust = round(max(0.5, self.angle_adjust - 0.05), 2) print("In angle_adjust increase",self.angle_adjust) def run(self): return self.angle_adjust
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armada_flexbe_states/src/armada_flexbe_states/concatenate_pointcloud_service_state.py
uml-robotics/armada_behaviors
0
136889
#!/usr/bin/env python import rospy from flexbe_core import EventState, Logger from flexbe_core.proxy import ProxyServiceCaller from sensor_msgs.msg import PointCloud2 from armada_flexbe_utilities.srv import ConcatenatePointCloud, ConcatenatePointCloudResponse, ConcatenatePointCloudRequest class concatenatePointCloudState(EventState): ''' Example for a state to demonstrate which functionality is available for state implementation. This example lets the behavior wait until the given target_time has passed since the behavior has been started. ># pointcloud_list List of PointCloud2 messages #> combined_pointcloud Concatenated PointCloud2 message <= continue Concatenated pointclouds successfully <= failed Something went wrong ''' def __init__(self): # Declare outcomes, input_keys, and output_keys by calling the super constructor with the corresponding arguments. super(concatenatePointCloudState, self).__init__(outcomes = ['continue', 'failed'], input_keys = ['pointcloud_list'], output_keys = ['combined_pointcloud']) def execute(self, userdata): # This method is called periodically while the state is active. # Main purpose is to check state conditions and trigger a corresponding outcome. # If no outcome is returned, the state will stay active. self._service_topic = '/concatenate_pointcloud' rospy.wait_for_service(self._service_topic) self._service = ProxyServiceCaller({self._service_topic: ConcatenatePointCloud}) try: service_response = self._service.call(self._service_topic, userdata.pointcloud_list) userdata.combined_pointcloud = service_response.cloud_out return 'continue' except: return 'failed' def on_enter(self, userdata): # This method is called when the state becomes active, i.e. a transition from another state to this one is taken. # It is primarily used to start actions which are associated with this state. Logger.loginfo('attempting to concatenate pointcloud...' ) def on_exit(self, userdata): # This method is called when an outcome is returned and another state gets active. # It can be used to stop possibly running processes started by on_enter. pass # Nothing to do in this state. def on_start(self): # This method is called when the behavior is started. # If possible, it is generally better to initialize used resources in the constructor # because if anything failed, the behavior would not even be started. pass # Nothing to do in this state. def on_stop(self): # This method is called whenever the behavior stops execution, also if it is cancelled. # Use this event to clean up things like claimed resources. pass # Nothing to do in this state.
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scify/__init__.py
DanielBok/scify
6
54133
<filename>scify/__init__.py # -*- coding: utf-8 -*- """Top-level package for scify.""" __author__ = """<NAME>""" __email__ = '<EMAIL>' __version__ = '0.1.0'
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igem2017/sdin/views/main_views.py
StrickerLee/SYSU-Software-2017
40
113910
<reponame>StrickerLee/SYSU-Software-2017 # -*- coding: utf-8 -*- from __future__ import unicode_literals from django.shortcuts import render, redirect from sdin.forms import * from sdin.models import * from django.contrib import messages from django.db import IntegrityError from django.core.exceptions import ObjectDoesNotExist from django.contrib.auth import authenticate, login, logout from django.contrib.auth.decorators import login_required from django.http import JsonResponse, HttpResponse import traceback import json Err = "Something wrong!" Inv = "Invalid form!" def index(request): return render(request, 'index.html') def login_view(request): if request.method == "POST": # Login action form = LoginForm(request.POST) if form.is_valid(): email = form.cleaned_data['email'] password = <PASSWORD>.cleaned_data['password'] user = authenticate(username = email, password = password) if user is not None: login(request, user) messages.success(request, "Login successfully!") next_url = request.POST.get('next') if next_url: return redirect(next_url) else: return redirect('/') else: messages.error(request, "Invalid Login!") else: messages.error(request, Inv) return render(request, 'login.html') elif request.method == 'GET': return render(request, 'login.html') @login_required def logout_view(request): logout(request) return redirect('/index') @login_required def interest_view(request): return render(request, 'interest.html') def register(request): if request.method == "POST": form = RegisterForm(request.POST) if form.is_valid(): try: user = User.objects.create_user(email = form.cleaned_data["email"], password = form.cleaned_data["password"], org = form.cleaned_data["org"], igem = form.cleaned_data["igem"] ) login(request, user) messages.success(request, "Register successfully!") return redirect('/interest') except IntegrityError: messages.error(request, "Email already exists!") except: messages.error(request, Err) else: messages.error(request, Inv) return render(request, 'register.html') def work(request): try: wk = Works.objects.get(TeamID = request.GET.get('id')) use_parts = wk.Use_parts.split(';') part = [] for item in use_parts: try: pt = Parts.objects.get(Name = item) if request.user.is_authenticated: try: FavoriteParts.objects.get(user = request.user, part = pt) favor = True except FavoriteParts.DoesNotExist: favor = False else: favor = False part.append({ 'id': pt.id, 'BBa': item, 'name': pt.secondName, 'isFavourite': favor}) except Parts.DoesNotExist: part.append({ 'id': request.GET.get('id'), 'BBa': item, 'name': pt.secondName, 'isFavourite': False}) if request.user.is_authenticated: try: UserFavorite.objects.get(user = request.user, circuit = wk.Circuit) favorite = True except UserFavorite.DoesNotExist: favorite = False else: favorite = False if wk.Img.all().count() == 0: Img = [wk.DefaultImg] else: Img = [i.URL for i in wk.Img.all()] Awards = wk.Award.split(';') while len(Awards) > 0 and Awards[-1] == '': Awards = Awards[: -1] relatedTeams = Trelation.objects.filter(first = wk) relatedTeams = list(map(lambda rt: { 'teamName': rt.second.Teamname, 'projectName': rt.second.Title, 'year': rt.second.Year, 'id': rt.second.TeamID }, relatedTeams)) keywords = TeamKeyword.objects.filter(Team = wk) keywords = list(map(lambda tk: [tk.keyword, tk.score], keywords)) keywords.sort(key = lambda tk: -tk[1]) keywords = list(map(lambda tk: tk[0], keywords))[:5] wk.ReadCount += 1 wk.save() context = { 'projectName': wk.Title, 'teamName': wk.Teamname, 'year': wk.Year, 'readCount': wk.ReadCount, 'medal': wk.Medal, 'rewards': Awards, 'description': wk.Description, 'isFavourite': favorite, 'images': Img, 'designId': -1 if wk.Circuit is None else wk.Circuit.id, 'part': part, 'logo': wk.logo, 'keywords': keywords, 'relatedTeams': relatedTeams } return render(request, 'work.html', context) except Works.DoesNotExist: return HttpResponse("Work Does Not Exist!") search_url = 'http://sdin.sysusoftware.info:10086' import requests import json trackTable = { 'artDesign': 'Art & Design', 'diagnostics': 'Diagnostics', 'energy': 'Energy', 'environment': 'Environment', 'foodEnergy': 'Food & Energy', 'foodAndNutrition': 'Food and Nutrition', 'foundationalAdvance': 'Foundational Advance', 'hardware': 'Hardware', 'healthMedicine': 'Health & Medicine', 'highSchool': 'High School', 'informationProcessing': 'Information Processing', 'manufacturing': 'Manufacturing', 'measurement': 'Measurement', 'newApplication': 'New Application', 'software': 'Software', 'therapeutics': 'Therapeutics'} def _get_work(w, request): if request.user.is_authenticated: try: UserFavorite.objects.get(user = request.user, circuit = w.Circuit) favourite = True except UserFavorite.DoesNotExist: favourite = False else: favourite = False awards = w.Award.split(';') while len(awards) > 0 and awards[-1] == '': awards = awards[:-1] return { 'id': w.TeamID, 'year': w.Year, 'teamName': w.Teamname, 'projectName': w.Title, 'school': w.Teamname, 'medal': w.Medal, 'description': w.SimpleDescription, 'chassis': w.Chassis, 'rewards': awards, 'isFavourite': favourite, 'logo': w.logo, 'IEF': w.IEF } def safety_level(s): try: return { 1: 'Low risk', 2: 'Moderate risk', 3: 'High risk' }[s] except KeyError: return 'Unknown risk' def _get_part(p, request): if request.user.is_authenticated: try: FavoriteParts.objects.get(user = request.user, part = p) favourite = True except FavoriteParts.DoesNotExist: favourite = False else: favourite = False return { 'id': p.id, 'name': p.Name, 'group': p.Group, 'date': p.DATE, 'description': p.Description, 'type': p.Type, 'releaseStatus': p.Release_status, 'sampleStatus': p.Sample_status, 'rating': p.Part_rating, 'use': p.Use, 'partResult': p.Part_results, 'safety': safety_level(p.Safety), 'isFavorite': favourite } def search_work(request): key = request.GET.get('q') lkey = key.lower() year = request.GET.get('year') if year == None: year = 'any' medal = request.GET.get('medal') if medal == None: medal = 'any' track = request.GET.get('track') if track == None: track = 'any' if track != 'any': track = trackTable[track] keys = key.split() true_keys = [] key_dict = {} q = Keyword.objects.all() d = list(filter(lambda x: x.name.lower() in lkey, q)) for i in d: if 'name' not in key_dict or len(key_dict['name']) < len(i.name): key_dict = i.__dict__ for i in keys: keyword_query = Keyword.objects.filter(name__contains = i) filter_key = False for j in keyword_query: if j._type == 'year' and (year == 'any' or year == j.name): year = j.name filter_key = True elif j._type == 'track' and (track == 'any' or track == j.name): track = j.name filter_key = True elif j._type == 'medal' and (medal == 'any' or medal == j.name): medal = j.name filter_key = True if not filter_key: true_keys.append(i) if 'link' in key_dict: key_dict['link'] = json.loads(key_dict['link'])[0] parts = [] works = [] keywords = [] if '_type' in key_dict and (key_dict['_type'] == 'team name' or key_dict['_type'] == 'special prizes'): _data = json.loads(key_dict['suggestedProject']) for d in _data: try: w = Works.objects.get(Year = d[1][0:4], Teamname = d[1][5:]) works.append(_get_work(w, request)) except: pass _data = json.loads(key_dict['suggestedPart']) for d in _data: try: w = Parts.objects.get(Name = d[1]) parts.append(_get_part(w, request)) except: pass key = ''.join(map(lambda x: str(x) + ' ', true_keys)) if len(key) > 0: key = key[:-1] key = key.lower() if request.user.is_authenticated and request.user.interest != 'None': interest = json.dumps(json.loads(request.user.interest)['interest']) else: interest = '[]' res = requests.get(search_url + "?key=" + key + "&interest=" + interest) try: result = json.loads(res.text) except: result = res.text if len(true_keys) == 0: q = Works.objects.all().order_by('-IEF') if year != 'any': q = q.filter(Year = year) if medal != 'any': q = q.filter(Medal = medal) if track != 'any': q = q.filter(Track = track) for w in q: works.append(_get_work(w, request)) if type(result) is dict: if parts == []: for item in result['parts']: try: p = Parts.objects.get(Name = item) parts.append(_get_part(p, request)) except Parts.DoesNotExist: pass if works == []: for item in result['teams']: try: s = item.split(' ') w = Works.objects.get(Teamname = s[0], Year = s[1]) if year is not None and year != 'any' and w.Year != int(year): continue if medal is not None and medal != 'any' and w.Medal != medal: continue if track is not None and track != 'any' and w.Track != track: continue works.append(_get_work(w, request)) except Works.DoesNotExist: pass keywords = result['keyWords'] context = { 'works': works, 'parts': parts, 'keywords': keywords, 'resultsCount': len(works), 'additional': key_dict} return render(request, 'search/work.html', context) def search_paper(request): key = request.GET.get('q') query = Papers.objects.filter(Title__contains = key) papers = [{ 'id': x.id, 'title': x.Title, 'author': x.Authors, 'DOI': x.DOI, 'abstract': x.Abstract if len(x.Abstract) <= 120 else x.Abstract[:117] + '...', 'JIF': x.JIF, 'logo': x.LogoURL, 'circuitId': x.Circuit.id} for x in query] context = { 'resultsCount': len(papers), 'papers': papers} print(context) return render(request, 'search/paper.html', context) def search_part(request): key = request.GET.get('q') if key.find('BBa_') != -1: query_set = Parts.objects.filter(Name__contains = key) parts = [_get_part(x, request) for x in query_set] context = { 'parts': parts, 'resultsCount': len(parts) } return render(request, 'search/part.html', context) lkey = key.lower() year = request.GET.get('year') if year == None: year = 'any' medal = request.GET.get('medal') if medal == None: medal = 'any' track = request.GET.get('track') if track == None: track = 'any' if track != 'any': track = trackTable[track] keys = key.split() true_keys = [] key_dict = {} q = Keyword.objects.all() d = list(filter(lambda x: x.name.lower() in lkey, q)) for i in d: if 'name' not in key_dict or len(key_dict['name']) < len(i.name): key_dict = i.__dict__ for i in keys: keyword_query = Keyword.objects.filter(name__contains = i) filter_key = False for j in keyword_query: if j._type == 'year' and (year == 'any' or year == j.name): year = j.name filter_key = True elif j._type == 'track' and (track == 'any' or track == j.name): track = j.name filter_key = True elif j._type == 'medal' and (medal == 'any' or medal == j.name): medal = j.name filter_key = True if not filter_key: true_keys.append(i) if 'link' in key_dict: key_dict['link'] = json.loads(key_dict['link']) parts = [] works = [] keywords = [] if '_type' in key_dict and (key_dict['_type'] == 'team name' or key_dict['_type'] == 'special prizes'): _data = json.loads(key_dict['suggestedProject']) for d in _data: try: w = Works.objects.get(Year = d[1][0:4], Teamname = d[1][5:]) works.append(_get_work(w, request)) except: pass _data = json.loads(key_dict['suggestedPart']) for d in _data: try: w = Parts.objects.get(Name = d[1]) parts.append(_get_part(w, request)) except: pass key = ''.join(map(lambda x: str(x) + ' ', true_keys)) if len(key) > 0: key = key[:-1] key = key.lower() if request.user.is_authenticated and request.user.interest != 'None': interest = json.dumps(json.loads(request.user.interest)['interest']) else: interest = '[]' res = requests.get(search_url + "?key=" + key + "&interest=" + interest) try: result = json.loads(res.text) except: result = res.text if len(true_keys) == 0: q = Works.objects.all().order_by('-IEF') if year != 'any': q = q.filter(Year = year) if medal != 'any': q = q.filter(Medal = medal) if track != 'any': q = q.filter(Track = track) for w in q: works.append(_get_work(w, request)) if type(result) is dict: if parts == []: for item in result['parts']: try: p = Parts.objects.get(Name = item) parts.append(_get_part(p, request)) except Parts.DoesNotExist: pass if works == []: for item in result['teams']: try: s = item.split(' ') w = Works.objects.get(Teamname = s[0], Year = s[1]) if year is not None and year != 'any' and w.Year != int(year): continue if medal is not None and medal != 'any' and w.Medal != medal: continue if track is not None and track != 'any' and w.Track != track: continue works.append(_get_work(w, request)) except Works.DoesNotExist: pass keywords = result['keyWords'] context = { 'works': works, 'parts': parts, 'keywords': keywords, 'resultsCount': len(parts), 'additional': key_dict} return render(request, 'search/part.html', context) def search_paper(request): key = request.GET.get('q') query = Papers.objects.filter(Abstract__contains = key) papers = [{ 'id': x.id, 'title': x.Title, 'author': x.Authors, 'DOI': x.DOI, 'abstract': x.Abstract if len(x.Abstract) <= 120 else x.Abstract[:117] + '...', 'JIF': x.JIF, 'logo': x.LogoURL, 'circuitId': x.Circuit.id} for x in query] context = { 'resultsCount': len(papers), 'papers': papers} print(context) return render(request, 'search/paper.html', context) return render(request, 'search/part.html', context) def paper(request): key = request.GET.get('id') try: paper = Papers.objects.get(pk = key) parts_query = CircuitParts.objects.filter(Circuit = paper.Circuit) part = [] for q in parts_query: try: pt = q.Part if request.user.is_authenticated: try: FavoriteParts.objects.get(user = request.user, part = pt) part.append({ 'id': pt.id, 'BBa': pt.Name, 'name': pt.secondName, 'isFavourite': True}) except FavoriteParts.DoesNotExist: part.append({ 'id': pt.id, 'BBa': pt.Name, 'name': pt.secondName, 'isFavourite': False}) except Parts.DoesNotExist: part.append({ 'id': pt.id, 'BBa': pt.Name, 'name': pt.secondName, 'isFavourite': False}) if request.user.is_authenticated: try: UserFavorite.objects.get(user = request.user, circuit = paper.Circuit) favorite = True except UserFavorite.DoesNotExist: favorite = False else: favorite = False context = { 'title': paper.Title, 'DOI': paper.DOI, 'authors': paper.Authors.split(','), 'abstract': paper.Abstract, 'JIF': paper.JIF, 'keywords': paper.Keywords, 'designId': paper.Circuit.id, 'articleURL': paper.ArticleURL, 'copyright': paper.Copyright, 'part': part } return render(request, 'paper.html', context) except Papers.DoesNotExist: return HttpResponse('Does not exist.') @login_required def interest(request): ''' GET /api/interest (get user interests) return: interest: ['xxx', 'xxx'] POST /api/interest (set user interests) interest: ['xxx', 'xxx'] return: success: true of false ''' try: if request.method == 'POST': request.user.interest = request.POST['data'] request.user.save() return JsonResponse({ 'success': True}) else: interests = request.user.interest if interests == 'None': interests = [] else: interests = json.loads(interests)['interest'] return JsonResponse({ 'success': True, 'interest': interests}) except: traceback.print_exc() return JsonResponse({ 'success': False}) import json with open(os.path.abspath(os.path.join(BASE_DIR, 'tools/preload/others/daotu.json'))) as f: daotu = json.load(f) def keywords(request): ''' GET /keywords ''' return JsonResponse(daotu)
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data_utils/split_train_val.py
aman0044/pytorch-classifier
0
44524
import torch from torchvision import datasets import shutil import argparse import os import numpy as np from tqdm import tqdm ########### Help ########### ''' #size = (h,w) python split_train_val.py \ --data_dir /Users/aman.gupta/Documents/self/datasets/blank_page_detection/letterbox_training_data/ \ --val_ratio 0.10 \ --output_dir /Users/aman.gupta/Documents/self/datasets/blank_page_detection/splitted_letterbox_training_data ''' ########################### if __name__ == '__main__': parser = argparse.ArgumentParser(description="this script splits classification data into train and val based on ratio provided by user") parser.add_argument("--data_dir",required = True,help="training data path") parser.add_argument("--val_ratio",default = 0.2,type = float, help="ratio of val in total data") parser.add_argument("--output_dir",required=False,default="./data/",type=str,help="dir to save images") args = parser.parse_args() os.makedirs(args.output_dir,exist_ok=True) data = datasets.ImageFolder(args.data_dir) imgs_info = data.imgs classes = data.classes output_folders = ['train','val'] for o_folder in output_folders: for folder in classes: os.makedirs(os.path.join(args.output_dir,o_folder,folder),exist_ok=True) print(f"Total classes:{len(classes)}") np.random.shuffle(imgs_info) num_samples = len(imgs_info) split = int(np.floor(args.val_ratio * num_samples)) train_info, test_info = imgs_info[split:], imgs_info[:split] print(f"processing train...") for info in tqdm(train_info): folder = classes[info[1]] source = info[0] file_name = os.path.basename(source) destination = os.path.join(args.output_dir,output_folders[0],folder,file_name) shutil.copy(source, destination) print(f"processing val...") for info in tqdm(test_info): folder = classes[info[1]] source = info[0] file_name = os.path.basename(source) destination = os.path.join(args.output_dir,output_folders[1],folder,file_name) shutil.copy(source, destination)
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custom_components/yoosee/const.py
shaonianzhentan/ha_yoosee_camera
0
31054
DOMAIN = "yoosee" PLATFORMS = ["camera"] DEFAULT_NAME = "Yoosee摄像头" VERSION = "1.1" SERVICE_PTZ = 'ptz'
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src/apps/devices/cubelib/emulator.py
ajintom/music_sync
0
19048
#!/bin/env python #using the wireframe module downloaded from http://www.petercollingridge.co.uk/ import mywireframe as wireframe import pygame from pygame import display from pygame.draw import * import time import numpy key_to_function = { pygame.K_LEFT: (lambda x: x.translateAll('x', -10)), pygame.K_RIGHT: (lambda x: x.translateAll('x', 10)), pygame.K_DOWN: (lambda x: x.translateAll('y', 10)), pygame.K_UP: (lambda x: x.translateAll('y', -10)), pygame.K_EQUALS: (lambda x: x.scaleAll(1.25)), pygame.K_MINUS: (lambda x: x.scaleAll( 0.8)), pygame.K_q: (lambda x: x.rotateAll('X', 0.1)), pygame.K_w: (lambda x: x.rotateAll('X', -0.1)), pygame.K_a: (lambda x: x.rotateAll('Y', 0.1)), pygame.K_s: (lambda x: x.rotateAll('Y', -0.1)), pygame.K_z: (lambda x: x.rotateAll('Z', 0.1)), pygame.K_x: (lambda x: x.rotateAll('Z', -0.1))} class ProjectionViewer: """ Displays 3D objects on a Pygame screen """ def __init__(self, width, height): self.width = width self.height = height self.screen = pygame.display.set_mode((width, height)) pygame.display.set_caption('Wireframe Display') self.background = (10,10,50) self.wireframes = {} self.displayNodes = True self.displayEdges = True self.nodeColour = (255,255,255) self.edgeColour = (200,200,200) self.nodeRadius = 3 #Modify to change size of the spheres def addWireframe(self, name, wireframe): """ Add a named wireframe object. """ self.wireframes[name] = wireframe def run(self): for event in pygame.event.get(): if event.type == pygame.KEYDOWN: if event.key in key_to_function: key_to_function[event.key](self) self.display() pygame.display.flip() def display(self): """ Draw the wireframes on the screen. """ self.screen.fill(self.background) for wireframe in self.wireframes.values(): if self.displayEdges: for edge in wireframe.edges: pygame.draw.aaline(self.screen, self.edgeColour, (edge.start.x, edge.start.y), (edge.stop.x, edge.stop.y), 1) if self.displayNodes: for node in wireframe.nodes: if node.visiblity: pygame.draw.circle(self.screen, self.nodeColour, (int(node.x), int(node.y)), self.nodeRadius, 0) def translateAll(self, axis, d): """ Translate all wireframes along a given axis by d units. """ for wireframe in self.wireframes.itervalues(): wireframe.translate(axis, d) def scaleAll(self, scale): """ Scale all wireframes by a given scale, centred on the centre of the screen. """ centre_x = self.width/2 centre_y = self.height/2 for wireframe in self.wireframes.itervalues(): wireframe.scale((centre_x, centre_y), scale) def rotateAll(self, axis, theta): """ Rotate all wireframe about their centre, along a given axis by a given angle. """ rotateFunction = 'rotate' + axis for wireframe in self.wireframes.itervalues(): centre = wireframe.findCentre() getattr(wireframe, rotateFunction)(centre, theta) def createCube(self,cube,X=[50,140], Y=[50,140], Z=[50,140]): cube.addNodes([(x,y,z) for x in X for y in Y for z in Z]) #adding the nodes of the cube framework. allnodes = [] cube.addEdges([(n,n+4) for n in range(0,4)]+[(n,n+1) for n in range(0,8,2)]+[(n,n+2) for n in (0,1,4,5)]) #creating edges of the cube framework. for i in range(0,10): for j in range(0,10): for k in range(0,10): allnodes.append((X[0]+(X[1]-X[0])/9 * i,Y[0]+(Y[1] - Y[0])/9 * j,Z[0] + (Z[1]-Z[0])/9 * k)) cube.addNodes(allnodes) #cube.outputNodes() self.addWireframe('cube',cube) def findIndex(coords): #Send coordinates of the points you want lit up. Will convert to neede indices = [] for nodes in coords: x,y,z = nodes index = x*100+y*10+z + 8 indices.append(index) return indices def findIndexArray(array): #Takes a 3-D numpy array containing bool of all the LED points. indices = [] for i in range(0,10): for j in range(0,10): for k in range(0,10): if(array[i][j][k] == 1): index = i*100+j*10+ k + 8 indices.append(index) return indices def wireframecube(size): if size % 2 == 1: size = size+1 half = size/2 start = 5 - half end = 5 + half - 1 cubecords = [(x,y,z) for x in (start,end) for y in (start,end) for z in range(start,end+1)]+[(x,z,y) for x in (start,end) for y in (start,end) for z in range(start,end+1)] + [(z,y,x) for x in (start,end) for y in (start,end) for z in range(start,end+1)] return cubecords def cubes(size): if size % 2 == 1: size = size+1 half = size/2 cubecords = [] for i in range(0,size): for j in range(0,size): for k in range(0,size): cubecords.append((5-half+i,5-half+j,5-half+k)) return cubecords if __name__ == '__main__': pv = ProjectionViewer(400, 300) allnodes =[] cube = wireframe.Wireframe() #storing all the nodes in this wireframe object. X = [50,140] Y = [50,140] Z = [50,140] pv.createCube(cube,X,Y,Z) YZface = findIndex((0,y,z) for y in range(0,10) for z in range(0,10)) count = 0 for k in range(1,150000): if k%5000 ==2500: count = (count+2)%11 cube.setVisible(findIndex(wireframecube(count))) pv.run()
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xbbo/search_algorithm/multi_fidelity/BOHB.py
zhanglei1172/XBBO
0
79643
<reponame>zhanglei1172/XBBO<gh_stars>0 ''' Reference: https://github.com/automl/DEHB ''' from typing import List import numpy as np # from xbbo.configspace.feature_space import Uniform2Gaussian from xbbo.search_algorithm.multi_fidelity.hyperband import HB from xbbo.configspace.space import DenseConfiguration, DenseConfigurationSpace from xbbo.core.trials import Trials, Trial from xbbo.search_algorithm.multi_fidelity.utils.bracket_manager import BasicConfigGenerator from xbbo.search_algorithm.tpe_optimizer import TPE from xbbo.search_algorithm.bo_optimizer import BO from xbbo.utils.constants import MAXINT, Key from .. import alg_register class BOHB_CG_TPE(BasicConfigGenerator, TPE): def __init__(self, cs, budget, max_pop_size, rng, **kwargs) -> None: BasicConfigGenerator.__init__(self, cs, budget, max_pop_size, rng, **kwargs) TPE.__init__(self, space=cs, **kwargs) self.reset(max_pop_size) class BOHB_CG(BasicConfigGenerator, BO): def __init__(self, cs, budget, max_pop_size, rng, **kwargs) -> None: BasicConfigGenerator.__init__(self, cs, budget, max_pop_size, rng, **kwargs) BO.__init__(self, space=cs, **kwargs) self.reset(max_pop_size) @property def kde_models(self): return (self.trials.trials_num) >= self.min_sample alg_marker = 'bohb' @alg_register.register(alg_marker) class BOHB(HB): name = alg_marker def __init__(self, space: DenseConfigurationSpace, budget_bound=[9, 729], # mutation_factor=0.5, # crossover_prob=0.5, # strategy='rand1_bin', eta: int = 3, seed: int = 42, round_limit: int = 1, bracket_limit=np.inf, bo_opt_name='prf', boundary_fix_type='random', **kwargs): self.bo_opt_name = bo_opt_name HB.__init__(self, space, budget_bound, eta, seed=seed, round_limit=round_limit, bracket_limit=bracket_limit, boundary_fix_type=boundary_fix_type, **kwargs) # Uniform2Gaussian.__init__(self,) # self._get_max_pop_sizes() # self._init_subpop(**kwargs) def _init_subpop(self, **kwargs): """ List of DE objects corresponding to the budgets (fidelities) """ self.cg = {} for i, b in enumerate(self._max_pop_size.keys()): if self.bo_opt_name.upper() == 'TPE': self.cg[b] = BOHB_CG_TPE( self.space, seed=self.rng.randint(MAXINT), initial_design="random", init_budget=0, budget=b, max_pop_size=self._max_pop_size[b], rng=self.rng, **kwargs) else: self.cg[b] = BOHB_CG( self.space, seed=self.rng.randint(MAXINT), initial_design="random", init_budget=0, budget=b, max_pop_size=self._max_pop_size[b], rng=self.rng, surrogate=self.bo_opt_name, **kwargs) # self.cg[b] = TPE( # self.space, # seed=self.rng.randint(MAXINT), # initial_design="random", # init_budget=0, # **kwargs) # self.cg[b].population = [None] * self._max_pop_size[b] # self.cg[b].population_fitness = [np.inf] * self._max_pop_size[b] def _acquire_candidate(self, bracket, budget): """ Generates/chooses a configuration based on the budget and iteration number """ # select a parent/target # select a parent/target parent_id = self._get_next_idx_for_subpop(budget, bracket) if budget != bracket.budgets[0]: if bracket.is_new_rung(): # TODO: check if generalizes to all budget spacings lower_budget, num_configs = bracket.get_lower_budget_promotions( budget) self._get_promotion_candidate(lower_budget, budget, num_configs) # else: # 每一列中的第一行，随机生成config else: if bracket.is_new_rung(): lower_budget, num_configs = bracket.get_lower_budget_promotions( budget) self.cg[budget].population_fitness[:] = np.inf for b in reversed(self.budgets): if self.cg[b].kde_models: break # trial = self.cg[b]._suggest()[0] trials = self.cg[b]._suggest(1) for i in range(len(trials)): self.cg[budget].population[parent_id+i] = trials[i].array # self.cg[budget].population[parent_id] = trial.array # parent_id = self._get_next_idx_for_subpop(budget, bracket) target = self.cg[budget].population[parent_id] # target = self.fix_boundary(target) return target, parent_id def _observe(self, trial_list): for trial in trial_list: self.trials.add_a_trial(trial, True) fitness = trial.observe_value job_info = trial.info # learner_train_time = job_info.get(Key.EVAL_TIME, 0) budget = job_info[Key.BUDGET] parent_id = job_info['parent_id'] individual = trial.array # TODO for bracket in self.active_brackets: if bracket.bracket_id == job_info['bracket_id']: # registering is IMPORTANT for Bracket Manager to perform SH bracket.register_job(budget) # may be new row # bracket job complete bracket.complete_job( budget) # IMPORTANT to perform synchronous SH self.cg[budget].population[parent_id] = individual self.cg[budget].population_fitness[parent_id] = fitness # updating incumbents if fitness < self.current_best_fitness: self.current_best = individual self.current_best_fitness = trial.observe_value self.current_best_trial = trial # for rung in range(bracket.n_rungs-1, bracket.current_rung, -1): # if self.cg[bracket.budgets[rung]].kde_models: # break # else: self.cg[budget]._observe([trial]) self._clean_inactive_brackets() opt_class = BOHB
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lib/handlers/base.py
WXSD-Sales/APMBot
0
73177
import json import tornado.web class BaseHandler(tornado.web.RequestHandler): def get_current_user(self): cookie = self.get_secure_cookie("sessionId", max_age_days=1, min_version=1) if cookie != None: cookie = cookie.decode('utf-8') cookie = json.loads(cookie) return cookie
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fhir.py
bionicles/playing_with_fhir
0
179385
"quantify shape and depth diversity of FHIR data" # conda create -n py39 python=3.9 # conda activate py39 # pip install rich, numpy # python fhir.py from dataclasses import dataclass from itertools import chain from typing import Dict, List, Optional, Tuple import json import os from plotly.subplots import make_subplots import plotly.graph_objects as go from rich import print import numpy as np Array = np.ndarray # data from https://synthetichealth.github.io/synthea/ VERSIONS = ("dstu2", "stu3", "r4") N_PATIENTS = 200 # use NONE to analyze all patient bundles in each folder def test_get_paths(): item1 = { "a": "bion", "b": {"c": "is", "d": "cool"}, } # (("a",), ("b", "c"), ("b", "d")) paths1 = get_paths(item1) # print(item1, "\n", paths1) assert paths1 == (("a",), ("b", "c"), ("b", "d")) item2 = {"a": ("bion", "is", "cool")} # (("a", 0), ("a", 1), ("a", 2)) paths2 = get_paths(item2) # print(item2, "\n", paths2) assert paths2 == (("a", 0), ("a", 1), ("a", 2)) item3 = {"a": {"b": ("bion", "is", "cool")}} paths3 = get_paths(item3) # print(item3, "\n", paths3) assert paths3 == (("a", "b", 0), ("a", "b", 1), ("a", "b", 2)) assert get_paths("bion") == ((),) assert get_paths(b"cool") == ((),) assert get_paths(True) == ((),) assert get_paths(None) == ((),) assert get_paths(1) == ((),) shape1 = {"a": "bion", "b": {"c": "is", "d": "cool"}} shape2 = {"a": "stuff", "b": {"c": True, "d": False}} assert get_paths(shape1) == get_paths(shape2) def get_paths( item: any, path: Tuple[any, ...] = () ) -> Tuple[Tuple[any, ...]]: # (()) or ((step0,), (step0, step1) ...) """ given a PyTree / collection, returns a tuple of path tuples, one per leaf. leaves are non-collection types (str, int, float, bool, bytes, None) get_paths({"a": "bion", "b": {"c": "is", "d": "cool"}}) = (("a",), ("b", "c"), ("b", "d")) get_paths({"a": ("bion", "is", "cool")}) = (("a", 0), ("a", 1), ("a", 2)) {"a": {"b": ("bion", "is", "cool")}} = (("a", "b", 0), ("a", "b", 1), ("a", "b", 2)) """ if isinstance(item, (str, int, float, bool, bytes, type(None))): return (path,) if isinstance(item, dict): nested = tuple(get_paths(value, path + (key,)) for key, value in item.items()) # unnest nested tuples return tuple(chain.from_iterable(nested)) if isinstance(item, (list, tuple)): nested = tuple( get_paths(value, path + (index,)) for index, value in enumerate(item) ) # unnest nested tuples return tuple(chain.from_iterable(nested)) raise TypeError(f"unsupported type: {type(item)}") def get_data(folder: str, n_patients: Optional[int] = N_PATIENTS) -> List[dict]: "get data from a folder" data = [] path = os.path.join("data", folder) for file in os.listdir(path): if file.endswith(".json"): filepath = os.path.join("data", folder, file) with open(filepath) as f: data.append(json.load(f)) if n_patients is not None and len(data) == n_patients: break return data def group_by_resource_type(bundles: List[dict]) -> Dict[str, List[dict]]: "group data by resource type" grouped = {} for bundle in bundles: for entry in bundle["entry"]: resource = entry["resource"] resource_type = resource["resourceType"] if resource_type not in grouped: grouped[resource_type] = [] grouped[resource_type].append(resource) return grouped def get_shapes_and_depths(grouped: dict) -> Tuple[Dict[str, tuple], Dict[str, Array]]: "get shapes and leaf depths of resources" shapes, depths = {}, {} for resource_type, instances in grouped.items(): if resource_type not in shapes: shapes[resource_type] = [] if resource_type not in depths: depths[resource_type] = [] for instance in instances: paths = get_paths(instance) leaf_depths = tuple(len(path) for path in paths) shapes[resource_type].append(len(paths)) depths[resource_type].extend(leaf_depths) shapes = {k: tuple(set(v)) for k, v in shapes.items()} depths = {k: np.array(v) for k, v in depths.items()} return shapes, depths @dataclass(frozen=True) class VersionStats: "statistics for a FHIR version" version: str n_patients: int counts: Dict[str, int] # {resource_type: count} depths: Dict[str, Array] # {resource_type: leaf_depths} shapes: Dict[str, tuple] # {resource_type: (shape, ...)} def get_version_stats(version: str, n_patients: int = N_PATIENTS) -> Dict[str, tuple]: "get shapes of resources" data = get_data(version, n_patients) grouped = group_by_resource_type(data) shapes, depths = get_shapes_and_depths(grouped) version_stats = VersionStats( version=version, n_patients=n_patients, counts={k: len(v) for k, v in grouped.items()}, depths=depths, shapes=shapes, ) return version_stats def show_version(stats: VersionStats) -> None: "renders a VersionStats to stdout" print("FHIR Version {stats.version}") print(" n_patients: {stats.n_patients}") for key in sorted(stats.counts.keys()): print(f" {key}: ") print(f" count: {stats.counts[key]}") print(f" n_shapes: {len(stats.shapes[key])}") print(f" avg_depth: {stats.depths[key].mean()}") print(f" max_depth: {stats.depths[key].max()}") @dataclass(frozen=True) class ResourceTypeStats: "statistics for a resource type" resource_type: str n_patients: int counts: Dict[str, int] # {version: count} depths: Dict[str, Array] # {version: depths} shapes: Dict[str, tuple] # {version: shapes} def get_resource_stats( stats: Dict[str, VersionStats] # {version: version_stats} ) -> Dict[str, ResourceTypeStats]: # {resource_type: resource_type_stats} "group the resources by type across FHIR versions" grouped = {} for version, version_stats in stats.items(): for resource_type, instances in version_stats.counts.items(): if resource_type not in grouped: grouped[resource_type] = ResourceTypeStats( resource_type=resource_type, n_patients=version_stats.n_patients, counts={}, depths={}, shapes={}, ) grouped[resource_type].counts[version] = instances grouped[resource_type].depths[version] = version_stats.depths[resource_type] grouped[resource_type].shapes[version] = version_stats.shapes[resource_type] return grouped def show_resource_stats(stats: ResourceTypeStats) -> None: "renders a ResourceTypeStats to stdout" print(f"Resource Type: {stats.resource_type}") for version in stats.counts.keys(): print(f" {version}: ") print(f" count: {stats.counts[version]}") print(f" n_shapes: {len(stats.shapes[version])}") print(f" avg_depth: {stats.depths[version].mean()}") print(f" max_depth: {stats.depths[version].max()}") def plot_lines_and_violins( all_version_stats: Dict[str, VersionStats], # {version: version_stats} all_resource_type_stats: Dict[ str, ResourceTypeStats ], # {resource_type: resource_type_stats} ) -> go.Figure: """ Plots 2 subfigures in 1 column top row: a (version, n_shapes) line per resource type top row: a (version, n_shapes) violin per version bottom row: a (version, depths) violin per version """ # make a figure with 2 rows and 1 column fig = make_subplots( rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.05, subplot_titles=("", ""), ) # label the figure fig.update_layout( title_text="Resource Polymorphism & Nesting Of FHIR Versions", xaxis_title="--- FHIR Version ---> ", yaxis_title="Count", width=1000, height=800, ) # make the top row # add a (version, n_shapes) line per resource_type for resource_type, resource_type_stats in all_resource_type_stats.items(): fig.add_trace( go.Scatter( x=list(resource_type_stats.counts.keys()), y=list( len(resource_type_stats.shapes[version]) for version in resource_type_stats.counts.keys() ), mode="lines", name=resource_type, ), row=1, col=1, ) # label the top row fig.update_yaxes( title_text="Polymorphism / # Unique Shapes (lower is better)", row=1, col=1 ) # add a (version, n_shapes) violin per version colors = {"dstu2": "red", "stu3": "green", "r4": "blue"} for version, version_stats in all_version_stats.items(): # group by version n_shapes = list(map(len, version_stats.shapes.values())) fig.add_trace( go.Violin( x=[version] * len(n_shapes), y=n_shapes, name=version, marker_color=colors[version], showlegend=False, legendgroup=version, ), row=1, col=1, ) # make the bottom row # add a (version, depths) violin per version for version, version_stats in all_version_stats.items(): # group all the depths for all the resource_types of this version depths = np.concatenate( [ version_stats.depths[resource_type] for resource_type in version_stats.counts.keys() ] ) fig.add_trace( go.Violin( x=[version] * len(depths), y=depths, name=version, marker_color=colors[version], legendgroup=version, ), row=2, col=1, ) # label the bottom row fig.update_yaxes(title_text="Nesting / Leaf Depth (lower is better)", row=2, col=1) return fig def plot_bars( all_resource_type_stats: Dict[ str, ResourceTypeStats ], # {resource_type: resource_type_stats} ) -> go.Figure: """ Plots 2 subfigures in 1 column top row: a (resource_type, n_shapes) bar group per resource_type, one bar per version bottom row: a (resource_type, depths) box plot group per resource_type, one box per version """ # make a figure with 2 rows and 1 column fig = make_subplots( rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.05, subplot_titles=("", ""), ) # label the figure fig.update_layout( title_text="Polymorphism & Nesting Of FHIR Resource Types", xaxis_title="Resource Type", yaxis_title="Count", barmode="group", width=1000, height=800, ) colors = {"dstu2": "red", "stu3": "green", "r4": "blue"} # make the top row # add a (resource_type, n_shapes) bar group per resource_type, one bar per version for version in colors.keys(): x = [ resource_type for resource_type in all_resource_type_stats.keys() if version in all_resource_type_stats[resource_type].counts ] y = [ len(resource_type_stats.shapes[version]) for resource_type_stats in all_resource_type_stats.values() if version in resource_type_stats.counts ] fig.add_trace( go.Bar( name=version, x=x, y=y, marker_color=colors[version], legendgroup=version, ), row=1, col=1, ) # label the top row fig.update_yaxes( title_text="Polymorphism / # Unique Shapes (lower is better)", row=1, col=1 ) # add a (resource_type, depths) bar group per resource_type, one box per version for version in colors.keys(): x = [ resource_type for resource_type in all_resource_type_stats.keys() if version in all_resource_type_stats[resource_type].counts ] y = [ resource_type_stats.depths[version].mean() for resource_type_stats in all_resource_type_stats.values() if version in resource_type_stats.counts ] fig.add_trace( go.Bar( name=version, x=x, y=y, marker_color=colors[version], legendgroup=version, showlegend=False, ), row=2, col=1, ) # label the bottom row fig.update_yaxes( title_text="Average Nesting / Leaf Depth (lower is better)", row=2, col=1 ) return fig def find_worst_offenders( all_resource_type_stats: Dict[str, ResourceTypeStats], version: str, ) -> Dict[str, ResourceTypeStats]: """ Finds the resource types with the worst polymorphing and nesting """ # find the resource type with the most number of shapes most_polymorphic_resource_type = None deepest_resource_type_by_mean = None deepest_resource_type_by_max = None for resource_type, resource_type_stats in all_resource_type_stats.items(): if version not in resource_type_stats.counts: continue shapes = resource_type_stats.shapes[version] depths = resource_type_stats.depths[version] if most_polymorphic_resource_type is None or len(shapes) > len( all_resource_type_stats[most_polymorphic_resource_type].shapes[version] ): most_polymorphic_resource_type = resource_type if ( deepest_resource_type_by_mean is None or depths.mean() > all_resource_type_stats[deepest_resource_type_by_mean] .depths[version] .mean() ): deepest_resource_type_by_mean = resource_type if ( deepest_resource_type_by_max is None or depths.max() > all_resource_type_stats[deepest_resource_type_by_max] .depths[version] .max() ): deepest_resource_type_by_max = resource_type return { "version": version, "most_polymorphic": all_resource_type_stats[most_polymorphic_resource_type], "deepest_by_mean": all_resource_type_stats[deepest_resource_type_by_mean], "deepest_by_max": all_resource_type_stats[deepest_resource_type_by_max], } # - the resource type with the most inconsistent data: # ImagingStudy with 177 different shapes in a sample of 977 ImagingStudy instances # - the resource type with most deeply nested data (on average): # ImagingStudy, which requires an average of 5.3 operations to access each leaf # - the resource type with most deeply nested data (worst case): # ExplanationOfBenefit has a leaf which requires 8 operations to access def show_worst_offenders(worst_offenders: dict) -> None: version = worst_offenders["version"] print(f"\nworst offenders in FHIR {version}\n") most_polymorphic = worst_offenders["most_polymorphic"] resource_type = most_polymorphic.resource_type n_shapes = len(most_polymorphic.shapes[version]) count = most_polymorphic.counts[version] print("the resource type with the most inconsistent data:") print( f"{resource_type}, with {n_shapes} unique shapes in a sample of {count} {resource_type} instances" ) print() deepest_by_mean = worst_offenders["deepest_by_mean"] resource_type = deepest_by_mean.resource_type mean_depth = deepest_by_mean.depths[version].mean() print("the resource type with most deeply nested data (on average):") print( f"{resource_type}, which requires an average of {mean_depth} operations to access each leaf" ) print() deepest_by_max = worst_offenders["deepest_by_max"] resource_type = deepest_by_max.resource_type max_depth = deepest_by_max.depths[version].max() print("the resource type with most deeply nested data (worst case):") print( f"{resource_type}, which has a leaf which requires {max_depth} operations to access" ) if __name__ == "__main__": version_stats = { version: get_version_stats(version, n_patients=N_PATIENTS) for version in VERSIONS } resource_stats = get_resource_stats(version_stats) # to make output.txt, uncomment this and run `python fhir.py > output.txt` for resource_type, resource_type_stats in resource_stats.items(): show_resource_stats(resource_type_stats) # to make worst.txt, uncomment this and run `python fhir.py > worst.txt` worst_offenders = find_worst_offenders(resource_stats, "r4") show_worst_offenders(worst_offenders) # to make plots, uncomment this and run `python fhir.py` # warning: violin plots are slow if you have a lot of data lines_and_violins = plot_lines_and_violins(version_stats, resource_stats) lines_and_violins.show() # lines_and_violins.write_image("by_fhir_version.png") bars = plot_bars(resource_stats) bars.show() # bars.write_image("by_resource_type.png")
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tests/unit/utils/test_views.py
rolandgeider/OpenSlides
0
37959
from unittest import TestCase from unittest.mock import MagicMock, patch from openslides.utils import views @patch('builtins.super') class SingleObjectMixinTest(TestCase): def test_get_object_cache(self, mock_super): """ Test that the method get_object caches his result. Tests that get_object from the django view is only called once, even if get_object on our class is called twice. """ view = views.SingleObjectMixin() view.get_object() view.get_object() mock_super().get_object.assert_called_once_with() def test_dispatch_with_existin_object(self, mock_super): view = views.SingleObjectMixin() view.object = 'old_object' view.get_object = MagicMock() view.dispatch() mock_super().dispatch.assert_called_with() self.assertEqual( view.object, 'old_object', "view.object should not be changed") self.assertFalse( view.get_object.called, "view.get_object() should not be called") def test_dispatch_without_existin_object(self, mock_super): view = views.SingleObjectMixin() view.get_object = MagicMock(return_value='new_object') view.dispatch() mock_super().dispatch.assert_called_with() self.assertEqual( view.object, 'new_object', "view.object should be changed") self.assertTrue( view.get_object.called, "view.get_object() should be called") class TestAPIView(TestCase): def test_class_creation(self): """ Tests that the APIView has all relevant methods """ http_methods = set(('get', 'post', 'put', 'patch', 'delete', 'head', 'options', 'trace')) self.assertTrue( http_methods.issubset(views.APIView.__dict__), "All http methods should be defined in the APIView") self.assertFalse( hasattr(views.APIView, 'method_call'), "The APIView should not have the method 'method_call'") class TestCSRFMixin(TestCase): @patch('builtins.super') def test_as_view(self, mock_super): """ Tests, that ensure_csrf_cookie is called. """ mock_super().as_view.return_value = 'super_view' with patch('openslides.utils.views.ensure_csrf_cookie') as ensure_csrf_cookie: views.CSRFMixin.as_view() ensure_csrf_cookie.assert_called_once_with('super_view')
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parsedmarc/template.py
dwt/dmarc-visualizer
5
144748
<reponame>dwt/dmarc-visualizer<gh_stars>1-10 #!/usr/bin/env python import sys import re import os possible_keys = os.environ.keys() def substituter(match): string_to_replace = match.group() environment_variable_name = string_to_replace[1:] if environment_variable_name in os.environ: return os.environ[environment_variable_name] else: return string_to_replace with open(sys.argv[1]) as template: print(re.sub(r'\$\w+', substituter, template.read()))
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Python/danger_mouse_game/character_profile.py
jastr945/PDXclass
0
154482
<filename>Python/danger_mouse_game/character_profile.py """ This portion of code begins the game play. The user gets an introduction to the game and chooses a major player character. The code instantiates a Mouse object. This instantiates an Inventory object for the Mouse. And the code then adds two Spell objects to the Mouse's inventory. Because the Spells each Mouse character starts with differ, the choice changes the game play. """ import character, item import room import inventory def create_character(): """ This function will go at the beginning of the game play. This function allows a user to choose a major player character from a menu. :return: """ """ :return: """ run_again = True while run_again == True: print(""" Welcome to the game Danger Mouse! Your goal will be to avoid danger while gathering enough food from rooms in the castle to last a day. You will find various spells to aid you. Keep an eye on your health, as you will need to eat throughout the day and also store food to bring home. Please choose a character to play: 1. Mortimer - a wise mouse with a keen understanding of the rats and dogs who occupy the castle. 2. Sydney - a clever mouse skilled at hiding and evasion from the rats, cats, dogs, and people who occupy the castle. 3. Aster - a brave mouse quick to cause fright in cats and people who occupy the castle. """) try: choice = input("Do you choose character 1, 2, or 3?\n:") if choice == '1' or choice == '2' or choice == '3': run_again = False except KeyError: continue if choice == '1': """ Creates character Mortimer. """ befriend_1 = item.Spell("befriend") befriend_2 = item.Spell("befriend") char_list = ['Mortimer', 'You are an elderly mouse who\'s body is worn, but who\'s smile is genuine.', 'library', [befriend_1, befriend_2]] elif choice == '2': """ Creates character Sydney. """ hide_1 = item.Spell("hide") hide_2 = item.Spell("hide") char_list = ['Sydney', 'You try to look at yourself, but you quickly dodge your own gaze and hide in the shadows.', 'nest', [hide_1, hide_2]] elif choice == '3': """ Creates character Aster. """ scare_1 = item.Spell("scare") scare_2 = item.Spell("scare") char_list = ['Aster', 'Your physical appearance is not notable, but you act with confidence that leaves others intimidated.', 'chapel', [scare_1, scare_2]] """ Uses variable player to instantiate a Mouse character and call the needed list of attributes. Returns the Mouse character. """ player = character.Mouse(char_list[0], char_list[1], char_list[2]) player.inventory.put_in_quiet(char_list[3][0]) player.inventory.put_in_quiet(char_list[3][1]) return player
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src/sims4communitylib/enums/enumtypes/common_int.py
velocist/TS4CheatsInfo
0
56186
<reponame>velocist/TS4CheatsInfo<gh_stars>0 """ The Sims 4 Community Library is licensed under the Creative Commons Attribution 4.0 International public license (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/legalcode Copyright (c) COLONOLNUTTY """ from sims4.utils import classproperty from collections import OrderedDict from typing import Iterator # noinspection PyBroadException try: # noinspection PyUnresolvedReferences from enum import Int except: # noinspection PyMissingOrEmptyDocstring class Int: # noinspection PyPropertyDefinition @property def name(self) -> str: pass # noinspection PyPropertyDefinition @property def value(self) -> int: pass # noinspection PyPropertyDefinition,PyMethodParameters @classproperty def values(cls) -> Iterator[int]: pass # noinspection PyPropertyDefinition,PyMethodParameters @classproperty def name_to_value(cls) -> OrderedDict: pass # noinspection PyPropertyDefinition,PyMethodParameters @classproperty def value_to_name(cls) -> OrderedDict: pass class CommonInt(Int): """An inheritable class that inherits from the vanilla Sims 4 enum.Int class so you don't have to. """ pass
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bmcs_beam/mxn/scripts/__init__.py
bmcs-group/bmcs_beam
1
26043
''' Created on Dec 18, 2016 @author: rch '''
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scripts/snippets/eval-clevr-instance-retrieval/eval-referential.py
Glaciohound/VCML
52
33995
#! /usr/bin/env python3 # -*- coding: utf-8 -*- # File : eval-referential.py # Author : <NAME>, <NAME> # Email : <EMAIL>, <EMAIL> # Date : 30.07.2019 # Last Modified Date: 16.10.2019 # Last Modified By : Chi Han, Jiayuan Mao # # This file is part of the VCML codebase # Distributed under MIT license # -*- coding: utf-8 -*- # File : eval-referential.py # Author : <NAME> # Email : <EMAIL> # Date : 07/30/2019 # # This file is part of eval-clevr-instance-retrieval. # Distributed under terms of the MIT license. import six import functools import sys from IPython.core import ultratb import numpy as np import jacinle.io as io import jacinle.random as random from jacinle.cli.argument import JacArgumentParser from jacinle.utils.tqdm import tqdm_gofor, get_current_tqdm from jacinle.utils.meter import GroupMeters sys.excepthook = ultratb.FormattedTB( mode='Plain', color_scheme='Linux', call_pdb=True) parser = JacArgumentParser() parser.add_argument('--scene-json', required=True, type='checked_file') parser.add_argument('--preds-json', required=True, type='checked_file') args = parser.parse_args() class Definition(object): annotation_attribute_names = ['color', 'material', 'shape', 'size'] annotation_relation_names = ['behind', 'front', 'left', 'right'] concepts = { 'color': ['gray', 'red', 'blue', 'green', 'brown', 'purple', 'cyan', 'yellow'], 'material': ['rubber', 'metal'], 'shape': ['cube', 'sphere', 'cylinder'], 'size': ['small', 'large'] } concept2attribute = { v: k for k, vs in concepts.items() for v in vs } relational_concepts = { 'spatial_relation': ['left', 'right', 'front', 'behind'] } synonyms = { "thing": ["thing", "object"], "sphere": ["sphere", "ball"], "cube": ["cube", "block"], "cylinder": ["cylinder"], "large": ["large", "big"], "small": ["small", "tiny"], "metal": ["metallic", "metal", "shiny"], "rubber": ["rubber", "matte"], } word2lemma = { v: k for k, vs in synonyms.items() for v in vs } def_ = Definition() def get_desc(obj): names = [obj[k] for k in def_.annotation_attribute_names] for i, n in enumerate(names): if n in def_.synonyms: names[i] = random.choice_list(def_.synonyms[n]) return names def run_desc_obj(obj, desc): for d in desc: dd = def_.word2lemma.get(d, d) if dd != obj[def_.concept2attribute[dd]]: return False return True def run_desc_pred(all_preds, desc): s = 10000 for d in desc: s = np.fmin(s, all_preds[d]) return s def test(index, all_objs, all_preds, meter): obj = all_objs[index] nr_descriptors = random.randint(1, 3) desc = random.choice_list(get_desc(obj), size=nr_descriptors) if isinstance(desc, six.string_types): desc = [desc] filtered_objs = [i for i, o in enumerate(all_objs) if not run_desc_obj(o, desc)] all_scores = run_desc_pred(all_preds, desc) rank = (all_scores[filtered_objs] > all_scores[index]).sum() # print(desc) # print(all_scores) # print(all_scores[index]) meter.update('r@01', rank <= 1) meter.update('r@02', rank <= 2) meter.update('r@03', rank <= 3) meter.update('r@04', rank <= 4) meter.update('r@05', rank <= 5) def transpose_scene(scene): ret = dict() for k in scene['0']: ret[k] = np.array([scene[str(o)][k] for o in range(len(scene))]) return ret def main(): scenes = io.load_json(args.scene_json)['scenes'] preds = io.load(args.preds_json) if isinstance(preds, dict): preds = list(preds.values()) if False: preds = [transpose_scene(s) for s in preds] # flattened_objs = [o for s in scenes for o in s['objects']] # flattened_preds = { # k: np.concatenate([np.array(p[k]) for p in preds], axis=0) # for k in preds[0] # } meter = GroupMeters() ''' for i, scene in tqdm_gofor(scenes, mininterval=0.5): for j in range(len(scene['objects'])): test(j, scene['objects'], preds[i], meter) ''' for i, pred in tqdm_gofor(preds, mininterval=0.5): scene = scenes[i] for j in range(len(scene['objects'])): test(j, scene['objects'], pred, meter) print(meter.format_simple('Results:', compressed=False)) if __name__ == '__main__': main()
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eth/tools/factories/transaction.py
kclowes/py-evm
0
164002
<gh_stars>0 from eth_utils.toolz import curry from eth.vm.spoof import ( SpoofTransaction, ) @curry def new_transaction( vm, from_, to, amount=0, private_key=None, gas_price=10, gas=100000, data=b'', nonce=None, chain_id=None): """ Create and return a transaction sending amount from <from_> to <to>. The transaction will be signed with the given private key. """ if nonce is None: nonce = vm.state.get_nonce(from_) tx = vm.create_unsigned_transaction( nonce=nonce, gas_price=gas_price, gas=gas, to=to, value=amount, data=data, ) if private_key: if chain_id is None: return tx.as_signed_transaction(private_key) else: return tx.as_signed_transaction(private_key, chain_id=chain_id) else: return SpoofTransaction(tx, from_=from_)
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main.py
jfmaes/transformationsuite
0
113009
<reponame>jfmaes/transformationsuite import argparse from transformer import Transformer from format import Formatter from Crypto.Hash import MD5 parser = argparse.ArgumentParser(description="Transformer next generation by jfmaes") #DONT FORGET TO PUT REQUIRED TRUE parser.add_argument("-f", "--file", help="the payload file", required=True) parser.add_argument("-x", "--xor", help="use xor encryption", action="store_true") parser.add_argument("-key", help="the xor key") parser.add_argument("-c", "--caesar", help="use caesar cipher", action="store_true") parser.add_argument("-rotation", help="the rotation to follow, can be + or - ") parser.add_argument("-b64","-base64","--base64", help= "base 64 encode payload", action="store_true") parser.add_argument("-rev","--reverse", help= "reverse payload", action="store_true") parser.add_argument("-o", "--output-file", help="the output file") parser.add_argument("-vba", help="format to vba", action="store_true") parser.add_argument("-csharp", help="format to csharp", action="store_true") parser.add_argument("-cpp", help="format to cpp", action="store_true") parser.add_argument("-raw", help="format to raw payload", action="store_true") parser.add_argument("-v", "--verbose", help="print shellcode to terminal", action="store_true") parser.add_argument("--no-transform", help="doesnt transform payload, just formats.", action="store_true") def check_args(args): if args.xor and not args.key: print(f"[!] XOR encryption needs a key") quit() if args.caesar and not args.rotation: print(f"[!] Caesar encryption needs a rotation") quit() if not args.verbose and not args.output_file: print(f"[!] Your payload needs to go somewhere. Use either verbose or outfile params, or both.") quit() def get_shellcode_from_file(inFile): try: with open(inFile, "rb") as shellcodeFileHandle: shellcodeBytes = bytearray(shellcodeFileHandle.read()) shellcodeFileHandle.close() print (f"[*] Payload file [{inFile}] successfully loaded") except IOError: print(f"[!] Could not open or read file [{inFile}]") quit() print("[*] MD5 hash of the initial payload: [{}]".format(MD5.new(shellcodeBytes).hexdigest())) print("[*] Payload size: [{}] bytes".format(len(shellcodeBytes))) return shellcodeBytes def main(args): transformer = Transformer() formatter = Formatter() data = get_shellcode_from_file(args.file) transform_blob = transformer.transform(args, data) if not args.no_transform: formatter.format(args, transform_blob) if args.no_transform: formatter.format(args, data) if __name__ == '__main__': args = parser.parse_args() check_args(args) main(args)
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departure/provider/tfl_tube/commons.py
Woll78/departure-python
4
60394
import os class TflTubeException(Exception): pass def check_env_vars(): if "TFL_APP_KEY" not in os.environ: raise TflTubeException("missing env var TFL_APP_KEY")
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dss/stepfunctions/__init__.py
ucsc-cgp/cgp-data-store
0
134594
import json import os import typing import logging from dss.util.aws import ARN from dss.util.aws.clients import stepfunctions # type: ignore from dss.util.aws import send_sns_msg """ The keys used to transfer step function invocation data over SNS to the dss-sfn-* Lambda. dss-sfn starts step function execution and configured with DLQ for resiliency """ SFN_TEMPLATE_KEY = 'sfn_template' SFN_EXECUTION_KEY = 'sfn_execution' SFN_INPUT_KEY = 'sfn_input' region = ARN.get_region() stage = os.environ["DSS_DEPLOYMENT_STAGE"] accountid = ARN.get_account_id() sfn_sns_topic = f"dss-sfn-{stage}" sfn_sns_topic_arn = f"arn:aws:sns:{region}:{accountid}:{sfn_sns_topic}" logger = logging.getLogger(__name__) def step_functions_arn(state_machine_name_template: str) -> str: """ The ARN of a state machine, with name derived from `state_machine_name_template`, with string formatting to replace {stage} with the dss deployment stage. """ sfn_name = state_machine_name_template.format(stage=stage) state_machine_arn = f"arn:aws:states:{region}:{accountid}:stateMachine:{sfn_name}" return state_machine_arn def step_functions_execution_arn(state_machine_name_template: str, execution_name: str) -> str: """ The ARN of a state machine execution, with name derived from `state_machine_name_template`, with string formatting to replace {stage} with the dss deployment stage. """ sfn_name = state_machine_name_template.format(stage=stage) state_machine_execution_arn = f"arn:aws:states:{region}:{accountid}:execution:{sfn_name}:{execution_name}" return state_machine_execution_arn def step_functions_invoke(state_machine_name_template: str, execution_name: str, input, attributes=None) -> typing.Any: """ Invoke a step functions state machine. The name of the state machine to be invoked will be derived from `state_machine_name_template`, with string formatting to replace {stage} with the dss deployment stage. """ message = { SFN_TEMPLATE_KEY: state_machine_name_template, SFN_EXECUTION_KEY: execution_name, SFN_INPUT_KEY: json.dumps(input) } logger.debug('Sending message: %s', str(message)) response = send_sns_msg(sfn_sns_topic_arn, message, attributes) return response def _step_functions_start_execution(state_machine_name_template: str, execution_name: str, execution_input: str) -> typing.Any: """ Invoke a step functions state machine. The name of the state machine to be invoked will be derived from `state_machine_name_template`, with string formatting to replace {stage} with the dss deployment stage. """ state_machine_arn = step_functions_arn(state_machine_name_template) response = stepfunctions.start_execution( stateMachineArn=state_machine_arn, name=execution_name, input=execution_input ) return response def step_functions_describe_execution(state_machine_name_template: str, execution_name: str) -> typing.Any: """ Return description of a step function execution, possible in-progress, completed, errored, etc. """ execution_arn = step_functions_execution_arn(state_machine_name_template, execution_name) resp = stepfunctions.describe_execution(executionArn=execution_arn) return resp def step_functions_list_executions( state_machine_name_template: str, status_filter: str=None, max_results_per_page: int=None ) -> typing.Iterable: """ List step function executions, performing paging in the background. """ state_machine_arn = step_functions_arn(state_machine_name_template) kwargs = dict(stateMachineArn=state_machine_arn) # type: typing.Dict[str, typing.Any] if max_results_per_page is not None: kwargs['maxResults'] = max_results_per_page if status_filter is not None: kwargs['statusFilter'] = status_filter paginator = stepfunctions.get_paginator('list_executions') page_iterator = paginator.paginate(**kwargs) for page in page_iterator: for ex in page['executions']: yield ex
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acictf/Move ZIG/code.py
benhunter/ctf
0
36164
#!/usr/bin/python3 import argparse import socket import base64 import binascii # 'argparse' is a very useful library for building python tools that are easy # to use from the command line. It greatly simplifies the input validation # and "usage" prompts which really help when trying to debug your own code. # parser = argparse.ArgumentParser(description="Solver for 'All Your Base' challenge") # parser.add_argument("ip", help="IP (or hostname) of remote instance") # parser.add_argument("port", type=int, help="port for remote instance") # args = parser.parse_args() ip = 'challenge.acictf.com' port = 47912 # This tells the computer that we want a new TCP "socket" socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # This says we want to connect to the given IP and port # socket.connect((args.ip, args.port)) socket.connect((ip, port)) # This gives us a file-like view of the connection which makes reading data # easier since it handles the buffering of lines for you. f = socket.makefile() # while True: # line = f.readline().strip() # This iterates over data from the server a line at a time. This can cause # some unexpected behavior like not seeing "prompts" until after you've sent # a reply for it (for example, you won't see "answer:" for this problem). # However, you can still send data and it will be handled correctly. # Handle the information from the server to extact the problem and build # the answer string. # pass # Fill this in with your logic # Send a response back to the server # answer = "Clearly not the answer..." # socket.send((answer + "\n").encode()) # The "\n" is important for the server's # interpretation of your answer, so make # sure there is only one sent for each # answer. def raw_dec(x): e = x.encode() b = bytes(e) i = int.from_bytes(b, byteorder='big') return i def b64_dec(x): b = base64.b64decode(x) i = int.from_bytes(b, byteorder='big') return i def hex_dec(x): # return int(binascii.unhexlify(x)) i = int(x, 16) return i def oct_dec(x): d = int(x, 8) return d def bin_dec(x): d = int(x, 2) return d def dec_raw(x): # return str(x) i = int(x).to_bytes(int(x).bit_length(), byteorder='big').strip(b'\x00') return i.decode() def dec_b64(x): by = x.to_bytes((x.bit_length() + 7) // 8, byteorder='big').strip(b'A') b64 = base64.b64encode(by) return b64.decode() def dec_hex(x): # by = x.to_bytes(x.bit_length(), byteorder='big') # h = binascii.hexlify(by) h = hex(x) return h[2:] def dec_oct(x): o = oct(x) s = str(o) return s[2:] def dec_bin(x): b = bin(x) s = str(b) return s[2:] # def read_to_dash(): # pass while True: line = f.readline().strip() if len(line) > 1 and line[0] == '-': break while True: line = f.readline().strip().split() print(line) encode = line[0] decode = line[2] print(encode, decode) src = f.readline().strip() print(src) # src to dec if encode == 'raw': dec = raw_dec(src) elif encode == 'b64': dec = b64_dec(src) elif encode == 'hex': dec = hex_dec(src) elif encode == 'dec': dec = int(src) elif encode == 'oct': dec = oct_dec(src) elif encode == 'bin': dec = bin_dec(src) # dec to target if decode == 'raw': target = dec_raw(dec) elif decode == 'b64': target = dec_b64(dec) elif decode == 'hex': target = dec_hex(dec) elif decode == 'dec': target = str(dec) elif decode == 'oct': target = dec_oct(dec) elif decode == 'bin': target = dec_bin(dec) # answer = "Clearly not the answer..." socket.send((target + "\n").encode()) # The "\n" is important for the server's # interpretation of your answer, so make # sure there is only one sent for each # answer. line = f.readline().strip() print(line) line = f.readline().strip() print(line) line = f.readline().strip() print(line) if 'incorrect' in line: print('hold up') line = f.readline().strip() print(line) # ACI{for_great_justice_618c35ec}
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confply/cpp_compiler/options/tool.py
graehu/confply
0
111502
echo = "echo" gcc = "gcc" gpp = "g++" emcc = "emcc" empp = "em++" cl = "cl" clang = "clang" clangpp = "clang++"
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plugins/python/test/testCustomEntity.py
shotgunsoftware/cplusplus-api
3
18104
<gh_stars>1-10 #!/usr/bin/env python import sys from shotgun import * try: if len(sys.argv) > 1: sg = Shotgun(sys.argv[1]) else: sg = Shotgun() ################################################################# # Find CustomEntity01 entities ################################################################# print "*" * 40, "findEntities - CustomEntity01", "*" * 40 for entity in sg.findEntities("CustomEntity01", FilterBy(), 5): #print entity #print "-" * 40 print "%s : %s" % (entity.sgProjectCode(), entity.getAttrValue("code")) ################################################################# # Find CustomEntity02 entities ################################################################# print "*" * 40, "findEntities - CustomEntity02", "*" * 40 for entity in sg.findEntities("CustomEntity02", FilterBy(), 5): #print entity #print "-" * 40 print "%s : %s" % (entity.sgProjectCode(), entity.getAttrValue("code")) except SgError, e: print "SgError:", e except Exception, e: print "Error:", e
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tdd_wallet/views/get_wallet_balance/request_response_mocks.py
kapeed2091/tdd_practice
0
164847
<filename>tdd_wallet/views/get_wallet_balance/request_response_mocks.py REQUEST_BODY_JSON = """ { "customer_ids": [ "string" ] } """ RESPONSE_200_JSON = """ { "customers_balance": [ { "balance": 1.1, "customer_id": "string" } ] } """
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pgmpy/tests/test_models/test_SEM.py
predictive-analytics-lab/pgmpy
0
10592
<filename>pgmpy/tests/test_models/test_SEM.py import os import unittest import numpy as np import networkx as nx import numpy.testing as npt from pgmpy.models import SEM, SEMGraph, SEMAlg class TestSEM(unittest.TestCase): def test_from_graph(self): self.demo = SEM.from_graph( ebunch=[ ("xi1", "x1"), ("xi1", "x2"), ("xi1", "x3"), ("xi1", "eta1"), ("eta1", "y1"), ("eta1", "y2"), ("eta1", "y3"), ("eta1", "y4"), ("eta1", "eta2"), ("xi1", "eta2"), ("eta2", "y5"), ("eta2", "y6"), ("eta2", "y7"), ("eta2", "y8"), ], latents=["xi1", "eta1", "eta2"], err_corr=[ ("y1", "y5"), ("y2", "y6"), ("y2", "y4"), ("y3", "y7"), ("y4", "y8"), ("y6", "y8"), ], ) self.assertSetEqual(self.demo.latents, {"xi1", "eta1", "eta2"}) self.assertSetEqual( self.demo.observed, {"x1", "x2", "x3", "y1", "y2", "y3", "y4", "y5", "y6", "y7", "y8"} ) self.assertListEqual( sorted(self.demo.graph.nodes()), [ "eta1", "eta2", "x1", "x2", "x3", "xi1", "y1", "y2", "y3", "y4", "y5", "y6", "y7", "y8", ], ) self.assertListEqual( sorted(self.demo.graph.edges()), sorted( [ ("eta1", "eta2"), ("eta1", "y1"), ("eta1", "y2"), ("eta1", "y3"), ("eta1", "y4"), ("eta2", "y5"), ("eta2", "y6"), ("eta2", "y7"), ("eta2", "y8"), ("xi1", "eta1"), ("xi1", "eta2"), ("xi1", "x1"), ("xi1", "x2"), ("xi1", "x3"), ] ), ) self.assertDictEqual(self.demo.graph.edges[("xi1", "x1")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("xi1", "x2")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("xi1", "x3")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("xi1", "eta1")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta1", "y1")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta1", "y2")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta1", "y3")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta1", "y4")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta1", "eta2")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("xi1", "eta2")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta2", "y5")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta2", "y6")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta2", "y7")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta2", "y8")], {"weight": np.NaN}) npt.assert_equal( nx.to_numpy_matrix( self.demo.err_graph, nodelist=sorted(self.demo.err_graph.nodes()), weight=None ), np.array( [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0], ] ), ) for edge in self.demo.err_graph.edges(): self.assertDictEqual(self.demo.err_graph.edges[edge], {"weight": np.NaN}) for node in self.demo.err_graph.nodes(): self.assertDictEqual(self.demo.err_graph.nodes[node], {"weight": np.NaN}) def test_from_lavaan(self): model_str = """# %load model.lav # measurement model ind60 =~ x1 + x2 + x3 dem60 =~ y1 + y2 + y3 + y4 dem65 =~ y5 + y6 + y7 + y8 # regressions dem60 ~ ind60 dem65 ~ ind60 + dem60 # residual correlations y1 ~~ y5 y2 ~~ y4 + y6 y3 ~~ y7 y4 ~~ y8 y6 ~~ y8 """ model_from_str = SEM.from_lavaan(string=model_str) with open("test_model.lav", "w") as f: f.write(model_str) model_from_file = SEM.from_lavaan(filename="test_model.lav") os.remove("test_model.lav") expected_edges = set( [ ("ind60", "x1"), ("ind60", "x2"), ("ind60", "x3"), ("ind60", "dem60"), ("ind60", "dem65"), ("dem60", "dem65"), ("dem60", "y1"), ("dem60", "y2"), ("dem60", "y3"), ("dem60", "y4"), ("dem65", "y5"), ("dem65", "y6"), ("dem65", "y7"), ("dem65", "y8"), ] ) # Undirected Graph, needs to handle when edges returned in reverse. expected_err_edges = set( [ ("y1", "y5"), ("y5", "y1"), ("y2", "y6"), ("y6", "y2"), ("y2", "y4"), ("y4", "y2"), ("y3", "y7"), ("y7", "y3"), ("y4", "y8"), ("y8", "y4"), ("y6", "y8"), ("y8", "y6"), ] ) expected_latents = set(["dem60", "dem65", "ind60"]) self.assertEqual(set(model_from_str.graph.edges()), expected_edges) self.assertEqual(set(model_from_file.graph.edges()), expected_edges) self.assertFalse(set(model_from_str.err_graph.edges()) - expected_err_edges) self.assertFalse(set(model_from_file.err_graph.edges()) - expected_err_edges) self.assertEqual(set(model_from_str.latents), expected_latents) self.assertEqual(set(model_from_file.latents), expected_latents) def test_from_lisrel(self): pass # TODO: Add this test when done writing the tests for SEMAlg def test_from_ram(self): pass # TODO: Add this. class TestSEMGraph(unittest.TestCase): def setUp(self): self.demo = SEMGraph( ebunch=[ ("xi1", "x1"), ("xi1", "x2"), ("xi1", "x3"), ("xi1", "eta1"), ("eta1", "y1"), ("eta1", "y2"), ("eta1", "y3"), ("eta1", "y4"), ("eta1", "eta2"), ("xi1", "eta2"), ("eta2", "y5"), ("eta2", "y6"), ("eta2", "y7"), ("eta2", "y8"), ], latents=["xi1", "eta1", "eta2"], err_corr=[ ("y1", "y5"), ("y2", "y6"), ("y2", "y4"), ("y3", "y7"), ("y4", "y8"), ("y6", "y8"), ], ) self.union = SEMGraph( ebunch=[ ("yrsmill", "unionsen"), ("age", "laboract"), ("age", "deferenc"), ("deferenc", "laboract"), ("deferenc", "unionsen"), ("laboract", "unionsen"), ], latents=[], err_corr=[("yrsmill", "age")], ) self.demo_params = SEMGraph( ebunch=[ ("xi1", "x1", 0.4), ("xi1", "x2", 0.5), ("xi1", "x3", 0.6), ("xi1", "eta1", 0.3), ("eta1", "y1", 1.1), ("eta1", "y2", 1.2), ("eta1", "y3", 1.3), ("eta1", "y4", 1.4), ("eta1", "eta2", 0.1), ("xi1", "eta2", 0.2), ("eta2", "y5", 0.7), ("eta2", "y6", 0.8), ("eta2", "y7", 0.9), ("eta2", "y8", 1.0), ], latents=["xi1", "eta1", "eta2"], err_corr=[ ("y1", "y5", 1.5), ("y2", "y6", 1.6), ("y2", "y4", 1.9), ("y3", "y7", 1.7), ("y4", "y8", 1.8), ("y6", "y8", 2.0), ], err_var={ "y1": 2.1, "y2": 2.2, "y3": 2.3, "y4": 2.4, "y5": 2.5, "y6": 2.6, "y7": 2.7, "y8": 2.8, "x1": 3.1, "x2": 3.2, "x3": 3.3, "eta1": 2.9, "eta2": 3.0, "xi1": 3.4, }, ) self.custom = SEMGraph( ebunch=[ ("xi1", "eta1"), ("xi1", "y1"), ("xi1", "y4"), ("xi1", "x1"), ("xi1", "x2"), ("y4", "y1"), ("y1", "eta2"), ("eta2", "y5"), ("y1", "eta1"), ("eta1", "y2"), ("eta1", "y3"), ], latents=["xi1", "eta1", "eta2"], err_corr=[("y1", "y2"), ("y2", "y3")], err_var={}, ) def test_demo_init(self): self.assertSetEqual(self.demo.latents, {"xi1", "eta1", "eta2"}) self.assertSetEqual( self.demo.observed, {"x1", "x2", "x3", "y1", "y2", "y3", "y4", "y5", "y6", "y7", "y8"} ) self.assertListEqual( sorted(self.demo.graph.nodes()), [ "eta1", "eta2", "x1", "x2", "x3", "xi1", "y1", "y2", "y3", "y4", "y5", "y6", "y7", "y8", ], ) self.assertListEqual( sorted(self.demo.graph.edges()), sorted( [ ("eta1", "eta2"), ("eta1", "y1"), ("eta1", "y2"), ("eta1", "y3"), ("eta1", "y4"), ("eta2", "y5"), ("eta2", "y6"), ("eta2", "y7"), ("eta2", "y8"), ("xi1", "eta1"), ("xi1", "eta2"), ("xi1", "x1"), ("xi1", "x2"), ("xi1", "x3"), ] ), ) self.assertDictEqual(self.demo.graph.edges[("xi1", "x1")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("xi1", "x2")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("xi1", "x3")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("xi1", "eta1")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta1", "y1")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta1", "y2")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta1", "y3")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta1", "y4")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta1", "eta2")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("xi1", "eta2")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta2", "y5")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta2", "y6")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta2", "y7")], {"weight": np.NaN}) self.assertDictEqual(self.demo.graph.edges[("eta2", "y8")], {"weight": np.NaN}) npt.assert_equal( nx.to_numpy_matrix( self.demo.err_graph, nodelist=sorted(self.demo.err_graph.nodes()), weight=None ), np.array( [ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0], ] ), ) for edge in self.demo.err_graph.edges(): self.assertDictEqual(self.demo.err_graph.edges[edge], {"weight": np.NaN}) for node in self.demo.err_graph.nodes(): self.assertDictEqual(self.demo.err_graph.nodes[node], {"weight": np.NaN}) def test_union_init(self): self.assertSetEqual(self.union.latents, set()) self.assertSetEqual( self.union.observed, {"yrsmill", "unionsen", "age", "laboract", "deferenc"} ) self.assertListEqual( sorted(self.union.graph.nodes()), sorted(["yrsmill", "unionsen", "age", "laboract", "deferenc"]), ) self.assertListEqual( sorted(self.union.graph.edges()), sorted( [ ("yrsmill", "unionsen"), ("age", "laboract"), ("age", "deferenc"), ("deferenc", "laboract"), ("deferenc", "unionsen"), ("laboract", "unionsen"), ] ), ) self.assertDictEqual(self.union.graph.edges[("yrsmill", "unionsen")], {"weight": np.NaN}) self.assertDictEqual(self.union.graph.edges[("age", "laboract")], {"weight": np.NaN}) self.assertDictEqual(self.union.graph.edges[("age", "deferenc")], {"weight": np.NaN}) self.assertDictEqual(self.union.graph.edges[("deferenc", "laboract")], {"weight": np.NaN}) self.assertDictEqual(self.union.graph.edges[("deferenc", "unionsen")], {"weight": np.NaN}) self.assertDictEqual(self.union.graph.edges[("laboract", "unionsen")], {"weight": np.NaN}) npt.assert_equal( nx.to_numpy_matrix( self.union.err_graph, nodelist=sorted(self.union.err_graph.nodes()), weight=None ), np.array( [ [0.0, 0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0, 0.0], ] ), ) for edge in self.union.err_graph.edges(): self.assertDictEqual(self.union.err_graph.edges[edge], {"weight": np.NaN}) for node in self.union.err_graph.nodes(): self.assertDictEqual(self.union.err_graph.nodes[node], {"weight": np.NaN}) def test_demo_param_init(self): self.assertDictEqual(self.demo_params.graph.edges[("xi1", "x1")], {"weight": 0.4}) self.assertDictEqual(self.demo_params.graph.edges[("xi1", "x2")], {"weight": 0.5}) self.assertDictEqual(self.demo_params.graph.edges[("xi1", "x3")], {"weight": 0.6}) self.assertDictEqual(self.demo_params.graph.edges[("xi1", "eta1")], {"weight": 0.3}) self.assertDictEqual(self.demo_params.graph.edges[("eta1", "y1")], {"weight": 1.1}) self.assertDictEqual(self.demo_params.graph.edges[("eta1", "y2")], {"weight": 1.2}) self.assertDictEqual(self.demo_params.graph.edges[("eta1", "y3")], {"weight": 1.3}) self.assertDictEqual(self.demo_params.graph.edges[("eta1", "y4")], {"weight": 1.4}) self.assertDictEqual(self.demo_params.graph.edges[("eta1", "eta2")], {"weight": 0.1}) self.assertDictEqual(self.demo_params.graph.edges[("xi1", "eta2")], {"weight": 0.2}) self.assertDictEqual(self.demo_params.graph.edges[("eta2", "y5")], {"weight": 0.7}) self.assertDictEqual(self.demo_params.graph.edges[("eta2", "y6")], {"weight": 0.8}) self.assertDictEqual(self.demo_params.graph.edges[("eta2", "y7")], {"weight": 0.9}) self.assertDictEqual(self.demo_params.graph.edges[("eta2", "y8")], {"weight": 1.0}) self.assertDictEqual(self.demo_params.err_graph.edges[("y1", "y5")], {"weight": 1.5}) self.assertDictEqual(self.demo_params.err_graph.edges[("y2", "y6")], {"weight": 1.6}) self.assertDictEqual(self.demo_params.err_graph.edges[("y2", "y4")], {"weight": 1.9}) self.assertDictEqual(self.demo_params.err_graph.edges[("y3", "y7")], {"weight": 1.7}) self.assertDictEqual(self.demo_params.err_graph.edges[("y4", "y8")], {"weight": 1.8}) self.assertDictEqual(self.demo_params.err_graph.edges[("y6", "y8")], {"weight": 2.0}) self.assertDictEqual(self.demo_params.err_graph.nodes["y1"], {"weight": 2.1}) self.assertDictEqual(self.demo_params.err_graph.nodes["y2"], {"weight": 2.2}) self.assertDictEqual(self.demo_params.err_graph.nodes["y3"], {"weight": 2.3}) self.assertDictEqual(self.demo_params.err_graph.nodes["y4"], {"weight": 2.4}) self.assertDictEqual(self.demo_params.err_graph.nodes["y5"], {"weight": 2.5}) self.assertDictEqual(self.demo_params.err_graph.nodes["y6"], {"weight": 2.6}) self.assertDictEqual(self.demo_params.err_graph.nodes["y7"], {"weight": 2.7}) self.assertDictEqual(self.demo_params.err_graph.nodes["y8"], {"weight": 2.8}) self.assertDictEqual(self.demo_params.err_graph.nodes["x1"], {"weight": 3.1}) self.assertDictEqual(self.demo_params.err_graph.nodes["x2"], {"weight": 3.2}) self.assertDictEqual(self.demo_params.err_graph.nodes["x3"], {"weight": 3.3}) self.assertDictEqual(self.demo_params.err_graph.nodes["eta1"], {"weight": 2.9}) self.assertDictEqual(self.demo_params.err_graph.nodes["eta2"], {"weight": 3.0}) def test_get_full_graph_struct(self): full_struct = self.union._get_full_graph_struct() self.assertFalse( set(full_struct.nodes()) - set( [ "yrsmill", "unionsen", "age", "laboract", "deferenc", ".yrsmill", ".unionsen", ".age", ".laboract", ".deferenc", "..ageyrsmill", "..yrsmillage", ] ) ) self.assertFalse( set(full_struct.edges()) - set( [ ("yrsmill", "unionsen"), ("age", "laboract"), ("age", "deferenc"), ("deferenc", "laboract"), ("deferenc", "unionsen"), ("laboract", "unionsen"), (".yrsmill", "yrsmill"), (".unionsen", "unionsen"), (".age", "age"), (".laboract", "laboract"), (".deferenc", "deferenc"), ("..ageyrsmill", ".age"), ("..ageyrsmill", ".yrsmill"), ("..yrsmillage", ".age"), ("..yrsmillage", ".yrsmill"), ] ) ) def test_active_trail_nodes(self): demo_nodes = ["x1", "x2", "x3", "y1", "y2", "y3", "y4", "y5", "y6", "y7", "y8"] for node in demo_nodes: self.assertSetEqual( self.demo.active_trail_nodes(node, struct="full")[node], set(demo_nodes) ) union_nodes = self.union.graph.nodes() active_trails = self.union.active_trail_nodes(list(union_nodes), struct="full") for node in union_nodes: self.assertSetEqual(active_trails[node], set(union_nodes)) self.assertSetEqual( self.union.active_trail_nodes("age", observed=["laboract", "deferenc", "unionsen"])[ "age" ], {"age", "yrsmill"}, ) def test_get_scaling_indicators(self): demo_scaling_indicators = self.demo.get_scaling_indicators() self.assertTrue(demo_scaling_indicators["eta1"] in ["y1", "y2", "y3", "y4"]) self.assertTrue(demo_scaling_indicators["eta2"] in ["y5", "y6", "y7", "y8"]) self.assertTrue(demo_scaling_indicators["xi1"] in ["x1", "x2", "x3"]) union_scaling_indicators = self.union.get_scaling_indicators() self.assertDictEqual(union_scaling_indicators, dict()) custom_scaling_indicators = self.custom.get_scaling_indicators() self.assertTrue(custom_scaling_indicators["xi1"] in ["x1", "x2", "y1", "y4"]) self.assertTrue(custom_scaling_indicators["eta1"] in ["y2", "y3"]) self.assertTrue(custom_scaling_indicators["eta2"] in ["y5"]) def test_to_lisrel(self): demo = SEMGraph( ebunch=[ ("xi1", "x1", 1.000), ("xi1", "x2", 2.180), ("xi1", "x3", 1.819), ("xi1", "eta1", 1.483), ("eta1", "y1", 1.000), ("eta1", "y2", 1.257), ("eta1", "y3", 1.058), ("eta1", "y4", 1.265), ("eta1", "eta2", 0.837), ("xi1", "eta2", 0.572), ("eta2", "y5", 1.000), ("eta2", "y6", 1.186), ("eta2", "y7", 1.280), ("eta2", "y8", 1.266), ], latents=["xi1", "eta1", "eta2"], err_corr=[ ("y1", "y5", 0.624), ("y2", "y6", 2.153), ("y2", "y4", 1.313), ("y3", "y7", 0.795), ("y4", "y8", 0.348), ("y6", "y8", 1.356), ], err_var={ "x1": 0.082, "x2": 0.120, "x3": 0.467, "y1": 1.891, "y2": 7.373, "y3": 5.067, "y4": 3.148, "y5": 2.351, "y6": 4.954, "y7": 3.431, "y8": 3.254, "xi1": 0.448, "eta1": 3.956, "eta2": 0.172, }, ) demo_lisrel = demo.to_lisrel() indexing = [] vars_ordered = [ "y1", "y2", "y3", "y4", "y5", "y6", "y7", "y8", "x1", "x2", "x3", "xi1", "eta1", "eta2", ] for var in vars_ordered: indexing.append(demo_lisrel.eta.index(var)) eta_reorder = [demo_lisrel.eta[i] for i in indexing] B_reorder = demo_lisrel.B[indexing, :][:, indexing] B_fixed_reorder = demo_lisrel.B_fixed_mask[indexing, :][:, indexing] zeta_reorder = demo_lisrel.zeta[indexing, :][:, indexing] zeta_fixed_reorder = demo_lisrel.zeta_fixed_mask[indexing, :][:, indexing] wedge_y_reorder = demo_lisrel.wedge_y[:, indexing] self.assertEqual(vars_ordered, eta_reorder) npt.assert_array_equal( B_reorder, np.array( [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], ] ), ) npt.assert_array_equal( zeta_reorder, np.array( [ [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], ] ), ) npt.assert_array_equal( B_fixed_reorder, np.array( [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.000, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.257, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.058, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.265, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.000], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.186], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.280], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.266], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.000, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.180, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.819, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.483, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.572, 0.837, 0], ] ), ) npt.assert_array_equal( zeta_fixed_reorder, np.array( [ [1.891, 0, 0, 0, 0.624, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 7.373, 0, 1.313, 0, 2.153, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 5.067, 0, 0, 0, 0.795, 0, 0, 0, 0, 0, 0, 0], [0, 1.313, 0, 3.148, 0, 0, 0, 0.348, 0, 0, 0, 0, 0, 0], [0.624, 0, 0, 0, 2.351, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 2.153, 0, 0, 0, 4.954, 0, 1.356, 0, 0, 0, 0, 0, 0], [0, 0, 0.795, 0, 0, 0, 3.431, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0.348, 0, 1.356, 0, 3.254, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0.082, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0.120, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.467, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.448, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.956, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.172], ] ), ) npt.assert_array_equal( demo_lisrel.wedge_y, np.array( [ [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0], ] ), ) def test_to_from_lisrel(self): demo_lisrel = self.demo.to_lisrel() union_lisrel = self.union.to_lisrel() demo_params_lisrel = self.demo_params.to_lisrel() custom_lisrel = self.custom.to_lisrel() demo_graph = demo_lisrel.to_SEMGraph() union_graph = union_lisrel.to_SEMGraph() demo_params_graph = demo_params_lisrel.to_SEMGraph() custom_graph = custom_lisrel.to_SEMGraph() # Test demo self.assertSetEqual(set(self.demo.graph.nodes()), set(demo_graph.graph.nodes())) self.assertSetEqual(set(self.demo.graph.edges()), set(demo_graph.graph.edges())) self.assertSetEqual(set(self.demo.err_graph.nodes()), set(demo_graph.err_graph.nodes())) npt.assert_array_equal( nx.to_numpy_matrix(self.demo.err_graph, nodelist=sorted(self.demo.err_graph.nodes())), nx.to_numpy_matrix(demo_graph, nodelist=sorted(demo_graph.err_graph.nodes())), ) self.assertSetEqual( set(self.demo.full_graph_struct.nodes()), set(demo_graph.full_graph_struct.nodes()) ) self.assertSetEqual( set(self.demo.full_graph_struct.edges()), set(demo_graph.full_graph_struct.edges()) ) self.assertSetEqual(self.demo.latents, demo_graph.latents) self.assertSetEqual(self.demo.observed, demo_graph.observed) # Test union self.assertSetEqual(set(self.union.graph.nodes()), set(union_graph.graph.nodes())) self.assertSetEqual(set(self.union.graph.edges()), set(union_graph.graph.edges())) self.assertSetEqual(set(self.union.err_graph.nodes()), set(union_graph.err_graph.nodes())) npt.assert_array_equal( nx.to_numpy_matrix(self.union.err_graph, nodelist=sorted(self.union.err_graph.nodes())), nx.to_numpy_matrix(union_graph, nodelist=sorted(union_graph.err_graph.nodes())), ) self.assertSetEqual( set(self.union.full_graph_struct.nodes()), set(union_graph.full_graph_struct.nodes()) ) self.assertSetEqual( set(self.union.full_graph_struct.edges()), set(union_graph.full_graph_struct.edges()) ) self.assertSetEqual(self.union.latents, union_graph.latents) self.assertSetEqual(self.union.observed, union_graph.observed) # Test demo_params self.assertSetEqual( set(self.demo_params.graph.nodes()), set(demo_params_graph.graph.nodes()) ) self.assertSetEqual( set(self.demo_params.graph.edges()), set(demo_params_graph.graph.edges()) ) self.assertSetEqual( set(self.demo_params.err_graph.nodes()), set(demo_params_graph.err_graph.nodes()) ) npt.assert_array_equal( nx.to_numpy_matrix( self.demo_params.err_graph, nodelist=sorted(self.demo_params.err_graph.nodes()), weight=None, ), nx.to_numpy_matrix( demo_graph.err_graph, nodelist=sorted(demo_params_graph.err_graph.nodes()), weight=None, ), ) self.assertSetEqual( set(self.demo_params.full_graph_struct.nodes()), set(demo_params_graph.full_graph_struct.nodes()), ) self.assertSetEqual( set(self.demo_params.full_graph_struct.edges()), set(demo_params_graph.full_graph_struct.edges()), ) self.assertSetEqual(self.demo_params.latents, demo_params_graph.latents) self.assertSetEqual(self.demo_params.observed, demo_params_graph.observed) # Test demo self.assertSetEqual(set(self.custom.graph.nodes()), set(custom_graph.graph.nodes())) self.assertSetEqual(set(self.custom.graph.edges()), set(custom_graph.graph.edges())) self.assertSetEqual(set(self.custom.err_graph.nodes()), set(custom_graph.err_graph.nodes())) npt.assert_array_equal( nx.to_numpy_matrix( self.custom.err_graph, nodelist=sorted(self.custom.err_graph.nodes()) ), nx.to_numpy_matrix(custom_graph, nodelist=sorted(custom_graph.err_graph.nodes())), ) self.assertSetEqual( set(self.custom.full_graph_struct.nodes()), set(custom_graph.full_graph_struct.nodes()) ) self.assertSetEqual( set(self.custom.full_graph_struct.edges()), set(custom_graph.full_graph_struct.edges()) ) self.assertSetEqual(self.custom.latents, custom_graph.latents) self.assertSetEqual(self.custom.observed, custom_graph.observed) def test_iv_transformations_demo(self): scale = {"eta1": "y1", "eta2": "y5", "xi1": "x1"} self.assertRaises(ValueError, self.demo._iv_transformations, "x1", "y1", scale) for y in ["y2", "y3", "y4"]: full_graph, dependent_var = self.demo._iv_transformations( X="eta1", Y=y, scaling_indicators=scale ) self.assertEqual(dependent_var, y) self.assertTrue((".y1", y) in full_graph.edges) self.assertFalse(("eta1", y) in full_graph.edges) for y in ["y6", "y7", "y8"]: full_graph, dependent_var = self.demo._iv_transformations( X="eta2", Y=y, scaling_indicators=scale ) self.assertEqual(dependent_var, y) self.assertTrue((".y5", y) in full_graph.edges) self.assertFalse(("eta2", y) in full_graph.edges) full_graph, dependent_var = self.demo._iv_transformations( X="xi1", Y="eta1", scaling_indicators=scale ) self.assertEqual(dependent_var, "y1") self.assertTrue((".eta1", "y1") in full_graph.edges()) self.assertTrue((".x1", "y1") in full_graph.edges()) self.assertFalse(("xi1", "eta1") in full_graph.edges()) full_graph, dependent_var = self.demo._iv_transformations( X="xi1", Y="eta2", scaling_indicators=scale ) self.assertEqual(dependent_var, "y5") self.assertTrue((".y1", "y5") in full_graph.edges()) self.assertTrue((".eta2", "y5") in full_graph.edges()) self.assertTrue((".x1", "y5") in full_graph.edges()) self.assertFalse(("eta1", "eta2") in full_graph.edges()) self.assertFalse(("xi1", "eta2") in full_graph.edges()) full_graph, dependent_var = self.demo._iv_transformations( X="eta1", Y="eta2", scaling_indicators=scale ) self.assertEqual(dependent_var, "y5") self.assertTrue((".y1", "y5") in full_graph.edges()) self.assertTrue((".eta2", "y5") in full_graph.edges()) self.assertTrue((".x1", "y5") in full_graph.edges()) self.assertFalse(("eta1", "eta2") in full_graph.edges()) self.assertFalse(("xi1", "eta2") in full_graph.edges()) def test_iv_transformations_union(self): scale = {} for u, v in self.union.graph.edges(): full_graph, dependent_var = self.union._iv_transformations( u, v, scaling_indicators=scale ) self.assertFalse((u, v) in full_graph.edges()) self.assertEqual(dependent_var, v) def test_get_ivs_demo(self): scale = {"eta1": "y1", "eta2": "y5", "xi1": "x1"} self.assertSetEqual( self.demo.get_ivs("eta1", "y2", scaling_indicators=scale), {"x1", "x2", "x3", "y3", "y7", "y8"}, ) self.assertSetEqual( self.demo.get_ivs("eta1", "y3", scaling_indicators=scale), {"x1", "x2", "x3", "y2", "y4", "y6", "y8"}, ) self.assertSetEqual( self.demo.get_ivs("eta1", "y4", scaling_indicators=scale), {"x1", "x2", "x3", "y3", "y6", "y7"}, ) self.assertSetEqual( self.demo.get_ivs("eta2", "y6", scaling_indicators=scale), {"x1", "x2", "x3", "y3", "y4", "y7"}, ) self.assertSetEqual( self.demo.get_ivs("eta2", "y7", scaling_indicators=scale), {"x1", "x2", "x3", "y2", "y4", "y6", "y8"}, ) self.assertSetEqual( self.demo.get_ivs("eta2", "y8", scaling_indicators=scale), {"x1", "x2", "x3", "y2", "y3", "y7"}, ) self.assertSetEqual( self.demo.get_ivs("xi1", "x2", scaling_indicators=scale), {"x3", "y1", "y2", "y3", "y4", "y5", "y6", "y7", "y8"}, ) self.assertSetEqual( self.demo.get_ivs("xi1", "x3", scaling_indicators=scale), {"x2", "y1", "y2", "y3", "y4", "y5", "y6", "y7", "y8"}, ) self.assertSetEqual( self.demo.get_ivs("xi1", "eta1", scaling_indicators=scale), {"x2", "x3"} ) self.assertSetEqual( self.demo.get_ivs("xi1", "eta2", scaling_indicators=scale), {"x2", "x3", "y2", "y3", "y4"}, ) self.assertSetEqual( self.demo.get_ivs("eta1", "eta2", scaling_indicators=scale), {"x2", "x3", "y2", "y3", "y4"}, ) def test_get_conditional_ivs_demo(self): scale = {"eta1": "y1", "eta2": "y5", "xi1": "x1"} self.assertEqual(self.demo.get_conditional_ivs("eta1", "y2", scaling_indicators=scale), []) self.assertEqual(self.demo.get_conditional_ivs("eta1", "y3", scaling_indicators=scale), []) self.assertEqual(self.demo.get_conditional_ivs("eta1", "y4", scaling_indicators=scale), []) self.assertEqual(self.demo.get_conditional_ivs("eta2", "y6", scaling_indicators=scale), []) self.assertEqual(self.demo.get_conditional_ivs("eta2", "y7", scaling_indicators=scale), []) self.assertEqual(self.demo.get_conditional_ivs("eta2", "y8", scaling_indicators=scale), []) self.assertEqual(self.demo.get_conditional_ivs("xi1", "x2", scaling_indicators=scale), []) self.assertEqual(self.demo.get_conditional_ivs("xi1", "x3", scaling_indicators=scale), []) self.assertEqual(self.demo.get_conditional_ivs("xi1", "eta1", scaling_indicators=scale), []) self.assertEqual(self.demo.get_conditional_ivs("xi1", "eta2", scaling_indicators=scale), []) self.assertEqual( self.demo.get_conditional_ivs("eta1", "eta2", scaling_indicators=scale), [] ) def test_get_ivs_union(self): scale = {} self.assertSetEqual( self.union.get_ivs("yrsmill", "unionsen", scaling_indicators=scale), set() ) self.assertSetEqual( self.union.get_ivs("deferenc", "unionsen", scaling_indicators=scale), set() ) self.assertSetEqual( self.union.get_ivs("laboract", "unionsen", scaling_indicators=scale), set() ) self.assertSetEqual( self.union.get_ivs("deferenc", "laboract", scaling_indicators=scale), set() ) self.assertSetEqual( self.union.get_ivs("age", "laboract", scaling_indicators=scale), {"yrsmill"} ) self.assertSetEqual( self.union.get_ivs("age", "deferenc", scaling_indicators=scale), {"yrsmill"} ) def test_get_conditional_ivs_union(self): self.assertEqual( self.union.get_conditional_ivs("yrsmill", "unionsen"), [("age", {"laboract", "deferenc"})], ) # This case wouldn't have conditonal IV if the Total effect between `deferenc` and # `unionsen` needs to be computed because one of the conditional variable lies on the # effect path. self.assertEqual( self.union.get_conditional_ivs("deferenc", "unionsen"), [("age", {"yrsmill", "laboract"})], ) self.assertEqual( self.union.get_conditional_ivs("laboract", "unionsen"), [("age", {"yrsmill", "deferenc"})], ) self.assertEqual(self.union.get_conditional_ivs("deferenc", "laboract"), []) self.assertEqual( self.union.get_conditional_ivs("age", "laboract"), [("yrsmill", {"deferenc"})] ) self.assertEqual(self.union.get_conditional_ivs("age", "deferenc"), []) def test_iv_transformations_custom(self): scale_custom = {"eta1": "y2", "eta2": "y5", "xi1": "x1"} full_graph, var = self.custom._iv_transformations( "xi1", "x2", scaling_indicators=scale_custom ) self.assertEqual(var, "x2") self.assertTrue((".x1", "x2") in full_graph.edges()) self.assertFalse(("xi1", "x2") in full_graph.edges()) full_graph, var = self.custom._iv_transformations( "xi1", "y4", scaling_indicators=scale_custom ) self.assertEqual(var, "y4") self.assertTrue((".x1", "y4") in full_graph.edges()) self.assertFalse(("xi1", "y4") in full_graph.edges()) full_graph, var = self.custom._iv_transformations( "xi1", "y1", scaling_indicators=scale_custom ) self.assertEqual(var, "y1") self.assertTrue((".x1", "y1") in full_graph.edges()) self.assertFalse(("xi1", "y1") in full_graph.edges()) self.assertFalse(("y4", "y1") in full_graph.edges()) full_graph, var = self.custom._iv_transformations( "xi1", "eta1", scaling_indicators=scale_custom ) self.assertEqual(var, "y2") self.assertTrue((".eta1", "y2") in full_graph.edges()) self.assertTrue((".x1", "y2") in full_graph.edges()) self.assertFalse(("y1", "eta1") in full_graph.edges()) self.assertFalse(("xi1", "eta1") in full_graph.edges()) full_graph, var = self.custom._iv_transformations( "y1", "eta1", scaling_indicators=scale_custom ) self.assertEqual(var, "y2") self.assertTrue((".eta1", "y2") in full_graph.edges()) self.assertTrue((".x1", "y2") in full_graph.edges()) self.assertFalse(("y1", "eta1") in full_graph.edges()) self.assertFalse(("xi1", "eta1") in full_graph.edges()) full_graph, var = self.custom._iv_transformations( "y1", "eta2", scaling_indicators=scale_custom ) self.assertEqual(var, "y5") self.assertTrue((".eta2", "y5") in full_graph.edges()) self.assertFalse(("y1", "eta2") in full_graph.edges()) full_graph, var = self.custom._iv_transformations( "y4", "y1", scaling_indicators=scale_custom ) self.assertEqual(var, "y1") self.assertFalse(("y4", "y1") in full_graph.edges()) full_graph, var = self.custom._iv_transformations( "eta1", "y3", scaling_indicators=scale_custom ) self.assertEqual(var, "y3") self.assertTrue((".y2", "y3") in full_graph.edges()) self.assertFalse(("eta1", "y3") in full_graph.edges()) def test_get_ivs_custom(self): scale_custom = {"eta1": "y2", "eta2": "y5", "xi1": "x1"} self.assertSetEqual( self.custom.get_ivs("xi1", "x2", scaling_indicators=scale_custom), {"y1", "y2", "y3", "y4", "y5"}, ) self.assertSetEqual( self.custom.get_ivs("xi1", "y4", scaling_indicators=scale_custom), {"x2"} ) self.assertSetEqual( self.custom.get_ivs("xi1", "y1", scaling_indicators=scale_custom), {"x2", "y4"} ) self.assertSetEqual( self.custom.get_ivs("xi1", "eta1", scaling_indicators=scale_custom), {"x2", "y4"} ) # TODO: Test this and fix. self.assertSetEqual( self.custom.get_ivs("y1", "eta1", scaling_indicators=scale_custom), {"x2", "y4", "y5"} ) self.assertSetEqual( self.custom.get_ivs("y1", "eta2", scaling_indicators=scale_custom), {"x1", "x2", "y2", "y3", "y4"}, ) self.assertSetEqual(self.custom.get_ivs("y4", "y1", scaling_indicators=scale_custom), set()) self.assertSetEqual( self.custom.get_ivs("eta1", "y3", scaling_indicators=scale_custom), {"x1", "x2", "y4"} ) def test_small_model_ivs(self): model1 = SEMGraph( ebunch=[("X", "Y"), ("I", "X"), ("W", "I")], latents=[], err_corr=[("W", "Y")], err_var={}, ) self.assertEqual(model1.get_conditional_ivs("X", "Y"), [("I", {"W"})]) model2 = SEMGraph( ebunch=[("x", "y"), ("z", "x"), ("w", "z"), ("w", "u"), ("u", "x"), ("u", "y")], latents=["u"], ) self.assertEqual(model2.get_conditional_ivs("x", "y"), [("z", {"w"})]) model3 = SEMGraph(ebunch=[("x", "y"), ("u", "x"), ("u", "y"), ("z", "x")], latents=["u"]) self.assertEqual(model3.get_ivs("x", "y"), {"z"}) model4 = SEMGraph(ebunch=[("x", "y"), ("z", "x"), ("u", "x"), ("u", "y")]) self.assertEqual(model4.get_conditional_ivs("x", "y"), [("z", {"u"})]) class TestSEMAlg(unittest.TestCase): def setUp(self): self.demo = SEMGraph( ebunch=[ ("xi1", "x1", 1.000), ("xi1", "x2", 2.180), ("xi1", "x3", 1.819), ("xi1", "eta1", 1.483), ("eta1", "y1", 1.000), ("eta1", "y2", 1.257), ("eta1", "y3", 1.058), ("eta1", "y4", 1.265), ("eta1", "eta2", 0.837), ("xi1", "eta2", 0.572), ("eta2", "y5", 1.000), ("eta2", "y6", 1.186), ("eta2", "y7", 1.280), ("eta2", "y8", 1.266), ], latents=["xi1", "eta1", "eta2"], err_corr=[ ("y1", "y5", 0.624), ("y2", "y6", 2.153), ("y2", "y4", 1.313), ("y3", "y7", 0.795), ("y4", "y8", 0.348), ("y6", "y8", 1.356), ], err_var={ "x1": 0.082, "x2": 0.120, "x3": 0.467, "y1": 1.891, "y2": 7.373, "y3": 5.067, "y4": 3.148, "y5": 2.351, "y6": 4.954, "y7": 3.431, "y8": 3.254, "xi1": 0.448, "eta1": 3.956, "eta2": 0.172, }, ) self.demo_lisrel = self.demo.to_lisrel() self.small_model = SEM.from_graph( ebunch=[("X", "Y", 0.3)], latents=[], err_var={"X": 0.1, "Y": 0.1} ) self.small_model_lisrel = self.small_model.to_lisrel() def test_generate_samples(self): samples = self.small_model_lisrel.generate_samples(n_samples=100) samples = self.demo_lisrel.generate_samples(n_samples=100)
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contessa/alembic/packages_migrations.py
mindartur/contessa
15
1607600
migration_map = { "0.0.0": "54f8985b0ee5", "0.2.4": "480e6618700d", "0.2.5": "a179e5ca0ad2", }
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brambling/forms/invites.py
Shivanjain023/django-brambling
8
98277
<reponame>Shivanjain023/django-brambling from django.forms import BaseFormSet import floppyforms as forms from brambling.utils.invites import ( get_invite_class, OrganizationOwnerInvite, OrganizationEditInvite, OrganizationViewInvite, EventEditInvite, EventViewInvite, ) class BaseInviteFormSet(BaseFormSet): def __init__(self, request, content, *args, **kwargs): self.request = request self.content = content super(BaseInviteFormSet, self).__init__(*args, **kwargs) def _construct_form(self, i, **kwargs): kwargs['request'] = self.request kwargs['content'] = self.content return super(BaseInviteFormSet, self)._construct_form(i, **kwargs) @property def empty_form(self): form = self.form( auto_id=self.auto_id, prefix=self.add_prefix('__prefix__'), empty_permitted=True, request=self.request, content=self.content, ) self.add_fields(form, None) return form def save(self): deleted_forms = set(self.deleted_forms) for form in self: if form not in deleted_forms and form.has_changed(): form.save() class BaseInviteForm(forms.Form): email = forms.EmailField() kind = forms.ChoiceField() choices = () def __init__(self, request, content, *args, **kwargs): super(BaseInviteForm, self).__init__(*args, **kwargs) self.request = request self.content = content self.fields['kind'].choices = self.choices self.fields['kind'].initial = self.choices[0][0] def save(self): invite_class = get_invite_class(self.cleaned_data['kind']) invite, created = invite_class.get_or_create( request=self.request, email=self.cleaned_data['email'], content=self.content, ) if created: invite.send() class EventAdminInviteForm(BaseInviteForm): choices = ( (EventEditInvite.slug, EventEditInvite.verbose_name), (EventViewInvite.slug, EventViewInvite.verbose_name), ) class OrganizationAdminInviteForm(BaseInviteForm): choices = ( (OrganizationOwnerInvite.slug, OrganizationOwnerInvite.verbose_name), (OrganizationEditInvite.slug, OrganizationEditInvite.verbose_name), (OrganizationViewInvite.slug, OrganizationViewInvite.verbose_name), )
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newsapp/migrations/0003_news.py
adi112100/newsapp
0
8815
# Generated by Django 3.0.8 on 2020-07-11 08:10 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('newsapp', '0002_auto_20200711_1124'), ] operations = [ migrations.CreateModel( name='News', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('date', models.DateTimeField()), ('indian_news', models.TextField()), ('national_news', models.TextField()), ('international_news', models.TextField()), ('bollywood_news', models.TextField()), ('lifestyle_news', models.TextField()), ('sport_news', models.TextField()), ('business_news', models.TextField()), ('sharemarket_news', models.TextField()), ('corona_news', models.TextField()), ('space_news', models.TextField()), ('motivation_news', models.TextField()), ], ), ]
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bob/blitz/examples/bob.example.project/bob/example/project/test.py
bioidiap/bob.blitz
0
106536
#!/usr/bin/env python # vim: set fileencoding=utf-8 : """Test Units """ def test_version(): from .script import version assert version.main() == 0
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src/TestBenchCliReporter/actions.py
StaudtEngineering/testbench-cli-reporter
0
138081
# Copyright 2021- imbus AG # # 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 annotations from typing import Dict, Optional, Union from zipfile import ZipFile from abc import ABC, abstractmethod from os import path from xml.etree import ElementTree as ET import sys import base64 from TestBenchCliReporter import questions from TestBenchCliReporter import testbench from TestBenchCliReporter.util import ( close_program, get_project_keys, XmlExportConfig, ImportConfig, login, pretty_print, ) def Action(class_name: str, parameters: dict[str, str]) -> AbstractAction: try: return globals()[class_name](parameters) except AttributeError: print(f"Failed to create class {class_name}") close_program() class AbstractAction(ABC): def __init__(self, parameters: Optional[Dict] = None): self.parameters = parameters or {} self.report_tmp_name = "" self.job_id = "" def prepare(self, connection_log: testbench.ConnectionLog) -> bool: return True @abstractmethod def trigger(self, connection_log: testbench.ConnectionLog) -> bool: raise NotImplementedError def wait(self, connection_log: testbench.ConnectionLog) -> bool: return True def poll(self, connection_log: testbench.ConnectionLog) -> bool: return True def finish(self, connection_log: testbench.ConnectionLog) -> bool: return True def export(self): return {"type": type(self).__name__, "parameters": self.parameters} class UnloggedAction(AbstractAction): def export(self): return None class ExportXMLReport(AbstractAction): def prepare(self, connection_log: testbench.ConnectionLog) -> bool: all_projects = connection_log.active_connection.get_all_projects() selected_project = questions.ask_to_select_project(all_projects) selected_tov = questions.ask_to_select_tov(selected_project) self.parameters["tovKey"] = selected_tov["key"]["serial"] self.parameters["projectPath"] = [ selected_project["name"], selected_tov["name"], ] selected_cycle = questions.ask_to_select_cycle(selected_tov, export=True) print(" Selection:") pretty_print( { "value": f"{' ' * 4 + selected_project['name']: <50}", "style": "#06c8ff bold italic", "end": None, }, {"value": f" projectKey: ", "end": None}, { "value": f"{selected_project['key']['serial']: >15}", "style": "#06c8ff bold italic", }, { "value": f"{' ' * 6 + selected_tov['name']: <50}", "style": "#06c8ff bold italic", "end": None, }, {"value": f" tovKey: ", "end": None}, { "value": f"{selected_tov['key']['serial']: >15}", "style": "#06c8ff bold italic", }, ) if selected_cycle == "NO_EXEC": self.parameters["cycleKey"] = None tttree_structure = connection_log.active_connection.get_tov_structure( self.parameters["tovKey"] ) else: pretty_print( { "value": f"{' ' * 8 + selected_cycle['name']: <50}", "style": "#06c8ff bold italic", "end": None, }, {"value": f" cycleKey: ", "end": None}, { "value": f"{selected_cycle['key']['serial']: >15}", "style": "#06c8ff bold italic", }, ) self.parameters["cycleKey"] = selected_cycle["key"]["serial"] self.parameters["projectPath"].append(selected_cycle["name"]) tttree_structure = ( connection_log.active_connection.get_test_cycle_structure( self.parameters["cycleKey"] ) ) self.parameters["reportRootUID"] = questions.ask_to_select_report_root_uid( tttree_structure ) all_filters = connection_log.active_connection.get_all_filters() self.parameters["filters"] = questions.ask_to_select_filters(all_filters) self.parameters["report_config"] = questions.ask_to_config_report() self.parameters["outputPath"] = questions.ask_for_output_path() return True def trigger(self, connection_log: testbench.ConnectionLog) -> Union[bool, str]: if not self.parameters.get("cycleKey"): if ( not self.parameters.get("tovKey") and len(self.parameters["projectPath"]) >= 2 ): all_projects = connection_log.active_connection.get_all_projects() ( project_key, self.parameters["tovKey"], self.parameters["cycleKey"], ) = get_project_keys(all_projects, *self.parameters["projectPath"]) self.job_id = connection_log.active_connection.trigger_xml_report_generation( self.parameters.get("tovKey"), self.parameters.get("cycleKey"), self.parameters.get("reportRootUID", "ROOT"), self.parameters.get("filters", []), self.parameters.get("report_config", XmlExportConfig["Itep Export"]), ) return self.job_id def wait(self, connection_log: testbench.ConnectionLog) -> Union[bool, str]: try: self.report_tmp_name = ( connection_log.active_connection.wait_for_tmp_xml_report_name( self.job_id ) ) return self.report_tmp_name except KeyError as e: print(f"{str(e)}") return False def poll(self, connection_log: testbench.ConnectionLog) -> bool: result = connection_log.active_connection.get_exp_job_result(self.job_id) if result is not None: self.report_tmp_name = result return result def finish(self, connection_log: testbench.ConnectionLog) -> bool: report = connection_log.active_connection.get_xml_report_data( self.report_tmp_name ) with open(self.parameters["outputPath"], "wb") as output_file: output_file.write(report) pretty_print( {"value": f"Report ", "end": None}, { "value": f'{path.abspath(self.parameters["outputPath"])}', "style": "#06c8ff bold italic", "end": None, }, {"value": f" was generated"}, ) return True class ImportExecutionResults(AbstractAction): def prepare(self, connection_log: testbench.ConnectionLog) -> bool: self.parameters["inputPath"] = questions.ask_for_input_path() project = version = cycle = None try: project, version, cycle = self.get_project_path_from_report() except: pass all_projects = connection_log.active_connection.get_all_projects() selected_project = questions.ask_to_select_project( all_projects, default=project ) selected_tov = questions.ask_to_select_tov(selected_project, default=version) self.parameters["cycleKey"] = questions.ask_to_select_cycle( selected_tov, default=cycle )["key"]["serial"] cycle_structure = connection_log.active_connection.get_test_cycle_structure( self.parameters["cycleKey"] ) self.parameters["reportRootUID"] = questions.ask_to_select_report_root_uid( cycle_structure ) available_testers = connection_log.active_connection.get_all_testers_of_project( selected_project["key"]["serial"] ) self.parameters["defaultTester"] = questions.ask_to_select_default_tester( available_testers ) all_filters = connection_log.active_connection.get_all_filters() self.parameters["filters"] = questions.ask_to_select_filters(all_filters) self.parameters["importConfig"] = questions.ask_to_config_import() return True def get_project_path_from_report(self): zip_file = ZipFile(self.parameters["inputPath"]) xml = ET.fromstring(zip_file.read("report.xml")) project = xml.find("./header/project").get("name") version = xml.find("./header/version").get("name") cycle = xml.find("./header/cycle").get("name") return project, version, cycle def trigger(self, connection_log: testbench.ConnectionLog) -> bool: if not self.parameters.get("cycleKey"): if len(self.parameters.get("projectPath", [])) != 3: self.parameters["projectPath"] = self.get_project_path_from_report() self.set_cycle_key_from_path(connection_log) with open(self.parameters["inputPath"], "rb") as execution_report: execution_report_base64 = base64.b64encode(execution_report.read()).decode() serverside_file_name = ( connection_log.active_connection.upload_execution_results( execution_report_base64 ) ) if serverside_file_name: self.job_id = ( connection_log.active_connection.trigger_execution_results_import( self.parameters["cycleKey"], self.parameters["reportRootUID"], serverside_file_name, self.parameters["defaultTester"], self.parameters["filters"], self.parameters.get("importConfig", ImportConfig["Typical"]), ) ) return True def set_cycle_key_from_path(self, connection_log): all_projects = connection_log.active_connection.get_all_projects() ( project_key, tov_key, self.parameters["cycleKey"], ) = get_project_keys(all_projects, *self.parameters["projectPath"]) if not self.parameters["cycleKey"]: raise ValueError("Invalid Config! 'cycleKey' missing.") def wait(self, connection_log: testbench.ConnectionLog) -> bool: self.report_tmp_name = connection_log.active_connection.wait_for_execution_results_import_to_finish( self.job_id ) return self.report_tmp_name def poll(self, connection_log: testbench.ConnectionLog) -> bool: result = connection_log.active_connection.get_imp_job_result(self.job_id) if result is not None: self.report_tmp_name = result return result def finish(self, connection_log: testbench.ConnectionLog) -> bool: if self.report_tmp_name: pretty_print( {"value": f"Report ", "end": None}, { "value": f'{path.abspath(self.parameters["inputPath"])}', "style": "#06c8ff bold italic", "end": None, }, {"value": f" was imported"}, ) return True class ExportActionLog(UnloggedAction): def prepare(self, connection_log: testbench.ConnectionLog): self.parameters["outputPath"] = questions.ask_for_output_path("config.json") return True def trigger(self, connection_log: testbench.ConnectionLog) -> bool: try: connection_log.export_as_json(self.parameters["outputPath"]) pretty_print( {"value": f"Config ", "end": None}, { "value": f'{path.abspath(self.parameters["outputPath"])}', "style": "#06c8ff bold italic", "end": None, }, {"value": f" was generated"}, ) return True except KeyError as e: print(f"{str(e)}") return False class ChangeConnection(UnloggedAction): def prepare(self, connection_log: testbench.ConnectionLog): self.parameters["newConnection"] = login() return True def trigger(self, connection_log: testbench.ConnectionLog) -> bool: connection_log.active_connection.close() connection_log.add_connection(self.parameters["newConnection"]) return True class Quit(UnloggedAction): def trigger(self, connection_log: testbench.ConnectionLog = None): print("Closing program.") sys.exit(0)
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python/ex23.py
kazushiyuuki/Lab-ICC
0
191178
<reponame>kazushiyuuki/Lab-ICC<gh_stars>0 n = int(input()) cont = 0 while cont < n: num = int(input()) if cont == 0: maior = num elif num > maior: maior = num cont += 1 print(maior)
[ 1, 529, 276, 1112, 420, 29958, 8637, 1878, 19881, 29884, 19267, 29914, 28632, 29899, 2965, 29907, 29966, 12443, 29918, 303, 1503, 29958, 29900, 13, 29876, 353, 938, 29898, 2080, 3101, 13, 1285, 353, 29871, 29900, 13, 8000, 640, 529, 302, 29901, 13, 1678, 954, 353, 938, 29898, 2080, 3101, 13, 1678, 565, 640, 1275, 29871, 29900, 29901, 13, 4706, 17136, 353, 954, 13, 1678, 25342, 954, 1405, 17136, 29901, 13, 4706, 17136, 353, 954, 13, 1678, 640, 4619, 29871, 29896, 13, 2158, 29898, 655, 1611, 29897, 13, 268, 2 ]
github/recorders/github/github_user_info_recorder.py
zvtvz/play-github
2
18428
# -*- coding: utf-8 -*- import argparse from github.accounts.github_account import GithubAccount from github.domain.github import GithubUser from github.recorders.github.common import get_result from zvdata.api import get_entities from zvdata.domain import get_db_session from zvdata.recorder import TimeSeriesDataRecorder from zvdata.utils.time_utils import day_offset_today, now_pd_timestamp class GithubUserInfoRecorder(TimeSeriesDataRecorder): entity_provider = 'github' entity_schema = GithubUser provider = 'github' data_schema = GithubUser url = 'https://api.github.com/users/{}' def __init__(self, codes=None, batch_size=50, force_update=True, sleeping_time=5, default_size=2000, one_shot=True, fix_duplicate_way='ignore', start_timestamp=None, end_timestamp=None) -> None: super().__init__('github_user', ['github'], None, codes, batch_size, force_update, sleeping_time, default_size, one_shot, fix_duplicate_way, start_timestamp, end_timestamp) self.seed = 0 def init_entities(self): if self.entity_provider == self.provider and self.entity_schema == self.data_schema: self.entity_session = self.session else: self.entity_session = get_db_session(provider=self.entity_provider, data_schema=self.entity_schema) # init the entity list self.entities = get_entities(session=self.entity_session, entity_type=self.entity_type, entity_ids=self.entity_ids, codes=self.codes, return_type='domain', provider=self.entity_provider, # 最近７天更新过的跳过 filters=[(GithubUser.updated_timestamp < day_offset_today( -7)) | (GithubUser.updated_timestamp.is_(None))], start_timestamp=self.start_timestamp, end_timestamp=self.end_timestamp) def record(self, entity_item, start, end, size, timestamps): self.seed += 1 the_url = self.url.format(entity_item.code) user_info = get_result(url=the_url, token=GithubAccount.get_token(seed=self.seed)) if user_info: user_info['updated_timestamp'] = now_pd_timestamp() return [user_info] return [] def get_data_map(self): return { 'site_admin': 'site_admin', 'name': 'name', 'avatar_url': 'avatar_url', 'gravatar_id': 'gravatar_id', 'company': 'company', 'blog': 'blog', 'location': 'location', 'email': 'email', 'hireable': 'hireable', 'bio': 'bio', 'public_repos': 'public_repos', 'public_gists': 'public_gists', 'followers': 'followers', 'following': 'following', 'updated_timestamp': 'updated_timestamp' } def generate_domain_id(self, security_item, original_data): return security_item.id def evaluate_start_end_size_timestamps(self, entity): latest_record = self.get_latest_saved_record(entity=entity) if latest_record: latest_timestamp = latest_record.updated_timestamp if latest_timestamp is not None: if (now_pd_timestamp() - latest_timestamp).days < 7: self.logger.info('entity_item:{},updated_timestamp:{},ignored'.format(entity.id, latest_timestamp)) return None, None, 0, None return None, None, self.default_size, None if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--start', help='start_timestamp', default='2015-01-01') parser.add_argument('--end', help='end_timestamp', default='2015-12-31') args = parser.parse_args() start = args.start end = args.end recorder = GithubUserInfoRecorder(start_timestamp=start, end_timestamp=end) recorder.run()
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new_test.py
kingsam91/Habari-zote
0
147890
<gh_stars>0 from news import News # Importing the contact class class TestNews(unittest.TestCase): ''' Test class that defines test cases for the news class behaviours. Args: unittest.TestCase: TestCase class that helps in creating test cases '''
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MITx-6.00.2x/ProblemSet5/ps5.py
maistrovas/My-Courses-Solutions
0
138643
# 6.00.2x Problem Set 5 # Graph optimization # Finding shortest paths through MIT buildings # import string # This imports everything from `graph.py` as if it was defined in this file! from graph import * # # Problem 2: Building up the Campus Map # # Before you write any code, write a couple of sentences here # describing how you will model this problem as a graph. # This is a helpful exercise to help you organize your # thoughts before you tackle a big design problem! # # My description of the problem: # # Nodes is a buildings, edges is the roads between them. Every path (edge) from building # to building has parameters (weights (Indoor)(Oundoor)) of distance. I should finde a combination # of paths (edges) fro building (NodeA) to building (NodeB) with smallest # summ of diatances(weights) whether it is Indoor or Oundoor. def load_map(mapFilename): """ Parses the map file and constructs a directed graph Parameters: mapFilename : name of the map file Assumes: Each entry in the map file consists of the following four positive integers, separated by a blank space: From To TotalDistance DistanceOutdoors e.g. 32 76 54 23 This entry would become an edge from 32 to 76. Returns: a directed graph representing the map """ inFile = open(mapFilename, 'r', 0) string_list = [] for line in inFile: wordlist = string.split(line) string_list.append(wordlist) nodes = set([]) edges = [] w_graph = WeightedDigraph() for strings in string_list: start = strings[0] dest = strings[1] tot_distance = float(strings[2]) outd_distance = float(strings[3]) start_node = Node(start) destin_node = Node(dest) w_edge = WeightedEdge(start_node, destin_node, tot_distance , outd_distance) try: w_graph.addNode(start_node) except ValueError: pass try: w_graph.addNode(destin_node) except ValueError: pass try: w_graph.addEdge(w_edge) except ValueError: pass return w_graph #mitMap = load_map("mit_map.txt") #nodes = mitMap.nodes #nodes = list(nodes) # Problem 3: Finding the Shortest Path using Brute Force Search # # State the optimization problem as a function to minimize # and what the constraints are # def getPaths(digraph, start, end): start = Node(start) end = Node(end) stack = [[start]] pathss = [] while stack: path = stack.pop() node = path[-1] if node == end: pathss.append(path[:]) continue children = digraph.childrenOf(node) if not children: continue stack += [path +[c] for c in children if c not in path] return pathss def getPathDistance(digraph, path): totalDist = outdDist = 0.0 for n1, n2 in zip(path,path[1:]): for edge in digraph.edges[n1]: if edge[0] == n2: totalDist += edge[1][0] outdDist +=edge[1][1] break return (totalDist, outdDist) def getShortestPath(digraph, paths, maxTotalDist, maxDistOutdoors): res = '' shortest = None for path in paths: totalDist, outdDist = getPathDistance(digraph, path) if totalDist <= maxTotalDist and outdDist <= maxDistOutdoors: if not shortest or totalDist < shortest: res, shortest = path, totalDist return res def bruteForceSearch(digraph, start, end, maxTotalDist, maxDistOutdoors): """ Finds the shortest path from start to end using brute-force approach. The total distance travelled on the path must not exceed maxTotalDist, and the distance spent outdoor on this path must not exceed maxDistOutdoors. Parameters: digraph: instance of class Digraph or its subclass start, end: start & end building numbers (strings) maxTotalDist : maximum total distance on a path (integer) maxDistOutdoors: maximum distance spent outdoors on a path (integer) Assumes: start and end are numbers for existing buildings in graph Returns: The shortest-path from start to end, represented by a list of building numbers (in strings), [n_1, n_2, ..., n_k], where there exists an edge from n_i to n_(i+1) in digraph, for all 1 <= i < k. If there exists no path that satisfies maxTotalDist and maxDistOutdoors constraints, then raises a ValueError. """ paths = getPaths(digraph, start, end) shortest = getShortestPath(digraph, paths, maxTotalDist, maxDistOutdoors) if shortest: return map(str, shortest) else: raise ValueError ('ValueError') # # Problem 4: Finding the Shorest Path using Optimized Search Method # def directedDFS_Defected(digraph, start, end, maxTotalDist, maxDistOutdoors, path = [], shortest = None): """ Finds the shortest path from start to end using directed depth-first. search approach. The total distance travelled on the path must not exceed maxTotalDist, and the distance spent outdoor on this path must not exceed maxDistOutdoors. Parameters: digraph: instance of class Digraph or its subclass start, end: start & end building numbers (strings) maxTotalDist : maximum total distance on a path (integer) maxDistOutdoors: maximum distance spent outdoors on a path (integer) Assumes: start and end are numbers for existing buildings in graph Returns: The shortest-path from start to end, represented by a list of building numbers (in strings), [n_1, n_2, ..., n_k], where there exists an edge from n_i to n_(i+1) in digraph, for all 1 <= i < k. If there exists no path that satisfies maxTotalDist and maxDistOutdoors constraints, then raises a ValueError. """ start = Node(start) end = Node(end) path = path + [start] if start == end: return path #continue #print digraph. print digraph.childrenOf(start) for node in digraph.childrenOf(start): print node not in path if node not in path: if shortest == None or len(path) < len (shortest): newPath = directedDFS(digraph, node, end,maxTotalDist,maxDistOutdoors, path, shortest) if newPath != None: return newPath #print path_list return shortest def getOptimizedPath(digraph, start, end, maxTotalDist, maxOutdoorDist): start, end = Node(start), Node(end) stack = [[start, n] for n in digraph.childrenOf(start) if n != start] res, shortest = "", None while stack: path = stack.pop() totalDist, outDist = getPathDistance(digraph, path) if totalDist > maxTotslDist or outDist > maxOutdoorDist: continue node = path[-1] if node == end: if not shortest or totalDist < shortest: res, shortest = path, totalDist continue children = digraph.childrenOf(node) if not children: continue stack += [path + [c] for c in children if c not in path] return res def directedDFS(digraph, start, end, maxTotalDist, maxDistOutdoors): res = getOptimizedPath(digraph, start, end, maxTotalDist, maxDistOutdoors) if res: return map(str, res) else: raise ValueError('ValueError') #mitMap = load_map("mit_map.txt") #print directedDFS(mitMap, '32', '56', 200, 200) #print getPaths(mitMap, '32', '56') # Uncomment below when ready to test #### NOTE! These tests may take a few minutes to run!! #### if __name__ == '__main__': mitMap = load_map("mit_map.txt") print isinstance(mitMap, Digraph) print isinstance(mitMap, WeightedDigraph) print 'nodes', mitMap.nodes print 'edges', mitMap.edges # # LARGE_DIST = 1000000 # # Test case 1 # print "---------------" # print "Test case 1:" # print "Find the shortest-path from Building 32 to 56" # expectedPath1 = ['32', '56'] #brutePath1 = bruteForceSearch(mitMap, '32', '56', LARGE_DIST, LARGE_DIST) # dfsPath1 = directedDFS(mitMap, '32', '56', LARGE_DIST, LARGE_DIST) # print "Expected: ", expectedPath1 #print "Brute-force: ", brutePath1 # print "DFS: ", dfsPath1 #print "Correct? BFS: {0}; DFS: {1}".format(expectedPath1 == brutePath1, expectedPath1 == dfsPath1) # # Test case 2 # print "---------------" # print "Test case 2:" # print "Find the shortest-path from Building 32 to 56 without going outdoors" # expectedPath2 = ['32', '36', '26', '16', '56'] # brutePath2 = bruteForceSearch(mitMap, '32', '56', LARGE_DIST, 0) # dfsPath2 = directedDFS(mitMap, '32', '56', LARGE_DIST, 0) # print "Expected: ", expectedPath2 # print "Brute-force: ", brutePath2 # print "DFS: ", dfsPath2 # print "Correct? BFS: {0}; DFS: {1}".format(expectedPath2 == brutePath2, expectedPath2 == dfsPath2) # Test case 3 # print "---------------" # print "Test case 3:" # print "Find the shortest-path from Building 2 to 9" # expectedPath3 = ['2', '3', '7', '9'] # brutePath3 = bruteForceSearch(mitMap, '2', '9', LARGE_DIST, LARGE_DIST) # dfsPath3 = directedDFS(mitMap, '2', '9', LARGE_DIST, LARGE_DIST) # print "Expected: ", expectedPath3 # print "Brute-force: ", brutePath3 # print "DFS: ", dfsPath3 # print "Correct? BFS: {0}; DFS: {1}".format(expectedPath3 == brutePath3, expectedPath3 == dfsPath3) # Test case 4 # print "---------------" # print "Test case 4:" # print "Find the shortest-path from Building 2 to 9 without going outdoors" # expectedPath4 = ['2', '4', '10', '13', '9'] # brutePath4 = bruteForceSearch(mitMap, '2', '9', LARGE_DIST, 0) # dfsPath4 = directedDFS(mitMap, '2', '9', LARGE_DIST, 0) # print "Expected: ", expectedPath4 # print "Brute-force: ", brutePath4 # print "DFS: ", dfsPath4 # print "Correct? BFS: {0}; DFS: {1}".format(expectedPath4 == brutePath4, expectedPath4 == dfsPath4) # Test case 5 # print "---------------" # print "Test case 5:" # print "Find the shortest-path from Building 1 to 32" # expectedPath5 = ['1', '4', '12', '32'] # brutePath5 = bruteForceSearch(mitMap, '1', '32', LARGE_DIST, LARGE_DIST) # dfsPath5 = directedDFS(mitMap, '1', '32', LARGE_DIST, LARGE_DIST) # print "Expected: ", expectedPath5 # print "Brute-force: ", brutePath5 # print "DFS: ", dfsPath5 # print "Correct? BFS: {0}; DFS: {1}".format(expectedPath5 == brutePath5, expectedPath5 == dfsPath5) # Test case 6 # print "---------------" # print "Test case 6:" # print "Find the shortest-path from Building 1 to 32 without going outdoors" # expectedPath6 = ['1', '3', '10', '4', '12', '24', '34', '36', '32'] # brutePath6 = bruteForceSearch(mitMap, '1', '32', LARGE_DIST, 0) # dfsPath6 = directedDFS(mitMap, '1', '32', LARGE_DIST, 0) # print "Expected: ", expectedPath6 # print "Brute-force: ", brutePath6 # print "DFS: ", dfsPath6 # print "Correct? BFS: {0}; DFS: {1}".format(expectedPath6 == brutePath6, expectedPath6 == dfsPath6) # Test case 7 # print "---------------" # print "Test case 7:" # print "Find the shortest-path from Building 8 to 50 without going outdoors" # bruteRaisedErr = 'No' # dfsRaisedErr = 'No' # try: # bruteForceSearch(mitMap, '8', '50', LARGE_DIST, 0) # except ValueError: # bruteRaisedErr = 'Yes' # try: # directedDFS(mitMap, '8', '50', LARGE_DIST, 0) # except ValueError: # dfsRaisedErr = 'Yes' # print "Expected: No such path! Should throw a value error." # print "Did brute force search raise an error?", bruteRaisedErr # print "Did DFS search raise an error?", dfsRaisedErr # Test case 8 # print "---------------" # print "Test case 8:" # print "Find the shortest-path from Building 10 to 32 without walking" # print "more than 100 meters in total" # bruteRaisedErr = 'No' # dfsRaisedErr = 'No' # try: # bruteForceSearch(mitMap, '10', '32', 100, LARGE_DIST) # except ValueError: # bruteRaisedErr = 'Yes' # try: # directedDFS(mitMap, '10', '32', 100, LARGE_DIST) # except ValueError: # dfsRaisedErr = 'Yes' # print "Expected: No such path! Should throw a value error." # print "Did brute force search raise an error?", bruteRaisedErr # print "Did DFS search raise an error?", dfsRaisedErr # # # # # # # # #
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src/radical/pilot/agent/launch_method/srun.py
radical-cybertools/radical.pilot
47
124033
<filename>src/radical/pilot/agent/launch_method/srun.py __copyright__ = "Copyright 2016, http://radical.rutgers.edu" __license__ = "MIT" import math import radical.utils as ru from .base import LaunchMethod # ------------------------------------------------------------------------------ # class Srun(LaunchMethod): ''' This launch method uses `srun` to place tasks into a slurm allocation. Srun has severe limitations compared to other launch methods, in that it does not allow to place a task on a specific set of nodes and cores, at least not in the general case. It is possible to select nodes as long as the task uses (a part of) a single node, or the task is using multiple nodes uniformly. Core pinning is only available on tasks which use exactly one full node (and in that case becomes useless for our purposes). We use srun in the following way: IF task <= nodesize OR task is uniformel THEN enforce node placement ELSE leave *all* placement to slurm ''' # -------------------------------------------------------------------------- # def __init__(self, name, lm_cfg, rm_info, log, prof): self._command: str = '' LaunchMethod.__init__(self, name, lm_cfg, rm_info, log, prof) # -------------------------------------------------------------------------- # def _init_from_scratch(self, env, env_sh): command = ru.which('srun') out, err, ret = ru.sh_callout('%s -V' % command) if ret: raise RuntimeError('cannot use srun [%s] [%s]' % (out, err)) self._version = out.split()[-1] self._log.debug('using srun from %s [%s]', command, self._version) lm_info = {'env' : env, 'env_sh' : env_sh, 'command': command} return lm_info # -------------------------------------------------------------------------- # def _init_from_info(self, lm_info): self._env = lm_info['env'] self._env_sh = lm_info['env_sh'] self._command = lm_info['command'] assert self._command # -------------------------------------------------------------------------- # def finalize(self): pass # -------------------------------------------------------------------------- # def can_launch(self, task): if not task['description']['executable']: return False, 'no executable' return True, '' # ------------------------------------------------------------------------- # def get_slurm_ver(self): major_version = int(self._version.split('.')[0].split()[-1]) return major_version # -------------------------------------------------------------------------- # def get_launcher_env(self): return ['export SLURM_CPU_BIND=verbose', # debug mapping '. $RP_PILOT_SANDBOX/%s' % self._env_sh] # -------------------------------------------------------------------------- # def get_launch_cmds(self, task, exec_path): uid = task['uid'] slots = task['slots'] td = task['description'] sbox = task['task_sandbox_path'] n_tasks = td['cpu_processes'] n_task_threads = td.get('cpu_threads', 1) n_gpus = td.get('gpu_processes', 1) # Alas, exact rank-to-core mapping seems only be available in Slurm when # tasks use full nodes - which in RP is rarely the case. We thus are # limited to specifying the list of nodes we want the processes to be # placed on, and otherwise have to rely on the `--exclusive` flag to get # a decent auto mapping. In cases where the scheduler did not place # the task we leave the node placement to srun as well. if not slots: nodefile = None n_nodes = int(math.ceil(float(n_tasks) / self._rm_info.get('cores_per_node', 1))) else: # the scheduler did place tasks - we can't honor the core and gpu # mapping (see above), but we at least honor the nodelist. nodelist = [rank['node_name'] for rank in slots['ranks']] nodefile = '%s/%s.nodes' % (sbox, uid) with ru.ru_open(nodefile, 'w') as fout: fout.write(','.join(nodelist)) fout.write('\n') n_nodes = len(set(nodelist)) # use `--exclusive` to ensure all tasks get individual resources. # do not use core binding: it triggers warnings on some installations # FIXME: warnings are triggered anyway :-( mapping = '--exclusive --cpu-bind=none ' \ + '--nodes %d ' % n_nodes \ + '--ntasks %d ' % n_tasks \ + '--gpus %d ' % (n_gpus * n_tasks) \ + '--cpus-per-task %d' % n_task_threads # check that gpus were requested to be allocated if self._rm_info.get('gpus'): mapping += ' --gpus-per-task %d' % n_gpus if nodefile: if self.get_slurm_ver() <= 18: mapping += ' --nodelist=%s' % ','.join(str(n) for n in nodelist) else: mapping += ' --nodefile=%s' % nodefile cmd = '%s %s %s' % (self._command, mapping, exec_path) return cmd.rstrip() # -------------------------------------------------------------------------- # def get_rank_cmd(self): # FIXME: does SRUN set a rank env? ret = 'test -z "$MPI_RANK" || export RP_RANK=$MPI_RANK\n' ret += 'test -z "$PMIX_RANK" || export RP_RANK=$PMIX_RANK\n' return ret # -------------------------------------------------------------------------- # def get_rank_exec(self, task, rank_id, rank): td = task['description'] task_exec = td['executable'] task_args = td.get('arguments') task_argstr = self._create_arg_string(task_args) command = '%s %s' % (task_exec, task_argstr) return command.rstrip() # ------------------------------------------------------------------------------
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tests/uservirtualrackattack.py
shlomimatichin/rackattack-virtual
0
102962
import subprocess import os import shutil import time from rackattack import clientfactory from tests import testlib import rackattack class UserVirtualRackAttack: MAXIMUM_VMS = 4 def __init__(self): assert '/usr' not in rackattack.__file__ self._requestPort = 3443 self._subscribePort = 3444 imageDir = os.path.join(os.getcwd(), "images.fortests") shutil.rmtree(imageDir, ignore_errors=True) self._popen = subprocess.Popen( ["sudo", "PYTHONPATH=.", "UPSETO_JOIN_PYTHON_NAMESPACES=Yes", "python", "rackattack/virtual/main.py", "--requestPort=%d" % self._requestPort, "--subscribePort=%d" % self._subscribePort, "--diskImagesDirectory=" + imageDir, "--serialLogsDirectory=" + imageDir, "--maximumVMs=%d" % self.MAXIMUM_VMS], close_fds=True, stderr=subprocess.STDOUT) testlib.waitForTCPServer(('localhost', self._requestPort)) time.sleep(0.5) # dnsmasq needs to be able to receive a SIGHUP def done(self): if self._popen.poll() is not None: raise Exception("Virtual RackAttack server terminated before it's time") subprocess.check_call(["sudo", "kill", str(self._popen.pid)], close_fds=True) self._popen.wait() def createClient(self): os.environ['RACKATTACK_PROVIDER'] = 'tcp://localhost:%d@tcp://localhost:%d' % ( self._requestPort, self._subscribePort) return clientfactory.factory()
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serve/api/predict.py
HalleyYoung/musicautobot
402
13952
import sys from . import app sys.path.append(str(app.config['LIB_PATH'])) from musicautobot.music_transformer import * from musicautobot.config import * from flask import Response, send_from_directory, send_file, request, jsonify from .save import to_s3 import torch import traceback torch.set_num_threads(4) data = load_data(app.config['DATA_PATH'], app.config['DATA_SAVE_NAME'], num_workers=1) learn = music_model_learner(data, pretrained_path=app.config['MUSIC_MODEL_PATH']) if torch.cuda.is_available(): learn.model.cuda() # learn.to_fp16(loss_scale=512) # fp16 not supported for cpu - https://github.com/pytorch/pytorch/issues/17699 @app.route('/predict/midi', methods=['POST']) def predict_midi(): args = request.form.to_dict() midi = request.files['midi'].read() print('THE ARGS PASSED:', args) bpm = float(args['bpm']) # (AS) TODO: get bpm from midi file instead temperatures = (float(args.get('noteTemp', 1.2)), float(args.get('durationTemp', 0.8))) n_words = int(args.get('nSteps', 200)) seed_len = int(args.get('seedLen', 12)) # debugging 1 - send exact midi back # with open('/tmp/test.mid', 'wb') as f: # f.write(midi) # return send_from_directory('/tmp', 'test.mid', mimetype='audio/midi') # debugging 2 - test music21 conversion # stream = file2stream(midi) # 1. # debugging 3 - test npenc conversion # seed_np = midi2npenc(midi) # music21 can handle bytes directly # stream = npenc2stream(seed_np, bpm=bpm) # debugging 4 - midi in, convert, midi out # stream = file2stream(midi) # 1. # midi_in = Path(stream.write("musicxml")) # print('Midi in:', midi_in) # stream_sep = separate_melody_chord(stream) # midi_out = Path(stream_sep.write("midi")) # print('Midi out:', midi_out) # s3_id = to_s3(midi_out, args) # result = { # 'result': s3_id # } # return jsonify(result) # Main logic try: full = predict_from_midi(learn, midi=midi, n_words=n_words, seed_len=seed_len, temperatures=temperatures) stream = separate_melody_chord(full.to_stream(bpm=bpm)) midi_out = Path(stream.write("midi")) print('Wrote to temporary file:', midi_out) except Exception as e: traceback.print_exc() return jsonify({'error': f'Failed to predict: {e}'}) s3_id = to_s3(midi_out, args) result = { 'result': s3_id } return jsonify(result) # return send_from_directory(midi_out.parent, midi_out.name, mimetype='audio/midi') # @app.route('/midi/song/<path:sid>') # def get_song_midi(sid): # return send_from_directory(file_path/data_dir, htlist[sid]['midi'], mimetype='audio/midi') @app.route('/midi/convert', methods=['POST']) def convert_midi(): args = request.form.to_dict() if 'midi' in request.files: midi = request.files['midi'].read() elif 'midi_path'in args: midi = args['midi_path'] stream = file2stream(midi) # 1. # stream = file2stream(midi).chordify() # 1. stream_out = Path(stream.write('musicxml')) return send_from_directory(stream_out.parent, stream_out.name, mimetype='xml')
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invo/LinearModels/AbsoluteDualityGap.py
rafidrm/invo
7
132305
<reponame>rafidrm/invo """ Absolute Duality Gap Inverse Optimization The absolute duality gap method for inverse optimization minimizes the aggregate duality gap between the primal and dual objective values for each observed decision. The problem is formulated as follows .. math:: \min_{\mathbf{c, y},\epsilon_1, \dots, \epsilon_Q} \quad & \sum_{q=1}^Q | \epsilon_q | \\text{s.t.}\quad\quad & \mathbf{A'y = c} & \mathbf{c'\hat{x}_q = b'y} + \epsilon_q, \quad \\forall q & \| \mathbf{c} \|_1 = 1 & \mathbf{y \geq 0} """ import cvxpy as cvx import numpy as np #import pudb from ..utils.invoutils import checkFeasibility, validateFOP class AbsoluteDualityGap(): """ Formulate an Absolute Duality Gap method of GMIO. Args: tol (int): Sets number of significant digits. Default is 8. verbose (bool): Sets displays. Default is False. force_feasible_method (bool): If set to True, then will enforce the hyperplane projection method regardless of feasible points. Default is False. normalize_c: Set to either 1 or np.inf. Decides the normalization constraint on c ban_constraints (list): A list of constraint indices to force to zero when solving. Default is none. Example: Suppose that the variables ``A`` and ``b`` are numpy matrices and ``points`` is a list of numpy arrays:: model = AbsoluteDualityGap() model.FOP(A, b) model.solve(points) print (model.c) """ def __init__(self, **kwargs): self._fop = False self._verbose = False self._solved = False self.tol = 8 self.solver = cvx.ECOS_BB self.force_feasible_method = False self.ban_constraints = [] self.normalize_c = 1 self._kwargs = self._initialize_kwargs(kwargs) def FOP(self, A, b): """ Create a forward optimization problem. Args: A (matrix): numpy matrix of shape :math:`m \\times n`. b (matrix): numpy matrix of shape :math:`m \\times 1`. Currently, the forward problem is constructed by the user supplying a constraint matrix ``A`` and vector ``b``. The forward problem is .. math:: \min_{\mathbf{x}} \quad&\mathbf{c'x} \\text{s.t} \quad&\mathbf{A x \geq b} """ #self.A = np.mat(A) #self.b = np.mat(b) self.A, self.b = validateFOP(A, b) self._fop = True def solve(self, points, **kwargs): """ Solves the inverse optimization problem. Args: points (list): list of numpy arrays, denoting the (optimal) observed points. Returns: error (float): the optimal value of the inverse optimization problem. First check if all of the points are feasible, in which case we can just project the points to each of the hyperplanes. Let :math:`\\bar{x}` denote the centroid of the points. Then, we just solve .. math:: \min_{i \in \mathcal{M}} \left\{ \\frac{\mathbf{a_i'\\bar{x} - }b_i }{\| \mathbf{a_i} \|_1} \\right\} Let :math:`i^*` denote the optimal index. The optimal cost and dual variables are .. math:: \mathbf{c^*} &= \mathbf{\\frac{a_{i^*}}{\|a_{i^*}\|}} \mathbf{y^*} &= \mathbf{\\frac{e_{i^*}}{\|a_{i^*}\|}} If not all of the points are feasible, then we need to solve an exponential number of optimization problems. Let :math:`\mathcal{C}^+, \mathcal{C}^- \subseteq \{ 1, \dots, n \}` be a partition of the index set of length ``n``. For each possible partition, we solve the following problem .. math:: \min_{\mathbf{c, y}, \epsilon_1,\dots,\epsilon_Q} \quad & \sum_{q=1}^Q | \epsilon_q | \\text{s.t.} \quad & \mathbf{A'y = c} & \mathbf{c'\hat{x}_q = b'y} + \epsilon_q, \quad \\forall q & \sum_{i \in \mathcal{C}^+} c_i + \sum_{i \in \mathcal{C}^-} c_i = 1 & c_i \geq 0, \quad i \in \mathcal{C}^+ & c_i \leq 0, \quad i \in \mathcal{C}^- & \mathbf{y \geq 0} """ self._kwargs = self._initialize_kwargs(kwargs) points = [np.mat(point).T for point in points] assert self._fop, 'No forward model given.' feasible = checkFeasibility(points, self.A, self.b, self.tol) if feasible or self.force_feasible_method: self.error = self._solveHyperplaneProjection(points) else: if self.normalize_c == 1: self.error = self._solveBruteForceNorm1(points) elif self.normalize_c == np.inf: self.error = self._solveBruteForceNormInf(points) else: return -1 return self.error def _solveHyperplaneProjection(self, points): m, n = self.A.shape errors = np.zeros(m) for i in range(m): if i in self.ban_constraints: errors[i] = 9999999 else: ai = self.A[i] / np.linalg.norm(self.A[i].T, self.normalize_c) bi = self.b[i] / np.linalg.norm(self.A[i].T, self.normalize_c) errors[i] = np.sum([ai * pt - bi for pt in points]) minInd = np.argmin(errors) self.c = self.A[minInd] / np.linalg.norm(self.A[minInd].T, self.normalize_c) self.c = self.c.tolist()[0] self.error = errors[minInd] self.dual = np.zeros(m) self.dual[minInd] = 1 / np.linalg.norm(self.A[minInd].T, self.normalize_c) self._solved = True return errors[minInd] def _baseBruteForceProblem(self, y, z, c, points): obj = cvx.Minimize(sum(z)) cons = [] cons.append(y >= 0) cons.append(self.A.T * y == c) for i in range(len(points)): chi = self.A * points[i] - self.b cons.append(z[i] >= y.T * chi) cons.append(z[i] >= -1 * y.T * chi) for i in self.ban_constraints: cons.append(y[i] == 0) return obj, cons def _solveBruteForceNorm1(self, points): m, n = self.A.shape nPoints = len(points) nFormulations = 2**n bestResult = np.inf for formulation in range(nFormulations): binFormulation = format(formulation, '0{}b'.format(n)) cSign = [int(i) for i in binFormulation] cSign = np.mat(cSign) cSign[cSign == 0] = -1 y = cvx.Variable(m) z = cvx.Variable(nPoints) c = cvx.Variable(n) obj, cons = self._baseBruteForceProblem(y, z, c, points) # add the normalization constraint cons.append(cSign * c == 1) for i in range(n): if cSign[0, i] == 1: cons.append(c[i] >= 0) else: cons.append(c[i] <= 0) prob = cvx.Problem(obj, cons) result = prob.solve(solver=self.solver) if result < bestResult: bestResult = result self.c = c.value / np.linalg.norm(c.value, 1) self.dual = y.value / np.linalg.norm(c.value, 1) self._solved = True self.error = bestResult self.dual = self.dual.T.tolist()[0] # reconvert to just a list self.c = self.c.T.tolist()[0] return self.error def _solveBruteForceNormInf(self, points): m, n = self.A.shape nPoints = len(points) bestResult = np.inf for j in range(n): y1 = cvx.Variable(m) z1 = cvx.Variable(nPoints) c1 = cvx.Variable(n) obj1, cons1 = self._baseBruteForceProblem(y1, z1, c1, points) # Add the normalization constraint cons1.append(c1 <= 1) cons1.append(c1 >= -1) cons1.append(c1[j] == 1) prob1 = cvx.Problem(obj1, cons1) result1 = prob1.solve(solver=self.solver) y2 = cvx.Variable(m) z2 = cvx.Variable(nPoints) c2 = cvx.Variable(n) obj2, cons2 = self._baseBruteForceProblem(y2, z2, c2, points) # Add the normalization constraint cons2.append(c2 <= 1) cons2.append(c2 >= -1) cons2.append(c2[j] == -1) prob2 = cvx.Problem(obj2, cons2) result2 = prob2.solve(solver=self.solver) optimalReform = np.argmin([result1, result2, bestResult]) if optimalReform == 0: bestResult = result1 self.c = c1.value / np.linalg.norm(c1.value, np.inf) self.dual = y1.value / np.linalg.norm(y1.value, np.inf) elif optimalReform == 1: bestResult = result2 self.c = c2.value / np.linalg.norm(c2.value, np.inf) self.dual = y2.value / np.linalg.norm(y2.value, np.inf) self._solved = True self.error = bestResult self.dual = self.dual.T.tolist()[0] # reconvert to just a list self.c = self.c.T.tolist()[0] return self.error def rho(self, points): """ Solves the goodness of fit. """ assert self._solved, 'you need to solve first.' m, n = self.A.shape numer = [ np.abs(np.dot(self.c, point) - np.dot(self.dual, self.b)) for point in points ] numer = sum(numer) denom = 0 for i in range(m): denomTerm = [ np.abs(np.dot(self.A[i], point) - self.b[i]) / np.linalg.norm( self.A[i].T, self.normalize_c) for point in points ] denom += sum(denomTerm) rho = 1 - numer / denom return rho[0, 0] def _initialize_kwargs(self, kwargs): if 'verbose' in kwargs: assert isinstance(kwargs['verbose'], bool), 'verbose needs to be True or False.' self._verbose = kwargs['verbose'] if 'tol' in kwargs: assert isinstance(kwargs['tol'], int), 'tolerance needs to be an integer.' self.tol = kwargs['tol'] if 'force_feasible_method' in kwargs: assert isinstance( kwargs['force_feasible_method'], bool), 'force feasible method needs to be True or False.' self.force_feasible_method = kwargs['force_feasible_method'] if 'ban_constraints' in kwargs: assert isinstance(kwargs['ban_constraints'], list), 'ban constraints needs to be a list.' self.ban_constraints = kwargs['ban_constraints'] if 'normalize_c' in kwargs: assert kwargs['normalize_c'] == 1 or kwargs['normalize_c'] == np.inf, 'normalize c with 1 or infinity norm.' self.normalize_c = kwargs['normalize_c'] if 'solver' in kwargs: if kwargs['solver'] in cvx.installed_solvers(): self.solver = getattr(cvx, kwargs['solver']) else: print('you do not have this solver.') return kwargs
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helpers.py
old-school-vienna/predict-future-sales-py
0
164133
<reponame>old-school-vienna/predict-future-sales-py<gh_stars>0 import os import typing from dataclasses import dataclass from datetime import datetime from pathlib import Path from typing import List, Dict import numpy as np import pandas as pd import tensorflow.python.keras as keras import tensorflow.python.keras.layers as kerasl from sklearn.preprocessing import MinMaxScaler @dataclass class Trainset: id: str x: np.array y: np.array y_min_max_scaler: MinMaxScaler def pivot(df: pd.DataFrame, grp_vars: List[str], col: str, val: str) -> pd.DataFrame: grpd = df.groupby(grp_vars).first() return grpd.pivot_table(index=grp_vars, columns=col, values=val, fill_value=0.0) def dd() -> Path: datadir = os.getenv("DATADIR") if datadir is None: raise RuntimeError("Environment variable DATADIR not defined") datadir_path = Path(datadir) if not datadir_path.exists(): raise RuntimeError(f"Directory {datadir_path} does not exist") return datadir_path _dt_start: datetime.date = datetime.strptime("1.1.2013", '%d.%m.%Y').date() def to_ds(date: str) -> int: dt: datetime.date = datetime.strptime(date, '%d.%m.%Y').date() diff = dt - _dt_start return diff.days def read_train_fillna() -> pd.DataFrame: file_name = dd() / 'in' / "df_train.csv" df_train = pd.read_csv(file_name) return df_train.fillna(value=0.0) # noinspection PyTypeChecker def category_dict() -> Dict[int, int]: file_name = dd() / 'in' / "items.csv" df = pd.read_csv(file_name) df = df[['item_id', 'item_category_id']] return pd.Series(df.item_category_id.values, index=df.item_id).to_dict() @dataclass class LayerConfig: size_relative: float @dataclass class ModelConfig: activation: str optimizer: str loss: str layers: typing.List[LayerConfig] def create_model(model_config: ModelConfig, input_size: int): model = keras.Sequential() model.add(kerasl.Dense(input_size, activation=model_config.activation)) for layer in model_config.layers: model.add(kerasl.Dense(int(layer.size_relative * input_size), activation=model_config.activation)) model.add(kerasl.Dense(1)) model.compile(optimizer=model_config.optimizer, loss=model_config.loss) return model
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setup.py
zhanglabtools/CIRCLET
1
103936
# -*- coding: utf-8 -*- """ @author: <NAME> """ import os import sys import shutil from subprocess import call from warnings import warn from setuptools import setup setup(name='CIRCLET', version='1.0', package_dir={'': 'src'}, packages=['CIRCLET'], package_data={ # And include any *.msg files found in the 'hello' package, too: 'CIRCLET': ['DATA/*','*.txt','DATA/RNA-seq/*','DATA/Hi-Cmaps/*','DATA/Nagano et al/*'], }, include_package_data=True )
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Robofont-scripts/select/unsel Alternates.py
casasin/RobofontTools
5
75654
# unselectGlyphsWithExtension.py f = CurrentFont() for gname in f.selection: if '.' in gname: f[gname].selected = 0
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get_data/get_tencent_data.py
blueberry686868/crawler_code
0
168177
import requests import json import time # 获取腾讯疫情数据 def get_tencent_data(): """ :return: 返回历史数据和当日详细数据 """ url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_h5' headers = { 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36' } r = requests.get(url, headers) # 把json格式转换成字典 res = json.loads(r.text) # 字典中有4个key，分别是lastUpdateTime, chinaTotal, chinaAdd, areaTree data_all = json.loads(res['data']) # 取出data_all中的areaTree这个key值,areaTree是一个列表，里面只有一个字典，索引0取出字典，得到省份数据 # 字典里面的key值为name(中国),today(全国今日新增),total(总计,包括现有确诊,已确诊,疑似,死亡...),children(省级数据) china_data = data_all['areaTree'][0] # china_data中key值为children为省级数据，china_data['children']是一个列表(每一个省份是一个字典) province_data = china_data['children'] # 循环遍历出每个省份的数据 # for province in province_data: # print(province) # 爬取历史数据 history_url = 'https://view.inews.qq.com/g2/getOnsInfo?name=disease_other&callback=jQuery34107549579501076509_1596161763386&_=1596161763387' headers = { 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36' } history_res = requests.get(history_url, headers) # 把字符串转换成字典 history_response_data = json.loads(history_res.text.replace('jQuery34107549579501076509_1596161763386(', '')[:-1]) # 获取字典中的data history_response = history_response_data.get('data') # 需要把response转换成字典,chinaDayList全国历史数据，是一个列表，每个元素就是一天的数据 # chinaDayAddList历史新增数据 history_data_all = json.loads(history_response) history = {} # 历史数据 for i in history_data_all['chinaDayList']: ds = "2020." + i['date'] # time.strptime是将字符串格式化成元组 tup = time.strptime(ds, "%Y.%m.%d") # time.strftime改变时间格式 ds = time.strftime("%Y-%m-%d", tup) confirm = i['confirm'] suspect = i['suspect'] heal = i['heal'] dead = i['dead'] history[ds] = {'confirm': confirm, 'suspect': suspect, 'heal': heal, 'dead': dead} for i in history_data_all['chinaDayAddList']: ds = '2020.' + i['date'] tup = time.strptime(ds, "%Y.%m.%d") ds = time.strftime("%Y-%m-%d", tup) confirm = i['confirm'] suspect = i['suspect'] heal = i['heal'] dead = i['dead'] history[ds].update({'confirm_add': confirm, 'suspect_add': suspect, 'heal_add': heal, 'dead_add': dead}) detail = [] # 当日详细数据 update_time = data_all['lastUpdateTime'] data_province = province_data # 中国各省 for pro_infos in data_province: province = pro_infos['name'] for city_infos in pro_infos['children']: city = city_infos['name'] confirm = city_infos['total']['confirm'] confirm_now = city_infos['total']['nowConfirm'] confirm_add = city_infos['today']['confirm'] heal = city_infos['total']['heal'] dead = city_infos['total']['dead'] detail.append([update_time, province, city, confirm, confirm_now, confirm_add, heal, dead]) return history, detail if __name__ == '__main__': history, details = get_tencent_data() print(history) print(details)
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slacker/workers/django.py
kmike/tornado-slacker
8
108353
<filename>slacker/workers/django.py from __future__ import absolute_import from django.core.urlresolvers import reverse from slacker.django_backend.conf import SLACKER_SERVER from .http import HttpWorker class DjangoWorker(HttpWorker): """ HttpWorker with django's defaults """ def __init__(self, server=None, path=None): server = server or SLACKER_SERVER path = path or reverse('slacker-execute') super(DjangoWorker, self).__init__(server, path)
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modules/GEM_500_COM3.py
ZeppW/ODMRGUI
0
34780
import pyvisa import time import numpy as np from struct import unpack import matplotlib.pyplot as plt rm = pyvisa.ResourceManager() rm.list_resources() GEM = rm.open_resource('ASRL3::INSTR') def Turn_on(): GEM.write('ON') GEM.write('POWER=001') print('Laser has initialized!!!') def SetPower(pw_mw): GEM.write('POWER='+str(pw_mw)) print('Power has changed to '+str(pw_mw)+' mW!!!Please wait...') time.sleep(10) def Turn_off(): GEM.write('POWER=001') print('Reset power!!!Please wait...') time.sleep(10) GEM.write('OFF') print('Laser has been disabled!!!') def PowerQ(): return GEM.query('POWER?').split()[0] def tempQ(): temp_list = [] i = 0 while i < 5: tt = GEM.query('LASTEMP?').split() if tt != []: tt = ''.join(list(tt[0])[:-1]) temp_list.append(float(tt)) i += 1 print(temp_list)
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dronecan/driver/timestamp_estimator.py
bugobliterator/pydronecan
0
1608435
# # Copyright (C) 2014-2016 UAVCAN Development Team <dronecan.org> # # This software is distributed under the terms of the MIT License. # # Author: <NAME> <<EMAIL>> # <NAME> <<EMAIL>> # from __future__ import division, absolute_import, print_function, unicode_literals import decimal class SourceTimeResolver: """ This class contains logic that recovers absolute value of a remote clock observable via small overflowing integer samples. For example, consider a remote system that reports timestamps as a 16-bit integer number of milliseconds that overflows every 60 seconds (this method is used in SLCAN for example). This class can recover the time difference in the remote clock domain between two arbitrary timestamps, even if the timestamp variable overflowed more than once between these events. """ def __init__(self, source_clock_overflow_period=None): """ Args: source_clock_overflow_period: Overflow period of the remote clock, in seconds. If not provided, the remote clock is considered to never overflow (i.e. absolute). """ self.source_clock_overflow_period = \ decimal.Decimal(source_clock_overflow_period) if source_clock_overflow_period else None if self.source_clock_overflow_period is not None and self.source_clock_overflow_period <= 0: raise ValueError('source_clock_overflow_period must be positive or None') # Internal states self._resolved_time = None self._prev_source_sample = None self._prev_target_sample = None def reset(self): """ Resets the internal logic; resolved time will start over. """ self._resolved_time = None self._prev_source_sample = None self._prev_target_sample = None def update(self, source_clock_sample, target_clock_sample): """ Args: source_clock_sample: Sample of the source clock, in seconds target_clock_sample: Sample of the target clock, in seconds Returns: Resolved absolute source clock value """ if self._resolved_time is None or self.source_clock_overflow_period is None: self._resolved_time = decimal.Decimal(source_clock_sample) self._prev_source_sample = source_clock_sample self._prev_target_sample = target_clock_sample else: # Time between updates in the target clock domain tgt_delta = target_clock_sample - self._prev_target_sample self._prev_target_sample = target_clock_sample assert tgt_delta >= 0 # Time between updates in the source clock domain src_delta = source_clock_sample - self._prev_source_sample self._prev_source_sample = source_clock_sample # Using the target clock we can resolve the integer ambiguity (number of overflows) full_cycles = int(round((tgt_delta - src_delta) / float(self.source_clock_overflow_period), 0)) # Updating the source clock now; in two steps, in order to avoid error accumulation in floats self._resolved_time += decimal.Decimal(full_cycles * self.source_clock_overflow_period) self._resolved_time += decimal.Decimal(src_delta) return self._resolved_time class TimestampEstimator: """ Based on "A Passive Solution to the Sensor Synchronization Problem" [<NAME> 2010] https://april.eecs.umich.edu/pdfs/olson2010.pdf """ DEFAULT_MAX_DRIFT_PPM = 200 DEFAULT_MAX_PHASE_ERROR_TO_RESYNC = 1. def __init__(self, max_rate_error=None, source_clock_overflow_period=None, fixed_delay=None, max_phase_error_to_resync=None): """ Args: max_rate_error: The max drift parameter must be not lower than maximum relative clock drift in PPM. If the max relative drift is guaranteed to be lower, reducing this value will improve estimation. The default covers vast majority of low-cost (and up) crystal oscillators. source_clock_overflow_period: How often the source clocks wraps over, in seconds. For example, for SLCAN this value is 60 seconds. If not provided, the source clock is considered to never wrap over. fixed_delay: This value will be unconditionally added to the delay estimations. Represented in seconds. Default is zero. For USB-interfaced sources it should be safe to use as much as 100 usec. max_phase_error_to_resync: When this value is exceeded, the estimator will start over. Defaults to a large value. """ self.max_rate_error = float(max_rate_error or (self.DEFAULT_MAX_DRIFT_PPM / 1e6)) self.fixed_delay = fixed_delay or 0 self.max_phase_error_to_resync = max_phase_error_to_resync or self.DEFAULT_MAX_PHASE_ERROR_TO_RESYNC if self.max_rate_error < 0: raise ValueError('max_rate_error must be non-negative') if self.fixed_delay < 0: raise ValueError('fixed_delay must be non-negative') if self.max_phase_error_to_resync <= 0: raise ValueError('max_phase_error_to_resync must be positive') # This is used to recover absolute source time self._source_time_resolver = SourceTimeResolver(source_clock_overflow_period=source_clock_overflow_period) # Refer to the paper for explanations self._p = None self._q = None # Statistics self._estimated_delay = 0.0 self._resync_count = 0 def update(self, source_clock_sample, target_clock_sample): """ Args: source_clock_sample: E.g. value received from the source system, in seconds target_clock_sample: E.g. target time sampled when the data arrived to the local system, in seconds Returns: Event timestamp converted to the target time domain. """ pi = float(self._source_time_resolver.update(source_clock_sample, target_clock_sample)) qi = target_clock_sample # Initialization if self._p is None: self._p = pi self._q = qi # Sync error - refer to the reference implementation of the algorithm self._estimated_delay = abs((pi - self._p) - (qi - self._q)) # Resynchronization (discarding known state) if self._estimated_delay > self.max_phase_error_to_resync: self._source_time_resolver.reset() self._resync_count += 1 self._p = pi = float(self._source_time_resolver.update(source_clock_sample, target_clock_sample)) self._q = qi # Offset options assert pi >= self._p offset = self._p - self._q - self.max_rate_error * (pi - self._p) - self.fixed_delay new_offset = pi - qi - self.fixed_delay # Updating p/q if the new offset is lower by magnitude if new_offset >= offset: offset = new_offset self._p = pi self._q = qi ti = pi - offset return ti @property def estimated_delay(self): """Estimated delay, updated in the last call to update()""" return self._estimated_delay @property def resync_count(self): return self._resync_count if __name__ == '__main__': # noinspection PyPackageRequirements import matplotlib.pyplot as plt # noinspection PyPackageRequirements import numpy import time if 1: estimator = TimestampEstimator() print(estimator.update(.0, 1000.0)) print(estimator.update(.1, 1000.1)) print(estimator.update(.2, 1000.1)) # Repeat print(estimator.update(.3, 1000.1)) # Repeat print(estimator.update(.4, 1000.2)) print(estimator.update(.5, 1000.3)) if 1: # Conversion from Real to Monotonic estimator = TimestampEstimator(max_rate_error=1e-5, fixed_delay=1e-6, max_phase_error_to_resync=1e-2) print('Initial mono to real:', time.time() - time.monotonic()) while True: mono = time.monotonic() real = time.time() est_real = estimator.update(mono, real) mono_to_real_offset = est_real - mono print(mono_to_real_offset) time.sleep(1) max_rate_error = None source_clock_range = 10 delay_min = 0.0001 delay_max = 0.02 num_samples = 200 x = range(num_samples) delays = numpy.random.uniform(delay_min, delay_max, size=num_samples) estimator = TimestampEstimator(max_rate_error=max_rate_error, fixed_delay=delay_min, source_clock_overflow_period=source_clock_range) source_clocks = [] estimated_times = [] offset_errors = [] estimated_delays = [] for i, delay in enumerate(delays): source_clock = i source_clocks.append(source_clock) target_clock = i + delay estimated_time = estimator.update(source_clock % source_clock_range, target_clock) estimated_times.append(estimated_time) offset_errors.append(estimated_time - source_clock) estimated_delays.append(estimator.estimated_delay) fig = plt.figure() ax1 = fig.add_subplot(211) ax1.plot(x, numpy.array(delays) * 1e3) ax1.plot(x, numpy.array(offset_errors) * 1e3) ax2 = fig.add_subplot(212) ax2.plot(x, (numpy.array(estimated_times) - numpy.array(source_clocks)) * 1e3) plt.show()
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pretalx_vimeo/recording.py
pretalx/pretalx-vimeo
0
133579
from pretalx.agenda.recording import BaseRecordingProvider class VimeoProvider(BaseRecordingProvider): def get_recording(self, submission): vimeo = getattr(submission, "vimeo_link", None) if vimeo: return {"iframe": vimeo.iframe, "csp_header": "https://player.vimeo.com"}
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hrnet/src/train.py
sentinel-hub/multi-temporal-super-resolution
34
121877
""" Python script to train HRNet + shiftNet for multi frame super resolution (MFSR) Credits: This code is adapted from ElementAI's HighRes-Net: https://github.com/ElementAI/HighRes-net """ import os import gc import json import argparse import datetime from functools import partial from collections import defaultdict, deque import numpy as np import cv2 as cv import torch import torch.optim as optim from torch.utils.data import DataLoader from sr.metrics import calculate_metrics from hrnet.src.DeepNetworks.HRNet import HRNet from hrnet.src.DeepNetworks.ShiftNet import ShiftNet from hrnet.src.utils import normalize_plotting from hrnet.src.utils import distributions_plot from sr.data_loader import ImagesetDataset, augment from sr.metrics import compute_perceptual_loss from tqdm.auto import tqdm from tensorboardX import SummaryWriter import wandb def register_batch(shiftNet, lrs, reference): """ Registers images against references. Args: shiftNet: torch.model lrs: tensor (batch size, views, C, W, H), images to shift reference: tensor (batch size, 1, C, W, H), reference images to shift Returns: thetas: tensor (batch size, views, 2) """ n_views = lrs.size(1) thetas = [] for i in range(n_views): # Add references as views concated = torch.cat([reference, lrs[:, i:i + 1]], 1) theta = shiftNet(concated) thetas.append(theta) thetas = torch.stack(thetas, 1) return thetas def apply_shifts(shiftNet, images, thetas, device): """ Applies sub-pixel translations to images with Lanczos interpolation. Args: shiftNet: torch.model images: tensor (batch size, views, C, W, H), images to shift thetas: tensor (batch size, views, 2), translation params Returns: new_images: tensor (batch size, views, C, W, H), warped images """ batch_size, n_views, channels, height, width = images.shape images = images.view(-1, channels, height, width) thetas = thetas.view(-1, 2) new_images = shiftNet.transform(thetas, images, device=device) return new_images.view(-1, n_views, channels, images.size(2), images.size(3)) def resize_batch_images(batch, fx=3, fy=3, interpolation=cv.INTER_CUBIC): resized = torch.tensor([cv.resize(np.moveaxis(img.detach().cpu().numpy(), 0, 2), None, fx=fx, fy=fy, interpolation=interpolation) for img in batch]) # The channel dimension was 1 and was lost by opencv... if resized.ndim < batch.ndim: b, w, h = resized.shape return resized.view(b, 1, w, h) return resized.permute([0, 3, 1, 2]) def save_per_sample_scores(val_score_lists, baseline_val_score_lists, val_names, filename): out_dict = {} for metric, batch_scores in val_score_lists.items(): out_dict[metric] = np.concatenate(batch_scores).tolist() for metric, batch_scores in baseline_val_score_lists.items(): out_dict[metric] = np.concatenate(batch_scores).tolist() out_dict['name'] = val_names with open(filename, 'w') as out_file: json.dump(out_dict, out_file) def trainAndGetBestModel(fusion_model, regis_model, optimizer, dataloaders, config, perceptual_loss_model=None): """ Trains HRNet and ShiftNet for Multi-Frame Super Resolution (MFSR), and saves best model. Args: fusion_model: torch.model, HRNet regis_model: torch.model, ShiftNet optimizer: torch.optim, optimizer to minimize loss dataloaders: dict, wraps train and validation dataloaders config: dict, configuration file perceptual_loss_model: model used for perceptual loss """ # Set params from config num_epochs = config['training']['num_epochs'] batch_size = config['training']['batch_size'] loss_metric = config['training']['loss_metric'] val_metrics = config['training']['validation_metrics'] apply_correction = config['training']['apply_correction'] use_reg_regularisation = config['training']['use_reg_regularization'] lambda_ = config['training']['lambda'] use_kl_div_loss = config['training']['use_kl_div_loss'] eta_ = config['training']['eta'] upscale_factor = config['network']['upscale_factor'] reg_offset = config['training']['reg_offset'] plot_chnls = config['visualization']['channels_to_plot'] distribution_sampling_proba = config['visualization']['distribution_sampling_proba'] assert loss_metric in ['MAE', 'MSE', 'SSIM', 'MIXED'] # Logging subfolder_pattern = 'batch_{}_time_{}'.format(batch_size, f"{datetime.datetime.now():%Y-%m-%d-%H-%M-%S-%f}") if config['training']['wandb']: wandb.watch(fusion_model) wandb.watch(regis_model) out_fusion = os.path.join(wandb.run.dir, 'HRNet.pth') out_regis = os.path.join(wandb.run.dir, 'ShiftNet.pth') out_val_scores = os.path.join(wandb.run.dir, 'val_scores.json') else: checkpoint_dir_run = os.path.join(config['paths']['checkpoint_dir'], subfolder_pattern) scores_dir_run = os.path.join(config['paths']['scores_dir'], subfolder_pattern) os.makedirs(checkpoint_dir_run, exist_ok=True) os.makedirs(scores_dir_run, exist_ok=True) out_fusion = os.path.join(checkpoint_dir_run, 'HRNet.pth') out_regis = os.path.join(checkpoint_dir_run, 'ShiftNet.pth') out_val_scores = os.path.join(scores_dir_run, 'val_scores.json') tb_logging_dir = config['paths']['tb_log_file_dir'] logging_dir = os.path.join(tb_logging_dir, subfolder_pattern) os.makedirs(logging_dir, exist_ok=True) writer = SummaryWriter(logging_dir) # Set backend device = torch.device('cuda' if torch.cuda.is_available() and config['training']['use_gpu'] else 'cpu') fusion_model.to(device) regis_model.to(device) # Iterate best_score_loss = np.Inf val_names_saved = False val_names = deque() for epoch in tqdm(range(0, num_epochs), desc='Epochs'): # Set train mode fusion_model.train() regis_model.train() # Reset epoch loss train_loss = 0. train_loss_reg = 0. train_loss_kl = 0. train_loss_perceptual = 0. for sample in tqdm(dataloaders['train'], desc='Training iter. %d' % epoch): # Reset parameter gradients optimizer.zero_grad() # Potentially transfer data to GPU lrs = sample['lr'].float().to(device, non_blocking=True) alphas = sample['alphas'].float().to(device, non_blocking=True) lrs_last = lrs[np.arange(len(alphas)), torch.sum(alphas, dim=1, dtype=torch.int64) - 1] hrs = sample['hr'].float().to(device, non_blocking=True) # Fuse multiple frames into (B, 1, upscale_factor*W, upscale_factor*H) srs = fusion_model(lrs, alphas) batch, c, w, h = srs.shape srs = srs.view(batch, 1, c, w, h) # Register batch wrt HR shifts = register_batch( regis_model, srs[:, :, :, reg_offset:-reg_offset, reg_offset:-reg_offset], reference=hrs[:, :, reg_offset:-reg_offset, reg_offset:-reg_offset].view(-1, 1, c, h-2*reg_offset, w-2*reg_offset)) srs_shifted = apply_shifts(regis_model, srs, shifts, device)[:, 0] # Training loss scores = calculate_metrics(hrs=hrs, srs=srs_shifted, metrics=loss_metric, apply_correction=apply_correction) loss = torch.mean(scores) if loss_metric == 'SSIM': loss = -1 * loss + 1 loss_registration = torch.mean(torch.linalg.norm(shifts, ord=2, dim=1)) if use_reg_regularisation: loss += lambda_ * loss_registration srs = srs.view(batch, c, w, h) kl_losses = 0 if use_kl_div_loss: for nc in np.arange(c): mean_diffs = srs[:, nc, ...].mean(dim=(1, 2)) - lrs_last[:, nc, ...].mean(dim=(1, 2)) tmp_kl_loss = torch.abs(mean_diffs).mean() loss += eta_ * tmp_kl_loss kl_losses += tmp_kl_loss del tmp_kl_loss kl_losses /= c # adding perceptual loss if perceptual_loss_model: feat_perceptual_loss, style_perceptual_loss = compute_perceptual_loss(hrs, srs, perceptual_loss_model) perceptual_loss = feat_perceptual_loss + style_perceptual_loss loss += config["perceptual_loss"]["weight"] * perceptual_loss del feat_perceptual_loss, style_perceptual_loss # Backprop loss.backward() optimizer.step() # Scale loss so that epoch loss is the average of batch losses num_batches = len(dataloaders['train'].dataset) / len(hrs) train_loss += loss.detach().item() / num_batches train_loss_reg += loss_registration.detach().item() / num_batches train_loss_kl += kl_losses / num_batches train_loss_perceptual += perceptual_loss.detach().item() / num_batches # Try releasing some memory del lrs, alphas, hrs, srs, srs_shifted, scores, loss, loss_registration, sample, kl_losses, perceptual_loss gc.collect() torch.cuda.empty_cache() # Set eval mode fusion_model.eval() val_scores = defaultdict(float) baseline_val_scores = defaultdict(float) val_score_lists = defaultdict(list) baseline_val_score_lists = defaultdict(list) lrs_ref = None hrs_ref = None srs_ref = None lin_interp_img = None distribution_s2, distribution_deimos, distribution_sr = [], [], [] # Run validation with torch.no_grad(): for sample in tqdm(dataloaders['val'], desc='Valid. iter. %d' % epoch): # Potentially transfer data to GPU lrs_cpu = sample['lr'].float() hrs_cpu = sample['hr'].float() lrs = lrs_cpu.to(device, non_blocking=True) hrs = hrs_cpu.to(device, non_blocking=True) alphas = sample['alphas'].float().to(device, non_blocking=True) # Inference srs = fusion_model(lrs, alphas) if np.random.random() < distribution_sampling_proba: # sampling.... for lr, hr, sr, a in zip(lrs_cpu, hrs_cpu, srs.cpu().numpy(), sample['alphas']): num_valid = torch.sum(a, dim=0, dtype=torch.int64).int() distribution_s2.append(lr[:num_valid, ...]) distribution_deimos.append(np.expand_dims(hr, 0)) distribution_sr.append(np.expand_dims(sr, 0)) # Update scores metrics = calculate_metrics(hrs, srs, val_metrics, apply_correction) for metric, batch_scores in metrics.items(): batch_scores = batch_scores.cpu() val_scores[metric] += torch.sum(batch_scores).item() val_score_lists[metric].append(batch_scores.numpy().flatten()) # First val. iter.: add names, calculate baseline if not val_names_saved: val_names.append(sample['name']) latest_s2_images = lrs_cpu[np.arange(len(alphas)), torch.sum(alphas, dim=1, dtype=torch.int64) - 1] lin_interp_imgs = resize_batch_images(latest_s2_images, fx=upscale_factor, fy=upscale_factor) baseline_metrics = calculate_metrics(hrs_cpu, lin_interp_imgs, val_metrics, apply_correction) for metric, batch_scores in baseline_metrics.items(): batch_scores = batch_scores.cpu() baseline_val_scores[f'{metric}_baseline'] += torch.sum(batch_scores).item() baseline_val_score_lists[f'{metric}_baseline'].append(batch_scores.numpy().flatten()) del baseline_metrics # Keep a reference for plotting if lrs_ref is None: lrs_ref = lrs_cpu[0].numpy() hrs_ref = hrs_cpu[0].numpy() lin_interp_img = lin_interp_imgs[0].numpy() if srs_ref is None: srs_ref = srs[0].cpu().numpy() # Try releasing some memory del lrs_cpu, hrs_cpu, lrs, alphas, hrs, srs, metrics, batch_scores, sample gc.collect() torch.cuda.empty_cache() s2 = np.concatenate(distribution_s2) deimos = np.concatenate(distribution_deimos) sresolved = np.concatenate(distribution_sr) # Compute the average scores per sample (note the sum instead of the mean above) n = len(dataloaders['val'].dataset) for metric in val_scores: val_scores[metric] /= n # Validation file identifiers if not val_names_saved: val_names = np.concatenate(val_names).tolist() val_names_saved = True for metric in baseline_val_scores: baseline_val_scores[metric] /= n # Save improved model val_loss_metric = loss_metric if not use_kl_div_loss else 'SSIM' val_scores_loss = val_scores[val_loss_metric] if val_loss_metric == 'SSIM': val_scores_loss = -1 * val_scores_loss + 1 if val_scores_loss < best_score_loss: print('Saving model (val. loss has improved).') torch.save(fusion_model.state_dict(), out_fusion) torch.save(regis_model.state_dict(), out_regis) save_per_sample_scores(val_score_lists, baseline_val_score_lists, val_names, out_val_scores) best_score_loss = val_scores_loss # Plotting lrs = lrs_ref srs = srs_ref hrs = hrs_ref normalized_srs = (srs - np.min(srs)) / np.max(srs) normalized_plot = normalized_srs[plot_chnls, ...] lrs_plot = np.array([normalize_plotting(x[plot_chnls, ...]) for x in lrs if np.any(x)]) error_map = hrs - srs writer.add_image('SR Image', normalize_plotting(normalized_plot), epoch, dataformats='HWC') writer.add_image('Error Map', normalize_plotting(error_map[plot_chnls, ...]), epoch, dataformats='HWC') writer.add_image('HR GT', normalize_plotting(hrs[plot_chnls, ...]), epoch, dataformats='HWC') writer.add_images('S2', np.moveaxis(lrs_plot, 3, 1), epoch, dataformats='NCHW') writer.add_scalar('train/loss', train_loss, epoch) for metric in val_metrics: writer.add_scalar('val/%s' % metric.lower(), val_scores[metric], epoch) # wandb if config['training']['wandb']: wandb.log({'Train loss': train_loss, 'Train loss registration': train_loss_reg, 'Train KL loss': train_loss_kl, 'Train loss perceptual': train_loss_perceptual}, step=epoch) wandb.log({'sr': [wandb.Image(normalize_plotting(normalized_plot), caption='SR Image')]}, step=epoch) wandb.log({'gt': [wandb.Image(normalize_plotting(hrs[plot_chnls, ...]), caption='HR GT')]}, step=epoch) wandb.log({'S2': [wandb.Image(x, caption='S2 GT') for x in lrs_plot]}, step=epoch) wandb.log({'S2 bilinear interpolation baseline': [wandb.Image(normalize_plotting(lin_interp_img[plot_chnls, ...]))]}, step=epoch) wandb.log({'Distributions': [wandb.Image(normalize_plotting(hrs[plot_chnls, ...]), caption='HR GT')]}, step=epoch) wandb.log({'Distribution Blue': [wandb.Image(distributions_plot(s2, deimos, sresolved, 0), caption='Band Blue')], 'Distribution Green': [wandb.Image(distributions_plot(s2, deimos, sresolved, 1), caption='Band Green')], 'Distributions Red': [wandb.Image(distributions_plot(s2, deimos, sresolved, 2), caption='Band Red')], 'DIstributions NIR': [wandb.Image(distributions_plot(s2, deimos, sresolved, 3), caption='Band NIR')] }, step=epoch) wandb.log(val_scores, step=epoch) wandb.log(baseline_val_scores, step=epoch) del lrs, srs, hrs, lrs_ref, srs_ref, hrs_ref del val_scores, baseline_val_scores, val_score_lists, baseline_val_score_lists gc.collect() writer.close() def main( config, data_df, filesystem=None, country_norm_df=None, normalize=True, norm_deimos_npz=None, norm_s2_npz=None, perceptual_loss_model=None, fusion_model = None, regis_model = None ): """ Given a configuration, trains HRNet and ShiftNet for Multi-Frame Super Resolution (MFSR), and saves best model. Args: config: dict, configuration file """ # Reproducibility options seed = config['training']['seed'] np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.backends.cudnn.enabled = True torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False channels_labels = config['training']['channels_labels'] channels_features = config['training']['channels_features'] lr_patch_size = config['training']['patch_size'] upscale_factor = config['network']['upscale_factor'] reg_offset = config['training']['reg_offset'] histogram_matching = config['training']['histogram_matching'] # Initialize the network based on the network configuration if fusion_model is None: fusion_model = HRNet(config["network"]) if regis_model is None: regis_model = ShiftNet(in_channel=len(channels_labels), patch_size=lr_patch_size*upscale_factor - 2*reg_offset) optimizer = optim.Adam(list(fusion_model.parameters()) + list(regis_model.parameters()), lr=config['training']['lr']) data_directory = config['paths']['prefix'] # Dataloaders batch_size = config['training']['batch_size'] n_workers = config['training']['n_workers'] n_views = config['training']['n_views'] use_augment = config['training']['augment'] aug_fn = partial(augment, permute_timestamps=False) if use_augment else None # Train data loader train_samples = data_df[data_df.train_test_validation == 'train'].singleton_npz_filename.values train_dataset = ImagesetDataset( imset_dir=data_directory, imset_npz_files=train_samples, time_first=True, filesystem=filesystem, country_norm_df=country_norm_df, normalize=normalize, norm_deimos_npz=norm_deimos_npz, norm_s2_npz=norm_s2_npz, channels_labels=channels_labels, channels_feats=channels_features, n_views=n_views, padding='zeros', transform=aug_fn, histogram_matching=histogram_matching) train_dataloader = DataLoader( train_dataset, batch_size=batch_size, shuffle=True, num_workers=n_workers, pin_memory=True) # Validation data loader validation_samples = data_df[data_df.train_test_validation == 'validation'].singleton_npz_filename.values val_dataset = ImagesetDataset( imset_dir=data_directory, imset_npz_files=validation_samples, time_first=True, filesystem=filesystem, country_norm_df=country_norm_df, normalize=normalize, norm_deimos_npz=norm_deimos_npz, norm_s2_npz=norm_s2_npz, channels_labels=channels_labels, channels_feats=channels_features, n_views=n_views, padding='zeros', transform=None, histogram_matching=False) val_dataloader = DataLoader( val_dataset, batch_size=batch_size, shuffle=False, num_workers=n_workers, pin_memory=True) dataloaders = {'train': train_dataloader, 'val': val_dataloader} # Train model torch.cuda.empty_cache() trainAndGetBestModel(fusion_model, regis_model, optimizer, dataloaders, config, perceptual_loss_model) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--config', help='path of the config file', default='config/config.json') args = parser.parse_args() assert os.path.isfile(args.config) with open(args.config, 'r') as read_file: config = json.load(read_file) main(config)
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pedal/tifa/feedbacks.py
acbart/python-analysis
14
73738
""" All of the feedback responses generated by TIFA. """ from pedal.utilities.operators import OPERATION_DESCRIPTION from pedal.core.report import MAIN_REPORT from pedal.core.feedback import FeedbackResponse class TifaFeedback(FeedbackResponse): """ Base class for all TIFA feedback """ muted = False category = FeedbackResponse.CATEGORIES.ALGORITHMIC kind = FeedbackResponse.KINDS.MISTAKE class action_after_return(TifaFeedback): """ Statement after return """ title = "Action after Return" message_template = ("You performed an action after already returning from " "a function, on line {location.line}. You can " "only return on a path once.") justification = ("TIFA visited a node not in the top scope when its " "*return variable was definitely set in this scope.") def __init__(self, location, **kwargs): super().__init__(location=location, **kwargs) class return_outside_function(TifaFeedback): """ Return statement outside of function """ title = "Return outside Function" message_template = ("You attempted to return outside of a function on line " "{location.line}. But you can only return from within " "a function.") justification = "TIFA visited a return node at the top level." def __init__(self, location, **kwargs): super().__init__(location=location, **kwargs) class multiple_return_types(TifaFeedback): """ Multiple returned types in single function """ title = "Multiple Return Types" message_template = ("Your function returned {actual} on line {location.line}, " "even though you defined it to return {expected}. " "Your function should return values consistently.") justification = ("TIFA visited a function definition with multiple returns " "that unequal types.") def __init__(self, location, expected, actual, **kwargs): super().__init__(location=location, expected=expected, actual=actual, **kwargs) class write_out_of_scope(TifaFeedback): """ Write out of Scope """ title = "Write Out of Scope" message_template = ("You attempted to write the variable {name_message} " "from a higher scope (outside the function) on line " "{location.line}. You should only use variables inside " "the function they were declared in.") justification = "TIFA stored to an existing variable not in this scope" def __init__(self, location, name, **kwargs): report = kwargs.get("report", MAIN_REPORT) super().__init__(location=location, name=name, name_message=report.format.name(name), **kwargs) class unconnected_blocks(TifaFeedback): """ Unconnected Blocks """ title = "Unconnected Blocks" message_template = ("It looks like you have unconnected blocks on line {location.line}. " "Before you run your program, you must make sure that all " "of your blocks are connected that there are no unfilled " "holes.") justification = "TIFA found a name equal to ___" def __init__(self, location, **kwargs): super().__init__(location=location, **kwargs) class iteration_problem(TifaFeedback): """ Iteration Problem """ title = "Iteration Problem" message_template = ("The variable {name_message} was iterated on line " "{location.line} but you used the same variable as the iteration " "variable. You should choose a different variable name " "for the iteration variable. Usually, the iteration variable " "is the singular form of the iteration list (e.g., " "`for a_dog in dogs:`).") justification = "TIFA visited a loop where the iteration list and target were the same." def __init__(self, location, name, **kwargs): report = kwargs.get("report", MAIN_REPORT) super().__init__(location=location, name=name, name_message=report.format.name(name), **kwargs) class initialization_problem(TifaFeedback): """ Initialization Problem """ title = "Initialization Problem" message_template = ("The variable {name_message} was used on line {location.line}, " "but it was not given a value on a previous line. " "You cannot use a variable until it has been given a value." ) justification = "TIFA read a variable that did not exist or was not previously set in this branch." def __init__(self, location, name, **kwargs): report = kwargs.get("report", MAIN_REPORT) super().__init__(location=location, name=name, name_message=report.format.name(name), **kwargs) class possible_initialization_problem(TifaFeedback): """ Possible Initialization Problem """ title = "Possible Initialization Problem" message_template = ("The variable {name_message} was used on line {location.line}, " "but it was possibly not given a value on a previous " "line. You cannot use a variable until it has been given " "a value. Check to make sure that this variable was " "declared in all of the branches of your decision." ) justification = "TIFA read a variable that was maybe set but not definitely set in this branch." def __init__(self, location, name, **kwargs): report = kwargs.get("report", MAIN_REPORT) super().__init__(location=location, name=name, name_message=report.format.name(name), **kwargs) class unused_variable(TifaFeedback): """ Unused Variable """ title = "Unused Variable" message_template = ("The {kind} {name_message} was given a {initialization} on line " "{location.line}, but was never used after that.") justification = ("TIFA stored a variable but it was not read any other time " "in the program.") def __init__(self, location, name, variable_type, **kwargs): report = kwargs.get("report", MAIN_REPORT) if variable_type.is_equal('function'): kind, initialization = 'function', 'definition' else: kind, initialization = 'variable', 'value' fields = {'location': location, 'name': name, 'type': variable_type, 'name_message': report.format.name(name), 'kind': kind, 'initialization': initialization} if 'fields' in kwargs: fields.update(kwargs.pop('fields')) super().__init__(location=location, fields=fields, **kwargs) class overwritten_variable(TifaFeedback): """ Overwritten Variable """ title = "Overwritten Variable" message_template = ("The variable {name_message} was given a value, but " "{name_message} was changed on line {location.line} " "before it was used. One of the times that you gave " "{name_message} a value was incorrect." ) justification = ("TIFA attempted to store to a variable that was previously " "stored but not read.") def __init__(self, location, name, **kwargs): report = kwargs.get("report", MAIN_REPORT) super().__init__(location=location, name=name, name_message=report.format.name(name), **kwargs) class iterating_over_non_list(TifaFeedback): """ Iterating over non-list """ title = "Iterating over Non-list" message_template = ("The {iter} is not a list, but you used it in the " "iteration on line {location.line}. You should only " "iterate over sequences like lists.") justification = ("TIFA visited a loop's iteration list whose type was" "not indexable.") def __init__(self, location, iter_name, **kwargs): report = kwargs.get("report", MAIN_REPORT) if iter_name is None: iter_list = "expression" else: iter_list = "variable " + report.format.name(iter_name) fields = {'location': location, 'name': iter_name, 'iter': iter_list} if 'fields' in kwargs: fields.update(kwargs.pop('fields')) super().__init__(location=location, fields=fields, **kwargs) class iterating_over_empty_list(TifaFeedback): """ Iterating over empty list """ title = "Iterating over empty list" message_template = ("The {iter} was set as an empty list, " "and then you attempted to use it in an iteration on line " "{location.line}. You should only iterate over non-empty lists." ) justification = "TIFA visited a loop's iteration list that was empty." def __init__(self, location, iter_name, **kwargs): report = kwargs.get("report", MAIN_REPORT) if iter_name is None: iter_list = "expression" else: iter_list = "variable " + report.format.name(iter_name) fields = {'location': location, 'name': iter_name, 'iter': iter_list} super().__init__(location=location, fields=fields, **kwargs) class incompatible_types(TifaFeedback): """ Incompatible types """ title = "Incompatible types" message_template = ("You used {op_name} operation with {left_name} and {right_name} on line " "{location.line}. But you can't do that with that operator. Make " "sure both sides of the operator are the right type." ) justification = "TIFA visited an operation with operands of the wrong type." def __init__(self, location, operation, left, right, **kwargs): op_name = OPERATION_DESCRIPTION.get(operation.__class__, str(operation)) left_name = left.singular_name right_name = right.singular_name fields = {'location': location, 'operation': operation, 'op_name': op_name, 'left': left, 'right': right, 'left_name': left_name, 'right_name': right_name} super().__init__(location=location, fields=fields, **kwargs) class invalid_indexing(TifaFeedback): """ Invalid Index """ title = "Invalid Index" message_template = ("You indexed {left_name} with {right_name} on line " "{location.line}. But you can't index {left_name} with " "{right_name}." ) justification = ("TIFA attempted to call an .index() operation on a type" " with a type that wasn't acceptable.") muted = True def __init__(self, location, left, right, **kwargs): left_name = left.singular_name right_name = right.singular_name fields = {'location': location, 'left': left, 'right': right, 'left_name': left_name, 'right_name': right_name} super().__init__(location=location, fields=fields, **kwargs) class parameter_type_mismatch(TifaFeedback): """ Parameter type mismatch """ title = "Parameter Type Mismatch" message_template = ("You defined the parameter {parameter_name_message} on line {location.line} " "as {parameter_type_name}. However, the argument passed to that parameter " "was {argument_type_name}. The formal parameter type must match the argument's type." ) justification = "TIFA visited a function definition where a parameter type and argument type were not equal." def __init__(self, location, parameter_name, parameter, argument, **kwargs): report = kwargs.get("report", MAIN_REPORT) parameter_type_name = parameter.singular_name argument_type_name = argument.singular_name fields = {'location': location, 'parameter_name': parameter_name, 'parameter_name_message': report.format.name(parameter_name), 'parameter_type': parameter, 'argument_type': argument, 'parameter_type_name': parameter_type_name, 'argument_type_name': argument_type_name} super().__init__(location=location, fields=fields, **kwargs) class read_out_of_scope(TifaFeedback): """ Read out of scope """ title = "Read out of Scope" message_template = ("You attempted to read the variable {name_message} " "from a different scope on line {location.line}. You " "should only use variables inside the function they " "were declared in." ) justification = "TIFA read a variable that did not exist in this scope but existed in another." def __init__(self, location, name, **kwargs): report = kwargs.get("report", MAIN_REPORT) super().__init__(location=location, name=name, name_message=report.format.name(name), **kwargs) # TODO: Complete these class type_changes(TifaFeedback): """ Type changes """ title = "Type Changes" message_template = ("The variable {name_message} changed type from {old} to " "{new} on line {location.line}.") justification = "" muted = True def __init__(self, location, name, old, new, **kwargs): report = kwargs.get("report", MAIN_REPORT) fields = {'location': location, 'name': name, 'name_message': report.format.name(name), 'old': old, 'new': new} super().__init__(location=location, fields=fields, **kwargs) class unnecessary_second_branch(TifaFeedback): """ Unnecessary second branch """ title = "Unnecessary Second Branch" message_template = ("You have an `if` statement where one of the two branches" " only has `pass` in its body, on line {location.line}." " You shouldn't need an empty body.") justification = "There is an else or if statement who's body is just pass." def __init__(self, location, **kwargs): super().__init__(location=location, **kwargs) class else_on_loop_body(TifaFeedback): """ Else on Loop body """ title = "Else on Loop Body" message_template = "TODO" justification = "" def __init__(self, location, **kwargs): super().__init__(location=location, **kwargs) class recursive_call(TifaFeedback): """ recursive call """ title = "Recursive Call" message_template = "TODO" justification = "" muted = True def __init__(self, location, name, **kwargs): super().__init__(location=location, name=name, **kwargs) class not_a_function(TifaFeedback): """ Not a function """ title = "Not a Function" message_template = ("You attempted to call {name} as if it" " was a function on line {location.line}. However," " that expression was actually a {called_type}.") justification = "" # TODO: Unmute? #muted = True def __init__(self, location, name, called_type, **kwargs): report = kwargs.get("report", MAIN_REPORT) singular_name = called_type.singular_name fields = {'location': location, 'name': name, 'called_type': called_type, 'singular_name': singular_name} super().__init__(fields=fields, **kwargs) class incorrect_arity(TifaFeedback): """ Incorrect arity """ title = "Incorrect Arity" message_template = ("The function {function_name_message} was given the " "wrong number of arguments.") justification = "" def __init__(self, location, function_name, **kwargs): report = kwargs.get("report", MAIN_REPORT) super().__init__(location=location, function_name=function_name, function_name_message=report.format.name(function_name), **kwargs) class module_not_found(TifaFeedback): """ Module not found """ title = "Module Not Found" message_template = "TODO" justification = "" muted = True def __init__(self, location, name, is_dynamic=False, error=None, **kwargs): fields = {"location": location, "name": name, "is_dynamic": is_dynamic, "error": error} super().__init__(location=location, fields=fields, **kwargs) class append_to_non_list(TifaFeedback): """ Append to non-list """ title = "Append to non-list" message_template = "TODO" justification = "" muted = True def __init__(self, location, name, actual_type, **kwargs): fields = {'location': location, "name": name, "actual_type": actual_type} super().__init__(location=location, fields=fields, **kwargs) class nested_function_definition(TifaFeedback): """ Function defined not at top-level """ message_template = ("The function {name_message} was defined inside of another" "block on line {location.line}. For instance, you may " "have placed it inside another function definition, or " "inside of a loop. Do not nest your function " "definition!") title = "Don't Nest Functions" justification = "Found a FunctionDef that was not at the top-level." muted = True unscored = True def __init__(self, location, name, **kwargs): report = kwargs.get("report", MAIN_REPORT) super().__init__(location=location, name=name, name_message=report.format.name(name), **kwargs) class unused_returned_value(TifaFeedback): """ Expr node had a non-None value """ title = "Did Not Use Function's Return Value" message_template = ("It looks like you called the {call_type} {name_message} on " "{location.line}, but failed to store the result in " "a variable or use it in an expression. You should " "remember to use the result!") justification = "Expression node calculated a non-None value." muted = True unscored = True def __init__(self, location, name, call_type, result_type, **kwargs): report = kwargs.get("report", MAIN_REPORT) fields = {'location': location, 'name': name, 'call_type': call_type, 'result_type': result_type, 'name_message': report.format.name(name)} super().__init__(fields=fields, location=location, **kwargs) ''' TODO: Finish these checks "Empty Body": [], # Any use of pass on its own "Malformed Conditional": [], # An if/else with empty else or if "Unnecessary Pass": [], # Any use of pass "Append to non-list": [], # Attempted to use the append method on a non-list "Used iteration list": [], # "Unused iteration variable": [], # "Type changes": [], # "Unknown functions": [], # "Not a function": [], # Attempt to call non-function as function "Recursive Call": [], "Incorrect Arity": [], "Aliased built-in": [], # "Method not in Type": [], # A method was used that didn't exist for that type "Submodule not found": [], "Module not found": [], "Else on loop body": [], # Used an Else on a For or While ''' # TODO: Equality instead of assignment
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baseDeConversao.py
wcalazans81/Mundo_02_Python
0
115280
print('\033[34m^=\033[m' * 27) print('Conversor de decimal para binário, octol e hexadecimal') print('\033[34m=^\033[m' * 27) print("""Opção [1] Binário Opção [2] Octal Opção [3] Hexadecimal""") print('\033[34m^=\033[m' * 27) num = int(input('Digite o valor que deseja converter: ')) op = int(input('Digite a opção de conversão que deseja: ')) if op == 1: print('O número {} convertido para Binário é {}'.format(num, bin(num)[2:])) elif op == 2: print('O número {} convertido para Octal é {}'.format(num, oct(num)[2:])) elif op == 3: print('O número {} convertido para Hexadecimal é {}'.format(num, hex(num)[2:])) else: print('\033[31mOpção inválida tente novamente !!!\033[m')
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clinica/pipelines/pet_linear/pet_linear_utils.py
Raelag0112/clinica
1
148024
# coding: utf8 # Functions used by nipype interface. # Initiate the pipeline def init_input_node(pet): from clinica.utils.filemanip import get_subject_id from clinica.utils.ux import print_begin_image # Extract image ID image_id = get_subject_id(pet) print_begin_image(image_id) return pet # Concatenate two transformation in one transformation list def concatenate_transforms(pet_to_t1w_tranform, t1w_to_mni_tranform): """Concatenate two input transformation files into a list. Args: transform1 (str): first transformation to apply transform2 (str): second transformation to apply Returns: transform_list (list of string): both transform files path in a list """ return [t1w_to_mni_tranform, pet_to_t1w_tranform] # Normalize the images based on the reference mask region def suvr_normalization(input_img, norm_img, ref_mask): """Normalize the input image according to the reference region. It uses nilearn `resample_to_img` and scipy `trim_mean` functions. This function is different than the one in other PET pipelines because there is a downsampling step. Args: input_img (str): image to be processed norm_img (str): image used to compute the mean of the reference region ref_mask (str): mask of the reference region Returns: output_img (nifty image): normalized nifty image mask_template (nifty image): output mask on disk """ import os import nibabel as nib import numpy as np from nilearn.image import resample_to_img from scipy.stats import trim_mean pet = nib.load(input_img) norm = nib.load(norm_img) mask = nib.load(ref_mask) # Downsample the pet image used for normalization so we can multiply it with the mask ds_img = resample_to_img(norm, mask, interpolation="nearest") # Compute the mean of the region region = np.multiply(ds_img.get_fdata(), mask.get_fdata(dtype="float32")) array_region = np.where(region != 0, region, np.nan).flatten() region_mean = trim_mean(array_region[~np.isnan(array_region)], 0.1) from clinica.utils.stream import cprint cprint(region_mean) # Divide the value of the image voxels by the computed mean data = pet.get_fdata(dtype="float32") / region_mean # Create and save the normalized image output_img = os.path.join( os.getcwd(), os.path.basename(input_img).split(".nii")[0] + "_suvr_normalized.nii.gz", ) normalized_img = nib.Nifti1Image(data, pet.affine, header=pet.header) normalized_img.to_filename(output_img) return output_img # It crops an image based on the reference. def crop_nifti(input_img, ref_crop): """Crop input image based on the reference. It uses nilearn `resample_to_img` function. Args: input_img (str): image to be processed ref_img (str): template used to crop the image Returns: output_img (nifty image): crop image on disk. crop_template (nifty image): output template on disk. """ import os import nibabel as nib import numpy as np from nilearn.image import resample_to_img basedir = os.getcwd() # resample the individual MRI into the cropped template image crop_img = resample_to_img(input_img, ref_crop, force_resample=True) output_img = os.path.join( basedir, os.path.basename(input_img).split(".nii")[0] + "_cropped.nii.gz" ) crop_img.to_filename(output_img) return output_img def rename_into_caps( in_bids_pet, fname_pet, fname_trans, suvr_reference_region, uncropped_image, fname_pet_in_t1w=None, ): """ Rename the outputs of the pipelines into CAPS format. Args: in_bids_pet (str): Input BIDS PET to extract the <source_file> fname_pet (str): Preprocessed PET file. fname_trans (str): Transformation file from PET to MRI space suvr_reference_region (str): SUVR mask name for file name output uncropped_image (bool): Pipeline argument for image cropping fname_pet_in_t1w (bool): Pipeline argument for saving intermediate file Returns: The different outputs in CAPS format """ import os from nipype.interfaces.utility import Rename from nipype.utils.filemanip import split_filename _, source_file_pet, _ = split_filename(in_bids_pet) # Rename into CAPS PET: rename_pet = Rename() rename_pet.inputs.in_file = fname_pet if not uncropped_image: suffix = f"_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_suvr-{suvr_reference_region}_pet.nii.gz" rename_pet.inputs.format_string = source_file_pet + suffix else: suffix = f"_space-MNI152NLin2009cSym_res-1x1x1_suvr-{suvr_reference_region}_pet.nii.gz" rename_pet.inputs.format_string = source_file_pet + suffix out_caps_pet = rename_pet.run().outputs.out_file # Rename into CAPS transformation file: rename_trans = Rename() rename_trans.inputs.in_file = fname_trans rename_trans.inputs.format_string = source_file_pet + "_space-T1w_rigid.mat" out_caps_trans = rename_trans.run().outputs.out_file # Rename intermediate PET in T1w MRI space if fname_pet_in_t1w is not None: rename_pet_in_t1w = Rename() rename_pet_in_t1w.inputs.in_file = fname_pet_in_t1w rename_pet_in_t1w.inputs.format_string = ( source_file_pet + "_space-T1w_pet.nii.gz" ) out_caps_pet_in_t1w = rename_pet_in_t1w.run().outputs.out_file else: out_caps_pet_in_t1w = None return out_caps_pet, out_caps_trans, out_caps_pet_in_t1w def print_end_pipeline(pet, final_file): """ Display end message for <subject_id> when <final_file> is connected. """ from clinica.utils.filemanip import get_subject_id from clinica.utils.ux import print_end_image print_end_image(get_subject_id(pet))
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KNN/iris_knn.py
artalukd/Data_Mining_Lab
2
199952
<filename>KNN/iris_knn.py # Example of kNN implemented from Scratch in Python import numpy as np from sklearn import datasets ''' Before we actually start with writing a nearest neighbor classifier, we need to think about the data, i.e. the testset. We will use the "iris" dataset provided by the datasets of the sklearn module. The data set consists of 50 samples from each of three species of Iris Iris setosa, Iris virginica and Iris versicolor. Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres.''' iris = datasets.load_iris() iris_data = iris.data iris_labels = iris.target print(iris_data[0], iris_data[79], iris_data[100]) print(iris_labels[0], iris_labels[79], iris_labels[100]) ''' We create a trainset from the sets above. We use permutation from np.random to split the data randomly. ''' np.random.seed(42) indices = np.random.permutation(len(iris_data)) n_training_samples = 12 trainset_data = iris_data[indices[:-n_training_samples]] trainset_labels = iris_labels[indices[:-n_training_samples]] testset_data = iris_data[indices[-n_training_samples:]] testset_labels = iris_labels[indices[-n_training_samples:]] print(trainset_data[:4], trainset_labels[:4]) print(testset_data[:4], testset_labels[:4]) ''' The following code is only necessary to visualize the data of our testset, not a part of the course. Our data consists of four values per iris item, so we will reduce the data to three values by dropping fourth value. You can change the dimensions on lines 48-50 This way, we are capable of depicting the data in 3-dimensional space: ''' import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D colours = ("r", "b") X = [] for iclass in range(3): X.append([[], [], []]) for i in range(len(trainset_data)): if trainset_labels[i] == iclass: X[iclass][0].append(trainset_data[i][0]) X[iclass][1].append(trainset_data[i][1]) X[iclass][2].append((trainset_data[i][2])) colours = ("r", "g", "y") fig = plt.figure() ax = fig.add_subplot(111, projection='3d') for iclass in range(3): ax.scatter(X[iclass][0], X[iclass][1], X[iclass][2], c=colours[iclass]) plt.show()
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python/bigadd.py
seckcoder/lang-learn
1
146483
import itertools def bigadd(a, b): z = [x+y for x,y in itertools.izip_longest(reversed(a),reversed(b),fillvalue=0)] res = [x+y for x,y in itertools.izip_longest([i%10 for i in z],[0] + [i/10 for i in z],fillvalue=0)] [x for x in itertools.dropwhile(lambda x: x == 0, reversed(res))] print bigadd([1,2,3], [4,5,6,8])
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accounts/urls.py
mishrakeshav/Django-Real-Estate-Website
0
22898
<reponame>mishrakeshav/Django-Real-Estate-Website from django.urls import path from . import views urlpatterns = [ path('login', views.login, name = 'login'), path('register', views.register, name = 'register'), path('logout', views.logout, name = 'logout'), path('dashboard', views.dashboard, name = 'dashboard'), ]
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models/cnn_stft.py
gumpy-hybridBCI/GUMPY-
27
21827
from .model import KerasModel import keras from keras.models import Sequential from keras.layers import Dense, Activation, Flatten from keras.layers import BatchNormalization, Dropout, Conv2D, MaxPooling2D import kapre from kapre.utils import Normalization2D from kapre.time_frequency import Spectrogram class CNN_STFT(KerasModel): def create_model(self, input_shape, dropout=0.5, print_summary=False): # basis of the CNN_STFT is a Sequential network model = Sequential() # spectrogram creation using STFT model.add(Spectrogram(n_dft = 128, n_hop = 16, input_shape = input_shape, return_decibel_spectrogram = False, power_spectrogram = 2.0, trainable_kernel = False, name = 'static_stft')) model.add(Normalization2D(str_axis = 'freq')) # Conv Block 1 model.add(Conv2D(filters = 24, kernel_size = (12, 12), strides = (1, 1), name = 'conv1', border_mode = 'same')) model.add(BatchNormalization(axis = 1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size = (2, 2), strides = (2,2), padding = 'valid', data_format = 'channels_last')) # Conv Block 2 model.add(Conv2D(filters = 48, kernel_size = (8, 8), name = 'conv2', border_mode = 'same')) model.add(BatchNormalization(axis = 1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size = (2, 2), strides = (2, 2), padding = 'valid', data_format = 'channels_last')) # Conv Block 3 model.add(Conv2D(filters = 96, kernel_size = (4, 4), name = 'conv3', border_mode = 'same')) model.add(BatchNormalization(axis = 1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size = (2, 2), strides = (2,2), padding = 'valid', data_format = 'channels_last')) model.add(Dropout(dropout)) # classificator model.add(Flatten()) model.add(Dense(2)) # two classes only model.add(Activation('softmax')) if print_summary: print(model.summary()) # compile the model model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy']) # assign model and return self.model = model return model
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xsInterface/containers/datasettings.py
CORE-GATECH-GROUP/xs-interface
0
165316
<filename>xsInterface/containers/datasettings.py # -*- coding: utf-8 -*- """datasettings.py The user needs to define the required data to be stored on the containers. This container stores all the attributes and settings for the required data. Created on Sat Mar 19 18:30:00 2022 @author: <NAME> and <NAME> Last updated on Tue Apr 01 11:30:00 2022 @author: <NAME> email: <EMAIL> """ import numpy as np from xsInterface.errors.checkerrors import _isint, _islist, _isbool, _inlist,\ _ispositive, _isstr, _isuniquelist, _isarray,\ _is1darray, _isequallength, _isBoundArray from xsInterface.containers.container_header import DATA_TYPES class DataSettings(): """ Stores the names and data that are expected to be stored on containers Parameters ----------- NG : int number of energy groups for multi-group parameters DN : int Delayed neutron groups for kinetic parameters macro : boolean indicate whether macro data is expected to be provided micro : boolean indicate whether micro data is expected to be provided kinetics : boolean indicate whether kinetic data is expected to be provided meta : boolean indicate whether meta data is expected to be provided isotopes : array ZZAAA0/1 for all the isotopes to be provided Attributes ----------- NG : int number of energy groups for multi-group parameters DN : int delayed neutron groups for kinetic parameters dataFlags : dict boolean flags to indicate the data types that are provided macro : dict contains all the macro attributes (e.g., ``abs``) micro : boolean contains all the micro attributes for all the isotopes (e.g., ``fiss``) kinetics : boolean contains all the kinetic attributes (e.g., ``beta``) meta : boolean contains all the metadata attributes (e.g., ``time``) Methods -------- AddData(dataType, attributes, attrDims=None): Add relevant macroscopic/microscopic/meta data Raises ------- TypeError If any of the parameters, e.g., ``NG``, ``DN`` are not integers. If any of the ``macro``, ``micro``, ``kinetics``, ``meta`` are not booleans. ValueError If ``NG`` is below one. If ``DN`` is below one. If ``isotopes`` list is not provided but ``micro`` data is expected. KeyError If ``dataType`` or ``frmt`` do not exist in DATA_TYPES or FRMT_OPTS. Examples --------- >>> rc = DataSettings(NG=2, DN=7, macro=True, micro=False, kinetics=True, >>> meta=False, isotopes=None) """ def __init__(self, NG, DN, macro=True, micro=False, kinetics=False, meta=False, isotopes=None): """Assign parameters that describe the required data to be provided""" # Check variables types _isint(NG, "number of energy groups") _isint(DN, "number of delayed neutron groups") _isbool(macro, "macro data") _isbool(micro, "micro data") _isbool(kinetics, "kinetics data") _isbool(meta, "meta data") # Check values/entries for different variables _ispositive(NG, "number of energy groups") _ispositive(DN, "number of delayed neutron groups") if micro: if isotopes is not None: isotopes = np.array(isotopes, dtype=int) else: raise ValueError("<isotopes> list/array must be provided") # Reset variables self.ng = NG # number of energy groups self.dn = DN # number of delayed neutron groups self.isotopes = isotopes self.dataFlags = {"macro": macro, "micro": micro, "kinetics": kinetics, "meta": meta} self.macro = [] self.micro = [] self.kinetics = [] self.meta = [] def AddData(self, dataType, attributes): """Add relevant macroscopic/microscopic/meta data Parameters ---------- dataType : ["macro", "micro", "kinetics", "meta"] type of data attributes : list of strings user-defined names for the provided data type (e.g., ``abs``) Examples -------- >>> rc.AddData("macro", ["abs", "nsf", "sct"], "array") >>> rc.AddData("kinetics", ["beta", "decay"], "array") """ # Error checking _isstr(dataType, "data types") _inlist(dataType, "data types", DATA_TYPES) if not self.dataFlags[dataType]: raise ValueError("Data type <{}> was disabled when DataSettings " "object was created".format(dataType)) _islist(attributes, "names of "+dataType+" attributes") _isuniquelist(attributes, "attribute names in ") # check if data is already populated data0 = getattr(self, dataType) if data0 == []: # data is new # define the specific dictionary for the selected data type attrList = attributes else: # data already exists attr0 = data0 # create a new/appended list of attributes attr1 = attr0 + attributes _isuniquelist(attr1, "attribute names in ") attrList = attr1 # set a muted attribute with the settings for the selected data type setattr(self, dataType, attrList) def _proofTest(self): """Check that data was inputted""" if self.dataFlags["macro"] and self.macro == []: raise ValueError("macro data is expected to be provided.") if self.dataFlags["micro"] and self.micro == []: raise ValueError("micro data is expected to be provided.") if self.dataFlags["kinetics"] and self.kinetics == []: raise ValueError("kinetics data is expected to be provided.") if self.dataFlags["meta"] and self.meta == []: raise ValueError("meta data is expected to be provided.")
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src/CrossQuantilogram/__init__.py
wangys96/CrossQuantilogram
12
197485
from .stationarybootstrap import Bootstrap from .crossquantilogram import CrossQuantilogram from .qtests import BoxPierceQ,LjungBoxQ from .utils import DescriptiveStatistics from .api import CQBS,CQBS_alphas,CQBS_years from .plot import bar_example,heatmap_example,rolling_example __doc__ = """The `Cross-Quantilogram`(CQ) is a correlation statistics that measures the quantile dependence between two time series. It can test the hypothesis that one time series has no directional predictability to another. Stationary bootstrap method helps establish the asymptotic distribution for CQ statistics and other corresponding test statistics."""
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waitlist_parser/waitlist_parser.py
Itsindigo/waitlist-parser
0
122255
<reponame>Itsindigo/waitlist-parser import os import sys from random import choice from string import ascii_lowercase from csv import DictReader, DictWriter if __name__ == "__main__": if len(sys.argv) > 1: my_file = sys.argv[-1] else: print("File path not provided") sys.exit(1) _, extension = os.path.splitext(my_file) if extension != '.csv': print("This script accepts CSV Filetypes only.") sys.exit(1) with open(my_file) as csvfile: records = [] waitlist = DictReader(csvfile) for row in waitlist: records.append(row) column_headers = records[0].keys() input = input('Enter the column header you would like to split: \n') if input not in column_headers: print("Input supplied not in column headings.... exiting.") sys.exit(1) for record in records: target = record[input] split_names = target.split(' ') del record[input] record['first %s' % input] = split_names[0] record['last %s' % input] = '' if len(split_names) > 1: record['last %s' % input] = ' '.join(split_names[1:]) output_filename = 'outfiles/waitlist-%s.csv' % (''.join(choice(ascii_lowercase) for i in range(4))) with open(output_filename, 'w') as outfile: writer = DictWriter(outfile, records[0].keys()) writer.writeheader() writer.writerows(records)
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content/migrations/0001_initial.py
Daniel-and-Zach/character_generator
0
179648
<gh_stars>0 # -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ] operations = [ migrations.CreateModel( name='Race', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('name', models.CharField(max_length=255, choices=[(b'Elf', b'Elf'), (b'Dwarf', b'Dwarf'), (b'Gnome', b'Gnome'), (b'Half-Elf', b'Half-Elf'), (b'Halfling', b'Halfling'), (b'Half-Orc', b'Half-Orc'), (b'Human', b'Human')])), ('size', models.CharField(max_length=255)), ('base_speed', models.IntegerField()), ('description', models.TextField()), ('history', models.TextField()), ], ), ]
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lab4_CNN_intro/create_dataset.py
j-adamczyk/Pattern_recognition
1
54441
from concurrent.futures.thread import ThreadPoolExecutor from io import BytesIO import os import cv2 import numpy as np import requests def get_img(url): try: headers = {"User-Agent": "Mozilla/5.0 (Macintosh; " "Intel Mac OS X 10_11_6) " "AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/61.0.3163.100 Safari/537.36"} response = requests.get(url, headers=headers, timeout=2) if response.status_code == requests.codes.ok: return response else: return None except Exception: return None def download_images_from_file(label): with open("links_" + label + ".txt") as file: URLs = file.readlines() URLs = [url.rstrip() for url in URLs] next_img_num = 0 num_threads = min(len(URLs), 30) for i in range(0, len(URLs), 50): part = URLs[i:i + 50] with ThreadPoolExecutor(num_threads) as executor: results = [result for result in executor.map(get_img, part) if result is not None] for result in results: filename = str(next_img_num) + ".png" filepath = os.path.join("dataset_2", label, filename) img_bytes = BytesIO(result.content) img = cv2.imdecode(np.frombuffer(img_bytes.read(), np.uint8), flags=1) if img is None: continue height, width = img.shape[:2] if height < width: new_height = 224 new_width = int((new_height / height) * width) else: new_width = 224 new_height = int((new_width / width) * height) img = cv2.resize(img, dsize=(new_width, new_height), interpolation=cv2.INTER_AREA) cv2.imwrite(filepath, img) next_img_num += 1 def create_csv(labels): labels.sort() text = "file label\n" for label_num, label in enumerate(labels): label_dir = os.path.join("dataset_2", label) filenames = os.listdir(label_dir) filenames = [os.path.join(label_dir, filename) for filename in filenames] for filename in filenames: # save numerical values for labels text += filename + " " + str(label_num) + "\n" with open("dataset_2.csv", "w") as file: file.write(text) if __name__ == '__main__': #for file in ["cats", "cats", "cats"]: # download_images_from_file(file) #labels = ["cat", "dog", "owl"] #create_csv(labels) #for file in ["deku", "naruto", "saitama"]: # download_images_from_file(file) labels = ["deku", "naruto", "saitama"] create_csv(labels)
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