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Simulator/bipartite_graph_matrix.py | nicoladainese96/CoopVM | 0 | 6613251 | # -*- coding: utf-8 -*-
"""
Created on Wed Aug 8 16:40:39 2018
@author: <EMAIL>
"""
def adjacency_matrix_rnd(S1 = 10, S2 = 10, p = 0.33, dir_name = 'graph', index = 1, index2 = 1, flag = False):
import networkx as nx
from networkx.algorithms import bipartite
import matplotlib.pyplot as plt
import os
from ensure_dir import ensure_dir
import my_print as my
import my_output as O
file_path = dir_name+'/prova.txt'
ensure_dir(file_path)
out_dir = os.path.dirname(file_path)
script_dir = os.getcwd()
os.chdir(out_dir)
#connectance IS NOT fixed, but its average <C> is equal to p
#anyway on 100 realization no significant difference is detected
G = bipartite.random_graph(S1, S2, p)
deg = list(G.degree())
deg1 = deg[:S1]
deg2 = deg[S1:]
pos = {x:[0,x] for x in range(S1)}
for j in range(S2):
pos[S1+j] = [1,j]
colors = ['r' for i in range(0,S1)]
for j in range(S2):
colors.append('b')
fig, [ax1, ax] = plt.subplots(1,2, figsize = (9,4))
ax1.set_title('Interazioni mutualistiche casuali')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G, pos = pos, node_color = colors, ax = ax1)
A = nx.to_numpy_matrix(G)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Configurazione casuale con C = '+format(p,'.3f'))
ax.set_autoscale_on(True)
plt.imshow(A1, cmap = 'Greys')
if index == 1:
fig.savefig('random_'+format(p,'.2f')+'.png')
plt.close()
if flag == False:
if index == 1:
my.print_tuple2(deg1, 'R_degree1-'+repr(index2), dir_name)
my.print_tuple2(deg2, 'R_degree2-'+repr(index2), dir_name)
else:
O.print_list_csv(deg1, 'deg1_R-'+repr(index), dir_name)
O.print_list_csv(deg2, 'deg2_R-'+repr(index), dir_name)
os.chdir(script_dir)
return A1
def adjacency_matrix_rnd2(S1 = 10, S2 = 10, p = 0.33, dir_name = 'graph', index = 1, index2 = 1, flag = False):
import networkx as nx
from networkx.algorithms import bipartite
import matplotlib.pyplot as plt
import os
from ensure_dir import ensure_dir
import my_print as my
import my_output as O
script_dir = os.getcwd()
#print('dir_name = ', dir_name)
#k is the number of non-null elements of the mutualistic matrix
#in this case p = C in each realization
k = int(round(S1*S2*p,0))
G = bipartite.gnmk_random_graph(S1, S2, k)
#G = bipartite.random_graph(S1, S2, p)
num_conn = G.number_of_edges()
if num_conn != k: #this checks out if the number of connections of the random graphs is really k
print('Problema numero connessioni.') #prints an alert otherwise
print('# = ', num_conn)
deg = list(G.degree())
deg1 = deg[:S1]
deg2 = deg[S1:]
pos = {x:[0,x] for x in range(S1)}
for j in range(S2):
pos[S1+j] = [1,j]
colors = ['r' for i in range(0,S1)]
for j in range(S2):
colors.append('b')
A = nx.to_numpy_matrix(G)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
if index == 1:
plt.style.use('seaborn')
file_path = dir_name+'/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
fig, [ax1, ax] = plt.subplots(1,2, figsize = (10,4))
#fig, [ax1, ax] = plt.subplots(1,2)
ax1.set_title('Interazioni mutualistiche casuali')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G, pos = pos, node_color = colors, ax = ax1)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Configurazione casuale con C = '+format(p,'.3f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.imshow(A1, cmap = 'Greys')
plt.tight_layout()
fig.savefig('random_'+format(p,'.2f')+'.png')
plt.close()
else:
file_path = dir_name+'/random_matrix/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
ax.set_title('Matrice random realizzazione {}'.format(index))
ax.set_xlabel('S2 = {}'.format(S2))
ax.set_ylabel('S1 = {}'.format(S1))
ax.imshow(A1, cmap = 'Greys')
fig.savefig('random_'+format(p,'.2f')+'-'+repr(index)+'.png')
plt.close()
#else:
# O.print_list_csv(deg1, 'deg1_R-'+repr(index), dir_name)
# O.print_list_csv(deg2, 'deg2_R-'+repr(index), dir_name)
os.chdir(script_dir)
return A1
def adjacency_matrix_nested (S1 = 10, S2 = 10, p = 0.33, dir_name = 'graph', index = 1, index2 = 1, flag = False):
#asymmetric nested (for low values of connectances)
import networkx as nx
import matplotlib.pyplot as plt
import os
from ensure_dir import ensure_dir
import my_print as my
import my_output as O
script_dir = os.getcwd()
file_path = dir_name+'/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
#attenzione, funziona bene solo con S1 = S2
G = nx.Graph()
nodes = [x for x in range(S1+S2)]
G.add_nodes_from(nodes)
#print(list(G.nodes()))
edges = []
for i in range(S1):
for j in range(S1, S1+S2-i):
edges.append((i,j))
G.add_edges_from(edges)
if p < 0.55:
G = adjust_edges(G, S1, S2, p) #this is where the actual edges are decided in most cases (C < 0.55)
if p > 0.55:
G = adjust_edges2(G, S1, S2, p) #this should be usless in all real-connectance cases
deg = list(G.degree())
deg1 = deg[:S1]
deg2 = deg[S1:]
pos = {x:[0,x] for x in range(S1)}
for j in range(S2):
pos[S1+j] = [1,j]
colors = ['r' for i in range(0,S1)]
for j in range(S2):
colors.append('b')
A = nx.to_numpy_matrix(G)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
#togliere quando finito
if index == 0:
plt.style.use('seaborn')
fig, [ax1, ax] = plt.subplots(1,2, figsize = (10,4))
ax1.set_title('Interazioni mutualistiche nidificate')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G, pos = pos, node_color = colors, ax=ax1)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Configurazione nidificata con C = '+format(p,'.3f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.tight_layout()
plt.imshow(A1, cmap = 'Greys')
#rimuovere questa parte
if index == 1:
plt.style.use('seaborn')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Nested I tipo con C = '+format(p,'.3f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.tight_layout()
plt.imshow(A1, cmap = 'Greys')
if index == 1:
fig.savefig('nested_'+format(p,'.2f')+'.png')
if flag == False:
if index == 1:
my.print_tuple2(deg1, 'N_degree1-'+repr(index2), dir_name)
my.print_tuple2(deg2, 'N_degree2-'+repr(index2), dir_name)
else:
O.print_list_csv(deg1, 'deg1_N-'+repr(index), dir_name)
O.print_list_csv(deg2, 'deg2_N-'+repr(index), dir_name)
plt.close()
os.chdir(script_dir)
return A1
def adjacency_matrix_nested2 (S1 = 10, S2 = 10, p = 0.33, dir_name = 'graph', index = 1, index2 = 1, flag = False):
#balanced nested
import networkx as nx
import matplotlib.pyplot as plt
import os
from ensure_dir import ensure_dir
import my_print as my
import my_output as O
script_dir = os.getcwd()
file_path = dir_name+'/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
#attenzione, funziona bene solo con S1 = S2
G = nx.Graph()
nodes = [x for x in range(S1+S2)]
G.add_nodes_from(nodes)
#print(list(G.nodes()))
edges = []
for i in range(S1):
for j in range(S1, S1+S2-i):
edges.append((i,j))
G.add_edges_from(edges)
if p < 0.55:
#this is where the actual edges are decided in most cases (C < 0.55) - different method
G = adjust_edges1(G, S1, S2, p)
if p > 0.55:
G = adjust_edges2(G, S1, S2, p)
deg = list(G.degree())
deg1 = deg[:S1]
deg2 = deg[S1:]
pos = {x:[0,x] for x in range(S1)}
for j in range(S2):
pos[S1+j] = [1,j]
colors = ['r' for i in range(0,S1)]
for j in range(S2):
colors.append('b')
A = nx.to_numpy_matrix(G)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
#rimettere if index == 1
if index == 0:
plt.style.use('seaborn')
fig, [ax1, ax] = plt.subplots(1,2, figsize = (10,4))
ax1.set_title('Interazioni mutualistiche nidificate')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G, pos = pos, node_color = colors, ax=ax1)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Configurazione nidificata con C = '+format(p,'.2f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.imshow(A1, cmap = 'Greys')
plt.tight_layout()
fig.savefig('nested_'+format(p,'.2f')+'_II.png')
if index == 1:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Nested II tipo con C = '+format(p,'.3f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.tight_layout()
plt.imshow(A1, cmap = 'Greys')
if index == 1:
fig.savefig('nested_'+format(p,'.2f')+'_II.png')
if flag == False:
if index == 1:
my.print_tuple2(deg1, 'N_degree1-'+repr(index2), dir_name)
my.print_tuple2(deg2, 'N_degree2-'+repr(index2), dir_name)
else:
O.print_list_csv(deg1, 'deg1_N-'+repr(index), dir_name)
O.print_list_csv(deg2, 'deg2_N-'+repr(index), dir_name)
plt.close()
os.chdir(script_dir)
return A1
def adjust_edges(G, S1, S2, C): #first method of adjusting edges - asymmetric for low Cs
import networkx as nx
in_edges = G.number_of_edges()
#print(in_edges)
fin_edges = int(round(C*S1*S2,0)) #number of final edges to be obtained
#print(fin_edges)
delta = in_edges - fin_edges #final < initial
#print(delta)
edges = list(G.edges())
#print(edges)
index = []
for i in range(len(edges)):
#we start with a triangular matrix [1/0]
#edges with lower values will be the first ones to be recided
#untill the request connectance is achieved
i_x_i = edges[i][0]*(edges[i][1]-S1) #this is the algorithm that assign a value to each edge
index.append(i_x_i)
#print(index)
M = max(index)
sort_edg = []
for i in range(0,M+1):
for j in range(len(edges)):
if index[j] == i:
sort_edg.append(edges[j]) #this list contains the edges ordered with increasing values
#print(sort_edg)
new_edges = sort_edg[:-delta]
F = nx.Graph()
nodes = [x for x in range(S1+S2)]
F.add_nodes_from(nodes)
F.add_edges_from(new_edges)
return F
def adjust_edges1(G, S1, S2, C): #second method of adjusting edges - more balanced
import networkx as nx
in_edges = G.number_of_edges()
#print('in edges = ', in_edges, '\n')
fin_edges = int(round(C*S1*S2,0))
#print('fin_edges = ', fin_edges, '\n')
delta = in_edges - fin_edges
#print('delta = ', delta, '\n')
edges = list(G.edges())
#print('edges = ', edges, '\n')
index = []
for i in range(len(edges)):
if i == 0:
i_x_i = (edges[i][0]+2)*(edges[i][1]-S1+2)
else:
i_x_i = (edges[i][0]+1)*(edges[i][1]-S1+1)
index.append(i_x_i)
#print('index = ', index, '\n')
M = max(index)
sort_edg = []
for i in range(0,M+1):
for j in range(len(edges)):
if index[j] == i:
sort_edg.append(edges[j])
#print(sort_edg)
new_edges = sort_edg[:-delta]
F = nx.Graph()
nodes = [x for x in range(S1+S2)]
F.add_nodes_from(nodes)
F.add_edges_from(new_edges)
return F
def adjust_edges2(G, S1, S2, C):
#import networkx as nx
Sq = S1*S2
conn = check_connectance (G, Sq)
while conn < C:
for i in range(S1):
if conn > C:
break
deg = G.degree(i)
if deg < S2:
G.add_edge(i,deg+S1)
conn = check_connectance(G, Sq)
else:
continue
return G
def check_connectance (G, Sq):
#import networkx as nx
in_edges = G.number_of_edges()
C = in_edges/(Sq)
return C
| # -*- coding: utf-8 -*-
"""
Created on Wed Aug 8 16:40:39 2018
@author: <EMAIL>
"""
def adjacency_matrix_rnd(S1 = 10, S2 = 10, p = 0.33, dir_name = 'graph', index = 1, index2 = 1, flag = False):
import networkx as nx
from networkx.algorithms import bipartite
import matplotlib.pyplot as plt
import os
from ensure_dir import ensure_dir
import my_print as my
import my_output as O
file_path = dir_name+'/prova.txt'
ensure_dir(file_path)
out_dir = os.path.dirname(file_path)
script_dir = os.getcwd()
os.chdir(out_dir)
#connectance IS NOT fixed, but its average <C> is equal to p
#anyway on 100 realization no significant difference is detected
G = bipartite.random_graph(S1, S2, p)
deg = list(G.degree())
deg1 = deg[:S1]
deg2 = deg[S1:]
pos = {x:[0,x] for x in range(S1)}
for j in range(S2):
pos[S1+j] = [1,j]
colors = ['r' for i in range(0,S1)]
for j in range(S2):
colors.append('b')
fig, [ax1, ax] = plt.subplots(1,2, figsize = (9,4))
ax1.set_title('Interazioni mutualistiche casuali')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G, pos = pos, node_color = colors, ax = ax1)
A = nx.to_numpy_matrix(G)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Configurazione casuale con C = '+format(p,'.3f'))
ax.set_autoscale_on(True)
plt.imshow(A1, cmap = 'Greys')
if index == 1:
fig.savefig('random_'+format(p,'.2f')+'.png')
plt.close()
if flag == False:
if index == 1:
my.print_tuple2(deg1, 'R_degree1-'+repr(index2), dir_name)
my.print_tuple2(deg2, 'R_degree2-'+repr(index2), dir_name)
else:
O.print_list_csv(deg1, 'deg1_R-'+repr(index), dir_name)
O.print_list_csv(deg2, 'deg2_R-'+repr(index), dir_name)
os.chdir(script_dir)
return A1
def adjacency_matrix_rnd2(S1 = 10, S2 = 10, p = 0.33, dir_name = 'graph', index = 1, index2 = 1, flag = False):
import networkx as nx
from networkx.algorithms import bipartite
import matplotlib.pyplot as plt
import os
from ensure_dir import ensure_dir
import my_print as my
import my_output as O
script_dir = os.getcwd()
#print('dir_name = ', dir_name)
#k is the number of non-null elements of the mutualistic matrix
#in this case p = C in each realization
k = int(round(S1*S2*p,0))
G = bipartite.gnmk_random_graph(S1, S2, k)
#G = bipartite.random_graph(S1, S2, p)
num_conn = G.number_of_edges()
if num_conn != k: #this checks out if the number of connections of the random graphs is really k
print('Problema numero connessioni.') #prints an alert otherwise
print('# = ', num_conn)
deg = list(G.degree())
deg1 = deg[:S1]
deg2 = deg[S1:]
pos = {x:[0,x] for x in range(S1)}
for j in range(S2):
pos[S1+j] = [1,j]
colors = ['r' for i in range(0,S1)]
for j in range(S2):
colors.append('b')
A = nx.to_numpy_matrix(G)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
if index == 1:
plt.style.use('seaborn')
file_path = dir_name+'/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
fig, [ax1, ax] = plt.subplots(1,2, figsize = (10,4))
#fig, [ax1, ax] = plt.subplots(1,2)
ax1.set_title('Interazioni mutualistiche casuali')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G, pos = pos, node_color = colors, ax = ax1)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Configurazione casuale con C = '+format(p,'.3f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.imshow(A1, cmap = 'Greys')
plt.tight_layout()
fig.savefig('random_'+format(p,'.2f')+'.png')
plt.close()
else:
file_path = dir_name+'/random_matrix/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
ax.set_title('Matrice random realizzazione {}'.format(index))
ax.set_xlabel('S2 = {}'.format(S2))
ax.set_ylabel('S1 = {}'.format(S1))
ax.imshow(A1, cmap = 'Greys')
fig.savefig('random_'+format(p,'.2f')+'-'+repr(index)+'.png')
plt.close()
#else:
# O.print_list_csv(deg1, 'deg1_R-'+repr(index), dir_name)
# O.print_list_csv(deg2, 'deg2_R-'+repr(index), dir_name)
os.chdir(script_dir)
return A1
def adjacency_matrix_nested (S1 = 10, S2 = 10, p = 0.33, dir_name = 'graph', index = 1, index2 = 1, flag = False):
#asymmetric nested (for low values of connectances)
import networkx as nx
import matplotlib.pyplot as plt
import os
from ensure_dir import ensure_dir
import my_print as my
import my_output as O
script_dir = os.getcwd()
file_path = dir_name+'/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
#attenzione, funziona bene solo con S1 = S2
G = nx.Graph()
nodes = [x for x in range(S1+S2)]
G.add_nodes_from(nodes)
#print(list(G.nodes()))
edges = []
for i in range(S1):
for j in range(S1, S1+S2-i):
edges.append((i,j))
G.add_edges_from(edges)
if p < 0.55:
G = adjust_edges(G, S1, S2, p) #this is where the actual edges are decided in most cases (C < 0.55)
if p > 0.55:
G = adjust_edges2(G, S1, S2, p) #this should be usless in all real-connectance cases
deg = list(G.degree())
deg1 = deg[:S1]
deg2 = deg[S1:]
pos = {x:[0,x] for x in range(S1)}
for j in range(S2):
pos[S1+j] = [1,j]
colors = ['r' for i in range(0,S1)]
for j in range(S2):
colors.append('b')
A = nx.to_numpy_matrix(G)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
#togliere quando finito
if index == 0:
plt.style.use('seaborn')
fig, [ax1, ax] = plt.subplots(1,2, figsize = (10,4))
ax1.set_title('Interazioni mutualistiche nidificate')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G, pos = pos, node_color = colors, ax=ax1)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Configurazione nidificata con C = '+format(p,'.3f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.tight_layout()
plt.imshow(A1, cmap = 'Greys')
#rimuovere questa parte
if index == 1:
plt.style.use('seaborn')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Nested I tipo con C = '+format(p,'.3f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.tight_layout()
plt.imshow(A1, cmap = 'Greys')
if index == 1:
fig.savefig('nested_'+format(p,'.2f')+'.png')
if flag == False:
if index == 1:
my.print_tuple2(deg1, 'N_degree1-'+repr(index2), dir_name)
my.print_tuple2(deg2, 'N_degree2-'+repr(index2), dir_name)
else:
O.print_list_csv(deg1, 'deg1_N-'+repr(index), dir_name)
O.print_list_csv(deg2, 'deg2_N-'+repr(index), dir_name)
plt.close()
os.chdir(script_dir)
return A1
def adjacency_matrix_nested2 (S1 = 10, S2 = 10, p = 0.33, dir_name = 'graph', index = 1, index2 = 1, flag = False):
#balanced nested
import networkx as nx
import matplotlib.pyplot as plt
import os
from ensure_dir import ensure_dir
import my_print as my
import my_output as O
script_dir = os.getcwd()
file_path = dir_name+'/prova.txt'
ensure_dir(file_path)
directory = os.path.dirname(file_path)
os.chdir(directory)
#attenzione, funziona bene solo con S1 = S2
G = nx.Graph()
nodes = [x for x in range(S1+S2)]
G.add_nodes_from(nodes)
#print(list(G.nodes()))
edges = []
for i in range(S1):
for j in range(S1, S1+S2-i):
edges.append((i,j))
G.add_edges_from(edges)
if p < 0.55:
#this is where the actual edges are decided in most cases (C < 0.55) - different method
G = adjust_edges1(G, S1, S2, p)
if p > 0.55:
G = adjust_edges2(G, S1, S2, p)
deg = list(G.degree())
deg1 = deg[:S1]
deg2 = deg[S1:]
pos = {x:[0,x] for x in range(S1)}
for j in range(S2):
pos[S1+j] = [1,j]
colors = ['r' for i in range(0,S1)]
for j in range(S2):
colors.append('b')
A = nx.to_numpy_matrix(G)
A2 = A.getA()
A1 = []
for x in range(S1):
A1.append(A2[x][S1:])
#rimettere if index == 1
if index == 0:
plt.style.use('seaborn')
fig, [ax1, ax] = plt.subplots(1,2, figsize = (10,4))
ax1.set_title('Interazioni mutualistiche nidificate')
ax1.set_axis_off()
ax1.set_autoscale_on(True)
nx.draw_networkx(G, pos = pos, node_color = colors, ax=ax1)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Configurazione nidificata con C = '+format(p,'.2f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.imshow(A1, cmap = 'Greys')
plt.tight_layout()
fig.savefig('nested_'+format(p,'.2f')+'_II.png')
if index == 1:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid()
xtics = [x-0.5 for x in range(0,S2+1)]
ytics = [x-0.5 for x in range(0,S1+1)]
ax.set_yticks(ytics)
ax.set_xticks(xtics)
ax.set_ylabel('Impollinatori')
ax.set_xlabel('Piante')
ax.set_title('Nested II tipo con C = '+format(p,'.3f'))
ax.set_autoscale_on(True)
plt.setp(ax.get_xticklabels(), visible=False)
plt.setp(ax.get_yticklabels(), visible=False)
plt.tight_layout()
plt.imshow(A1, cmap = 'Greys')
if index == 1:
fig.savefig('nested_'+format(p,'.2f')+'_II.png')
if flag == False:
if index == 1:
my.print_tuple2(deg1, 'N_degree1-'+repr(index2), dir_name)
my.print_tuple2(deg2, 'N_degree2-'+repr(index2), dir_name)
else:
O.print_list_csv(deg1, 'deg1_N-'+repr(index), dir_name)
O.print_list_csv(deg2, 'deg2_N-'+repr(index), dir_name)
plt.close()
os.chdir(script_dir)
return A1
def adjust_edges(G, S1, S2, C): #first method of adjusting edges - asymmetric for low Cs
import networkx as nx
in_edges = G.number_of_edges()
#print(in_edges)
fin_edges = int(round(C*S1*S2,0)) #number of final edges to be obtained
#print(fin_edges)
delta = in_edges - fin_edges #final < initial
#print(delta)
edges = list(G.edges())
#print(edges)
index = []
for i in range(len(edges)):
#we start with a triangular matrix [1/0]
#edges with lower values will be the first ones to be recided
#untill the request connectance is achieved
i_x_i = edges[i][0]*(edges[i][1]-S1) #this is the algorithm that assign a value to each edge
index.append(i_x_i)
#print(index)
M = max(index)
sort_edg = []
for i in range(0,M+1):
for j in range(len(edges)):
if index[j] == i:
sort_edg.append(edges[j]) #this list contains the edges ordered with increasing values
#print(sort_edg)
new_edges = sort_edg[:-delta]
F = nx.Graph()
nodes = [x for x in range(S1+S2)]
F.add_nodes_from(nodes)
F.add_edges_from(new_edges)
return F
def adjust_edges1(G, S1, S2, C): #second method of adjusting edges - more balanced
import networkx as nx
in_edges = G.number_of_edges()
#print('in edges = ', in_edges, '\n')
fin_edges = int(round(C*S1*S2,0))
#print('fin_edges = ', fin_edges, '\n')
delta = in_edges - fin_edges
#print('delta = ', delta, '\n')
edges = list(G.edges())
#print('edges = ', edges, '\n')
index = []
for i in range(len(edges)):
if i == 0:
i_x_i = (edges[i][0]+2)*(edges[i][1]-S1+2)
else:
i_x_i = (edges[i][0]+1)*(edges[i][1]-S1+1)
index.append(i_x_i)
#print('index = ', index, '\n')
M = max(index)
sort_edg = []
for i in range(0,M+1):
for j in range(len(edges)):
if index[j] == i:
sort_edg.append(edges[j])
#print(sort_edg)
new_edges = sort_edg[:-delta]
F = nx.Graph()
nodes = [x for x in range(S1+S2)]
F.add_nodes_from(nodes)
F.add_edges_from(new_edges)
return F
def adjust_edges2(G, S1, S2, C):
#import networkx as nx
Sq = S1*S2
conn = check_connectance (G, Sq)
while conn < C:
for i in range(S1):
if conn > C:
break
deg = G.degree(i)
if deg < S2:
G.add_edge(i,deg+S1)
conn = check_connectance(G, Sq)
else:
continue
return G
def check_connectance (G, Sq):
#import networkx as nx
in_edges = G.number_of_edges()
C = in_edges/(Sq)
return C
| en | 0.662721 | # -*- coding: utf-8 -*- Created on Wed Aug 8 16:40:39 2018 @author: <EMAIL> #connectance IS NOT fixed, but its average <C> is equal to p #anyway on 100 realization no significant difference is detected #print('dir_name = ', dir_name) #k is the number of non-null elements of the mutualistic matrix #in this case p = C in each realization #G = bipartite.random_graph(S1, S2, p) #this checks out if the number of connections of the random graphs is really k #prints an alert otherwise #fig, [ax1, ax] = plt.subplots(1,2) #else: # O.print_list_csv(deg1, 'deg1_R-'+repr(index), dir_name) # O.print_list_csv(deg2, 'deg2_R-'+repr(index), dir_name) #asymmetric nested (for low values of connectances) #attenzione, funziona bene solo con S1 = S2 #print(list(G.nodes())) #this is where the actual edges are decided in most cases (C < 0.55) #this should be usless in all real-connectance cases #togliere quando finito #rimuovere questa parte #balanced nested #attenzione, funziona bene solo con S1 = S2 #print(list(G.nodes())) #this is where the actual edges are decided in most cases (C < 0.55) - different method #rimettere if index == 1 #first method of adjusting edges - asymmetric for low Cs #print(in_edges) #number of final edges to be obtained #print(fin_edges) #final < initial #print(delta) #print(edges) #we start with a triangular matrix [1/0] #edges with lower values will be the first ones to be recided #untill the request connectance is achieved #this is the algorithm that assign a value to each edge #print(index) #this list contains the edges ordered with increasing values #print(sort_edg) #second method of adjusting edges - more balanced #print('in edges = ', in_edges, '\n') #print('fin_edges = ', fin_edges, '\n') #print('delta = ', delta, '\n') #print('edges = ', edges, '\n') #print('index = ', index, '\n') #print(sort_edg) #import networkx as nx #import networkx as nx | 2.742739 | 3 |
1_strategy/rubber_duck.py | hypersport/Head-First-Design-Patterns-Python | 0 | 6613252 | <reponame>hypersport/Head-First-Design-Patterns-Python
from duck import Duck
from fly_no_way import FlyNoWay
from squeak import Squeak
class RubberDuck(Duck):
def __init__(self):
self.set_fly_behavior(FlyNoWay())
self.set_quack_behavior(Squeak())
def display(self):
print('I\'m a real Rubber Duck')
| from duck import Duck
from fly_no_way import FlyNoWay
from squeak import Squeak
class RubberDuck(Duck):
def __init__(self):
self.set_fly_behavior(FlyNoWay())
self.set_quack_behavior(Squeak())
def display(self):
print('I\'m a real Rubber Duck') | none | 1 | 2.651242 | 3 | |
exercises/simple-cipher/simple_cipher_test.py | akashsara/python | 1 | 6613253 | import unittest
import re
from simple_cipher import Cipher
# Tests adapted from `problem-specifications//canonical-data.json` @ v2.0.0
class SimpleCipherTest(unittest.TestCase):
# Utility functions
def setUp(self):
try:
self.assertRaisesRegex
except AttributeError:
self.assertRaisesRegex = self.assertRaisesRegexp
def assertRaisesWithMessage(self, exception):
return self.assertRaisesRegex(exception, r".+")
class RandomKeyCipherTest(SimpleCipherTest):
def test_can_encode(self):
cipher = Cipher()
plaintext = 'aaaaaaaaaa'
self.assertEqual(cipher.encode(plaintext), cipher.key[:len(plaintext)])
def test_can_decode(self):
cipher = Cipher()
plaintext = 'aaaaaaaaaa'
self.assertEqual(cipher.decode(cipher.key[:len(plaintext)]), plaintext)
def test_is_reversible(self):
cipher = Cipher()
plaintext = 'abcdefghij'
self.assertEqual(cipher.decode(cipher.encode(plaintext)), plaintext)
def test_key_is_only_made_of_lowercase_letters(self):
self.assertIsNotNone(re.match('^[a-z]+$', Cipher().key))
class SubstitutionCipherTest(SimpleCipherTest):
def test_can_encode(self):
cipher = Cipher('abcdefghij')
self.assertEqual(cipher.encode('aaaaaaaaaa'), cipher.key)
def test_can_decode(self):
cipher = Cipher('abcdefghij')
self.assertEqual(cipher.decode(cipher.key), 'aaaaaaaaaa')
def test_is_reversible(self):
cipher = Cipher('abcdefghij')
plaintext = 'abcdefghij'
self.assertEqual(cipher.decode(cipher.encode(plaintext)), plaintext)
def test_can_double_shift_encode(self):
plaintext = 'iamapandabear'
cipher = Cipher(plaintext)
self.assertEqual(cipher.encode(plaintext), 'qayaeaagaciai')
def test_can_wrap_on_encode(self):
cipher = Cipher('abcdefghij')
self.assertEqual(cipher.encode('zzzzzzzzzz'), 'zabcdefghi')
def test_can_wrap_on_decode(self):
cipher = Cipher('abcdefghij')
self.assertEqual(cipher.decode('zabcdefghi'), 'zzzzzzzzzz')
def test_can_encode_messages_longer_than_key(self):
cipher = Cipher('abc')
self.assertEqual(cipher.encode('iamapandabear'), 'iboaqcnecbfcr')
def test_can_decode_messages_longer_than_key(self):
cipher = Cipher('abc')
self.assertEqual(cipher.decode('iboaqcnecbfcr'), 'iamapandabear')
if __name__ == '__main__':
unittest.main()
| import unittest
import re
from simple_cipher import Cipher
# Tests adapted from `problem-specifications//canonical-data.json` @ v2.0.0
class SimpleCipherTest(unittest.TestCase):
# Utility functions
def setUp(self):
try:
self.assertRaisesRegex
except AttributeError:
self.assertRaisesRegex = self.assertRaisesRegexp
def assertRaisesWithMessage(self, exception):
return self.assertRaisesRegex(exception, r".+")
class RandomKeyCipherTest(SimpleCipherTest):
def test_can_encode(self):
cipher = Cipher()
plaintext = 'aaaaaaaaaa'
self.assertEqual(cipher.encode(plaintext), cipher.key[:len(plaintext)])
def test_can_decode(self):
cipher = Cipher()
plaintext = 'aaaaaaaaaa'
self.assertEqual(cipher.decode(cipher.key[:len(plaintext)]), plaintext)
def test_is_reversible(self):
cipher = Cipher()
plaintext = 'abcdefghij'
self.assertEqual(cipher.decode(cipher.encode(plaintext)), plaintext)
def test_key_is_only_made_of_lowercase_letters(self):
self.assertIsNotNone(re.match('^[a-z]+$', Cipher().key))
class SubstitutionCipherTest(SimpleCipherTest):
def test_can_encode(self):
cipher = Cipher('abcdefghij')
self.assertEqual(cipher.encode('aaaaaaaaaa'), cipher.key)
def test_can_decode(self):
cipher = Cipher('abcdefghij')
self.assertEqual(cipher.decode(cipher.key), 'aaaaaaaaaa')
def test_is_reversible(self):
cipher = Cipher('abcdefghij')
plaintext = 'abcdefghij'
self.assertEqual(cipher.decode(cipher.encode(plaintext)), plaintext)
def test_can_double_shift_encode(self):
plaintext = 'iamapandabear'
cipher = Cipher(plaintext)
self.assertEqual(cipher.encode(plaintext), 'qayaeaagaciai')
def test_can_wrap_on_encode(self):
cipher = Cipher('abcdefghij')
self.assertEqual(cipher.encode('zzzzzzzzzz'), 'zabcdefghi')
def test_can_wrap_on_decode(self):
cipher = Cipher('abcdefghij')
self.assertEqual(cipher.decode('zabcdefghi'), 'zzzzzzzzzz')
def test_can_encode_messages_longer_than_key(self):
cipher = Cipher('abc')
self.assertEqual(cipher.encode('iamapandabear'), 'iboaqcnecbfcr')
def test_can_decode_messages_longer_than_key(self):
cipher = Cipher('abc')
self.assertEqual(cipher.decode('iboaqcnecbfcr'), 'iamapandabear')
if __name__ == '__main__':
unittest.main()
| en | 0.801145 | # Tests adapted from `problem-specifications//canonical-data.json` @ v2.0.0 # Utility functions | 3.119959 | 3 |
src/dungeonbot/tests/test_die_roll.py | tlake/dungeonbot_backup | 0 | 6613254 | # from flask_testing import TestCase
from dungeonbot.conftest import BaseTest
from dungeonbot.plugins.helpers.die_roll import DieRoll
class DieRollTest(BaseTest):
"""Test Helper file: die_roll."""
def test_basic_init(self):
"""Test attributes of DieRoll object created with a basic roll string."""
roll = DieRoll("1d20", flag=None)
assert roll.roll_str == "1d20"
assert roll.operator == "+"
assert roll.action == roll.roll_die
assert roll.modifier == 0
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_a_init(self):
"""Test attributes of DieRoll object created with a roll string with advantage."""
roll = DieRoll("1d20", flag="a")
assert roll.roll_str == "1d20"
assert roll.operator == "+"
assert roll.action == roll.advantage
assert roll.modifier == 0
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_d_init(self):
"""Test attribuetes of DieRoll object created with a roll string with disadvantage."""
roll = DieRoll("1d20", flag="d")
assert roll.roll_str == "1d20"
assert roll.operator == "+"
assert roll.action == roll.disadvantage
assert roll.modifier == 0
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_positive_init(self):
"""Test attributes of DieRoll object created with a roll string with a plus."""
roll = DieRoll("1d20+32", flag=None)
assert roll.roll_str == "1d20+32"
assert roll.operator == "+"
assert roll.action == roll.roll_die
assert roll.modifier == 32
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_negative_init(self):
"""Test attributes of DieRoll object created with a roll string with a minus."""
roll = DieRoll("1d20-32", flag=None)
assert roll.roll_str == "1d20-32"
assert roll.operator == "-"
assert roll.action == roll.roll_die
assert roll.modifier == -32
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_negative_with_flag(self):
"""Test attributes with a minus and a flag."""
roll = DieRoll("1d20-32", flag="d")
assert roll.roll_str == "1d20-32"
assert roll.operator == "-"
assert roll.action == roll.disadvantage
assert roll.modifier == -32
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_print_results(self):
"""Test print results, basic."""
roll = DieRoll("1d20", flag=None)
roll_result = 10
message = "*[ 10 ]* _(1d20 = 10 + 0) (min 1, max 20) _"
assert roll.print_results(roll_result) == message
def test_print_results_named(self):
"""Test print results with a name."""
roll = DieRoll("1d20", flag=None)
roll_result = 10
name = "blue"
message = "*[ 10 ]* _(1d20 = 10 + 0) (min 1, max 20) _ with blue"
assert roll.print_results(roll_result, name=name) == message
def test_roll_die(self):
"""Test method roll_die."""
roll = DieRoll("1d20-1", flag=None)
assert roll.action == roll.roll_die
assert roll.roll_die() in range(roll.min_roll, roll.max_roll + 1)
def test_advantage(self):
"""Test method advantage."""
roll = DieRoll("1d20+1", flag="a")
assert roll.action == roll.advantage
assert roll.advantage() in range(roll.min_roll, roll.max_roll + 1)
def test_disadvantage(self):
"""Test disadvantage method."""
roll = DieRoll("1d20+1", flag="d")
assert roll.action == roll.disadvantage
assert roll.disadvantage() in range(roll.min_roll, roll.max_roll + 1)
| # from flask_testing import TestCase
from dungeonbot.conftest import BaseTest
from dungeonbot.plugins.helpers.die_roll import DieRoll
class DieRollTest(BaseTest):
"""Test Helper file: die_roll."""
def test_basic_init(self):
"""Test attributes of DieRoll object created with a basic roll string."""
roll = DieRoll("1d20", flag=None)
assert roll.roll_str == "1d20"
assert roll.operator == "+"
assert roll.action == roll.roll_die
assert roll.modifier == 0
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_a_init(self):
"""Test attributes of DieRoll object created with a roll string with advantage."""
roll = DieRoll("1d20", flag="a")
assert roll.roll_str == "1d20"
assert roll.operator == "+"
assert roll.action == roll.advantage
assert roll.modifier == 0
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_d_init(self):
"""Test attribuetes of DieRoll object created with a roll string with disadvantage."""
roll = DieRoll("1d20", flag="d")
assert roll.roll_str == "1d20"
assert roll.operator == "+"
assert roll.action == roll.disadvantage
assert roll.modifier == 0
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_positive_init(self):
"""Test attributes of DieRoll object created with a roll string with a plus."""
roll = DieRoll("1d20+32", flag=None)
assert roll.roll_str == "1d20+32"
assert roll.operator == "+"
assert roll.action == roll.roll_die
assert roll.modifier == 32
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_negative_init(self):
"""Test attributes of DieRoll object created with a roll string with a minus."""
roll = DieRoll("1d20-32", flag=None)
assert roll.roll_str == "1d20-32"
assert roll.operator == "-"
assert roll.action == roll.roll_die
assert roll.modifier == -32
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_negative_with_flag(self):
"""Test attributes with a minus and a flag."""
roll = DieRoll("1d20-32", flag="d")
assert roll.roll_str == "1d20-32"
assert roll.operator == "-"
assert roll.action == roll.disadvantage
assert roll.modifier == -32
assert roll.message == ""
assert roll.number == 1
assert roll.sides == 20
assert roll.min_roll == roll.number
assert roll.max_roll == roll.sides * roll.number
def test_print_results(self):
"""Test print results, basic."""
roll = DieRoll("1d20", flag=None)
roll_result = 10
message = "*[ 10 ]* _(1d20 = 10 + 0) (min 1, max 20) _"
assert roll.print_results(roll_result) == message
def test_print_results_named(self):
"""Test print results with a name."""
roll = DieRoll("1d20", flag=None)
roll_result = 10
name = "blue"
message = "*[ 10 ]* _(1d20 = 10 + 0) (min 1, max 20) _ with blue"
assert roll.print_results(roll_result, name=name) == message
def test_roll_die(self):
"""Test method roll_die."""
roll = DieRoll("1d20-1", flag=None)
assert roll.action == roll.roll_die
assert roll.roll_die() in range(roll.min_roll, roll.max_roll + 1)
def test_advantage(self):
"""Test method advantage."""
roll = DieRoll("1d20+1", flag="a")
assert roll.action == roll.advantage
assert roll.advantage() in range(roll.min_roll, roll.max_roll + 1)
def test_disadvantage(self):
"""Test disadvantage method."""
roll = DieRoll("1d20+1", flag="d")
assert roll.action == roll.disadvantage
assert roll.disadvantage() in range(roll.min_roll, roll.max_roll + 1)
| en | 0.769884 | # from flask_testing import TestCase Test Helper file: die_roll. Test attributes of DieRoll object created with a basic roll string. Test attributes of DieRoll object created with a roll string with advantage. Test attribuetes of DieRoll object created with a roll string with disadvantage. Test attributes of DieRoll object created with a roll string with a plus. Test attributes of DieRoll object created with a roll string with a minus. Test attributes with a minus and a flag. Test print results, basic. Test print results with a name. Test method roll_die. Test method advantage. Test disadvantage method. | 3.030823 | 3 |
Seeder/contracts/signals.py | WebarchivCZ/Seeder | 8 | 6613255 | <gh_stars>1-10
# pylint: disable=W0613
from . import constants
from django.dispatch import receiver
from django.db.models.signals import post_save
from contracts.models import Contract
from source import constants as source_constants
@receiver(signal=post_save, sender=Contract)
def process_contract_change(instance, created, **kwargs):
"""
If the contract is marked as valid then source is accepted
"""
if not created and instance.state in constants.STATE_CONVERSION:
instance.sources.filter(
state=source_constants.STATE_ACCEPTED_BY_STAFF
).update(state = constants.STATE_CONVERSION[instance.state])
| # pylint: disable=W0613
from . import constants
from django.dispatch import receiver
from django.db.models.signals import post_save
from contracts.models import Contract
from source import constants as source_constants
@receiver(signal=post_save, sender=Contract)
def process_contract_change(instance, created, **kwargs):
"""
If the contract is marked as valid then source is accepted
"""
if not created and instance.state in constants.STATE_CONVERSION:
instance.sources.filter(
state=source_constants.STATE_ACCEPTED_BY_STAFF
).update(state = constants.STATE_CONVERSION[instance.state]) | en | 0.92603 | # pylint: disable=W0613 If the contract is marked as valid then source is accepted | 1.973728 | 2 |
easy/1021-remove-outermost-parentheses.py | changmeng72/leecode_python3 | 0 | 6613256 | <filename>easy/1021-remove-outermost-parentheses.py
class Solution:
def removeOuterParentheses(self, s: str) -> str:
r =[]
start = 0
count = 1
for i in range(1,len(s)):
if(s[i]=='('):
count += 1
else:
count -=1
if(count==0):
r.append(s[start+1:i])
start = i+1
return ''.join(r) | <filename>easy/1021-remove-outermost-parentheses.py
class Solution:
def removeOuterParentheses(self, s: str) -> str:
r =[]
start = 0
count = 1
for i in range(1,len(s)):
if(s[i]=='('):
count += 1
else:
count -=1
if(count==0):
r.append(s[start+1:i])
start = i+1
return ''.join(r) | none | 1 | 3.446983 | 3 | |
hwt/serializer/generic/context.py | mgielda/hwt | 0 | 6613257 | from copy import copy
class SerializerCtx():
"""
Serializer context
:ivar scope: instance of NameScope used to check id availability
:ivar indent: number of visual indentations for code in this context
:ivar createTmpVarFn: function (sugestedName, dtype) returns variable
this function will be called to create tmp variables
:ivar constCache: constant cache to extract frequently used large constant
values from code (visual improvement)
:ivar currentUnit: current Unit instance or None
"""
def __init__(self, scope, indent: int, createTmpVarFn, constCache=None):
self.scope = scope
self.indent = indent
self.currentUnit = None
if createTmpVarFn is None:
self.createTmpVarFn = self.defaultCreateTmpVarFn
else:
self.createTmpVarFn = createTmpVarFn
self.constCache = constCache
def defaultCreateTmpVarFn(self, sugestedName, dtype):
raise NotImplementedError()
def withIndent(self, indent=1):
"""
Create copy of this context with increased indent
"""
ctx = copy(self)
ctx.indent += indent
return ctx
| from copy import copy
class SerializerCtx():
"""
Serializer context
:ivar scope: instance of NameScope used to check id availability
:ivar indent: number of visual indentations for code in this context
:ivar createTmpVarFn: function (sugestedName, dtype) returns variable
this function will be called to create tmp variables
:ivar constCache: constant cache to extract frequently used large constant
values from code (visual improvement)
:ivar currentUnit: current Unit instance or None
"""
def __init__(self, scope, indent: int, createTmpVarFn, constCache=None):
self.scope = scope
self.indent = indent
self.currentUnit = None
if createTmpVarFn is None:
self.createTmpVarFn = self.defaultCreateTmpVarFn
else:
self.createTmpVarFn = createTmpVarFn
self.constCache = constCache
def defaultCreateTmpVarFn(self, sugestedName, dtype):
raise NotImplementedError()
def withIndent(self, indent=1):
"""
Create copy of this context with increased indent
"""
ctx = copy(self)
ctx.indent += indent
return ctx
| en | 0.644386 | Serializer context :ivar scope: instance of NameScope used to check id availability :ivar indent: number of visual indentations for code in this context :ivar createTmpVarFn: function (sugestedName, dtype) returns variable this function will be called to create tmp variables :ivar constCache: constant cache to extract frequently used large constant values from code (visual improvement) :ivar currentUnit: current Unit instance or None Create copy of this context with increased indent | 2.580711 | 3 |
py_headless_daw/dsp_utils/drum_synth/one_shot_oscillator.py | hq9000/py-headless-daw | 22 | 6613258 | <reponame>hq9000/py-headless-daw
import numpy as np
import math
from py_headless_daw.dsp_utils.adsr_envelope import ADSREnvelope
from py_headless_daw.dsp_utils.wave_producer_interface import WaveProducerInterface
class OneShotOscillator(WaveProducerInterface):
# different signals https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.sawtooth.html
TYPE_SINE: str = "sine"
TYPE_NOISE: str = "noise"
TYPE_TRIANGLE: str = "triangle"
def __init__(self):
self.pitch_envelope: ADSREnvelope = self._create_default_envelope()
self.volume_envelope: ADSREnvelope = self._create_default_envelope()
self.zero_frequency: float = 440.0
self.frequency_range: float = 440.0
self.wave = self.TYPE_SINE
self.distortion: float = 0.0
self.initial_phase: float = 0.0
self.volume: float = 1.0
def render_to_buffer(self, output_buffer: np.ndarray, sample_rate: int, start_sample: int,
mode: str = WaveProducerInterface.MODE_REPLACE):
if output_buffer.ndim != 1:
raise ValueError(f"buffers given to an oscillator are supposed to be mono, "
f"this one has {output_buffer.ndim} dimensions instead of 1")
volume_env_wave = np.copy(output_buffer)
self.volume_envelope.render_to_buffer(volume_env_wave, sample_rate, start_sample)
oscillation_wave = np.copy(output_buffer)
self._generate_oscillation_wave(oscillation_wave, sample_rate, start_sample)
np.multiply(oscillation_wave, volume_env_wave, out=oscillation_wave)
if WaveProducerInterface.MODE_REPLACE == mode:
np.copyto(output_buffer, oscillation_wave)
elif WaveProducerInterface.MODE_MIX == mode:
np.add(output_buffer, oscillation_wave, out=output_buffer)
else:
raise ValueError(f'unknown producing mode {mode}')
def _create_default_envelope(self) -> ADSREnvelope:
return ADSREnvelope(attack_time=0, decay_time=1, sustain_level=1, sustain_time=0, release_time=1)
def _generate_oscillation_wave(self, out_buffer: np.ndarray, sample_rate, start_sample):
phase: float = self.initial_phase
for i in range(out_buffer.shape[0]):
relative_frequency = self.pitch_envelope.get_one_value(start_sample + i, sample_rate)
real_frequency = self.zero_frequency + self.frequency_range * relative_frequency
out_buffer[i] = self._wave_function(self.wave, self.distortion, phase)
if real_frequency > 0:
samples_in_period = (1 / real_frequency) * sample_rate
oscillator_step_for_one_sample = 2 * math.pi / samples_in_period
phase += oscillator_step_for_one_sample
def _wave_function(self, waveform: str, distortion: float, phase: float) -> float:
return math.sin(phase)
| import numpy as np
import math
from py_headless_daw.dsp_utils.adsr_envelope import ADSREnvelope
from py_headless_daw.dsp_utils.wave_producer_interface import WaveProducerInterface
class OneShotOscillator(WaveProducerInterface):
# different signals https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.sawtooth.html
TYPE_SINE: str = "sine"
TYPE_NOISE: str = "noise"
TYPE_TRIANGLE: str = "triangle"
def __init__(self):
self.pitch_envelope: ADSREnvelope = self._create_default_envelope()
self.volume_envelope: ADSREnvelope = self._create_default_envelope()
self.zero_frequency: float = 440.0
self.frequency_range: float = 440.0
self.wave = self.TYPE_SINE
self.distortion: float = 0.0
self.initial_phase: float = 0.0
self.volume: float = 1.0
def render_to_buffer(self, output_buffer: np.ndarray, sample_rate: int, start_sample: int,
mode: str = WaveProducerInterface.MODE_REPLACE):
if output_buffer.ndim != 1:
raise ValueError(f"buffers given to an oscillator are supposed to be mono, "
f"this one has {output_buffer.ndim} dimensions instead of 1")
volume_env_wave = np.copy(output_buffer)
self.volume_envelope.render_to_buffer(volume_env_wave, sample_rate, start_sample)
oscillation_wave = np.copy(output_buffer)
self._generate_oscillation_wave(oscillation_wave, sample_rate, start_sample)
np.multiply(oscillation_wave, volume_env_wave, out=oscillation_wave)
if WaveProducerInterface.MODE_REPLACE == mode:
np.copyto(output_buffer, oscillation_wave)
elif WaveProducerInterface.MODE_MIX == mode:
np.add(output_buffer, oscillation_wave, out=output_buffer)
else:
raise ValueError(f'unknown producing mode {mode}')
def _create_default_envelope(self) -> ADSREnvelope:
return ADSREnvelope(attack_time=0, decay_time=1, sustain_level=1, sustain_time=0, release_time=1)
def _generate_oscillation_wave(self, out_buffer: np.ndarray, sample_rate, start_sample):
phase: float = self.initial_phase
for i in range(out_buffer.shape[0]):
relative_frequency = self.pitch_envelope.get_one_value(start_sample + i, sample_rate)
real_frequency = self.zero_frequency + self.frequency_range * relative_frequency
out_buffer[i] = self._wave_function(self.wave, self.distortion, phase)
if real_frequency > 0:
samples_in_period = (1 / real_frequency) * sample_rate
oscillator_step_for_one_sample = 2 * math.pi / samples_in_period
phase += oscillator_step_for_one_sample
def _wave_function(self, waveform: str, distortion: float, phase: float) -> float:
return math.sin(phase) | en | 0.393301 | # different signals https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.sawtooth.html | 2.590454 | 3 |
scripts/make_spreadsheet.py | ElementAI/data-augmentation-with-llms | 2 | 6613259 | <gh_stars>1-10
import os, json, pickle
from openpyxl import Workbook
from openpyxl.styles import Font
DATASET_NAME = "banking77"
def load_data():
data_dir = f"./data/{DATASET_NAME}"
opj = os.path.join
ds_full_suite = pickle.load(open(opj(data_dir, "full/data_full_suite.pkl"), "rb"))
generated_samples = pickle.load(open(opj(data_dir, "full/al_dataset.pkl"), "rb"))[
"generated"
]
id2name = json.load(open(opj(data_dir, "id2name.json")))
return ds_full_suite, generated_samples, id2name
def massage_data(ds_full_suite, generated_samples, id2name):
workbooks = {}
for engine in generated_samples:
workbooks[engine] = gather_workbook_data(
ds_full_suite, generated_samples[engine], id2name
)
return workbooks
def gather_workbook_data(ds_full_suite, generated_samples, id2name):
workbook_data = {}
for domain in ds_full_suite:
# generate prompt column
prompt_data = ds_full_suite[domain]["F"]["train"]
for text, intent_id in zip(prompt_data["text"], prompt_data["intent"]):
intent_name = id2name[str(intent_id)].replace("?", "")
sheet_name = f"{domain}<>{intent_name}"
if sheet_name not in workbook_data:
workbook_data[sheet_name] = {
"prompt": [],
"generated": [],
"oracle_prediction": [],
}
workbook_data[sheet_name]["prompt"].append(text)
# add generated data, and oracle prediction data
for text, oracle_intent_id, org_intent_id in zip(
generated_samples["text"],
generated_samples["intent"],
generated_samples["old_intent"],
):
oracle_pred = id2name[str(oracle_intent_id)].replace("?", "")
org_intent_name = id2name[str(org_intent_id)].replace("?", "")
sheet_name = f"{domain}<>{org_intent_name}"
if sheet_name not in workbook_data:
# print(f"sheet {sheet_name} doesn't exist")
continue
workbook_data[sheet_name]["generated"].append(text)
workbook_data[sheet_name]["oracle_prediction"].append(oracle_pred)
return workbook_data
def create_excel_sheet(name, data):
wb = Workbook()
wb.remove(wb.active) # remove the empty "Sheet"
# create different sheets
for sheet_name in data:
org_intent = sheet_name.split("<>", 1)[1]
ws = wb.create_sheet(sheet_name)
prompts = data[sheet_name]["prompt"]
generated = data[sheet_name]["generated"]
oracle_predictions = data[sheet_name]["oracle_prediction"]
############# compute some quantities for formatting ##############
# max width of column A
max_sent_length = max(map(len, prompts + generated))
# max width of column B
max_pred_length = max(map(len, oracle_predictions))
total_faithful_samples = oracle_predictions.count(org_intent)
############# compute end #################
# add the first column
ws.append(["Sentences", "Oracle Predictions"])
# add the sentences column
for irow in range(len(prompts + generated)):
if irow < len(prompts):
ws.append([prompts[irow]])
else:
new_irow = irow - len(prompts)
ws.append([generated[new_irow], oracle_predictions[new_irow]])
# some analysis
ws["C1"] = "Total faithful samples"
ws["C2"] = f"{total_faithful_samples}/{len(generated)}"
ws["C3"] = f"{total_faithful_samples/len(generated)*100:.2f}%"
# adjust column widths
ws.column_dimensions["A"].width = max_sent_length
ws.column_dimensions["B"].width = max_pred_length
ws.column_dimensions["C"].width = len("Total faithful samples")
# increase font size
n_rows = len(prompts + generated)
for col, n_rows in [("A", n_rows), ("B", n_rows), ("C", 3)]:
for i in range(1, n_rows + 2):
ws[f"{col}{i}"].font = Font(size=14)
# bold the first row
ws["A1"].font = Font(bold=True, size=14)
ws["B1"].font = Font(bold=True, size=14)
ws["C1"].font = Font(bold=True, size=14)
# delete this useless sheet
# sort sheets based on fidelity (ws['C3'] is the fidelity)
wb._sheets.sort(key=lambda ws: float(ws["C3"].value[:-1]))
wb.active = 0
save_folder = f"spreadsheets/{DATASET_NAME}"
if not os.path.exists(save_folder):
os.mkdir(save_folder)
wb.save(os.path.join(save_folder, f"{name}.xlsx"))
if __name__ == "__main__":
workbooks = massage_data(*load_data())
for engine_temp, data in workbooks.items():
if "_" in engine_temp and engine_temp.split("_")[1] != "1.0":
continue
create_excel_sheet(engine_temp, data)
| import os, json, pickle
from openpyxl import Workbook
from openpyxl.styles import Font
DATASET_NAME = "banking77"
def load_data():
data_dir = f"./data/{DATASET_NAME}"
opj = os.path.join
ds_full_suite = pickle.load(open(opj(data_dir, "full/data_full_suite.pkl"), "rb"))
generated_samples = pickle.load(open(opj(data_dir, "full/al_dataset.pkl"), "rb"))[
"generated"
]
id2name = json.load(open(opj(data_dir, "id2name.json")))
return ds_full_suite, generated_samples, id2name
def massage_data(ds_full_suite, generated_samples, id2name):
workbooks = {}
for engine in generated_samples:
workbooks[engine] = gather_workbook_data(
ds_full_suite, generated_samples[engine], id2name
)
return workbooks
def gather_workbook_data(ds_full_suite, generated_samples, id2name):
workbook_data = {}
for domain in ds_full_suite:
# generate prompt column
prompt_data = ds_full_suite[domain]["F"]["train"]
for text, intent_id in zip(prompt_data["text"], prompt_data["intent"]):
intent_name = id2name[str(intent_id)].replace("?", "")
sheet_name = f"{domain}<>{intent_name}"
if sheet_name not in workbook_data:
workbook_data[sheet_name] = {
"prompt": [],
"generated": [],
"oracle_prediction": [],
}
workbook_data[sheet_name]["prompt"].append(text)
# add generated data, and oracle prediction data
for text, oracle_intent_id, org_intent_id in zip(
generated_samples["text"],
generated_samples["intent"],
generated_samples["old_intent"],
):
oracle_pred = id2name[str(oracle_intent_id)].replace("?", "")
org_intent_name = id2name[str(org_intent_id)].replace("?", "")
sheet_name = f"{domain}<>{org_intent_name}"
if sheet_name not in workbook_data:
# print(f"sheet {sheet_name} doesn't exist")
continue
workbook_data[sheet_name]["generated"].append(text)
workbook_data[sheet_name]["oracle_prediction"].append(oracle_pred)
return workbook_data
def create_excel_sheet(name, data):
wb = Workbook()
wb.remove(wb.active) # remove the empty "Sheet"
# create different sheets
for sheet_name in data:
org_intent = sheet_name.split("<>", 1)[1]
ws = wb.create_sheet(sheet_name)
prompts = data[sheet_name]["prompt"]
generated = data[sheet_name]["generated"]
oracle_predictions = data[sheet_name]["oracle_prediction"]
############# compute some quantities for formatting ##############
# max width of column A
max_sent_length = max(map(len, prompts + generated))
# max width of column B
max_pred_length = max(map(len, oracle_predictions))
total_faithful_samples = oracle_predictions.count(org_intent)
############# compute end #################
# add the first column
ws.append(["Sentences", "Oracle Predictions"])
# add the sentences column
for irow in range(len(prompts + generated)):
if irow < len(prompts):
ws.append([prompts[irow]])
else:
new_irow = irow - len(prompts)
ws.append([generated[new_irow], oracle_predictions[new_irow]])
# some analysis
ws["C1"] = "Total faithful samples"
ws["C2"] = f"{total_faithful_samples}/{len(generated)}"
ws["C3"] = f"{total_faithful_samples/len(generated)*100:.2f}%"
# adjust column widths
ws.column_dimensions["A"].width = max_sent_length
ws.column_dimensions["B"].width = max_pred_length
ws.column_dimensions["C"].width = len("Total faithful samples")
# increase font size
n_rows = len(prompts + generated)
for col, n_rows in [("A", n_rows), ("B", n_rows), ("C", 3)]:
for i in range(1, n_rows + 2):
ws[f"{col}{i}"].font = Font(size=14)
# bold the first row
ws["A1"].font = Font(bold=True, size=14)
ws["B1"].font = Font(bold=True, size=14)
ws["C1"].font = Font(bold=True, size=14)
# delete this useless sheet
# sort sheets based on fidelity (ws['C3'] is the fidelity)
wb._sheets.sort(key=lambda ws: float(ws["C3"].value[:-1]))
wb.active = 0
save_folder = f"spreadsheets/{DATASET_NAME}"
if not os.path.exists(save_folder):
os.mkdir(save_folder)
wb.save(os.path.join(save_folder, f"{name}.xlsx"))
if __name__ == "__main__":
workbooks = massage_data(*load_data())
for engine_temp, data in workbooks.items():
if "_" in engine_temp and engine_temp.split("_")[1] != "1.0":
continue
create_excel_sheet(engine_temp, data) | en | 0.569877 | # generate prompt column # add generated data, and oracle prediction data # print(f"sheet {sheet_name} doesn't exist") # remove the empty "Sheet" # create different sheets ############# compute some quantities for formatting ############## # max width of column A # max width of column B ############# compute end ################# # add the first column # add the sentences column # some analysis # adjust column widths # increase font size # bold the first row # delete this useless sheet # sort sheets based on fidelity (ws['C3'] is the fidelity) | 2.667352 | 3 |
CardPotential.py | PChild/card-counter | 0 | 6613260 | import os
import tbapy
tba = tbapy.TBA(os.getenv("TBA_KEY"))
for event in tba.events(year=2019):
if event['event_type'] in range(0, 10):
for match in tba.event_matches(event['key']):
if | import os
import tbapy
tba = tbapy.TBA(os.getenv("TBA_KEY"))
for event in tba.events(year=2019):
if event['event_type'] in range(0, 10):
for match in tba.event_matches(event['key']):
if | none | 1 | 2.54503 | 3 | |
coursera/python_programming_basics/1_week_09_lesson_rubles.py | anklav24/Python-Education | 0 | 6613261 | a = int(input())
b = int(input())
c = int(input())
d = int(input())
cost1 = a * 100 + b
cost2 = c * 100 + d
total_cost = cost1 + cost2
print(total_cost // 100, total_cost % 100, sep=',')
| a = int(input())
b = int(input())
c = int(input())
d = int(input())
cost1 = a * 100 + b
cost2 = c * 100 + d
total_cost = cost1 + cost2
print(total_cost // 100, total_cost % 100, sep=',')
| none | 1 | 3.634464 | 4 | |
yolov4_pytorch/model/module/__init__.py | Lornatang/YOLOv4-PyTorch | 19 | 6613262 | <reponame>Lornatang/YOLOv4-PyTorch
# Copyright 2020 Lorna Authors. 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 .activations import HardSwish
from .activations import MemoryEfficientMish
from .activations import MemoryEfficientSwish
from .activations import Mish
from .activations import MishImplementation
from .activations import Swish
from .activations import SwishImplementation
from .common import Concat
from .common import Focus
from .common import check_anchor_order
from .conv import C3
from .conv import Conv
from .conv import ConvBNMish
from .conv import CrossConv
from .conv import DWConv
from .conv import MixConv2d
from .conv import MobileNetConv
from .conv import autopad
from .head import SPP
from .layer import Detect
from .layer import YOLO
from .layer import parse_model
from .neck import Bottleneck
from .neck import BottleneckCSP
from .neck import YOLOv4_Bottleneck
__all__ = [
"HardSwish",
"MemoryEfficientMish",
"MemoryEfficientSwish",
"Mish",
"MishImplementation",
"Swish",
"SwishImplementation",
"Concat",
"Focus",
"check_anchor_order",
"C3",
"Conv",
"ConvBNMish",
"CrossConv",
"DWConv",
"MixConv2d",
"MobileNetConv",
"autopad",
"SPP",
"Detect",
"YOLO",
"parse_model",
"Bottleneck",
"BottleneckCSP",
"YOLOv4_Bottleneck",
]
| # Copyright 2020 Lorna Authors. 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 .activations import HardSwish
from .activations import MemoryEfficientMish
from .activations import MemoryEfficientSwish
from .activations import Mish
from .activations import MishImplementation
from .activations import Swish
from .activations import SwishImplementation
from .common import Concat
from .common import Focus
from .common import check_anchor_order
from .conv import C3
from .conv import Conv
from .conv import ConvBNMish
from .conv import CrossConv
from .conv import DWConv
from .conv import MixConv2d
from .conv import MobileNetConv
from .conv import autopad
from .head import SPP
from .layer import Detect
from .layer import YOLO
from .layer import parse_model
from .neck import Bottleneck
from .neck import BottleneckCSP
from .neck import YOLOv4_Bottleneck
__all__ = [
"HardSwish",
"MemoryEfficientMish",
"MemoryEfficientSwish",
"Mish",
"MishImplementation",
"Swish",
"SwishImplementation",
"Concat",
"Focus",
"check_anchor_order",
"C3",
"Conv",
"ConvBNMish",
"CrossConv",
"DWConv",
"MixConv2d",
"MobileNetConv",
"autopad",
"SPP",
"Detect",
"YOLO",
"parse_model",
"Bottleneck",
"BottleneckCSP",
"YOLOv4_Bottleneck",
] | en | 0.814997 | # Copyright 2020 Lorna Authors. 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. # ============================================================================== | 1.045587 | 1 |
qutilities/line_delay.py | Emigon/qutilities | 0 | 6613263 | """ processing methods to remove line delay from a resonance
author: <NAME>
"""
import numpy as np
import sympy as sp
from fitkit import *
from fitkit.decimate import *
from .circle import *
from qutilities import *
def pm_line_delay(b_tau = (0, 0, 25/3e8)):
""" returns a Parametric1D model for the line delay
Params:
tau: The line delay
Args: Parameter bounds as required by Parametric1D
"""
tau, f = sp.symbols('tau f')
return Parametric1D(sp.exp(-2j*np.pi*tau*f), {'tau': b_tau})
def rm_line_delay(s21, k = 10, N = 201):
""" remove the line delay from s21
Args:
s21: A Signal1D representation of the s21 data. Using approximately
10*fwhm of the resonance is recommended
k: The number of samples from the beggining of the phase response
used to estimate the initial gradient before optimiser polishing
N: The number of points to decimate s21 by when fitting tau using
the non-linear optimiser
Returns:
s21: The input s21 data with the line delay removed from the phase
response
model: The Parametric1D model for the line delay
"""
# unwrap the phase and fit linear model to obtain a starting point for tau
phi = np.unwrap(np.angle(s21.values))
p = np.poly1d(np.polyfit(s21.x[:k], phi[:k], 1))
tau_0 = p.c[0]/(2*np.pi) # we expect this to be negative
rough = s21*Signal1D(np.exp(-2j*np.pi*tau_0*s21.x), xraw = s21.x)
# construct the model and a circle fitting based error function
pm_neg = pm_line_delay(b_tau = (-np.abs(tau_0), 0, np.abs(tau_0)))
def errf(v, self, sig1d, _):
try:
pm_neg.v['tau'] = v[0]*pm_neg.v['tau']
except:
pass # return the same result as the previously set parameter!
return circle_fit(sig1d*pm_neg(sig1d.x))[-1]
# subsample to speed up circle fitting
subsample = decimate_by_derivative(rough, N)
shgo_opts = {'n': 100, 'iters': 1, 'sampling_method': 'sobol'}
pm_neg.fit(subsample, method = 'shgo', errf = errf, opts = shgo_opts)
tau_f = -(pm_neg.v['tau'] + tau_0)
bounds = (tau_f - .5*np.abs(tau_f), tau_f, tau_f + .5*np.abs(tau_f))
ld_model = pm_line_delay(b_tau = bounds)
return s21*Signal1D(np.exp(2j*np.pi*tau_f*s21.x), xraw = s21.x), ld_model
| """ processing methods to remove line delay from a resonance
author: <NAME>
"""
import numpy as np
import sympy as sp
from fitkit import *
from fitkit.decimate import *
from .circle import *
from qutilities import *
def pm_line_delay(b_tau = (0, 0, 25/3e8)):
""" returns a Parametric1D model for the line delay
Params:
tau: The line delay
Args: Parameter bounds as required by Parametric1D
"""
tau, f = sp.symbols('tau f')
return Parametric1D(sp.exp(-2j*np.pi*tau*f), {'tau': b_tau})
def rm_line_delay(s21, k = 10, N = 201):
""" remove the line delay from s21
Args:
s21: A Signal1D representation of the s21 data. Using approximately
10*fwhm of the resonance is recommended
k: The number of samples from the beggining of the phase response
used to estimate the initial gradient before optimiser polishing
N: The number of points to decimate s21 by when fitting tau using
the non-linear optimiser
Returns:
s21: The input s21 data with the line delay removed from the phase
response
model: The Parametric1D model for the line delay
"""
# unwrap the phase and fit linear model to obtain a starting point for tau
phi = np.unwrap(np.angle(s21.values))
p = np.poly1d(np.polyfit(s21.x[:k], phi[:k], 1))
tau_0 = p.c[0]/(2*np.pi) # we expect this to be negative
rough = s21*Signal1D(np.exp(-2j*np.pi*tau_0*s21.x), xraw = s21.x)
# construct the model and a circle fitting based error function
pm_neg = pm_line_delay(b_tau = (-np.abs(tau_0), 0, np.abs(tau_0)))
def errf(v, self, sig1d, _):
try:
pm_neg.v['tau'] = v[0]*pm_neg.v['tau']
except:
pass # return the same result as the previously set parameter!
return circle_fit(sig1d*pm_neg(sig1d.x))[-1]
# subsample to speed up circle fitting
subsample = decimate_by_derivative(rough, N)
shgo_opts = {'n': 100, 'iters': 1, 'sampling_method': 'sobol'}
pm_neg.fit(subsample, method = 'shgo', errf = errf, opts = shgo_opts)
tau_f = -(pm_neg.v['tau'] + tau_0)
bounds = (tau_f - .5*np.abs(tau_f), tau_f, tau_f + .5*np.abs(tau_f))
ld_model = pm_line_delay(b_tau = bounds)
return s21*Signal1D(np.exp(2j*np.pi*tau_f*s21.x), xraw = s21.x), ld_model
| en | 0.834293 | processing methods to remove line delay from a resonance author: <NAME> returns a Parametric1D model for the line delay Params: tau: The line delay Args: Parameter bounds as required by Parametric1D remove the line delay from s21 Args: s21: A Signal1D representation of the s21 data. Using approximately 10*fwhm of the resonance is recommended k: The number of samples from the beggining of the phase response used to estimate the initial gradient before optimiser polishing N: The number of points to decimate s21 by when fitting tau using the non-linear optimiser Returns: s21: The input s21 data with the line delay removed from the phase response model: The Parametric1D model for the line delay # unwrap the phase and fit linear model to obtain a starting point for tau # we expect this to be negative # construct the model and a circle fitting based error function # return the same result as the previously set parameter! # subsample to speed up circle fitting | 2.950867 | 3 |
src/bo/strategy_bo.py | Asconius/trading-bot | 2 | 6613264 | import math
from datetime import datetime
from decimal import Decimal
from typing import Tuple
from pandas import DataFrame
from src.constants import ZERO
from src.dto.attempt_dto import AttemptDTO
from src.enums.action_enum import ActionEnum
from src.utils.utils import Utils
class StrategyBO:
# noinspection DuplicatedCode
@staticmethod
def counter_cyclical(frame: DataFrame, ticker: str, date: datetime,
attempt: AttemptDTO) -> Tuple[ActionEnum, Decimal]:
end_close: Decimal = frame.at[date, ticker]
if attempt is None:
attempt = AttemptDTO()
start_close: Decimal = Utils.day_delta_value(frame, ticker, date, attempt.distance_buy)
if not math.isnan(start_close):
percent: Decimal = start_close / end_close
if not math.isnan(start_close) and not math.isnan(end_close) and percent > attempt.delta_buy:
return ActionEnum.BUY, Utils.number(attempt.amount_buy, end_close)
start_close: Decimal = Utils.day_delta_value(frame, ticker, date, attempt.distance_sell)
if not math.isnan(start_close):
percent: Decimal = end_close / start_close
if not math.isnan(start_close) and not math.isnan(end_close) and percent > attempt.delta_sell:
return ActionEnum.SELL, Utils.number(attempt.amount_sell, end_close)
return ActionEnum.NONE, ZERO
| import math
from datetime import datetime
from decimal import Decimal
from typing import Tuple
from pandas import DataFrame
from src.constants import ZERO
from src.dto.attempt_dto import AttemptDTO
from src.enums.action_enum import ActionEnum
from src.utils.utils import Utils
class StrategyBO:
# noinspection DuplicatedCode
@staticmethod
def counter_cyclical(frame: DataFrame, ticker: str, date: datetime,
attempt: AttemptDTO) -> Tuple[ActionEnum, Decimal]:
end_close: Decimal = frame.at[date, ticker]
if attempt is None:
attempt = AttemptDTO()
start_close: Decimal = Utils.day_delta_value(frame, ticker, date, attempt.distance_buy)
if not math.isnan(start_close):
percent: Decimal = start_close / end_close
if not math.isnan(start_close) and not math.isnan(end_close) and percent > attempt.delta_buy:
return ActionEnum.BUY, Utils.number(attempt.amount_buy, end_close)
start_close: Decimal = Utils.day_delta_value(frame, ticker, date, attempt.distance_sell)
if not math.isnan(start_close):
percent: Decimal = end_close / start_close
if not math.isnan(start_close) and not math.isnan(end_close) and percent > attempt.delta_sell:
return ActionEnum.SELL, Utils.number(attempt.amount_sell, end_close)
return ActionEnum.NONE, ZERO
| en | 0.3074 | # noinspection DuplicatedCode | 2.457064 | 2 |
examples/wisePigs/runMe.py | tyler-utah/PBDD | 13 | 6613265 | <reponame>tyler-utah/PBDD
#!/usr/bin/python2.7
#<NAME>
#Sept 18, 2011
#This example shows the second more complicated Lewis Carroll puzzle.
#See wiseYoungPigs.txt for the formula.
import sys
sys.path.append("../../include/")
sys.path.append("../../../../PyBool/include/")
import BDD
if __name__ == "__main__":
#creating and initializing the BDD
x = BDD.bdd_init("wiseYoungPigs.txt")
#building the tree
BDD.ite_build(x)
print "Can wise young pigs go up in balloons?"
if BDD.sat_count(x) == 0:
print "No"
else:
print "Yes, and here's how:"
print ""
print BDD.any_sat(x)
BDD.dot_bdd(x,"my_dot.dot")
#BDD.dot_bdd(x, "myDot.dot")
| #!/usr/bin/python2.7
#<NAME>
#Sept 18, 2011
#This example shows the second more complicated Lewis Carroll puzzle.
#See wiseYoungPigs.txt for the formula.
import sys
sys.path.append("../../include/")
sys.path.append("../../../../PyBool/include/")
import BDD
if __name__ == "__main__":
#creating and initializing the BDD
x = BDD.bdd_init("wiseYoungPigs.txt")
#building the tree
BDD.ite_build(x)
print "Can wise young pigs go up in balloons?"
if BDD.sat_count(x) == 0:
print "No"
else:
print "Yes, and here's how:"
print ""
print BDD.any_sat(x)
BDD.dot_bdd(x,"my_dot.dot")
#BDD.dot_bdd(x, "myDot.dot") | en | 0.713934 | #!/usr/bin/python2.7 #<NAME> #Sept 18, 2011 #This example shows the second more complicated Lewis Carroll puzzle. #See wiseYoungPigs.txt for the formula. #creating and initializing the BDD #building the tree #BDD.dot_bdd(x, "myDot.dot") | 3.460672 | 3 |
ABC144/ABC144c copy.py | VolgaKurvar/AtCoder | 0 | 6613266 | # ABC144c
import math
def main():
import sys
input = sys.stdin.readline
sys.setrecursionlimit(10**6)
# map(int,input().split())
n = int(input())
ans = 10**12
for i in range(n//2+1):
for j in range(n//2+1):
if(i*j == n):
ans = min(ans, i+j)
print(ans-2)
if __name__ == '__main__':
main()
| # ABC144c
import math
def main():
import sys
input = sys.stdin.readline
sys.setrecursionlimit(10**6)
# map(int,input().split())
n = int(input())
ans = 10**12
for i in range(n//2+1):
for j in range(n//2+1):
if(i*j == n):
ans = min(ans, i+j)
print(ans-2)
if __name__ == '__main__':
main()
| ceb | 0.249579 | # ABC144c # map(int,input().split()) | 2.55739 | 3 |
example/qubole_presto_api_example.py | sanketsaurav/qds-sdk-py | 42 | 6613267 |
"""
This is the sample code used for submitting a Presto query (PrestoCommand) and getting the result back to local file.
Similar way can be followed for HiveCommand etc.
"""
import sys
import string
from ConfigParser import SafeConfigParser
from qds_sdk.qubole import Qubole
from qds_sdk.commands import *
import boto
import time
# Used for generating file name to download the result
def get_random_filename(size=10):
return "/tmp/result_" + str(int(time.time())) + ".tsv"
# Returning content from the file
def get_content(filename):
with open(filename, 'r') as content_file:
content = content_file.read()
return content
# Executing given query
def execute_query(query):
if query is None or query == "":
return None
cmd = PrestoCommand.create(query=query)
query_id = str(cmd.id)
print "Starting Command with id: " + query_id + "\nProgress: =>",
while not PrestoCommand.is_done(cmd.status):
print "\b=>",
cmd = PrestoCommand.find(cmd.id)
time.sleep(5)
print cmd.get_log()
if PrestoCommand.is_success(cmd.status):
print "\nCommand Executed: Completed successfully"
else:
print "\nCommand Executed: Failed!!!. The status returned is: " + str(cmd.status)
return cmd
# Downloading the result
def get_results(command):
if command is None:
return None
filename = get_random_filename(10)
print filename
fp = open(filename, 'w')
command.get_results(fp, delim="\n")
print "Starting Result fetch with Command id: " + str(command.id) + "\nProgress: =>",
while not PrestoCommand.is_done(command.status):
print "\b=>",
time.sleep(5)
if PrestoCommand.is_success(command.status):
print "\nCommand Executed: Results fetch completed successfully"
else:
print "\nCommand Executed: Result fetch for original command " + str(command.id) + "Failed!!!. The status returned is: " + str(command.status)
fp.close()
content = get_content(filename)
return content
if __name__ == '__main__':
# Stting API token
Qubole.configure(api_token='<PASSWORD>')
get_results(execute_query("select * from default.cities limit 100;"))
|
"""
This is the sample code used for submitting a Presto query (PrestoCommand) and getting the result back to local file.
Similar way can be followed for HiveCommand etc.
"""
import sys
import string
from ConfigParser import SafeConfigParser
from qds_sdk.qubole import Qubole
from qds_sdk.commands import *
import boto
import time
# Used for generating file name to download the result
def get_random_filename(size=10):
return "/tmp/result_" + str(int(time.time())) + ".tsv"
# Returning content from the file
def get_content(filename):
with open(filename, 'r') as content_file:
content = content_file.read()
return content
# Executing given query
def execute_query(query):
if query is None or query == "":
return None
cmd = PrestoCommand.create(query=query)
query_id = str(cmd.id)
print "Starting Command with id: " + query_id + "\nProgress: =>",
while not PrestoCommand.is_done(cmd.status):
print "\b=>",
cmd = PrestoCommand.find(cmd.id)
time.sleep(5)
print cmd.get_log()
if PrestoCommand.is_success(cmd.status):
print "\nCommand Executed: Completed successfully"
else:
print "\nCommand Executed: Failed!!!. The status returned is: " + str(cmd.status)
return cmd
# Downloading the result
def get_results(command):
if command is None:
return None
filename = get_random_filename(10)
print filename
fp = open(filename, 'w')
command.get_results(fp, delim="\n")
print "Starting Result fetch with Command id: " + str(command.id) + "\nProgress: =>",
while not PrestoCommand.is_done(command.status):
print "\b=>",
time.sleep(5)
if PrestoCommand.is_success(command.status):
print "\nCommand Executed: Results fetch completed successfully"
else:
print "\nCommand Executed: Result fetch for original command " + str(command.id) + "Failed!!!. The status returned is: " + str(command.status)
fp.close()
content = get_content(filename)
return content
if __name__ == '__main__':
# Stting API token
Qubole.configure(api_token='<PASSWORD>')
get_results(execute_query("select * from default.cities limit 100;"))
| en | 0.764827 | This is the sample code used for submitting a Presto query (PrestoCommand) and getting the result back to local file. Similar way can be followed for HiveCommand etc. # Used for generating file name to download the result # Returning content from the file # Executing given query # Downloading the result # Stting API token | 2.686478 | 3 |
cloudseg/models/__init__.py | elrichgro/irccam-pmodwrc | 1 | 6613268 | <gh_stars>1-10
from cloudseg.models.unet.unet import UNet
from cloudseg.models.deeplab.deeplab import DeepLab
def get_model(name, **kwargs):
models = {"unet": UNet, "deeplab": DeepLab}
return models[name](**kwargs)
| from cloudseg.models.unet.unet import UNet
from cloudseg.models.deeplab.deeplab import DeepLab
def get_model(name, **kwargs):
models = {"unet": UNet, "deeplab": DeepLab}
return models[name](**kwargs) | none | 1 | 1.715789 | 2 | |
api/Payload/runner.py | DeltaMeter/compilebox | 0 | 6613269 | import sys
import subprocess
import os
from multiprocessing import Pool
from optparse import OptionParser
if not os.path.exists('results'):
os.makedirs('results')
errors = open('results/errors.txt', 'w')
interpreter = ''
def shellExec(filename):
print filename
testResult = subprocess.call([interpreter + " " + filename], stdout=subprocess.PIPE, stderr=errors, shell=True)
if testResult == 0:
return filename
return ""
def runPrograms(files, options):
#interpreted languages are already 'compiled',
compileResult = 0
if options.compiler:
compileResult = subprocess.call(options.compiler + ' ' + options.compileTargets, stdout=subprocess.PIPE, stderr=errors, shell=True)
print "Compile Result: %s" % compileResult
#compilation was successful or unnecessary, so now we run the code
if compileResult == 0:
testPool = Pool(processes=8)
tests = testPool.map_async(shellExec, files)
passedTests = filter((lambda x: x != ""), tests.get(timeout=1))
failedTests = filter((lambda x: x not in passedTests), files)
print "Passed " + ' '.join(passedTests)
print "Failed " + ' '.join(failedTests)
passedResults = open('results/passed.txt', 'w')
passedResults.write(' '.join(passedTests))
passedResults.close()
failedResults = open('results/failed.txt', 'w')
failedResults.write(' '.join(failedTests))
failedResults.close()
errors.close()
open('results/completed', 'w').close()
if __name__ == "__main__":
optParser = OptionParser()
optParser.add_option('-c', '--compiler', dest='compiler', help='compiler, i.e. javac')
optParser.add_option('-t', '--compileTargets', dest='compileTargets', help='compile targets, i.e., *.java')
optParser.add_option('-i', '--interpreter', dest='interpreter', help='interpreter, i.e. java')
(options, args) = optParser.parse_args()
interpreter = options.interpreter
runPrograms(args, options)
| import sys
import subprocess
import os
from multiprocessing import Pool
from optparse import OptionParser
if not os.path.exists('results'):
os.makedirs('results')
errors = open('results/errors.txt', 'w')
interpreter = ''
def shellExec(filename):
print filename
testResult = subprocess.call([interpreter + " " + filename], stdout=subprocess.PIPE, stderr=errors, shell=True)
if testResult == 0:
return filename
return ""
def runPrograms(files, options):
#interpreted languages are already 'compiled',
compileResult = 0
if options.compiler:
compileResult = subprocess.call(options.compiler + ' ' + options.compileTargets, stdout=subprocess.PIPE, stderr=errors, shell=True)
print "Compile Result: %s" % compileResult
#compilation was successful or unnecessary, so now we run the code
if compileResult == 0:
testPool = Pool(processes=8)
tests = testPool.map_async(shellExec, files)
passedTests = filter((lambda x: x != ""), tests.get(timeout=1))
failedTests = filter((lambda x: x not in passedTests), files)
print "Passed " + ' '.join(passedTests)
print "Failed " + ' '.join(failedTests)
passedResults = open('results/passed.txt', 'w')
passedResults.write(' '.join(passedTests))
passedResults.close()
failedResults = open('results/failed.txt', 'w')
failedResults.write(' '.join(failedTests))
failedResults.close()
errors.close()
open('results/completed', 'w').close()
if __name__ == "__main__":
optParser = OptionParser()
optParser.add_option('-c', '--compiler', dest='compiler', help='compiler, i.e. javac')
optParser.add_option('-t', '--compileTargets', dest='compileTargets', help='compile targets, i.e., *.java')
optParser.add_option('-i', '--interpreter', dest='interpreter', help='interpreter, i.e. java')
(options, args) = optParser.parse_args()
interpreter = options.interpreter
runPrograms(args, options)
| en | 0.850634 | #interpreted languages are already 'compiled', #compilation was successful or unnecessary, so now we run the code | 2.66102 | 3 |
SBCorpus-py/SBCorpus-py/reader.py | johnwdubois/rezonator_v2 | 3 | 6613270 | #!/usr/bin/python3
#from __future__ import print_function
#from xml import *
import SBCorpus
from SBCorpus.metadata import metadata
import re
class SBCorpusReader():
def __init__(self):
#self.corpus = SBCorpus.xmlreader()
self.corpus = SBCorpus.corpus
self.positions={
'ID':0,
'NAME':1,
'GENDER':2,
'AGE':3,
'HOMETOWN':4,
'HOMESTATE':5,
'CURRENTSTATE':6,
'EDUCATION':7,
'YEARSEDUCATION':8,
'OCCUPATION':9,
'ETHNICITY':10,
'TEXTS':11
}
def copy_object(self, obj):
if type(obj) in (str,int,float):
output=obj
elif type(obj)==list:
output=[]
for item in obj:
output+=[self.copy_object(item)]
elif type(obj)==tuple:
output=[]
for item in obj:
output+=[self.copy_object(item)]
output=tuple(output)
elif type(obj)==dict:
output={}
for key in list(obj):
output[key]=self.copy_object(obj[key])
return output
def copy_part(self, output, source, text=None, turn=None, IU=None, word=None, tiers=None):
if text != None:
if turn != None:
if text not in output:
output[text] = {'name':source[text]['name']}
if IU != None:
if turn not in output[text]:
output[text][turn] = {'ID':source[text][turn]['ID']}
if word != None:
if IU not in output[text][turn]:
output[text][turn][IU] = {'start':source[text][turn][IU]['start'], 'end':source[text][turn][IU]['end']}
if tiers != None:
if word not in output[text][turn][IU]:
output[text][turn][IU][word] = {}
for tier in tiers:
if word not in output[text][turn][IU][word]:
output[text][turn][IU][word][tier] = self.copy_object(source[text][turn][IU][word][tier])
elif tiers == None:
output[text][turn][IU][word] = self.copy_object(source[text][turn][IU][word])
elif word == None:
output[text][turn][IU] = self.copy_object(source[text][turn][IU])
elif IU == None:
output[text][turn] = self.copy_object(source[text][turn])
elif turn == None:
output[text] = self.copy_object(source[text])
elif text == None:
output = self.copy_object(source)
return output
def get_range(self, terms):
if ':' in terms:
nrange=[]
for i in range(int(terms.split(':')[0]),int(terms.split(':')[1])+1):
nrange.append(i)
terms = nrange
else:
if re.match('^\d*$', terms) == None:
terms = [terms]
else:
terms = [int(terms)]
return terms
def get_parameters(self, identifier, negative=False, capsearch=True):
identifiers = {}
remove = {}
if type(identifier) in [list,tuple]:
return identifier
elif type(identifier) in [int, float]:
return [identifier]
if ',' in identifier:
identifier = identifier.split(',')
elif type(identifier) == str:
identifier = [identifier]
for parameter in identifier:
if '!=' in parameter:
search,terms = parameter.split('!=')
if capsearch == True:
search = search.upper()
terms = self.get_range(terms)
if search not in remove:
remove[search] = terms
else:
remove[search] += terms
elif '=' in parameter:
search,terms = parameter.split('=')
if capsearch == True:
search = search.upper()
terms = self.get_range(terms)
if search not in identifiers:
identifiers[search] = terms
else:
identifiers[search] += terms
else:
if 'generic' not in identifiers:
identifiers['generic'] = self.get_range(parameter)
else:
identifiers['generic'] += self.get_range(parameter)
if 'generic' in identifiers:
return identifiers['generic']
else:
if negative == True:
return identifiers, remove
else:
return identifiers
def generator(self, extract, level):
output = []
for text in extract:
if type(text) == int:
if level == 'text':
output += [text]
else:
for turn in extract[text]:
if type(turn) == int:
if level == 'turn':
output += [(text, turn)]
else:
for IU in extract[text][turn]:
if type(IU) == int:
if level == 'IU':
output += [(text, turn, IU)]
else:
for word in extract[text][turn][IU]:
if type(word) == int:
if level == 'word':
output += [(text, turn, IU, word)]
output.sort()
return output
def getParticipants(self, identifier='all', info='all'):
if identifier != 'all':
identifiers,remove = self.get_parameters(identifier, negative=True)
containing = []
outtakes=[]
for key in identifiers:
for participant in metadata:
for term in identifiers[key]:
pos = participant[self.positions[key]]
if type(pos) in [int,float,str]:
if term == pos:
containing.append(participant)
elif type(pos) in [list,tuple]:
if term in pos:
containing.append(participant)
else:
outtakes.append(participant)
output = []
for participant in containing:
for key in remove:
for term in remove[key]:
pos = participant[self.positions[key]]
if type(pos) in [int,float,str]:
if term == pos:
outtakes.append(participant)
elif type(pos) in [list,tuple]:
if term in pos:
outtakes.append(participant)
else:
output=[]
outtakes=[]
containing = self.copy_object(metadata)
for participant in containing:
if participant not in outtakes:
output += [participant]
if info == 'all':
return output
else:
output = [x[self.positions[info]] for x in output]
newoutput = []
for element in output:
if type(element) == list:
newoutput += [e for e in element if e not in newoutput]
elif element not in newoutput:
newoutput += [element]
return newoutput
def printParticipants(self, identifier):
participants = self.getParticipants(identifier, info='all')
output = []
for key in self.positions:
output.append(key)
for participant in participants:
for key in output:
value = participant[self.positions[key]]
if type(value) in [int,float]:
value = str(value)
elif type(value) == list:
value = ', '.join([str(v) for v in value])
print (key+': '+value)
print()
def format_time(self, flt, decimal=3):
output = str(flt)
if decimal > 0:
if '.' not in output:
output += '.'
before = output.split('.')[0]
after = output.split('.')[1]
zeros = decimal - len(after)
if zeros >= 0:
output += '0'*zeros
return output
elif zeros < 0:
if int(after[zeros]) < 5:
return output[:zeros]
else:
after = str(int(after[:zeros])+1)
if len(after) > decimal:
return str(int(before)+1) + '.' + after[1:]
else:
return before + '.' + after
def getTexts(self, subset=None, textlist='all', participants='all'):
if subset == None:
subset = self.copy_object(self.corpus)
output = {}
if textlist != 'all':
textlist = self.get_parameters(textlist)
else:
textlist = [i for i in range(1,61)]
if participants == 'all':
ppl = self.getParticipants(info='ID')
ppl = self.getParticipants(participants, 'TEXTS')
for txt in textlist:
if txt in ppl and txt in subset:
output[txt] = self.copy_object(subset[txt])
return output
def getTurns(self, subset=None, textlist='all', turnlist='all', IUlist='all', participants='all', containing='any', afterTurn='any', beforeTurn='any', offset=0, before='any', after='any', at='any', minlength='any', maxlength='any'):
subset = self.getTexts(subset=subset, textlist=textlist, participants=participants)
if (containing,IUlist) != ('any','all'):
IUs = self.getIUs(subset=subset, IUlist=IUlist, containing=containing)
else:
IUs=subset
list(IUs)
turns = {}
for text,turn in self.generator(IUs, 'turn'):
turns = self.copy_part(turns, subset, text, turn)
output = {}
if afterTurn != 'any':
turns_after = {}
for text,turn in self.generator(afterTurn, 'turn'):
if text in turns:
if turn+1 in turns[text]:
turns_after = self.copy_part(turns_after, turns, text, turn+1)
turns = turns_after
del turns_after
if beforeTurn != 'any':
turns_before = {}
for text,turn in self.generator(beforeTurn, 'turn'):
if text in turns:
if turn-1 in turns[text]:
turns_before = self.copy_part(turns_before, turns, text, turn-1)
turns = turns_before
del turns_before
IDs = [0] + self.getParticipants(participants, 'ID')
if turnlist != 'all':
turnlist = self.get_parameters(turnlist)
for text,turn in self.generator(turns,'turn'):
accept = True
if turn+offset in turns[text]:
if turns[text][turn]['ID'] not in IDs:
accept = False
if turnlist != 'all':
if turn + offset not in turnlist:
accept = False
ius = [iu for iu in turns[text][turn+offset] if (type(iu) == int)]
if ius != []:
start = turns[text][turn+offset][min(ius)]['start']
end = turns[text][turn+offset][max(ius)]['end']
if type(maxlength) in [int, float]:
if end - start > maxlength:
accept = False
if type(minlength) in [int, float]:
if end - start < minlength:
accept = False
if type(at) in [int, float]:
if at < start or at > end:
accept = False
if type(after) in [int, float]:
if start < after:
accept = False
if type(before) in [int, float]:
if end > before:
accept = False
elif (maxlength,minlength,at,after,before) != ('any','any','any','any','any'):
accept = False
print(ius)
else:
accept = False
if accept == True:
output = self.copy_part(output,turns,text,turn+offset)
return output
def getIUs(self, subset=None, textlist='all', turnlist='all', IUlist='all', participants='all', containing='any', after='any', at='any', before='any', maxlength='any', minlength='any'):
subset = self.getTexts(subset=subset,participants=participants,textlist=textlist)
if (turnlist,participants) != ('any','all'):
subset = self.getTurns(subset, turnlist=turnlist, participants=participants)
IUs = {}
if containing != 'any':
words = self.getWords(subset=subset,containing=containing)
for text,turn,IU in self.generator(words,'IU'):
IUs = self.copy_part(IUs, subset, text, turn, IU)
subset = IUs
del IUs
output={}
if IUlist != 'all':
IUlist = self.get_parameters(IUlist)
for text,turn,IU in self.generator(subset, 'IU'):
iu = subset[text][turn][IU]
accept = True
if IUlist != 'all':
if IU not in IUlist:
accept = False
if type(maxlength) in [int, float]:
if iu['end'] - iu['start'] > maxlength:
accept = False
if type(minlength) in [int, float]:
if iu['end'] - iu['start'] < minlength:
accept = False
if type(at) in [int, float]:
if at < iu['start'] or at > iu['end']:
accept = False
if type(after) in [int, float]:
if iu['start'] < after:
accept = False
if type(before) in [int, float]:
if iu['end'] > before:
accept = False
if accept == True:
output = self.copy_part(output, subset, text, turn, IU)
return output
def getWords(self, subset=None, textlist='all', turnlist='all', IUlist='all', participants='all', containing='all', tier='dt', aslist=False, unit='word', fromstart='any', fromend='any'):
output = {}
subset = self.getIUs(subset=subset, textlist=textlist, turnlist=turnlist, IUlist=IUlist, participants=participants)
if containing != 'all':
containing,remove = self.get_parameters(containing, negative=True)
for text,turn,IU,word in self.generator(subset, 'word'):
accept = True
if type(fromstart) == int:
if word > fromstart:
accept = False
if type(fromend) == int:
for i in range(0,fromend):
if word+i not in subset[text][turn][IU]:
accept = False
if containing != 'all':
for search in containing:
for term in containing[search]:
if search.lower() in ['dt','word']:
if term[0:2] == "r'" and term[-1] == "'":
if re.match(term[2:-1],subset[text][turn][IU][word][search.lower()]) == None:
accept = False
elif term != subset[text][turn][IU][word][search.lower()]:
accept = False
elif search.lower() == 'manner':
if term not in subset[text][turn][IU][word]['manner'] and term.upper() not in subset[text][turn][IU][word]['manner']:
accept = False
elif search == 'POS':
if term != subset[text][turn][IU][word]['POS']:
accept = False
for search in remove:
for term in remove[search]:
if search.lower() in ['dt','word']:
if term[0:2] == "r'" and term[-1] == "'":
if re.match(term[2:-1],subset[text][turn][IU][word][search.lower()]) != None:
accept = False
elif term == subset[text][turn][IU][word][search.lower()]:
accept = False
elif search.lower() == 'manner':
if term not in subset[text][turn][IU][word]['manner'] and term.upper() not in subset[text][turn][IU][word]['manner']:
accept = False
elif search == 'POS':
if term != subset[text][turn][IU][word]['POS']:
accept = False
if accept == True:
output = self.copy_part(output, subset, text, turn, IU, word)
if aslist == True:
if unit == 'IU':
output = self.listWords(output, tier=tier, IUs=True)
elif unit == 'word':
output = self.listWords(output, tier=tier, IUs=False)
return output
def combineSubsets(self, excerpt1, excerpt2=None):
if type(excerpt1) == list:
output = self.copy_object(excerpt1[0])
for subset in excerpt1[1:]:
for text,turn,IU,word in self.generator(subset, 'word'):
output = self.copy_part(output,subset,text,turn,IU,word)
elif type(excerpt1) == dict and excerpt2 != None:
output = self.copy_object(excerpt1)
for text,turn,IU,word in self.generator(excerpt2, 'word'):
output = self.copy_part(output,excerpt2,text,turn,IU,word)
return output
def getWindow(self, subset='all', castunit='IU', outputunit='IU', size=10, shift=5):
output = {}
complete = [0]
if subset == 'all':
subset = self.getTexts()
if castunit in ['millisecond', 'ms']:
size = float(size)/1000.000
shift = float(shift)/1000.000
minus = 0.001
castunit = 's'
if outputunit == 'word':
print('No data for timing of words. Returning output in IUs.')
return self.getWindow(subset=subset,castunit=castunit,outputunit='IU',size=size,shift=shift)
elif castunit in ['second', 's']:
minus = 1.000
castunit = 's'
if outputunit == 'word':
print('No data for timing of words. Returning output in IUs.')
return self.getWindow(subset=subset,castunit=castunit,outputunit='IU',size=size,shift=shift)
elif castunit in ['minute', 'm']:
size = float(size)*60.000
shift = float(shift)*60.000
minus = 60.000
castunit = 's'
if outputunit == 'word':
print('No data for timing of words. Returning output in IUs.')
return self.getWindow(subset=subset,castunit=castunit,outputunit='IU',size=size,shift=shift)
elif castunit == 'word':
if outputunit == 'IU':
words = self.getWindow(subset=subset, castunit=castunit, outputunit='word', size=size, shift=shift)
for window in range(0,len(words)):
if window+1 not in output:
output[window+1] = {}
for text,turn,iu in self.generator(words[window], 'IU'):
output[window+1] = self.copy_part(output[window+1], subset, text, turn, iu)
elif outputunit == 'turn':
words = self.getWindows(subset=subset, castunit=castunit, outputunit='word', size=size, shift=shift)
for window in range(0,len(words)):
if window+1 not in output:
output[window+1] = {}
for text,turn in self.generator(words[window], 'turn'):
output[window+1] = self.copy_part(output[window+1], subset, text, turn)
elif castunit == 'IU':
if outputunit == 'turn':
IUs = self.getWindows(subset=subset, castunit=castunit, outputunit='IU', size=size, shift=shift)
for window in range(0,len(IUs)):
if window+1 not in output:
output[window+1] = {}
for text,turn in self.generator(IUs[window], 'turn'):
output[window+1] = self.copy_part(output[window+1], subset, text, turn)
if outputunit == 'word':
return self.getWindow(subset=subset, castunit=castunit, outputunit='IU', size=size, shift=shift)
elif castunit == 'turn':
if outputunit in ('IU','word'):
return self.getWindow(subset=subset, castunit=castunit, outputunit='turn', size=size, shift=shift)
point = size
for text in self.generator(subset,'text'):
if outputunit == 'word':
if castunit == 'word':
complete = [0]
wordno=0
for txt,trn,iu,wd in self.generator({text:subset[text]},'word'):
end = False
i = max(complete)+1
while end == False:
number = size*2
if i not in output:
output[i] = {}
isize = len(self.listWords(words=output[i], IUs=False))
point = size + ((i-1) * shift)
if number == isize or wordno > size + ((i-1) * shift) + size:
complete.append(i)
elif point + size >= wordno and point - size <= wordno:
output[i] = self.copy_part(output[i], subset, text, trn, iu, wd)
else:
end = True
i+=1
wordno += 1
elif outputunit == 'IU':
i = 1
end = False
if castunit == 'IU':
while end == False:
minIU = str(point - size)
maxIU = str(point + size - 1)
IUrange = ':'.join([minIU,maxIU])
window = self.getIUs(subset=subset,textlist=text,IUlist=IUrange)
if window == {}:
end = True
else:
output[i] = window
i+=1
point += shift
elif castunit == 's':
while end == False:
minIU = point - size
maxIU = point + size - minus
window = self.combineSubsets([self.getIUs(subset=subset,textlist=text,after=minIU,before=maxIU),
self.getIUs(subset=subset,textlist=text,at=minIU),
self.getIUs(subset=subset,textlist=text,at=maxIU)])
if window == {}:
end = True
else:
output[i] = window
i+=1
point += shift
elif outputunit == 'turn':
i = 1
end = False
if castunit == 's':
while end == False:
minturn = point - size
maxturn = point + size - minus
window = self.combineSubsets([self.getTurns(subset=subset,textlist=text,after=minturn,before=maxturn),
self.getTurns(subset=subset,textlist=text,at=minturn),
self.getTurns(subset=subset,textlist=text,at=maxturn)])
if window == {}:
end = True
else:
output[i] = window
i+=1
point += shift
return [output[window] for window in output if output[window] != {}]
def printSubset(self, subset, title=True, tier='dt', timestamps=True, labels=True, numberedlines=False, decimal=3):
output = ''
for text in subset:
output += '\n'
header = subset[text]['name']+' ('
turns = [t for t in list(subset[text]) if (type(t) == int)]
turns.sort()
turn1 = min(turns)
IUlist = [iu for iu in list(subset[text][turn1]) if (type(iu) == int)]
IUlist.sort()
IU1 = min(IUlist)
lstturn = max(turns)
IUlist = [iu for iu in list(subset[text][lstturn]) if (type(iu) == int)]
lstIU = max(IUlist)
header += self.format_time(subset[text][turn1][IU1]['start'], decimal) + ' - ' + self.format_time(subset[text][lstturn][lstIU]['end'], decimal) + ')\n'
if title == True:
output += header
for turn in turns:
IUlist = [i for i in subset[text][turn] if (type(i) == int)]
IUlist.sort()
label = subset[text][turn]['ID']
if label == 0:
label = 'OTHER'
else:
label = self.getParticipants('ID='+str(label),'NAME')[-1]
IU1 = min(IUlist)
turn_start = self.format_time(subset[text][turn][IU1]['start'], decimal)
turn_end = self.format_time(subset[text][turn][IU1]['end'], decimal)
if numberedlines == True:
output += str(IU1)+'\t'
if timestamps == True:
output += turn_start + '\t' + turn_end + '\t'
if labels == True:
output += label + ';\t'
IUtext = []
words = [wd for wd in list(subset[text][turn][IU1]) if (type(wd) == int)]
words.sort()
for word in words:
IUtext += [subset[text][turn][IU1][word][tier]]
output += ' '.join(IUtext) + '\n'
if len(IUlist) > 1:
for IU in IUlist[1:]:
IUtext = []
turn_start = self.format_time(subset[text][turn][IU]['start'], decimal)
turn_end = self.format_time(subset[text][turn][IU]['end'], decimal)
if numberedlines == True:
output += str(IU)+'\t'
if timestamps == True:
output += turn_start + '\t' + turn_end + '\t'
if labels == True:
output += '\t'
IUtext = []
words = [wd for wd in list(subset[text][turn][IU]) if (type(wd) == int)]
words.sort()
for word in words:
IUtext += [subset[text][turn][IU][word][tier]]
for word in IUtext:
pass
output += ' '.join(IUtext) + '\n'
print(output)
def listWords(self, words='all', tier='dt', IUs=True):
wordlist=[]
IUlist=[]
if words == 'all':
words == getTexts()
prevIU = int
for text,turn,IU,word in self.generator(words,'word'):
if IUs == True:
if prevIU != IU:
if prevIU != int:
wordlist += [[IUlist]]
IUlist = []
IUlist += [words[text][turn][IU][word][tier]]
prevIU = IU
else:
wordlist += [words[text][turn][IU][word][tier]]
if IUlist != []:
wordlist += IUlist
return wordlist
| #!/usr/bin/python3
#from __future__ import print_function
#from xml import *
import SBCorpus
from SBCorpus.metadata import metadata
import re
class SBCorpusReader():
def __init__(self):
#self.corpus = SBCorpus.xmlreader()
self.corpus = SBCorpus.corpus
self.positions={
'ID':0,
'NAME':1,
'GENDER':2,
'AGE':3,
'HOMETOWN':4,
'HOMESTATE':5,
'CURRENTSTATE':6,
'EDUCATION':7,
'YEARSEDUCATION':8,
'OCCUPATION':9,
'ETHNICITY':10,
'TEXTS':11
}
def copy_object(self, obj):
if type(obj) in (str,int,float):
output=obj
elif type(obj)==list:
output=[]
for item in obj:
output+=[self.copy_object(item)]
elif type(obj)==tuple:
output=[]
for item in obj:
output+=[self.copy_object(item)]
output=tuple(output)
elif type(obj)==dict:
output={}
for key in list(obj):
output[key]=self.copy_object(obj[key])
return output
def copy_part(self, output, source, text=None, turn=None, IU=None, word=None, tiers=None):
if text != None:
if turn != None:
if text not in output:
output[text] = {'name':source[text]['name']}
if IU != None:
if turn not in output[text]:
output[text][turn] = {'ID':source[text][turn]['ID']}
if word != None:
if IU not in output[text][turn]:
output[text][turn][IU] = {'start':source[text][turn][IU]['start'], 'end':source[text][turn][IU]['end']}
if tiers != None:
if word not in output[text][turn][IU]:
output[text][turn][IU][word] = {}
for tier in tiers:
if word not in output[text][turn][IU][word]:
output[text][turn][IU][word][tier] = self.copy_object(source[text][turn][IU][word][tier])
elif tiers == None:
output[text][turn][IU][word] = self.copy_object(source[text][turn][IU][word])
elif word == None:
output[text][turn][IU] = self.copy_object(source[text][turn][IU])
elif IU == None:
output[text][turn] = self.copy_object(source[text][turn])
elif turn == None:
output[text] = self.copy_object(source[text])
elif text == None:
output = self.copy_object(source)
return output
def get_range(self, terms):
if ':' in terms:
nrange=[]
for i in range(int(terms.split(':')[0]),int(terms.split(':')[1])+1):
nrange.append(i)
terms = nrange
else:
if re.match('^\d*$', terms) == None:
terms = [terms]
else:
terms = [int(terms)]
return terms
def get_parameters(self, identifier, negative=False, capsearch=True):
identifiers = {}
remove = {}
if type(identifier) in [list,tuple]:
return identifier
elif type(identifier) in [int, float]:
return [identifier]
if ',' in identifier:
identifier = identifier.split(',')
elif type(identifier) == str:
identifier = [identifier]
for parameter in identifier:
if '!=' in parameter:
search,terms = parameter.split('!=')
if capsearch == True:
search = search.upper()
terms = self.get_range(terms)
if search not in remove:
remove[search] = terms
else:
remove[search] += terms
elif '=' in parameter:
search,terms = parameter.split('=')
if capsearch == True:
search = search.upper()
terms = self.get_range(terms)
if search not in identifiers:
identifiers[search] = terms
else:
identifiers[search] += terms
else:
if 'generic' not in identifiers:
identifiers['generic'] = self.get_range(parameter)
else:
identifiers['generic'] += self.get_range(parameter)
if 'generic' in identifiers:
return identifiers['generic']
else:
if negative == True:
return identifiers, remove
else:
return identifiers
def generator(self, extract, level):
output = []
for text in extract:
if type(text) == int:
if level == 'text':
output += [text]
else:
for turn in extract[text]:
if type(turn) == int:
if level == 'turn':
output += [(text, turn)]
else:
for IU in extract[text][turn]:
if type(IU) == int:
if level == 'IU':
output += [(text, turn, IU)]
else:
for word in extract[text][turn][IU]:
if type(word) == int:
if level == 'word':
output += [(text, turn, IU, word)]
output.sort()
return output
def getParticipants(self, identifier='all', info='all'):
if identifier != 'all':
identifiers,remove = self.get_parameters(identifier, negative=True)
containing = []
outtakes=[]
for key in identifiers:
for participant in metadata:
for term in identifiers[key]:
pos = participant[self.positions[key]]
if type(pos) in [int,float,str]:
if term == pos:
containing.append(participant)
elif type(pos) in [list,tuple]:
if term in pos:
containing.append(participant)
else:
outtakes.append(participant)
output = []
for participant in containing:
for key in remove:
for term in remove[key]:
pos = participant[self.positions[key]]
if type(pos) in [int,float,str]:
if term == pos:
outtakes.append(participant)
elif type(pos) in [list,tuple]:
if term in pos:
outtakes.append(participant)
else:
output=[]
outtakes=[]
containing = self.copy_object(metadata)
for participant in containing:
if participant not in outtakes:
output += [participant]
if info == 'all':
return output
else:
output = [x[self.positions[info]] for x in output]
newoutput = []
for element in output:
if type(element) == list:
newoutput += [e for e in element if e not in newoutput]
elif element not in newoutput:
newoutput += [element]
return newoutput
def printParticipants(self, identifier):
participants = self.getParticipants(identifier, info='all')
output = []
for key in self.positions:
output.append(key)
for participant in participants:
for key in output:
value = participant[self.positions[key]]
if type(value) in [int,float]:
value = str(value)
elif type(value) == list:
value = ', '.join([str(v) for v in value])
print (key+': '+value)
print()
def format_time(self, flt, decimal=3):
output = str(flt)
if decimal > 0:
if '.' not in output:
output += '.'
before = output.split('.')[0]
after = output.split('.')[1]
zeros = decimal - len(after)
if zeros >= 0:
output += '0'*zeros
return output
elif zeros < 0:
if int(after[zeros]) < 5:
return output[:zeros]
else:
after = str(int(after[:zeros])+1)
if len(after) > decimal:
return str(int(before)+1) + '.' + after[1:]
else:
return before + '.' + after
def getTexts(self, subset=None, textlist='all', participants='all'):
if subset == None:
subset = self.copy_object(self.corpus)
output = {}
if textlist != 'all':
textlist = self.get_parameters(textlist)
else:
textlist = [i for i in range(1,61)]
if participants == 'all':
ppl = self.getParticipants(info='ID')
ppl = self.getParticipants(participants, 'TEXTS')
for txt in textlist:
if txt in ppl and txt in subset:
output[txt] = self.copy_object(subset[txt])
return output
def getTurns(self, subset=None, textlist='all', turnlist='all', IUlist='all', participants='all', containing='any', afterTurn='any', beforeTurn='any', offset=0, before='any', after='any', at='any', minlength='any', maxlength='any'):
subset = self.getTexts(subset=subset, textlist=textlist, participants=participants)
if (containing,IUlist) != ('any','all'):
IUs = self.getIUs(subset=subset, IUlist=IUlist, containing=containing)
else:
IUs=subset
list(IUs)
turns = {}
for text,turn in self.generator(IUs, 'turn'):
turns = self.copy_part(turns, subset, text, turn)
output = {}
if afterTurn != 'any':
turns_after = {}
for text,turn in self.generator(afterTurn, 'turn'):
if text in turns:
if turn+1 in turns[text]:
turns_after = self.copy_part(turns_after, turns, text, turn+1)
turns = turns_after
del turns_after
if beforeTurn != 'any':
turns_before = {}
for text,turn in self.generator(beforeTurn, 'turn'):
if text in turns:
if turn-1 in turns[text]:
turns_before = self.copy_part(turns_before, turns, text, turn-1)
turns = turns_before
del turns_before
IDs = [0] + self.getParticipants(participants, 'ID')
if turnlist != 'all':
turnlist = self.get_parameters(turnlist)
for text,turn in self.generator(turns,'turn'):
accept = True
if turn+offset in turns[text]:
if turns[text][turn]['ID'] not in IDs:
accept = False
if turnlist != 'all':
if turn + offset not in turnlist:
accept = False
ius = [iu for iu in turns[text][turn+offset] if (type(iu) == int)]
if ius != []:
start = turns[text][turn+offset][min(ius)]['start']
end = turns[text][turn+offset][max(ius)]['end']
if type(maxlength) in [int, float]:
if end - start > maxlength:
accept = False
if type(minlength) in [int, float]:
if end - start < minlength:
accept = False
if type(at) in [int, float]:
if at < start or at > end:
accept = False
if type(after) in [int, float]:
if start < after:
accept = False
if type(before) in [int, float]:
if end > before:
accept = False
elif (maxlength,minlength,at,after,before) != ('any','any','any','any','any'):
accept = False
print(ius)
else:
accept = False
if accept == True:
output = self.copy_part(output,turns,text,turn+offset)
return output
def getIUs(self, subset=None, textlist='all', turnlist='all', IUlist='all', participants='all', containing='any', after='any', at='any', before='any', maxlength='any', minlength='any'):
subset = self.getTexts(subset=subset,participants=participants,textlist=textlist)
if (turnlist,participants) != ('any','all'):
subset = self.getTurns(subset, turnlist=turnlist, participants=participants)
IUs = {}
if containing != 'any':
words = self.getWords(subset=subset,containing=containing)
for text,turn,IU in self.generator(words,'IU'):
IUs = self.copy_part(IUs, subset, text, turn, IU)
subset = IUs
del IUs
output={}
if IUlist != 'all':
IUlist = self.get_parameters(IUlist)
for text,turn,IU in self.generator(subset, 'IU'):
iu = subset[text][turn][IU]
accept = True
if IUlist != 'all':
if IU not in IUlist:
accept = False
if type(maxlength) in [int, float]:
if iu['end'] - iu['start'] > maxlength:
accept = False
if type(minlength) in [int, float]:
if iu['end'] - iu['start'] < minlength:
accept = False
if type(at) in [int, float]:
if at < iu['start'] or at > iu['end']:
accept = False
if type(after) in [int, float]:
if iu['start'] < after:
accept = False
if type(before) in [int, float]:
if iu['end'] > before:
accept = False
if accept == True:
output = self.copy_part(output, subset, text, turn, IU)
return output
def getWords(self, subset=None, textlist='all', turnlist='all', IUlist='all', participants='all', containing='all', tier='dt', aslist=False, unit='word', fromstart='any', fromend='any'):
output = {}
subset = self.getIUs(subset=subset, textlist=textlist, turnlist=turnlist, IUlist=IUlist, participants=participants)
if containing != 'all':
containing,remove = self.get_parameters(containing, negative=True)
for text,turn,IU,word in self.generator(subset, 'word'):
accept = True
if type(fromstart) == int:
if word > fromstart:
accept = False
if type(fromend) == int:
for i in range(0,fromend):
if word+i not in subset[text][turn][IU]:
accept = False
if containing != 'all':
for search in containing:
for term in containing[search]:
if search.lower() in ['dt','word']:
if term[0:2] == "r'" and term[-1] == "'":
if re.match(term[2:-1],subset[text][turn][IU][word][search.lower()]) == None:
accept = False
elif term != subset[text][turn][IU][word][search.lower()]:
accept = False
elif search.lower() == 'manner':
if term not in subset[text][turn][IU][word]['manner'] and term.upper() not in subset[text][turn][IU][word]['manner']:
accept = False
elif search == 'POS':
if term != subset[text][turn][IU][word]['POS']:
accept = False
for search in remove:
for term in remove[search]:
if search.lower() in ['dt','word']:
if term[0:2] == "r'" and term[-1] == "'":
if re.match(term[2:-1],subset[text][turn][IU][word][search.lower()]) != None:
accept = False
elif term == subset[text][turn][IU][word][search.lower()]:
accept = False
elif search.lower() == 'manner':
if term not in subset[text][turn][IU][word]['manner'] and term.upper() not in subset[text][turn][IU][word]['manner']:
accept = False
elif search == 'POS':
if term != subset[text][turn][IU][word]['POS']:
accept = False
if accept == True:
output = self.copy_part(output, subset, text, turn, IU, word)
if aslist == True:
if unit == 'IU':
output = self.listWords(output, tier=tier, IUs=True)
elif unit == 'word':
output = self.listWords(output, tier=tier, IUs=False)
return output
def combineSubsets(self, excerpt1, excerpt2=None):
if type(excerpt1) == list:
output = self.copy_object(excerpt1[0])
for subset in excerpt1[1:]:
for text,turn,IU,word in self.generator(subset, 'word'):
output = self.copy_part(output,subset,text,turn,IU,word)
elif type(excerpt1) == dict and excerpt2 != None:
output = self.copy_object(excerpt1)
for text,turn,IU,word in self.generator(excerpt2, 'word'):
output = self.copy_part(output,excerpt2,text,turn,IU,word)
return output
def getWindow(self, subset='all', castunit='IU', outputunit='IU', size=10, shift=5):
output = {}
complete = [0]
if subset == 'all':
subset = self.getTexts()
if castunit in ['millisecond', 'ms']:
size = float(size)/1000.000
shift = float(shift)/1000.000
minus = 0.001
castunit = 's'
if outputunit == 'word':
print('No data for timing of words. Returning output in IUs.')
return self.getWindow(subset=subset,castunit=castunit,outputunit='IU',size=size,shift=shift)
elif castunit in ['second', 's']:
minus = 1.000
castunit = 's'
if outputunit == 'word':
print('No data for timing of words. Returning output in IUs.')
return self.getWindow(subset=subset,castunit=castunit,outputunit='IU',size=size,shift=shift)
elif castunit in ['minute', 'm']:
size = float(size)*60.000
shift = float(shift)*60.000
minus = 60.000
castunit = 's'
if outputunit == 'word':
print('No data for timing of words. Returning output in IUs.')
return self.getWindow(subset=subset,castunit=castunit,outputunit='IU',size=size,shift=shift)
elif castunit == 'word':
if outputunit == 'IU':
words = self.getWindow(subset=subset, castunit=castunit, outputunit='word', size=size, shift=shift)
for window in range(0,len(words)):
if window+1 not in output:
output[window+1] = {}
for text,turn,iu in self.generator(words[window], 'IU'):
output[window+1] = self.copy_part(output[window+1], subset, text, turn, iu)
elif outputunit == 'turn':
words = self.getWindows(subset=subset, castunit=castunit, outputunit='word', size=size, shift=shift)
for window in range(0,len(words)):
if window+1 not in output:
output[window+1] = {}
for text,turn in self.generator(words[window], 'turn'):
output[window+1] = self.copy_part(output[window+1], subset, text, turn)
elif castunit == 'IU':
if outputunit == 'turn':
IUs = self.getWindows(subset=subset, castunit=castunit, outputunit='IU', size=size, shift=shift)
for window in range(0,len(IUs)):
if window+1 not in output:
output[window+1] = {}
for text,turn in self.generator(IUs[window], 'turn'):
output[window+1] = self.copy_part(output[window+1], subset, text, turn)
if outputunit == 'word':
return self.getWindow(subset=subset, castunit=castunit, outputunit='IU', size=size, shift=shift)
elif castunit == 'turn':
if outputunit in ('IU','word'):
return self.getWindow(subset=subset, castunit=castunit, outputunit='turn', size=size, shift=shift)
point = size
for text in self.generator(subset,'text'):
if outputunit == 'word':
if castunit == 'word':
complete = [0]
wordno=0
for txt,trn,iu,wd in self.generator({text:subset[text]},'word'):
end = False
i = max(complete)+1
while end == False:
number = size*2
if i not in output:
output[i] = {}
isize = len(self.listWords(words=output[i], IUs=False))
point = size + ((i-1) * shift)
if number == isize or wordno > size + ((i-1) * shift) + size:
complete.append(i)
elif point + size >= wordno and point - size <= wordno:
output[i] = self.copy_part(output[i], subset, text, trn, iu, wd)
else:
end = True
i+=1
wordno += 1
elif outputunit == 'IU':
i = 1
end = False
if castunit == 'IU':
while end == False:
minIU = str(point - size)
maxIU = str(point + size - 1)
IUrange = ':'.join([minIU,maxIU])
window = self.getIUs(subset=subset,textlist=text,IUlist=IUrange)
if window == {}:
end = True
else:
output[i] = window
i+=1
point += shift
elif castunit == 's':
while end == False:
minIU = point - size
maxIU = point + size - minus
window = self.combineSubsets([self.getIUs(subset=subset,textlist=text,after=minIU,before=maxIU),
self.getIUs(subset=subset,textlist=text,at=minIU),
self.getIUs(subset=subset,textlist=text,at=maxIU)])
if window == {}:
end = True
else:
output[i] = window
i+=1
point += shift
elif outputunit == 'turn':
i = 1
end = False
if castunit == 's':
while end == False:
minturn = point - size
maxturn = point + size - minus
window = self.combineSubsets([self.getTurns(subset=subset,textlist=text,after=minturn,before=maxturn),
self.getTurns(subset=subset,textlist=text,at=minturn),
self.getTurns(subset=subset,textlist=text,at=maxturn)])
if window == {}:
end = True
else:
output[i] = window
i+=1
point += shift
return [output[window] for window in output if output[window] != {}]
def printSubset(self, subset, title=True, tier='dt', timestamps=True, labels=True, numberedlines=False, decimal=3):
output = ''
for text in subset:
output += '\n'
header = subset[text]['name']+' ('
turns = [t for t in list(subset[text]) if (type(t) == int)]
turns.sort()
turn1 = min(turns)
IUlist = [iu for iu in list(subset[text][turn1]) if (type(iu) == int)]
IUlist.sort()
IU1 = min(IUlist)
lstturn = max(turns)
IUlist = [iu for iu in list(subset[text][lstturn]) if (type(iu) == int)]
lstIU = max(IUlist)
header += self.format_time(subset[text][turn1][IU1]['start'], decimal) + ' - ' + self.format_time(subset[text][lstturn][lstIU]['end'], decimal) + ')\n'
if title == True:
output += header
for turn in turns:
IUlist = [i for i in subset[text][turn] if (type(i) == int)]
IUlist.sort()
label = subset[text][turn]['ID']
if label == 0:
label = 'OTHER'
else:
label = self.getParticipants('ID='+str(label),'NAME')[-1]
IU1 = min(IUlist)
turn_start = self.format_time(subset[text][turn][IU1]['start'], decimal)
turn_end = self.format_time(subset[text][turn][IU1]['end'], decimal)
if numberedlines == True:
output += str(IU1)+'\t'
if timestamps == True:
output += turn_start + '\t' + turn_end + '\t'
if labels == True:
output += label + ';\t'
IUtext = []
words = [wd for wd in list(subset[text][turn][IU1]) if (type(wd) == int)]
words.sort()
for word in words:
IUtext += [subset[text][turn][IU1][word][tier]]
output += ' '.join(IUtext) + '\n'
if len(IUlist) > 1:
for IU in IUlist[1:]:
IUtext = []
turn_start = self.format_time(subset[text][turn][IU]['start'], decimal)
turn_end = self.format_time(subset[text][turn][IU]['end'], decimal)
if numberedlines == True:
output += str(IU)+'\t'
if timestamps == True:
output += turn_start + '\t' + turn_end + '\t'
if labels == True:
output += '\t'
IUtext = []
words = [wd for wd in list(subset[text][turn][IU]) if (type(wd) == int)]
words.sort()
for word in words:
IUtext += [subset[text][turn][IU][word][tier]]
for word in IUtext:
pass
output += ' '.join(IUtext) + '\n'
print(output)
def listWords(self, words='all', tier='dt', IUs=True):
wordlist=[]
IUlist=[]
if words == 'all':
words == getTexts()
prevIU = int
for text,turn,IU,word in self.generator(words,'word'):
if IUs == True:
if prevIU != IU:
if prevIU != int:
wordlist += [[IUlist]]
IUlist = []
IUlist += [words[text][turn][IU][word][tier]]
prevIU = IU
else:
wordlist += [words[text][turn][IU][word][tier]]
if IUlist != []:
wordlist += IUlist
return wordlist
| en | 0.164391 | #!/usr/bin/python3 #from __future__ import print_function #from xml import * #self.corpus = SBCorpus.xmlreader() | 2.741103 | 3 |
DetectorSendDataSrc/ConfigManager.py | KeenHausOrg/DetectorSendData | 0 | 6613271 | <reponame>KeenHausOrg/DetectorSendData<gh_stars>0
import json
class ConfigManager:
TWILIO_ACCOUNT_SID = ""
TWILIO_SECRET = ""
TWILIO_APIKEY = ""
TWILIO_RECEIVERNUMBER = ""
TWILIO_SENDERNUMBER = ""
IMGUR_BEARERTOKEN = ""
IMGUR_APIURL = ""
def __init__(self):
with open('ChrisSecrets.json') as f:
data = json.load(f)
self.TWILIO_ACCOUNT_SID = data['twilio']['AccountSID']
self.TWILIO_SECRET = data['twilio']['Secret']
self.TWILIO_APIKEY = data['twilio']['APIKey']
self.TWILIO_RECEIVERNUMBER = data['twilio']['ReceiverPhone']
self.TWILIO_SENDERNUMBER = data['twilio']['SenderPhone']
self.IMGUR_APIURL = data['imgur']['ApiUrl']
self.IMGUR_BEARERTOKEN = data['imgur']['BearerToken']
| import json
class ConfigManager:
TWILIO_ACCOUNT_SID = ""
TWILIO_SECRET = ""
TWILIO_APIKEY = ""
TWILIO_RECEIVERNUMBER = ""
TWILIO_SENDERNUMBER = ""
IMGUR_BEARERTOKEN = ""
IMGUR_APIURL = ""
def __init__(self):
with open('ChrisSecrets.json') as f:
data = json.load(f)
self.TWILIO_ACCOUNT_SID = data['twilio']['AccountSID']
self.TWILIO_SECRET = data['twilio']['Secret']
self.TWILIO_APIKEY = data['twilio']['APIKey']
self.TWILIO_RECEIVERNUMBER = data['twilio']['ReceiverPhone']
self.TWILIO_SENDERNUMBER = data['twilio']['SenderPhone']
self.IMGUR_APIURL = data['imgur']['ApiUrl']
self.IMGUR_BEARERTOKEN = data['imgur']['BearerToken'] | none | 1 | 2.744715 | 3 | |
flatcoke/settings/base.py | flatcoke/django-structure | 6 | 6613272 | """
Django settings for flatcoke project.
Generated by 'django-admin startproject' using Django 2.0.1.
For more information on this file, see
https://docs.djangoproject.com/en/2.0/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.0/ref/settings/
"""
import os
import datetime
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = os.environ.get('SECRET_KEY', '<KEY>')
# SECURITY WARNING: don't run with debug turned on in production!
ALLOWED_HOSTS = ["*"]
INTERNAL_IPS = ['127.0.0.1']
STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'django_extensions',
'debug_toolbar',
'api',
'api.users',
'api.posts',
'rest_framework', # For rest api
'rest_framework_swagger', # Doc
'cacheops', # ORM cache
'corsheaders', # for CORS
# https://github.com/celery/django-celery-results/issues/19
'django_celery_results', # Worker result (It doesn't work)
'django_celery_beat', # Worker scheduler
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'corsheaders.middleware.CorsMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
'debug_toolbar.middleware.DebugToolbarMiddleware',
'api.middleware.Response403To401Middleware'
]
ROOT_URLCONF = 'flatcoke.urls'
AUTH_USER_MODEL = 'users.User'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'flatcoke.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.0/ref/settings/#databases
db_user = os.environ.get('DB_USER', 'cola')
db_password = os.environ.get('DB_PASSWORD', '<PASSWORD>')
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.mysql',
'NAME': os.environ.get('DATABASE', 'flatcoke'),
'USER': db_user,
'PASSWORD': <PASSWORD>,
'HOST': os.environ.get('DB_HOST', '127.0.0.1'),
'PORT': os.environ.get('DB_PORT', '3306'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': ('django.contrib.auth.password_validation.'
'UserAttributeSimilarityValidator'),
},
{
'NAME': ('django.contrib.auth.password_validation.'
'MinimumLengthValidator'),
},
{
'NAME': ('django.contrib.auth.password_validation.'
'CommonPasswordValidator'),
},
{
'NAME': ('django.contrib.auth.password_validation.'
'NumericPasswordValidator'),
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.0/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.0/howto/static-files/
STATIC_URL = '/static/'
LOGGING = {
'version': 1,
'filters': {
'require_debug_true': {
'()': 'django.utils.log.RequireDebugTrue',
}
},
'handlers': {
'console': {
'level': 'DEBUG',
'filters': ['require_debug_true'],
'class': 'logging.StreamHandler',
}
},
'loggers': {
'django.db.backends': {
'level': 'DEBUG',
'handlers': ['console'],
}
}
}
REST_FRAMEWORK = {
'DEFAULT_PAGINATION_CLASS': ('rest_framework.pagination.'
'PageNumberPagination'),
'PAGE_SIZE': 10,
'DEFAULT_PERMISSION_CLASSES': (
'rest_framework.permissions.IsAuthenticated',
),
'DEFAULT_AUTHENTICATION_CLASSES': (
'rest_framework_jwt.authentication.JSONWebTokenAuthentication',
'rest_framework.authentication.SessionAuthentication',
'rest_framework.authentication.BasicAuthentication',
),
}
SWAGGER_SETTINGS = {
'SECURITY_DEFINITIONS': {
'api_key': {
'type': 'apiKey',
'in': 'header',
'name': 'Authorization'
}
},
'USE_SESSION_AUTH': True,
'LOGIN_URL': 'rest_framework:login',
'LOGOUT_URL': 'rest_framework:logout',
}
SHELL_PLUS_PRINT_SQL = True # commend shell_plug with logging sql
SITE_ID = 1
CACHEOPS_REDIS = {
'host': os.environ.get('REDIS_HOST', 'localhost'),
'port': os.environ.get('REDIS_PORT', '6379'),
'db': os.environ.get('REDIS_DB', 0),
}
CACHEOPS_DEFAULTS = {
'timeout': 60 * 60
}
CACHEOPS = {
'users.user': {'ops': 'get', 'timeout': 60 * 15},
'*.*': {},
}
JWT_AUTH = {
'JWT_ENCODE_HANDLER':
'rest_framework_jwt.utils.jwt_encode_handler',
'JWT_DECODE_HANDLER':
'rest_framework_jwt.utils.jwt_decode_handler',
'JWT_PAYLOAD_HANDLER':
'rest_framework_jwt.utils.jwt_payload_handler',
'JWT_PAYLOAD_GET_USER_ID_HANDLER':
'rest_framework_jwt.utils.jwt_get_user_id_from_payload_handler',
'JWT_RESPONSE_PAYLOAD_HANDLER':
'rest_framework_jwt.utils.jwt_response_payload_handler',
'JWT_SECRET_KEY': os.environ.get('SECRET_KEY', 'secret'),
'JWT_GET_USER_SECRET_KEY': None,
'JWT_PUBLIC_KEY': None,
'JWT_PRIVATE_KEY': None,
'JWT_ALGORITHM': 'HS256',
'JWT_VERIFY': True,
'JWT_VERIFY_EXPIRATION': True,
'JWT_LEEWAY': 0,
'JWT_EXPIRATION_DELTA': datetime.timedelta(seconds=7200),
'JWT_AUDIENCE': None,
'JWT_ISSUER': None,
'JWT_ALLOW_REFRESH': True,
'JWT_REFRESH_EXPIRATION_DELTA': datetime.timedelta(days=7),
'JWT_AUTH_HEADER_PREFIX': 'JWT',
'JWT_AUTH_COOKIE': None,
}
CELERY_BROKER_URL = os.environ.get('CELERY_BROKER_URL',
'redis://redis:6379/0')
CELERY_RESULT_BACKEND = 'django-db'
CELERY_ACCEPT_CONTENT = ['application/json']
CELERY_TASK_SERIALIZER = 'json'
CELERY_RESULT_SERIALIZER = 'json'
CELERY_TIMEZONE = 'Asia/Seoul'
CORS_ORIGIN_ALLOW_ALL = True
| """
Django settings for flatcoke project.
Generated by 'django-admin startproject' using Django 2.0.1.
For more information on this file, see
https://docs.djangoproject.com/en/2.0/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.0/ref/settings/
"""
import os
import datetime
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = os.environ.get('SECRET_KEY', '<KEY>')
# SECURITY WARNING: don't run with debug turned on in production!
ALLOWED_HOSTS = ["*"]
INTERNAL_IPS = ['127.0.0.1']
STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'django_extensions',
'debug_toolbar',
'api',
'api.users',
'api.posts',
'rest_framework', # For rest api
'rest_framework_swagger', # Doc
'cacheops', # ORM cache
'corsheaders', # for CORS
# https://github.com/celery/django-celery-results/issues/19
'django_celery_results', # Worker result (It doesn't work)
'django_celery_beat', # Worker scheduler
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'corsheaders.middleware.CorsMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
'debug_toolbar.middleware.DebugToolbarMiddleware',
'api.middleware.Response403To401Middleware'
]
ROOT_URLCONF = 'flatcoke.urls'
AUTH_USER_MODEL = 'users.User'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'flatcoke.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.0/ref/settings/#databases
db_user = os.environ.get('DB_USER', 'cola')
db_password = os.environ.get('DB_PASSWORD', '<PASSWORD>')
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.mysql',
'NAME': os.environ.get('DATABASE', 'flatcoke'),
'USER': db_user,
'PASSWORD': <PASSWORD>,
'HOST': os.environ.get('DB_HOST', '127.0.0.1'),
'PORT': os.environ.get('DB_PORT', '3306'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': ('django.contrib.auth.password_validation.'
'UserAttributeSimilarityValidator'),
},
{
'NAME': ('django.contrib.auth.password_validation.'
'MinimumLengthValidator'),
},
{
'NAME': ('django.contrib.auth.password_validation.'
'CommonPasswordValidator'),
},
{
'NAME': ('django.contrib.auth.password_validation.'
'NumericPasswordValidator'),
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.0/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.0/howto/static-files/
STATIC_URL = '/static/'
LOGGING = {
'version': 1,
'filters': {
'require_debug_true': {
'()': 'django.utils.log.RequireDebugTrue',
}
},
'handlers': {
'console': {
'level': 'DEBUG',
'filters': ['require_debug_true'],
'class': 'logging.StreamHandler',
}
},
'loggers': {
'django.db.backends': {
'level': 'DEBUG',
'handlers': ['console'],
}
}
}
REST_FRAMEWORK = {
'DEFAULT_PAGINATION_CLASS': ('rest_framework.pagination.'
'PageNumberPagination'),
'PAGE_SIZE': 10,
'DEFAULT_PERMISSION_CLASSES': (
'rest_framework.permissions.IsAuthenticated',
),
'DEFAULT_AUTHENTICATION_CLASSES': (
'rest_framework_jwt.authentication.JSONWebTokenAuthentication',
'rest_framework.authentication.SessionAuthentication',
'rest_framework.authentication.BasicAuthentication',
),
}
SWAGGER_SETTINGS = {
'SECURITY_DEFINITIONS': {
'api_key': {
'type': 'apiKey',
'in': 'header',
'name': 'Authorization'
}
},
'USE_SESSION_AUTH': True,
'LOGIN_URL': 'rest_framework:login',
'LOGOUT_URL': 'rest_framework:logout',
}
SHELL_PLUS_PRINT_SQL = True # commend shell_plug with logging sql
SITE_ID = 1
CACHEOPS_REDIS = {
'host': os.environ.get('REDIS_HOST', 'localhost'),
'port': os.environ.get('REDIS_PORT', '6379'),
'db': os.environ.get('REDIS_DB', 0),
}
CACHEOPS_DEFAULTS = {
'timeout': 60 * 60
}
CACHEOPS = {
'users.user': {'ops': 'get', 'timeout': 60 * 15},
'*.*': {},
}
JWT_AUTH = {
'JWT_ENCODE_HANDLER':
'rest_framework_jwt.utils.jwt_encode_handler',
'JWT_DECODE_HANDLER':
'rest_framework_jwt.utils.jwt_decode_handler',
'JWT_PAYLOAD_HANDLER':
'rest_framework_jwt.utils.jwt_payload_handler',
'JWT_PAYLOAD_GET_USER_ID_HANDLER':
'rest_framework_jwt.utils.jwt_get_user_id_from_payload_handler',
'JWT_RESPONSE_PAYLOAD_HANDLER':
'rest_framework_jwt.utils.jwt_response_payload_handler',
'JWT_SECRET_KEY': os.environ.get('SECRET_KEY', 'secret'),
'JWT_GET_USER_SECRET_KEY': None,
'JWT_PUBLIC_KEY': None,
'JWT_PRIVATE_KEY': None,
'JWT_ALGORITHM': 'HS256',
'JWT_VERIFY': True,
'JWT_VERIFY_EXPIRATION': True,
'JWT_LEEWAY': 0,
'JWT_EXPIRATION_DELTA': datetime.timedelta(seconds=7200),
'JWT_AUDIENCE': None,
'JWT_ISSUER': None,
'JWT_ALLOW_REFRESH': True,
'JWT_REFRESH_EXPIRATION_DELTA': datetime.timedelta(days=7),
'JWT_AUTH_HEADER_PREFIX': 'JWT',
'JWT_AUTH_COOKIE': None,
}
CELERY_BROKER_URL = os.environ.get('CELERY_BROKER_URL',
'redis://redis:6379/0')
CELERY_RESULT_BACKEND = 'django-db'
CELERY_ACCEPT_CONTENT = ['application/json']
CELERY_TASK_SERIALIZER = 'json'
CELERY_RESULT_SERIALIZER = 'json'
CELERY_TIMEZONE = 'Asia/Seoul'
CORS_ORIGIN_ALLOW_ALL = True
| en | 0.661927 | Django settings for flatcoke project. Generated by 'django-admin startproject' using Django 2.0.1. For more information on this file, see https://docs.djangoproject.com/en/2.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.0/ref/settings/ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! # SECURITY WARNING: don't run with debug turned on in production! # Application definition # For rest api # Doc # ORM cache # for CORS # https://github.com/celery/django-celery-results/issues/19 # Worker result (It doesn't work) # Worker scheduler # Database # https://docs.djangoproject.com/en/2.0/ref/settings/#databases # Password validation # https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators # Internationalization # https://docs.djangoproject.com/en/2.0/topics/i18n/ # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.0/howto/static-files/ # commend shell_plug with logging sql | 1.743474 | 2 |
hihope_neptune-oh_hid/00_src/v0.1/device/winnermicro/neptune/w800_adapter/copy_library.py | dawmlight/vendor_oh_fun | 1 | 6613273 | <gh_stars>1-10
#! usr/bin/env python
import sys
import shutil
import os
import argparse
def main():
parser = argparse.ArgumentParser(description='Copy prebuilt library to out dir.')
parser.add_argument('--source', help='Source file')
parser.add_argument('--target', help='Target file')
args = parser.parse_args()
source = args.source
target = args.target
if (source or target) is None:
print("Source file or taget file is None.")
return -1
print("source is {}".format(source))
print("target is {}".format(target))
try:
shutil.copyfile(source, target)
except Exception as e:
print("Copy {} to {} failed: {}".format(source, target, e))
return -1
else:
return
if __name__ == "__main__":
sys.exit(main())
| #! usr/bin/env python
import sys
import shutil
import os
import argparse
def main():
parser = argparse.ArgumentParser(description='Copy prebuilt library to out dir.')
parser.add_argument('--source', help='Source file')
parser.add_argument('--target', help='Target file')
args = parser.parse_args()
source = args.source
target = args.target
if (source or target) is None:
print("Source file or taget file is None.")
return -1
print("source is {}".format(source))
print("target is {}".format(target))
try:
shutil.copyfile(source, target)
except Exception as e:
print("Copy {} to {} failed: {}".format(source, target, e))
return -1
else:
return
if __name__ == "__main__":
sys.exit(main()) | hu | 0.132117 | #! usr/bin/env python | 2.745389 | 3 |
auths/email.py | horsehair/unimate | 0 | 6613274 | <gh_stars>0
def message(domain, uidb, token):
return f"아래 링크를 클릭하면 학교 이메일 인증이 완료됩니다.\n\n회원가입 링크 : http://{domain}/activate/{uidb}/{token}\n\n감사합니다."
| def message(domain, uidb, token):
return f"아래 링크를 클릭하면 학교 이메일 인증이 완료됩니다.\n\n회원가입 링크 : http://{domain}/activate/{uidb}/{token}\n\n감사합니다." | none | 1 | 1.401092 | 1 | |
examples/example-plugin/ExampleWidget.py | physimals/quantiphyse | 9 | 6613275 | <reponame>physimals/quantiphyse
"""
Quantiphyse - Example processing widget
Copyright (c) 2013-2018 University of Oxford
"""
from __future__ import division, unicode_literals, absolute_import, print_function
import sys, os, warnings
import time
import traceback
import re
import tempfile
import nibabel as nib
import numpy as np
import pyqtgraph as pg
from PySide import QtCore, QtGui
from quantiphyse.utils import QpException, debug
from quantiphyse.gui.widgets import QpWidget, BatchButton, HelpButton
DESC = """This is an example of how to create a plugin widget"""
class ExamplePluginWidget(QpWidget):
def __init__(self, **kwargs):
super(ExamplePluginWidget, self).__init__(name="Example Plugin",
desc="An example plugin",
group="Examples", **kwargs)
def init_ui(self):
layout = QtWidgets.QVBoxLayout()
self.setLayout(layout)
hbox = QtWidgets.QHBoxLayout()
hbox.addWidget(QtWidgets.QLabel('<font size="5">Example Plugin Widget</font>'))
hbox.addStretch(1)
hbox.addWidget(BatchButton(self))
hbox.addWidget(HelpButton(self, "example_plugin"))
layout.addLayout(hbox)
desc = QtWidgets.QLabel(DESC)
desc.setWordWrap(True)
layout.addWidget(desc)
layout.addWidget(QtWidgets.QLabel(""))
def activate(self):
self.ivm.sig_all_data.connect(self.data_changed)
def deactivate(self):
self.ivm.sig_all_data.disconnect(self.data_changed)
def data_changed(self, data):
pass
def batch_options(self):
return "ExamplePluginProcess", {}
class ExampleWidget(QpWidget):
"""
Widget for setting a threshold to the image data inside the ROI. This is saved as data
"""
def __init__(self, **kwargs):
super(ExampleWidget, self).__init__(name="Threshold", desc="Threshold data", icon="quantiphyse", **kwargs)
def init_ui(self):
main_vbox = QtWidgets.QVBoxLayout()
hbox = QtWidgets.QHBoxLayout()
hbox.addWidget(QtWidgets.QLabel('<font size="5">Threshold volume</font>'))
hbox.addStretch(1)
hbox.addWidget(HelpButton(self))
main_vbox.addLayout(hbox)
explanation = QtWidgets.QLabel('This is a basic example of a \n'
'widget for development purposes. \n'
'A DCE-MRI image and ROI are \n'
'loaded normally and clicking run \n'
'creates a data set which only shows values \n'
'in the ROI above a defined threshold.')
main_vbox.addWidget(explanation)
hbox = QtWidgets.QHBoxLayout()
self.b1 = QtWidgets.QPushButton('Process', self)
self.b1.clicked.connect(self.run_threshold)
hbox.addWidget(self.b1)
hbox.addStretch(1)
hbox.addWidget(QtWidgets.QLabel('ROI threshold value:'))
self.val_t1 = QtWidgets.QLineEdit('1', self)
hbox.addWidget(self.val_t1)
main_vbox.addLayout(hbox)
hbox = QtWidgets.QHBoxLayout()
hbox.addStretch(1)
hbox.addWidget(QtWidgets.QLabel('Slice to threshold:'))
self.val_s1 = QtWidgets.QLineEdit('0', self)
hbox.addWidget(self.val_s1)
main_vbox.addLayout(hbox)
main_vbox.addStretch(1)
self.setLayout(main_vbox)
def run_threshold(self):
# Check if an image and roi exists or otherwise throw an error
if self.ivm.vol is None:
raise QpException("No data loaded")
if self.ivm.current_roi is None:
raise QpException("No ROI loaded")
slice = int(self.val_s1.text())
thresh = float(self.val_t1.text())
img = self.ivm.vol[:, :, :, slice1]
img = img * (self.ivm.current_roi > 0)
img[img1 < thresh] = 0
self.ivm.add_data(img, name='thresh', make_current=True)
| """
Quantiphyse - Example processing widget
Copyright (c) 2013-2018 University of Oxford
"""
from __future__ import division, unicode_literals, absolute_import, print_function
import sys, os, warnings
import time
import traceback
import re
import tempfile
import nibabel as nib
import numpy as np
import pyqtgraph as pg
from PySide import QtCore, QtGui
from quantiphyse.utils import QpException, debug
from quantiphyse.gui.widgets import QpWidget, BatchButton, HelpButton
DESC = """This is an example of how to create a plugin widget"""
class ExamplePluginWidget(QpWidget):
def __init__(self, **kwargs):
super(ExamplePluginWidget, self).__init__(name="Example Plugin",
desc="An example plugin",
group="Examples", **kwargs)
def init_ui(self):
layout = QtWidgets.QVBoxLayout()
self.setLayout(layout)
hbox = QtWidgets.QHBoxLayout()
hbox.addWidget(QtWidgets.QLabel('<font size="5">Example Plugin Widget</font>'))
hbox.addStretch(1)
hbox.addWidget(BatchButton(self))
hbox.addWidget(HelpButton(self, "example_plugin"))
layout.addLayout(hbox)
desc = QtWidgets.QLabel(DESC)
desc.setWordWrap(True)
layout.addWidget(desc)
layout.addWidget(QtWidgets.QLabel(""))
def activate(self):
self.ivm.sig_all_data.connect(self.data_changed)
def deactivate(self):
self.ivm.sig_all_data.disconnect(self.data_changed)
def data_changed(self, data):
pass
def batch_options(self):
return "ExamplePluginProcess", {}
class ExampleWidget(QpWidget):
"""
Widget for setting a threshold to the image data inside the ROI. This is saved as data
"""
def __init__(self, **kwargs):
super(ExampleWidget, self).__init__(name="Threshold", desc="Threshold data", icon="quantiphyse", **kwargs)
def init_ui(self):
main_vbox = QtWidgets.QVBoxLayout()
hbox = QtWidgets.QHBoxLayout()
hbox.addWidget(QtWidgets.QLabel('<font size="5">Threshold volume</font>'))
hbox.addStretch(1)
hbox.addWidget(HelpButton(self))
main_vbox.addLayout(hbox)
explanation = QtWidgets.QLabel('This is a basic example of a \n'
'widget for development purposes. \n'
'A DCE-MRI image and ROI are \n'
'loaded normally and clicking run \n'
'creates a data set which only shows values \n'
'in the ROI above a defined threshold.')
main_vbox.addWidget(explanation)
hbox = QtWidgets.QHBoxLayout()
self.b1 = QtWidgets.QPushButton('Process', self)
self.b1.clicked.connect(self.run_threshold)
hbox.addWidget(self.b1)
hbox.addStretch(1)
hbox.addWidget(QtWidgets.QLabel('ROI threshold value:'))
self.val_t1 = QtWidgets.QLineEdit('1', self)
hbox.addWidget(self.val_t1)
main_vbox.addLayout(hbox)
hbox = QtWidgets.QHBoxLayout()
hbox.addStretch(1)
hbox.addWidget(QtWidgets.QLabel('Slice to threshold:'))
self.val_s1 = QtWidgets.QLineEdit('0', self)
hbox.addWidget(self.val_s1)
main_vbox.addLayout(hbox)
main_vbox.addStretch(1)
self.setLayout(main_vbox)
def run_threshold(self):
# Check if an image and roi exists or otherwise throw an error
if self.ivm.vol is None:
raise QpException("No data loaded")
if self.ivm.current_roi is None:
raise QpException("No ROI loaded")
slice = int(self.val_s1.text())
thresh = float(self.val_t1.text())
img = self.ivm.vol[:, :, :, slice1]
img = img * (self.ivm.current_roi > 0)
img[img1 < thresh] = 0
self.ivm.add_data(img, name='thresh', make_current=True) | en | 0.725523 | Quantiphyse - Example processing widget Copyright (c) 2013-2018 University of Oxford This is an example of how to create a plugin widget Widget for setting a threshold to the image data inside the ROI. This is saved as data # Check if an image and roi exists or otherwise throw an error | 2.419493 | 2 |
src/nmdc/scripts/__main__.py | microbiomedata/pynmdc | 0 | 6613276 | <reponame>microbiomedata/pynmdc
"""
CLI tools for NMDC
"""
import click
import json
import jsonschema
from jsonschema import validate
from nmdc import __version__
from nmdc.scripts.gff2json import NMDCGFFLoader
@click.group(help=f"""NMDC Tools v{__version__}.""")
def nmdccli():
"""
NMDC command line tools.
"""
pass
@nmdccli.command()
@click.argument('gff', type=click.File('r'))
@click.option('-of',
help='output file name for genome feature json',
required=True,
type=click.File(mode='w'))
@click.option('-oa',
help='output file name for functioanl annotation json',
required=True,
type=click.File(mode='w'))
@click.option('-ai',
help='Activity index',
required=True,
type=str)
def gff2json(gff, of, oa, ai):
"""
Convert GFF3 to NMDC JSON format.
"""
INDENT = 2
converter = NMDCGFFLoader(gff, ai)
jobj = converter.model
features = []
annotations = []
for record in jobj.keys():
for feature in jobj[record].keys():
entry = jobj[record][feature]
features.append(entry['genome_feature_set'])
annotations.extend(list(entry['functional_annotation_set'].values()))
of.write(json.dumps({'genome_feature_set': features}, indent=INDENT))
oa.write(json.dumps({'functional_annotation_set': annotations}, indent=INDENT))
@nmdccli.command('validate')
@click.option('--schema', help='The NMDC metadata JSON Schema', required=True, type=click.Path())
@click.option('--file', help='The file with JSON data to validate', required=True, type=click.Path())
def validate_json(schema, file):
"""
Validate JSON as per NMDC Metadata schema.
"""
try:
validate(instance=json.load(open(file)), schema=json.load(open(schema)))
print("Given JSON data is valid")
except jsonschema.exceptions.ValidationError as err:
print(err)
print("Given JSON data is not valid")
if __name__ == '__main__':
nmdccli()
| """
CLI tools for NMDC
"""
import click
import json
import jsonschema
from jsonschema import validate
from nmdc import __version__
from nmdc.scripts.gff2json import NMDCGFFLoader
@click.group(help=f"""NMDC Tools v{__version__}.""")
def nmdccli():
"""
NMDC command line tools.
"""
pass
@nmdccli.command()
@click.argument('gff', type=click.File('r'))
@click.option('-of',
help='output file name for genome feature json',
required=True,
type=click.File(mode='w'))
@click.option('-oa',
help='output file name for functioanl annotation json',
required=True,
type=click.File(mode='w'))
@click.option('-ai',
help='Activity index',
required=True,
type=str)
def gff2json(gff, of, oa, ai):
"""
Convert GFF3 to NMDC JSON format.
"""
INDENT = 2
converter = NMDCGFFLoader(gff, ai)
jobj = converter.model
features = []
annotations = []
for record in jobj.keys():
for feature in jobj[record].keys():
entry = jobj[record][feature]
features.append(entry['genome_feature_set'])
annotations.extend(list(entry['functional_annotation_set'].values()))
of.write(json.dumps({'genome_feature_set': features}, indent=INDENT))
oa.write(json.dumps({'functional_annotation_set': annotations}, indent=INDENT))
@nmdccli.command('validate')
@click.option('--schema', help='The NMDC metadata JSON Schema', required=True, type=click.Path())
@click.option('--file', help='The file with JSON data to validate', required=True, type=click.Path())
def validate_json(schema, file):
"""
Validate JSON as per NMDC Metadata schema.
"""
try:
validate(instance=json.load(open(file)), schema=json.load(open(schema)))
print("Given JSON data is valid")
except jsonschema.exceptions.ValidationError as err:
print(err)
print("Given JSON data is not valid")
if __name__ == '__main__':
nmdccli() | en | 0.61103 | CLI tools for NMDC NMDC Tools v{__version__}. NMDC command line tools. Convert GFF3 to NMDC JSON format. Validate JSON as per NMDC Metadata schema. | 2.395115 | 2 |
modu/tests/slurm-test-shell.py | zamalchi/script-runner-bottle-app | 0 | 6613277 | <gh_stars>0
#!/usr/bin/env python
# from __future__ import print_function
import code
import readline
# context provides the slurm module
# from context import slurm
import modu.slurm as slurm
import modu.color_printer as cp
print("----------------------------------------------------------------------")
cp.printWarn("import modu.slurm as slurm")
cp.printWarn("import modu.color_printer as cp")
cp.printWarn("states = slurm.Slurm.getNonEmptyStates()")
print("----------------------------------------------------------------------")
states = slurm.Slurm.getNonEmptyStates()
vars = globals().copy()
vars.update(locals())
shell = code.InteractiveConsole(vars)
shell.interact()
| #!/usr/bin/env python
# from __future__ import print_function
import code
import readline
# context provides the slurm module
# from context import slurm
import modu.slurm as slurm
import modu.color_printer as cp
print("----------------------------------------------------------------------")
cp.printWarn("import modu.slurm as slurm")
cp.printWarn("import modu.color_printer as cp")
cp.printWarn("states = slurm.Slurm.getNonEmptyStates()")
print("----------------------------------------------------------------------")
states = slurm.Slurm.getNonEmptyStates()
vars = globals().copy()
vars.update(locals())
shell = code.InteractiveConsole(vars)
shell.interact() | en | 0.308232 | #!/usr/bin/env python # from __future__ import print_function # context provides the slurm module # from context import slurm | 2.205076 | 2 |
dscore/plot.py | thodson-usgs/dscore | 1 | 6613278 | import matplotlib.pyplot as plt
import seaborn as sns
tick_label_size = 9
def ilamb_card(scores, vmin=0, vmax=100,
component_labels=None,
score_label=None,
cmap='RdYlBu',
reverse_cmap=False,
hlines=None,
cbar=False,
annot_kws={"size": 8},
ax=None):
"""
Parameters
----------
scores : pandas.DataFrame
DataFrame with rows for each component
vmin : float
vmax : float
score_labels : list
hlines : list
rows to place horizontal lines
"""
if ax is None:
fig, ax = plt.subplots(1)
if component_labels is None:
component_labels = scores.index
# if score_label is None and hasattr(scores, 'name'):
# score_label = scores.name
g = sns.heatmap(scores.round().astype(int),
vmin=vmin,
vmax=vmax,
yticklabels=component_labels,
annot=True,
fmt='d',
annot_kws=annot_kws,
cmap=cmap,
cbar=cbar,
square=True,
ax=ax)
ax.set_xticklabels(
ax.get_xticklabels(),
rotation=90)
if hlines is not None:
ax.hlines(hlines, *ax.get_xlim(), colors='k')
ax.xaxis.set_ticks_position('top')
ax.xaxis.set_label_position('top')
ax.set_xlabel(score_label)
ax.set_ylabel('Component')
ax.tick_params(axis='y', labelsize=tick_label_size)
ax.tick_params(axis='x', labelsize=tick_label_size)
return g
def multi_panel_card(score_cards,
score_labels,
component_labels=None,
hlines=None,
figsize=None,
tight_layout=True):
"""Helper function for plotting multiple score cards with the same components
"""
fig, ax = plt.subplots(1, len(score_cards), figsize=figsize)
fig.subplots_adjust(hspace=0)
for i, score in enumerate(score_cards):
ilamb_card(score,
score_label=score_labels[i],
hlines=hlines,
ax=ax[i])
#import pdb; pdb.set_trace()
if not ax[i].get_subplotspec().is_first_col():
ax[i].set_yticklabels([])
ax[i].tick_params(left=False)
ax[i].set_ylabel('')
fig.tight_layout()
return fig
| import matplotlib.pyplot as plt
import seaborn as sns
tick_label_size = 9
def ilamb_card(scores, vmin=0, vmax=100,
component_labels=None,
score_label=None,
cmap='RdYlBu',
reverse_cmap=False,
hlines=None,
cbar=False,
annot_kws={"size": 8},
ax=None):
"""
Parameters
----------
scores : pandas.DataFrame
DataFrame with rows for each component
vmin : float
vmax : float
score_labels : list
hlines : list
rows to place horizontal lines
"""
if ax is None:
fig, ax = plt.subplots(1)
if component_labels is None:
component_labels = scores.index
# if score_label is None and hasattr(scores, 'name'):
# score_label = scores.name
g = sns.heatmap(scores.round().astype(int),
vmin=vmin,
vmax=vmax,
yticklabels=component_labels,
annot=True,
fmt='d',
annot_kws=annot_kws,
cmap=cmap,
cbar=cbar,
square=True,
ax=ax)
ax.set_xticklabels(
ax.get_xticklabels(),
rotation=90)
if hlines is not None:
ax.hlines(hlines, *ax.get_xlim(), colors='k')
ax.xaxis.set_ticks_position('top')
ax.xaxis.set_label_position('top')
ax.set_xlabel(score_label)
ax.set_ylabel('Component')
ax.tick_params(axis='y', labelsize=tick_label_size)
ax.tick_params(axis='x', labelsize=tick_label_size)
return g
def multi_panel_card(score_cards,
score_labels,
component_labels=None,
hlines=None,
figsize=None,
tight_layout=True):
"""Helper function for plotting multiple score cards with the same components
"""
fig, ax = plt.subplots(1, len(score_cards), figsize=figsize)
fig.subplots_adjust(hspace=0)
for i, score in enumerate(score_cards):
ilamb_card(score,
score_label=score_labels[i],
hlines=hlines,
ax=ax[i])
#import pdb; pdb.set_trace()
if not ax[i].get_subplotspec().is_first_col():
ax[i].set_yticklabels([])
ax[i].tick_params(left=False)
ax[i].set_ylabel('')
fig.tight_layout()
return fig
| en | 0.496586 | Parameters ---------- scores : pandas.DataFrame DataFrame with rows for each component vmin : float vmax : float score_labels : list hlines : list rows to place horizontal lines # if score_label is None and hasattr(scores, 'name'): # score_label = scores.name Helper function for plotting multiple score cards with the same components #import pdb; pdb.set_trace() | 2.852977 | 3 |
Tornado/app.py | lowjack1/Bitmap-Tornado | 4 | 6613279 | __author__ = "lowjack"
import views
import tornado.ioloop
import tornado.web
import tornado.httpserver
from settings import port, static_path, template_path
class Application(tornado.web.Application):
def __init__(self):
urls = [
(r"/", views.MainHandler),
(r"/home", views.MainHandler),
(r"/contact", views.ContactHandler),
(r"/about", views.AboutusHandler),
(r"/more", views.MoreHandler),
(r"/search", views.SearchHandler),
]
setting = {
"template_path": template_path,
"static_path": static_path,
"xsrf_cookies": True
}
tornado.web.Application.__init__(self, urls, **setting)
def main():
app = Application()
http_server = tornado.httpserver.HTTPServer(app)
http_server.listen(port)
print("Starting development server at http://127.0.0.1:%d" %(port))
print("Quit the server with CONTROL-C.")
try:
tornado.ioloop.IOLoop.instance().start()
except KeyboardInterrupt:
print(" Server Closed.")
if __name__ == "__main__":
main()
| __author__ = "lowjack"
import views
import tornado.ioloop
import tornado.web
import tornado.httpserver
from settings import port, static_path, template_path
class Application(tornado.web.Application):
def __init__(self):
urls = [
(r"/", views.MainHandler),
(r"/home", views.MainHandler),
(r"/contact", views.ContactHandler),
(r"/about", views.AboutusHandler),
(r"/more", views.MoreHandler),
(r"/search", views.SearchHandler),
]
setting = {
"template_path": template_path,
"static_path": static_path,
"xsrf_cookies": True
}
tornado.web.Application.__init__(self, urls, **setting)
def main():
app = Application()
http_server = tornado.httpserver.HTTPServer(app)
http_server.listen(port)
print("Starting development server at http://127.0.0.1:%d" %(port))
print("Quit the server with CONTROL-C.")
try:
tornado.ioloop.IOLoop.instance().start()
except KeyboardInterrupt:
print(" Server Closed.")
if __name__ == "__main__":
main()
| none | 1 | 2.404387 | 2 | |
app.py | ezronis/fitmetrix-booking-tool | 0 | 6613280 | import requests
import browsercookie
import FitMetrixAPI as fmAPI
import utils
import data
import creds
from bs4 import BeautifulSoup
# grabbing cookies from chrome browser
cj = browsercookie.chrome()
s = requests.Session()
# setting cookies for this session
#s.cookies = cj
# logging in and printing title of html response
l = fmAPI.login(s, creds.username, creds.password)
print(utils.get_html_title(l.content))
# iterating through class names in classes array
for keys, values in data.classes.items():
# getting schedule
day = utils.get_vip_date(2018, 11, 12, keys)
sched = fmAPI.get_schedule(s, day, day)
'''
# creating record of schedule for requested date range
Html_file = open("html/sched_test" + (day) + ".html","w")
Html_file.write(str(sched.content))
Html_file.close()
'''
# validating "title" of schedule, <span id="wmrhDateCurr">October 31, 2018</span>
soup = BeautifulSoup(sched.content, 'html.parser')
print(soup.find(id="wmrhDateCurr").next_element)
# getting appointment ids for classes in sched.html
apptids = utils.get_apptids(str(sched.content))
print(str(apptids))
for c in values:
print('class: '+ c)
class_index = []
class_index = utils.get_class_index(str(sched.content), c)
print(class_index)
for i in class_index:
print('index of current class:' + str(i))
apptid = apptids[i]
print('apptid:' + str(apptid))
booking = fmAPI.book_spot1(s, apptid, data.spot)
print(utils.get_html_title(booking.content))
print('classes have been booked!') | import requests
import browsercookie
import FitMetrixAPI as fmAPI
import utils
import data
import creds
from bs4 import BeautifulSoup
# grabbing cookies from chrome browser
cj = browsercookie.chrome()
s = requests.Session()
# setting cookies for this session
#s.cookies = cj
# logging in and printing title of html response
l = fmAPI.login(s, creds.username, creds.password)
print(utils.get_html_title(l.content))
# iterating through class names in classes array
for keys, values in data.classes.items():
# getting schedule
day = utils.get_vip_date(2018, 11, 12, keys)
sched = fmAPI.get_schedule(s, day, day)
'''
# creating record of schedule for requested date range
Html_file = open("html/sched_test" + (day) + ".html","w")
Html_file.write(str(sched.content))
Html_file.close()
'''
# validating "title" of schedule, <span id="wmrhDateCurr">October 31, 2018</span>
soup = BeautifulSoup(sched.content, 'html.parser')
print(soup.find(id="wmrhDateCurr").next_element)
# getting appointment ids for classes in sched.html
apptids = utils.get_apptids(str(sched.content))
print(str(apptids))
for c in values:
print('class: '+ c)
class_index = []
class_index = utils.get_class_index(str(sched.content), c)
print(class_index)
for i in class_index:
print('index of current class:' + str(i))
apptid = apptids[i]
print('apptid:' + str(apptid))
booking = fmAPI.book_spot1(s, apptid, data.spot)
print(utils.get_html_title(booking.content))
print('classes have been booked!') | en | 0.779951 | # grabbing cookies from chrome browser # setting cookies for this session #s.cookies = cj # logging in and printing title of html response # iterating through class names in classes array # getting schedule # creating record of schedule for requested date range Html_file = open("html/sched_test" + (day) + ".html","w") Html_file.write(str(sched.content)) Html_file.close() # validating "title" of schedule, <span id="wmrhDateCurr">October 31, 2018</span> # getting appointment ids for classes in sched.html | 2.657457 | 3 |
setup.py | consbio/awsecret | 0 | 6613281 | <reponame>consbio/awsecret
from setuptools import setup
from awsecret import VERSION
setup(
name='awsecret',
description='Secure credential storage in S3.',
keywords='password,aws,s3,credentials,encryption',
version=VERSION,
packages=['awsecret'],
install_requires=['pycrypto', 'boto', 'click'],
url='https://github.com/consbio/awsecret',
license='BSD',
entry_points={
'console_scripts': ['awsec=awsecret.cli:main']
}
)
| from setuptools import setup
from awsecret import VERSION
setup(
name='awsecret',
description='Secure credential storage in S3.',
keywords='password,aws,s3,credentials,encryption',
version=VERSION,
packages=['awsecret'],
install_requires=['pycrypto', 'boto', 'click'],
url='https://github.com/consbio/awsecret',
license='BSD',
entry_points={
'console_scripts': ['awsec=awsecret.cli:main']
}
) | none | 1 | 1.472923 | 1 | |
antarest/eventbus/business/interfaces.py | AntaresSimulatorTeam/antaREST | 2 | 6613282 | <reponame>AntaresSimulatorTeam/antaREST
from abc import abstractmethod
from typing import List
from antarest.core.interfaces.eventbus import Event
class IEventBusBackend:
@abstractmethod
def push_event(self, event: Event) -> None:
pass
@abstractmethod
def get_events(self) -> List[Event]:
pass
@abstractmethod
def clear_events(self) -> None:
pass
| from abc import abstractmethod
from typing import List
from antarest.core.interfaces.eventbus import Event
class IEventBusBackend:
@abstractmethod
def push_event(self, event: Event) -> None:
pass
@abstractmethod
def get_events(self) -> List[Event]:
pass
@abstractmethod
def clear_events(self) -> None:
pass | none | 1 | 2.603846 | 3 | |
launcher.py | Benny84/discord-music-bot-modis | 0 | 6613283 | import modis
DISCORD_TOKEN = "<KEY>"
CLIENT_ID = "380719766471442433"
modis.console(
discord_token=DISCORD_TOKEN,
discord_client_id=CLIENT_ID
)
| import modis
DISCORD_TOKEN = "<KEY>"
CLIENT_ID = "380719766471442433"
modis.console(
discord_token=DISCORD_TOKEN,
discord_client_id=CLIENT_ID
)
| none | 1 | 1.380524 | 1 | |
bead/archive.py | krisztianfekete/lib | 1 | 6613284 | import os
import pathlib
import re
from cached_property import cached_property
from tracelog import TRACELOG
from .bead import UnpackableBead
from . import meta
from . import tech
from .ziparchive import ZipArchive
from .exceptions import InvalidArchive
persistence = tech.persistence
__all__ = ('Archive', 'InvalidArchive')
CACHE_CONTENT_ID = 'content_id'
CACHE_INPUT_MAP = 'input_map'
def _cached_zip_attribute(cache_key: str, ziparchive_attribute):
"""Make a cache accessor @property with a self.ziparchive.attribute fallback
raises InvalidArchive if the attribute is not cached
and the backing ziparchive is not valid.
"""
def maybe_cached_attr(self):
try:
return self.cache[cache_key]
except LookupError:
return getattr(self.ziparchive, ziparchive_attribute)
return property(maybe_cached_attr)
class Archive(UnpackableBead):
def __init__(self, filename, box_name=''):
self.archive_filename = filename
self.archive_path = pathlib.Path(filename)
self.box_name = box_name
self.name = bead_name_from_file_path(filename)
self.cache = {}
self.load_cache()
# Check that we can get access to metadata
# - either through the cache or through the archive
# The resulting archive can still be invalid and die unexpectedly later with
# InvalidArchive exception, as these are potentially cached values
self.meta_version
self.freeze_time
self.kind
def load_cache(self):
try:
try:
self.cache = persistence.loads(self.cache_path.read_text())
except persistence.ReadError:
TRACELOG(f"Ignoring existing, malformed bead meta cache {self.cache_path}")
except FileNotFoundError:
pass
def save_cache(self):
try:
self.cache_path.write_text(persistence.dumps(self.cache))
except FileNotFoundError:
pass
@property
def cache_path(self):
if self.archive_path.suffix != '.zip':
raise FileNotFoundError(f'Archive can not have cache {self.archive_path}')
return self.archive_path.with_suffix('.xmeta')
meta_version = _cached_zip_attribute(meta.META_VERSION, 'meta_version')
content_id = _cached_zip_attribute(CACHE_CONTENT_ID, 'content_id')
kind = _cached_zip_attribute(meta.KIND, 'kind')
freeze_time_str = _cached_zip_attribute(meta.FREEZE_TIME, 'freeze_time_str')
@property
def input_map(self):
try:
return self.cache[CACHE_INPUT_MAP]
except LookupError:
return self.ziparchive.input_map
@input_map.setter
def input_map(self, input_map):
self.cache[CACHE_INPUT_MAP] = input_map
self.save_cache()
@cached_property
def ziparchive(self):
ziparchive = ZipArchive(self.archive_filename, self.box_name)
self._check_and_populate_cache(ziparchive)
return ziparchive
def _check_and_populate_cache(self, ziparchive):
def ensure(cache_key, value):
try:
if self.cache[cache_key] != value:
raise InvalidArchive(
'Cache disagrees with zip meta', self.archive_filename, cache_key)
except KeyError:
self.cache[cache_key] = value
ensure(meta.META_VERSION, ziparchive.meta_version)
ensure(CACHE_CONTENT_ID, ziparchive.content_id)
ensure(meta.KIND, ziparchive.kind)
ensure(meta.FREEZE_TIME, ziparchive.freeze_time_str)
ensure(meta.INPUTS, ziparchive.meta[meta.INPUTS])
# need not match
self.cache.setdefault(CACHE_INPUT_MAP, ziparchive.input_map)
def validate(self):
self.ziparchive.validate()
@property
def inputs(self):
try:
return tuple(meta.parse_inputs({meta.INPUTS: self.cache[meta.INPUTS]}))
except LookupError:
return self.ziparchive.inputs
def extract_dir(self, zip_dir, fs_dir):
return self.ziparchive.extract_dir(zip_dir, fs_dir)
def extract_file(self, zip_path, fs_path):
return self.ziparchive.extract_file(zip_path, fs_path)
def unpack_code_to(self, fs_dir):
self.ziparchive.unpack_code_to(fs_dir)
def unpack_data_to(self, fs_dir):
self.ziparchive.unpack_data_to(fs_dir)
def unpack_meta_to(self, workspace):
workspace.meta = self.ziparchive.meta
workspace.input_map = self.input_map
def bead_name_from_file_path(path):
'''
Parse bead name from a file path.
Might return a simpler name than intended
'''
name_with_timestamp, ext = os.path.splitext(os.path.basename(path))
# assert ext == '.zip' # not enforced to allow having beads with different extensions
name = re.sub('_[0-9]{8}(?:[tT][-+0-9]*)?$', '', name_with_timestamp)
return name
assert 'bead-2015v3' == bead_name_from_file_path('bead-2015v3.zip')
assert 'bead-2015v3' == bead_name_from_file_path('bead-2015v3_20150923.zip')
assert 'bead-2015v3' == bead_name_from_file_path('bead-2015v3_20150923T010203012345+0200.zip')
assert 'bead-2015v3' == bead_name_from_file_path('bead-2015v3_20150923T010203012345-0200.zip')
assert 'bead-2015v3' == bead_name_from_file_path('path/to/bead-2015v3_20150923.zip')
| import os
import pathlib
import re
from cached_property import cached_property
from tracelog import TRACELOG
from .bead import UnpackableBead
from . import meta
from . import tech
from .ziparchive import ZipArchive
from .exceptions import InvalidArchive
persistence = tech.persistence
__all__ = ('Archive', 'InvalidArchive')
CACHE_CONTENT_ID = 'content_id'
CACHE_INPUT_MAP = 'input_map'
def _cached_zip_attribute(cache_key: str, ziparchive_attribute):
"""Make a cache accessor @property with a self.ziparchive.attribute fallback
raises InvalidArchive if the attribute is not cached
and the backing ziparchive is not valid.
"""
def maybe_cached_attr(self):
try:
return self.cache[cache_key]
except LookupError:
return getattr(self.ziparchive, ziparchive_attribute)
return property(maybe_cached_attr)
class Archive(UnpackableBead):
def __init__(self, filename, box_name=''):
self.archive_filename = filename
self.archive_path = pathlib.Path(filename)
self.box_name = box_name
self.name = bead_name_from_file_path(filename)
self.cache = {}
self.load_cache()
# Check that we can get access to metadata
# - either through the cache or through the archive
# The resulting archive can still be invalid and die unexpectedly later with
# InvalidArchive exception, as these are potentially cached values
self.meta_version
self.freeze_time
self.kind
def load_cache(self):
try:
try:
self.cache = persistence.loads(self.cache_path.read_text())
except persistence.ReadError:
TRACELOG(f"Ignoring existing, malformed bead meta cache {self.cache_path}")
except FileNotFoundError:
pass
def save_cache(self):
try:
self.cache_path.write_text(persistence.dumps(self.cache))
except FileNotFoundError:
pass
@property
def cache_path(self):
if self.archive_path.suffix != '.zip':
raise FileNotFoundError(f'Archive can not have cache {self.archive_path}')
return self.archive_path.with_suffix('.xmeta')
meta_version = _cached_zip_attribute(meta.META_VERSION, 'meta_version')
content_id = _cached_zip_attribute(CACHE_CONTENT_ID, 'content_id')
kind = _cached_zip_attribute(meta.KIND, 'kind')
freeze_time_str = _cached_zip_attribute(meta.FREEZE_TIME, 'freeze_time_str')
@property
def input_map(self):
try:
return self.cache[CACHE_INPUT_MAP]
except LookupError:
return self.ziparchive.input_map
@input_map.setter
def input_map(self, input_map):
self.cache[CACHE_INPUT_MAP] = input_map
self.save_cache()
@cached_property
def ziparchive(self):
ziparchive = ZipArchive(self.archive_filename, self.box_name)
self._check_and_populate_cache(ziparchive)
return ziparchive
def _check_and_populate_cache(self, ziparchive):
def ensure(cache_key, value):
try:
if self.cache[cache_key] != value:
raise InvalidArchive(
'Cache disagrees with zip meta', self.archive_filename, cache_key)
except KeyError:
self.cache[cache_key] = value
ensure(meta.META_VERSION, ziparchive.meta_version)
ensure(CACHE_CONTENT_ID, ziparchive.content_id)
ensure(meta.KIND, ziparchive.kind)
ensure(meta.FREEZE_TIME, ziparchive.freeze_time_str)
ensure(meta.INPUTS, ziparchive.meta[meta.INPUTS])
# need not match
self.cache.setdefault(CACHE_INPUT_MAP, ziparchive.input_map)
def validate(self):
self.ziparchive.validate()
@property
def inputs(self):
try:
return tuple(meta.parse_inputs({meta.INPUTS: self.cache[meta.INPUTS]}))
except LookupError:
return self.ziparchive.inputs
def extract_dir(self, zip_dir, fs_dir):
return self.ziparchive.extract_dir(zip_dir, fs_dir)
def extract_file(self, zip_path, fs_path):
return self.ziparchive.extract_file(zip_path, fs_path)
def unpack_code_to(self, fs_dir):
self.ziparchive.unpack_code_to(fs_dir)
def unpack_data_to(self, fs_dir):
self.ziparchive.unpack_data_to(fs_dir)
def unpack_meta_to(self, workspace):
workspace.meta = self.ziparchive.meta
workspace.input_map = self.input_map
def bead_name_from_file_path(path):
'''
Parse bead name from a file path.
Might return a simpler name than intended
'''
name_with_timestamp, ext = os.path.splitext(os.path.basename(path))
# assert ext == '.zip' # not enforced to allow having beads with different extensions
name = re.sub('_[0-9]{8}(?:[tT][-+0-9]*)?$', '', name_with_timestamp)
return name
assert 'bead-2015v3' == bead_name_from_file_path('bead-2015v3.zip')
assert 'bead-2015v3' == bead_name_from_file_path('bead-2015v3_20150923.zip')
assert 'bead-2015v3' == bead_name_from_file_path('bead-2015v3_20150923T010203012345+0200.zip')
assert 'bead-2015v3' == bead_name_from_file_path('bead-2015v3_20150923T010203012345-0200.zip')
assert 'bead-2015v3' == bead_name_from_file_path('path/to/bead-2015v3_20150923.zip')
| en | 0.829691 | Make a cache accessor @property with a self.ziparchive.attribute fallback raises InvalidArchive if the attribute is not cached and the backing ziparchive is not valid. # Check that we can get access to metadata # - either through the cache or through the archive # The resulting archive can still be invalid and die unexpectedly later with # InvalidArchive exception, as these are potentially cached values # need not match Parse bead name from a file path. Might return a simpler name than intended # assert ext == '.zip' # not enforced to allow having beads with different extensions | 2.126763 | 2 |
gfm/gfm.py | rcmdnk/gfm | 0 | 6613285 | #!/usr/bin/env python
"""
Gmail Filter Manager
https://github.com/rcmdnk/gfm
"""
from __future__ import print_function
import os
import sys
import xml.etree.ElementTree as ET
import argparse
import xml.dom.minidom
import httplib2
from ruamel.yaml.scalarstring import DoubleQuotedScalarString
import ruamel.yaml
from oauth2client.file import Storage
from oauth2client.tools import run_flow, argparser
from oauth2client.client import OAuth2WebServerFlow
from apiclient.discovery import build
__prog__ = "gfm"
__description__ = __doc__
__author__ = 'rcmdnk'
__copyright__ = 'Copyright (c) 2018 rcmdnk'
__credits__ = ['rcmdnk']
__license__ = 'MIT'
__version__ = 'v0.0.1'
__date__ = '14/Jul/2018'
__maintainer__ = 'rcmdnk'
__email__ = '<EMAIL>'
__status__ = 'Prototype'
AUTH_FILE = os.environ['HOME'] + '/.config/gmail_filter/auth'
GOOGLE_CLIENT_ID = '937185253369-2er0fqahlnpn7tgou1i4mi2for07mhci.'\
'apps.googleusercontent.com'
GOOGLE_CLIENT_SECRET = '<KEY>'
class GmailFilterManager():
def __init__(self, **kw):
self.opt = {}
for k, v in kw.items():
self.opt[k] = v
# Set defaults if it is called w/o args (check dummy in argparser)
if "dummy" not in self.opt:
self.opt = vars(self.get_parser().parse_args())
if self.opt["client_id"] is None:
self.opt["client_id"] = GOOGLE_CLIENT_ID
if self.opt["client_secret"] is None:
self.opt["client_secret"] = GOOGLE_CLIENT_SECRET
self.service = None
self.address = None
self.filters = None
self.filters_api = None
self.filters_xml = None
self.labels = None
self.dic_xml2api = {
"hasTheWord": "query",
"doesNotHaveTheWord": "negatedQuery",
"sizeOperator": "sizeComparison",
}
self.dic_api2xml = {v: k for k, v in self.dic_xml2api.items()}
self.dic_label_xml2api = {
"shouldArchive": ("removeLabelIds", "INBOX"),
"shouldMarkAsRead": ("removeLabelIds", "UNREAD"),
"shouldStar": ("addLabelIds", "STARRED"),
"shouldTrash": ("addLabelIds", "TRASH"),
"shouldNeverSpam": ("removeLabelIds", "SPAM"),
"shouldAlwaysMarkAsImportant": ("addLabelIds", "IMPORTANT"),
"shouldNeverMarkAsImportant": ("removeLabelIds", "IMPORTANT"),
"smartLabelToApply": ("addLabelIds", "CATEGORY_"),
}
self.dic_label_api2xml = {v: k for k, v
in self.dic_label_xml2api.items()}
self.dic_size_xml2api = {
"s_sl": "larger",
"s_ss": "smaller",
}
self.dic_size_api2xml = {v: k for k, v
in self.dic_size_xml2api.items()}
if ((isinstance(self.opt["command"], str)
and self.opt["command"] == "")
or (isinstance(self.opt["command"], str)
and not self.opt["command"])
or (self.opt["command"] is None)):
return
if isinstance(self.opt["command"], str):
self.opt["command"] = [self.opt["command"]]
for command in self.opt["command"]:
if command == "xml2yaml":
self.read_xml()
self.write_yaml()
elif command == "yaml2xml":
self.read_yaml()
self.yaml2xml()
self.write_xml()
elif command == "get":
self.get()
elif command == "put":
self.put()
elif command == "show_filters":
self.show_filters()
elif command == "show_filters_xml":
self.show_filters_xml()
elif command == "show_filters_api":
self.show_filters_api()
elif command == "show_labels_api":
self.show_labels_api()
else:
raise ValueError("Invalid command: %s" % command)
def clean(self):
self.filters = None
self.filters_api = None
self.labels = None
def authentication(self, storage):
return run_flow(
OAuth2WebServerFlow(
client_id=self.opt["client_id"],
client_secret=self.opt["client_secret"],
scope=['https://www.googleapis.com/auth/gmail.modify']),
storage, argparser.parse_args([]))
def build_service(self, rebuild=False):
conf_dir = os.path.dirname(self.opt["auth_file"])
if not os.path.isdir(conf_dir):
os.makedirs(conf_dir)
storage = Storage(self.opt["auth_file"])
credentials = storage.get()
if rebuild or credentials is None or credentials.invalid:
credentials = self.authentication(storage)
http = httplib2.Http()
http = credentials.authorize(http)
service = build('gmail', 'v1', http=http)
prof = service.users().getProfile(userId='me').execute()
self.opt["address "] = prof['emailAddress']
if self.opt["debug"]:
print("My address: %s" % self.opt["address"])
return service
def get_service(self):
if self.service is None:
self.service = self.build_service()
return self.service
def dump_xml(self, stream=sys.stdout):
my_filter = xml.dom.minidom.parseString(
ET.tostring(self.filters_xml)).toprettyxml(
indent=" ", encoding="utf-8")
if sys.version_info.major > 2:
my_filter = my_filter.decode()
stream.write(my_filter)
def write_xml(self):
with open(self.opt["output_xml"], "w") as f:
self.dump_xml(f)
def read_xml(self):
namespaces = {str(x[0]) if x[0] != "" else "atom": x[1]
for _, x in ET.iterparse(self.opt["input_xml"],
events=['start-ns'])}
for k, v in namespaces.items():
if k == "atom":
k = ""
ET.register_namespace(k, v)
tree = ET.parse(self.opt["input_xml"])
self.filters_xml = tree.getroot()
for e in self.filters_xml.iter('*'):
if e.text is not None:
e.text = e.text.strip()
if e.tail is not None:
e.tail = e.tail.strip()
filter_list = []
for e in self.filters_xml.findall("./atom:entry", namespaces):
properties = {}
for p in e.findall("./apps:property", namespaces):
name = p.get("name")
value = p.get("value")
properties[name] = DoubleQuotedScalarString(value)
if "size" not in properties:
for noneed in ["sizeOperator", "sizeUnit"]:
if noneed in properties:
del properties[noneed]
filter_list.append(properties)
self.filters = {"namespaces": namespaces, "filter": filter_list}
def show_filters_xml(self):
self.read_xml()
if self.opt["raw"]:
self.dump_xml()
else:
self.dump_yaml()
def dump_yaml(self, stream=sys.stdout):
yaml = ruamel.yaml.YAML()
yaml.indent(mapping=2, sequence=4, offset=2)
yaml.dump(self.filters, stream=stream)
def write_yaml(self):
with open(self.opt["output_yaml"], "w") as stream:
self.dump_yaml(stream)
def read_yaml(self):
yaml = ruamel.yaml.YAML()
with open(self.opt["input_yaml"], "r") as f:
self.filters = yaml.load(f)
if "namespaces" in self.filters:
if "" in self.filters["namespaces"]:
self.filters["namespaces"]["atom"] =\
self.filters["namespaces"][""]
del self.filters["namespaces"][""]
else:
self.filters["namespaces"] = {
"atom": "http://www.w3.org/2005/Atom",
"apps": "http://schemas.google.com/apps/2006"
}
def show_filters(self):
self.read_yaml()
self.dump_yaml()
def yaml2xml(self):
for k, v in self.filters["namespaces"].items():
if k == "atom":
k = ""
ET.register_namespace(k, v)
self.filters_xml = ET.Element('feed')
for f in self.filters["filters"]:
if "label" in f:
labels = f["label"] if isinstance(
f["label"], list) else [f["label"]]
del f["label"]
else:
labels = [None]
for label in labels:
entry = ET.SubElement(
self.filters_xml,
"{" + self.filters["namespaces"]["atom"] + "}" + 'entry')
properties = f
if label is not None:
properties["label"] = label
for k, v in properties.items():
ET.SubElement(
entry,
"{" + self.filters["namespaces"]["apps"] + "}property",
attrib={"name": k, "value": v}
)
def get_filters(self):
if self.filters_api is not None:
return
self.filters_api = self.get_service().users().settings(
).filters().list(userId='me').execute()
def show_filters_api(self):
self.get_filters()
if self.opt["raw"]:
print(self.filters_api)
return
for f in self.filters_api["filter"]:
print("criteria:")
for a in f["criteria"]:
print(" %s: %s" % (a, f["criteria"][a]))
print("action:")
for a in f["action"]:
print(" %s: %s" % (a, f["action"][a]))
print("")
def get(self):
self.get_filters()
self.filters = {
"filter": [],
"namespaces": {
"apps": "http://schemas.google.com/apps/2006",
"atom": "http://www.w3.org/2005/Atom",
}
}
for f in self.filters_api["filter"]:
xml_filter = {}
for k, v in f["criteria"].items():
key, value = self.criteria_api2xml(k, v)
xml_filter[key] = value
if key == "size":
xml_filter["sizeUnit"] = "s_sb"
for k, v in f["action"].items():
if k == "addLabelIds":
for label in v:
if ("addLabelIds", label) in self.dic_label_api2xml:
xml_filter[self.dic_label_api2xml[
("addLabelIds", label)]] = "true"
continue
if "label" not in xml_filter:
xml_filter["label"] = []
xml_filter["label"].append(self.label_id2name(label))
elif k == "removeLabelIds":
for label in v:
if ("removeLabelIds", label) in self.dic_label_api2xml:
xml_filter[self.dic_label_api2xml[
("removeLabelIds", label)]] = "true"
continue
else:
xml_filter[k] = v
self.filters["filter"].append(xml_filter)
self.write_yaml()
def criteria_api2xml(self, key, value):
key_out = key
value_out = value
if key in self.dic_api2xml:
key_out = self.dic_api2xml[key]
if key == "sizeComparison":
value_out = self.dic_size_api2xml[value]
return key_out, value_out
def action_api2xml(self, key, value):
key_out = key
value_out = value
if key in self.dic_label_api2xml:
key_out = self.dic_api2xml[key]
if key == "sizeComparison":
value_out = self.dic_size_api2xml[value]
if key == "query":
key_out = "hasTheWord"
elif key == "query":
key_out = "hasTheWord"
return key_out, value_out
def get_labels(self):
if self.labels is not None:
return
self.labels = self.get_service().users().labels().list(
userId='me').execute()["labels"]
def show_labels_api(self):
self.get_labels()
if self.opt["raw"]:
for l in self.labels:
print(l)
return
print("===User labels===")
for l in sorted(filter(lambda x: x["type"] == "user", self.labels),
key=lambda x: x["name"]):
print("%s: %s" % (l["name"], l["id"]))
print("\n===System labels===")
for l in sorted(filter(lambda x: x["type"] != "user", self.labels),
key=lambda x: x["name"]):
print("%s: %s" % (l["name"], l["id"]))
def label_id2name(self, label_id):
self.get_labels()
candidates = filter(lambda x: x["id"] == label_id, self.labels)
if len(candidates) != 1:
print("Wrong label id? id: %s, candidates: %s" %
(label_id, str(candidates)))
sys.exit(1)
return candidates[0]["name"]
def label_name2id(self, name):
self.get_labels()
candidates = filter(lambda x: x["name"] == name, self.labels)
if len(candidates) != 1:
print("Wrong label name? candidates: %s" % str(candidates))
sys.exit(1)
return candidates[0]["id"]
def put(self):
pass
@staticmethod
def get_parser():
input_xml_parser = argparse.ArgumentParser(add_help=False)
input_xml_parser.add_argument(
"-x", "--input_xml", action="store", dest="input_xml",
default="mailFilters.xml", help="Input XML file name")
input_yaml_parser = argparse.ArgumentParser(add_help=False)
input_yaml_parser.add_argument(
"-y", "--input_yaml", action="store", dest="input_yaml",
default="mailFilters.yaml", help="Input YAML file name")
output_xml_parser = argparse.ArgumentParser(add_help=False)
output_xml_parser.add_argument(
"-X", "--output_xml", action="store", dest="output_xml",
default="filters.xml", help="Output XML file name")
output_yaml_parser = argparse.ArgumentParser(add_help=False)
output_yaml_parser.add_argument(
"-Y", "--output_yaml", action="store", dest="output_yaml",
default="mailFilters.yaml", help="Output YAML file name")
auth_file_parser = argparse.ArgumentParser(add_help=False)
auth_file_parser.add_argument(
"--auth_file", action="store", dest="auth_file",
default=AUTH_FILE, help="Gmail API authentication file")
client_id_parser = argparse.ArgumentParser(add_help=False)
client_id_parser.add_argument(
"--client_id", action="store", dest="client_id",
default=None, help="Google Client ID")
client_secret_parser = argparse.ArgumentParser(add_help=False)
client_secret_parser.add_argument(
"--client_secret", action="store", dest="client_secret",
default=None, help="Google Client ID")
raw_parser = argparse.ArgumentParser(add_help=False)
raw_parser.add_argument(
"-r", "--raw", action="store_true", dest="raw", default=False,
help="Show raw output")
debug_parser = argparse.ArgumentParser(add_help=False)
debug_parser.add_argument(
"-d", "--debug", action="store_true", dest="debug", default=False,
help="Enable debug mode")
dummy_parser = argparse.ArgumentParser(add_help=False)
dummy_parser.add_argument(
"--dummy", action="store_true", dest="dummy", default=True,
help=argparse.SUPPRESS)
parser = argparse.ArgumentParser(
prog=__prog__,
add_help=True,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=__description__,
parents=[input_xml_parser, input_yaml_parser, output_xml_parser,
output_yaml_parser, auth_file_parser,
client_id_parser, client_secret_parser,
raw_parser, debug_parser,
dummy_parser],
)
subparsers = parser.add_subparsers(
title="subcommands", metavar="[command]", help="", dest="command")
desc = "Convert filters from XML to YAML"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [input_xml_parser, output_yaml_parser, debug_parser]
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["x2y"]
subparsers.add_parser("xml2yaml", **kwargs)
desc = "Convert filters from YAML to XML"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [input_yaml_parser, output_xml_parser, debug_parser]
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["y2x"]
subparsers.add_parser("yaml2xml", **kwargs)
desc = "Get filters by using API and make YAML"
subparsers.add_parser(
"get", description=desc, help=desc,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=[output_yaml_parser, auth_file_parser,
client_id_parser, client_secret_parser, debug_parser])
desc = "Put filters in YAML file to Gmail server by using API"
subparsers.add_parser(
"put", description=desc, help=desc,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=[input_yaml_parser, auth_file_parser,
client_id_parser, client_secret_parser, debug_parser])
desc = "Show filters in YAML"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [input_yaml_parser, debug_parser],
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["show", "s"]
subparsers.add_parser("show_filters", **kwargs)
desc = "Show filters in XML"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [input_xml_parser, raw_parser, debug_parser],
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["show_xml", "sx"]
subparsers.add_parser("show_filter_xml", **kwargs)
desc = "Show filters taken by API"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [auth_file_parser, client_id_parser,
client_secret_parser, raw_parser, debug_parser],
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["show_api", "sa"]
subparsers.add_parser("show_filterapi", **kwargs)
desc = "Show labels taken by API"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [auth_file_parser, client_id_parser,
client_secret_parser, raw_parser, debug_parser],
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["show_labels", "sl"]
subparsers.add_parser("show_labels_api", **kwargs)
return parser
def main():
parser = GmailFilterManager.get_parser()
if len(sys.argv) == 1:
parser.print_help()
return
args = parser.parse_args()
GmailFilterManager(**vars(args))
if __name__ == '__main__':
main()
| #!/usr/bin/env python
"""
Gmail Filter Manager
https://github.com/rcmdnk/gfm
"""
from __future__ import print_function
import os
import sys
import xml.etree.ElementTree as ET
import argparse
import xml.dom.minidom
import httplib2
from ruamel.yaml.scalarstring import DoubleQuotedScalarString
import ruamel.yaml
from oauth2client.file import Storage
from oauth2client.tools import run_flow, argparser
from oauth2client.client import OAuth2WebServerFlow
from apiclient.discovery import build
__prog__ = "gfm"
__description__ = __doc__
__author__ = 'rcmdnk'
__copyright__ = 'Copyright (c) 2018 rcmdnk'
__credits__ = ['rcmdnk']
__license__ = 'MIT'
__version__ = 'v0.0.1'
__date__ = '14/Jul/2018'
__maintainer__ = 'rcmdnk'
__email__ = '<EMAIL>'
__status__ = 'Prototype'
AUTH_FILE = os.environ['HOME'] + '/.config/gmail_filter/auth'
GOOGLE_CLIENT_ID = '937185253369-2er0fqahlnpn7tgou1i4mi2for07mhci.'\
'apps.googleusercontent.com'
GOOGLE_CLIENT_SECRET = '<KEY>'
class GmailFilterManager():
def __init__(self, **kw):
self.opt = {}
for k, v in kw.items():
self.opt[k] = v
# Set defaults if it is called w/o args (check dummy in argparser)
if "dummy" not in self.opt:
self.opt = vars(self.get_parser().parse_args())
if self.opt["client_id"] is None:
self.opt["client_id"] = GOOGLE_CLIENT_ID
if self.opt["client_secret"] is None:
self.opt["client_secret"] = GOOGLE_CLIENT_SECRET
self.service = None
self.address = None
self.filters = None
self.filters_api = None
self.filters_xml = None
self.labels = None
self.dic_xml2api = {
"hasTheWord": "query",
"doesNotHaveTheWord": "negatedQuery",
"sizeOperator": "sizeComparison",
}
self.dic_api2xml = {v: k for k, v in self.dic_xml2api.items()}
self.dic_label_xml2api = {
"shouldArchive": ("removeLabelIds", "INBOX"),
"shouldMarkAsRead": ("removeLabelIds", "UNREAD"),
"shouldStar": ("addLabelIds", "STARRED"),
"shouldTrash": ("addLabelIds", "TRASH"),
"shouldNeverSpam": ("removeLabelIds", "SPAM"),
"shouldAlwaysMarkAsImportant": ("addLabelIds", "IMPORTANT"),
"shouldNeverMarkAsImportant": ("removeLabelIds", "IMPORTANT"),
"smartLabelToApply": ("addLabelIds", "CATEGORY_"),
}
self.dic_label_api2xml = {v: k for k, v
in self.dic_label_xml2api.items()}
self.dic_size_xml2api = {
"s_sl": "larger",
"s_ss": "smaller",
}
self.dic_size_api2xml = {v: k for k, v
in self.dic_size_xml2api.items()}
if ((isinstance(self.opt["command"], str)
and self.opt["command"] == "")
or (isinstance(self.opt["command"], str)
and not self.opt["command"])
or (self.opt["command"] is None)):
return
if isinstance(self.opt["command"], str):
self.opt["command"] = [self.opt["command"]]
for command in self.opt["command"]:
if command == "xml2yaml":
self.read_xml()
self.write_yaml()
elif command == "yaml2xml":
self.read_yaml()
self.yaml2xml()
self.write_xml()
elif command == "get":
self.get()
elif command == "put":
self.put()
elif command == "show_filters":
self.show_filters()
elif command == "show_filters_xml":
self.show_filters_xml()
elif command == "show_filters_api":
self.show_filters_api()
elif command == "show_labels_api":
self.show_labels_api()
else:
raise ValueError("Invalid command: %s" % command)
def clean(self):
self.filters = None
self.filters_api = None
self.labels = None
def authentication(self, storage):
return run_flow(
OAuth2WebServerFlow(
client_id=self.opt["client_id"],
client_secret=self.opt["client_secret"],
scope=['https://www.googleapis.com/auth/gmail.modify']),
storage, argparser.parse_args([]))
def build_service(self, rebuild=False):
conf_dir = os.path.dirname(self.opt["auth_file"])
if not os.path.isdir(conf_dir):
os.makedirs(conf_dir)
storage = Storage(self.opt["auth_file"])
credentials = storage.get()
if rebuild or credentials is None or credentials.invalid:
credentials = self.authentication(storage)
http = httplib2.Http()
http = credentials.authorize(http)
service = build('gmail', 'v1', http=http)
prof = service.users().getProfile(userId='me').execute()
self.opt["address "] = prof['emailAddress']
if self.opt["debug"]:
print("My address: %s" % self.opt["address"])
return service
def get_service(self):
if self.service is None:
self.service = self.build_service()
return self.service
def dump_xml(self, stream=sys.stdout):
my_filter = xml.dom.minidom.parseString(
ET.tostring(self.filters_xml)).toprettyxml(
indent=" ", encoding="utf-8")
if sys.version_info.major > 2:
my_filter = my_filter.decode()
stream.write(my_filter)
def write_xml(self):
with open(self.opt["output_xml"], "w") as f:
self.dump_xml(f)
def read_xml(self):
namespaces = {str(x[0]) if x[0] != "" else "atom": x[1]
for _, x in ET.iterparse(self.opt["input_xml"],
events=['start-ns'])}
for k, v in namespaces.items():
if k == "atom":
k = ""
ET.register_namespace(k, v)
tree = ET.parse(self.opt["input_xml"])
self.filters_xml = tree.getroot()
for e in self.filters_xml.iter('*'):
if e.text is not None:
e.text = e.text.strip()
if e.tail is not None:
e.tail = e.tail.strip()
filter_list = []
for e in self.filters_xml.findall("./atom:entry", namespaces):
properties = {}
for p in e.findall("./apps:property", namespaces):
name = p.get("name")
value = p.get("value")
properties[name] = DoubleQuotedScalarString(value)
if "size" not in properties:
for noneed in ["sizeOperator", "sizeUnit"]:
if noneed in properties:
del properties[noneed]
filter_list.append(properties)
self.filters = {"namespaces": namespaces, "filter": filter_list}
def show_filters_xml(self):
self.read_xml()
if self.opt["raw"]:
self.dump_xml()
else:
self.dump_yaml()
def dump_yaml(self, stream=sys.stdout):
yaml = ruamel.yaml.YAML()
yaml.indent(mapping=2, sequence=4, offset=2)
yaml.dump(self.filters, stream=stream)
def write_yaml(self):
with open(self.opt["output_yaml"], "w") as stream:
self.dump_yaml(stream)
def read_yaml(self):
yaml = ruamel.yaml.YAML()
with open(self.opt["input_yaml"], "r") as f:
self.filters = yaml.load(f)
if "namespaces" in self.filters:
if "" in self.filters["namespaces"]:
self.filters["namespaces"]["atom"] =\
self.filters["namespaces"][""]
del self.filters["namespaces"][""]
else:
self.filters["namespaces"] = {
"atom": "http://www.w3.org/2005/Atom",
"apps": "http://schemas.google.com/apps/2006"
}
def show_filters(self):
self.read_yaml()
self.dump_yaml()
def yaml2xml(self):
for k, v in self.filters["namespaces"].items():
if k == "atom":
k = ""
ET.register_namespace(k, v)
self.filters_xml = ET.Element('feed')
for f in self.filters["filters"]:
if "label" in f:
labels = f["label"] if isinstance(
f["label"], list) else [f["label"]]
del f["label"]
else:
labels = [None]
for label in labels:
entry = ET.SubElement(
self.filters_xml,
"{" + self.filters["namespaces"]["atom"] + "}" + 'entry')
properties = f
if label is not None:
properties["label"] = label
for k, v in properties.items():
ET.SubElement(
entry,
"{" + self.filters["namespaces"]["apps"] + "}property",
attrib={"name": k, "value": v}
)
def get_filters(self):
if self.filters_api is not None:
return
self.filters_api = self.get_service().users().settings(
).filters().list(userId='me').execute()
def show_filters_api(self):
self.get_filters()
if self.opt["raw"]:
print(self.filters_api)
return
for f in self.filters_api["filter"]:
print("criteria:")
for a in f["criteria"]:
print(" %s: %s" % (a, f["criteria"][a]))
print("action:")
for a in f["action"]:
print(" %s: %s" % (a, f["action"][a]))
print("")
def get(self):
self.get_filters()
self.filters = {
"filter": [],
"namespaces": {
"apps": "http://schemas.google.com/apps/2006",
"atom": "http://www.w3.org/2005/Atom",
}
}
for f in self.filters_api["filter"]:
xml_filter = {}
for k, v in f["criteria"].items():
key, value = self.criteria_api2xml(k, v)
xml_filter[key] = value
if key == "size":
xml_filter["sizeUnit"] = "s_sb"
for k, v in f["action"].items():
if k == "addLabelIds":
for label in v:
if ("addLabelIds", label) in self.dic_label_api2xml:
xml_filter[self.dic_label_api2xml[
("addLabelIds", label)]] = "true"
continue
if "label" not in xml_filter:
xml_filter["label"] = []
xml_filter["label"].append(self.label_id2name(label))
elif k == "removeLabelIds":
for label in v:
if ("removeLabelIds", label) in self.dic_label_api2xml:
xml_filter[self.dic_label_api2xml[
("removeLabelIds", label)]] = "true"
continue
else:
xml_filter[k] = v
self.filters["filter"].append(xml_filter)
self.write_yaml()
def criteria_api2xml(self, key, value):
key_out = key
value_out = value
if key in self.dic_api2xml:
key_out = self.dic_api2xml[key]
if key == "sizeComparison":
value_out = self.dic_size_api2xml[value]
return key_out, value_out
def action_api2xml(self, key, value):
key_out = key
value_out = value
if key in self.dic_label_api2xml:
key_out = self.dic_api2xml[key]
if key == "sizeComparison":
value_out = self.dic_size_api2xml[value]
if key == "query":
key_out = "hasTheWord"
elif key == "query":
key_out = "hasTheWord"
return key_out, value_out
def get_labels(self):
if self.labels is not None:
return
self.labels = self.get_service().users().labels().list(
userId='me').execute()["labels"]
def show_labels_api(self):
self.get_labels()
if self.opt["raw"]:
for l in self.labels:
print(l)
return
print("===User labels===")
for l in sorted(filter(lambda x: x["type"] == "user", self.labels),
key=lambda x: x["name"]):
print("%s: %s" % (l["name"], l["id"]))
print("\n===System labels===")
for l in sorted(filter(lambda x: x["type"] != "user", self.labels),
key=lambda x: x["name"]):
print("%s: %s" % (l["name"], l["id"]))
def label_id2name(self, label_id):
self.get_labels()
candidates = filter(lambda x: x["id"] == label_id, self.labels)
if len(candidates) != 1:
print("Wrong label id? id: %s, candidates: %s" %
(label_id, str(candidates)))
sys.exit(1)
return candidates[0]["name"]
def label_name2id(self, name):
self.get_labels()
candidates = filter(lambda x: x["name"] == name, self.labels)
if len(candidates) != 1:
print("Wrong label name? candidates: %s" % str(candidates))
sys.exit(1)
return candidates[0]["id"]
def put(self):
pass
@staticmethod
def get_parser():
input_xml_parser = argparse.ArgumentParser(add_help=False)
input_xml_parser.add_argument(
"-x", "--input_xml", action="store", dest="input_xml",
default="mailFilters.xml", help="Input XML file name")
input_yaml_parser = argparse.ArgumentParser(add_help=False)
input_yaml_parser.add_argument(
"-y", "--input_yaml", action="store", dest="input_yaml",
default="mailFilters.yaml", help="Input YAML file name")
output_xml_parser = argparse.ArgumentParser(add_help=False)
output_xml_parser.add_argument(
"-X", "--output_xml", action="store", dest="output_xml",
default="filters.xml", help="Output XML file name")
output_yaml_parser = argparse.ArgumentParser(add_help=False)
output_yaml_parser.add_argument(
"-Y", "--output_yaml", action="store", dest="output_yaml",
default="mailFilters.yaml", help="Output YAML file name")
auth_file_parser = argparse.ArgumentParser(add_help=False)
auth_file_parser.add_argument(
"--auth_file", action="store", dest="auth_file",
default=AUTH_FILE, help="Gmail API authentication file")
client_id_parser = argparse.ArgumentParser(add_help=False)
client_id_parser.add_argument(
"--client_id", action="store", dest="client_id",
default=None, help="Google Client ID")
client_secret_parser = argparse.ArgumentParser(add_help=False)
client_secret_parser.add_argument(
"--client_secret", action="store", dest="client_secret",
default=None, help="Google Client ID")
raw_parser = argparse.ArgumentParser(add_help=False)
raw_parser.add_argument(
"-r", "--raw", action="store_true", dest="raw", default=False,
help="Show raw output")
debug_parser = argparse.ArgumentParser(add_help=False)
debug_parser.add_argument(
"-d", "--debug", action="store_true", dest="debug", default=False,
help="Enable debug mode")
dummy_parser = argparse.ArgumentParser(add_help=False)
dummy_parser.add_argument(
"--dummy", action="store_true", dest="dummy", default=True,
help=argparse.SUPPRESS)
parser = argparse.ArgumentParser(
prog=__prog__,
add_help=True,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=__description__,
parents=[input_xml_parser, input_yaml_parser, output_xml_parser,
output_yaml_parser, auth_file_parser,
client_id_parser, client_secret_parser,
raw_parser, debug_parser,
dummy_parser],
)
subparsers = parser.add_subparsers(
title="subcommands", metavar="[command]", help="", dest="command")
desc = "Convert filters from XML to YAML"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [input_xml_parser, output_yaml_parser, debug_parser]
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["x2y"]
subparsers.add_parser("xml2yaml", **kwargs)
desc = "Convert filters from YAML to XML"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [input_yaml_parser, output_xml_parser, debug_parser]
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["y2x"]
subparsers.add_parser("yaml2xml", **kwargs)
desc = "Get filters by using API and make YAML"
subparsers.add_parser(
"get", description=desc, help=desc,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=[output_yaml_parser, auth_file_parser,
client_id_parser, client_secret_parser, debug_parser])
desc = "Put filters in YAML file to Gmail server by using API"
subparsers.add_parser(
"put", description=desc, help=desc,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
parents=[input_yaml_parser, auth_file_parser,
client_id_parser, client_secret_parser, debug_parser])
desc = "Show filters in YAML"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [input_yaml_parser, debug_parser],
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["show", "s"]
subparsers.add_parser("show_filters", **kwargs)
desc = "Show filters in XML"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [input_xml_parser, raw_parser, debug_parser],
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["show_xml", "sx"]
subparsers.add_parser("show_filter_xml", **kwargs)
desc = "Show filters taken by API"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [auth_file_parser, client_id_parser,
client_secret_parser, raw_parser, debug_parser],
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["show_api", "sa"]
subparsers.add_parser("show_filterapi", **kwargs)
desc = "Show labels taken by API"
kwargs = {
"description": desc, "help": desc,
"formatter_class": argparse.ArgumentDefaultsHelpFormatter,
"parents": [auth_file_parser, client_id_parser,
client_secret_parser, raw_parser, debug_parser],
}
if sys.version_info.major > 2:
kwargs["aliases"] = ["show_labels", "sl"]
subparsers.add_parser("show_labels_api", **kwargs)
return parser
def main():
parser = GmailFilterManager.get_parser()
if len(sys.argv) == 1:
parser.print_help()
return
args = parser.parse_args()
GmailFilterManager(**vars(args))
if __name__ == '__main__':
main()
| en | 0.627759 | #!/usr/bin/env python Gmail Filter Manager https://github.com/rcmdnk/gfm # Set defaults if it is called w/o args (check dummy in argparser) | 2.286493 | 2 |
cybox/objects/pipe_object.py | Mattlk13/python-cybox | 40 | 6613286 | # Copyright (c) 2017, The MITRE Corporation. All rights reserved.
# See LICENSE.txt for complete terms.
from mixbox import fields
import cybox.bindings.pipe_object as pipe_binding
from cybox.common import ObjectProperties, String
class Pipe(ObjectProperties):
_namespace = 'http://cybox.mitre.org/objects#PipeObject-2'
_XSI_NS = "PipeObj"
_XSI_TYPE = "PipeObjectType"
_binding = pipe_binding
_binding_class = pipe_binding.PipeObjectType
name = fields.TypedField("Name", String)
named = fields.TypedField("named")
| # Copyright (c) 2017, The MITRE Corporation. All rights reserved.
# See LICENSE.txt for complete terms.
from mixbox import fields
import cybox.bindings.pipe_object as pipe_binding
from cybox.common import ObjectProperties, String
class Pipe(ObjectProperties):
_namespace = 'http://cybox.mitre.org/objects#PipeObject-2'
_XSI_NS = "PipeObj"
_XSI_TYPE = "PipeObjectType"
_binding = pipe_binding
_binding_class = pipe_binding.PipeObjectType
name = fields.TypedField("Name", String)
named = fields.TypedField("named")
| en | 0.821898 | # Copyright (c) 2017, The MITRE Corporation. All rights reserved. # See LICENSE.txt for complete terms. #PipeObject-2' | 2.148387 | 2 |
exercism/python/armstrong-numbers/armstrong_numbers.py | TGITS/programming-workouts | 0 | 6613287 | <gh_stars>0
def is_armstrong_number(number):
number_as_string = str(number)
power = len(number_as_string)
return number == sum([int(digit)**power for digit in number_as_string])
| def is_armstrong_number(number):
number_as_string = str(number)
power = len(number_as_string)
return number == sum([int(digit)**power for digit in number_as_string]) | none | 1 | 3.75345 | 4 | |
flashCards.py | simonbaeuerle/image2txt | 0 | 6613288 | """
Read image files from ./imgs_flashCards
Perform OCR scan on images
Save text to txt file
Created 01.05.2020 by <NAME>
"""
import pytesseract
from PIL import Image
import os
# Repository root directory
root = "C:\\Users\\Simon\\Git\\image2txt\\"
# If you don't have tesseract executable in your PATH, include the following:
pytesseract.pytesseract.tesseract_cmd = "C:\\Program Files\\Tesseract-OCR\\tesseract.exe"
TESSDATA_PREFIX = "C:\\Program Files\\Tesseract-OCR"
# Set working directory to repository root directory
os.chdir(root)
# Path to folder with images to be scanned
path = os.path.join(root, "imgs_flashCards\\")
# Loop over images
result = ""
for i in range(1,5):
# Image name template
imgfile = str(i) + ".jpeg"
# Append images to result string
result += "\n \n ---------------------------------------------------- \n \n"
result += pytesseract.image_to_string(Image.open(os.path.join(path, imgfile)))
# Output scanned text to txt file
txtFile = open("Output.txt", "w")
txtFile.write(result)
txtFile.close() | """
Read image files from ./imgs_flashCards
Perform OCR scan on images
Save text to txt file
Created 01.05.2020 by <NAME>
"""
import pytesseract
from PIL import Image
import os
# Repository root directory
root = "C:\\Users\\Simon\\Git\\image2txt\\"
# If you don't have tesseract executable in your PATH, include the following:
pytesseract.pytesseract.tesseract_cmd = "C:\\Program Files\\Tesseract-OCR\\tesseract.exe"
TESSDATA_PREFIX = "C:\\Program Files\\Tesseract-OCR"
# Set working directory to repository root directory
os.chdir(root)
# Path to folder with images to be scanned
path = os.path.join(root, "imgs_flashCards\\")
# Loop over images
result = ""
for i in range(1,5):
# Image name template
imgfile = str(i) + ".jpeg"
# Append images to result string
result += "\n \n ---------------------------------------------------- \n \n"
result += pytesseract.image_to_string(Image.open(os.path.join(path, imgfile)))
# Output scanned text to txt file
txtFile = open("Output.txt", "w")
txtFile.write(result)
txtFile.close() | en | 0.852235 | Read image files from ./imgs_flashCards Perform OCR scan on images Save text to txt file Created 01.05.2020 by <NAME> # Repository root directory # If you don't have tesseract executable in your PATH, include the following: # Set working directory to repository root directory # Path to folder with images to be scanned # Loop over images # Image name template # Append images to result string # Output scanned text to txt file | 3.433743 | 3 |
cards/models.py | zachtib/DraftingWithFriends | 0 | 6613289 | from django.db import models
class MagicSet(models.Model):
id = models.UUIDField(primary_key=True)
code = models.CharField(max_length=5)
name = models.CharField(max_length=100)
class Card(models.Model):
id = models.UUIDField(primary_key=True)
name = models.CharField(max_length=200)
mana_cost = models.CharField(max_length=20)
class CardFace(models.Model):
card = models.ForeignKey(Card, related_name='faces', on_delete=models.CASCADE)
name = models.CharField(max_length=200)
mana_cost = models.CharField(max_length=20)
class Printing(models.Model):
magic_set = models.ForeignKey(MagicSet, related_name='printings', on_delete=models.CASCADE)
card = models.ForeignKey(Card, related_name='printings', on_delete=models.CASCADE)
| from django.db import models
class MagicSet(models.Model):
id = models.UUIDField(primary_key=True)
code = models.CharField(max_length=5)
name = models.CharField(max_length=100)
class Card(models.Model):
id = models.UUIDField(primary_key=True)
name = models.CharField(max_length=200)
mana_cost = models.CharField(max_length=20)
class CardFace(models.Model):
card = models.ForeignKey(Card, related_name='faces', on_delete=models.CASCADE)
name = models.CharField(max_length=200)
mana_cost = models.CharField(max_length=20)
class Printing(models.Model):
magic_set = models.ForeignKey(MagicSet, related_name='printings', on_delete=models.CASCADE)
card = models.ForeignKey(Card, related_name='printings', on_delete=models.CASCADE)
| none | 1 | 2.296904 | 2 | |
python-codes/m2_curso_em_video_estruturas_de_controle/ex068.0.py | lucasportella/learning_repo | 0 | 6613290 | from random import randint
print('=-'* 15)
print('VAMOS JOGAR PAR OU ÍMPAR')
print('=-'* 15)
v = 0
while True:
pc = randint(0,10)
valor = int(input('Digite um valor: '))
if (valor + pc) % 2 == 0:
resultadosoma = 'PAR'
else: resultadosoma = 'ÍMPAR'
escolha = str(input('Par ou ímpar? [P/I] ')).upper().strip()
print('-' * 30)
print(f'Você jogou {valor} e o computador jogou {pc}. Total de {valor + pc} deu {resultadosoma}')
print('-' * 30)
if escolha in 'PPAR':
if (valor + pc) % 2 == 0:
print('Você GANHOU!!')
print('Vamos jogar novamente...')
print('=-' * 15)
v += 1
else:
print('Voce PERDEU!!')
print('-=' * 15)
print(f'GAME OVER! Você venceu {v} vez(es).')
break
elif escolha in 'ÍIMPARÍMPAR':
if (valor + pc) % 2 != 0:
print('Você GANHOU!!')
print('Vamos jogar novamente...')
print('=-' * 15)
v += 1
else:
print('Você PERDEU!!')
print('-=' * 15)
print(f'GAME OVER! Você venceu {v} vez(es).')
break
| from random import randint
print('=-'* 15)
print('VAMOS JOGAR PAR OU ÍMPAR')
print('=-'* 15)
v = 0
while True:
pc = randint(0,10)
valor = int(input('Digite um valor: '))
if (valor + pc) % 2 == 0:
resultadosoma = 'PAR'
else: resultadosoma = 'ÍMPAR'
escolha = str(input('Par ou ímpar? [P/I] ')).upper().strip()
print('-' * 30)
print(f'Você jogou {valor} e o computador jogou {pc}. Total de {valor + pc} deu {resultadosoma}')
print('-' * 30)
if escolha in 'PPAR':
if (valor + pc) % 2 == 0:
print('Você GANHOU!!')
print('Vamos jogar novamente...')
print('=-' * 15)
v += 1
else:
print('Voce PERDEU!!')
print('-=' * 15)
print(f'GAME OVER! Você venceu {v} vez(es).')
break
elif escolha in 'ÍIMPARÍMPAR':
if (valor + pc) % 2 != 0:
print('Você GANHOU!!')
print('Vamos jogar novamente...')
print('=-' * 15)
v += 1
else:
print('Você PERDEU!!')
print('-=' * 15)
print(f'GAME OVER! Você venceu {v} vez(es).')
break
| none | 1 | 3.720107 | 4 | |
datadrivenpdes/core/__init__.py | snes-chalmers/data-driven-advection | 26 | 6613291 | <filename>datadrivenpdes/core/__init__.py
"""Core functionality."""
from datadrivenpdes.core import builders
from datadrivenpdes.core import equations
from datadrivenpdes.core import geometry
from datadrivenpdes.core import grids
from datadrivenpdes.core import integrate
from datadrivenpdes.core import models
from datadrivenpdes.core import polynomials
from datadrivenpdes.core import readers
from datadrivenpdes.core import states
from datadrivenpdes.core import tensor_ops
from datadrivenpdes.core import utils
| <filename>datadrivenpdes/core/__init__.py
"""Core functionality."""
from datadrivenpdes.core import builders
from datadrivenpdes.core import equations
from datadrivenpdes.core import geometry
from datadrivenpdes.core import grids
from datadrivenpdes.core import integrate
from datadrivenpdes.core import models
from datadrivenpdes.core import polynomials
from datadrivenpdes.core import readers
from datadrivenpdes.core import states
from datadrivenpdes.core import tensor_ops
from datadrivenpdes.core import utils
| en | 0.90823 | Core functionality. | 1.191905 | 1 |
synapse/rest/admin/background_updates.py | dsonck92/synapse | 9,945 | 6613292 | <filename>synapse/rest/admin/background_updates.py
# Copyright 2021 The Matrix.org Foundation C.I.C.
#
# 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.
import logging
from http import HTTPStatus
from typing import TYPE_CHECKING, Tuple
from synapse.api.errors import SynapseError
from synapse.http.servlet import (
RestServlet,
assert_params_in_dict,
parse_json_object_from_request,
)
from synapse.http.site import SynapseRequest
from synapse.rest.admin._base import admin_patterns, assert_requester_is_admin
from synapse.types import JsonDict
if TYPE_CHECKING:
from synapse.server import HomeServer
logger = logging.getLogger(__name__)
class BackgroundUpdateEnabledRestServlet(RestServlet):
"""Allows temporarily disabling background updates"""
PATTERNS = admin_patterns("/background_updates/enabled$")
def __init__(self, hs: "HomeServer"):
self._auth = hs.get_auth()
self._data_stores = hs.get_datastores()
async def on_GET(self, request: SynapseRequest) -> Tuple[int, JsonDict]:
await assert_requester_is_admin(self._auth, request)
# We need to check that all configured databases have updates enabled.
# (They *should* all be in sync.)
enabled = all(db.updates.enabled for db in self._data_stores.databases)
return HTTPStatus.OK, {"enabled": enabled}
async def on_POST(self, request: SynapseRequest) -> Tuple[int, JsonDict]:
await assert_requester_is_admin(self._auth, request)
body = parse_json_object_from_request(request)
enabled = body.get("enabled", True)
if not isinstance(enabled, bool):
raise SynapseError(
HTTPStatus.BAD_REQUEST, "'enabled' parameter must be a boolean"
)
for db in self._data_stores.databases:
db.updates.enabled = enabled
# If we're re-enabling them ensure that we start the background
# process again.
if enabled:
db.updates.start_doing_background_updates()
return HTTPStatus.OK, {"enabled": enabled}
class BackgroundUpdateRestServlet(RestServlet):
"""Fetch information about background updates"""
PATTERNS = admin_patterns("/background_updates/status$")
def __init__(self, hs: "HomeServer"):
self._auth = hs.get_auth()
self._data_stores = hs.get_datastores()
async def on_GET(self, request: SynapseRequest) -> Tuple[int, JsonDict]:
await assert_requester_is_admin(self._auth, request)
# We need to check that all configured databases have updates enabled.
# (They *should* all be in sync.)
enabled = all(db.updates.enabled for db in self._data_stores.databases)
current_updates = {}
for db in self._data_stores.databases:
update = db.updates.get_current_update()
if not update:
continue
current_updates[db.name()] = {
"name": update.name,
"total_item_count": update.total_item_count,
"total_duration_ms": update.total_duration_ms,
"average_items_per_ms": update.average_items_per_ms(),
}
return HTTPStatus.OK, {"enabled": enabled, "current_updates": current_updates}
class BackgroundUpdateStartJobRestServlet(RestServlet):
"""Allows to start specific background updates"""
PATTERNS = admin_patterns("/background_updates/start_job$")
def __init__(self, hs: "HomeServer"):
self._auth = hs.get_auth()
self._store = hs.get_datastore()
async def on_POST(self, request: SynapseRequest) -> Tuple[int, JsonDict]:
await assert_requester_is_admin(self._auth, request)
body = parse_json_object_from_request(request)
assert_params_in_dict(body, ["job_name"])
job_name = body["job_name"]
if job_name == "populate_stats_process_rooms":
jobs = [
{
"update_name": "populate_stats_process_rooms",
"progress_json": "{}",
},
]
elif job_name == "regenerate_directory":
jobs = [
{
"update_name": "populate_user_directory_createtables",
"progress_json": "{}",
"depends_on": "",
},
{
"update_name": "populate_user_directory_process_rooms",
"progress_json": "{}",
"depends_on": "populate_user_directory_createtables",
},
{
"update_name": "populate_user_directory_process_users",
"progress_json": "{}",
"depends_on": "populate_user_directory_process_rooms",
},
{
"update_name": "populate_user_directory_cleanup",
"progress_json": "{}",
"depends_on": "populate_user_directory_process_users",
},
]
else:
raise SynapseError(HTTPStatus.BAD_REQUEST, "Invalid job_name")
try:
await self._store.db_pool.simple_insert_many(
table="background_updates",
values=jobs,
desc=f"admin_api_run_{job_name}",
)
except self._store.db_pool.engine.module.IntegrityError:
raise SynapseError(
HTTPStatus.BAD_REQUEST,
"Job %s is already in queue of background updates." % (job_name,),
)
self._store.db_pool.updates.start_doing_background_updates()
return HTTPStatus.OK, {}
| <filename>synapse/rest/admin/background_updates.py
# Copyright 2021 The Matrix.org Foundation C.I.C.
#
# 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.
import logging
from http import HTTPStatus
from typing import TYPE_CHECKING, Tuple
from synapse.api.errors import SynapseError
from synapse.http.servlet import (
RestServlet,
assert_params_in_dict,
parse_json_object_from_request,
)
from synapse.http.site import SynapseRequest
from synapse.rest.admin._base import admin_patterns, assert_requester_is_admin
from synapse.types import JsonDict
if TYPE_CHECKING:
from synapse.server import HomeServer
logger = logging.getLogger(__name__)
class BackgroundUpdateEnabledRestServlet(RestServlet):
"""Allows temporarily disabling background updates"""
PATTERNS = admin_patterns("/background_updates/enabled$")
def __init__(self, hs: "HomeServer"):
self._auth = hs.get_auth()
self._data_stores = hs.get_datastores()
async def on_GET(self, request: SynapseRequest) -> Tuple[int, JsonDict]:
await assert_requester_is_admin(self._auth, request)
# We need to check that all configured databases have updates enabled.
# (They *should* all be in sync.)
enabled = all(db.updates.enabled for db in self._data_stores.databases)
return HTTPStatus.OK, {"enabled": enabled}
async def on_POST(self, request: SynapseRequest) -> Tuple[int, JsonDict]:
await assert_requester_is_admin(self._auth, request)
body = parse_json_object_from_request(request)
enabled = body.get("enabled", True)
if not isinstance(enabled, bool):
raise SynapseError(
HTTPStatus.BAD_REQUEST, "'enabled' parameter must be a boolean"
)
for db in self._data_stores.databases:
db.updates.enabled = enabled
# If we're re-enabling them ensure that we start the background
# process again.
if enabled:
db.updates.start_doing_background_updates()
return HTTPStatus.OK, {"enabled": enabled}
class BackgroundUpdateRestServlet(RestServlet):
"""Fetch information about background updates"""
PATTERNS = admin_patterns("/background_updates/status$")
def __init__(self, hs: "HomeServer"):
self._auth = hs.get_auth()
self._data_stores = hs.get_datastores()
async def on_GET(self, request: SynapseRequest) -> Tuple[int, JsonDict]:
await assert_requester_is_admin(self._auth, request)
# We need to check that all configured databases have updates enabled.
# (They *should* all be in sync.)
enabled = all(db.updates.enabled for db in self._data_stores.databases)
current_updates = {}
for db in self._data_stores.databases:
update = db.updates.get_current_update()
if not update:
continue
current_updates[db.name()] = {
"name": update.name,
"total_item_count": update.total_item_count,
"total_duration_ms": update.total_duration_ms,
"average_items_per_ms": update.average_items_per_ms(),
}
return HTTPStatus.OK, {"enabled": enabled, "current_updates": current_updates}
class BackgroundUpdateStartJobRestServlet(RestServlet):
"""Allows to start specific background updates"""
PATTERNS = admin_patterns("/background_updates/start_job$")
def __init__(self, hs: "HomeServer"):
self._auth = hs.get_auth()
self._store = hs.get_datastore()
async def on_POST(self, request: SynapseRequest) -> Tuple[int, JsonDict]:
await assert_requester_is_admin(self._auth, request)
body = parse_json_object_from_request(request)
assert_params_in_dict(body, ["job_name"])
job_name = body["job_name"]
if job_name == "populate_stats_process_rooms":
jobs = [
{
"update_name": "populate_stats_process_rooms",
"progress_json": "{}",
},
]
elif job_name == "regenerate_directory":
jobs = [
{
"update_name": "populate_user_directory_createtables",
"progress_json": "{}",
"depends_on": "",
},
{
"update_name": "populate_user_directory_process_rooms",
"progress_json": "{}",
"depends_on": "populate_user_directory_createtables",
},
{
"update_name": "populate_user_directory_process_users",
"progress_json": "{}",
"depends_on": "populate_user_directory_process_rooms",
},
{
"update_name": "populate_user_directory_cleanup",
"progress_json": "{}",
"depends_on": "populate_user_directory_process_users",
},
]
else:
raise SynapseError(HTTPStatus.BAD_REQUEST, "Invalid job_name")
try:
await self._store.db_pool.simple_insert_many(
table="background_updates",
values=jobs,
desc=f"admin_api_run_{job_name}",
)
except self._store.db_pool.engine.module.IntegrityError:
raise SynapseError(
HTTPStatus.BAD_REQUEST,
"Job %s is already in queue of background updates." % (job_name,),
)
self._store.db_pool.updates.start_doing_background_updates()
return HTTPStatus.OK, {}
| en | 0.819577 | # Copyright 2021 The Matrix.org Foundation C.I.C. # # 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. Allows temporarily disabling background updates # We need to check that all configured databases have updates enabled. # (They *should* all be in sync.) # If we're re-enabling them ensure that we start the background # process again. Fetch information about background updates # We need to check that all configured databases have updates enabled. # (They *should* all be in sync.) Allows to start specific background updates | 2.073012 | 2 |
machine_learning/torch_autoencoder/src/data/tqdmupto.py | iimuz/til | 4 | 6613293 | <filename>machine_learning/torch_autoencoder/src/data/tqdmupto.py
"""tqdm for downloader."""
import sys
if "ipykernel" in sys.modules:
from tqdm.notebook import tqdm
else:
from tqdm import tqdm
class TqdmUpTo(tqdm):
"""Provides `update_to(n)` which uses `tqdm.update(delta_n)`.
Args:
tqdm (tqdm): tqdm
"""
def update_to(self, b: int = 1, bsize: int = 1, tsize: int = None):
""" update function
Args:
b (int, optional): Number of blocks transferred. Defaults to 1.
bsize (int, optional): Size of each block (in tqdm units). Defaults to 1.
tsize ([type], optional): Total size (in tqdm units). Defaults to None.
"""
if tsize is not None:
self.total = tsize
self.update(b * bsize - self.n)
| <filename>machine_learning/torch_autoencoder/src/data/tqdmupto.py
"""tqdm for downloader."""
import sys
if "ipykernel" in sys.modules:
from tqdm.notebook import tqdm
else:
from tqdm import tqdm
class TqdmUpTo(tqdm):
"""Provides `update_to(n)` which uses `tqdm.update(delta_n)`.
Args:
tqdm (tqdm): tqdm
"""
def update_to(self, b: int = 1, bsize: int = 1, tsize: int = None):
""" update function
Args:
b (int, optional): Number of blocks transferred. Defaults to 1.
bsize (int, optional): Size of each block (in tqdm units). Defaults to 1.
tsize ([type], optional): Total size (in tqdm units). Defaults to None.
"""
if tsize is not None:
self.total = tsize
self.update(b * bsize - self.n)
| en | 0.421154 | tqdm for downloader. Provides `update_to(n)` which uses `tqdm.update(delta_n)`. Args: tqdm (tqdm): tqdm update function Args: b (int, optional): Number of blocks transferred. Defaults to 1. bsize (int, optional): Size of each block (in tqdm units). Defaults to 1. tsize ([type], optional): Total size (in tqdm units). Defaults to None. | 2.647347 | 3 |
pyvod/vod.py | sixP-NaraKa/pyvod-chat | 0 | 6613294 | <reponame>sixP-NaraKa/pyvod-chat
"""
pyvod-chat - a simple tool to download a past Twitch.tv broadcasts (VOD) chat comments!
Available on GitHub (+ documentation): https://github.com/sixP-NaraKa/pyvod-chat
"""
import os
from collections import namedtuple
import requests
import dotenv
from .vodchat import VODChat
from .exceptions import TwitchApiException
# check for a .env file and get the "twitch-client-id" which we need to identify the application for use with the API
# this is NOT the same as the Client-Secret, which we do not need here
# if there is no such Client-ID or it is empty, we use a default Client-ID
dotenv.load_dotenv()
_client_id = os.getenv("twitch-client-id")
_client_id = _client_id if _client_id else "r52h1i1phlvyxs0sdi3ooam1b3w62g"
# needed request headers
_headers = {"client-id": _client_id, "accept": "application/vnd.twitchtv.v5+json"}
# additional API url
vod_url = "https://api.twitch.tv/v5/videos/{vod_id}"
class VOD:
""" Represents a Twitch.tv VOD (video-on-demand).
The main entry point, responsible for getting the VODChat via `get_videochat()`
as well as some basic information about the VOD itself and the channel the VOD belongs to (see below).
Additional Class Attributes
-----
The following are class attributes which contain basic information about the VOD and its associated channel.
- `vod_title`:
the title of the VOD
- `vod_length`:
the length of the VOD in hours
- `vod_date`:
the date when the broadcast has been streamed
- `vod_views`:
the total amount of VOD views
- `channel`:
the name of the channel associated with the VOD
- `channel_id`:
the channel ID
- `channel_views`:
total channel views
- `channel_followers`:
total channel followers
- `channel_broadcaster_type`:
whether the channel is partnered or a affiliate
:param vod_id: the VOD ID to fetch the information for
"""
def __init__(self, vod_id):
self.vod_id = str(vod_id)
self._basic_data = self._get_basic_data()
self.vod_title = self._basic_data.title
self.vod_length = self._basic_data.vod_length
self.vod_date = self._basic_data.created_at
self.vod_game = self._basic_data.game
self.vod_views = self._basic_data.views
self.channel = self._basic_data.channel_name
self.channel_id = self._basic_data.channel_id
self.channel_views = self._basic_data.channel_views
self.channel_followers = self._basic_data.channel_followers
self.channel_broadcaster_type = self._basic_data.channel_type
def __repr__(self):
return "<VOD vod_title={0.vod_title!r} vod_length={0.vod_length!r} vod_date={0.vod_date!r} " \
"vod_game={0.vod_game!r} vod_views={0.vod_views!r} " \
"channel={0.channel!r} channel_id={0.channel_id!r} channel_views={0.channel_views!r} " \
"channel_followers={0.channel_followers!r} channel_broadcaster_type={0.channel_broadcaster_type!r}>"\
.format(self)
def _get_basic_data(self) -> namedtuple:
""" Gets some basic information in regards to the VOD and the channel associated with the VOD.
:return: the basic data as a `namedtuple`
"""
response = requests.get(url=vod_url.format(vod_id=self.vod_id), headers=_headers)
response_body = response.json()
if response.status_code != 200:
msg_from_twitch = response_body["message"]
raise TwitchApiException(
"Twitch API responded with '{1}' (status code {0}). Expected 200 (OK)."
.format(response.status_code, msg_from_twitch)
)
BasicData = namedtuple("BasicData", "title views created_at game vod_length "
"channel_name channel_id channel_date "
"channel_views channel_followers channel_type"
)
data = BasicData(
response_body["title"], # VOD title
response_body["views"], # VOD views
response_body["created_at"], # VOD stream date
response_body["game"], # what game has been streamed
response_body["length"], # VOD length in seconds (seconds / 3600 = hours)
response_body["channel"]["display_name"], # channel name (streamer name)
response_body["channel"]["_id"], # channel ID
response_body["channel"]["created_at"], # channel creation date
response_body["channel"]["views"], # total channel views
response_body["channel"]["followers"], # total channel followers
response_body["channel"]["broadcaster_type"] # broadcaster type (i.e. partner or affiliate, etc.)
)
data = data._replace(vod_length=round(float(data.vod_length) / 3600, 2))
return data
def get_vodchat(self) -> VODChat:
""" Gets the VODChat associated with the `vod_id`.
:return: the VODChat
"""
vod_chat = VODChat(vod_id=self.vod_id, _basic_vod_data=self._basic_data, _headers=_headers)
return vod_chat
| """
pyvod-chat - a simple tool to download a past Twitch.tv broadcasts (VOD) chat comments!
Available on GitHub (+ documentation): https://github.com/sixP-NaraKa/pyvod-chat
"""
import os
from collections import namedtuple
import requests
import dotenv
from .vodchat import VODChat
from .exceptions import TwitchApiException
# check for a .env file and get the "twitch-client-id" which we need to identify the application for use with the API
# this is NOT the same as the Client-Secret, which we do not need here
# if there is no such Client-ID or it is empty, we use a default Client-ID
dotenv.load_dotenv()
_client_id = os.getenv("twitch-client-id")
_client_id = _client_id if _client_id else "r52h1i1phlvyxs0sdi3ooam1b3w62g"
# needed request headers
_headers = {"client-id": _client_id, "accept": "application/vnd.twitchtv.v5+json"}
# additional API url
vod_url = "https://api.twitch.tv/v5/videos/{vod_id}"
class VOD:
""" Represents a Twitch.tv VOD (video-on-demand).
The main entry point, responsible for getting the VODChat via `get_videochat()`
as well as some basic information about the VOD itself and the channel the VOD belongs to (see below).
Additional Class Attributes
-----
The following are class attributes which contain basic information about the VOD and its associated channel.
- `vod_title`:
the title of the VOD
- `vod_length`:
the length of the VOD in hours
- `vod_date`:
the date when the broadcast has been streamed
- `vod_views`:
the total amount of VOD views
- `channel`:
the name of the channel associated with the VOD
- `channel_id`:
the channel ID
- `channel_views`:
total channel views
- `channel_followers`:
total channel followers
- `channel_broadcaster_type`:
whether the channel is partnered or a affiliate
:param vod_id: the VOD ID to fetch the information for
"""
def __init__(self, vod_id):
self.vod_id = str(vod_id)
self._basic_data = self._get_basic_data()
self.vod_title = self._basic_data.title
self.vod_length = self._basic_data.vod_length
self.vod_date = self._basic_data.created_at
self.vod_game = self._basic_data.game
self.vod_views = self._basic_data.views
self.channel = self._basic_data.channel_name
self.channel_id = self._basic_data.channel_id
self.channel_views = self._basic_data.channel_views
self.channel_followers = self._basic_data.channel_followers
self.channel_broadcaster_type = self._basic_data.channel_type
def __repr__(self):
return "<VOD vod_title={0.vod_title!r} vod_length={0.vod_length!r} vod_date={0.vod_date!r} " \
"vod_game={0.vod_game!r} vod_views={0.vod_views!r} " \
"channel={0.channel!r} channel_id={0.channel_id!r} channel_views={0.channel_views!r} " \
"channel_followers={0.channel_followers!r} channel_broadcaster_type={0.channel_broadcaster_type!r}>"\
.format(self)
def _get_basic_data(self) -> namedtuple:
""" Gets some basic information in regards to the VOD and the channel associated with the VOD.
:return: the basic data as a `namedtuple`
"""
response = requests.get(url=vod_url.format(vod_id=self.vod_id), headers=_headers)
response_body = response.json()
if response.status_code != 200:
msg_from_twitch = response_body["message"]
raise TwitchApiException(
"Twitch API responded with '{1}' (status code {0}). Expected 200 (OK)."
.format(response.status_code, msg_from_twitch)
)
BasicData = namedtuple("BasicData", "title views created_at game vod_length "
"channel_name channel_id channel_date "
"channel_views channel_followers channel_type"
)
data = BasicData(
response_body["title"], # VOD title
response_body["views"], # VOD views
response_body["created_at"], # VOD stream date
response_body["game"], # what game has been streamed
response_body["length"], # VOD length in seconds (seconds / 3600 = hours)
response_body["channel"]["display_name"], # channel name (streamer name)
response_body["channel"]["_id"], # channel ID
response_body["channel"]["created_at"], # channel creation date
response_body["channel"]["views"], # total channel views
response_body["channel"]["followers"], # total channel followers
response_body["channel"]["broadcaster_type"] # broadcaster type (i.e. partner or affiliate, etc.)
)
data = data._replace(vod_length=round(float(data.vod_length) / 3600, 2))
return data
def get_vodchat(self) -> VODChat:
""" Gets the VODChat associated with the `vod_id`.
:return: the VODChat
"""
vod_chat = VODChat(vod_id=self.vod_id, _basic_vod_data=self._basic_data, _headers=_headers)
return vod_chat | en | 0.851669 | pyvod-chat - a simple tool to download a past Twitch.tv broadcasts (VOD) chat comments! Available on GitHub (+ documentation): https://github.com/sixP-NaraKa/pyvod-chat # check for a .env file and get the "twitch-client-id" which we need to identify the application for use with the API # this is NOT the same as the Client-Secret, which we do not need here # if there is no such Client-ID or it is empty, we use a default Client-ID # needed request headers # additional API url Represents a Twitch.tv VOD (video-on-demand). The main entry point, responsible for getting the VODChat via `get_videochat()` as well as some basic information about the VOD itself and the channel the VOD belongs to (see below). Additional Class Attributes ----- The following are class attributes which contain basic information about the VOD and its associated channel. - `vod_title`: the title of the VOD - `vod_length`: the length of the VOD in hours - `vod_date`: the date when the broadcast has been streamed - `vod_views`: the total amount of VOD views - `channel`: the name of the channel associated with the VOD - `channel_id`: the channel ID - `channel_views`: total channel views - `channel_followers`: total channel followers - `channel_broadcaster_type`: whether the channel is partnered or a affiliate :param vod_id: the VOD ID to fetch the information for Gets some basic information in regards to the VOD and the channel associated with the VOD. :return: the basic data as a `namedtuple` # VOD title # VOD views # VOD stream date # what game has been streamed # VOD length in seconds (seconds / 3600 = hours) # channel name (streamer name) # channel ID # channel creation date # total channel views # total channel followers # broadcaster type (i.e. partner or affiliate, etc.) Gets the VODChat associated with the `vod_id`. :return: the VODChat | 2.767197 | 3 |
test/test_histogram.py | zachjweiner/pystella | 14 | 6613295 | <gh_stars>10-100
__copyright__ = "Copyright (C) 2019 <NAME>"
__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 numpy as np
import pyopencl as cl
import pyopencl.clrandom as clr
import pystella as ps
import pytest
from common import get_errs
from pyopencl.tools import ( # noqa
pytest_generate_tests_for_pyopencl as pytest_generate_tests)
@pytest.mark.filterwarnings(
"ignore::pyopencl.characterize.CLCharacterizationWarning")
@pytest.mark.filterwarnings("ignore::loopy.diagnostic.LoopyAdvisory")
@pytest.mark.parametrize("dtype", ["float64"])
@pytest.mark.parametrize("num_bins", [123, 1024, 1493])
@pytest.mark.parametrize("_N", [256, 1200])
def test_trivial_histogram(ctx_factory, grid_shape, proc_shape, dtype,
num_bins, _N, timing=False):
ctx = ctx_factory()
grid_shape = (_N,)*3
queue = cl.CommandQueue(ctx)
h = 1
mpi = ps.DomainDecomposition(proc_shape, h, grid_shape=grid_shape)
rank_shape, _ = mpi.get_rank_shape_start(grid_shape)
histograms = {
"a": (13., 1),
"b": (10.3, 2),
"c": (100.9, 3),
}
hist = ps.Histogrammer(mpi, histograms, num_bins, dtype, rank_shape=rank_shape)
result = hist(queue)
for key, (_b, weight) in histograms.items():
res = result[key]
b = int(np.floor(_b))
expected = weight * np.product(grid_shape)
assert res[b] == expected, \
f"{key}: result={res[b]}, {expected=}, ratio={res[b]/expected}"
assert np.all(res[res != res[b]] == 0.)
@pytest.mark.filterwarnings(
"ignore::pyopencl.characterize.CLCharacterizationWarning")
@pytest.mark.filterwarnings("ignore::loopy.diagnostic.LoopyAdvisory")
@pytest.mark.parametrize("dtype", ["float64", "float32"])
@pytest.mark.parametrize("num_bins", [123, 1024, 1493])
def test_histogram(ctx_factory, grid_shape, proc_shape, dtype, num_bins,
timing=False):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
h = 1
mpi = ps.DomainDecomposition(proc_shape, h, grid_shape=grid_shape)
rank_shape, _ = mpi.get_rank_shape_start(grid_shape)
if np.dtype(dtype) in (np.dtype("float64"), np.dtype("complex128")):
max_rtol, avg_rtol = 1e-10, 1e-11
else:
max_rtol, avg_rtol = 5e-4, 5e-5
from pymbolic import var
_fx = ps.Field("fx")
histograms = {
"count": (var("abs")(_fx) * num_bins, 1),
"squared": (var("abs")(_fx) * num_bins, _fx**2),
}
hist = ps.Histogrammer(mpi, histograms, num_bins, dtype, rank_shape=rank_shape)
rng = clr.ThreefryGenerator(ctx, seed=12321)
fx = rng.uniform(queue, rank_shape, dtype)
fx_h = fx.get()
result = hist(queue, fx=fx)
res = result["count"]
assert np.sum(res.astype("int64")) == np.product(grid_shape), \
f"Count histogram doesn't sum to grid_size ({np.sum(res)})"
bins = np.linspace(0, 1, num_bins+1).astype(dtype)
weights = np.ones_like(fx_h)
np_res = np.histogram(fx_h, bins=bins, weights=weights)[0]
np_res = mpi.allreduce(np_res)
max_err, avg_err = get_errs(res, np_res)
assert max_err < max_rtol and avg_err < avg_rtol, \
f"Histogrammer inaccurate for grid_shape={grid_shape}" \
f": {max_err=}, {avg_err=}"
res = result["squared"]
np_res = np.histogram(fx_h, bins=bins, weights=fx_h**2)[0]
np_res = mpi.allreduce(np_res)
max_err, avg_err = get_errs(res, np_res)
assert max_err < max_rtol and avg_err < avg_rtol, \
f"Histogrammer with weights inaccurate for grid_shape={grid_shape}" \
f": {max_err=}, {avg_err=}"
if timing:
from common import timer
t = timer(lambda: hist(queue, fx=fx))
print(f"histogram took {t:.3f} ms for {grid_shape=}, {dtype=}")
@pytest.mark.filterwarnings(
"ignore::pyopencl.characterize.CLCharacterizationWarning")
@pytest.mark.filterwarnings("ignore::loopy.diagnostic.LoopyAdvisory")
@pytest.mark.parametrize("dtype", ["float64", "float32"])
def test_field_histogram(ctx_factory, grid_shape, proc_shape, dtype, timing=False):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
h = 1
mpi = ps.DomainDecomposition(proc_shape, h, grid_shape=grid_shape)
rank_shape, _ = mpi.get_rank_shape_start(grid_shape)
pencil_shape = tuple(Ni + 2 * h for Ni in rank_shape)
num_bins = 432
if np.dtype(dtype) in (np.dtype("float64"), np.dtype("complex128")):
max_rtol, avg_rtol = 1e-10, 1e-11
else:
max_rtol, avg_rtol = 5e-4, 5e-5
hist = ps.FieldHistogrammer(mpi, num_bins, dtype,
rank_shape=rank_shape, halo_shape=h)
rng = clr.ThreefryGenerator(ctx, seed=12321)
fx = rng.uniform(queue, (2, 2)+pencil_shape, dtype, a=-1.2, b=3.)
fx_h = fx.get()[..., h:-h, h:-h, h:-h]
result = hist(fx)
outer_shape = fx.shape[:-3]
from itertools import product
slices = list(product(*[range(n) for n in outer_shape]))
for slc in slices:
res = result["linear"][slc]
np_res = np.histogram(fx_h[slc], bins=result["linear_bins"][slc])[0]
np_res = mpi.allreduce(np_res)
max_err, avg_err = get_errs(res, np_res)
assert max_err < max_rtol and avg_err < avg_rtol, \
f"linear Histogrammer inaccurate for grid_shape={grid_shape}" \
f": {max_err=}, {avg_err=}"
res = result["log"][slc]
bins = result["log_bins"][slc]
# avoid FPA comparison issues
# numpy sometimes doesn't count the actual maximum/minimum
eps = 1e-14 if np.dtype(dtype) == np.dtype("float64") else 1e-4
bins[0] *= (1 - eps)
bins[-1] *= (1 + eps)
np_res = np.histogram(np.abs(fx_h[slc]), bins=bins)[0]
np_res = mpi.allreduce(np_res)
norm = np.maximum(np.abs(res), np.abs(np_res))
norm[norm == 0.] = 1.
max_err, avg_err = get_errs(res, np_res)
assert max_err < max_rtol and avg_err < avg_rtol, \
f"log Histogrammer inaccurate for grid_shape={grid_shape}" \
f": {max_err=}, {avg_err=}"
if timing:
from common import timer
t = timer(lambda: hist(fx[0, 0]))
print(f"field histogram took {t:.3f} ms for {grid_shape=}, {dtype=}")
if __name__ == "__main__":
from common import parser
args = parser.parse_args()
test_trivial_histogram(
ps.choose_device_and_make_context,
grid_shape=args.grid_shape, proc_shape=args.proc_shape,
dtype="float64", timing=args.timing, num_bins=1493, _N=1200,
)
test_histogram(
ps.choose_device_and_make_context,
grid_shape=args.grid_shape, proc_shape=args.proc_shape,
dtype=args.dtype, timing=args.timing, num_bins=1001,
)
test_field_histogram(
ps.choose_device_and_make_context,
grid_shape=args.grid_shape, proc_shape=args.proc_shape,
dtype=args.dtype, timing=args.timing
)
| __copyright__ = "Copyright (C) 2019 <NAME>"
__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 numpy as np
import pyopencl as cl
import pyopencl.clrandom as clr
import pystella as ps
import pytest
from common import get_errs
from pyopencl.tools import ( # noqa
pytest_generate_tests_for_pyopencl as pytest_generate_tests)
@pytest.mark.filterwarnings(
"ignore::pyopencl.characterize.CLCharacterizationWarning")
@pytest.mark.filterwarnings("ignore::loopy.diagnostic.LoopyAdvisory")
@pytest.mark.parametrize("dtype", ["float64"])
@pytest.mark.parametrize("num_bins", [123, 1024, 1493])
@pytest.mark.parametrize("_N", [256, 1200])
def test_trivial_histogram(ctx_factory, grid_shape, proc_shape, dtype,
num_bins, _N, timing=False):
ctx = ctx_factory()
grid_shape = (_N,)*3
queue = cl.CommandQueue(ctx)
h = 1
mpi = ps.DomainDecomposition(proc_shape, h, grid_shape=grid_shape)
rank_shape, _ = mpi.get_rank_shape_start(grid_shape)
histograms = {
"a": (13., 1),
"b": (10.3, 2),
"c": (100.9, 3),
}
hist = ps.Histogrammer(mpi, histograms, num_bins, dtype, rank_shape=rank_shape)
result = hist(queue)
for key, (_b, weight) in histograms.items():
res = result[key]
b = int(np.floor(_b))
expected = weight * np.product(grid_shape)
assert res[b] == expected, \
f"{key}: result={res[b]}, {expected=}, ratio={res[b]/expected}"
assert np.all(res[res != res[b]] == 0.)
@pytest.mark.filterwarnings(
"ignore::pyopencl.characterize.CLCharacterizationWarning")
@pytest.mark.filterwarnings("ignore::loopy.diagnostic.LoopyAdvisory")
@pytest.mark.parametrize("dtype", ["float64", "float32"])
@pytest.mark.parametrize("num_bins", [123, 1024, 1493])
def test_histogram(ctx_factory, grid_shape, proc_shape, dtype, num_bins,
timing=False):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
h = 1
mpi = ps.DomainDecomposition(proc_shape, h, grid_shape=grid_shape)
rank_shape, _ = mpi.get_rank_shape_start(grid_shape)
if np.dtype(dtype) in (np.dtype("float64"), np.dtype("complex128")):
max_rtol, avg_rtol = 1e-10, 1e-11
else:
max_rtol, avg_rtol = 5e-4, 5e-5
from pymbolic import var
_fx = ps.Field("fx")
histograms = {
"count": (var("abs")(_fx) * num_bins, 1),
"squared": (var("abs")(_fx) * num_bins, _fx**2),
}
hist = ps.Histogrammer(mpi, histograms, num_bins, dtype, rank_shape=rank_shape)
rng = clr.ThreefryGenerator(ctx, seed=12321)
fx = rng.uniform(queue, rank_shape, dtype)
fx_h = fx.get()
result = hist(queue, fx=fx)
res = result["count"]
assert np.sum(res.astype("int64")) == np.product(grid_shape), \
f"Count histogram doesn't sum to grid_size ({np.sum(res)})"
bins = np.linspace(0, 1, num_bins+1).astype(dtype)
weights = np.ones_like(fx_h)
np_res = np.histogram(fx_h, bins=bins, weights=weights)[0]
np_res = mpi.allreduce(np_res)
max_err, avg_err = get_errs(res, np_res)
assert max_err < max_rtol and avg_err < avg_rtol, \
f"Histogrammer inaccurate for grid_shape={grid_shape}" \
f": {max_err=}, {avg_err=}"
res = result["squared"]
np_res = np.histogram(fx_h, bins=bins, weights=fx_h**2)[0]
np_res = mpi.allreduce(np_res)
max_err, avg_err = get_errs(res, np_res)
assert max_err < max_rtol and avg_err < avg_rtol, \
f"Histogrammer with weights inaccurate for grid_shape={grid_shape}" \
f": {max_err=}, {avg_err=}"
if timing:
from common import timer
t = timer(lambda: hist(queue, fx=fx))
print(f"histogram took {t:.3f} ms for {grid_shape=}, {dtype=}")
@pytest.mark.filterwarnings(
"ignore::pyopencl.characterize.CLCharacterizationWarning")
@pytest.mark.filterwarnings("ignore::loopy.diagnostic.LoopyAdvisory")
@pytest.mark.parametrize("dtype", ["float64", "float32"])
def test_field_histogram(ctx_factory, grid_shape, proc_shape, dtype, timing=False):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
h = 1
mpi = ps.DomainDecomposition(proc_shape, h, grid_shape=grid_shape)
rank_shape, _ = mpi.get_rank_shape_start(grid_shape)
pencil_shape = tuple(Ni + 2 * h for Ni in rank_shape)
num_bins = 432
if np.dtype(dtype) in (np.dtype("float64"), np.dtype("complex128")):
max_rtol, avg_rtol = 1e-10, 1e-11
else:
max_rtol, avg_rtol = 5e-4, 5e-5
hist = ps.FieldHistogrammer(mpi, num_bins, dtype,
rank_shape=rank_shape, halo_shape=h)
rng = clr.ThreefryGenerator(ctx, seed=12321)
fx = rng.uniform(queue, (2, 2)+pencil_shape, dtype, a=-1.2, b=3.)
fx_h = fx.get()[..., h:-h, h:-h, h:-h]
result = hist(fx)
outer_shape = fx.shape[:-3]
from itertools import product
slices = list(product(*[range(n) for n in outer_shape]))
for slc in slices:
res = result["linear"][slc]
np_res = np.histogram(fx_h[slc], bins=result["linear_bins"][slc])[0]
np_res = mpi.allreduce(np_res)
max_err, avg_err = get_errs(res, np_res)
assert max_err < max_rtol and avg_err < avg_rtol, \
f"linear Histogrammer inaccurate for grid_shape={grid_shape}" \
f": {max_err=}, {avg_err=}"
res = result["log"][slc]
bins = result["log_bins"][slc]
# avoid FPA comparison issues
# numpy sometimes doesn't count the actual maximum/minimum
eps = 1e-14 if np.dtype(dtype) == np.dtype("float64") else 1e-4
bins[0] *= (1 - eps)
bins[-1] *= (1 + eps)
np_res = np.histogram(np.abs(fx_h[slc]), bins=bins)[0]
np_res = mpi.allreduce(np_res)
norm = np.maximum(np.abs(res), np.abs(np_res))
norm[norm == 0.] = 1.
max_err, avg_err = get_errs(res, np_res)
assert max_err < max_rtol and avg_err < avg_rtol, \
f"log Histogrammer inaccurate for grid_shape={grid_shape}" \
f": {max_err=}, {avg_err=}"
if timing:
from common import timer
t = timer(lambda: hist(fx[0, 0]))
print(f"field histogram took {t:.3f} ms for {grid_shape=}, {dtype=}")
if __name__ == "__main__":
from common import parser
args = parser.parse_args()
test_trivial_histogram(
ps.choose_device_and_make_context,
grid_shape=args.grid_shape, proc_shape=args.proc_shape,
dtype="float64", timing=args.timing, num_bins=1493, _N=1200,
)
test_histogram(
ps.choose_device_and_make_context,
grid_shape=args.grid_shape, proc_shape=args.proc_shape,
dtype=args.dtype, timing=args.timing, num_bins=1001,
)
test_field_histogram(
ps.choose_device_and_make_context,
grid_shape=args.grid_shape, proc_shape=args.proc_shape,
dtype=args.dtype, timing=args.timing
) | en | 0.765683 | 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. # noqa # avoid FPA comparison issues # numpy sometimes doesn't count the actual maximum/minimum | 1.802699 | 2 |
ilxutils/ilxutils/graph_edge_cases.py | tmsincomb/pyontutils | 11 | 6613296 | <reponame>tmsincomb/pyontutils
from pyontutils.utils import *
from pyontutils.core import *
from pyontutils.closed_namespaces import *
edge_cases = {
#Definitions
'definition:': 'definition:',
'skos:definition': 'definition:',
'definition': 'definition:',
'NIFRID:birnlexDefinition': 'definition:',
'NIFRID:externallySourcedDefinition': 'definition:',
#ExistingIds
'ilxtr:existingIds': 'ilxtr:existingIds',
#LABELS
'rdfs:label': 'rdfs:label',
'skos:prefLabel': 'rdfs:label',
#SUPERCLASSES
'rdfs:subClassOf': 'rdfs:subClassOf',
#SYNONYMS
'oboInOwl:hasExactSynonym': 'NIFRID:synonym',
#'oboInOwl:hasNarrowSynonym' : 'NIFRID:synonym',
#'oboInOwl:hasBroadSynonym' : 'NIFRID:synonym',
'oboInOwl:hasRelatedSynonym': 'NIFRID:synonym',
'go:systematic_synonym': 'NIFRID:synonym',
'NIFRID:synonym': 'NIFRID:synonym',
#TYPE
'rdf:type': 'rdf:type',
}
full = {
#'':None, # safety (now managed directly in the curies file)
#'EHDAA2':'http://purl.obolibrary.org/obo/EHDAA2_', # FIXME needs to go in curie map?
'hasRole': 'http://purl.obolibrary.org/obo/RO_0000087',
'inheresIn': 'http://purl.obolibrary.org/obo/RO_0000052',
'bearerOf': 'http://purl.obolibrary.org/obo/RO_0000053',
'participatesIn': 'http://purl.obolibrary.org/obo/RO_0000056',
'hasParticipant': 'http://purl.obolibrary.org/obo/RO_0000057',
'adjacentTo': 'http://purl.obolibrary.org/obo/RO_0002220',
'derivesFrom': 'http://purl.obolibrary.org/obo/RO_0001000',
'derivesInto': 'http://purl.obolibrary.org/obo/RO_0001001',
'agentIn': 'http://purl.obolibrary.org/obo/RO_0002217',
'hasAgent': 'http://purl.obolibrary.org/obo/RO_0002218',
'containedIn': 'http://purl.obolibrary.org/obo/RO_0001018',
'contains': 'http://purl.obolibrary.org/obo/RO_0001019',
'locatedIn': 'http://purl.obolibrary.org/obo/RO_0001025',
'locationOf': 'http://purl.obolibrary.org/obo/RO_0001015',
'toward': 'http://purl.obolibrary.org/obo/RO_0002503',
'replacedBy': 'http://purl.obolibrary.org/obo/IAO_0100001',
'hasCurStatus': 'http://purl.obolibrary.org/obo/IAO_0000114',
'definition': 'http://purl.obolibrary.org/obo/IAO_0000115',
'editorNote': 'http://purl.obolibrary.org/obo/IAO_0000116',
'termEditor': 'http://purl.obolibrary.org/obo/IAO_0000117',
'altTerm': 'http://purl.obolibrary.org/obo/IAO_0000118',
'defSource': 'http://purl.obolibrary.org/obo/IAO_0000119',
'termsMerged': 'http://purl.obolibrary.org/obo/IAO_0000227',
'obsReason': 'http://purl.obolibrary.org/obo/IAO_0000231',
'curatorNote': 'http://purl.obolibrary.org/obo/IAO_0000232',
'importedFrom': 'http://purl.obolibrary.org/obo/IAO_0000412',
'partOf': 'http://purl.obolibrary.org/obo/BFO_0000050',
'hasPart': 'http://purl.obolibrary.org/obo/BFO_0000051',
}
normal = {
'ILX': 'http://uri.interlex.org/base/ilx_',
'ilx': 'http://uri.interlex.org/base/',
'ilxr': 'http://uri.interlex.org/base/readable/',
'ilxtr': 'http://uri.interlex.org/tgbugs/uris/readable/',
# for obo files with 'fake' namespaces, http://uri.interlex.org/fakeobo/uris/ eqiv to purl.obolibrary.org/
'fobo': 'http://uri.interlex.org/fakeobo/uris/obo/',
'PROTEGE': 'http://protege.stanford.edu/plugins/owl/protege#',
'ILXREPLACE': 'http://ILXREPLACE.org/',
'TEMP': interlex_namespace('temp/uris'),
'FIXME': 'http://FIXME.org/',
'NIFTTL': 'http://ontology.neuinfo.org/NIF/ttl/',
'NIFRET': 'http://ontology.neuinfo.org/NIF/Retired/NIF-Retired.owl#',
'NLXWIKI': 'http://neurolex.org/wiki/',
'dc': 'http://purl.org/dc/elements/1.1/',
'dcterms': 'http://purl.org/dc/terms/',
'dctypes':
'http://purl.org/dc/dcmitype/', # FIXME there is no agreement on qnames
# FIXME a thought: was # intentionally used to increase user privacy? or is this just happenstance?
'nsu': 'http://www.FIXME.org/nsupper#',
'oboInOwl': 'http://www.geneontology.org/formats/oboInOwl#',
'owl': 'http://www.w3.org/2002/07/owl#',
'ro': 'http://www.obofoundry.org/ro/ro.owl#',
'skos': 'http://www.w3.org/2004/02/skos/core#',
'rdf': 'http://www.w3.org/1999/02/22-rdf-syntax-ns#',
'rdfs': 'http://www.w3.org/2000/01/rdf-schema#',
'prov': 'http://www.w3.org/ns/prov#',
}
expo = {
'ilxtr:existingId': 'ilxtr:identifier',
'oboInOwl:hasAlternativeId': 'ilxtr:identifier',
'NIFRID:isReplacedByClass': 'replacedBy:',
'skos:editorialNote': 'editorNote:',
'ncbitaxon:has_rank': 'NIFRID:hasTaxonRank',
}
| from pyontutils.utils import *
from pyontutils.core import *
from pyontutils.closed_namespaces import *
edge_cases = {
#Definitions
'definition:': 'definition:',
'skos:definition': 'definition:',
'definition': 'definition:',
'NIFRID:birnlexDefinition': 'definition:',
'NIFRID:externallySourcedDefinition': 'definition:',
#ExistingIds
'ilxtr:existingIds': 'ilxtr:existingIds',
#LABELS
'rdfs:label': 'rdfs:label',
'skos:prefLabel': 'rdfs:label',
#SUPERCLASSES
'rdfs:subClassOf': 'rdfs:subClassOf',
#SYNONYMS
'oboInOwl:hasExactSynonym': 'NIFRID:synonym',
#'oboInOwl:hasNarrowSynonym' : 'NIFRID:synonym',
#'oboInOwl:hasBroadSynonym' : 'NIFRID:synonym',
'oboInOwl:hasRelatedSynonym': 'NIFRID:synonym',
'go:systematic_synonym': 'NIFRID:synonym',
'NIFRID:synonym': 'NIFRID:synonym',
#TYPE
'rdf:type': 'rdf:type',
}
full = {
#'':None, # safety (now managed directly in the curies file)
#'EHDAA2':'http://purl.obolibrary.org/obo/EHDAA2_', # FIXME needs to go in curie map?
'hasRole': 'http://purl.obolibrary.org/obo/RO_0000087',
'inheresIn': 'http://purl.obolibrary.org/obo/RO_0000052',
'bearerOf': 'http://purl.obolibrary.org/obo/RO_0000053',
'participatesIn': 'http://purl.obolibrary.org/obo/RO_0000056',
'hasParticipant': 'http://purl.obolibrary.org/obo/RO_0000057',
'adjacentTo': 'http://purl.obolibrary.org/obo/RO_0002220',
'derivesFrom': 'http://purl.obolibrary.org/obo/RO_0001000',
'derivesInto': 'http://purl.obolibrary.org/obo/RO_0001001',
'agentIn': 'http://purl.obolibrary.org/obo/RO_0002217',
'hasAgent': 'http://purl.obolibrary.org/obo/RO_0002218',
'containedIn': 'http://purl.obolibrary.org/obo/RO_0001018',
'contains': 'http://purl.obolibrary.org/obo/RO_0001019',
'locatedIn': 'http://purl.obolibrary.org/obo/RO_0001025',
'locationOf': 'http://purl.obolibrary.org/obo/RO_0001015',
'toward': 'http://purl.obolibrary.org/obo/RO_0002503',
'replacedBy': 'http://purl.obolibrary.org/obo/IAO_0100001',
'hasCurStatus': 'http://purl.obolibrary.org/obo/IAO_0000114',
'definition': 'http://purl.obolibrary.org/obo/IAO_0000115',
'editorNote': 'http://purl.obolibrary.org/obo/IAO_0000116',
'termEditor': 'http://purl.obolibrary.org/obo/IAO_0000117',
'altTerm': 'http://purl.obolibrary.org/obo/IAO_0000118',
'defSource': 'http://purl.obolibrary.org/obo/IAO_0000119',
'termsMerged': 'http://purl.obolibrary.org/obo/IAO_0000227',
'obsReason': 'http://purl.obolibrary.org/obo/IAO_0000231',
'curatorNote': 'http://purl.obolibrary.org/obo/IAO_0000232',
'importedFrom': 'http://purl.obolibrary.org/obo/IAO_0000412',
'partOf': 'http://purl.obolibrary.org/obo/BFO_0000050',
'hasPart': 'http://purl.obolibrary.org/obo/BFO_0000051',
}
normal = {
'ILX': 'http://uri.interlex.org/base/ilx_',
'ilx': 'http://uri.interlex.org/base/',
'ilxr': 'http://uri.interlex.org/base/readable/',
'ilxtr': 'http://uri.interlex.org/tgbugs/uris/readable/',
# for obo files with 'fake' namespaces, http://uri.interlex.org/fakeobo/uris/ eqiv to purl.obolibrary.org/
'fobo': 'http://uri.interlex.org/fakeobo/uris/obo/',
'PROTEGE': 'http://protege.stanford.edu/plugins/owl/protege#',
'ILXREPLACE': 'http://ILXREPLACE.org/',
'TEMP': interlex_namespace('temp/uris'),
'FIXME': 'http://FIXME.org/',
'NIFTTL': 'http://ontology.neuinfo.org/NIF/ttl/',
'NIFRET': 'http://ontology.neuinfo.org/NIF/Retired/NIF-Retired.owl#',
'NLXWIKI': 'http://neurolex.org/wiki/',
'dc': 'http://purl.org/dc/elements/1.1/',
'dcterms': 'http://purl.org/dc/terms/',
'dctypes':
'http://purl.org/dc/dcmitype/', # FIXME there is no agreement on qnames
# FIXME a thought: was # intentionally used to increase user privacy? or is this just happenstance?
'nsu': 'http://www.FIXME.org/nsupper#',
'oboInOwl': 'http://www.geneontology.org/formats/oboInOwl#',
'owl': 'http://www.w3.org/2002/07/owl#',
'ro': 'http://www.obofoundry.org/ro/ro.owl#',
'skos': 'http://www.w3.org/2004/02/skos/core#',
'rdf': 'http://www.w3.org/1999/02/22-rdf-syntax-ns#',
'rdfs': 'http://www.w3.org/2000/01/rdf-schema#',
'prov': 'http://www.w3.org/ns/prov#',
}
expo = {
'ilxtr:existingId': 'ilxtr:identifier',
'oboInOwl:hasAlternativeId': 'ilxtr:identifier',
'NIFRID:isReplacedByClass': 'replacedBy:',
'skos:editorialNote': 'editorNote:',
'ncbitaxon:has_rank': 'NIFRID:hasTaxonRank',
} | en | 0.596399 | #Definitions #ExistingIds #LABELS #SUPERCLASSES #SYNONYMS #'oboInOwl:hasNarrowSynonym' : 'NIFRID:synonym', #'oboInOwl:hasBroadSynonym' : 'NIFRID:synonym', #TYPE #'':None, # safety (now managed directly in the curies file) #'EHDAA2':'http://purl.obolibrary.org/obo/EHDAA2_', # FIXME needs to go in curie map? # for obo files with 'fake' namespaces, http://uri.interlex.org/fakeobo/uris/ eqiv to purl.obolibrary.org/ #', #', # FIXME there is no agreement on qnames # FIXME a thought: was # intentionally used to increase user privacy? or is this just happenstance? #', #', #', #', #', #', #', #', | 1.566781 | 2 |
main.py | code-s-witch/project2 | 0 | 6613297 | from search import graph
import networkx as nx
def main():
adjlist_file = './data/tiny_network.adjlist'
start = '<NAME>'
#* CASE 1: If there's no end node, just return a list with the order of traversal
# instantiate a graph object with a start node only since we only care about traversal here not shortest path
bfs_traversal = graph.Graph(adjlist_file).bfs(start)
networkx_graph = nx.read_adjlist(adjlist_file, create_using=nx.DiGraph, delimiter=";") #for testing using nx API
# assert that all nodes are being traversed (ie. returns the right number of nodes)
assert(nx.number_of_nodes(networkx_graph) == len(bfs_traversal))
assert all(node in networkx_graph for node in bfs_traversal) == True
# assert that all nodes are being traversed in the right order
nx_bfs_traversal_nodes = list(nx.bfs_tree(networkx_graph, start).nodes)
assert all([True for node in range(len(bfs_traversal)) if bfs_traversal[node] == nx_bfs_traversal_nodes[node]]) == True
#Case 2
adjlist_file = './data/small_file.adjlist'
start = 'A'
end = 'G'
truth_set = ['A', 'C', 'G']
bfs_path = graph.Graph(adjlist_file).bfs(start, end)
"""Source: https://www.geeksforgeeks.org/python-check-if-two-lists-are-identical/"""
assert(len(truth_set)== len(bfs_path) and len(truth_set) == sum([1 for i, j in zip(truth_set, bfs_path) if i == j]))
print(bfs_path)
#* CASE 3: If there is an end node and a path does not exist, return None
#strategy - give the bfs search method a end node that does not exist to esnure no path exists (Brenda's suggestion)
adjlist_file = './data/citation_network.adjlist'
start = '<NAME>'
end = '<NAME>'
# instantiate a graph object with a start node only since we only care about traversal here not shortest path
bfs_path = graph.Graph(adjlist_file).bfs(start, end)
assert(bfs_path == None)
if __name__ == "__main__":
main()
| from search import graph
import networkx as nx
def main():
adjlist_file = './data/tiny_network.adjlist'
start = '<NAME>'
#* CASE 1: If there's no end node, just return a list with the order of traversal
# instantiate a graph object with a start node only since we only care about traversal here not shortest path
bfs_traversal = graph.Graph(adjlist_file).bfs(start)
networkx_graph = nx.read_adjlist(adjlist_file, create_using=nx.DiGraph, delimiter=";") #for testing using nx API
# assert that all nodes are being traversed (ie. returns the right number of nodes)
assert(nx.number_of_nodes(networkx_graph) == len(bfs_traversal))
assert all(node in networkx_graph for node in bfs_traversal) == True
# assert that all nodes are being traversed in the right order
nx_bfs_traversal_nodes = list(nx.bfs_tree(networkx_graph, start).nodes)
assert all([True for node in range(len(bfs_traversal)) if bfs_traversal[node] == nx_bfs_traversal_nodes[node]]) == True
#Case 2
adjlist_file = './data/small_file.adjlist'
start = 'A'
end = 'G'
truth_set = ['A', 'C', 'G']
bfs_path = graph.Graph(adjlist_file).bfs(start, end)
"""Source: https://www.geeksforgeeks.org/python-check-if-two-lists-are-identical/"""
assert(len(truth_set)== len(bfs_path) and len(truth_set) == sum([1 for i, j in zip(truth_set, bfs_path) if i == j]))
print(bfs_path)
#* CASE 3: If there is an end node and a path does not exist, return None
#strategy - give the bfs search method a end node that does not exist to esnure no path exists (Brenda's suggestion)
adjlist_file = './data/citation_network.adjlist'
start = '<NAME>'
end = '<NAME>'
# instantiate a graph object with a start node only since we only care about traversal here not shortest path
bfs_path = graph.Graph(adjlist_file).bfs(start, end)
assert(bfs_path == None)
if __name__ == "__main__":
main()
| en | 0.841602 | #* CASE 1: If there's no end node, just return a list with the order of traversal # instantiate a graph object with a start node only since we only care about traversal here not shortest path #for testing using nx API # assert that all nodes are being traversed (ie. returns the right number of nodes) # assert that all nodes are being traversed in the right order #Case 2 Source: https://www.geeksforgeeks.org/python-check-if-two-lists-are-identical/ #* CASE 3: If there is an end node and a path does not exist, return None #strategy - give the bfs search method a end node that does not exist to esnure no path exists (Brenda's suggestion) # instantiate a graph object with a start node only since we only care about traversal here not shortest path | 3.751383 | 4 |
src/ab_testing/server_starter.py | JouniVatanen/NLP-and-Deep-Learning | 1 | 6613298 | <gh_stars>1-10
# From the course: Bayesin Machine Learning in Python: A/B Testing
# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future
import numpy as np
from flask import Flask, jsonify, request
from scipy.stats import beta
# create an app
app = Flask(__name__)
# define bandits
# there's no "pull arm" here
# since that's technically now the user/client
class Bandit:
def __init__(self, name):
self.name = name
def sample(self):
# TODO
return 1
# TODO - what else does the Bandit need to do?
# initialize bandits
banditA = Bandit('A')
banditB = Bandit('B')
@app.route('/get_ad')
def get_ad():
# TODO
return jsonify({'advertisement_id': 'A'})
@app.route('/click_ad', methods=['POST'])
def click_ad():
result = 'OK'
if request.form['advertisement_id'] == 'A':
# TODO
pass
elif request.form['advertisement_id'] == 'B':
# TODO
pass
else:
result = 'Invalid Input.'
# nothing to return really
return jsonify({'result': result})
if __name__ == '__main__':
app.run(host='127.0.0.1', port='8888') | # From the course: Bayesin Machine Learning in Python: A/B Testing
# https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing
# https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing
from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future
import numpy as np
from flask import Flask, jsonify, request
from scipy.stats import beta
# create an app
app = Flask(__name__)
# define bandits
# there's no "pull arm" here
# since that's technically now the user/client
class Bandit:
def __init__(self, name):
self.name = name
def sample(self):
# TODO
return 1
# TODO - what else does the Bandit need to do?
# initialize bandits
banditA = Bandit('A')
banditB = Bandit('B')
@app.route('/get_ad')
def get_ad():
# TODO
return jsonify({'advertisement_id': 'A'})
@app.route('/click_ad', methods=['POST'])
def click_ad():
result = 'OK'
if request.form['advertisement_id'] == 'A':
# TODO
pass
elif request.form['advertisement_id'] == 'B':
# TODO
pass
else:
result = 'Invalid Input.'
# nothing to return really
return jsonify({'result': result})
if __name__ == '__main__':
app.run(host='127.0.0.1', port='8888') | en | 0.839657 | # From the course: Bayesin Machine Learning in Python: A/B Testing # https://deeplearningcourses.com/c/bayesian-machine-learning-in-python-ab-testing # https://www.udemy.com/bayesian-machine-learning-in-python-ab-testing # Note: you may need to update your version of future # sudo pip install -U future # create an app # define bandits # there's no "pull arm" here # since that's technically now the user/client # TODO # TODO - what else does the Bandit need to do? # initialize bandits # TODO # TODO # TODO # nothing to return really | 2.975045 | 3 |
backend/biz/migrations/0008_auto_20200504_0823.py | Critter-Inc/critter | 0 | 6613299 | <reponame>Critter-Inc/critter
# Generated by Django 3.0.5 on 2020-05-04 08:23
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('biz', '0007_auto_20200502_1700'),
]
operations = [
migrations.AlterUniqueTogether(
name='hours',
unique_together={('biz', 'weekday', 'from_hour')},
),
]
| # Generated by Django 3.0.5 on 2020-05-04 08:23
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('biz', '0007_auto_20200502_1700'),
]
operations = [
migrations.AlterUniqueTogether(
name='hours',
unique_together={('biz', 'weekday', 'from_hour')},
),
] | en | 0.776979 | # Generated by Django 3.0.5 on 2020-05-04 08:23 | 1.637087 | 2 |
openid_connect_op/decorators.py | rerobins/django-openid-op | 2 | 6613300 | <filename>openid_connect_op/decorators.py
import base64
from functools import wraps
from django.conf import settings
from django.http.response import HttpResponseForbidden
from django.utils import timezone
from openid_connect_op.models import OpenIDToken
def get_access_token_from_auth_header(auth_header):
auth_header = auth_header.strip()
if not auth_header.startswith('Bearer '):
raise AttributeError('Not a Bearer token')
return auth_header[7:]
def get_access_token_from_post_data(request):
post_data = request.POST
if request.content_type != 'application/x-www-form-urlencoded':
return None
if 'access_token' not in post_data:
return None
return post_data['access_token']
def get_access_token_from_uri_query(get_data):
if 'access_token' not in get_data:
return None
return get_data['access_token']
def extract_access_token(request, forbidden_on_not_present):
auth_header = request.META.get('HTTP_AUTHORIZATION', None)
access_token = None
try:
if auth_header is not None:
access_token = get_access_token_from_auth_header(auth_header)
if request.method == 'POST' and not access_token:
access_token = get_access_token_from_post_data(request)
if not access_token:
access_token = get_access_token_from_uri_query(request.GET)
if not access_token:
if forbidden_on_not_present:
return HttpResponseForbidden('No access token provided')
else:
return None
try:
db_access_token = OpenIDToken.objects.get(token_hash=OpenIDToken.get_token_hash(access_token))
if db_access_token.expiration < timezone.now():
return HttpResponseForbidden('Expired access token')
return db_access_token
except OpenIDToken.DoesNotExist:
return HttpResponseForbidden('Provided access token %s not found' % access_token)
except BaseException as e:
return HttpResponseForbidden('Access error: %s' % e)
def access_token_required(disabled_settings=None):
def wrapper(func):
"""
Check that access token is present on the request and is valid. If not, returns HttpResponseForbidden.
request is annotated with the database access token, i.e. isinstance(req.openid_access_token, OpenIDToken) == True
"""
@wraps(func)
def inner(request, *args, **kwargs):
if disabled_settings:
if getattr(settings, disabled_settings, False):
request.openid_access_token = None
return func(request, *args, **kwargs)
db_access_token = extract_access_token(request, True)
if isinstance(db_access_token, HttpResponseForbidden):
return db_access_token
request.openid_access_token = db_access_token
return func(request, *args, **kwargs)
return inner
return wrapper
__all__ = ('access_token_required',)
| <filename>openid_connect_op/decorators.py
import base64
from functools import wraps
from django.conf import settings
from django.http.response import HttpResponseForbidden
from django.utils import timezone
from openid_connect_op.models import OpenIDToken
def get_access_token_from_auth_header(auth_header):
auth_header = auth_header.strip()
if not auth_header.startswith('Bearer '):
raise AttributeError('Not a Bearer token')
return auth_header[7:]
def get_access_token_from_post_data(request):
post_data = request.POST
if request.content_type != 'application/x-www-form-urlencoded':
return None
if 'access_token' not in post_data:
return None
return post_data['access_token']
def get_access_token_from_uri_query(get_data):
if 'access_token' not in get_data:
return None
return get_data['access_token']
def extract_access_token(request, forbidden_on_not_present):
auth_header = request.META.get('HTTP_AUTHORIZATION', None)
access_token = None
try:
if auth_header is not None:
access_token = get_access_token_from_auth_header(auth_header)
if request.method == 'POST' and not access_token:
access_token = get_access_token_from_post_data(request)
if not access_token:
access_token = get_access_token_from_uri_query(request.GET)
if not access_token:
if forbidden_on_not_present:
return HttpResponseForbidden('No access token provided')
else:
return None
try:
db_access_token = OpenIDToken.objects.get(token_hash=OpenIDToken.get_token_hash(access_token))
if db_access_token.expiration < timezone.now():
return HttpResponseForbidden('Expired access token')
return db_access_token
except OpenIDToken.DoesNotExist:
return HttpResponseForbidden('Provided access token %s not found' % access_token)
except BaseException as e:
return HttpResponseForbidden('Access error: %s' % e)
def access_token_required(disabled_settings=None):
def wrapper(func):
"""
Check that access token is present on the request and is valid. If not, returns HttpResponseForbidden.
request is annotated with the database access token, i.e. isinstance(req.openid_access_token, OpenIDToken) == True
"""
@wraps(func)
def inner(request, *args, **kwargs):
if disabled_settings:
if getattr(settings, disabled_settings, False):
request.openid_access_token = None
return func(request, *args, **kwargs)
db_access_token = extract_access_token(request, True)
if isinstance(db_access_token, HttpResponseForbidden):
return db_access_token
request.openid_access_token = db_access_token
return func(request, *args, **kwargs)
return inner
return wrapper
__all__ = ('access_token_required',)
| en | 0.771609 | Check that access token is present on the request and is valid. If not, returns HttpResponseForbidden. request is annotated with the database access token, i.e. isinstance(req.openid_access_token, OpenIDToken) == True | 2.157284 | 2 |
src/hyperparameters_exploration/HyperparametersExploration.py | philipco/structured_noise | 0 | 6613301 | <filename>src/hyperparameters_exploration/HyperparametersExploration.py
"""
Created by <NAME>, 18th January 2022.
"""
import matplotlib
matplotlib.rcParams.update({
"pgf.texsystem": "pdflatex",
'font.family': 'serif',
'text.usetex': True,
'pgf.rcfonts': False,
'text.latex.preamble': r'\usepackage{amsfonts}'
})
import hashlib
import os
import sys
import numpy as np
from matplotlib import pyplot as plt
from src.PickleHandler import pickle_saver, pickle_loader
from src.Utilities import create_folder_if_not_existing
from src.hyperparameters_exploration import Explorer
from src.hyperparameters_exploration.Hyperparameters import Hyperparameters
from src.hyperparameters_exploration.Metric import Metric
class Exploration:
def __init__(self, name, hyperparameters: Hyperparameters, explorer: Explorer, metrics: Metric):
# super().__init__()
self.name = name
self.hyperparameters = hyperparameters
self.explorer = explorer
self.metrics = metrics
self.nb_runs = 2
self.results = np.zeros((self.explorer.nb_outputs, self.nb_runs, self.hyperparameters.nb_hyperparams))
self.string_before_hash = str(self.hyperparameters.range_hyperparameters)
self.hash_string = self.explorer.function.__name__ + "-" + hashlib.shake_256(self.string_before_hash.encode()).hexdigest(4) # returns a hash value of length 2*4
self.pickle_folder = "./pickle/exploration/"
self.pictures_folder = "./pictures/exploration/"
create_folder_if_not_existing(self.pickle_folder)
create_folder_if_not_existing(self.pictures_folder)
def run_exploration(self):
print("====> Starting exploration : ", self.name)
for idx_param in range(self.hyperparameters.nb_hyperparams):
param = self.hyperparameters.range_hyperparameters[idx_param]
print("Hyperparameter's value:", param)
# self.blockPrint()
for idx_run in range(self.nb_runs):
output = self.explorer.explore(param)
for i in range(len(output)):
self.results[i, idx_run, idx_param] = self.metrics.compute(output[i])
pickle_saver(self, self.pickle_folder + self.hash_string)
self.enablePrint()
def load(self):
self.results = pickle_loader(self.pickle_folder + self.hash_string).results[:,:,:-1]
self.hyperparameters.range_hyperparameters = self.hyperparameters.range_hyperparameters[:-1]
self.hyperparameters.nb_hyperparams -= 1
def plot_exploration(self):
fig, ax = plt.subplots(figsize=(8, 7))
for i in range(len(self.explorer.outputs_label)):
plt.errorbar(range(self.hyperparameters.nb_hyperparams), np.mean(self.results[i], axis=0),
yerr=np.std(self.results[i], axis=0),
label=self.explorer.outputs_label[i],
lw=4)
plt.xticks([i for i in range(0, len(self.hyperparameters.range_hyperparameters))],
self.hyperparameters.range_hyperparameters,
rotation=30, fontsize=15)
plt.yticks(fontsize=15)
ax.set_xlabel(self.hyperparameters.x_axis_label, fontsize=15)
ax.set_ylabel(self.metrics.y_axis_label, fontsize=15)
plt.title(self.hyperparameters.name, fontsize=15)
plt.legend(loc='best', fontsize=15)
ax.grid()
plt.savefig('{0}.eps'.format(self.pictures_folder + self.hash_string), format='eps')
plt.close()
# Disable
def blockPrint(self):
sys.stdout = open(os.devnull, 'w')
# Restore
def enablePrint(self):
sys.stdout = sys.__stdout__
| <filename>src/hyperparameters_exploration/HyperparametersExploration.py
"""
Created by <NAME>, 18th January 2022.
"""
import matplotlib
matplotlib.rcParams.update({
"pgf.texsystem": "pdflatex",
'font.family': 'serif',
'text.usetex': True,
'pgf.rcfonts': False,
'text.latex.preamble': r'\usepackage{amsfonts}'
})
import hashlib
import os
import sys
import numpy as np
from matplotlib import pyplot as plt
from src.PickleHandler import pickle_saver, pickle_loader
from src.Utilities import create_folder_if_not_existing
from src.hyperparameters_exploration import Explorer
from src.hyperparameters_exploration.Hyperparameters import Hyperparameters
from src.hyperparameters_exploration.Metric import Metric
class Exploration:
def __init__(self, name, hyperparameters: Hyperparameters, explorer: Explorer, metrics: Metric):
# super().__init__()
self.name = name
self.hyperparameters = hyperparameters
self.explorer = explorer
self.metrics = metrics
self.nb_runs = 2
self.results = np.zeros((self.explorer.nb_outputs, self.nb_runs, self.hyperparameters.nb_hyperparams))
self.string_before_hash = str(self.hyperparameters.range_hyperparameters)
self.hash_string = self.explorer.function.__name__ + "-" + hashlib.shake_256(self.string_before_hash.encode()).hexdigest(4) # returns a hash value of length 2*4
self.pickle_folder = "./pickle/exploration/"
self.pictures_folder = "./pictures/exploration/"
create_folder_if_not_existing(self.pickle_folder)
create_folder_if_not_existing(self.pictures_folder)
def run_exploration(self):
print("====> Starting exploration : ", self.name)
for idx_param in range(self.hyperparameters.nb_hyperparams):
param = self.hyperparameters.range_hyperparameters[idx_param]
print("Hyperparameter's value:", param)
# self.blockPrint()
for idx_run in range(self.nb_runs):
output = self.explorer.explore(param)
for i in range(len(output)):
self.results[i, idx_run, idx_param] = self.metrics.compute(output[i])
pickle_saver(self, self.pickle_folder + self.hash_string)
self.enablePrint()
def load(self):
self.results = pickle_loader(self.pickle_folder + self.hash_string).results[:,:,:-1]
self.hyperparameters.range_hyperparameters = self.hyperparameters.range_hyperparameters[:-1]
self.hyperparameters.nb_hyperparams -= 1
def plot_exploration(self):
fig, ax = plt.subplots(figsize=(8, 7))
for i in range(len(self.explorer.outputs_label)):
plt.errorbar(range(self.hyperparameters.nb_hyperparams), np.mean(self.results[i], axis=0),
yerr=np.std(self.results[i], axis=0),
label=self.explorer.outputs_label[i],
lw=4)
plt.xticks([i for i in range(0, len(self.hyperparameters.range_hyperparameters))],
self.hyperparameters.range_hyperparameters,
rotation=30, fontsize=15)
plt.yticks(fontsize=15)
ax.set_xlabel(self.hyperparameters.x_axis_label, fontsize=15)
ax.set_ylabel(self.metrics.y_axis_label, fontsize=15)
plt.title(self.hyperparameters.name, fontsize=15)
plt.legend(loc='best', fontsize=15)
ax.grid()
plt.savefig('{0}.eps'.format(self.pictures_folder + self.hash_string), format='eps')
plt.close()
# Disable
def blockPrint(self):
sys.stdout = open(os.devnull, 'w')
# Restore
def enablePrint(self):
sys.stdout = sys.__stdout__
| en | 0.686406 | Created by <NAME>, 18th January 2022. # super().__init__() # returns a hash value of length 2*4 # self.blockPrint() # Disable # Restore | 2.773563 | 3 |
code/genetic_algorithm/callbacks.py | ahillbs/minimum_scan_cover | 0 | 6613302 | import pathlib
import numpy as np
from . import GeneticAlgorithm
from database import Task
class SaveCallback:
def __init__(self, iterations, population_amount, task: Task, session):
#self.generations = np.empty(
# (iterations, population_amount), dtype=object)
self.session = session
self.task = task
return
def __call__(self, gen_algo: GeneticAlgorithm):
if not self.session or not self.task:
return
try:
self.session.add_all(gen_algo.genomes.tolist())
self.session.commit()
#self.generations[gen_algo.generation][:] = gen_algo.genomes[:]
#with open(self.savePath, 'wb') as fd_instances:
# pickle.dump(self.generations[:gen_algo.generation+1], fd_instances)
except Exception as e:
print("Exception while calling the iteration end callback:", e)
def update_callback(gen_algo: GeneticAlgorithm):
mean = np.mean(gen_algo.fitness_val)
max_fitness = np.max(gen_algo.fitness_val)
min_fitness = np.min(gen_algo.fitness_val)
gen_algo.termCon.tqdm.set_description_str(
"Evolve feature genomes. Fitness values: Mean: {0}, max: {1}, min: {2}".format(mean, max_fitness, min_fitness)
)
| import pathlib
import numpy as np
from . import GeneticAlgorithm
from database import Task
class SaveCallback:
def __init__(self, iterations, population_amount, task: Task, session):
#self.generations = np.empty(
# (iterations, population_amount), dtype=object)
self.session = session
self.task = task
return
def __call__(self, gen_algo: GeneticAlgorithm):
if not self.session or not self.task:
return
try:
self.session.add_all(gen_algo.genomes.tolist())
self.session.commit()
#self.generations[gen_algo.generation][:] = gen_algo.genomes[:]
#with open(self.savePath, 'wb') as fd_instances:
# pickle.dump(self.generations[:gen_algo.generation+1], fd_instances)
except Exception as e:
print("Exception while calling the iteration end callback:", e)
def update_callback(gen_algo: GeneticAlgorithm):
mean = np.mean(gen_algo.fitness_val)
max_fitness = np.max(gen_algo.fitness_val)
min_fitness = np.min(gen_algo.fitness_val)
gen_algo.termCon.tqdm.set_description_str(
"Evolve feature genomes. Fitness values: Mean: {0}, max: {1}, min: {2}".format(mean, max_fitness, min_fitness)
)
| en | 0.40184 | #self.generations = np.empty( # (iterations, population_amount), dtype=object) #self.generations[gen_algo.generation][:] = gen_algo.genomes[:] #with open(self.savePath, 'wb') as fd_instances: # pickle.dump(self.generations[:gen_algo.generation+1], fd_instances) | 2.329611 | 2 |
examples/src/ActiveX/LinkingVideoActiveXControl.py | aspose-slides/Aspose.Slides-for-Python-via-.NET | 0 | 6613303 | import aspose.slides as slides
def activex_linking_video_activex_control():
#ExStart:LinkingVideoActiveXControl
# The path to the documents directory.
dataDir = "./examples/data/"
outDir = "./examples/out/"
# Instantiate Presentation class that represents PPTX file
with slides.Presentation(dataDir + "activex_template.pptx") as presentation:
# Create empty presentation instance
with slides.Presentation() as newPresentation:
# Remove default slide
newPresentation.slides.remove_at(0)
# Clone slide with Media Player ActiveX Control
newPresentation.slides.insert_clone(0, presentation.slides[0])
# Access the Media Player ActiveX control and set the video path
control = newPresentation.slides[0].controls[0]
control.properties.remove("URL")
control.properties.add("URL", dataDir + "video.mp4")
# Save the Presentation
newPresentation.save(outDir + "activex_linking_video_activex_control_out.pptx", slides.export.SaveFormat.PPTX)
#ExEnd:LinkingVideoActiveXControl | import aspose.slides as slides
def activex_linking_video_activex_control():
#ExStart:LinkingVideoActiveXControl
# The path to the documents directory.
dataDir = "./examples/data/"
outDir = "./examples/out/"
# Instantiate Presentation class that represents PPTX file
with slides.Presentation(dataDir + "activex_template.pptx") as presentation:
# Create empty presentation instance
with slides.Presentation() as newPresentation:
# Remove default slide
newPresentation.slides.remove_at(0)
# Clone slide with Media Player ActiveX Control
newPresentation.slides.insert_clone(0, presentation.slides[0])
# Access the Media Player ActiveX control and set the video path
control = newPresentation.slides[0].controls[0]
control.properties.remove("URL")
control.properties.add("URL", dataDir + "video.mp4")
# Save the Presentation
newPresentation.save(outDir + "activex_linking_video_activex_control_out.pptx", slides.export.SaveFormat.PPTX)
#ExEnd:LinkingVideoActiveXControl | en | 0.67752 | #ExStart:LinkingVideoActiveXControl # The path to the documents directory. # Instantiate Presentation class that represents PPTX file # Create empty presentation instance # Remove default slide # Clone slide with Media Player ActiveX Control # Access the Media Player ActiveX control and set the video path # Save the Presentation #ExEnd:LinkingVideoActiveXControl | 2.913956 | 3 |
Day-009/01-dictionary-comprehensions.py | arvimal/100DaysofCode-Python | 1 | 6613304 | #!/usr/bin/env python3
# Dictionary comprehensions originated in Python v3,
# but were backported to v2.
# Example 1
# This creates a key and value from a range
print({i: str(i) for i in range(5)})
# {0: '0', 1: '1', 2: '2', 3: '3', 4: '4'}
# Example 2
# Swap the key and value pairs in a dict
dict_1 = {0: "Hello", 1: "World!"}
print({value: key for key, value in dict_1.items()})
| #!/usr/bin/env python3
# Dictionary comprehensions originated in Python v3,
# but were backported to v2.
# Example 1
# This creates a key and value from a range
print({i: str(i) for i in range(5)})
# {0: '0', 1: '1', 2: '2', 3: '3', 4: '4'}
# Example 2
# Swap the key and value pairs in a dict
dict_1 = {0: "Hello", 1: "World!"}
print({value: key for key, value in dict_1.items()})
| en | 0.677107 | #!/usr/bin/env python3 # Dictionary comprehensions originated in Python v3, # but were backported to v2. # Example 1 # This creates a key and value from a range # {0: '0', 1: '1', 2: '2', 3: '3', 4: '4'} # Example 2 # Swap the key and value pairs in a dict | 4.252643 | 4 |
TopicExtractor/src/utils/database.py | npnkbabu/mymlproject | 0 | 6613305 | <reponame>npnkbabu/mymlproject
import psycopg2
import os
import json
import pandas as pd
BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
CONFIG_PATH = os.path.join(BASE_DIR,'config')
CONFIG_FILE = 'database.json'
class NewsDatabase():
def __init__(self):
print('database instantiated')
@staticmethod
def getConnection():
with open(os.path.join(CONFIG_PATH,CONFIG_FILE), 'r') as file:
config = json.load(file)
return psycopg2.connect(
user = os.getenv('MYMLPROJECT_NEWSDB_USER'),
password = os.getenv('<PASSWORD>'),
host = config['host'],
port = config['port'],
database = os.getenv('MYMLPROJECT_NEWSDB_DATABASE')
)
@staticmethod
def dumpSources(sources):
try:
connection = NewsDatabase.getConnection()
cursor = connection.cursor()
sql = '''TRUNCATE TABLE newsdata.source_details RESTART IDENTITY CASCADE'''
cursor.execute(sql)
print('removed {} rows from source_details'.format(cursor.rowcount))
sql = '''insert into newsdata.source_details (source_id,content) values (%s,%s)'''
record_to_insert=[]
for src in sources:
record_to_insert.append((1,json.dumps(src)))
cursor.executemany(sql,record_to_insert)
connection.commit()
print('{} sources inserted into database'.format(cursor.rowcount))
except(Exception, psycopg2.Error) as error:
print('Error connecting to PostgreSQL database', error)
connection = None
except:
print('error occured')
connection = None
finally:
if connection != None:
cursor.close()
connection.close()
print('PostgreSQL connection is now closed')
@staticmethod
def dumpNewsData(data):
try:
connection = NewsDatabase.getConnection()
cursor = connection.cursor()
#truncate news_data
sql = '''TRUNCATE TABLE newsdata.news_data RESTART IDENTITY CASCADE'''
cursor.execute(sql)
print('removed {} rows from news_data'.format(cursor.rowcount))
#truncate article
sql = '''TRUNCATE TABLE newsdata.article RESTART IDENTITY CASCADE'''
cursor.execute(sql)
print('removed {} rows from article'.format(cursor.rowcount))
sql = '''insert into newsdata.news_data (content) values (%s) RETURNING news_data_id'''
sqlArticle = '''insert into newsdata.article (news_data_id,content) values (%s,%s)'''
for newsdata in data:
cursor.execute(sql,(json.dumps(newsdata),))
news_data_id = cursor.fetchone()[0]
for article in newsdata:
cursor.execute(sqlArticle,(news_data_id,json.dumps(article)))
connection.commit()
print('{} news_data inserted into database'.format(cursor.rowcount))
except(Exception, psycopg2.Error) as error:
print('Error connecting to PostgreSQL database', error)
connection = None
except:
print('error occured')
connection = None
finally:
if connection != None:
cursor.close()
connection.close()
print('PostgreSQL connection is now closed')
@staticmethod
def getArticlesData():
print('getting articles data')
try:
connection = NewsDatabase.getConnection()
cursor = connection.cursor()
sqlArticle = '''select article_id,content->>'content' as content from newsdata.article order by article_id '''
cursor.execute(sqlArticle)
tuples = cursor.fetchall()
print('{} articles retrieved'.format(len(tuples)))
return pd.DataFrame(tuples,columns=['article_id','content'])
except(Exception, psycopg2.Error) as error:
print('Error connecting to PostgreSQL database', error)
connection = None
except:
print('error occured')
connection = None
finally:
if connection != None:
cursor.close()
connection.close()
print('PostgreSQL connection is now closed')
@staticmethod
def dumpFeatures(lstData):
try:
connection = NewsDatabase.getConnection()
cursor = connection.cursor()
#drop existing features
sql = '''TRUNCATE TABLE newsdata.features RESTART IDENTITY CASCADE'''
cursor.execute(sql)
print('removed {} rows from features'.format(cursor.rowcount))
#store tokens and tfidf into files
sql = '''insert into newsdata.features (id2word_file,corpus_file,model_file,processeddata_file) values (%s,%s,%s,%s)'''
cursor.execute(sql,(lstData[0],lstData[1],lstData[2],lstData[3]))
connection.commit()
print('{} features inserted into database'.format(cursor.rowcount))
except(Exception, psycopg2.Error) as error:
print('Error connecting to PostgreSQL database', error)
connection = None
except:
print('error occured')
connection = None
finally:
if connection != None:
cursor.close()
connection.close()
print('PostgreSQL connection is now closed')
@staticmethod
def GetFeatures():
try:
connection = NewsDatabase.getConnection()
cursor = connection.cursor()
#load tokens and tfidf into files
sql = '''select id2word_file, corpus_file, model_file,processeddata_file from newsdata.features'''
cursor.execute(sql)
tuples = cursor.fetchall()
return [x for x in tuples[0]]
except(Exception, psycopg2.Error) as error:
print('Error connecting to PostgreSQL database', error)
connection = None
except:
print('error occured')
connection = None
finally:
if connection != None:
cursor.close()
connection.close()
print('PostgreSQL connection is now closed')
| import psycopg2
import os
import json
import pandas as pd
BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
CONFIG_PATH = os.path.join(BASE_DIR,'config')
CONFIG_FILE = 'database.json'
class NewsDatabase():
def __init__(self):
print('database instantiated')
@staticmethod
def getConnection():
with open(os.path.join(CONFIG_PATH,CONFIG_FILE), 'r') as file:
config = json.load(file)
return psycopg2.connect(
user = os.getenv('MYMLPROJECT_NEWSDB_USER'),
password = os.getenv('<PASSWORD>'),
host = config['host'],
port = config['port'],
database = os.getenv('MYMLPROJECT_NEWSDB_DATABASE')
)
@staticmethod
def dumpSources(sources):
try:
connection = NewsDatabase.getConnection()
cursor = connection.cursor()
sql = '''TRUNCATE TABLE newsdata.source_details RESTART IDENTITY CASCADE'''
cursor.execute(sql)
print('removed {} rows from source_details'.format(cursor.rowcount))
sql = '''insert into newsdata.source_details (source_id,content) values (%s,%s)'''
record_to_insert=[]
for src in sources:
record_to_insert.append((1,json.dumps(src)))
cursor.executemany(sql,record_to_insert)
connection.commit()
print('{} sources inserted into database'.format(cursor.rowcount))
except(Exception, psycopg2.Error) as error:
print('Error connecting to PostgreSQL database', error)
connection = None
except:
print('error occured')
connection = None
finally:
if connection != None:
cursor.close()
connection.close()
print('PostgreSQL connection is now closed')
@staticmethod
def dumpNewsData(data):
try:
connection = NewsDatabase.getConnection()
cursor = connection.cursor()
#truncate news_data
sql = '''TRUNCATE TABLE newsdata.news_data RESTART IDENTITY CASCADE'''
cursor.execute(sql)
print('removed {} rows from news_data'.format(cursor.rowcount))
#truncate article
sql = '''TRUNCATE TABLE newsdata.article RESTART IDENTITY CASCADE'''
cursor.execute(sql)
print('removed {} rows from article'.format(cursor.rowcount))
sql = '''insert into newsdata.news_data (content) values (%s) RETURNING news_data_id'''
sqlArticle = '''insert into newsdata.article (news_data_id,content) values (%s,%s)'''
for newsdata in data:
cursor.execute(sql,(json.dumps(newsdata),))
news_data_id = cursor.fetchone()[0]
for article in newsdata:
cursor.execute(sqlArticle,(news_data_id,json.dumps(article)))
connection.commit()
print('{} news_data inserted into database'.format(cursor.rowcount))
except(Exception, psycopg2.Error) as error:
print('Error connecting to PostgreSQL database', error)
connection = None
except:
print('error occured')
connection = None
finally:
if connection != None:
cursor.close()
connection.close()
print('PostgreSQL connection is now closed')
@staticmethod
def getArticlesData():
print('getting articles data')
try:
connection = NewsDatabase.getConnection()
cursor = connection.cursor()
sqlArticle = '''select article_id,content->>'content' as content from newsdata.article order by article_id '''
cursor.execute(sqlArticle)
tuples = cursor.fetchall()
print('{} articles retrieved'.format(len(tuples)))
return pd.DataFrame(tuples,columns=['article_id','content'])
except(Exception, psycopg2.Error) as error:
print('Error connecting to PostgreSQL database', error)
connection = None
except:
print('error occured')
connection = None
finally:
if connection != None:
cursor.close()
connection.close()
print('PostgreSQL connection is now closed')
@staticmethod
def dumpFeatures(lstData):
try:
connection = NewsDatabase.getConnection()
cursor = connection.cursor()
#drop existing features
sql = '''TRUNCATE TABLE newsdata.features RESTART IDENTITY CASCADE'''
cursor.execute(sql)
print('removed {} rows from features'.format(cursor.rowcount))
#store tokens and tfidf into files
sql = '''insert into newsdata.features (id2word_file,corpus_file,model_file,processeddata_file) values (%s,%s,%s,%s)'''
cursor.execute(sql,(lstData[0],lstData[1],lstData[2],lstData[3]))
connection.commit()
print('{} features inserted into database'.format(cursor.rowcount))
except(Exception, psycopg2.Error) as error:
print('Error connecting to PostgreSQL database', error)
connection = None
except:
print('error occured')
connection = None
finally:
if connection != None:
cursor.close()
connection.close()
print('PostgreSQL connection is now closed')
@staticmethod
def GetFeatures():
try:
connection = NewsDatabase.getConnection()
cursor = connection.cursor()
#load tokens and tfidf into files
sql = '''select id2word_file, corpus_file, model_file,processeddata_file from newsdata.features'''
cursor.execute(sql)
tuples = cursor.fetchall()
return [x for x in tuples[0]]
except(Exception, psycopg2.Error) as error:
print('Error connecting to PostgreSQL database', error)
connection = None
except:
print('error occured')
connection = None
finally:
if connection != None:
cursor.close()
connection.close()
print('PostgreSQL connection is now closed') | en | 0.391369 | TRUNCATE TABLE newsdata.source_details RESTART IDENTITY CASCADE insert into newsdata.source_details (source_id,content) values (%s,%s) #truncate news_data TRUNCATE TABLE newsdata.news_data RESTART IDENTITY CASCADE #truncate article TRUNCATE TABLE newsdata.article RESTART IDENTITY CASCADE insert into newsdata.news_data (content) values (%s) RETURNING news_data_id insert into newsdata.article (news_data_id,content) values (%s,%s) select article_id,content->>'content' as content from newsdata.article order by article_id #drop existing features TRUNCATE TABLE newsdata.features RESTART IDENTITY CASCADE #store tokens and tfidf into files insert into newsdata.features (id2word_file,corpus_file,model_file,processeddata_file) values (%s,%s,%s,%s) #load tokens and tfidf into files select id2word_file, corpus_file, model_file,processeddata_file from newsdata.features | 2.713269 | 3 |
posicao.py | Alisson-tech/automacao_email | 0 | 6613306 | <filename>posicao.py
import pyautogui
import time
time.sleep(5)
a = pyautogui.position()
print(a)
| <filename>posicao.py
import pyautogui
import time
time.sleep(5)
a = pyautogui.position()
print(a)
| none | 1 | 2.358453 | 2 | |
bethelper/__main__.py | charstnut/Betrayal-helper | 0 | 6613307 | <reponame>charstnut/Betrayal-helper<filename>bethelper/__main__.py
"""
This file is the main script for the helper module
"""
class Game:
### Game class
def __init__(self, num_players: int):
if num_players < 3 or num_players > 5:
raise ValueError("Number of players can only be 3 to 5.")
self._num_player = num_players
self._families = [] # A list of families/players in this game
def init_game(self):
"""
Initialize the game
"""
pass
def status(self):
"""
Print the status of the game
"""
self.print_families()
self.print_map()
print("Currently it's {}'s turn.".format(self.current_player()))
def print_families(self):
"""
Print all the families in order and list their attributes
"""
for f in self._families:
print("Family member: {}".format(f.name))
if __name__ == '__main__':
game = Game(num_players=3)
| """
This file is the main script for the helper module
"""
class Game:
### Game class
def __init__(self, num_players: int):
if num_players < 3 or num_players > 5:
raise ValueError("Number of players can only be 3 to 5.")
self._num_player = num_players
self._families = [] # A list of families/players in this game
def init_game(self):
"""
Initialize the game
"""
pass
def status(self):
"""
Print the status of the game
"""
self.print_families()
self.print_map()
print("Currently it's {}'s turn.".format(self.current_player()))
def print_families(self):
"""
Print all the families in order and list their attributes
"""
for f in self._families:
print("Family member: {}".format(f.name))
if __name__ == '__main__':
game = Game(num_players=3) | en | 0.874943 | This file is the main script for the helper module ### Game class # A list of families/players in this game Initialize the game Print the status of the game Print all the families in order and list their attributes | 4.089895 | 4 |
FullDatasetFeatures.py | altuwairqi-s/MVS-using-CNN-and-LSTM | 14 | 6613308 | <reponame>altuwairqi-s/MVS-using-CNN-and-LSTM
from __future__ import print_function
import os, sys, numpy as np
import argparse
from scipy import misc
import caffe
import tempfile
from math import ceil
import cv2
import scipy.io as sio
from matplotlib import pyplot as plt
DatasetFolder = '/media/imlab/IMLab Server Data/Ubuntu/TanveerHussain/MVS/Training'
proto = '/media/imlab/IMLab Server Data/Ubuntu/TanveerHussain/MVS/Models/alexnet.prototxt'
model = '/media/imlab/IMLab Server Data/Ubuntu/TanveerHussain/MVS/Models/alexnet-model.caffemodel'
caffe.set_mode_cpu()
net = caffe.Net(proto, model, caffe.TEST)
img_mean = np.array([103.94, 116.78, 123.68], dtype=np.float32)
DatabaseFeautres = []
DatabaseLabel = []
for folderName in os.listdir(DatasetFolder):
print(folderName)
subFolder = DatasetFolder+'/'+ folderName
for filename in os.listdir(subFolder):
vidcap = cv2.VideoCapture(DatasetFolder+'/'+ folderName +'/'+filename)
print('Feature Extraction of : ',filename)
videolength = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
videoFeatures=[]
frame_no=-1;
while (frame_no < videolength-1): #(videolength%30)
frame_no = frame_no + 1
vidcap.set(1,frame_no)
ret0,img0 = vidcap.read()
if(ret0 == 1):
resized_image = caffe.io.resize_image(img0,[224,224])
transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape})
transformer.set_transpose('data',(2, 0, 1))
transformer.set_channel_swap('data', (2, 1, 0))
transformer.set_raw_scale('data', 255)
transformer.set_mean('data',img_mean)
net.blobs['data'].reshape(1, 3, 224, 224)
net.blobs['data'].data[...] = transformer.preprocess('data', resized_image)
net.forward()
features = net.blobs['fc1000'].data[0].reshape(1,1000)
bb = np.matrix(features)
features = bb.max(0)
videoFeatures.append(features)
print(frame_no % 15)
if frame_no % 15 == 14:
aa = np.asarray(videoFeatures)
DatabaseFeautres.append(aa)
DatabaseLabel.append(folderName)
videoFeatures=[]
#np.save('DatabaseFeaturesList',DatabaseFeautres)
#np.save('DatabaseLabelList',DatabaseLabel)
##################### One Hot and Train Test spilt
TotalFeatures= []
for sample in DatabaseFeautres:
TotalFeatures.append(sample.reshape([1,15000]))
TotalFeatures = np.asarray(TotalFeatures)
TotalFeatures = TotalFeatures.reshape([len(DatabaseFeautres),15000])
OneHotArray = []
kk=1;
for i in range(len(DatabaseFeautres)-1):
OneHotArray.append(kk)
if (DatabaseLabel[i] != DatabaseLabel[i+1]):
kk=kk+1;
OneHot= np.zeros([len(DatabaseFeautres),2], dtype='int');
for i in range(len(DatabaseFeautres)-1):
print(i)
OneHot[i,OneHotArray[i]-1] = 1
np.save('MVS_TotalFeatures',TotalFeatures)
sio.savemat('MVS_Labels.mat', mdict={'DatabaseLabel': OneHot})
sio.savemat('MVS_TotalFeatures.mat', mdict={'TotalFeatures': TotalFeatures},appendmat=True, format='5',
long_field_names=False, do_compression=True, oned_as='row')
#import random
#list=[]
#for i in range(1500):
# r=random.randint(1,7999)
# if r not in list: list.append(r)
#
#
#import os, sys, numpy as np
#
#
#
#DatasetFolder = '/media/imlab/IMLab Server Data/Datasets/UCF101/UCF-101'
#
#
#
#for folderName in os.listdir(DatasetFolder):
# print(folderName)
#
| from __future__ import print_function
import os, sys, numpy as np
import argparse
from scipy import misc
import caffe
import tempfile
from math import ceil
import cv2
import scipy.io as sio
from matplotlib import pyplot as plt
DatasetFolder = '/media/imlab/IMLab Server Data/Ubuntu/TanveerHussain/MVS/Training'
proto = '/media/imlab/IMLab Server Data/Ubuntu/TanveerHussain/MVS/Models/alexnet.prototxt'
model = '/media/imlab/IMLab Server Data/Ubuntu/TanveerHussain/MVS/Models/alexnet-model.caffemodel'
caffe.set_mode_cpu()
net = caffe.Net(proto, model, caffe.TEST)
img_mean = np.array([103.94, 116.78, 123.68], dtype=np.float32)
DatabaseFeautres = []
DatabaseLabel = []
for folderName in os.listdir(DatasetFolder):
print(folderName)
subFolder = DatasetFolder+'/'+ folderName
for filename in os.listdir(subFolder):
vidcap = cv2.VideoCapture(DatasetFolder+'/'+ folderName +'/'+filename)
print('Feature Extraction of : ',filename)
videolength = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))
videoFeatures=[]
frame_no=-1;
while (frame_no < videolength-1): #(videolength%30)
frame_no = frame_no + 1
vidcap.set(1,frame_no)
ret0,img0 = vidcap.read()
if(ret0 == 1):
resized_image = caffe.io.resize_image(img0,[224,224])
transformer = caffe.io.Transformer({'data':net.blobs['data'].data.shape})
transformer.set_transpose('data',(2, 0, 1))
transformer.set_channel_swap('data', (2, 1, 0))
transformer.set_raw_scale('data', 255)
transformer.set_mean('data',img_mean)
net.blobs['data'].reshape(1, 3, 224, 224)
net.blobs['data'].data[...] = transformer.preprocess('data', resized_image)
net.forward()
features = net.blobs['fc1000'].data[0].reshape(1,1000)
bb = np.matrix(features)
features = bb.max(0)
videoFeatures.append(features)
print(frame_no % 15)
if frame_no % 15 == 14:
aa = np.asarray(videoFeatures)
DatabaseFeautres.append(aa)
DatabaseLabel.append(folderName)
videoFeatures=[]
#np.save('DatabaseFeaturesList',DatabaseFeautres)
#np.save('DatabaseLabelList',DatabaseLabel)
##################### One Hot and Train Test spilt
TotalFeatures= []
for sample in DatabaseFeautres:
TotalFeatures.append(sample.reshape([1,15000]))
TotalFeatures = np.asarray(TotalFeatures)
TotalFeatures = TotalFeatures.reshape([len(DatabaseFeautres),15000])
OneHotArray = []
kk=1;
for i in range(len(DatabaseFeautres)-1):
OneHotArray.append(kk)
if (DatabaseLabel[i] != DatabaseLabel[i+1]):
kk=kk+1;
OneHot= np.zeros([len(DatabaseFeautres),2], dtype='int');
for i in range(len(DatabaseFeautres)-1):
print(i)
OneHot[i,OneHotArray[i]-1] = 1
np.save('MVS_TotalFeatures',TotalFeatures)
sio.savemat('MVS_Labels.mat', mdict={'DatabaseLabel': OneHot})
sio.savemat('MVS_TotalFeatures.mat', mdict={'TotalFeatures': TotalFeatures},appendmat=True, format='5',
long_field_names=False, do_compression=True, oned_as='row')
#import random
#list=[]
#for i in range(1500):
# r=random.randint(1,7999)
# if r not in list: list.append(r)
#
#
#import os, sys, numpy as np
#
#
#
#DatasetFolder = '/media/imlab/IMLab Server Data/Datasets/UCF101/UCF-101'
#
#
#
#for folderName in os.listdir(DatasetFolder):
# print(folderName)
# | en | 0.348264 | #(videolength%30) #np.save('DatabaseFeaturesList',DatabaseFeautres) #np.save('DatabaseLabelList',DatabaseLabel) ##################### One Hot and Train Test spilt #import random #list=[] #for i in range(1500): # r=random.randint(1,7999) # if r not in list: list.append(r) # # #import os, sys, numpy as np # # # #DatasetFolder = '/media/imlab/IMLab Server Data/Datasets/UCF101/UCF-101' # # # #for folderName in os.listdir(DatasetFolder): # print(folderName) # | 2.298062 | 2 |
pyfuppes/filter.py | MrFuppes/pyFuppes | 1 | 6613309 | # -*- coding: utf-8 -*-
r"""
Created on Mon Aug 20 11:23:26 2018
@author: <NAME>, florian\obersteiner\\kit\edu
"""
import numpy as np
from numba import njit
from scipy.interpolate import interp1d
###############################################################################
def mask_repeated(a, N, atol=1e-6):
"""
given an array a that consists of sections of repeated elements, mask
those elements in a section that repeat more than N times
on SO:
https://stackoverflow.com/a/58482894/10197418
Parameters
----------
a : 1d array
N : int
mask element if it repeats more than n times
atol : float, optional
absolute tolerance to check for equality. The default is 1e-6.
Returns
-------
boolean mask
"""
mask = np.ones(a.size, np.bool_)
mask[N:] = ~np.isclose(a[N:], a[:-N], atol=atol, equal_nan=True)
return mask
###############################################################################
@njit
def mask_repeated_nb(arr, n, atol=1e-6):
"""
numba version of mask_repeated(). Also works with input of type float.
Parameters
----------
arr : 1d array
n : int
mask element if it repeats more than n times
atol : float, optional
absolute tolerance to check for equality. The default is 1e-6.
Returns
-------
boolean mask
"""
mask = np.ones(arr.shape, np.bool_)
current = arr[0]
count = 0
for idx, item in enumerate(arr):
if abs(item-current) < atol:
count += 1
else:
current = item
count = 1
mask[idx] = count <= n
return mask
###############################################################################
@njit
def mask_jumps(arr, thrsh, look_ahead, abs_delta=False):
"""
check the elements of array "arr" if the delta between element and
following element(s) exceed a threshold "trsh". How many elements to
look ahead is defined by "look_ahead"
"""
n_el = arr.shape[0]
mask = np.ones(arr.shape).astype(np.bool_)
i = 0
while i < n_el-1:
cur, nxt = arr[i], arr[i+1]
delta_0 = np.absolute(nxt-cur) if abs_delta else nxt-cur
if delta_0 > thrsh:
for value in arr[i+1:i+look_ahead+1]:
delta_1 = np.absolute(value-cur) if abs_delta else value-cur
if delta_1 > thrsh:
mask[i+1] = False
i += 1
else:
break
i += 1
return mask
###############################################################################
def filter_jumps(arr, thrsh, look_ahead,
abs_delta=False,
vmiss=np.nan,
remove_repeated=False,
interpol_jumps=False, interpol_kind='linear'):
"""
wrapper around mask_jumps()
! interpolation assumes equidistant spacing of the independent variable of
which arr depends !
"""
if not isinstance(arr, np.ndarray):
raise ValueError("input array must be of class numpy ndarray.")
if arr.ndim > 1:
raise ValueError("input array must be numpy 1d array.")
if not isinstance(look_ahead, int):
raise ValueError("parameter look_ahead must be an integer.")
if look_ahead >= arr.shape[0] or look_ahead < 1:
raise ValueError(f"parameter look_ahead must be >=1 and <{arr.shape[0]}.")
result = arr.copy() # do not touch the input...
if not np.isnan(vmiss):
result[vmiss] = np.nan
if remove_repeated:
result[~mask_repeated(result)] = np.nan
mask = mask_jumps(result, thrsh, look_ahead, abs_delta=abs_delta)
result[~mask] = np.nan
if interpol_jumps:
f_ip = interp1d(np.arange(0, result.shape[0])[mask], result[mask],
kind=interpol_kind, fill_value='extrapolate')
result = f_ip(np.arange(0, result.shape[0]))
return (result, mask)
return (result, mask)
###############################################################################
def filter_jumps_np(v, max_delta, no_val=np.nan, use_abs_delta=True,
reset_buffer_after=3, remove_doubles=False,
interpol_jumps=False, interpol_kind='linear'):
"""
if v is dependent on another variable x (e.g. time) and if that x
is not equidistant, do NOT use interpolation.
Parameters
----------
v : np 1d array
data to filter.
max_delta : float
defines "jump".
no_val : float, optional
missing value placeholder. The default is np.nan.
use_abs_delta : boolean, optional
use the absolute delta to identify jumps. The default is True.
reset_buffer_after : int, optional
how many elements to wait until reset. The default is 3.
remove_doubles : boolean, optional
remove elements that are repeated once. The default is False.
interpol_jumps : boolean, optional
decide to interpolate filtered values. The default is False.
interpol_kind : string, optional
how to interpolate, see scipy.interpolate.interp1d.
The default is 'linear'.
Returns
-------
dict. 'filtered': filtered data
'ix_del': idices of deleted elements
'ix_rem': indices of remaining elements
"""
ix_del = np.full(v.shape[0], -1, dtype=int) # deletion index
ix_rem = np.full(v.shape[0], -1, dtype=int) # remaining index
buffer = [False, 0]
for ix, v_ix in enumerate(v):
if any([~np.isfinite(v_ix), v_ix == no_val, np.isnan(v_ix)]):
ix_rem[ix] = ix
continue # skip line if value is np.nan
if not buffer[0]:
buffer[0] = v_ix
ix_rem[ix] = ix
continue # fill buffer if not done so yet
if use_abs_delta:
delta = abs(v_ix-buffer[0])
else:
delta = v_ix-buffer[0]
if delta > max_delta: # jump found!
v[ix] = no_val
ix_del[ix] = ix
buffer[1] += 1
if reset_buffer_after:
if buffer[1] == reset_buffer_after:
buffer = [v_ix, 0]
else: # no jump,...
buffer[0] = v_ix
if remove_doubles: # check for double values...
if delta == 0.: # double found!
v[ix] = no_val
ix_del[ix] = ix
else: # no double
ix_rem[ix] = ix
else:
ix_rem[ix] = ix
w_valid = np.where(ix_del != -1)
ix_del = ix_del[w_valid]
w_valid = np.where(ix_rem != -1)
ix_rem = ix_rem[w_valid]
if interpol_jumps:
tmp_x = (np.arange(0, v.shape[0]))[ix_rem]
tmp_y = v[ix_rem]
f_ip = interp1d(tmp_x, tmp_y,
kind=interpol_kind, fill_value='extrapolate')
filtered = f_ip(np.arange(0, v.shape[0]))
else:
w_valid = np.where(v != no_val)
filtered = v[w_valid]
return {'filtered': filtered,
'ix_del': ix_del,
'ix_rem': ix_rem}
###############################################################################
def del_at_edge(v, n_cut, add=2, out_len='same'):
"""
assume v to be a 1D array which contains blocks of NaNs.
returns: v with "more NaNs", i.e. range of NaN-blocks is extended by n_cut.
"""
tf = np.isfinite(v)*1.
mask = np.convolve(tf, np.ones((int(n_cut+add),))/int(n_cut+add),
mode=out_len)
if tf[0] > 0.9:
mask[0] = 1.
if tf[-1] > 0.9:
mask[-1] = 1.
mask[np.where(mask < 0.999)] = np.nan
return v * mask
############################################################################### | # -*- coding: utf-8 -*-
r"""
Created on Mon Aug 20 11:23:26 2018
@author: <NAME>, florian\obersteiner\\kit\edu
"""
import numpy as np
from numba import njit
from scipy.interpolate import interp1d
###############################################################################
def mask_repeated(a, N, atol=1e-6):
"""
given an array a that consists of sections of repeated elements, mask
those elements in a section that repeat more than N times
on SO:
https://stackoverflow.com/a/58482894/10197418
Parameters
----------
a : 1d array
N : int
mask element if it repeats more than n times
atol : float, optional
absolute tolerance to check for equality. The default is 1e-6.
Returns
-------
boolean mask
"""
mask = np.ones(a.size, np.bool_)
mask[N:] = ~np.isclose(a[N:], a[:-N], atol=atol, equal_nan=True)
return mask
###############################################################################
@njit
def mask_repeated_nb(arr, n, atol=1e-6):
"""
numba version of mask_repeated(). Also works with input of type float.
Parameters
----------
arr : 1d array
n : int
mask element if it repeats more than n times
atol : float, optional
absolute tolerance to check for equality. The default is 1e-6.
Returns
-------
boolean mask
"""
mask = np.ones(arr.shape, np.bool_)
current = arr[0]
count = 0
for idx, item in enumerate(arr):
if abs(item-current) < atol:
count += 1
else:
current = item
count = 1
mask[idx] = count <= n
return mask
###############################################################################
@njit
def mask_jumps(arr, thrsh, look_ahead, abs_delta=False):
"""
check the elements of array "arr" if the delta between element and
following element(s) exceed a threshold "trsh". How many elements to
look ahead is defined by "look_ahead"
"""
n_el = arr.shape[0]
mask = np.ones(arr.shape).astype(np.bool_)
i = 0
while i < n_el-1:
cur, nxt = arr[i], arr[i+1]
delta_0 = np.absolute(nxt-cur) if abs_delta else nxt-cur
if delta_0 > thrsh:
for value in arr[i+1:i+look_ahead+1]:
delta_1 = np.absolute(value-cur) if abs_delta else value-cur
if delta_1 > thrsh:
mask[i+1] = False
i += 1
else:
break
i += 1
return mask
###############################################################################
def filter_jumps(arr, thrsh, look_ahead,
abs_delta=False,
vmiss=np.nan,
remove_repeated=False,
interpol_jumps=False, interpol_kind='linear'):
"""
wrapper around mask_jumps()
! interpolation assumes equidistant spacing of the independent variable of
which arr depends !
"""
if not isinstance(arr, np.ndarray):
raise ValueError("input array must be of class numpy ndarray.")
if arr.ndim > 1:
raise ValueError("input array must be numpy 1d array.")
if not isinstance(look_ahead, int):
raise ValueError("parameter look_ahead must be an integer.")
if look_ahead >= arr.shape[0] or look_ahead < 1:
raise ValueError(f"parameter look_ahead must be >=1 and <{arr.shape[0]}.")
result = arr.copy() # do not touch the input...
if not np.isnan(vmiss):
result[vmiss] = np.nan
if remove_repeated:
result[~mask_repeated(result)] = np.nan
mask = mask_jumps(result, thrsh, look_ahead, abs_delta=abs_delta)
result[~mask] = np.nan
if interpol_jumps:
f_ip = interp1d(np.arange(0, result.shape[0])[mask], result[mask],
kind=interpol_kind, fill_value='extrapolate')
result = f_ip(np.arange(0, result.shape[0]))
return (result, mask)
return (result, mask)
###############################################################################
def filter_jumps_np(v, max_delta, no_val=np.nan, use_abs_delta=True,
reset_buffer_after=3, remove_doubles=False,
interpol_jumps=False, interpol_kind='linear'):
"""
if v is dependent on another variable x (e.g. time) and if that x
is not equidistant, do NOT use interpolation.
Parameters
----------
v : np 1d array
data to filter.
max_delta : float
defines "jump".
no_val : float, optional
missing value placeholder. The default is np.nan.
use_abs_delta : boolean, optional
use the absolute delta to identify jumps. The default is True.
reset_buffer_after : int, optional
how many elements to wait until reset. The default is 3.
remove_doubles : boolean, optional
remove elements that are repeated once. The default is False.
interpol_jumps : boolean, optional
decide to interpolate filtered values. The default is False.
interpol_kind : string, optional
how to interpolate, see scipy.interpolate.interp1d.
The default is 'linear'.
Returns
-------
dict. 'filtered': filtered data
'ix_del': idices of deleted elements
'ix_rem': indices of remaining elements
"""
ix_del = np.full(v.shape[0], -1, dtype=int) # deletion index
ix_rem = np.full(v.shape[0], -1, dtype=int) # remaining index
buffer = [False, 0]
for ix, v_ix in enumerate(v):
if any([~np.isfinite(v_ix), v_ix == no_val, np.isnan(v_ix)]):
ix_rem[ix] = ix
continue # skip line if value is np.nan
if not buffer[0]:
buffer[0] = v_ix
ix_rem[ix] = ix
continue # fill buffer if not done so yet
if use_abs_delta:
delta = abs(v_ix-buffer[0])
else:
delta = v_ix-buffer[0]
if delta > max_delta: # jump found!
v[ix] = no_val
ix_del[ix] = ix
buffer[1] += 1
if reset_buffer_after:
if buffer[1] == reset_buffer_after:
buffer = [v_ix, 0]
else: # no jump,...
buffer[0] = v_ix
if remove_doubles: # check for double values...
if delta == 0.: # double found!
v[ix] = no_val
ix_del[ix] = ix
else: # no double
ix_rem[ix] = ix
else:
ix_rem[ix] = ix
w_valid = np.where(ix_del != -1)
ix_del = ix_del[w_valid]
w_valid = np.where(ix_rem != -1)
ix_rem = ix_rem[w_valid]
if interpol_jumps:
tmp_x = (np.arange(0, v.shape[0]))[ix_rem]
tmp_y = v[ix_rem]
f_ip = interp1d(tmp_x, tmp_y,
kind=interpol_kind, fill_value='extrapolate')
filtered = f_ip(np.arange(0, v.shape[0]))
else:
w_valid = np.where(v != no_val)
filtered = v[w_valid]
return {'filtered': filtered,
'ix_del': ix_del,
'ix_rem': ix_rem}
###############################################################################
def del_at_edge(v, n_cut, add=2, out_len='same'):
"""
assume v to be a 1D array which contains blocks of NaNs.
returns: v with "more NaNs", i.e. range of NaN-blocks is extended by n_cut.
"""
tf = np.isfinite(v)*1.
mask = np.convolve(tf, np.ones((int(n_cut+add),))/int(n_cut+add),
mode=out_len)
if tf[0] > 0.9:
mask[0] = 1.
if tf[-1] > 0.9:
mask[-1] = 1.
mask[np.where(mask < 0.999)] = np.nan
return v * mask
############################################################################### | en | 0.400724 | # -*- coding: utf-8 -*- Created on Mon Aug 20 11:23:26 2018
@author: <NAME>, florian\obersteiner\\kit\edu ############################################################################### given an array a that consists of sections of repeated elements, mask
those elements in a section that repeat more than N times
on SO:
https://stackoverflow.com/a/58482894/10197418
Parameters
----------
a : 1d array
N : int
mask element if it repeats more than n times
atol : float, optional
absolute tolerance to check for equality. The default is 1e-6.
Returns
-------
boolean mask ############################################################################### numba version of mask_repeated(). Also works with input of type float.
Parameters
----------
arr : 1d array
n : int
mask element if it repeats more than n times
atol : float, optional
absolute tolerance to check for equality. The default is 1e-6.
Returns
-------
boolean mask ############################################################################### check the elements of array "arr" if the delta between element and
following element(s) exceed a threshold "trsh". How many elements to
look ahead is defined by "look_ahead" ############################################################################### wrapper around mask_jumps()
! interpolation assumes equidistant spacing of the independent variable of
which arr depends ! # do not touch the input... ############################################################################### if v is dependent on another variable x (e.g. time) and if that x
is not equidistant, do NOT use interpolation.
Parameters
----------
v : np 1d array
data to filter.
max_delta : float
defines "jump".
no_val : float, optional
missing value placeholder. The default is np.nan.
use_abs_delta : boolean, optional
use the absolute delta to identify jumps. The default is True.
reset_buffer_after : int, optional
how many elements to wait until reset. The default is 3.
remove_doubles : boolean, optional
remove elements that are repeated once. The default is False.
interpol_jumps : boolean, optional
decide to interpolate filtered values. The default is False.
interpol_kind : string, optional
how to interpolate, see scipy.interpolate.interp1d.
The default is 'linear'.
Returns
-------
dict. 'filtered': filtered data
'ix_del': idices of deleted elements
'ix_rem': indices of remaining elements # deletion index # remaining index # skip line if value is np.nan # fill buffer if not done so yet # jump found! # no jump,... # check for double values... # double found! # no double ############################################################################### assume v to be a 1D array which contains blocks of NaNs.
returns: v with "more NaNs", i.e. range of NaN-blocks is extended by n_cut. ############################################################################### | 3.300761 | 3 |
uplink/converters/standard.py | SakornW/uplink | 0 | 6613310 | <reponame>SakornW/uplink
# Standard library imports
import json
# Local imports
from uplink.converters import interfaces, register_default_converter_factory
class Cast(interfaces.Converter):
def __init__(self, caster, converter):
self._cast = caster
self._converter = converter
def set_chain(self, chain):
self._converter.set_chain(chain)
def convert(self, value):
if callable(self._cast):
value = self._cast(value)
return self._converter(value)
class RequestBodyConverter(interfaces.Converter):
@staticmethod
def _default_json_dumper(obj):
return obj.__dict__ # pragma: no cover
def convert(self, value):
if isinstance(value, str):
return value
dumped = json.dumps(value, default=self._default_json_dumper)
return json.loads(dumped)
class StringConverter(interfaces.Converter):
def convert(self, value):
return str(value)
@register_default_converter_factory
class StandardConverter(interfaces.Factory):
"""
The default converter, this class seeks to provide sane alternatives
for (de)serialization when all else fails -- e.g., no other
converters could handle a particular type.
"""
def create_response_body_converter(self, type_, *args, **kwargs):
if isinstance(type_, interfaces.Converter):
return type_
def create_request_body_converter(self, type_, *args, **kwargs):
return Cast(type_, RequestBodyConverter()) # pragma: no cover
def create_string_converter(self, type_, *args, **kwargs):
return Cast(type_, StringConverter()) # pragma: no cover
| # Standard library imports
import json
# Local imports
from uplink.converters import interfaces, register_default_converter_factory
class Cast(interfaces.Converter):
def __init__(self, caster, converter):
self._cast = caster
self._converter = converter
def set_chain(self, chain):
self._converter.set_chain(chain)
def convert(self, value):
if callable(self._cast):
value = self._cast(value)
return self._converter(value)
class RequestBodyConverter(interfaces.Converter):
@staticmethod
def _default_json_dumper(obj):
return obj.__dict__ # pragma: no cover
def convert(self, value):
if isinstance(value, str):
return value
dumped = json.dumps(value, default=self._default_json_dumper)
return json.loads(dumped)
class StringConverter(interfaces.Converter):
def convert(self, value):
return str(value)
@register_default_converter_factory
class StandardConverter(interfaces.Factory):
"""
The default converter, this class seeks to provide sane alternatives
for (de)serialization when all else fails -- e.g., no other
converters could handle a particular type.
"""
def create_response_body_converter(self, type_, *args, **kwargs):
if isinstance(type_, interfaces.Converter):
return type_
def create_request_body_converter(self, type_, *args, **kwargs):
return Cast(type_, RequestBodyConverter()) # pragma: no cover
def create_string_converter(self, type_, *args, **kwargs):
return Cast(type_, StringConverter()) # pragma: no cover | en | 0.638023 | # Standard library imports # Local imports # pragma: no cover The default converter, this class seeks to provide sane alternatives for (de)serialization when all else fails -- e.g., no other converters could handle a particular type. # pragma: no cover # pragma: no cover | 2.314465 | 2 |
simple_racing/racing/pitstop_race.py | sebanie15/simple_racing | 0 | 6613311 | <reponame>sebanie15/simple_racing
# !/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: sebanie15
"""
from random import randint
from .race import Race
from .race_data import RaceData
class PitstopRace(Race):
def __init__(self, track, laps, cars, pitstop=10):
super().__init__(track, laps, cars)
self._pitstop_time = pitstop
@property
def pitstop_time(self):
return self.pitstop_time
@pitstop_time.setter
def pitstop_time(self, minutes=10):
self.pitstop_time = minutes / 60
def __car_timestamp(self, car):
car_speed = randint(car.max_speed // 2, car.max_speed + 1)
calc_distance = (self.__time_scale / 60) * car_speed
loop_time = 0.0
fuel_consumption = 0.0
tanked = False
if car.odometer + calc_distance > self.track.total_length:
calc_distance = self.track.total_length - car.odometer
if car.fuel_level > 0 and car.odometer < self.track.total_length:
fuel_consumption, loop_time = car.drive(distance=calc_distance, speed=car_speed)
if self.__is_end_of_fuel(car):
car.tank_fuel(car.tank_capacity)
tanked = True
result = RaceData(self.__racing_time, car.name, car_speed, car.odometer, fuel_consumption, self.__calc_lap(car),
loop_time, tanked)
return result, self.__is_finished(car)
| # !/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: sebanie15
"""
from random import randint
from .race import Race
from .race_data import RaceData
class PitstopRace(Race):
def __init__(self, track, laps, cars, pitstop=10):
super().__init__(track, laps, cars)
self._pitstop_time = pitstop
@property
def pitstop_time(self):
return self.pitstop_time
@pitstop_time.setter
def pitstop_time(self, minutes=10):
self.pitstop_time = minutes / 60
def __car_timestamp(self, car):
car_speed = randint(car.max_speed // 2, car.max_speed + 1)
calc_distance = (self.__time_scale / 60) * car_speed
loop_time = 0.0
fuel_consumption = 0.0
tanked = False
if car.odometer + calc_distance > self.track.total_length:
calc_distance = self.track.total_length - car.odometer
if car.fuel_level > 0 and car.odometer < self.track.total_length:
fuel_consumption, loop_time = car.drive(distance=calc_distance, speed=car_speed)
if self.__is_end_of_fuel(car):
car.tank_fuel(car.tank_capacity)
tanked = True
result = RaceData(self.__racing_time, car.name, car_speed, car.odometer, fuel_consumption, self.__calc_lap(car),
loop_time, tanked)
return result, self.__is_finished(car) | en | 0.267253 | # !/usr/bin/env python3 # -*- coding: utf-8 -*- @author: sebanie15 | 3.301518 | 3 |
csc work/final/w15/Q8.py | mdnu/snake | 1 | 6613312 | def last_phonemes(phoneme_list):
''' (list of str) -> list of str
Return the last vowel phoneme and any subequent consonant phoneme(s) from
phoneme_list, in the same order as they appear in phoneme_list.
>>> last_phonemes(['AE1', 'B', 'S', 'IH0', 'N', 'TH'])
['IH0', 'N', 'TH']
>>> last_phonemes(['IH0', 'N'])
['IH0', 'N']
'''
vowels = 'AEIOU'
last_phonemes_list = []
candidate_phoneme = ''
for i in range(len(phoneme_list)):
if phoneme_list[i][0] in vowels:
if phoneme_list[i] > candidate_phoneme:
candidate_phoneme = phoneme_list[i]
last_phonemes_list = phoneme_list[phoneme_list.index(candidate_phoneme):]
return last_phonemes_list
def build_rhyming_dict(words_to_phonemes):
# complete the function body (7 MARKS)
''' (dict of {str: list of str}) -> dict of {str: list of str}
Return a dict where the keys are the same as the keys in word_to_phonemes
and the value for each key is a list of all words that rhyme with the key.
Two words rhyme if and only if they are different and their last
vowel phonemes and all subsequent consonant phoneme(s) after the
last vowel phonemes match.
>>> words_to_phonemes = read_pronunciation(open('dictionary.txt'))
>>> words_to_rhyming_words = build_rhyming_dict(words_to_phonemes)
>>> words_to_rhyming_words['CRAIG']
['BAIG', 'BEGUE', 'FLAIG', 'HAGUE', 'HAIG', 'LAPHROAIG', 'MACIAG',
'MCCAGUE', 'MCCAIG', 'MCKAIG', 'MCQUAIG', 'MCTAGUE',
'NEST-EGG', 'O'LAGUE', 'PLAGUE', 'RAGUE', 'SPRAGUE', 'VAGUE']
>>> # Notice that 'CRAIG' is not in the list of words that rhyme with 'CRAIG'
'''
# recall that two words rhyme iff their last vowel phonemes and
# all subsequent consonant phonemes matched.
words_to_rhyming_words = {}
for word in words_to_phonemes:
rhyming_words = []
for potential_rhyme in words_to_phonemes:
if (last_phonemes(words_to_phonemes[word]) == last_phonemes(words_to_phonemes[potential_rhyme])):
rhyming_words.append(potential_rhyme)
rhyming_words.remove(word)
words_to_rhyming_words[word] = rhyming_words
return words_to_rhyming_words
# suppose we had a solution which had a runtime which was quadratic in the
# length of the words_to_phonemes pythoin dict. If it took 1 second for
# build_rhyming_dict to run for a words_to_phonemes dict with 1000 words,
# then if we doubled the length to 2000 words, then we expect it to take
# 4 seconds, since if a = 1000, f(a) = 1 second, then f(2a) = (2^2) = 4 | def last_phonemes(phoneme_list):
''' (list of str) -> list of str
Return the last vowel phoneme and any subequent consonant phoneme(s) from
phoneme_list, in the same order as they appear in phoneme_list.
>>> last_phonemes(['AE1', 'B', 'S', 'IH0', 'N', 'TH'])
['IH0', 'N', 'TH']
>>> last_phonemes(['IH0', 'N'])
['IH0', 'N']
'''
vowels = 'AEIOU'
last_phonemes_list = []
candidate_phoneme = ''
for i in range(len(phoneme_list)):
if phoneme_list[i][0] in vowels:
if phoneme_list[i] > candidate_phoneme:
candidate_phoneme = phoneme_list[i]
last_phonemes_list = phoneme_list[phoneme_list.index(candidate_phoneme):]
return last_phonemes_list
def build_rhyming_dict(words_to_phonemes):
# complete the function body (7 MARKS)
''' (dict of {str: list of str}) -> dict of {str: list of str}
Return a dict where the keys are the same as the keys in word_to_phonemes
and the value for each key is a list of all words that rhyme with the key.
Two words rhyme if and only if they are different and their last
vowel phonemes and all subsequent consonant phoneme(s) after the
last vowel phonemes match.
>>> words_to_phonemes = read_pronunciation(open('dictionary.txt'))
>>> words_to_rhyming_words = build_rhyming_dict(words_to_phonemes)
>>> words_to_rhyming_words['CRAIG']
['BAIG', 'BEGUE', 'FLAIG', 'HAGUE', 'HAIG', 'LAPHROAIG', 'MACIAG',
'MCCAGUE', 'MCCAIG', 'MCKAIG', 'MCQUAIG', 'MCTAGUE',
'NEST-EGG', 'O'LAGUE', 'PLAGUE', 'RAGUE', 'SPRAGUE', 'VAGUE']
>>> # Notice that 'CRAIG' is not in the list of words that rhyme with 'CRAIG'
'''
# recall that two words rhyme iff their last vowel phonemes and
# all subsequent consonant phonemes matched.
words_to_rhyming_words = {}
for word in words_to_phonemes:
rhyming_words = []
for potential_rhyme in words_to_phonemes:
if (last_phonemes(words_to_phonemes[word]) == last_phonemes(words_to_phonemes[potential_rhyme])):
rhyming_words.append(potential_rhyme)
rhyming_words.remove(word)
words_to_rhyming_words[word] = rhyming_words
return words_to_rhyming_words
# suppose we had a solution which had a runtime which was quadratic in the
# length of the words_to_phonemes pythoin dict. If it took 1 second for
# build_rhyming_dict to run for a words_to_phonemes dict with 1000 words,
# then if we doubled the length to 2000 words, then we expect it to take
# 4 seconds, since if a = 1000, f(a) = 1 second, then f(2a) = (2^2) = 4 | en | 0.846239 | (list of str) -> list of str Return the last vowel phoneme and any subequent consonant phoneme(s) from phoneme_list, in the same order as they appear in phoneme_list. >>> last_phonemes(['AE1', 'B', 'S', 'IH0', 'N', 'TH']) ['IH0', 'N', 'TH'] >>> last_phonemes(['IH0', 'N']) ['IH0', 'N'] # complete the function body (7 MARKS) (dict of {str: list of str}) -> dict of {str: list of str} Return a dict where the keys are the same as the keys in word_to_phonemes and the value for each key is a list of all words that rhyme with the key. Two words rhyme if and only if they are different and their last vowel phonemes and all subsequent consonant phoneme(s) after the last vowel phonemes match. >>> words_to_phonemes = read_pronunciation(open('dictionary.txt')) >>> words_to_rhyming_words = build_rhyming_dict(words_to_phonemes) >>> words_to_rhyming_words['CRAIG'] ['BAIG', 'BEGUE', 'FLAIG', 'HAGUE', 'HAIG', 'LAPHROAIG', 'MACIAG', 'MCCAGUE', 'MCCAIG', 'MCKAIG', 'MCQUAIG', 'MCTAGUE', 'NEST-EGG', 'O'LAGUE', 'PLAGUE', 'RAGUE', 'SPRAGUE', 'VAGUE'] >>> # Notice that 'CRAIG' is not in the list of words that rhyme with 'CRAIG' # recall that two words rhyme iff their last vowel phonemes and # all subsequent consonant phonemes matched. # suppose we had a solution which had a runtime which was quadratic in the # length of the words_to_phonemes pythoin dict. If it took 1 second for # build_rhyming_dict to run for a words_to_phonemes dict with 1000 words, # then if we doubled the length to 2000 words, then we expect it to take # 4 seconds, since if a = 1000, f(a) = 1 second, then f(2a) = (2^2) = 4 | 3.781206 | 4 |
plotting/plot_pub_pk.py | sjforeman/RadioFisher | 3 | 6613313 | <reponame>sjforeman/RadioFisher
#!/usr/bin/python
"""
Process EOS Fisher matrices and plot P(k).
"""
import numpy as np
import pylab as P
from rfwrapper import rf
import matplotlib.patches
import matplotlib.cm
from units import *
from mpi4py import MPI
import os
import euclid
cosmo = rf.experiments.cosmo
#names = ["GBT", "BINGO", "WSRT", "APERTIF", "JVLA", "ASKAP", "KAT7", "MeerKAT", "SKA1mid", "SKA1MK", "iSKA1MK", "aSKA1MK", "SKA1MK_A0"]
names = ["SKA1MK",] #["SKA1mid",] ["MeerKAT",]
colours = ['#22AD1A', '#3399FF', '#ED7624']
# Fiducial value and plotting
fig = P.figure()
ax = fig.add_subplot(111)
for k in range(len(names)):
root = "output/" + names[k]
# Load cosmo fns.
dat = np.atleast_2d( np.genfromtxt(root+"-cosmofns-zc.dat") ).T
zc, Hc, dAc, Dc, fc = dat
z, H, dA, D, f = np.genfromtxt(root+"-cosmofns-smooth.dat").T
kc = np.genfromtxt(root+"-fisher-kc.dat").T
# Load Fisher matrices as fn. of z
Nbins = zc.size
F_list = [np.genfromtxt(root+"-fisher-full-%d.dat" % i) for i in range(Nbins)]
# Save P(k) rebinning info
#np.savetxt(root+"-rebin-Fbase-%d.dat" % i, np.array(binning_info['F_base']) )
#np.savetxt(root+"-rebin-cumul-%d.dat" % i, np.array(binning_info['cumul']) )
#np.savetxt(root+"-rebin-kgrid-%d.dat" % i, np.array(binning_info['kgrid']) )
#np.savetxt(root+"-rebin-Vfac-%d.dat" % i, np.array([binning_info['Vfac'],]) )
# EOS FISHER MATRIX
# Actually, (aperp, apar) are (D_A, H)
pnames = ['A', 'b_HI', 'Tb', 'sigma_NL', 'sigma8', 'n_s', 'f', 'aperp', 'apar',
'omegak', 'omegaDE', 'w0', 'wa', 'h', 'gamma']
pnames += ["pk%d" % i for i in range(kc.size)]
zfns = [1,]
F, lbls = rf.combined_fisher_matrix( F_list,
expand=zfns, names=pnames,
exclude=[2,4,5,6,7,8 ] )
# Remove elements with zero diagonal (completely unconstrained)
zero_idxs = np.where(np.diag(F) == 0.)[0]
print "Zero idxs:", zero_idxs
F = rf.fisher_with_excluded_params(F, excl=zero_idxs)
lbls = lbls[:-zero_idxs.size]
#rf.plot_corrmat(F, lbls)
# Overlay error ellipses as a fn. of z
p1 = rf.indexes_for_sampled_fns(4, zc.size, zfns)
#p2 = rf.indexes_for_sampled_fns(5, zc.size, zfns)
# Full covmat
cov = np.linalg.inv(F)
diags = np.sqrt(np.diag(cov))
# Reduced covmat
#F2 = rf.fisher_with_excluded_params(F, excl=[l for l in range(19, 55)])
#cov2 = np.linalg.inv(F2)
#diags2 = np.sqrt(np.diag(cov2))
# Print diags.
for i in range(diags.size):
#if i < diags2.size:
# print "%2d %10s %3.4f %3.4f" % (i, lbls[i], diags[i], diags2[i])
#else:
print "%2d %10s %3.4f" % (i, lbls[i], diags[i])
exit()
P.subplot(111)
idxs = np.array([l for l in range(7, 43)])
print idxs, idxs.size
P.plot(kc[:idxs.size], diags[idxs], lw=1.5)
P.xscale('log')
P.ylim((0., 0.2))
P.show()
exit()
print "Cond.", np.linalg.cond(F)
F[-1,:] *= 1e5; F[:,-1] *= 1e5
F[-2,:] *= 1e5; F[:,-2] *= 1e5
F[-3,:] *= 1e5; F[:,-3] *= 1e5
print "Cond.", np.linalg.cond(F)
#print np.diag(F)
#print F[-1,:]
rf.plot_corrmat(F, lbls)
P.show()
exit()
#ax.legend((l for l in lines), (lbl for lbl in labels), prop={'size':'x-large'})
#ax.set_xlim((-1.31, -0.70))
#ax.set_ylim((-0.7, 0.7))
P.ylim((0., 1.))
P.xscale('log')
ax.set_xlabel(r"$k$", fontdict={'fontsize':'20'})
ax.set_ylabel(r"$P(k)$", fontdict={'fontsize':'20'})
fontsize = 16.
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
P.tight_layout()
P.show()
| #!/usr/bin/python
"""
Process EOS Fisher matrices and plot P(k).
"""
import numpy as np
import pylab as P
from rfwrapper import rf
import matplotlib.patches
import matplotlib.cm
from units import *
from mpi4py import MPI
import os
import euclid
cosmo = rf.experiments.cosmo
#names = ["GBT", "BINGO", "WSRT", "APERTIF", "JVLA", "ASKAP", "KAT7", "MeerKAT", "SKA1mid", "SKA1MK", "iSKA1MK", "aSKA1MK", "SKA1MK_A0"]
names = ["SKA1MK",] #["SKA1mid",] ["MeerKAT",]
colours = ['#22AD1A', '#3399FF', '#ED7624']
# Fiducial value and plotting
fig = P.figure()
ax = fig.add_subplot(111)
for k in range(len(names)):
root = "output/" + names[k]
# Load cosmo fns.
dat = np.atleast_2d( np.genfromtxt(root+"-cosmofns-zc.dat") ).T
zc, Hc, dAc, Dc, fc = dat
z, H, dA, D, f = np.genfromtxt(root+"-cosmofns-smooth.dat").T
kc = np.genfromtxt(root+"-fisher-kc.dat").T
# Load Fisher matrices as fn. of z
Nbins = zc.size
F_list = [np.genfromtxt(root+"-fisher-full-%d.dat" % i) for i in range(Nbins)]
# Save P(k) rebinning info
#np.savetxt(root+"-rebin-Fbase-%d.dat" % i, np.array(binning_info['F_base']) )
#np.savetxt(root+"-rebin-cumul-%d.dat" % i, np.array(binning_info['cumul']) )
#np.savetxt(root+"-rebin-kgrid-%d.dat" % i, np.array(binning_info['kgrid']) )
#np.savetxt(root+"-rebin-Vfac-%d.dat" % i, np.array([binning_info['Vfac'],]) )
# EOS FISHER MATRIX
# Actually, (aperp, apar) are (D_A, H)
pnames = ['A', 'b_HI', 'Tb', 'sigma_NL', 'sigma8', 'n_s', 'f', 'aperp', 'apar',
'omegak', 'omegaDE', 'w0', 'wa', 'h', 'gamma']
pnames += ["pk%d" % i for i in range(kc.size)]
zfns = [1,]
F, lbls = rf.combined_fisher_matrix( F_list,
expand=zfns, names=pnames,
exclude=[2,4,5,6,7,8 ] )
# Remove elements with zero diagonal (completely unconstrained)
zero_idxs = np.where(np.diag(F) == 0.)[0]
print "Zero idxs:", zero_idxs
F = rf.fisher_with_excluded_params(F, excl=zero_idxs)
lbls = lbls[:-zero_idxs.size]
#rf.plot_corrmat(F, lbls)
# Overlay error ellipses as a fn. of z
p1 = rf.indexes_for_sampled_fns(4, zc.size, zfns)
#p2 = rf.indexes_for_sampled_fns(5, zc.size, zfns)
# Full covmat
cov = np.linalg.inv(F)
diags = np.sqrt(np.diag(cov))
# Reduced covmat
#F2 = rf.fisher_with_excluded_params(F, excl=[l for l in range(19, 55)])
#cov2 = np.linalg.inv(F2)
#diags2 = np.sqrt(np.diag(cov2))
# Print diags.
for i in range(diags.size):
#if i < diags2.size:
# print "%2d %10s %3.4f %3.4f" % (i, lbls[i], diags[i], diags2[i])
#else:
print "%2d %10s %3.4f" % (i, lbls[i], diags[i])
exit()
P.subplot(111)
idxs = np.array([l for l in range(7, 43)])
print idxs, idxs.size
P.plot(kc[:idxs.size], diags[idxs], lw=1.5)
P.xscale('log')
P.ylim((0., 0.2))
P.show()
exit()
print "Cond.", np.linalg.cond(F)
F[-1,:] *= 1e5; F[:,-1] *= 1e5
F[-2,:] *= 1e5; F[:,-2] *= 1e5
F[-3,:] *= 1e5; F[:,-3] *= 1e5
print "Cond.", np.linalg.cond(F)
#print np.diag(F)
#print F[-1,:]
rf.plot_corrmat(F, lbls)
P.show()
exit()
#ax.legend((l for l in lines), (lbl for lbl in labels), prop={'size':'x-large'})
#ax.set_xlim((-1.31, -0.70))
#ax.set_ylim((-0.7, 0.7))
P.ylim((0., 1.))
P.xscale('log')
ax.set_xlabel(r"$k$", fontdict={'fontsize':'20'})
ax.set_ylabel(r"$P(k)$", fontdict={'fontsize':'20'})
fontsize = 16.
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
P.tight_layout()
P.show() | en | 0.47903 | #!/usr/bin/python Process EOS Fisher matrices and plot P(k). #names = ["GBT", "BINGO", "WSRT", "APERTIF", "JVLA", "ASKAP", "KAT7", "MeerKAT", "SKA1mid", "SKA1MK", "iSKA1MK", "aSKA1MK", "SKA1MK_A0"] #["SKA1mid",] ["MeerKAT",] # Fiducial value and plotting # Load cosmo fns. # Load Fisher matrices as fn. of z # Save P(k) rebinning info #np.savetxt(root+"-rebin-Fbase-%d.dat" % i, np.array(binning_info['F_base']) ) #np.savetxt(root+"-rebin-cumul-%d.dat" % i, np.array(binning_info['cumul']) ) #np.savetxt(root+"-rebin-kgrid-%d.dat" % i, np.array(binning_info['kgrid']) ) #np.savetxt(root+"-rebin-Vfac-%d.dat" % i, np.array([binning_info['Vfac'],]) ) # EOS FISHER MATRIX # Actually, (aperp, apar) are (D_A, H) # Remove elements with zero diagonal (completely unconstrained) #rf.plot_corrmat(F, lbls) # Overlay error ellipses as a fn. of z #p2 = rf.indexes_for_sampled_fns(5, zc.size, zfns) # Full covmat # Reduced covmat #F2 = rf.fisher_with_excluded_params(F, excl=[l for l in range(19, 55)]) #cov2 = np.linalg.inv(F2) #diags2 = np.sqrt(np.diag(cov2)) # Print diags. #if i < diags2.size: # print "%2d %10s %3.4f %3.4f" % (i, lbls[i], diags[i], diags2[i]) #else: #print np.diag(F) #print F[-1,:] #ax.legend((l for l in lines), (lbl for lbl in labels), prop={'size':'x-large'}) #ax.set_xlim((-1.31, -0.70)) #ax.set_ylim((-0.7, 0.7)) | 2.055647 | 2 |
src/sqlencode/__utilfuncs__.py | mtaboun/sqlencode | 0 | 6613314 | <filename>src/sqlencode/__utilfuncs__.py
def combine_queries(query1: str, query2: str) -> str:
return "Hello"
| <filename>src/sqlencode/__utilfuncs__.py
def combine_queries(query1: str, query2: str) -> str:
return "Hello"
| none | 1 | 1.37021 | 1 | |
utils/commonUtil.py | xia-deng/lawerWeb | 0 | 6613315 | <gh_stars>0
import re
from django.db.models import QuerySet
from xpinyin import Pinyin
class CommonUtil:
@staticmethod
def toString(Obj):
objtype=type(Obj)
print(objtype)
@staticmethod
def querySetToList(querySet,field=""):
if(type(querySet)==QuerySet):
list=[]
list=[', '.join(x) for x in querySet]
return list
return None
@staticmethod
def cn_to_pinyin(cn_words):
pinyin = Pinyin().get_pinyin(cn_words).replace('-','').lower()
pattern = re.compile('\\w*')
result1 = pattern.findall(pinyin)
result1 = ''.join(result1)
return (result1.replace("_",''))
| import re
from django.db.models import QuerySet
from xpinyin import Pinyin
class CommonUtil:
@staticmethod
def toString(Obj):
objtype=type(Obj)
print(objtype)
@staticmethod
def querySetToList(querySet,field=""):
if(type(querySet)==QuerySet):
list=[]
list=[', '.join(x) for x in querySet]
return list
return None
@staticmethod
def cn_to_pinyin(cn_words):
pinyin = Pinyin().get_pinyin(cn_words).replace('-','').lower()
pattern = re.compile('\\w*')
result1 = pattern.findall(pinyin)
result1 = ''.join(result1)
return (result1.replace("_",'')) | none | 1 | 2.421498 | 2 | |
weasyl/searchtag.py | hyena/weasyl | 0 | 6613316 | # searchtag.py
import re
import sqlalchemy as sa
from libweasyl import staff
from weasyl import define as d
from weasyl import files
from weasyl import ignoreuser
from weasyl import macro as m
from weasyl import orm
from weasyl import welcome
from weasyl.cache import region
from weasyl.error import WeasylError
_TAG_DELIMITER = re.compile(r"[\s,]+")
def select(submitid=None, charid=None, journalid=None):
return d.execute("SELECT st.title FROM searchtag st"
" INNER JOIN searchmap%s sm USING (tagid)"
" WHERE sm.targetid = %i"
" ORDER BY st.title",
[
"submit" if submitid else "char" if charid else "journal",
submitid if submitid else charid if charid else journalid
], options="within")
def select_with_artist_tags(submitid):
db = d.connect()
tags = (
db.query(orm.Tag.title, orm.SubmissionTag.is_artist_tag)
.join(orm.SubmissionTag)
.filter_by(targetid=submitid)
.order_by(orm.Tag.title)
.all())
ret = []
artist_tags = set()
for tag, is_artist_tag in tags:
ret.append(tag)
if is_artist_tag:
artist_tags.add(tag)
return ret, artist_tags
def can_remove_tags(userid, ownerid):
return userid == ownerid or userid in staff.MODS or 'k' not in d.get_config(ownerid)
def removable_tags(userid, ownerid, tags, artist_tags):
if not can_remove_tags(userid, ownerid):
return [tag for tag in tags if tag not in artist_tags]
else:
return tags
def select_list(map_table, targetids):
if not targetids:
return {}
mt = map_table
q = (
d.sa
.select([mt.c.targetid, d.sa.func.array_agg(mt.c.tagid)])
.select_from(mt)
.where(mt.c.targetid.in_(targetids))
.group_by(mt.c.targetid))
db = d.connect()
return dict(list(db.execute(q)))
@region.cache_on_arguments()
def get_or_create(name):
name = d.get_search_tag(name)
tag = d.engine.execute(
'INSERT INTO searchtag (title) VALUES (%(name)s) ON CONFLICT (title) DO NOTHING RETURNING tagid',
name=name).scalar()
if tag is not None:
return tag
return d.engine.execute(
'SELECT tagid FROM searchtag WHERE title = %(name)s',
name=name).scalar()
def get_ids(names):
result = d.engine.execute(
"SELECT tagid, title FROM searchtag WHERE title = ANY (%(names)s)",
names=list(names))
return {row.title: row.tagid for row in result}
def suggest(userid, target):
if not target:
return []
if userid:
block = d.execute("SELECT tagid FROM blocktag WHERE userid = %i", [userid], options="within")
query = list()
target = d.get_search_tag(target)
statement = ["SELECT title FROM searchtag WHERE title LIKE '%s%%'"]
if userid and block:
statement.append(" AND tagid NOT IN %s" % (d.sql_number_list(block),))
for i in d.execute("".join(statement + [" ORDER BY title LIMIT 10"]), [target], options="within"):
query.append(i)
statement = ["SELECT title FROM searchtag WHERE title LIKE '%%%s%%' AND title NOT LIKE '%s%%'"]
if userid and block:
statement.append(" AND tagid NOT IN %s" % (d.sql_number_list(block),))
for i in d.execute("".join(statement + [" ORDER BY title LIMIT 5"]), [target, target], options="within"):
query.append(i)
return query
def tag_array(tagids):
if not tagids:
return None
st = d.meta.tables['searchtag']
return sa.func.array(
sa.select([st.c.title])
.where(st.c.tagid.in_(tagids))
.as_scalar())
def parse_tags(text):
tags = set()
for i in _TAG_DELIMITER.split(text):
tag = d.get_search_tag(i)
if tag:
tags.add(tag)
return tags
def associate(userid, tags, submitid=None, charid=None, journalid=None):
targetid = d.get_targetid(submitid, charid, journalid)
# Assign table, feature, ownerid
if submitid:
table, feature = "searchmapsubmit", "submit"
ownerid = d.get_ownerid(submitid=targetid)
elif charid:
table, feature = "searchmapchar", "char"
ownerid = d.get_ownerid(charid=targetid)
else:
table, feature = "searchmapjournal", "journal"
ownerid = d.get_ownerid(journalid=targetid)
# Check permissions and invalid target
if not ownerid:
raise WeasylError("TargetRecordMissing")
elif userid != ownerid and "g" in d.get_config(userid):
raise WeasylError("InsufficientPermissions")
elif ignoreuser.check(ownerid, userid):
raise WeasylError("contentOwnerIgnoredYou")
# Determine previous tags
existing = d.engine.execute(
"SELECT tagid, settings FROM {} WHERE targetid = %(target)s".format(table),
target=targetid).fetchall()
# Determine tag titles and tagids
query = d.engine.execute(
"SELECT tagid, title FROM searchtag WHERE title = ANY (%(tags)s)",
tags=list(tags)).fetchall()
newtags = list(tags - {x.title for x in query})
if newtags:
query.extend(
d.engine.execute(
"INSERT INTO searchtag (title) SELECT * FROM UNNEST (%(newtags)s) AS title RETURNING tagid, title",
newtags=newtags
).fetchall())
existing_tagids = {t.tagid for t in existing}
entered_tagids = {t.tagid for t in query}
# Assign added and removed
added = entered_tagids - existing_tagids
removed = existing_tagids - entered_tagids
# Check removed artist tags
if not can_remove_tags(userid, ownerid):
existing_artist_tags = {t.tagid for t in existing if 'a' in t.settings}
removed.difference_update(existing_artist_tags)
entered_tagids.update(existing_artist_tags)
# Remove tags
if removed:
d.engine.execute(
"DELETE FROM {} WHERE targetid = %(target)s AND tagid = ANY (%(removed)s)".format(table),
target=targetid, removed=list(removed))
if added:
d.engine.execute(
"INSERT INTO {} SELECT tag, %(target)s FROM UNNEST (%(added)s) AS tag".format(table),
target=targetid, added=list(added))
if userid == ownerid:
d.execute(
"UPDATE %s SET settings = settings || 'a' WHERE targetid = %i AND tagid IN %s",
[table, targetid, d.sql_number_list(list(added))])
if submitid:
d.engine.execute(
'INSERT INTO submission_tags (submitid, tags) VALUES (%(submission)s, %(tags)s) '
'ON CONFLICT (submitid) DO UPDATE SET tags = %(tags)s',
submission=submitid, tags=list(entered_tagids))
db = d.connect()
db.execute(
d.meta.tables['tag_updates'].insert()
.values(submitid=submitid, userid=userid,
added=tag_array(added), removed=tag_array(removed)))
if userid != ownerid:
welcome.tag_update_insert(ownerid, submitid)
files.append(
"%stag.%s.%s.log" % (m.MACRO_SYS_LOG_PATH, feature, d.get_timestamp()),
"-%sID %i -T %i -UID %i -X %s\n" % (feature[0].upper(), targetid, d.get_time(), userid,
" ".join(tags)))
def tag_history(submitid):
db = d.connect()
tu = d.meta.tables['tag_updates']
pr = d.meta.tables['profile']
return db.execute(
sa.select([pr.c.username, tu.c.updated_at, tu.c.added, tu.c.removed])
.select_from(tu.join(pr, tu.c.userid == pr.c.userid))
.where(tu.c.submitid == submitid)
.order_by(tu.c.updated_at.desc()))
| # searchtag.py
import re
import sqlalchemy as sa
from libweasyl import staff
from weasyl import define as d
from weasyl import files
from weasyl import ignoreuser
from weasyl import macro as m
from weasyl import orm
from weasyl import welcome
from weasyl.cache import region
from weasyl.error import WeasylError
_TAG_DELIMITER = re.compile(r"[\s,]+")
def select(submitid=None, charid=None, journalid=None):
return d.execute("SELECT st.title FROM searchtag st"
" INNER JOIN searchmap%s sm USING (tagid)"
" WHERE sm.targetid = %i"
" ORDER BY st.title",
[
"submit" if submitid else "char" if charid else "journal",
submitid if submitid else charid if charid else journalid
], options="within")
def select_with_artist_tags(submitid):
db = d.connect()
tags = (
db.query(orm.Tag.title, orm.SubmissionTag.is_artist_tag)
.join(orm.SubmissionTag)
.filter_by(targetid=submitid)
.order_by(orm.Tag.title)
.all())
ret = []
artist_tags = set()
for tag, is_artist_tag in tags:
ret.append(tag)
if is_artist_tag:
artist_tags.add(tag)
return ret, artist_tags
def can_remove_tags(userid, ownerid):
return userid == ownerid or userid in staff.MODS or 'k' not in d.get_config(ownerid)
def removable_tags(userid, ownerid, tags, artist_tags):
if not can_remove_tags(userid, ownerid):
return [tag for tag in tags if tag not in artist_tags]
else:
return tags
def select_list(map_table, targetids):
if not targetids:
return {}
mt = map_table
q = (
d.sa
.select([mt.c.targetid, d.sa.func.array_agg(mt.c.tagid)])
.select_from(mt)
.where(mt.c.targetid.in_(targetids))
.group_by(mt.c.targetid))
db = d.connect()
return dict(list(db.execute(q)))
@region.cache_on_arguments()
def get_or_create(name):
name = d.get_search_tag(name)
tag = d.engine.execute(
'INSERT INTO searchtag (title) VALUES (%(name)s) ON CONFLICT (title) DO NOTHING RETURNING tagid',
name=name).scalar()
if tag is not None:
return tag
return d.engine.execute(
'SELECT tagid FROM searchtag WHERE title = %(name)s',
name=name).scalar()
def get_ids(names):
result = d.engine.execute(
"SELECT tagid, title FROM searchtag WHERE title = ANY (%(names)s)",
names=list(names))
return {row.title: row.tagid for row in result}
def suggest(userid, target):
if not target:
return []
if userid:
block = d.execute("SELECT tagid FROM blocktag WHERE userid = %i", [userid], options="within")
query = list()
target = d.get_search_tag(target)
statement = ["SELECT title FROM searchtag WHERE title LIKE '%s%%'"]
if userid and block:
statement.append(" AND tagid NOT IN %s" % (d.sql_number_list(block),))
for i in d.execute("".join(statement + [" ORDER BY title LIMIT 10"]), [target], options="within"):
query.append(i)
statement = ["SELECT title FROM searchtag WHERE title LIKE '%%%s%%' AND title NOT LIKE '%s%%'"]
if userid and block:
statement.append(" AND tagid NOT IN %s" % (d.sql_number_list(block),))
for i in d.execute("".join(statement + [" ORDER BY title LIMIT 5"]), [target, target], options="within"):
query.append(i)
return query
def tag_array(tagids):
if not tagids:
return None
st = d.meta.tables['searchtag']
return sa.func.array(
sa.select([st.c.title])
.where(st.c.tagid.in_(tagids))
.as_scalar())
def parse_tags(text):
tags = set()
for i in _TAG_DELIMITER.split(text):
tag = d.get_search_tag(i)
if tag:
tags.add(tag)
return tags
def associate(userid, tags, submitid=None, charid=None, journalid=None):
targetid = d.get_targetid(submitid, charid, journalid)
# Assign table, feature, ownerid
if submitid:
table, feature = "searchmapsubmit", "submit"
ownerid = d.get_ownerid(submitid=targetid)
elif charid:
table, feature = "searchmapchar", "char"
ownerid = d.get_ownerid(charid=targetid)
else:
table, feature = "searchmapjournal", "journal"
ownerid = d.get_ownerid(journalid=targetid)
# Check permissions and invalid target
if not ownerid:
raise WeasylError("TargetRecordMissing")
elif userid != ownerid and "g" in d.get_config(userid):
raise WeasylError("InsufficientPermissions")
elif ignoreuser.check(ownerid, userid):
raise WeasylError("contentOwnerIgnoredYou")
# Determine previous tags
existing = d.engine.execute(
"SELECT tagid, settings FROM {} WHERE targetid = %(target)s".format(table),
target=targetid).fetchall()
# Determine tag titles and tagids
query = d.engine.execute(
"SELECT tagid, title FROM searchtag WHERE title = ANY (%(tags)s)",
tags=list(tags)).fetchall()
newtags = list(tags - {x.title for x in query})
if newtags:
query.extend(
d.engine.execute(
"INSERT INTO searchtag (title) SELECT * FROM UNNEST (%(newtags)s) AS title RETURNING tagid, title",
newtags=newtags
).fetchall())
existing_tagids = {t.tagid for t in existing}
entered_tagids = {t.tagid for t in query}
# Assign added and removed
added = entered_tagids - existing_tagids
removed = existing_tagids - entered_tagids
# Check removed artist tags
if not can_remove_tags(userid, ownerid):
existing_artist_tags = {t.tagid for t in existing if 'a' in t.settings}
removed.difference_update(existing_artist_tags)
entered_tagids.update(existing_artist_tags)
# Remove tags
if removed:
d.engine.execute(
"DELETE FROM {} WHERE targetid = %(target)s AND tagid = ANY (%(removed)s)".format(table),
target=targetid, removed=list(removed))
if added:
d.engine.execute(
"INSERT INTO {} SELECT tag, %(target)s FROM UNNEST (%(added)s) AS tag".format(table),
target=targetid, added=list(added))
if userid == ownerid:
d.execute(
"UPDATE %s SET settings = settings || 'a' WHERE targetid = %i AND tagid IN %s",
[table, targetid, d.sql_number_list(list(added))])
if submitid:
d.engine.execute(
'INSERT INTO submission_tags (submitid, tags) VALUES (%(submission)s, %(tags)s) '
'ON CONFLICT (submitid) DO UPDATE SET tags = %(tags)s',
submission=submitid, tags=list(entered_tagids))
db = d.connect()
db.execute(
d.meta.tables['tag_updates'].insert()
.values(submitid=submitid, userid=userid,
added=tag_array(added), removed=tag_array(removed)))
if userid != ownerid:
welcome.tag_update_insert(ownerid, submitid)
files.append(
"%stag.%s.%s.log" % (m.MACRO_SYS_LOG_PATH, feature, d.get_timestamp()),
"-%sID %i -T %i -UID %i -X %s\n" % (feature[0].upper(), targetid, d.get_time(), userid,
" ".join(tags)))
def tag_history(submitid):
db = d.connect()
tu = d.meta.tables['tag_updates']
pr = d.meta.tables['profile']
return db.execute(
sa.select([pr.c.username, tu.c.updated_at, tu.c.added, tu.c.removed])
.select_from(tu.join(pr, tu.c.userid == pr.c.userid))
.where(tu.c.submitid == submitid)
.order_by(tu.c.updated_at.desc()))
| en | 0.619622 | # searchtag.py # Assign table, feature, ownerid # Check permissions and invalid target # Determine previous tags # Determine tag titles and tagids # Assign added and removed # Check removed artist tags # Remove tags | 2.255568 | 2 |
tests/__init__.py | nullp0tr/Bluew | 17 | 6613317 | <filename>tests/__init__.py
from bluew import * | <filename>tests/__init__.py
from bluew import * | none | 1 | 1.017169 | 1 | |
rbac/tests/test_review.py | shawnmckinney/py-fortress | 16 | 6613318 | '''
@copyright: 2022 - Symas Corporation
'''
import unittest
from rbac import review
from rbac.model import User, Role, Perm
from rbac.cli.utils import print_ln, print_entity
class BasicTestSuite(unittest.TestCase):
"""These tests the py-fortress review functions."""
class TestReivewMgr(unittest.TestCase):
"""
Test the review funcs
"""
def test01_assigned_users(self):
"""
Test the assigned users method
"""
print_ln('test_assigned_users')
try:
rList = review.find_roles(Role(name='py-role*'))
for rle in rList:
print_ln("Assigned users role=" + rle.name)
uList = review.assigned_users(rle)
for user in uList:
print_ln("Assigned user=" + user, 1)
except Exception as e:
self.fail('test_assigned_users failed, exception=' + e.msg)
def test02_assigned_roles(self):
"""
Test the assigned roles method
"""
print_ln('test_assigned_roles')
try:
uList = review.find_users(User(uid='py-user*'))
for usr in uList:
print_ln("Assigned roles user=" + usr.uid)
rList = review.assigned_roles(usr)
for role in rList:
print_entity(role, "Assigned role", 1)
except Exception as e:
self.fail('test_assigned_roles failed, exception=' + e.msg)
def test03_perm_roles(self):
"""
Test the perm roles method
"""
print_ln('test16_perm_roles')
try:
pList = review.find_perms(Perm(obj_name='py-obj*', op_name='*'))
for perm in pList:
print_ln("Role Perm obj name=" + perm.obj_name + ', op=' + perm.op_name + ', id=' + perm.obj_id)
rList = review.perm_roles(perm)
for role in rList:
print_ln("Assigned role=" + role, 1)
except Exception as e:
self.fail('test16_perm_roles failed, exception=' + e.msg)
def test04_role_perms(self):
"""
Test the role perms method
"""
print_ln('test_role_perms')
try:
rList = review.find_roles(Role(name='py-role*'))
for rle in rList:
print_ln("Perm Roles name=" + rle.name)
pList = review.role_perms(rle)
for perm in pList:
print_ln("Assigned perm obj name=" + perm.obj_name + ', op name=' + perm.op_name + ', obj id=' + perm.obj_id, 1)
except Exception as e:
self.fail('test_role_perms failed, exception=' + e.msg)
def test05_user_perms(self):
"""
Test the user perms method
"""
print_ln('test_user_perms')
try:
uList = review.find_users(User(uid='py-user*'))
for usr in uList:
print_ln("Assigned perms user=" + usr.uid)
pList = review.user_perms(usr)
for perm in pList:
print_ln("Assigned perm obj name=" + perm.obj_name + ', op name=' + perm.op_name + ', obj id=' + perm.obj_id, 1)
except Exception as e:
self.fail('test_user_perms failed, exception=' + e.msg)
def test06_perm_users(self):
"""
Test the perm users method
"""
print_ln('test_perm_users')
try:
pList = review.find_perms(Perm(obj_name='py-obj*', op_name='*'))
for perm in pList:
print_ln("Perm obj name=" + perm.obj_name + ', op=' + perm.op_name + ', id=' + perm.obj_id)
uList = review.perm_users(perm)
for user in uList:
print_ln("Assigned user=" + user.uid, 1)
except Exception as e:
self.fail('test_perm_users failed, exception=' + e.msg)
def suite():
suite = unittest.TestSuite()
suite.addTest(TestReivewMgr('test01_assigned_users'))
suite.addTest(TestReivewMgr('test02_assigned_roles'))
suite.addTest(TestReivewMgr('test03_perm_roles'))
suite.addTest(TestReivewMgr('test04_role_perms'))
suite.addTest(TestReivewMgr('test05_user_perms'))
suite.addTest(TestReivewMgr('test06_perm_users'))
return suite
if __name__ == '__main__':
runner = unittest.TextTestRunner(failfast=True)
runner.run(suite()) | '''
@copyright: 2022 - Symas Corporation
'''
import unittest
from rbac import review
from rbac.model import User, Role, Perm
from rbac.cli.utils import print_ln, print_entity
class BasicTestSuite(unittest.TestCase):
"""These tests the py-fortress review functions."""
class TestReivewMgr(unittest.TestCase):
"""
Test the review funcs
"""
def test01_assigned_users(self):
"""
Test the assigned users method
"""
print_ln('test_assigned_users')
try:
rList = review.find_roles(Role(name='py-role*'))
for rle in rList:
print_ln("Assigned users role=" + rle.name)
uList = review.assigned_users(rle)
for user in uList:
print_ln("Assigned user=" + user, 1)
except Exception as e:
self.fail('test_assigned_users failed, exception=' + e.msg)
def test02_assigned_roles(self):
"""
Test the assigned roles method
"""
print_ln('test_assigned_roles')
try:
uList = review.find_users(User(uid='py-user*'))
for usr in uList:
print_ln("Assigned roles user=" + usr.uid)
rList = review.assigned_roles(usr)
for role in rList:
print_entity(role, "Assigned role", 1)
except Exception as e:
self.fail('test_assigned_roles failed, exception=' + e.msg)
def test03_perm_roles(self):
"""
Test the perm roles method
"""
print_ln('test16_perm_roles')
try:
pList = review.find_perms(Perm(obj_name='py-obj*', op_name='*'))
for perm in pList:
print_ln("Role Perm obj name=" + perm.obj_name + ', op=' + perm.op_name + ', id=' + perm.obj_id)
rList = review.perm_roles(perm)
for role in rList:
print_ln("Assigned role=" + role, 1)
except Exception as e:
self.fail('test16_perm_roles failed, exception=' + e.msg)
def test04_role_perms(self):
"""
Test the role perms method
"""
print_ln('test_role_perms')
try:
rList = review.find_roles(Role(name='py-role*'))
for rle in rList:
print_ln("Perm Roles name=" + rle.name)
pList = review.role_perms(rle)
for perm in pList:
print_ln("Assigned perm obj name=" + perm.obj_name + ', op name=' + perm.op_name + ', obj id=' + perm.obj_id, 1)
except Exception as e:
self.fail('test_role_perms failed, exception=' + e.msg)
def test05_user_perms(self):
"""
Test the user perms method
"""
print_ln('test_user_perms')
try:
uList = review.find_users(User(uid='py-user*'))
for usr in uList:
print_ln("Assigned perms user=" + usr.uid)
pList = review.user_perms(usr)
for perm in pList:
print_ln("Assigned perm obj name=" + perm.obj_name + ', op name=' + perm.op_name + ', obj id=' + perm.obj_id, 1)
except Exception as e:
self.fail('test_user_perms failed, exception=' + e.msg)
def test06_perm_users(self):
"""
Test the perm users method
"""
print_ln('test_perm_users')
try:
pList = review.find_perms(Perm(obj_name='py-obj*', op_name='*'))
for perm in pList:
print_ln("Perm obj name=" + perm.obj_name + ', op=' + perm.op_name + ', id=' + perm.obj_id)
uList = review.perm_users(perm)
for user in uList:
print_ln("Assigned user=" + user.uid, 1)
except Exception as e:
self.fail('test_perm_users failed, exception=' + e.msg)
def suite():
suite = unittest.TestSuite()
suite.addTest(TestReivewMgr('test01_assigned_users'))
suite.addTest(TestReivewMgr('test02_assigned_roles'))
suite.addTest(TestReivewMgr('test03_perm_roles'))
suite.addTest(TestReivewMgr('test04_role_perms'))
suite.addTest(TestReivewMgr('test05_user_perms'))
suite.addTest(TestReivewMgr('test06_perm_users'))
return suite
if __name__ == '__main__':
runner = unittest.TextTestRunner(failfast=True)
runner.run(suite()) | en | 0.682593 | @copyright: 2022 - Symas Corporation These tests the py-fortress review functions. Test the review funcs Test the assigned users method Test the assigned roles method Test the perm roles method Test the role perms method Test the user perms method Test the perm users method | 2.671585 | 3 |
main.py | Kanata-Bifang/Nerual-networks-for-video-transfer | 2 | 6613319 | <reponame>Kanata-Bifang/Nerual-networks-for-video-transfer
from models.videonet import *
from models.fast_style_neural import *
from options import get_options
import os
def get_model(opt):
if opt.model == 'videonet':
return videoNet(opt)
elif opt.model == 'style':
return styleNet(opt)
else:
raise NotImplementedError('Such model doesn\'t exist.')
def train(opt):
opt.is_train = True
if not os.path.exists(opt.save_path):
os.mkdir(opt.save_path)
model = get_model(opt)
model.train()
def test(opt):
opt.is_train = False
if not os.path.exists(opt.save_path):
os.mkdir(opt.save_path)
model = get_model(opt)
model.test(opt.load_epoch)
if __name__ == '__main__':
opt = get_options()
if not opt.eval:
train(opt)
else:
test(opt)
| from models.videonet import *
from models.fast_style_neural import *
from options import get_options
import os
def get_model(opt):
if opt.model == 'videonet':
return videoNet(opt)
elif opt.model == 'style':
return styleNet(opt)
else:
raise NotImplementedError('Such model doesn\'t exist.')
def train(opt):
opt.is_train = True
if not os.path.exists(opt.save_path):
os.mkdir(opt.save_path)
model = get_model(opt)
model.train()
def test(opt):
opt.is_train = False
if not os.path.exists(opt.save_path):
os.mkdir(opt.save_path)
model = get_model(opt)
model.test(opt.load_epoch)
if __name__ == '__main__':
opt = get_options()
if not opt.eval:
train(opt)
else:
test(opt) | none | 1 | 2.350088 | 2 | |
src/timeatlas/models/NN/dataloader/prediction_dataloader.py | fredmontet/timeatlas | 10 | 6613320 | <reponame>fredmontet/timeatlas
try:
from torch.utils.data import Subset
except ModuleNotFoundError:
raise ModuleNotFoundError("Pytorch not found. Install with pip install torch")
from .base_dataloader import BaseDataLoader
class PredictionDataLoader(BaseDataLoader):
def __init__(self, dataset, *args, **kwargs):
super(PredictionDataLoader, self).__init__(dataset=dataset, *args, **kwargs)
def split(self, train: float, test: float or None = None, validation: float or None = None):
"""Split Dataset into train, test and validation
Splitting a dataset into train, test and validation by percentages.
Args:
train: percentage of train split
test: percentage of test split
validation: percentage of validation split
Returns: Subset of the DataSet
"""
idx = list(range(len(self.dataset)))
n = len(self.dataset)
if validation is None:
n_train = int(n * train)
train_idx = idx[:n_train]
test_idx = idx[n_train:]
train_set = Subset(self.dataset, train_idx)
test_set = Subset(self.dataset, test_idx)
return train_set, test_set
else:
if test is None:
test = 1 - train - validation
n_train = int(n * train)
n_test = int(n * test)
train_idx = idx[:n_train]
val_idx = idx[n_train:(n_train + n_test)]
test_idx = idx[(n_train + n_test):]
train_set = Subset(self.dataset, train_idx)
val_set = Subset(self.dataset, val_idx)
test_set = Subset(self.dataset, test_idx)
return train_set, val_set, test_set
| try:
from torch.utils.data import Subset
except ModuleNotFoundError:
raise ModuleNotFoundError("Pytorch not found. Install with pip install torch")
from .base_dataloader import BaseDataLoader
class PredictionDataLoader(BaseDataLoader):
def __init__(self, dataset, *args, **kwargs):
super(PredictionDataLoader, self).__init__(dataset=dataset, *args, **kwargs)
def split(self, train: float, test: float or None = None, validation: float or None = None):
"""Split Dataset into train, test and validation
Splitting a dataset into train, test and validation by percentages.
Args:
train: percentage of train split
test: percentage of test split
validation: percentage of validation split
Returns: Subset of the DataSet
"""
idx = list(range(len(self.dataset)))
n = len(self.dataset)
if validation is None:
n_train = int(n * train)
train_idx = idx[:n_train]
test_idx = idx[n_train:]
train_set = Subset(self.dataset, train_idx)
test_set = Subset(self.dataset, test_idx)
return train_set, test_set
else:
if test is None:
test = 1 - train - validation
n_train = int(n * train)
n_test = int(n * test)
train_idx = idx[:n_train]
val_idx = idx[n_train:(n_train + n_test)]
test_idx = idx[(n_train + n_test):]
train_set = Subset(self.dataset, train_idx)
val_set = Subset(self.dataset, val_idx)
test_set = Subset(self.dataset, test_idx)
return train_set, val_set, test_set | en | 0.820326 | Split Dataset into train, test and validation Splitting a dataset into train, test and validation by percentages. Args: train: percentage of train split test: percentage of test split validation: percentage of validation split Returns: Subset of the DataSet | 3.109659 | 3 |
polog/handlers/file/locks/abstract_single_lock.py | pomponchik/polog | 30 | 6613321 | class AbstractSingleLock:
"""
Все единичные классы блокировок, унаследованные от данного класса:
1. Должны переопределить методы .acquire() и .release().
2. Являются отключаемыми. То есть, после вызова метода .off() у их экземпляров они просто не работают.
"""
active = True
def off(self):
"""
Отключение блокировки.
После вызова данного метода методы .acquire() и .release() перестают работать. Откатить это нельзя, операция одноразовая, поэтому рекомендуется применять при инициализации экземпляра отнаследованного класса.
"""
self.acquire = self.empty_acquire
self.release = self.empty_release
self.active = False
def acquire(self):
"""
Взять лок.
Должно быть переопределено наследником.
"""
raise NotImplementedError('The basic action for the blocking class is not spelled out.')
def release(self):
"""
Отпустить лок.
Должно быть переопределено наследником.
"""
raise NotImplementedError('The basic action for the blocking class is not spelled out.')
def empty_acquire(self):
"""
Сделать вид, что взял лок.
"""
pass
def empty_release(self):
"""
Сделать вид, что отпустил лок.
"""
pass
| class AbstractSingleLock:
"""
Все единичные классы блокировок, унаследованные от данного класса:
1. Должны переопределить методы .acquire() и .release().
2. Являются отключаемыми. То есть, после вызова метода .off() у их экземпляров они просто не работают.
"""
active = True
def off(self):
"""
Отключение блокировки.
После вызова данного метода методы .acquire() и .release() перестают работать. Откатить это нельзя, операция одноразовая, поэтому рекомендуется применять при инициализации экземпляра отнаследованного класса.
"""
self.acquire = self.empty_acquire
self.release = self.empty_release
self.active = False
def acquire(self):
"""
Взять лок.
Должно быть переопределено наследником.
"""
raise NotImplementedError('The basic action for the blocking class is not spelled out.')
def release(self):
"""
Отпустить лок.
Должно быть переопределено наследником.
"""
raise NotImplementedError('The basic action for the blocking class is not spelled out.')
def empty_acquire(self):
"""
Сделать вид, что взял лок.
"""
pass
def empty_release(self):
"""
Сделать вид, что отпустил лок.
"""
pass
| ru | 0.99639 | Все единичные классы блокировок, унаследованные от данного класса: 1. Должны переопределить методы .acquire() и .release(). 2. Являются отключаемыми. То есть, после вызова метода .off() у их экземпляров они просто не работают. Отключение блокировки. После вызова данного метода методы .acquire() и .release() перестают работать. Откатить это нельзя, операция одноразовая, поэтому рекомендуется применять при инициализации экземпляра отнаследованного класса. Взять лок. Должно быть переопределено наследником. Отпустить лок. Должно быть переопределено наследником. Сделать вид, что взял лок. Сделать вид, что отпустил лок. | 2.807217 | 3 |
PyChat/client/protocol/FileClientProtocol.py | leosartaj/PyChat | 7 | 6613322 | #!/usr/bin/env python2
##
# PyChat
# https://github.com/leosartaj/PyChat.git
#
# Copyright (c) 2014 <NAME>
# Licensed under the MIT license.
##
# system import
import os
import struct
import cPickle as pickle
# twisted imports
from twisted.python import log
from twisted.protocols import basic
# user import
from FileSender import FileSender
from PyChat import command as cmd
# prefix for commands
SERVER_PREFIX = cmd.SERVER_PREFIX
def dict_to_pickle(pickle_dict):
"""
convert from a dictionary to pickle
"""
return pickle.dumps(pickle_dict)
def pickle_to_dict(pickle_str):
"""
convert from pickle to dictionary
"""
return pickle.loads(pickle_str)
class FileClientProtocol(basic.Int32StringReceiver):
"""
Implements file transfer protocol
"""
def connectionMade(self):
self.chatproto = self.factory.chatproto
self._register()
self.sending = False
self.sfile = [None, None]
self.rfile = {}
def _register(self):
"""
Register with the ftp server
send the refrence to the chatproto
"""
self.sendString(self.chatproto.setName)
if self.factory.deferred:
deferred, self.factory.deferred = self.factory.deferred, None
deferred.callback(self)
def stringReceived(self, line):
"""
Handles recieved file lines
"""
peername, line = self._parse(line)
if line:
self.update(peername, line)
def _parse(self, line):
"""
Parse line for commands
returns string to be logged
otherwise simply returns line without change
"""
peername, line = cmd.extractFirst(line)
comd, val = cmd.parse(line, SERVER_PREFIX)
if comd == 'eof':
value = 'File sent: %s' %(self.sfile)
peername = 'me'
self._reset()
else:
pickle_dict = pickle_to_dict(val)
value = self._parseDict(pickle_dict)
return peername, value
def _parseDict(self, pickle_dict):
"""
Parses the pickle_dict
Takes measures on the files
"""
fName = pickle_dict['filename']
if pickle_dict.has_key('eof'):
value = self._closeFile(fName)
elif pickle_dict.has_key('fail'):
value = self._closeFile(fName, False)
elif pickle_dict.has_key('line'):
saveline = pickle_dict['line']
value = self._saveFile(fName, saveline)
else:
return None
return value
def update(self, name, msg, logit=True):
"""
Updates the gui
logs the messages if logit set to true
"""
self.chatproto.update(name, msg)
if logit:
log.msg(msg)
def status(self):
"""
gives the status of sending and receiving
returns a tuple sending(bool)
"""
return self.sending
def sendFile(self, fName):
"""
Sends file to the server
"""
self.sending = True
filename = os.path.basename(fName)
self.sfile = filename
fileprotocol = FileSender()
sendfile, startsend = fileprotocol.beginFileTransfer(fName, self.transport, self.transform)
sendfile.addCallback(self._endTransfer)
sendfile.addErrback(self._sendingFailed)
startsend.callback(1)
def _getDict(self):
"""
Returns a dictionary
"""
pickle_dict = {}
pickle_dict['filename'] = self.sfile
return pickle_dict
def transform(self, line):
"""
Transforms a line to be saved in a file
"""
pickle_dict = self._getDict()
pickle_dict['line'] = line
pickle_str = dict_to_pickle(pickle_dict)
# prefix, for the protocol as file sender does not use sendString
prefix = struct.pack(self.structFormat, len(pickle_str))
pickle_str = prefix + pickle_str
return pickle_str
def _endTransfer(self, *args):
"""
End file transfer
"""
pickle_dict = self._getDict()
pickle_dict['eof'] = True
pickle_str = dict_to_pickle(pickle_dict)
self.sendString(pickle_str)
self.sendString(cmd.servercmd('eof', self.sfile))
def _sendingFailed(self, exc):
log.msg(exc)
msg = 'File Sending failed'
self.update('me', msg)
pickle_dict = self._getDict()
pickle_dict['fail'] = True
pickle_str = dict_to_pickle(pickle_dict)
self.sendString(pickle_str)
self.sendString(cmd.servercmd('fail', self.sfile))
def _reset(self):
"""
Reset the variables
"""
self.sending = False
self.sfile = [None, None]
def _initFile(self, fName='unnamed', dire=os.getcwd(), prefix='pychat_'):
"""
opens a file
returns the handler
"""
filename = os.path.basename(fName)
path = os.path.join(dire, prefix + filename)
handler = open(path, 'w')
return handler
def _saveFile(self, fName, fline):
"""
Parses the line
saves the line in the file
returns the result string
"""
if not self.rfile.has_key(fName):
handler = self._initFile(fName)
self.rfile[fName] = handler
value = 'Recieving: ' + fName
elif self.rfile.has_key(fName):
handler = self.rfile[fName]
value = None
else:
return
handler.write(fline)
return value
def _closeFile(self, fName, status=True):
"""
safely closes the file
cleans up rfiles dict
returns the result
"""
handler = self.rfile[fName]
handler.close()
del self.rfile[fName]
if status:
value = 'Recieved: ' + fName
else:
value = 'File could not be received: ' + fName
return value
| #!/usr/bin/env python2
##
# PyChat
# https://github.com/leosartaj/PyChat.git
#
# Copyright (c) 2014 <NAME>
# Licensed under the MIT license.
##
# system import
import os
import struct
import cPickle as pickle
# twisted imports
from twisted.python import log
from twisted.protocols import basic
# user import
from FileSender import FileSender
from PyChat import command as cmd
# prefix for commands
SERVER_PREFIX = cmd.SERVER_PREFIX
def dict_to_pickle(pickle_dict):
"""
convert from a dictionary to pickle
"""
return pickle.dumps(pickle_dict)
def pickle_to_dict(pickle_str):
"""
convert from pickle to dictionary
"""
return pickle.loads(pickle_str)
class FileClientProtocol(basic.Int32StringReceiver):
"""
Implements file transfer protocol
"""
def connectionMade(self):
self.chatproto = self.factory.chatproto
self._register()
self.sending = False
self.sfile = [None, None]
self.rfile = {}
def _register(self):
"""
Register with the ftp server
send the refrence to the chatproto
"""
self.sendString(self.chatproto.setName)
if self.factory.deferred:
deferred, self.factory.deferred = self.factory.deferred, None
deferred.callback(self)
def stringReceived(self, line):
"""
Handles recieved file lines
"""
peername, line = self._parse(line)
if line:
self.update(peername, line)
def _parse(self, line):
"""
Parse line for commands
returns string to be logged
otherwise simply returns line without change
"""
peername, line = cmd.extractFirst(line)
comd, val = cmd.parse(line, SERVER_PREFIX)
if comd == 'eof':
value = 'File sent: %s' %(self.sfile)
peername = 'me'
self._reset()
else:
pickle_dict = pickle_to_dict(val)
value = self._parseDict(pickle_dict)
return peername, value
def _parseDict(self, pickle_dict):
"""
Parses the pickle_dict
Takes measures on the files
"""
fName = pickle_dict['filename']
if pickle_dict.has_key('eof'):
value = self._closeFile(fName)
elif pickle_dict.has_key('fail'):
value = self._closeFile(fName, False)
elif pickle_dict.has_key('line'):
saveline = pickle_dict['line']
value = self._saveFile(fName, saveline)
else:
return None
return value
def update(self, name, msg, logit=True):
"""
Updates the gui
logs the messages if logit set to true
"""
self.chatproto.update(name, msg)
if logit:
log.msg(msg)
def status(self):
"""
gives the status of sending and receiving
returns a tuple sending(bool)
"""
return self.sending
def sendFile(self, fName):
"""
Sends file to the server
"""
self.sending = True
filename = os.path.basename(fName)
self.sfile = filename
fileprotocol = FileSender()
sendfile, startsend = fileprotocol.beginFileTransfer(fName, self.transport, self.transform)
sendfile.addCallback(self._endTransfer)
sendfile.addErrback(self._sendingFailed)
startsend.callback(1)
def _getDict(self):
"""
Returns a dictionary
"""
pickle_dict = {}
pickle_dict['filename'] = self.sfile
return pickle_dict
def transform(self, line):
"""
Transforms a line to be saved in a file
"""
pickle_dict = self._getDict()
pickle_dict['line'] = line
pickle_str = dict_to_pickle(pickle_dict)
# prefix, for the protocol as file sender does not use sendString
prefix = struct.pack(self.structFormat, len(pickle_str))
pickle_str = prefix + pickle_str
return pickle_str
def _endTransfer(self, *args):
"""
End file transfer
"""
pickle_dict = self._getDict()
pickle_dict['eof'] = True
pickle_str = dict_to_pickle(pickle_dict)
self.sendString(pickle_str)
self.sendString(cmd.servercmd('eof', self.sfile))
def _sendingFailed(self, exc):
log.msg(exc)
msg = 'File Sending failed'
self.update('me', msg)
pickle_dict = self._getDict()
pickle_dict['fail'] = True
pickle_str = dict_to_pickle(pickle_dict)
self.sendString(pickle_str)
self.sendString(cmd.servercmd('fail', self.sfile))
def _reset(self):
"""
Reset the variables
"""
self.sending = False
self.sfile = [None, None]
def _initFile(self, fName='unnamed', dire=os.getcwd(), prefix='pychat_'):
"""
opens a file
returns the handler
"""
filename = os.path.basename(fName)
path = os.path.join(dire, prefix + filename)
handler = open(path, 'w')
return handler
def _saveFile(self, fName, fline):
"""
Parses the line
saves the line in the file
returns the result string
"""
if not self.rfile.has_key(fName):
handler = self._initFile(fName)
self.rfile[fName] = handler
value = 'Recieving: ' + fName
elif self.rfile.has_key(fName):
handler = self.rfile[fName]
value = None
else:
return
handler.write(fline)
return value
def _closeFile(self, fName, status=True):
"""
safely closes the file
cleans up rfiles dict
returns the result
"""
handler = self.rfile[fName]
handler.close()
del self.rfile[fName]
if status:
value = 'Recieved: ' + fName
else:
value = 'File could not be received: ' + fName
return value
| en | 0.694874 | #!/usr/bin/env python2 ## # PyChat # https://github.com/leosartaj/PyChat.git # # Copyright (c) 2014 <NAME> # Licensed under the MIT license. ## # system import # twisted imports # user import # prefix for commands convert from a dictionary to pickle convert from pickle to dictionary Implements file transfer protocol Register with the ftp server send the refrence to the chatproto Handles recieved file lines Parse line for commands returns string to be logged otherwise simply returns line without change Parses the pickle_dict Takes measures on the files Updates the gui logs the messages if logit set to true gives the status of sending and receiving returns a tuple sending(bool) Sends file to the server Returns a dictionary Transforms a line to be saved in a file # prefix, for the protocol as file sender does not use sendString End file transfer Reset the variables opens a file returns the handler Parses the line saves the line in the file returns the result string safely closes the file cleans up rfiles dict returns the result | 2.505916 | 3 |
ILI/IDEC.py | arodriguezca/DeepOutbreak | 1 | 6613323 | <reponame>arodriguezca/DeepOutbreak
"""
Toy implementation for Improved Deep Embedded Clustering as described in paper:
<NAME>, <NAME>, <NAME>, <NAME>. Improved Deep Embedded Clustering with Local Structure
Preservation. IJCAI 2017.
The Autoencoder is pretrained directly in an end-to-end manner, NOT greedy layer-wise training. So the results are
different with what reported in the paper.
Usage:
No pretrained autoencoder weights available:
python IDEC.py mnist
python IDEC.py usps
python IDEC.py reutersidf10k --n_clusters 4
Weights of Pretrained autoencoder for mnist are in './ae_weights/mnist_ae_weights.h5':
python IDEC.py mnist --ae_weights ./ae_weights/mnist_ae_weights.h5
Author:
<NAME>. 2017.4.30
"""
from time import time
import numpy as np
from keras.models import Model
from keras.optimizers import SGD
from keras.utils.vis_utils import plot_model
from sklearn.cluster import KMeans
from sklearn import metrics
from DEC import cluster_acc, ClusteringLayer, autoencoder
class IDEC(object):
def __init__(self,
dims,
n_clusters=4,
alpha=1.0):
super(IDEC, self).__init__()
self.dims = dims
self.input_dim = dims[0]
self.n_stacks = len(self.dims) - 1
self.n_clusters = n_clusters
self.alpha = alpha
self.autoencoder = autoencoder(self.dims)
hidden = self.autoencoder.get_layer(name='encoder_%d' % (self.n_stacks - 1)).output
self.encoder = Model(inputs=self.autoencoder.input, outputs=hidden)
# prepare IDEC model
clustering_layer = ClusteringLayer(self.n_clusters, alpha=self.alpha, name='clustering')(hidden)
self.model = Model(inputs=self.autoencoder.input,
outputs=[clustering_layer, self.autoencoder.output])
self.pretrained = False
self.centers = []
self.y_pred = []
def pretrain(self, x, batch_size=256, epochs=200, optimizer='adam'):
print('...Pretraining...')
self.autoencoder.compile(loss='mse', optimizer=optimizer) # SGD(lr=0.01, momentum=0.9),
self.autoencoder.fit(x, x, batch_size=batch_size, epochs=epochs)
self.autoencoder.save_weights('ae_weights.h5')
print('Pretrained weights are saved to ./ae_weights.h5')
self.pretrained = True
def load_weights(self, weights_path): # load weights of IDEC model
self.model.load_weights(weights_path)
def extract_feature(self, x): # extract features from before clustering layer
return self.encoder.predict(x)
def predict_clusters(self, x): # predict cluster labels using the output of clustering layer
q, _ = self.model.predict(x, verbose=0)
return q.argmax(1)
@staticmethod
def target_distribution(q): # target distribution P which enhances the discrimination of soft label Q
weight = q ** 2 / q.sum(0)
return (weight.T / weight.sum(1)).T
def compile(self, loss=['kld', 'mse'], loss_weights=[1, 1], optimizer='adam'):
self.model.compile(loss=loss, loss_weights=loss_weights, optimizer=optimizer)
def fit(self, x, y=None, batch_size=256, maxiter=2e4, tol=1e-3, update_interval=140,
ae_weights=None, save_dir='./results/idec'):
print('Update interval', update_interval)
save_interval = int(x.shape[0] / batch_size) * 5 # 5 epochs
save_interval = 50
print('Save interval', save_interval)
# Step 1: pretrain
if not self.pretrained and ae_weights is None:
print('...pretraining autoencoders using default hyper-parameters:')
print(' optimizer=\'adam\'; epochs=200')
self.pretrain(x, batch_size)
self.pretrained = True
elif ae_weights is not None:
self.autoencoder.load_weights(ae_weights)
print('ae_weights is loaded successfully.')
# Step 2: initialize cluster centers using k-means
print('Initializing cluster centers with k-means.')
kmeans = KMeans(n_clusters=self.n_clusters, n_init=4)
self.y_pred = kmeans.fit_predict(self.encoder.predict(x))
y_pred_last = np.copy(self.y_pred)
self.model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])
# Step 3: deep clustering
# logging file
import csv, os
if not os.path.exists(save_dir):
os.makedirs(save_dir)
logfile = open(save_dir + '/idec_log.csv', 'w')
logwriter = csv.DictWriter(logfile, fieldnames=['iter', 'acc', 'nmi', 'ari', 'L', 'Lc', 'Lr'])
logwriter.writeheader()
loss = [0, 0, 0]
index = 0
for ite in range(int(maxiter)):
if ite % update_interval == 0:
q, _ = self.model.predict(x, verbose=0)
p = self.target_distribution(q) # update the auxiliary target distribution p
# evaluate the clustering performance
self.y_pred = q.argmax(1)
if y is not None:
acc = np.round(cluster_acc(y, self.y_pred), 5)
nmi = np.round(metrics.normalized_mutual_info_score(y, self.y_pred), 5)
ari = np.round(metrics.adjusted_rand_score(y, self.y_pred), 5)
loss = np.round(loss, 5)
logwriter.writerow(dict(iter=ite, acc=acc, nmi=nmi, ari=ari, L=loss[0], Lc=loss[1], Lr=loss[2]))
print('Iter-%d: ACC= %.4f, NMI= %.4f, ARI= %.4f; L= %.5f, Lc= %.5f, Lr= %.5f'
% (ite, acc, nmi, ari, loss[0], loss[1], loss[2]))
# check stop criterion
delta_label = np.sum(self.y_pred != y_pred_last).astype(np.float32) / self.y_pred.shape[0]
y_pred_last = np.copy(self.y_pred)
if ite > 0 and delta_label < tol:
print('delta_label ', delta_label, '< tol ', tol)
print('Reached tolerance threshold. Stopping training.')
logfile.close()
break
# train on batch
if (index + 1) * batch_size > x.shape[0]:
loss = self.model.train_on_batch(x=x[index * batch_size::],
y=[p[index * batch_size::], x[index * batch_size::]])
index = 0
else:
loss = self.model.train_on_batch(x=x[index * batch_size:(index + 1) * batch_size],
y=[p[index * batch_size:(index + 1) * batch_size],
x[index * batch_size:(index + 1) * batch_size]])
index += 1
# save intermediate model
if ite % save_interval == 0:
# save IDEC model checkpoints
print('saving model to: ' + save_dir + '/IDEC_model_' + str(ite) + '.h5')
self.model.save_weights(save_dir + '/IDEC_model_' + str(ite) + '.h5')
ite += 1
# save the trained model
logfile.close()
print('saving model to: ' + save_dir + '/IDEC_model_final.h5')
self.model.save_weights(save_dir + '/IDEC_model_final.h5')
return self.y_pred
if __name__ == "__main__":
# setting the hyper parameters
import argparse
parser = argparse.ArgumentParser(description='train',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('dataset', default='mydata', choices=['mnist', 'usps', 'reutersidf10k', 'pendigits', 'mydata'])
parser.add_argument('--n_clusters', default=4, type=int)
parser.add_argument('--batch_size', default=256, type=int)
parser.add_argument('--maxiter', default=2e4, type=int)
parser.add_argument('--pretrain_epochs', default=200, type=int)
parser.add_argument('--gamma', default=0.1, type=float,
help='coefficient of clustering loss')
parser.add_argument('--update_interval', default=1, type=int)
parser.add_argument('--tol', default=0.001, type=float)
parser.add_argument('--ae_weights', default=None)
parser.add_argument('--save_dir', default='results/idec')
args = parser.parse_args()
print(args)
# load dataset
optimizer = 'adam' # SGD(lr=0.01, momentum=0.99)
from datasets import load_mnist, load_reuters, load_usps, load_pendigits, load_mydata
if args.dataset == 'mnist': # recommends: n_clusters=10, update_interval=140
x, y = load_mnist()
elif args.dataset == 'usps': # recommends: n_clusters=10, update_interval=30
x, y = load_usps('data/usps')
elif args.dataset == 'pendigits':
x, y = load_pendigits('data/pendigits')
elif args.dataset == 'reutersidf10k': # recommends: n_clusters=4, update_interval=20
x, y = load_reuters('data/reuters')
elif args.dataset == 'mydata': # recommends: n_clusters=4, update_interval=20
x, y = load_mydata(path = './data/mydata')
if args.update_interval == 0: # one epoch
args.update_interval = int(x.shape[0]/args.batch_size)
# Define IDEC model
idec = IDEC(dims=[x.shape[-1], 500, 500, 2000, 18], n_clusters=args.n_clusters)
#plot_model(idec.model, to_file='idec_model.png', show_shapes=True)
idec.model.summary()
t0 = time()
# Pretrain autoencoders before clustering
if args.ae_weights is None:
idec.pretrain(x, batch_size=args.batch_size, epochs=args.pretrain_epochs, optimizer=optimizer)
# begin clustering, time not include pretraining part.
idec.compile(loss=['kld', 'mse'], loss_weights=[args.gamma, 1], optimizer=optimizer)
idec.fit(x, y=y, batch_size=args.batch_size, tol=args.tol, maxiter=args.maxiter,
update_interval=args.update_interval, ae_weights=args.ae_weights, save_dir=args.save_dir)
# Show the final results
y_pred = idec.y_pred
print(y_pred)
print('acc:', cluster_acc(y, y_pred))
print('clustering time: %d seconds.' % int(time() - t0))
embed = idec.encoder.predict(x)
year = 1999
emd_file = open("deep_embedding", 'w')
for embedding in embed:
emd_file.write(str(year)+':')
for vals in embedding:
emd_file.write('\t'+str(vals))
emd_file.write('\n')
year+=1
emd_file.close()
year = 1999
cl_file = open("deep_clustering", 'w')
for cl in y_pred:
cl_file.write(str(year)+'\t'+str(cl))
emd_file.write('\n')
year+=1
cl_file.close()
| """
Toy implementation for Improved Deep Embedded Clustering as described in paper:
<NAME>, <NAME>, <NAME>, <NAME>. Improved Deep Embedded Clustering with Local Structure
Preservation. IJCAI 2017.
The Autoencoder is pretrained directly in an end-to-end manner, NOT greedy layer-wise training. So the results are
different with what reported in the paper.
Usage:
No pretrained autoencoder weights available:
python IDEC.py mnist
python IDEC.py usps
python IDEC.py reutersidf10k --n_clusters 4
Weights of Pretrained autoencoder for mnist are in './ae_weights/mnist_ae_weights.h5':
python IDEC.py mnist --ae_weights ./ae_weights/mnist_ae_weights.h5
Author:
<NAME>. 2017.4.30
"""
from time import time
import numpy as np
from keras.models import Model
from keras.optimizers import SGD
from keras.utils.vis_utils import plot_model
from sklearn.cluster import KMeans
from sklearn import metrics
from DEC import cluster_acc, ClusteringLayer, autoencoder
class IDEC(object):
def __init__(self,
dims,
n_clusters=4,
alpha=1.0):
super(IDEC, self).__init__()
self.dims = dims
self.input_dim = dims[0]
self.n_stacks = len(self.dims) - 1
self.n_clusters = n_clusters
self.alpha = alpha
self.autoencoder = autoencoder(self.dims)
hidden = self.autoencoder.get_layer(name='encoder_%d' % (self.n_stacks - 1)).output
self.encoder = Model(inputs=self.autoencoder.input, outputs=hidden)
# prepare IDEC model
clustering_layer = ClusteringLayer(self.n_clusters, alpha=self.alpha, name='clustering')(hidden)
self.model = Model(inputs=self.autoencoder.input,
outputs=[clustering_layer, self.autoencoder.output])
self.pretrained = False
self.centers = []
self.y_pred = []
def pretrain(self, x, batch_size=256, epochs=200, optimizer='adam'):
print('...Pretraining...')
self.autoencoder.compile(loss='mse', optimizer=optimizer) # SGD(lr=0.01, momentum=0.9),
self.autoencoder.fit(x, x, batch_size=batch_size, epochs=epochs)
self.autoencoder.save_weights('ae_weights.h5')
print('Pretrained weights are saved to ./ae_weights.h5')
self.pretrained = True
def load_weights(self, weights_path): # load weights of IDEC model
self.model.load_weights(weights_path)
def extract_feature(self, x): # extract features from before clustering layer
return self.encoder.predict(x)
def predict_clusters(self, x): # predict cluster labels using the output of clustering layer
q, _ = self.model.predict(x, verbose=0)
return q.argmax(1)
@staticmethod
def target_distribution(q): # target distribution P which enhances the discrimination of soft label Q
weight = q ** 2 / q.sum(0)
return (weight.T / weight.sum(1)).T
def compile(self, loss=['kld', 'mse'], loss_weights=[1, 1], optimizer='adam'):
self.model.compile(loss=loss, loss_weights=loss_weights, optimizer=optimizer)
def fit(self, x, y=None, batch_size=256, maxiter=2e4, tol=1e-3, update_interval=140,
ae_weights=None, save_dir='./results/idec'):
print('Update interval', update_interval)
save_interval = int(x.shape[0] / batch_size) * 5 # 5 epochs
save_interval = 50
print('Save interval', save_interval)
# Step 1: pretrain
if not self.pretrained and ae_weights is None:
print('...pretraining autoencoders using default hyper-parameters:')
print(' optimizer=\'adam\'; epochs=200')
self.pretrain(x, batch_size)
self.pretrained = True
elif ae_weights is not None:
self.autoencoder.load_weights(ae_weights)
print('ae_weights is loaded successfully.')
# Step 2: initialize cluster centers using k-means
print('Initializing cluster centers with k-means.')
kmeans = KMeans(n_clusters=self.n_clusters, n_init=4)
self.y_pred = kmeans.fit_predict(self.encoder.predict(x))
y_pred_last = np.copy(self.y_pred)
self.model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])
# Step 3: deep clustering
# logging file
import csv, os
if not os.path.exists(save_dir):
os.makedirs(save_dir)
logfile = open(save_dir + '/idec_log.csv', 'w')
logwriter = csv.DictWriter(logfile, fieldnames=['iter', 'acc', 'nmi', 'ari', 'L', 'Lc', 'Lr'])
logwriter.writeheader()
loss = [0, 0, 0]
index = 0
for ite in range(int(maxiter)):
if ite % update_interval == 0:
q, _ = self.model.predict(x, verbose=0)
p = self.target_distribution(q) # update the auxiliary target distribution p
# evaluate the clustering performance
self.y_pred = q.argmax(1)
if y is not None:
acc = np.round(cluster_acc(y, self.y_pred), 5)
nmi = np.round(metrics.normalized_mutual_info_score(y, self.y_pred), 5)
ari = np.round(metrics.adjusted_rand_score(y, self.y_pred), 5)
loss = np.round(loss, 5)
logwriter.writerow(dict(iter=ite, acc=acc, nmi=nmi, ari=ari, L=loss[0], Lc=loss[1], Lr=loss[2]))
print('Iter-%d: ACC= %.4f, NMI= %.4f, ARI= %.4f; L= %.5f, Lc= %.5f, Lr= %.5f'
% (ite, acc, nmi, ari, loss[0], loss[1], loss[2]))
# check stop criterion
delta_label = np.sum(self.y_pred != y_pred_last).astype(np.float32) / self.y_pred.shape[0]
y_pred_last = np.copy(self.y_pred)
if ite > 0 and delta_label < tol:
print('delta_label ', delta_label, '< tol ', tol)
print('Reached tolerance threshold. Stopping training.')
logfile.close()
break
# train on batch
if (index + 1) * batch_size > x.shape[0]:
loss = self.model.train_on_batch(x=x[index * batch_size::],
y=[p[index * batch_size::], x[index * batch_size::]])
index = 0
else:
loss = self.model.train_on_batch(x=x[index * batch_size:(index + 1) * batch_size],
y=[p[index * batch_size:(index + 1) * batch_size],
x[index * batch_size:(index + 1) * batch_size]])
index += 1
# save intermediate model
if ite % save_interval == 0:
# save IDEC model checkpoints
print('saving model to: ' + save_dir + '/IDEC_model_' + str(ite) + '.h5')
self.model.save_weights(save_dir + '/IDEC_model_' + str(ite) + '.h5')
ite += 1
# save the trained model
logfile.close()
print('saving model to: ' + save_dir + '/IDEC_model_final.h5')
self.model.save_weights(save_dir + '/IDEC_model_final.h5')
return self.y_pred
if __name__ == "__main__":
# setting the hyper parameters
import argparse
parser = argparse.ArgumentParser(description='train',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('dataset', default='mydata', choices=['mnist', 'usps', 'reutersidf10k', 'pendigits', 'mydata'])
parser.add_argument('--n_clusters', default=4, type=int)
parser.add_argument('--batch_size', default=256, type=int)
parser.add_argument('--maxiter', default=2e4, type=int)
parser.add_argument('--pretrain_epochs', default=200, type=int)
parser.add_argument('--gamma', default=0.1, type=float,
help='coefficient of clustering loss')
parser.add_argument('--update_interval', default=1, type=int)
parser.add_argument('--tol', default=0.001, type=float)
parser.add_argument('--ae_weights', default=None)
parser.add_argument('--save_dir', default='results/idec')
args = parser.parse_args()
print(args)
# load dataset
optimizer = 'adam' # SGD(lr=0.01, momentum=0.99)
from datasets import load_mnist, load_reuters, load_usps, load_pendigits, load_mydata
if args.dataset == 'mnist': # recommends: n_clusters=10, update_interval=140
x, y = load_mnist()
elif args.dataset == 'usps': # recommends: n_clusters=10, update_interval=30
x, y = load_usps('data/usps')
elif args.dataset == 'pendigits':
x, y = load_pendigits('data/pendigits')
elif args.dataset == 'reutersidf10k': # recommends: n_clusters=4, update_interval=20
x, y = load_reuters('data/reuters')
elif args.dataset == 'mydata': # recommends: n_clusters=4, update_interval=20
x, y = load_mydata(path = './data/mydata')
if args.update_interval == 0: # one epoch
args.update_interval = int(x.shape[0]/args.batch_size)
# Define IDEC model
idec = IDEC(dims=[x.shape[-1], 500, 500, 2000, 18], n_clusters=args.n_clusters)
#plot_model(idec.model, to_file='idec_model.png', show_shapes=True)
idec.model.summary()
t0 = time()
# Pretrain autoencoders before clustering
if args.ae_weights is None:
idec.pretrain(x, batch_size=args.batch_size, epochs=args.pretrain_epochs, optimizer=optimizer)
# begin clustering, time not include pretraining part.
idec.compile(loss=['kld', 'mse'], loss_weights=[args.gamma, 1], optimizer=optimizer)
idec.fit(x, y=y, batch_size=args.batch_size, tol=args.tol, maxiter=args.maxiter,
update_interval=args.update_interval, ae_weights=args.ae_weights, save_dir=args.save_dir)
# Show the final results
y_pred = idec.y_pred
print(y_pred)
print('acc:', cluster_acc(y, y_pred))
print('clustering time: %d seconds.' % int(time() - t0))
embed = idec.encoder.predict(x)
year = 1999
emd_file = open("deep_embedding", 'w')
for embedding in embed:
emd_file.write(str(year)+':')
for vals in embedding:
emd_file.write('\t'+str(vals))
emd_file.write('\n')
year+=1
emd_file.close()
year = 1999
cl_file = open("deep_clustering", 'w')
for cl in y_pred:
cl_file.write(str(year)+'\t'+str(cl))
emd_file.write('\n')
year+=1
cl_file.close() | en | 0.633961 | Toy implementation for Improved Deep Embedded Clustering as described in paper: <NAME>, <NAME>, <NAME>, <NAME>. Improved Deep Embedded Clustering with Local Structure Preservation. IJCAI 2017. The Autoencoder is pretrained directly in an end-to-end manner, NOT greedy layer-wise training. So the results are different with what reported in the paper. Usage: No pretrained autoencoder weights available: python IDEC.py mnist python IDEC.py usps python IDEC.py reutersidf10k --n_clusters 4 Weights of Pretrained autoencoder for mnist are in './ae_weights/mnist_ae_weights.h5': python IDEC.py mnist --ae_weights ./ae_weights/mnist_ae_weights.h5 Author: <NAME>. 2017.4.30 # prepare IDEC model # SGD(lr=0.01, momentum=0.9), # load weights of IDEC model # extract features from before clustering layer # predict cluster labels using the output of clustering layer # target distribution P which enhances the discrimination of soft label Q # 5 epochs # Step 1: pretrain # Step 2: initialize cluster centers using k-means # Step 3: deep clustering # logging file # update the auxiliary target distribution p # evaluate the clustering performance # check stop criterion # train on batch # save intermediate model # save IDEC model checkpoints # save the trained model # setting the hyper parameters # load dataset # SGD(lr=0.01, momentum=0.99) # recommends: n_clusters=10, update_interval=140 # recommends: n_clusters=10, update_interval=30 # recommends: n_clusters=4, update_interval=20 # recommends: n_clusters=4, update_interval=20 # one epoch # Define IDEC model #plot_model(idec.model, to_file='idec_model.png', show_shapes=True) # Pretrain autoencoders before clustering # begin clustering, time not include pretraining part. # Show the final results | 2.692522 | 3 |
Archives/Model Testing/Testing_2.py | sukumarh/Helio-Learning | 2 | 6613324 | import datetime as dt
from Helper_Functions import File_Ops as File_IO
from Helper_Functions import Plotting_Ops as Plotter
from Helper_Functions import Dataset_Ops as Data
def main():
print('Model Testing')
start_time_train = dt.datetime(2003, 1, 1, 0, 0, 0)
stop_time_train = dt.datetime(2003, 1, 10, 0, 0, 0)
directory = 'Processed_Data/2004/01_01.p'
test_dataset, is_success = File_IO.read_data(directory)
file_name = 'Trained_Models/svr_multi_feature_1.p'
data, is_read_successful = File_IO.read_model(file_name)
if is_read_successful:
print('Model retrieved')
if data[0] == 'single_svr':
y_test = test_dataset[:, 0].T[: 20000]
X_test = test_dataset[:, 2:][: 20000]
svr_model = data[1]
# y_hat = svr.fit(X_train, y_train).predict(X_train)
# Plotter.plot_svr(X_train, y_hat, y_train, svr, svr.kernel, 'm', 'r',
# [min(X_train) - 0.1, max(X_train) + 0.1, min(y_train) - 1, max(y_train) + 1])
y_hat = svr_model.predict(X_test)
test_score = svr_model.score(X_test, y_test)
print(f'Test Score = {test_score}')
# Plotter.plot_svr(X_test, y_hat, y_test, svr, svr.kernel, 'm', 'b', mode='test')
# elif data[0] == 'Non_Linear_Reg_SVR_Tuned_Splitted_Dataset':
# [clf_neg, clf_pos, svr_pos, svr_neg] = data[1]
# X_test_pos, X_test_neg, y_test_pos, y_test_neg = Data.split_pos_and_neg_set(X_test, y_test)
#
# y_hat_neg = svr_neg.predict(X_test_neg)
# y_hat_pos = svr_pos.predict(X_test_pos)
#
# test_score_neg = svr_neg.score(X_test_neg, y_test_neg)
# test_score_pos = svr_pos.score(X_test_pos, y_test_pos)
#
# print(f'Positive Test Score = {test_score_pos}')
# print(f'Negative Test Score = {test_score_neg}')
#
# Plotter.plot_svr_combined([X_test_neg, X_test_pos], [y_hat_neg, y_hat_pos], [y_test_neg, y_test_pos],
# [svr_neg.kernel, svr_pos.kernel], ['m', 'g'], ['b', 'b'],
# [svr_neg, svr_pos],
# mode='test')
if __name__ == '__main__':
main()
| import datetime as dt
from Helper_Functions import File_Ops as File_IO
from Helper_Functions import Plotting_Ops as Plotter
from Helper_Functions import Dataset_Ops as Data
def main():
print('Model Testing')
start_time_train = dt.datetime(2003, 1, 1, 0, 0, 0)
stop_time_train = dt.datetime(2003, 1, 10, 0, 0, 0)
directory = 'Processed_Data/2004/01_01.p'
test_dataset, is_success = File_IO.read_data(directory)
file_name = 'Trained_Models/svr_multi_feature_1.p'
data, is_read_successful = File_IO.read_model(file_name)
if is_read_successful:
print('Model retrieved')
if data[0] == 'single_svr':
y_test = test_dataset[:, 0].T[: 20000]
X_test = test_dataset[:, 2:][: 20000]
svr_model = data[1]
# y_hat = svr.fit(X_train, y_train).predict(X_train)
# Plotter.plot_svr(X_train, y_hat, y_train, svr, svr.kernel, 'm', 'r',
# [min(X_train) - 0.1, max(X_train) + 0.1, min(y_train) - 1, max(y_train) + 1])
y_hat = svr_model.predict(X_test)
test_score = svr_model.score(X_test, y_test)
print(f'Test Score = {test_score}')
# Plotter.plot_svr(X_test, y_hat, y_test, svr, svr.kernel, 'm', 'b', mode='test')
# elif data[0] == 'Non_Linear_Reg_SVR_Tuned_Splitted_Dataset':
# [clf_neg, clf_pos, svr_pos, svr_neg] = data[1]
# X_test_pos, X_test_neg, y_test_pos, y_test_neg = Data.split_pos_and_neg_set(X_test, y_test)
#
# y_hat_neg = svr_neg.predict(X_test_neg)
# y_hat_pos = svr_pos.predict(X_test_pos)
#
# test_score_neg = svr_neg.score(X_test_neg, y_test_neg)
# test_score_pos = svr_pos.score(X_test_pos, y_test_pos)
#
# print(f'Positive Test Score = {test_score_pos}')
# print(f'Negative Test Score = {test_score_neg}')
#
# Plotter.plot_svr_combined([X_test_neg, X_test_pos], [y_hat_neg, y_hat_pos], [y_test_neg, y_test_pos],
# [svr_neg.kernel, svr_pos.kernel], ['m', 'g'], ['b', 'b'],
# [svr_neg, svr_pos],
# mode='test')
if __name__ == '__main__':
main()
| en | 0.200436 | # y_hat = svr.fit(X_train, y_train).predict(X_train) # Plotter.plot_svr(X_train, y_hat, y_train, svr, svr.kernel, 'm', 'r', # [min(X_train) - 0.1, max(X_train) + 0.1, min(y_train) - 1, max(y_train) + 1]) # Plotter.plot_svr(X_test, y_hat, y_test, svr, svr.kernel, 'm', 'b', mode='test') # elif data[0] == 'Non_Linear_Reg_SVR_Tuned_Splitted_Dataset': # [clf_neg, clf_pos, svr_pos, svr_neg] = data[1] # X_test_pos, X_test_neg, y_test_pos, y_test_neg = Data.split_pos_and_neg_set(X_test, y_test) # # y_hat_neg = svr_neg.predict(X_test_neg) # y_hat_pos = svr_pos.predict(X_test_pos) # # test_score_neg = svr_neg.score(X_test_neg, y_test_neg) # test_score_pos = svr_pos.score(X_test_pos, y_test_pos) # # print(f'Positive Test Score = {test_score_pos}') # print(f'Negative Test Score = {test_score_neg}') # # Plotter.plot_svr_combined([X_test_neg, X_test_pos], [y_hat_neg, y_hat_pos], [y_test_neg, y_test_pos], # [svr_neg.kernel, svr_pos.kernel], ['m', 'g'], ['b', 'b'], # [svr_neg, svr_pos], # mode='test') | 2.601698 | 3 |
python3/even_tree.py | ahavrylyuk/hackerrank | 0 | 6613325 | <filename>python3/even_tree.py
#! /usr/bin/env python
from collections import defaultdict
def dfs_visit(parent, g, start):
for v in g[start]:
counts[start].add(v)
if v not in parent:
parent.append(v)
subtree = dfs_visit(parent, g, v)
for u in subtree:
if u != start:
counts[start].add(u)
return counts[start]
def read_line():
return (int(x) for x in input().split())
if __name__ == '__main__':
g = defaultdict(set)
n, m = read_line()
for _ in range(m):
vi, ui = read_line()
g[vi].add(ui)
g[ui].add(vi)
start = next(iter(g))
parent = [start]
counts = defaultdict(set)
dfs_visit(parent, g, start)
from pprint import pprint
pprint(counts)
print(sum(1 for x in counts.values() if len(x) % 2 is 0))
| <filename>python3/even_tree.py
#! /usr/bin/env python
from collections import defaultdict
def dfs_visit(parent, g, start):
for v in g[start]:
counts[start].add(v)
if v not in parent:
parent.append(v)
subtree = dfs_visit(parent, g, v)
for u in subtree:
if u != start:
counts[start].add(u)
return counts[start]
def read_line():
return (int(x) for x in input().split())
if __name__ == '__main__':
g = defaultdict(set)
n, m = read_line()
for _ in range(m):
vi, ui = read_line()
g[vi].add(ui)
g[ui].add(vi)
start = next(iter(g))
parent = [start]
counts = defaultdict(set)
dfs_visit(parent, g, start)
from pprint import pprint
pprint(counts)
print(sum(1 for x in counts.values() if len(x) % 2 is 0))
| ru | 0.148623 | #! /usr/bin/env python | 3.584033 | 4 |
Duke/scripts/main.py | remram44/duke | 0 | 6613326 | <reponame>remram44/duke
import numpy as np
from Duke.agg_functions import *
from Duke.dataset_descriptor import DatasetDescriptor
from Duke.utils import mean_of_rows
def main(
dataset_path='/vectorizationdata/KnowledgeGraph2Vec/duke-dev/data/185_baseball.csv',
tree_path='../ontologies/class-tree_dbpedia_2016-10.json',
embedding_path='/vectorizationdata/KnowledgeGraph2Vec/duke-dev/embeddings/wiki2vec/en.model',
row_agg_func=mean_of_rows,
tree_agg_func=parent_children_funcs(np.mean, max),
source_agg_func=mean_of_rows,
max_num_samples = 1e6,
verbose=True,
):
duke = DatasetDescriptor(
dataset=dataset_path,
tree=tree_path,
embedding=embedding_path,
row_agg_func=row_agg_func,
tree_agg_func=tree_agg_func,
source_agg_func=source_agg_func,
max_num_samples=max_num_samples,
verbose=verbose,
)
print('initialized duke dataset descriptor \n')
out = duke.get_top_n_words(10)
print("The top N=%d words are"%10)
print(out)
return duke.get_dataset_description()
if __name__ == '__main__':
main()
| import numpy as np
from Duke.agg_functions import *
from Duke.dataset_descriptor import DatasetDescriptor
from Duke.utils import mean_of_rows
def main(
dataset_path='/vectorizationdata/KnowledgeGraph2Vec/duke-dev/data/185_baseball.csv',
tree_path='../ontologies/class-tree_dbpedia_2016-10.json',
embedding_path='/vectorizationdata/KnowledgeGraph2Vec/duke-dev/embeddings/wiki2vec/en.model',
row_agg_func=mean_of_rows,
tree_agg_func=parent_children_funcs(np.mean, max),
source_agg_func=mean_of_rows,
max_num_samples = 1e6,
verbose=True,
):
duke = DatasetDescriptor(
dataset=dataset_path,
tree=tree_path,
embedding=embedding_path,
row_agg_func=row_agg_func,
tree_agg_func=tree_agg_func,
source_agg_func=source_agg_func,
max_num_samples=max_num_samples,
verbose=verbose,
)
print('initialized duke dataset descriptor \n')
out = duke.get_top_n_words(10)
print("The top N=%d words are"%10)
print(out)
return duke.get_dataset_description()
if __name__ == '__main__':
main() | none | 1 | 2.60565 | 3 | |
scripts/libplatform.py | sjl3110/TF-M | 0 | 6613327 | Path = '/home/sjl/work/tf-m/libplatform_b'
total = 0
for line in open(Path,"r"):
content = line.split()
total = total + int(content[3])
print("total = %d" % total)
Path2 = '/home/sjl/work/tf-m/libplatform'
total = 0
for line in open(Path2,"r"):
if line.find("otal") > 0:
content = line.split()
total = total + int(content[1])
print("total = %d" % total) | Path = '/home/sjl/work/tf-m/libplatform_b'
total = 0
for line in open(Path,"r"):
content = line.split()
total = total + int(content[3])
print("total = %d" % total)
Path2 = '/home/sjl/work/tf-m/libplatform'
total = 0
for line in open(Path2,"r"):
if line.find("otal") > 0:
content = line.split()
total = total + int(content[1])
print("total = %d" % total) | none | 1 | 2.771983 | 3 | |
MultiAtlasSegmenter/LabelPropagation/LabelPropagationAndSelectionEvaluation.py | mabelzunce/MuscleSegmentation | 0 | 6613328 | #! python3
# This script compares multiple label propagation and selection cases for all the cases in a library.
from __future__ import print_function
import SimpleITK as sitk
import numpy as np
import sys
import os
import NormalizedCrossCorrelationMetrics as NCC
import MajorityVoting as MV
import PostprocessingLabels as PP
from DynamicLabelFusionWithSimilarityWeights import DynamicLabelFusionWithLocalSimilarityWeights as DynamicLocalLabelling
from DynamicLabelFusionWithSimilarityWeights import DynamicLabelFusionWithSimilarityWeights as DynamicLabelling
############################### TARGET FOLDER ###################################
# The target folder needs to have all the files that are saved by the plugin when intermediates files are saved.
libraryVersion = 'V1.2'
segType = 'BSplineStandardGradDesc_NMI_2000iters_2000samples'
numberOfSelectedAtlases = 19 # I need all the atlases, but if the segmentation was ran in dbug mode I'll have all anyways.
excludeFemurs = True # The femurs have been segmented in a few atlases, but because they are only in a few of them, it
# introduces erros in the undecided label.
numLabels = 11
if excludeFemurs:
numLabels = 9
numLabelWithoutUndecided = numLabels - 1
maskedRegistration = True
libraryCases = ''
libraryPath = 'D:\\Martin\\Segmentation\\AtlasLibrary\\' + libraryVersion + '\\NativeResolutionAndSize2\\'
targetPath = 'D:\\MuscleSegmentationEvaluation\\SegmentationWithPython\\' + libraryVersion + '\\TestWithLibrary\\Nonrigid{0}_N{1}_MaxProb_Mask\\'.format(segType, numberOfSelectedAtlases)
targetPath = 'D:\\MuscleSegmentationEvaluation\\SegmentationWithPython\\' + libraryVersion + '\\TestWithLibrary\\{0}_N{1}_{2}\\'.format(segType, numberOfSelectedAtlases, maskedRegistration)
outputPath = 'D:\\MuscleSegmentationEvaluation\\SegmentationWithPython\\' + libraryVersion + '\\TestWithLibrary\\{0}_N{1}_{2}_LibSize\\'.format(segType, numberOfSelectedAtlases, maskedRegistration)
# Exponential weights:
if not os.path.exists(outputPath):
os.mkdir(outputPath)
expWeight=2
outputPath = outputPath + '\\expweightFusion_{0}\\'.format(expWeight)
if not os.path.exists(outputPath):
os.mkdir(outputPath)
extensionImages = 'mhd'
regImagesFilenameEnd = '_to_target.mhd'
regLabelsFilenameEnd = '_to_target_labels.mhd'
# Look for the raw files in the library:
dirWithCases = os.listdir(targetPath)
#dirWithCases = dirWithCases[10:]
atlasImagesNames = []
atlasLabelsNames = []
# Label fusion strategies to include in the analysis
processWithMajorityVoting = True
processWithSTAPLES = True
processWithGlobalWeightedVoting = True
processWithRoiWeightedVoting = True
# Name of atlases in order to be included:
atlasNamesInOrderOfInclusion = ['ID00001', 'ID00002', 'ID00003', 'ID00005', 'ID00006', 'ID00008', 'ID00010', 'ID00011',
'ID00013', 'ID00014', 'ID00021', 'ID00029', 'ID00061','L0511645', '7390413', '7386347', 'L0045955',
'L0324841','L0364068','L0029976']
# Instead of using all cases from dirWithCases, I select a few of them for the library:
numberOfCases = 8
# Process multiple number of cases selection:
#[4,6,8,10,12,14,17]:
for numberOfCases in [15, 17]:
atlasesLibrary = atlasNamesInOrderOfInclusion[0:numberOfCases]
# For the segmentation, we evaluate all of them:
for filenameCases in dirWithCases:
outputPathThisLibrarySize = outputPath + 'LibrarySize{0}\\'.format(numberOfCases)
if not os.path.exists(outputPathThisLibrarySize):
os.mkdir(outputPathThisLibrarySize)
if os.path.isdir(targetPath + filenameCases):
caseName = filenameCases
# Data path were all the registered images:
dataPath = targetPath + filenameCases + "\\"
# Output path inside this folder:
outputPathThisCase = outputPathThisLibrarySize + caseName + "\\"
if not os.path.exists(outputPathThisCase):
os.mkdir(outputPathThisCase)
# Create a log file:
log = open(outputPathThisCase + 'log.txt', 'w')
# First read the target image:
targetImage = sitk.ReadImage(dataPath + "input_registration.mhd")
# get labels from library path:
targetLabels = sitk.ReadImage(libraryPath + caseName + "_labels.mhd")
# Look for the header files of the registered images:
files = os.listdir(dataPath)
# Get the images and labels filenames:
registeredFilenames = []
registeredLabelsFilenames = []
for filename in files:
if filename.endswith(regImagesFilenameEnd):
i = filename.find(regImagesFilenameEnd)
nameThisAtlas = filename[0:i]
filenameLabels = nameThisAtlas + regLabelsFilenameEnd
if os.path.isfile(dataPath + filenameLabels) & (nameThisAtlas in atlasesLibrary):
registeredFilenames.append(filename)
registeredLabelsFilenames.append(filenameLabels)
# Need to create a dictionary with all the registered images (too much memory?):
registeredImage = []
labelsImage = []
for i in range(0, len(registeredFilenames)):
# Read image:
registeredImage.append(sitk.ReadImage(dataPath + registeredFilenames[i]))
# Call the local similarity metric and save the image:
labelImage = sitk.ReadImage(dataPath + registeredLabelsFilenames[i])
# Remove femurs if indicated:
if excludeFemurs:
maskFilter = sitk.MaskImageFilter()
maskFilter.SetOutsideValue(0)
maskFilter.SetMaskingValue(9)
labelImage = maskFilter.Execute(labelImage, labelImage)
maskFilter.SetMaskingValue(10)
labelImage = maskFilter.Execute(labelImage, labelImage)
labelsImage.append(labelImage)
# Select the N most similar cases:
# Get similarity weights for each label mask for each atlas
lnccValues = np.zeros(len(registeredImage))
# Get a similarity metric for each label:
for i in range(0, len(registeredImage)):
# Using the similarity of the full image:
maskThisLabel = sitk.GreaterEqual(targetImage, 0) # Use all the voxels.
lncc = NCC.RoiNormalizedCrossCorrelationAsInITK(targetImage, registeredImage[i], maskThisLabel)
lnccValues[i] = lncc
# Sort indices for atlas selection and voting:
indicesSorted = np.argsort(lnccValues)
# Write log:
log.write('Similarity metric values (lncc): {0}\n'.format(lncc))
selectedAtlasesValues = range(2, len(registeredImage))
jaccard = np.zeros((len(selectedAtlasesValues), numLabels))
dice = np.zeros((len(selectedAtlasesValues), numLabels))
volumeSimilarity = np.zeros((len(selectedAtlasesValues), numLabels))
fn = np.zeros((len(selectedAtlasesValues), numLabels))
fp = np.zeros((len(selectedAtlasesValues), numLabels))
jaccardAll = np.zeros((len(selectedAtlasesValues), 1))
diceAll = np.zeros((len(selectedAtlasesValues), 1))
volumeSimilarityAll = np.zeros((len(selectedAtlasesValues), 1))
fnAll = np.zeros((len(selectedAtlasesValues), 1))
fpAll = np.zeros((len(selectedAtlasesValues), 1))
if processWithMajorityVoting:
j = 0
for numSelectedAtlases in selectedAtlasesValues:
# Selected atlases:
indicesSelected = indicesSorted[0:numSelectedAtlases]
# Now do the atlas selection (I can't access given indices so need a for):
propagatedLabels = list()
for index in indicesSelected:
propagatedLabels.append(labelsImage[index])
##################### LABEL FUSION WITH MAJORITY VOTING #################################
outputLabels = sitk.LabelVoting(propagatedLabels, numLabels)
# After label voting I will have undecided voxels, add an undecided solving step:
outputLabels = MV.SetUndecidedVoxelsUsingDistances(outputLabels, numLabels)
#Get largest connceted regions for each label:
#outputLabels = PP.FilterUnconnectedRegion(outputLabels, numLabels-1) # Not necessary for the undecided label
log.write('Indices of selected atlases: {0}\n'.format(indicesSelected))
# Write the results:
sitk.WriteImage(outputLabels, outputPathThisCase + "segmentedImage_MajVot_{0}.mhd".format(numSelectedAtlases))
# Get metrics:
overlap_measures_filter = sitk.LabelOverlapMeasuresImageFilter()
overlap_measures_filter.SetGlobalDefaultCoordinateTolerance(1e-2)
# First get a general metric:
overlap_measures_filter.Execute(targetLabels, outputLabels)
jaccardAll[j] = overlap_measures_filter.GetJaccardCoefficient()
diceAll[j] = overlap_measures_filter.GetDiceCoefficient()
volumeSimilarityAll[j] = overlap_measures_filter.GetVolumeSimilarity()
fnAll[j] = overlap_measures_filter.GetFalseNegativeError()
fpAll[j] = overlap_measures_filter.GetFalsePositiveError()
log.write('Dice All Labels: {0}\n'.format(diceAll[j]))
# Overlap measures for each label:
for labelIndex in range(0, numLabels): # The index needs to be icnreased as the base number is 1.
overlap_measures_filter.Execute(targetLabels == (labelIndex+1), outputLabels == (labelIndex+1))
jaccard[j, labelIndex] = overlap_measures_filter.GetJaccardCoefficient()
dice[j, labelIndex] = overlap_measures_filter.GetDiceCoefficient()
volumeSimilarity[j, labelIndex] = overlap_measures_filter.GetVolumeSimilarity()
fn[j, labelIndex] = overlap_measures_filter.GetFalseNegativeError()
fp[j, labelIndex] = overlap_measures_filter.GetFalsePositiveError()
log.write('Dice Label {0}: {1}\n'.format(labelIndex, dice[j, labelIndex]))
j = j+1
np.savetxt(outputPathThisCase + "jaccard.csv", jaccard, delimiter=",")
np.savetxt(outputPathThisCase + "dice.csv", dice, delimiter=",")
np.savetxt(outputPathThisCase + "volumeSimilarity.csv", volumeSimilarity, delimiter=",")
np.savetxt(outputPathThisCase + "fn.csv", fn, delimiter=",")
np.savetxt(outputPathThisCase + "fp.csv", fp, delimiter=",")
np.savetxt(outputPathThisCase + "jaccardAll.csv", jaccardAll, delimiter=",")
np.savetxt(outputPathThisCase + "diceAll.csv", diceAll, delimiter=",")
np.savetxt(outputPathThisCase + "volumeSimilarityAll.csv", volumeSimilarityAll, delimiter=",")
np.savetxt(outputPathThisCase + "fnAll.csv", fnAll, delimiter=",")
np.savetxt(outputPathThisCase + "fpAll.csv", fpAll, delimiter=",")
# Now repeat for STAPLEs:
if processWithSTAPLES:
j = 0
for numSelectedAtlases in range(2, len(registeredImage)):
# Selected atlases:
indicesSelected = indicesSorted[0:numSelectedAtlases]
# Now do the atlas selection (I can't access given indices so need a for):
propagatedLabels = list()
for index in indicesSelected:
propagatedLabels.append(labelsImage[index])
##################### LABEL FUSION WITH MAJORITY VOTING #################################
multilabelStaple = sitk.MultiLabelSTAPLEImageFilter()
multilabelStaple.SetTerminationUpdateThreshold(1e-4)
multilabelStaple.SetMaximumNumberOfIterations(30)
multilabelStaple.SetLabelForUndecidedPixels(numLabels)
outputLabelsSTAPLES = multilabelStaple.Execute(propagatedLabels)
log.write('Indices of selected atlases STAPLES: {0}\n'.format(indicesSelected))
sitk.WriteImage(outputLabelsSTAPLES,
outputPathThisCase + "segmentedImageSTAPLES_{0}.mhd".format(numSelectedAtlases))
# Get metrics:
overlap_measures_filter = sitk.LabelOverlapMeasuresImageFilter()
overlap_measures_filter.SetGlobalDefaultCoordinateTolerance(1e-2)
# Overlap measures for each label:
for labelIndex in range(0, numLabels):
overlap_measures_filter.Execute(targetLabels == (labelIndex+1), outputLabelsSTAPLES == (labelIndex+1))
jaccard[j, labelIndex] = overlap_measures_filter.GetJaccardCoefficient()
dice[j, labelIndex] = overlap_measures_filter.GetDiceCoefficient()
volumeSimilarity[j, labelIndex] = overlap_measures_filter.GetVolumeSimilarity()
fn[j, labelIndex] = overlap_measures_filter.GetFalseNegativeError()
fp[j, labelIndex] = overlap_measures_filter.GetFalsePositiveError()
log.write('STAPLES Dice Label {0}: {1}\n'.format(labelIndex, dice[j, labelIndex]))
j = j + 1
np.savetxt(outputPathThisCase + "jaccardSTAPLES.csv", jaccard, delimiter=",")
np.savetxt(outputPathThisCase + "diceSTAPLES.csv", dice, delimiter=",")
np.savetxt(outputPathThisCase + "volumeSimilaritySTAPLES.csv", volumeSimilarity, delimiter=",")
np.savetxt(outputPathThisCase + "fnSTAPLES.csv", fn, delimiter=",")
np.savetxt(outputPathThisCase + "fpSTAPLES.csv", fp, delimiter=",")
##################### LABEL FUSION WITH DYNAMIC WEIGHTING VOTING #################################
if processWithGlobalWeightedVoting:
j = 0
for numSelectedAtlases in selectedAtlasesValues:
# Call function for global weighted voting:
registeredAtlases = {'image': registeredImage, 'labels': labelsImage}
outputLabelsGWV = DynamicLabelling(targetImage, registeredAtlases, numLabelWithoutUndecided, numSelectedAtlases=numSelectedAtlases,
expWeight=expWeight, useOnlyLabelVoxels=True, outputPath=outputPathThisCase, debug=0)
# Write the results:
sitk.WriteImage(outputLabelsGWV,
outputPathThisCase + "segmentedImageGWV_{0}.mhd".format(numSelectedAtlases))
# Get metrics:
overlap_measures_filter = sitk.LabelOverlapMeasuresImageFilter()
overlap_measures_filter.SetGlobalDefaultCoordinateTolerance(1e-2)
# Overlap measures for each label:
for labelIndex in range(0, numLabelWithoutUndecided): # The index needs to be icnreased as the base number is 1.
overlap_measures_filter.Execute(targetLabels == (labelIndex + 1), outputLabelsGWV == (labelIndex + 1))
jaccard[j, labelIndex] = overlap_measures_filter.GetJaccardCoefficient()
dice[j, labelIndex] = overlap_measures_filter.GetDiceCoefficient()
volumeSimilarity[j, labelIndex] = overlap_measures_filter.GetVolumeSimilarity()
fn[j, labelIndex] = overlap_measures_filter.GetFalseNegativeError()
fp[j, labelIndex] = overlap_measures_filter.GetFalsePositiveError()
log.write('GWV Dice Label {0}: {1}\n'.format(labelIndex, dice[j, labelIndex]))
j = j + 1
np.savetxt(outputPathThisCase + "jaccardGWV.csv", jaccard, delimiter=",")
np.savetxt(outputPathThisCase + "diceGWV.csv", dice, delimiter=",")
np.savetxt(outputPathThisCase + "volumeSimilarityGWV.csv", volumeSimilarity, delimiter=",")
np.savetxt(outputPathThisCase + "fnGWV.csv", fn, delimiter=",")
np.savetxt(outputPathThisCase + "fpGWV.csv", fp, delimiter=",")
if processWithRoiWeightedVoting:
j = 0
for numSelectedAtlases in selectedAtlasesValues:
# Call function for global weighted voting:
registeredAtlases = {'image': registeredImage, 'labels': labelsImage}
outputLabelsLWV = DynamicLocalLabelling(targetImage, registeredAtlases, numLabelWithoutUndecided, numSelectedAtlases = numSelectedAtlases,
expWeight=expWeight, outputPath=outputPathThisCase, debug = 0)
# Write the results:
sitk.WriteImage(outputLabelsLWV,
outputPathThisCase + "segmentedImageRWV_{0}.mhd".format(numSelectedAtlases))
# Get metrics:
overlap_measures_filter = sitk.LabelOverlapMeasuresImageFilter()
overlap_measures_filter.SetGlobalDefaultCoordinateTolerance(1e-2)
# Overlap measures for each label:
for labelIndex in range(0, numLabelWithoutUndecided): # The index needs to be icnreased as the base number is 1.
overlap_measures_filter.Execute(targetLabels == (labelIndex + 1),
outputLabelsLWV == (labelIndex + 1))
jaccard[j, labelIndex] = overlap_measures_filter.GetJaccardCoefficient()
dice[j, labelIndex] = overlap_measures_filter.GetDiceCoefficient()
volumeSimilarity[j, labelIndex] = overlap_measures_filter.GetVolumeSimilarity()
fn[j, labelIndex] = overlap_measures_filter.GetFalseNegativeError()
fp[j, labelIndex] = overlap_measures_filter.GetFalsePositiveError()
log.write('LWV Dice Label {0}: {1}\n'.format(labelIndex, dice[j, labelIndex]))
j = j + 1
np.savetxt(outputPathThisCase + "jaccardRWV.csv", jaccard, delimiter=",")
np.savetxt(outputPathThisCase + "diceRWV.csv", dice, delimiter=",")
np.savetxt(outputPathThisCase + "volumeSimilarityRWV.csv", volumeSimilarity, delimiter=",")
np.savetxt(outputPathThisCase + "fnRWV.csv", fn, delimiter=",")
np.savetxt(outputPathThisCase + "fpRWV.csv", fp, delimiter=",")
log.close()
| #! python3
# This script compares multiple label propagation and selection cases for all the cases in a library.
from __future__ import print_function
import SimpleITK as sitk
import numpy as np
import sys
import os
import NormalizedCrossCorrelationMetrics as NCC
import MajorityVoting as MV
import PostprocessingLabels as PP
from DynamicLabelFusionWithSimilarityWeights import DynamicLabelFusionWithLocalSimilarityWeights as DynamicLocalLabelling
from DynamicLabelFusionWithSimilarityWeights import DynamicLabelFusionWithSimilarityWeights as DynamicLabelling
############################### TARGET FOLDER ###################################
# The target folder needs to have all the files that are saved by the plugin when intermediates files are saved.
libraryVersion = 'V1.2'
segType = 'BSplineStandardGradDesc_NMI_2000iters_2000samples'
numberOfSelectedAtlases = 19 # I need all the atlases, but if the segmentation was ran in dbug mode I'll have all anyways.
excludeFemurs = True # The femurs have been segmented in a few atlases, but because they are only in a few of them, it
# introduces erros in the undecided label.
numLabels = 11
if excludeFemurs:
numLabels = 9
numLabelWithoutUndecided = numLabels - 1
maskedRegistration = True
libraryCases = ''
libraryPath = 'D:\\Martin\\Segmentation\\AtlasLibrary\\' + libraryVersion + '\\NativeResolutionAndSize2\\'
targetPath = 'D:\\MuscleSegmentationEvaluation\\SegmentationWithPython\\' + libraryVersion + '\\TestWithLibrary\\Nonrigid{0}_N{1}_MaxProb_Mask\\'.format(segType, numberOfSelectedAtlases)
targetPath = 'D:\\MuscleSegmentationEvaluation\\SegmentationWithPython\\' + libraryVersion + '\\TestWithLibrary\\{0}_N{1}_{2}\\'.format(segType, numberOfSelectedAtlases, maskedRegistration)
outputPath = 'D:\\MuscleSegmentationEvaluation\\SegmentationWithPython\\' + libraryVersion + '\\TestWithLibrary\\{0}_N{1}_{2}_LibSize\\'.format(segType, numberOfSelectedAtlases, maskedRegistration)
# Exponential weights:
if not os.path.exists(outputPath):
os.mkdir(outputPath)
expWeight=2
outputPath = outputPath + '\\expweightFusion_{0}\\'.format(expWeight)
if not os.path.exists(outputPath):
os.mkdir(outputPath)
extensionImages = 'mhd'
regImagesFilenameEnd = '_to_target.mhd'
regLabelsFilenameEnd = '_to_target_labels.mhd'
# Look for the raw files in the library:
dirWithCases = os.listdir(targetPath)
#dirWithCases = dirWithCases[10:]
atlasImagesNames = []
atlasLabelsNames = []
# Label fusion strategies to include in the analysis
processWithMajorityVoting = True
processWithSTAPLES = True
processWithGlobalWeightedVoting = True
processWithRoiWeightedVoting = True
# Name of atlases in order to be included:
atlasNamesInOrderOfInclusion = ['ID00001', 'ID00002', 'ID00003', 'ID00005', 'ID00006', 'ID00008', 'ID00010', 'ID00011',
'ID00013', 'ID00014', 'ID00021', 'ID00029', 'ID00061','L0511645', '7390413', '7386347', 'L0045955',
'L0324841','L0364068','L0029976']
# Instead of using all cases from dirWithCases, I select a few of them for the library:
numberOfCases = 8
# Process multiple number of cases selection:
#[4,6,8,10,12,14,17]:
for numberOfCases in [15, 17]:
atlasesLibrary = atlasNamesInOrderOfInclusion[0:numberOfCases]
# For the segmentation, we evaluate all of them:
for filenameCases in dirWithCases:
outputPathThisLibrarySize = outputPath + 'LibrarySize{0}\\'.format(numberOfCases)
if not os.path.exists(outputPathThisLibrarySize):
os.mkdir(outputPathThisLibrarySize)
if os.path.isdir(targetPath + filenameCases):
caseName = filenameCases
# Data path were all the registered images:
dataPath = targetPath + filenameCases + "\\"
# Output path inside this folder:
outputPathThisCase = outputPathThisLibrarySize + caseName + "\\"
if not os.path.exists(outputPathThisCase):
os.mkdir(outputPathThisCase)
# Create a log file:
log = open(outputPathThisCase + 'log.txt', 'w')
# First read the target image:
targetImage = sitk.ReadImage(dataPath + "input_registration.mhd")
# get labels from library path:
targetLabels = sitk.ReadImage(libraryPath + caseName + "_labels.mhd")
# Look for the header files of the registered images:
files = os.listdir(dataPath)
# Get the images and labels filenames:
registeredFilenames = []
registeredLabelsFilenames = []
for filename in files:
if filename.endswith(regImagesFilenameEnd):
i = filename.find(regImagesFilenameEnd)
nameThisAtlas = filename[0:i]
filenameLabels = nameThisAtlas + regLabelsFilenameEnd
if os.path.isfile(dataPath + filenameLabels) & (nameThisAtlas in atlasesLibrary):
registeredFilenames.append(filename)
registeredLabelsFilenames.append(filenameLabels)
# Need to create a dictionary with all the registered images (too much memory?):
registeredImage = []
labelsImage = []
for i in range(0, len(registeredFilenames)):
# Read image:
registeredImage.append(sitk.ReadImage(dataPath + registeredFilenames[i]))
# Call the local similarity metric and save the image:
labelImage = sitk.ReadImage(dataPath + registeredLabelsFilenames[i])
# Remove femurs if indicated:
if excludeFemurs:
maskFilter = sitk.MaskImageFilter()
maskFilter.SetOutsideValue(0)
maskFilter.SetMaskingValue(9)
labelImage = maskFilter.Execute(labelImage, labelImage)
maskFilter.SetMaskingValue(10)
labelImage = maskFilter.Execute(labelImage, labelImage)
labelsImage.append(labelImage)
# Select the N most similar cases:
# Get similarity weights for each label mask for each atlas
lnccValues = np.zeros(len(registeredImage))
# Get a similarity metric for each label:
for i in range(0, len(registeredImage)):
# Using the similarity of the full image:
maskThisLabel = sitk.GreaterEqual(targetImage, 0) # Use all the voxels.
lncc = NCC.RoiNormalizedCrossCorrelationAsInITK(targetImage, registeredImage[i], maskThisLabel)
lnccValues[i] = lncc
# Sort indices for atlas selection and voting:
indicesSorted = np.argsort(lnccValues)
# Write log:
log.write('Similarity metric values (lncc): {0}\n'.format(lncc))
selectedAtlasesValues = range(2, len(registeredImage))
jaccard = np.zeros((len(selectedAtlasesValues), numLabels))
dice = np.zeros((len(selectedAtlasesValues), numLabels))
volumeSimilarity = np.zeros((len(selectedAtlasesValues), numLabels))
fn = np.zeros((len(selectedAtlasesValues), numLabels))
fp = np.zeros((len(selectedAtlasesValues), numLabels))
jaccardAll = np.zeros((len(selectedAtlasesValues), 1))
diceAll = np.zeros((len(selectedAtlasesValues), 1))
volumeSimilarityAll = np.zeros((len(selectedAtlasesValues), 1))
fnAll = np.zeros((len(selectedAtlasesValues), 1))
fpAll = np.zeros((len(selectedAtlasesValues), 1))
if processWithMajorityVoting:
j = 0
for numSelectedAtlases in selectedAtlasesValues:
# Selected atlases:
indicesSelected = indicesSorted[0:numSelectedAtlases]
# Now do the atlas selection (I can't access given indices so need a for):
propagatedLabels = list()
for index in indicesSelected:
propagatedLabels.append(labelsImage[index])
##################### LABEL FUSION WITH MAJORITY VOTING #################################
outputLabels = sitk.LabelVoting(propagatedLabels, numLabels)
# After label voting I will have undecided voxels, add an undecided solving step:
outputLabels = MV.SetUndecidedVoxelsUsingDistances(outputLabels, numLabels)
#Get largest connceted regions for each label:
#outputLabels = PP.FilterUnconnectedRegion(outputLabels, numLabels-1) # Not necessary for the undecided label
log.write('Indices of selected atlases: {0}\n'.format(indicesSelected))
# Write the results:
sitk.WriteImage(outputLabels, outputPathThisCase + "segmentedImage_MajVot_{0}.mhd".format(numSelectedAtlases))
# Get metrics:
overlap_measures_filter = sitk.LabelOverlapMeasuresImageFilter()
overlap_measures_filter.SetGlobalDefaultCoordinateTolerance(1e-2)
# First get a general metric:
overlap_measures_filter.Execute(targetLabels, outputLabels)
jaccardAll[j] = overlap_measures_filter.GetJaccardCoefficient()
diceAll[j] = overlap_measures_filter.GetDiceCoefficient()
volumeSimilarityAll[j] = overlap_measures_filter.GetVolumeSimilarity()
fnAll[j] = overlap_measures_filter.GetFalseNegativeError()
fpAll[j] = overlap_measures_filter.GetFalsePositiveError()
log.write('Dice All Labels: {0}\n'.format(diceAll[j]))
# Overlap measures for each label:
for labelIndex in range(0, numLabels): # The index needs to be icnreased as the base number is 1.
overlap_measures_filter.Execute(targetLabels == (labelIndex+1), outputLabels == (labelIndex+1))
jaccard[j, labelIndex] = overlap_measures_filter.GetJaccardCoefficient()
dice[j, labelIndex] = overlap_measures_filter.GetDiceCoefficient()
volumeSimilarity[j, labelIndex] = overlap_measures_filter.GetVolumeSimilarity()
fn[j, labelIndex] = overlap_measures_filter.GetFalseNegativeError()
fp[j, labelIndex] = overlap_measures_filter.GetFalsePositiveError()
log.write('Dice Label {0}: {1}\n'.format(labelIndex, dice[j, labelIndex]))
j = j+1
np.savetxt(outputPathThisCase + "jaccard.csv", jaccard, delimiter=",")
np.savetxt(outputPathThisCase + "dice.csv", dice, delimiter=",")
np.savetxt(outputPathThisCase + "volumeSimilarity.csv", volumeSimilarity, delimiter=",")
np.savetxt(outputPathThisCase + "fn.csv", fn, delimiter=",")
np.savetxt(outputPathThisCase + "fp.csv", fp, delimiter=",")
np.savetxt(outputPathThisCase + "jaccardAll.csv", jaccardAll, delimiter=",")
np.savetxt(outputPathThisCase + "diceAll.csv", diceAll, delimiter=",")
np.savetxt(outputPathThisCase + "volumeSimilarityAll.csv", volumeSimilarityAll, delimiter=",")
np.savetxt(outputPathThisCase + "fnAll.csv", fnAll, delimiter=",")
np.savetxt(outputPathThisCase + "fpAll.csv", fpAll, delimiter=",")
# Now repeat for STAPLEs:
if processWithSTAPLES:
j = 0
for numSelectedAtlases in range(2, len(registeredImage)):
# Selected atlases:
indicesSelected = indicesSorted[0:numSelectedAtlases]
# Now do the atlas selection (I can't access given indices so need a for):
propagatedLabels = list()
for index in indicesSelected:
propagatedLabels.append(labelsImage[index])
##################### LABEL FUSION WITH MAJORITY VOTING #################################
multilabelStaple = sitk.MultiLabelSTAPLEImageFilter()
multilabelStaple.SetTerminationUpdateThreshold(1e-4)
multilabelStaple.SetMaximumNumberOfIterations(30)
multilabelStaple.SetLabelForUndecidedPixels(numLabels)
outputLabelsSTAPLES = multilabelStaple.Execute(propagatedLabels)
log.write('Indices of selected atlases STAPLES: {0}\n'.format(indicesSelected))
sitk.WriteImage(outputLabelsSTAPLES,
outputPathThisCase + "segmentedImageSTAPLES_{0}.mhd".format(numSelectedAtlases))
# Get metrics:
overlap_measures_filter = sitk.LabelOverlapMeasuresImageFilter()
overlap_measures_filter.SetGlobalDefaultCoordinateTolerance(1e-2)
# Overlap measures for each label:
for labelIndex in range(0, numLabels):
overlap_measures_filter.Execute(targetLabels == (labelIndex+1), outputLabelsSTAPLES == (labelIndex+1))
jaccard[j, labelIndex] = overlap_measures_filter.GetJaccardCoefficient()
dice[j, labelIndex] = overlap_measures_filter.GetDiceCoefficient()
volumeSimilarity[j, labelIndex] = overlap_measures_filter.GetVolumeSimilarity()
fn[j, labelIndex] = overlap_measures_filter.GetFalseNegativeError()
fp[j, labelIndex] = overlap_measures_filter.GetFalsePositiveError()
log.write('STAPLES Dice Label {0}: {1}\n'.format(labelIndex, dice[j, labelIndex]))
j = j + 1
np.savetxt(outputPathThisCase + "jaccardSTAPLES.csv", jaccard, delimiter=",")
np.savetxt(outputPathThisCase + "diceSTAPLES.csv", dice, delimiter=",")
np.savetxt(outputPathThisCase + "volumeSimilaritySTAPLES.csv", volumeSimilarity, delimiter=",")
np.savetxt(outputPathThisCase + "fnSTAPLES.csv", fn, delimiter=",")
np.savetxt(outputPathThisCase + "fpSTAPLES.csv", fp, delimiter=",")
##################### LABEL FUSION WITH DYNAMIC WEIGHTING VOTING #################################
if processWithGlobalWeightedVoting:
j = 0
for numSelectedAtlases in selectedAtlasesValues:
# Call function for global weighted voting:
registeredAtlases = {'image': registeredImage, 'labels': labelsImage}
outputLabelsGWV = DynamicLabelling(targetImage, registeredAtlases, numLabelWithoutUndecided, numSelectedAtlases=numSelectedAtlases,
expWeight=expWeight, useOnlyLabelVoxels=True, outputPath=outputPathThisCase, debug=0)
# Write the results:
sitk.WriteImage(outputLabelsGWV,
outputPathThisCase + "segmentedImageGWV_{0}.mhd".format(numSelectedAtlases))
# Get metrics:
overlap_measures_filter = sitk.LabelOverlapMeasuresImageFilter()
overlap_measures_filter.SetGlobalDefaultCoordinateTolerance(1e-2)
# Overlap measures for each label:
for labelIndex in range(0, numLabelWithoutUndecided): # The index needs to be icnreased as the base number is 1.
overlap_measures_filter.Execute(targetLabels == (labelIndex + 1), outputLabelsGWV == (labelIndex + 1))
jaccard[j, labelIndex] = overlap_measures_filter.GetJaccardCoefficient()
dice[j, labelIndex] = overlap_measures_filter.GetDiceCoefficient()
volumeSimilarity[j, labelIndex] = overlap_measures_filter.GetVolumeSimilarity()
fn[j, labelIndex] = overlap_measures_filter.GetFalseNegativeError()
fp[j, labelIndex] = overlap_measures_filter.GetFalsePositiveError()
log.write('GWV Dice Label {0}: {1}\n'.format(labelIndex, dice[j, labelIndex]))
j = j + 1
np.savetxt(outputPathThisCase + "jaccardGWV.csv", jaccard, delimiter=",")
np.savetxt(outputPathThisCase + "diceGWV.csv", dice, delimiter=",")
np.savetxt(outputPathThisCase + "volumeSimilarityGWV.csv", volumeSimilarity, delimiter=",")
np.savetxt(outputPathThisCase + "fnGWV.csv", fn, delimiter=",")
np.savetxt(outputPathThisCase + "fpGWV.csv", fp, delimiter=",")
if processWithRoiWeightedVoting:
j = 0
for numSelectedAtlases in selectedAtlasesValues:
# Call function for global weighted voting:
registeredAtlases = {'image': registeredImage, 'labels': labelsImage}
outputLabelsLWV = DynamicLocalLabelling(targetImage, registeredAtlases, numLabelWithoutUndecided, numSelectedAtlases = numSelectedAtlases,
expWeight=expWeight, outputPath=outputPathThisCase, debug = 0)
# Write the results:
sitk.WriteImage(outputLabelsLWV,
outputPathThisCase + "segmentedImageRWV_{0}.mhd".format(numSelectedAtlases))
# Get metrics:
overlap_measures_filter = sitk.LabelOverlapMeasuresImageFilter()
overlap_measures_filter.SetGlobalDefaultCoordinateTolerance(1e-2)
# Overlap measures for each label:
for labelIndex in range(0, numLabelWithoutUndecided): # The index needs to be icnreased as the base number is 1.
overlap_measures_filter.Execute(targetLabels == (labelIndex + 1),
outputLabelsLWV == (labelIndex + 1))
jaccard[j, labelIndex] = overlap_measures_filter.GetJaccardCoefficient()
dice[j, labelIndex] = overlap_measures_filter.GetDiceCoefficient()
volumeSimilarity[j, labelIndex] = overlap_measures_filter.GetVolumeSimilarity()
fn[j, labelIndex] = overlap_measures_filter.GetFalseNegativeError()
fp[j, labelIndex] = overlap_measures_filter.GetFalsePositiveError()
log.write('LWV Dice Label {0}: {1}\n'.format(labelIndex, dice[j, labelIndex]))
j = j + 1
np.savetxt(outputPathThisCase + "jaccardRWV.csv", jaccard, delimiter=",")
np.savetxt(outputPathThisCase + "diceRWV.csv", dice, delimiter=",")
np.savetxt(outputPathThisCase + "volumeSimilarityRWV.csv", volumeSimilarity, delimiter=",")
np.savetxt(outputPathThisCase + "fnRWV.csv", fn, delimiter=",")
np.savetxt(outputPathThisCase + "fpRWV.csv", fp, delimiter=",")
log.close()
| en | 0.814213 | #! python3 # This script compares multiple label propagation and selection cases for all the cases in a library. ############################### TARGET FOLDER ################################### # The target folder needs to have all the files that are saved by the plugin when intermediates files are saved. # I need all the atlases, but if the segmentation was ran in dbug mode I'll have all anyways. # The femurs have been segmented in a few atlases, but because they are only in a few of them, it # introduces erros in the undecided label. # Exponential weights: # Look for the raw files in the library: #dirWithCases = dirWithCases[10:] # Label fusion strategies to include in the analysis # Name of atlases in order to be included: # Instead of using all cases from dirWithCases, I select a few of them for the library: # Process multiple number of cases selection: #[4,6,8,10,12,14,17]: # For the segmentation, we evaluate all of them: # Data path were all the registered images: # Output path inside this folder: # Create a log file: # First read the target image: # get labels from library path: # Look for the header files of the registered images: # Get the images and labels filenames: # Need to create a dictionary with all the registered images (too much memory?): # Read image: # Call the local similarity metric and save the image: # Remove femurs if indicated: # Select the N most similar cases: # Get similarity weights for each label mask for each atlas # Get a similarity metric for each label: # Using the similarity of the full image: # Use all the voxels. # Sort indices for atlas selection and voting: # Write log: # Selected atlases: # Now do the atlas selection (I can't access given indices so need a for): ##################### LABEL FUSION WITH MAJORITY VOTING ################################# # After label voting I will have undecided voxels, add an undecided solving step: #Get largest connceted regions for each label: #outputLabels = PP.FilterUnconnectedRegion(outputLabels, numLabels-1) # Not necessary for the undecided label # Write the results: # Get metrics: # First get a general metric: # Overlap measures for each label: # The index needs to be icnreased as the base number is 1. # Now repeat for STAPLEs: # Selected atlases: # Now do the atlas selection (I can't access given indices so need a for): ##################### LABEL FUSION WITH MAJORITY VOTING ################################# # Get metrics: # Overlap measures for each label: ##################### LABEL FUSION WITH DYNAMIC WEIGHTING VOTING ################################# # Call function for global weighted voting: # Write the results: # Get metrics: # Overlap measures for each label: # The index needs to be icnreased as the base number is 1. # Call function for global weighted voting: # Write the results: # Get metrics: # Overlap measures for each label: # The index needs to be icnreased as the base number is 1. | 1.756758 | 2 |
kvack/cli.py | radowit/kvack | 0 | 6613329 | """Console script for kvack."""
import sys
from typing import Any, cast
import click
import toml
ConfigType = dict[str, dict[str, dict[str, dict[str, dict[str, Any]]]]] # type: ignore
@click.group() # type: ignore
def main() -> None:
pass
@main.command() # type: ignore
def gen() -> None:
config = cast(ConfigType, toml.load("pyproject.toml"))
tools = config["tool"]["kvack"]["tool"]
for name, options in tools.items():
config["tool"].setdefault(name, {}).update(options)
with open("pyproject.toml", "w+") as pyproject_file:
toml.dump(config, pyproject_file)
click.echo(f"Tool configs generated: {', '.join(tools)}")
if __name__ == "__main__":
sys.exit(main()) # type: ignore
| """Console script for kvack."""
import sys
from typing import Any, cast
import click
import toml
ConfigType = dict[str, dict[str, dict[str, dict[str, dict[str, Any]]]]] # type: ignore
@click.group() # type: ignore
def main() -> None:
pass
@main.command() # type: ignore
def gen() -> None:
config = cast(ConfigType, toml.load("pyproject.toml"))
tools = config["tool"]["kvack"]["tool"]
for name, options in tools.items():
config["tool"].setdefault(name, {}).update(options)
with open("pyproject.toml", "w+") as pyproject_file:
toml.dump(config, pyproject_file)
click.echo(f"Tool configs generated: {', '.join(tools)}")
if __name__ == "__main__":
sys.exit(main()) # type: ignore
| en | 0.258968 | Console script for kvack. # type: ignore # type: ignore # type: ignore # type: ignore | 2.263063 | 2 |
scraper/scraper/items.py | Georgitanev/py_django_scrape | 0 | 6613330 | """ Parliament Pipeline"""
#
# See documentation in:
# https://docs.scrapy.org/en/latest/topics/items.html
import scrapy
class ParliamentPipeline(scrapy.Item):
""" Parliament Pipeline"""
name = scrapy.Field()
date_born = scrapy.Field()
place_born = scrapy.Field()
profession = scrapy.Field()
lang = scrapy.Field()
party = scrapy.Field()
email = scrapy.Field()
fb = scrapy.Field()
url = scrapy.Field()
pp = scrapy.Field()
dob = scrapy.Field()
| """ Parliament Pipeline"""
#
# See documentation in:
# https://docs.scrapy.org/en/latest/topics/items.html
import scrapy
class ParliamentPipeline(scrapy.Item):
""" Parliament Pipeline"""
name = scrapy.Field()
date_born = scrapy.Field()
place_born = scrapy.Field()
profession = scrapy.Field()
lang = scrapy.Field()
party = scrapy.Field()
email = scrapy.Field()
fb = scrapy.Field()
url = scrapy.Field()
pp = scrapy.Field()
dob = scrapy.Field()
| en | 0.593407 | Parliament Pipeline # # See documentation in: # https://docs.scrapy.org/en/latest/topics/items.html Parliament Pipeline | 2.217769 | 2 |
src/MapMyNotesApplication/MapMyNotesApplication/models/user.py | Ryan-Gouldsmith/MajorProject-MapMyNotes | 0 | 6613331 | from MapMyNotesApplication import database
from sqlalchemy import Column, Integer, String
"""
Interacts with the SQLAlchemy model
"""
class User(database.Model):
#SQL Alchemy model specific attributes
__tablename__ = 'users'
id = Column(Integer, primary_key=True)
email_address = Column(String(100), nullable=False)
def __init__(self, email_address):
"""
Creates a new instance of the user
Parameters
----------
email_address: String representation of the user
"""
self.email_address = email_address
def save(self):
"""
Saves the current instance to the database
Returns
-------
False if there is an error.
"""
if len(self.email_address) > 100:
return False
database.session.add(self)
database.session.commit()
@staticmethod
def find_user_by_email_address(email_address):
"""
Uses the SQLAlchemy API's to extract the email address from the database
Parameters
----------
email_address: String representation of the email address that's been attempted to be discovered
Returns
-------
An instance of User if email address has been found
None if there has been no results found.
"""
return User.query.filter_by(email_address=email_address).one_or_none()
| from MapMyNotesApplication import database
from sqlalchemy import Column, Integer, String
"""
Interacts with the SQLAlchemy model
"""
class User(database.Model):
#SQL Alchemy model specific attributes
__tablename__ = 'users'
id = Column(Integer, primary_key=True)
email_address = Column(String(100), nullable=False)
def __init__(self, email_address):
"""
Creates a new instance of the user
Parameters
----------
email_address: String representation of the user
"""
self.email_address = email_address
def save(self):
"""
Saves the current instance to the database
Returns
-------
False if there is an error.
"""
if len(self.email_address) > 100:
return False
database.session.add(self)
database.session.commit()
@staticmethod
def find_user_by_email_address(email_address):
"""
Uses the SQLAlchemy API's to extract the email address from the database
Parameters
----------
email_address: String representation of the email address that's been attempted to be discovered
Returns
-------
An instance of User if email address has been found
None if there has been no results found.
"""
return User.query.filter_by(email_address=email_address).one_or_none()
| en | 0.869058 | Interacts with the SQLAlchemy model #SQL Alchemy model specific attributes Creates a new instance of the user Parameters ---------- email_address: String representation of the user Saves the current instance to the database Returns ------- False if there is an error. Uses the SQLAlchemy API's to extract the email address from the database Parameters ---------- email_address: String representation of the email address that's been attempted to be discovered Returns ------- An instance of User if email address has been found None if there has been no results found. | 3.615662 | 4 |
Pronote.py | Yaya-Cout/Python | 5 | 6613332 | <filename>Pronote.py
import pronotepy
from pronotepy.ent import ac_reunion
# importing ent specific function, you do not need to import anything if you dont use an ent
client = pronotepy.Client('https://demo.index-education.net/pronote/eleve.html',
username='demonstration',
password='<PASSWORD>')
# ent=ac_reunion) # ent specific
if client.logged_in:
print(len(client.messages()))
| <filename>Pronote.py
import pronotepy
from pronotepy.ent import ac_reunion
# importing ent specific function, you do not need to import anything if you dont use an ent
client = pronotepy.Client('https://demo.index-education.net/pronote/eleve.html',
username='demonstration',
password='<PASSWORD>')
# ent=ac_reunion) # ent specific
if client.logged_in:
print(len(client.messages()))
| en | 0.630045 | # importing ent specific function, you do not need to import anything if you dont use an ent # ent=ac_reunion) # ent specific | 2.437379 | 2 |
Alphabet/CaptialAlphabet/Static_cap_letter_while_loop/WHILE_LOOP_G.py | Polamreddykrishnareddy/PatternPackage | 0 | 6613333 | <reponame>Polamreddykrishnareddy/PatternPackage
#G
row=0
while row<6:
col =0
while col<6:
if (col==0 and row!=0)or(row==0 and col==1)or(row==0 and col==2) or (row==3 and col!=1) or (col==5 and row!=0 and row!=1 and row!=2) or(row==4 and col==3) or (row==5 and col!=4):
print("*",end=" ")
else:
print(" ",end=" ")
col +=1
row +=1
print()
| #G
row=0
while row<6:
col =0
while col<6:
if (col==0 and row!=0)or(row==0 and col==1)or(row==0 and col==2) or (row==3 and col!=1) or (col==5 and row!=0 and row!=1 and row!=2) or(row==4 and col==3) or (row==5 and col!=4):
print("*",end=" ")
else:
print(" ",end=" ")
col +=1
row +=1
print() | none | 1 | 3.976588 | 4 | |
net/wyun/tests/db/test_mysql_host6_uxb.py | michaelyin/im2markup-prep | 3 | 6613334 | <reponame>michaelyin/im2markup-prep<gh_stars>1-10
from unittest import TestCase
from net.wyun.mer.ink.sample import Sample
import numpy as np
import MySQLdb
import _mysql
'''
test mysql db
'''
class TestEquation(TestCase):
def setUp(self):
# Open database connection
self.db = MySQLdb.connect("host6", 3308, "uxb", "uxb123", "uxb", charset = 'utf8',use_unicode=True)
# ending the test
def tearDown(self):
"""Cleaning up after the test"""
print "\nTest:tearDown_:begin"
# disconnect from server
self.db.close()
def test_mysql(self):
'''
test fancy index
array of indices on the matrix
used a lot in 2-D matrix value update
:return:
'''
# prepare a cursor object using cursor() method
cursor = self.db.cursor()
sql = "select id, scg_ink from hw_record where request_at like '2018-09-17%' order by request_at asc;"
cnt = 0
try:
# Execute the SQL command
# id | bigint(20) | NO | PRI | NULL | auto_increment |
# | create_t | datetime | NO | | NULL | |
# | image_name | varchar(255) | NO | | NULL | |
# | latex | varchar(255) | NO | | NULL | |
# | verified | bit(1) | YES | | NULL | |
# | file_name
cursor.execute(sql)
# Fetch all the rows in a list of lists.
results = cursor.fetchall()
print 'total records: ', len(results)
for row in results:
cnt = cnt +1
id, scg = row[0], row[1]
#print 'type of verified: ', type(verified)
# Now print fetched result
print 'saving scg... ', id
self.save_scg_2_file(cnt, scg)
#if cnt>10: break
except:
print "Error: unable to fecth data"
raise
print 'total records: ', len(results)
def save_scg_2_file(self, i, scg):
textfile = open('temp/2017-09-17/' + str(i) + '_scg.txt', 'w')
textfile.write(scg)
textfile.close() | from unittest import TestCase
from net.wyun.mer.ink.sample import Sample
import numpy as np
import MySQLdb
import _mysql
'''
test mysql db
'''
class TestEquation(TestCase):
def setUp(self):
# Open database connection
self.db = MySQLdb.connect("host6", 3308, "uxb", "uxb123", "uxb", charset = 'utf8',use_unicode=True)
# ending the test
def tearDown(self):
"""Cleaning up after the test"""
print "\nTest:tearDown_:begin"
# disconnect from server
self.db.close()
def test_mysql(self):
'''
test fancy index
array of indices on the matrix
used a lot in 2-D matrix value update
:return:
'''
# prepare a cursor object using cursor() method
cursor = self.db.cursor()
sql = "select id, scg_ink from hw_record where request_at like '2018-09-17%' order by request_at asc;"
cnt = 0
try:
# Execute the SQL command
# id | bigint(20) | NO | PRI | NULL | auto_increment |
# | create_t | datetime | NO | | NULL | |
# | image_name | varchar(255) | NO | | NULL | |
# | latex | varchar(255) | NO | | NULL | |
# | verified | bit(1) | YES | | NULL | |
# | file_name
cursor.execute(sql)
# Fetch all the rows in a list of lists.
results = cursor.fetchall()
print 'total records: ', len(results)
for row in results:
cnt = cnt +1
id, scg = row[0], row[1]
#print 'type of verified: ', type(verified)
# Now print fetched result
print 'saving scg... ', id
self.save_scg_2_file(cnt, scg)
#if cnt>10: break
except:
print "Error: unable to fecth data"
raise
print 'total records: ', len(results)
def save_scg_2_file(self, i, scg):
textfile = open('temp/2017-09-17/' + str(i) + '_scg.txt', 'w')
textfile.write(scg)
textfile.close() | en | 0.588999 | test mysql db # Open database connection # ending the test Cleaning up after the test # disconnect from server test fancy index array of indices on the matrix used a lot in 2-D matrix value update :return: # prepare a cursor object using cursor() method # Execute the SQL command # id | bigint(20) | NO | PRI | NULL | auto_increment | # | create_t | datetime | NO | | NULL | | # | image_name | varchar(255) | NO | | NULL | | # | latex | varchar(255) | NO | | NULL | | # | verified | bit(1) | YES | | NULL | | # | file_name # Fetch all the rows in a list of lists. #print 'type of verified: ', type(verified) # Now print fetched result #if cnt>10: break | 2.990426 | 3 |
MDEncoder/utils/plot.py | alanklam/MDEncoder | 0 | 6613335 | <reponame>alanklam/MDEncoder
import matplotlib.pyplot as plt
import numpy as np
def plot_energy(xdata,ydata,frame=[],fname='',xylab=('X','Y'),xscale=(-1,1,0.2),yscale=(-1,1,0.2),cscale=(0,5),bins=60,Temperature=300):
x , y = xdata , ydata
h , xe, ye = np.histogram2d(x,y,bins=bins,range=[[xscale[0],xscale[1]],[yscale[0],yscale[1]]])
X, Y = np.meshgrid(xe,ye)
plt.figure(figsize=(6,5))
RT = 0.00198588*Temperature
F = -RT*np.log((h.T)/np.sum(h))
F = F - np.min(F)
plt.pcolormesh(X,Y,F, cmap='jet')
plt.clim(cscale[0],cscale[1])
c = plt.colorbar()
c.ax.set_ylabel('Kcal/mol',fontsize=20)
for n, f in enumerate(frame):
plt.text(x[f],y[f], str(n+1) ,fontsize=26)
def rounding(x):
return int(x*10)/10
plt.xlabel(xylab[0],fontsize=22)
plt.ylabel(xylab[1],fontsize=22)
plt.xticks(np.arange(rounding(xscale[0]),rounding(xscale[1])+xscale[2],step=xscale[2]),fontsize=15)
plt.yticks(np.arange(rounding(yscale[0]),rounding(yscale[1])+yscale[2],step=yscale[2]),fontsize=15)
plt.xlim([xscale[0],xscale[1]])
plt.ylim([yscale[0],yscale[1]])
plt.tight_layout()
fig = plt.gcf()
if len(fname)>0:
fig.savefig(fname,dpi=200)
return fig | import matplotlib.pyplot as plt
import numpy as np
def plot_energy(xdata,ydata,frame=[],fname='',xylab=('X','Y'),xscale=(-1,1,0.2),yscale=(-1,1,0.2),cscale=(0,5),bins=60,Temperature=300):
x , y = xdata , ydata
h , xe, ye = np.histogram2d(x,y,bins=bins,range=[[xscale[0],xscale[1]],[yscale[0],yscale[1]]])
X, Y = np.meshgrid(xe,ye)
plt.figure(figsize=(6,5))
RT = 0.00198588*Temperature
F = -RT*np.log((h.T)/np.sum(h))
F = F - np.min(F)
plt.pcolormesh(X,Y,F, cmap='jet')
plt.clim(cscale[0],cscale[1])
c = plt.colorbar()
c.ax.set_ylabel('Kcal/mol',fontsize=20)
for n, f in enumerate(frame):
plt.text(x[f],y[f], str(n+1) ,fontsize=26)
def rounding(x):
return int(x*10)/10
plt.xlabel(xylab[0],fontsize=22)
plt.ylabel(xylab[1],fontsize=22)
plt.xticks(np.arange(rounding(xscale[0]),rounding(xscale[1])+xscale[2],step=xscale[2]),fontsize=15)
plt.yticks(np.arange(rounding(yscale[0]),rounding(yscale[1])+yscale[2],step=yscale[2]),fontsize=15)
plt.xlim([xscale[0],xscale[1]])
plt.ylim([yscale[0],yscale[1]])
plt.tight_layout()
fig = plt.gcf()
if len(fname)>0:
fig.savefig(fname,dpi=200)
return fig | none | 1 | 2.542895 | 3 | |
Evolutive Computing/real.py | Jelaque/Computacion-Bioinspirada | 0 | 6613336 | <gh_stars>0
import random as rand
import numpy as np
import math
def scale(X, x_min, x_max):
nom = (X-X.min(axis=0))*(x_max-x_min)
denom = int(X.max(axis=0) - X.min(axis=0))
denom[denom==0] = 1
return x_min + nom/denom
def r_genetic(limit,dec,n_genes,N,p_cross,p_mut,func,it,mode):
population = rget_population(dec,n_genes,N,limit)
print('Poblacion inicial')
print_pop_initial(population)
fitness = rdecode(population,func)
print_fitness(population,fitness,N)
i = 1
it = it + 1
while i < it:
print('****Iteracion ',i,'****')
parents = rmating_pool(population, fitness, mode)
population = rselect_parents(population, parents, fitness, p_cross, p_mut)
fitness = rdecode(population,func)
print('Nueva Poblacion')
print_pop_initial(population)
print_fitness(population,fitness,N)
i += 1
def print_pop_initial(population):
i = 1
for row in population:
print(i,')\t\t',row)
i += 1
print()
def print_fitness(population,fitness,n):
i = 1
print('Calcular la Aptitud para cada Individudo')
j = 0
for row in population:
print(i,')\t\t',row,'\t\t',fitness[j])
i += 1
j += 1
print()
def rget_population(dec,n_genes,N,limit):
population = np.random.rand(N,n_genes)
for i in range(0,2):
population[:,i] = np.interp(population[:,i], (min(population[:,i]), max(population[:,i])), (-10,10))
population = np.around(population, decimals = dec)
return population
def rdecode(population, func):
fitness = np.zeros(population.shape[0])
for i in range(0,population.shape[0]):
fitness[i] = func(population[i])
return fitness
def rmating_pool(population, fitness, mode):
n = population.shape[0]
parents = np.zeros(n)
for i in range(0,n):
a = rand.randint(0,n-1)
b = rand.randint(0,n-1)
val = mode(fitness[a], fitness[b])
if val == fitness[a]:
parents[i] = a
else:
parents[i] = b
print(a+1,'\t\t',b+1,'\t\t=>\t\t',int(parents[i])+1,'\t\t=>\t\t',population[int(parents[i])])
print()
return parents
def rselect_parents(population, parents, fitness, p_cross, p_mut):
population = np.around(population,decimals=5)
p_new = population
n = population.shape[0]
for i in range(0,n):
a = rand.randint(0,n-1)
b = rand.randint(0,n-1)
r = population[int(parents[a])]
s = population[int(parents[b])]
pcss = 0
print('Seleccion de Padres')
print(a+1,'\t',b+1,' => ',int(parents[a])+1,' - ',int(parents[b])+1,' => ',r,' - ',s)
if rand.uniform(0,1) >= p_cross:
beta1 = rand.uniform(-0.5,1.5)
beta2 = rand.uniform(-0.5,1.5)
z = r
x = r
y = s
x = [z[0],y[0]]
y = [z[1],y[1]]
H = abs(x[0]-x[1])
lim = H*beta1
v1 = round(rand.uniform(min(x)-lim,max(x)+lim),5)
H = abs(y[0]-y[1])
lim = H*beta2
v2 = round(rand.uniform(min(y)-lim,max(y)+lim),5)
if(v1 > 10 and v1 < -10) or (v2 > 10 and v2 < -10):
if fitness[int(parents[a])] < fitness[int(parents[b])]:
p_new[i] = r
else:
p_new[i] = s
else:
p_new[i] = [v1,v2]
pcss = 1
print('Cruzamiento')
else:
if fitness[int(parents[a])] < fitness[int(parents[b])]:
p_new[i] = r
else:
p_new[i] = s
print('Sin Cruzamiento')
print(p_new[i])
if rand.uniform(0,1) >= p_mut:
d = mutation(p_new[i])
if(d[0] <= 10 and d[0] >= -10) or (d[1] <= 10 and d[1] >= -10):
p_new[i] = d
print('Mutacion')
else:
print('Sin mutacion')
print(p_new[i])
print()
print()
return p_new
def mutation(vec):
for i in range(0,len(vec)):
vec[i] = round(rand.uniform(vec[i]-0.3,vec[i]+0.3),5)
return vec
p_cross = 0.75
p_mut = 0.5
n = 5000
def f(x):
return -math.cos(x[0])*math.cos(x[1])*math.exp(-math.pow(x[0]-math.pi,2)-math.pow(x[1]-math.pi,2))
print(f([8.29,-1.21]))
print('Parametros:')
print('- Cantidad de Individuos: ',16)
print('- Cantidad de Genes por Individuo: ',2)
print('- Selección por torneo (2)')
print('- Probabilidad de Cruzamiento: ',p_cross)
print('- Cruzamiento BLX-Alpha, Alpha = ',0.5)
print('- Probabilidad de Mutación: ',p_mut)
print('- Mutación Uniforme')
print('- Cantidad de Iteraciones: ',n)
r_genetic([[-100,100],[-100,100]],5,2,16,p_cross,p_mut,f,n,min)
| import random as rand
import numpy as np
import math
def scale(X, x_min, x_max):
nom = (X-X.min(axis=0))*(x_max-x_min)
denom = int(X.max(axis=0) - X.min(axis=0))
denom[denom==0] = 1
return x_min + nom/denom
def r_genetic(limit,dec,n_genes,N,p_cross,p_mut,func,it,mode):
population = rget_population(dec,n_genes,N,limit)
print('Poblacion inicial')
print_pop_initial(population)
fitness = rdecode(population,func)
print_fitness(population,fitness,N)
i = 1
it = it + 1
while i < it:
print('****Iteracion ',i,'****')
parents = rmating_pool(population, fitness, mode)
population = rselect_parents(population, parents, fitness, p_cross, p_mut)
fitness = rdecode(population,func)
print('Nueva Poblacion')
print_pop_initial(population)
print_fitness(population,fitness,N)
i += 1
def print_pop_initial(population):
i = 1
for row in population:
print(i,')\t\t',row)
i += 1
print()
def print_fitness(population,fitness,n):
i = 1
print('Calcular la Aptitud para cada Individudo')
j = 0
for row in population:
print(i,')\t\t',row,'\t\t',fitness[j])
i += 1
j += 1
print()
def rget_population(dec,n_genes,N,limit):
population = np.random.rand(N,n_genes)
for i in range(0,2):
population[:,i] = np.interp(population[:,i], (min(population[:,i]), max(population[:,i])), (-10,10))
population = np.around(population, decimals = dec)
return population
def rdecode(population, func):
fitness = np.zeros(population.shape[0])
for i in range(0,population.shape[0]):
fitness[i] = func(population[i])
return fitness
def rmating_pool(population, fitness, mode):
n = population.shape[0]
parents = np.zeros(n)
for i in range(0,n):
a = rand.randint(0,n-1)
b = rand.randint(0,n-1)
val = mode(fitness[a], fitness[b])
if val == fitness[a]:
parents[i] = a
else:
parents[i] = b
print(a+1,'\t\t',b+1,'\t\t=>\t\t',int(parents[i])+1,'\t\t=>\t\t',population[int(parents[i])])
print()
return parents
def rselect_parents(population, parents, fitness, p_cross, p_mut):
population = np.around(population,decimals=5)
p_new = population
n = population.shape[0]
for i in range(0,n):
a = rand.randint(0,n-1)
b = rand.randint(0,n-1)
r = population[int(parents[a])]
s = population[int(parents[b])]
pcss = 0
print('Seleccion de Padres')
print(a+1,'\t',b+1,' => ',int(parents[a])+1,' - ',int(parents[b])+1,' => ',r,' - ',s)
if rand.uniform(0,1) >= p_cross:
beta1 = rand.uniform(-0.5,1.5)
beta2 = rand.uniform(-0.5,1.5)
z = r
x = r
y = s
x = [z[0],y[0]]
y = [z[1],y[1]]
H = abs(x[0]-x[1])
lim = H*beta1
v1 = round(rand.uniform(min(x)-lim,max(x)+lim),5)
H = abs(y[0]-y[1])
lim = H*beta2
v2 = round(rand.uniform(min(y)-lim,max(y)+lim),5)
if(v1 > 10 and v1 < -10) or (v2 > 10 and v2 < -10):
if fitness[int(parents[a])] < fitness[int(parents[b])]:
p_new[i] = r
else:
p_new[i] = s
else:
p_new[i] = [v1,v2]
pcss = 1
print('Cruzamiento')
else:
if fitness[int(parents[a])] < fitness[int(parents[b])]:
p_new[i] = r
else:
p_new[i] = s
print('Sin Cruzamiento')
print(p_new[i])
if rand.uniform(0,1) >= p_mut:
d = mutation(p_new[i])
if(d[0] <= 10 and d[0] >= -10) or (d[1] <= 10 and d[1] >= -10):
p_new[i] = d
print('Mutacion')
else:
print('Sin mutacion')
print(p_new[i])
print()
print()
return p_new
def mutation(vec):
for i in range(0,len(vec)):
vec[i] = round(rand.uniform(vec[i]-0.3,vec[i]+0.3),5)
return vec
p_cross = 0.75
p_mut = 0.5
n = 5000
def f(x):
return -math.cos(x[0])*math.cos(x[1])*math.exp(-math.pow(x[0]-math.pi,2)-math.pow(x[1]-math.pi,2))
print(f([8.29,-1.21]))
print('Parametros:')
print('- Cantidad de Individuos: ',16)
print('- Cantidad de Genes por Individuo: ',2)
print('- Selección por torneo (2)')
print('- Probabilidad de Cruzamiento: ',p_cross)
print('- Cruzamiento BLX-Alpha, Alpha = ',0.5)
print('- Probabilidad de Mutación: ',p_mut)
print('- Mutación Uniforme')
print('- Cantidad de Iteraciones: ',n)
r_genetic([[-100,100],[-100,100]],5,2,16,p_cross,p_mut,f,n,min) | none | 1 | 3.175378 | 3 | |
code/global_coherence/step-1-1-slc2bursts.py | RichardScottOZ/openSAR | 0 | 6613337 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import sys
import shutil
import time
import numpy as np
import py_gamma as pg
import atexit
import glob
import zipfile
import json
def myargsparse():
import argparse
thisprog=os.path.basename(sys.argv[0])
############################################################################
# define some strings for parsing and help
############################################################################
epilog=\
"""**********************************************************************************************************
\n* GAMMA S1 InSAR processor, v1.0, 2020-12-14, oc *
\n* Import a list of S1 SLCs from the same datatake and store as individual bursts *
\n* *
\n* Input and options: *
\n* 1) SLC zipfile *
\n* 2) Output directory *
\n* 3) Directory containing S1 precision orbits AUX_POEORB (optional) *
\n* *
\n* Output: *
\n* Burst SLCs and associated par and tops_par files *
\n* *
\n***********************************************************************************************************
\nEXAMPLES:
\n{thisprog} -l $PATH/slclist* -o /home/user/ -p /nas/qc.sentinel1.eo.esa.int/aux_poeorb/
""".format(thisprog=thisprog)
help_slclist=\
'''List of S1 SLC zipfiles'''
help_outdir=\
'''Output directory '''
help_porb=\
'''Directory containing S1 precision orbits AUX_POEORB'''
help_q=\
'''Verbose Mode'''
help_r=\
'''Create temporary directory in ramdisk Mode'''
p = argparse.ArgumentParser(usage=None,description=epilog,prog=thisprog,formatter_class=argparse.RawDescriptionHelpFormatter)
p.add_argument("-o",required=False,help=help_outdir,action='store',dest='outdir',default=os.getcwd())
p.add_argument("-z",required=True,help=help_slclist,action='store',dest='ziplist',default=None,nargs='*')
p.add_argument("-p",required=False,help=help_porb,action='store',dest='porb',default=False)
p.add_argument("-v","--verbose",required=False,help=help_q,action='store_true',default=False)
p.add_argument("-r","--ramdisk",required=False,help=help_r,action='store_true',default=False)
args=p.parse_args()
args.outdir = args.outdir.rstrip('/') + '/'
return args
#########################################################################
# Function to be called by atexit when script stops
def delfun(dirname):
shutil.rmtree(dirname)
#########################################################################
def S1_import(args):
# Start time
start = time.time()
##########################################################
# Define Input/Output filenames/processing parameters #
##########################################################
ziplist = args.ziplist # List of S1 zipfiles
outdir = args.outdir # Output directory
porb = args.porb # Precision orbits
q = args.verbose # Verbose mode
ramdisk = args.ramdisk # Use ramdisk
outdir=outdir.rstrip('/')
ziplist=list(ziplist)
imno=len(ziplist)
ints=10
intv=10
if imno<11:
ints=int(100/(imno))
intv=int(100/(imno))
print('Import SLCs [%]: 0..', end='', flush=True)
for i,f in enumerate(ziplist):
f=f.rstrip()
# Obtain information from filename
filename=f.rpartition('/')[-1]
sat=filename.split('_')[0]
mode=filename.split('_')[4][-2:]
acqtime=filename.split('_')[5]
acqdate=filename.split('_')[5].split('T')[0]
orbit=filename.split('_')[7]
datatake=filename.split('_')[8]
# Define second polarization
if mode == 'DV':
pols=['vv','vh']
elif mode == 'DH':
pols=['hh','hv']
elif mode == 'SH':
pols='hh'
elif mode == 'SV':
pols='vv'
elif mode == 'HH':
pols='hh'
elif mode == 'VV':
pols='vv'
elif mode == 'HV':
pols='hv'
elif mode == 'VH':
pols='vh'
# Output directory
outdir = outdir.rstrip('/') + '/'
if os.path.isdir(outdir) == False:
os.mkdir(outdir)
# Define temporary working directory and filenames
tfile = orbit + '_' + datatake + '_' + acqtime
if i==0:
if ramdisk:
tpath = '/dev/shm/gamma_' + tfile + '_' + str(abs(np.random.randn())).replace('.','') + '/'
else:
tpath = outdir + '/gamma_' + tfile + '_' + str(abs(np.random.randn())).replace('.','') + '/'
os.mkdir(tpath)
os.chdir(tpath)
# Delete temporary working directory when script stops
atexit.register(delfun, tpath)
# Define name of log files
logout= outdir + tfile + '.log'
errout= outdir + tfile + '.err'
# Determine relative orbit
if sat=='S1B':
relpath=(int(int(orbit)-26-175*(np.floor((int(orbit)-27)/175))))
elif sat=='S1A':
relpath=(int(int(orbit)-72-175*(np.floor((int(orbit)-73)/175))))
relpath=f'{relpath:03d}'
# Unzip
z=zipfile.ZipFile(f)
product_path=tpath + filename.rstrip('zip') + 'SAFE'
refburst_resource='/cluster/raid/home/oliver/Scripts/JPL/sentinel1_reference_burst_offsets.json'
with open(refburst_resource,'r') as f:
refbursts=json.load(f)
# Import GRD
for p in pols:
for iw in ['iw1','iw2','iw3']:
flist=[x.filename for x in z.filelist if not x.is_dir() and x.filename.find(p) > -1 and x.filename.find(iw) > -1]
for u in flist:
z.extract(u)
# Input
tiff = glob.glob( product_path + '/measurement/*' + iw + '*' + p + '*tiff' )[0]
lead1 = glob.glob( product_path + '/annotation/*' + iw + '*' + p + '*xml' )[0]
lead2 = glob.glob( product_path + '/annotation/calibration/calibration-*' + iw + '*' + p + '*xml' )[0]
lead3 = glob.glob( product_path + '/annotation/calibration/noise-*' + iw + '*' + p + '*xml' )[0]
# Output
slc = tpath + tfile + '_' + p + '_' + iw + '.slc'
slcpar = tpath + tfile + '_' + p + '_' + iw + '.slc.par'
slctpar = tpath + tfile + '_' + p + '_' + iw + '.slc.tops_par'
# Import SLC
pg.par_S1_SLC(tiff,lead1,lead2,lead3,slcpar,slc,slctpar,1,'-', logf = logout, errf = errout, stdout_flag = q, stderr_flag = q)
# Update orbit state vectors
if porb:
if os.path.isdir(porb):
pg.OPOD_vec(slcpar,porb, logf = logout, errf = errout, stdout_flag = q, stderr_flag = q)
# Create tabfile
full_tabf=tpath + tfile + '_' + iw + '_' + p + '_fulltab'
full_tab=[slc, slcpar, slctpar]
pg.write_tab(full_tab, full_tabf)
# Copy individual bursts
pardict=pg.ParFile(slctpar)
nobursts=int(pardict.get_value('number_of_bursts'))
for b in range(1,nobursts+1):
burstid = pardict.get_value('burst_asc_node_' + str(b))[0]
burstid = str(np.floor(100*np.float32(burstid))/100)
s1path=relpath.lstrip('0')
refburst=float(refbursts[str(s1path)][iw[-1]])
burstid_integer=np.floor(float(burstid))
burstid_decimal=float(burstid)-burstid_integer
# Three cases
# 1. burstid just below integer
if burstid_decimal > 0.9 and refburst< 0.1:
burstid_integer+=1
burstid=str(int((burstid_integer+refburst)*100))
# 2. burstid just above integer
elif burstid_decimal < 0.1 and refburst > 0.9:
burstid_integer-=1
burstid=str(int((burstid_integer+refburst)*100))
# 3. burstid and refburst in same integer range
else:
burstid=str(int((burstid_integer+refburst)*100))
slcb = outdir + '/' + relpath + '_' + iw + '_' + p + '_' + burstid + '_' + acqdate + '.slc'
slcbpar = outdir + '/' + relpath + '_' + iw + '_' + p + '_' + burstid + '_' + acqdate + '.slc.par'
slcbtpar = outdir + '/' + relpath + '_' + iw + '_' + p + '_' + burstid + '_' + acqdate + '.slc.tops_par'
out_tab=[slcb, slcbpar, slcbtpar]
out_tabf=tpath + tfile + '_' + iw + '_' + p + '_' + burstid + '_tab'
pg.write_tab(out_tab, out_tabf)
# Create burst tab
bursttabf=tpath + tfile + '_' + iw + '_' + p + '_bursttab'
bursttab=[b,b]
pg.write_tab(bursttab, bursttabf)
pg.SLC_copy_ScanSAR(full_tabf,out_tabf, bursttabf,1, logf = logout, errf = errout, stdout_flag = q, stderr_flag = q)
os.remove(out_tabf)
os.remove(bursttabf)
os.remove(full_tabf)
os.remove(slc)
os.remove(slcpar)
os.remove(slctpar)
shutil.rmtree(product_path)
prct_coreg=np.floor(100*i/imno)
if prct_coreg>=ints:
print(str(ints), end='..', flush=True)
ints=ints+intv
print(('100 Done'))
#########################################################################################
# Compute and display execution time #
#########################################################################################
end = time.time()
diff=end-start
print(('Processed in ' + str(diff) + ' s'))
#########################################################################
def main():
args=myargsparse()
S1_import(args)
if __name__ == '__main__':
main() | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import sys
import shutil
import time
import numpy as np
import py_gamma as pg
import atexit
import glob
import zipfile
import json
def myargsparse():
import argparse
thisprog=os.path.basename(sys.argv[0])
############################################################################
# define some strings for parsing and help
############################################################################
epilog=\
"""**********************************************************************************************************
\n* GAMMA S1 InSAR processor, v1.0, 2020-12-14, oc *
\n* Import a list of S1 SLCs from the same datatake and store as individual bursts *
\n* *
\n* Input and options: *
\n* 1) SLC zipfile *
\n* 2) Output directory *
\n* 3) Directory containing S1 precision orbits AUX_POEORB (optional) *
\n* *
\n* Output: *
\n* Burst SLCs and associated par and tops_par files *
\n* *
\n***********************************************************************************************************
\nEXAMPLES:
\n{thisprog} -l $PATH/slclist* -o /home/user/ -p /nas/qc.sentinel1.eo.esa.int/aux_poeorb/
""".format(thisprog=thisprog)
help_slclist=\
'''List of S1 SLC zipfiles'''
help_outdir=\
'''Output directory '''
help_porb=\
'''Directory containing S1 precision orbits AUX_POEORB'''
help_q=\
'''Verbose Mode'''
help_r=\
'''Create temporary directory in ramdisk Mode'''
p = argparse.ArgumentParser(usage=None,description=epilog,prog=thisprog,formatter_class=argparse.RawDescriptionHelpFormatter)
p.add_argument("-o",required=False,help=help_outdir,action='store',dest='outdir',default=os.getcwd())
p.add_argument("-z",required=True,help=help_slclist,action='store',dest='ziplist',default=None,nargs='*')
p.add_argument("-p",required=False,help=help_porb,action='store',dest='porb',default=False)
p.add_argument("-v","--verbose",required=False,help=help_q,action='store_true',default=False)
p.add_argument("-r","--ramdisk",required=False,help=help_r,action='store_true',default=False)
args=p.parse_args()
args.outdir = args.outdir.rstrip('/') + '/'
return args
#########################################################################
# Function to be called by atexit when script stops
def delfun(dirname):
shutil.rmtree(dirname)
#########################################################################
def S1_import(args):
# Start time
start = time.time()
##########################################################
# Define Input/Output filenames/processing parameters #
##########################################################
ziplist = args.ziplist # List of S1 zipfiles
outdir = args.outdir # Output directory
porb = args.porb # Precision orbits
q = args.verbose # Verbose mode
ramdisk = args.ramdisk # Use ramdisk
outdir=outdir.rstrip('/')
ziplist=list(ziplist)
imno=len(ziplist)
ints=10
intv=10
if imno<11:
ints=int(100/(imno))
intv=int(100/(imno))
print('Import SLCs [%]: 0..', end='', flush=True)
for i,f in enumerate(ziplist):
f=f.rstrip()
# Obtain information from filename
filename=f.rpartition('/')[-1]
sat=filename.split('_')[0]
mode=filename.split('_')[4][-2:]
acqtime=filename.split('_')[5]
acqdate=filename.split('_')[5].split('T')[0]
orbit=filename.split('_')[7]
datatake=filename.split('_')[8]
# Define second polarization
if mode == 'DV':
pols=['vv','vh']
elif mode == 'DH':
pols=['hh','hv']
elif mode == 'SH':
pols='hh'
elif mode == 'SV':
pols='vv'
elif mode == 'HH':
pols='hh'
elif mode == 'VV':
pols='vv'
elif mode == 'HV':
pols='hv'
elif mode == 'VH':
pols='vh'
# Output directory
outdir = outdir.rstrip('/') + '/'
if os.path.isdir(outdir) == False:
os.mkdir(outdir)
# Define temporary working directory and filenames
tfile = orbit + '_' + datatake + '_' + acqtime
if i==0:
if ramdisk:
tpath = '/dev/shm/gamma_' + tfile + '_' + str(abs(np.random.randn())).replace('.','') + '/'
else:
tpath = outdir + '/gamma_' + tfile + '_' + str(abs(np.random.randn())).replace('.','') + '/'
os.mkdir(tpath)
os.chdir(tpath)
# Delete temporary working directory when script stops
atexit.register(delfun, tpath)
# Define name of log files
logout= outdir + tfile + '.log'
errout= outdir + tfile + '.err'
# Determine relative orbit
if sat=='S1B':
relpath=(int(int(orbit)-26-175*(np.floor((int(orbit)-27)/175))))
elif sat=='S1A':
relpath=(int(int(orbit)-72-175*(np.floor((int(orbit)-73)/175))))
relpath=f'{relpath:03d}'
# Unzip
z=zipfile.ZipFile(f)
product_path=tpath + filename.rstrip('zip') + 'SAFE'
refburst_resource='/cluster/raid/home/oliver/Scripts/JPL/sentinel1_reference_burst_offsets.json'
with open(refburst_resource,'r') as f:
refbursts=json.load(f)
# Import GRD
for p in pols:
for iw in ['iw1','iw2','iw3']:
flist=[x.filename for x in z.filelist if not x.is_dir() and x.filename.find(p) > -1 and x.filename.find(iw) > -1]
for u in flist:
z.extract(u)
# Input
tiff = glob.glob( product_path + '/measurement/*' + iw + '*' + p + '*tiff' )[0]
lead1 = glob.glob( product_path + '/annotation/*' + iw + '*' + p + '*xml' )[0]
lead2 = glob.glob( product_path + '/annotation/calibration/calibration-*' + iw + '*' + p + '*xml' )[0]
lead3 = glob.glob( product_path + '/annotation/calibration/noise-*' + iw + '*' + p + '*xml' )[0]
# Output
slc = tpath + tfile + '_' + p + '_' + iw + '.slc'
slcpar = tpath + tfile + '_' + p + '_' + iw + '.slc.par'
slctpar = tpath + tfile + '_' + p + '_' + iw + '.slc.tops_par'
# Import SLC
pg.par_S1_SLC(tiff,lead1,lead2,lead3,slcpar,slc,slctpar,1,'-', logf = logout, errf = errout, stdout_flag = q, stderr_flag = q)
# Update orbit state vectors
if porb:
if os.path.isdir(porb):
pg.OPOD_vec(slcpar,porb, logf = logout, errf = errout, stdout_flag = q, stderr_flag = q)
# Create tabfile
full_tabf=tpath + tfile + '_' + iw + '_' + p + '_fulltab'
full_tab=[slc, slcpar, slctpar]
pg.write_tab(full_tab, full_tabf)
# Copy individual bursts
pardict=pg.ParFile(slctpar)
nobursts=int(pardict.get_value('number_of_bursts'))
for b in range(1,nobursts+1):
burstid = pardict.get_value('burst_asc_node_' + str(b))[0]
burstid = str(np.floor(100*np.float32(burstid))/100)
s1path=relpath.lstrip('0')
refburst=float(refbursts[str(s1path)][iw[-1]])
burstid_integer=np.floor(float(burstid))
burstid_decimal=float(burstid)-burstid_integer
# Three cases
# 1. burstid just below integer
if burstid_decimal > 0.9 and refburst< 0.1:
burstid_integer+=1
burstid=str(int((burstid_integer+refburst)*100))
# 2. burstid just above integer
elif burstid_decimal < 0.1 and refburst > 0.9:
burstid_integer-=1
burstid=str(int((burstid_integer+refburst)*100))
# 3. burstid and refburst in same integer range
else:
burstid=str(int((burstid_integer+refburst)*100))
slcb = outdir + '/' + relpath + '_' + iw + '_' + p + '_' + burstid + '_' + acqdate + '.slc'
slcbpar = outdir + '/' + relpath + '_' + iw + '_' + p + '_' + burstid + '_' + acqdate + '.slc.par'
slcbtpar = outdir + '/' + relpath + '_' + iw + '_' + p + '_' + burstid + '_' + acqdate + '.slc.tops_par'
out_tab=[slcb, slcbpar, slcbtpar]
out_tabf=tpath + tfile + '_' + iw + '_' + p + '_' + burstid + '_tab'
pg.write_tab(out_tab, out_tabf)
# Create burst tab
bursttabf=tpath + tfile + '_' + iw + '_' + p + '_bursttab'
bursttab=[b,b]
pg.write_tab(bursttab, bursttabf)
pg.SLC_copy_ScanSAR(full_tabf,out_tabf, bursttabf,1, logf = logout, errf = errout, stdout_flag = q, stderr_flag = q)
os.remove(out_tabf)
os.remove(bursttabf)
os.remove(full_tabf)
os.remove(slc)
os.remove(slcpar)
os.remove(slctpar)
shutil.rmtree(product_path)
prct_coreg=np.floor(100*i/imno)
if prct_coreg>=ints:
print(str(ints), end='..', flush=True)
ints=ints+intv
print(('100 Done'))
#########################################################################################
# Compute and display execution time #
#########################################################################################
end = time.time()
diff=end-start
print(('Processed in ' + str(diff) + ' s'))
#########################################################################
def main():
args=myargsparse()
S1_import(args)
if __name__ == '__main__':
main() | de | 0.291475 | #!/usr/bin/env python3 # -*- coding: utf-8 -*- ############################################################################ # define some strings for parsing and help ############################################################################ ********************************************************************************************************** \n* GAMMA S1 InSAR processor, v1.0, 2020-12-14, oc * \n* Import a list of S1 SLCs from the same datatake and store as individual bursts * \n* * \n* Input and options: * \n* 1) SLC zipfile * \n* 2) Output directory * \n* 3) Directory containing S1 precision orbits AUX_POEORB (optional) * \n* * \n* Output: * \n* Burst SLCs and associated par and tops_par files * \n* * \n*********************************************************************************************************** \nEXAMPLES: \n{thisprog} -l $PATH/slclist* -o /home/user/ -p /nas/qc.sentinel1.eo.esa.int/aux_poeorb/ List of S1 SLC zipfiles Output directory Directory containing S1 precision orbits AUX_POEORB Verbose Mode Create temporary directory in ramdisk Mode ######################################################################### # Function to be called by atexit when script stops ######################################################################### # Start time ########################################################## # Define Input/Output filenames/processing parameters # ########################################################## # List of S1 zipfiles # Output directory # Precision orbits # Verbose mode # Use ramdisk # Obtain information from filename # Define second polarization # Output directory # Define temporary working directory and filenames # Delete temporary working directory when script stops # Define name of log files # Determine relative orbit # Unzip # Import GRD # Input # Output # Import SLC # Update orbit state vectors # Create tabfile # Copy individual bursts # Three cases # 1. burstid just below integer # 2. burstid just above integer # 3. burstid and refburst in same integer range # Create burst tab ######################################################################################### # Compute and display execution time # ######################################################################################### ######################################################################### | 2.411273 | 2 |
example/urls.py | ifanrx/django-ajax-upload-widget | 47 | 6613338 | from django.conf.urls.defaults import patterns, url
urlpatterns = patterns('example.views',
url(r'^add/$', 'add_edit_product', name='example-add-product'),
url(r'^edit/(?P<product_id>\d+)/$', 'add_edit_product', name='example-edit-product'),
)
| from django.conf.urls.defaults import patterns, url
urlpatterns = patterns('example.views',
url(r'^add/$', 'add_edit_product', name='example-add-product'),
url(r'^edit/(?P<product_id>\d+)/$', 'add_edit_product', name='example-edit-product'),
)
| none | 1 | 1.782382 | 2 | |
projectq/cengines/_basicmapper_test.py | ssc1729/ProjectQ | 795 | 6613339 | <gh_stars>100-1000
# -*- coding: utf-8 -*-
# Copyright 2018 ProjectQ-Framework (www.projectq.ch)
#
# 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.
"""Tests for projectq.cengines._basicmapper.py."""
from projectq.cengines import DummyEngine, _basicmapper
from projectq.meta import LogicalQubitIDTag
from projectq.ops import Allocate, BasicGate, Command, Deallocate, FlushGate, Measure
from projectq.types import WeakQubitRef
def test_basic_mapper_engine_send_cmd_with_mapped_ids():
mapper = _basicmapper.BasicMapperEngine()
mapper.current_mapping = {0: 3, 1: 2, 2: 1, 3: 0}
backend = DummyEngine(save_commands=True)
backend.is_last_engine = True
mapper.next_engine = backend
# generate a few commands
qb0 = WeakQubitRef(engine=None, idx=0)
qb1 = WeakQubitRef(engine=None, idx=1)
qb2 = WeakQubitRef(engine=None, idx=2)
qb3 = WeakQubitRef(engine=None, idx=3)
cmd0 = Command(engine=None, gate=Allocate, qubits=([qb0],), controls=[], tags=[])
cmd1 = Command(engine=None, gate=Deallocate, qubits=([qb1],), controls=[], tags=[])
cmd2 = Command(engine=None, gate=Measure, qubits=([qb2],), controls=[], tags=["SomeTag"])
cmd3 = Command(
engine=None,
gate=BasicGate(),
qubits=([qb0, qb1], [qb2]),
controls=[qb3],
tags=[],
)
cmd4 = Command(None, FlushGate(), ([WeakQubitRef(None, -1)],))
mapper._send_cmd_with_mapped_ids(cmd0)
mapper._send_cmd_with_mapped_ids(cmd1)
mapper._send_cmd_with_mapped_ids(cmd2)
mapper._send_cmd_with_mapped_ids(cmd3)
mapper._send_cmd_with_mapped_ids(cmd4)
rcmd0 = backend.received_commands[0]
rcmd1 = backend.received_commands[1]
rcmd2 = backend.received_commands[2]
rcmd3 = backend.received_commands[3]
rcmd4 = backend.received_commands[4]
assert rcmd0.gate == Allocate
assert rcmd0.qubits == ([qb3],)
assert rcmd1.gate == Deallocate
assert rcmd1.qubits == ([qb2],)
assert rcmd2.gate == Measure
assert rcmd2.qubits == ([qb1],)
assert rcmd2.tags == ["SomeTag", LogicalQubitIDTag(2)]
assert rcmd3.gate == BasicGate()
assert rcmd3.qubits == ([qb3, qb2], [qb1])
assert rcmd3.control_qubits == [qb0]
assert len(rcmd4.qubits) == 1
assert len(rcmd4.qubits[0]) == 1
assert rcmd4.qubits[0][0].id == -1
| # -*- coding: utf-8 -*-
# Copyright 2018 ProjectQ-Framework (www.projectq.ch)
#
# 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.
"""Tests for projectq.cengines._basicmapper.py."""
from projectq.cengines import DummyEngine, _basicmapper
from projectq.meta import LogicalQubitIDTag
from projectq.ops import Allocate, BasicGate, Command, Deallocate, FlushGate, Measure
from projectq.types import WeakQubitRef
def test_basic_mapper_engine_send_cmd_with_mapped_ids():
mapper = _basicmapper.BasicMapperEngine()
mapper.current_mapping = {0: 3, 1: 2, 2: 1, 3: 0}
backend = DummyEngine(save_commands=True)
backend.is_last_engine = True
mapper.next_engine = backend
# generate a few commands
qb0 = WeakQubitRef(engine=None, idx=0)
qb1 = WeakQubitRef(engine=None, idx=1)
qb2 = WeakQubitRef(engine=None, idx=2)
qb3 = WeakQubitRef(engine=None, idx=3)
cmd0 = Command(engine=None, gate=Allocate, qubits=([qb0],), controls=[], tags=[])
cmd1 = Command(engine=None, gate=Deallocate, qubits=([qb1],), controls=[], tags=[])
cmd2 = Command(engine=None, gate=Measure, qubits=([qb2],), controls=[], tags=["SomeTag"])
cmd3 = Command(
engine=None,
gate=BasicGate(),
qubits=([qb0, qb1], [qb2]),
controls=[qb3],
tags=[],
)
cmd4 = Command(None, FlushGate(), ([WeakQubitRef(None, -1)],))
mapper._send_cmd_with_mapped_ids(cmd0)
mapper._send_cmd_with_mapped_ids(cmd1)
mapper._send_cmd_with_mapped_ids(cmd2)
mapper._send_cmd_with_mapped_ids(cmd3)
mapper._send_cmd_with_mapped_ids(cmd4)
rcmd0 = backend.received_commands[0]
rcmd1 = backend.received_commands[1]
rcmd2 = backend.received_commands[2]
rcmd3 = backend.received_commands[3]
rcmd4 = backend.received_commands[4]
assert rcmd0.gate == Allocate
assert rcmd0.qubits == ([qb3],)
assert rcmd1.gate == Deallocate
assert rcmd1.qubits == ([qb2],)
assert rcmd2.gate == Measure
assert rcmd2.qubits == ([qb1],)
assert rcmd2.tags == ["SomeTag", LogicalQubitIDTag(2)]
assert rcmd3.gate == BasicGate()
assert rcmd3.qubits == ([qb3, qb2], [qb1])
assert rcmd3.control_qubits == [qb0]
assert len(rcmd4.qubits) == 1
assert len(rcmd4.qubits[0]) == 1
assert rcmd4.qubits[0][0].id == -1 | en | 0.842128 | # -*- coding: utf-8 -*- # Copyright 2018 ProjectQ-Framework (www.projectq.ch) # # 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. Tests for projectq.cengines._basicmapper.py. # generate a few commands | 1.87275 | 2 |
yunionclient/api/cloudkubeclusters.py | yunionyun/python_yunionsdk | 3 | 6613340 | <filename>yunionclient/api/cloudkubeclusters.py<gh_stars>1-10
from yunionclient.common import base
class CloudKubeCluster(base.ResourceBase):
pass
class CloudKubeClusterManager(base.StandaloneManager):
resource_class = CloudKubeCluster
keyword = 'cloud_kube_cluster'
keyword_plural = 'cloud_kube_clusters'
_columns = ["Id", "Name", "Description"]
| <filename>yunionclient/api/cloudkubeclusters.py<gh_stars>1-10
from yunionclient.common import base
class CloudKubeCluster(base.ResourceBase):
pass
class CloudKubeClusterManager(base.StandaloneManager):
resource_class = CloudKubeCluster
keyword = 'cloud_kube_cluster'
keyword_plural = 'cloud_kube_clusters'
_columns = ["Id", "Name", "Description"]
| none | 1 | 1.805372 | 2 | |
main/migrations/0042_auto_20200821_1342.py | alecstein/mataroa-personal | 30 | 6613341 | <gh_stars>10-100
# Generated by Django 3.1 on 2020-08-21 13:42
import uuid
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("main", "0041_auto_20200820_2107"),
]
operations = [
migrations.AlterField(
model_name="postnotification",
name="unsubscribe_key",
field=models.UUIDField(default=uuid.uuid4, unique=True),
),
]
| # Generated by Django 3.1 on 2020-08-21 13:42
import uuid
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("main", "0041_auto_20200820_2107"),
]
operations = [
migrations.AlterField(
model_name="postnotification",
name="unsubscribe_key",
field=models.UUIDField(default=uuid.uuid4, unique=True),
),
] | en | 0.814931 | # Generated by Django 3.1 on 2020-08-21 13:42 | 1.553744 | 2 |
test/__init__.py | JukeboxPipeline/jukebox-core | 2 | 6613342 | """ The tests module holds all unittests that we run.
To run them use tox. Just go to the project root and run the tox command.
Because some tools and modules need access to the database we have to establish a test database.
This is automatically done, when djadapter is imported and the tesing environment is initialized (env var JUKEBOX_TESTING).
The test db name will be saved in another env var ``TEST_DB``.
The test db name will be the name of the default database preceded by ``test_``.
As for now, we do not destroy the test db at the end of the test. It will be destroyed when the test runs again.
This is easier, when running multiple python instances simultaniously.
"""
import os
os.environ['JUKEBOX_TESTING'] = 'True'
| """ The tests module holds all unittests that we run.
To run them use tox. Just go to the project root and run the tox command.
Because some tools and modules need access to the database we have to establish a test database.
This is automatically done, when djadapter is imported and the tesing environment is initialized (env var JUKEBOX_TESTING).
The test db name will be saved in another env var ``TEST_DB``.
The test db name will be the name of the default database preceded by ``test_``.
As for now, we do not destroy the test db at the end of the test. It will be destroyed when the test runs again.
This is easier, when running multiple python instances simultaniously.
"""
import os
os.environ['JUKEBOX_TESTING'] = 'True'
| en | 0.794782 | The tests module holds all unittests that we run. To run them use tox. Just go to the project root and run the tox command. Because some tools and modules need access to the database we have to establish a test database. This is automatically done, when djadapter is imported and the tesing environment is initialized (env var JUKEBOX_TESTING). The test db name will be saved in another env var ``TEST_DB``. The test db name will be the name of the default database preceded by ``test_``. As for now, we do not destroy the test db at the end of the test. It will be destroyed when the test runs again. This is easier, when running multiple python instances simultaniously. | 2.428664 | 2 |
data/preprocess_persona-chat.py | adamlin120/lm | 0 | 6613343 | <gh_stars>0
import json
from pathlib import Path
SPLITS = ["train", "valid", "test"]
SILENCE = "__ SILENCE __"
def main():
dir = Path("./persona-chat/personachat_self_original.json")
output_path = Path(f"persona.processed.json")
output_debug_path = Path(f"persona.processed.debug.json")
raw_data = json.loads(dir.read_text())
datasets = {k: {} for k in SPLITS}
for split, dials in raw_data.items():
num_instance = len(dials)
for i, dial in enumerate(dials):
if split == "valid" and i > num_instance // 2:
split = "test"
dial_idx = f"{split}_{i}"
turns = dial["utterances"][-1]["history"]
turns.append(dial["utterances"][-1]["candidates"][-1])
if any(turn == SILENCE for turn in turns):
continue
datasets[split][dial_idx] = turns
assert all(split in datasets for split in SPLITS)
output_path.write_text(json.dumps(datasets, indent=2))
debug_dataset = {
split: {dial_idx: turns for dial_idx, turns in list(dataset.items())[:10]}
for split, dataset in datasets.items()
}
output_debug_path.write_text(json.dumps(debug_dataset, indent=2))
if __name__ == "__main__":
main()
| import json
from pathlib import Path
SPLITS = ["train", "valid", "test"]
SILENCE = "__ SILENCE __"
def main():
dir = Path("./persona-chat/personachat_self_original.json")
output_path = Path(f"persona.processed.json")
output_debug_path = Path(f"persona.processed.debug.json")
raw_data = json.loads(dir.read_text())
datasets = {k: {} for k in SPLITS}
for split, dials in raw_data.items():
num_instance = len(dials)
for i, dial in enumerate(dials):
if split == "valid" and i > num_instance // 2:
split = "test"
dial_idx = f"{split}_{i}"
turns = dial["utterances"][-1]["history"]
turns.append(dial["utterances"][-1]["candidates"][-1])
if any(turn == SILENCE for turn in turns):
continue
datasets[split][dial_idx] = turns
assert all(split in datasets for split in SPLITS)
output_path.write_text(json.dumps(datasets, indent=2))
debug_dataset = {
split: {dial_idx: turns for dial_idx, turns in list(dataset.items())[:10]}
for split, dataset in datasets.items()
}
output_debug_path.write_text(json.dumps(debug_dataset, indent=2))
if __name__ == "__main__":
main() | none | 1 | 2.749389 | 3 | |
authors/apps/articles/tests/test_reviews.py | andela/ah-the-answer-backend | 0 | 6613344 | from .commons import *
class TestReviewModel(BaseSetup):
def setUp(self):
user = User.objects.create(username="johndoe")
article = Article.objects.create(
title="Test title",
body="This is a very awesome article on testing tests",
description="Written by testing tester",
tags=[],
author=user
)
self.review_body = "<NAME>"
self.rating_value = int(5)
self.review = ReviewsModel(
article=article,
review_body=self.review_body,
rating_value=self.rating_value,
reviewed_by=user,
)
def test_model_can_create_review(self):
"""Test whether model can create a record"""
initial = ReviewsModel.objects.count()
self.review.save()
updated = ReviewsModel.objects.count()
self.assertNotEqual(initial, updated)
class ReviewTestCase(BaseSetup):
def test_user_can_create_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
def test_user_can_get_reviews(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client2.get(
reverse('articles:review', kwargs={
"slug": Article.objects.get().slug
}),
format="json"
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertContains(response, "I really liked the article")
def test_user_can_edit_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client2.put(reverse('articles:alter-review', kwargs={
"slug": Article.objects.get().slug, 'username': "Pete"
}), data={
"review": {
"review_body": "I did not liked the article",
"rating_value": 3
}
},
format="json")
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_user_can_delete_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client2.delete(reverse('articles:alter-review', kwargs={
"slug": Article.objects.get().slug, 'username': "Pete"
}))
self.assertEqual(response.status_code, status.HTTP_200_OK)
response_delete = self.client2.get(
reverse('articles:review', kwargs={
"slug": Article.objects.get().slug
}),
format="json"
)
self.assertEqual(response_delete.status_code,
status.HTTP_404_NOT_FOUND)
self.assertRaises(Exception)
def test_unauthenticated_user_cant_create_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.client2.logout()
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_review_creation_with_wrong_values(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client2.post(
reverse('articles:review', kwargs={
"slug": Article.objects.get().slug
}),
data={
"review": {
"review_body": "I really liked the article",
"rating_value": True
}
},
format="json"
)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def test_user_cannot_review_own_article(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client.post(
reverse('articles:review', kwargs={
"slug": Article.objects.get().slug
}),
data={
"review": {
"review_body": "I really liked the article",
"rating_value": 5
}
},
format="json"
)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def test_user_cant_edit_non_existent_review(self):
self.test_user_can_delete_review()
response = self.client2.delete(reverse('articles:alter-review', kwargs={
"slug": Article.objects.get().slug, 'username': User.objects.filter(username="Pete").first()
}))
self.assertEqual(response.status_code,
status.HTTP_404_NOT_FOUND)
self.assertRaises(Exception)
def test_user_cant_review_non_existent_article(self):
response = self.client2.post(
reverse('articles:review', kwargs={
"slug": "fake_article"
}),
data={
"review": {
"review_body": "I really liked the article",
"rating_value": 5
}
},
format="json"
)
self.assertEqual(response.status_code,
status.HTTP_404_NOT_FOUND)
self.assertRaises(Exception)
def test_malformed_url(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client.put(
reverse('articles:alter-review', kwargs={
"slug": Article.objects.get().slug, "username": "None"
}),
data={
"review": {
"review_body": "I really liked the article",
"rating_value": 5
}
},
format="json"
)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
def test_reviewer_cannot_double_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response_duplicate = self.post_review()
self.assertEqual(response_duplicate.status_code, status.HTTP_403_FORBIDDEN)
def test_user_cant_get_inexistent_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client2.get(
reverse('articles:review', kwargs={
"slug": Article.objects.get().slug
}),
format="json"
)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
def test_user_cant_edit_anothers_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response2 = self.post_review2()
self.assertEqual(response2.status_code, status.HTTP_201_CREATED)
edit_response = self.client2.put(reverse('articles:alter-review', kwargs={
"slug": Article.objects.get().slug, 'username': "Jane"
}), data={
"review": {
"review_body": "I did not liked the article",
"rating_value": 3
}
},
format="json")
self.assertEqual(edit_response.status_code, status.HTTP_403_FORBIDDEN)
| from .commons import *
class TestReviewModel(BaseSetup):
def setUp(self):
user = User.objects.create(username="johndoe")
article = Article.objects.create(
title="Test title",
body="This is a very awesome article on testing tests",
description="Written by testing tester",
tags=[],
author=user
)
self.review_body = "<NAME>"
self.rating_value = int(5)
self.review = ReviewsModel(
article=article,
review_body=self.review_body,
rating_value=self.rating_value,
reviewed_by=user,
)
def test_model_can_create_review(self):
"""Test whether model can create a record"""
initial = ReviewsModel.objects.count()
self.review.save()
updated = ReviewsModel.objects.count()
self.assertNotEqual(initial, updated)
class ReviewTestCase(BaseSetup):
def test_user_can_create_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
def test_user_can_get_reviews(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client2.get(
reverse('articles:review', kwargs={
"slug": Article.objects.get().slug
}),
format="json"
)
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertContains(response, "I really liked the article")
def test_user_can_edit_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client2.put(reverse('articles:alter-review', kwargs={
"slug": Article.objects.get().slug, 'username': "Pete"
}), data={
"review": {
"review_body": "I did not liked the article",
"rating_value": 3
}
},
format="json")
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_user_can_delete_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client2.delete(reverse('articles:alter-review', kwargs={
"slug": Article.objects.get().slug, 'username': "Pete"
}))
self.assertEqual(response.status_code, status.HTTP_200_OK)
response_delete = self.client2.get(
reverse('articles:review', kwargs={
"slug": Article.objects.get().slug
}),
format="json"
)
self.assertEqual(response_delete.status_code,
status.HTTP_404_NOT_FOUND)
self.assertRaises(Exception)
def test_unauthenticated_user_cant_create_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.client2.logout()
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)
def test_review_creation_with_wrong_values(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client2.post(
reverse('articles:review', kwargs={
"slug": Article.objects.get().slug
}),
data={
"review": {
"review_body": "I really liked the article",
"rating_value": True
}
},
format="json"
)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def test_user_cannot_review_own_article(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client.post(
reverse('articles:review', kwargs={
"slug": Article.objects.get().slug
}),
data={
"review": {
"review_body": "I really liked the article",
"rating_value": 5
}
},
format="json"
)
self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
def test_user_cant_edit_non_existent_review(self):
self.test_user_can_delete_review()
response = self.client2.delete(reverse('articles:alter-review', kwargs={
"slug": Article.objects.get().slug, 'username': User.objects.filter(username="Pete").first()
}))
self.assertEqual(response.status_code,
status.HTTP_404_NOT_FOUND)
self.assertRaises(Exception)
def test_user_cant_review_non_existent_article(self):
response = self.client2.post(
reverse('articles:review', kwargs={
"slug": "fake_article"
}),
data={
"review": {
"review_body": "I really liked the article",
"rating_value": 5
}
},
format="json"
)
self.assertEqual(response.status_code,
status.HTTP_404_NOT_FOUND)
self.assertRaises(Exception)
def test_malformed_url(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client.put(
reverse('articles:alter-review', kwargs={
"slug": Article.objects.get().slug, "username": "None"
}),
data={
"review": {
"review_body": "I really liked the article",
"rating_value": 5
}
},
format="json"
)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
def test_reviewer_cannot_double_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response_duplicate = self.post_review()
self.assertEqual(response_duplicate.status_code, status.HTTP_403_FORBIDDEN)
def test_user_cant_get_inexistent_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.client2.get(
reverse('articles:review', kwargs={
"slug": Article.objects.get().slug
}),
format="json"
)
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
def test_user_cant_edit_anothers_review(self):
response = self.post_article()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response = self.post_review()
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
response2 = self.post_review2()
self.assertEqual(response2.status_code, status.HTTP_201_CREATED)
edit_response = self.client2.put(reverse('articles:alter-review', kwargs={
"slug": Article.objects.get().slug, 'username': "Jane"
}), data={
"review": {
"review_body": "I did not liked the article",
"rating_value": 3
}
},
format="json")
self.assertEqual(edit_response.status_code, status.HTTP_403_FORBIDDEN)
| en | 0.457368 | Test whether model can create a record | 2.415828 | 2 |
actiereg/_basic/admin.py | albertvisser/actiereg | 0 | 6613345 | <gh_stars>0
"""register models to the admin site
"""
from django.contrib import admin
import actiereg._basic.models as my
admin.site.register(my.Status)
admin.site.register(my.Soort)
admin.site.register(my.Page)
admin.site.register(my.Actie)
admin.site.register(my.Event)
admin.site.register(my.SortOrder)
admin.site.register(my.Selection)
admin.site.register(my.Worker)
| """register models to the admin site
"""
from django.contrib import admin
import actiereg._basic.models as my
admin.site.register(my.Status)
admin.site.register(my.Soort)
admin.site.register(my.Page)
admin.site.register(my.Actie)
admin.site.register(my.Event)
admin.site.register(my.SortOrder)
admin.site.register(my.Selection)
admin.site.register(my.Worker) | en | 0.862266 | register models to the admin site | 1.804322 | 2 |
test/test_cli.py | bagheera-lang/bagheera-python | 0 | 6613346 | from bagheera import cli
assert cli.main() is None
| from bagheera import cli
assert cli.main() is None
| none | 1 | 1.038945 | 1 | |
lib/rucio/db/sqla/migrate_repo/versions/3ad36e2268b0_create_collection_replicas_updates_table.py | balrampariyarath/rucio | 1 | 6613347 | <reponame>balrampariyarath/rucio
# Copyright European Organization for Nuclear Research (CERN)
#
# 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
#
# Authors:
# - <NAME>, <<EMAIL>>, 2015
# - <NAME>, <<EMAIL>>, 2017
"""Create collection_replicas_updates table
Revision ID: 3ad36e2268b0
Revises: ae2a56fcc89
Create Date: 2015-03-16 15:32:59.620185
"""
from alembic import context
from alembic.op import (create_table, create_primary_key, add_column,
create_check_constraint, create_index,
drop_constraint, drop_column, drop_table, drop_index)
import sqlalchemy as sa
from rucio.db.sqla.constants import DIDType
from rucio.db.sqla.types import GUID
# revision identifiers, used by Alembic.
revision = '3ad36e2268b0'
down_revision = '42db2617c364'
def upgrade():
'''
upgrade method
'''
if context.get_context().dialect.name != 'sqlite':
add_column('collection_replicas', sa.Column('available_replicas_cnt', sa.BigInteger()))
add_column('collection_replicas', sa.Column('available_bytes', sa.BigInteger()))
create_table('updated_col_rep',
sa.Column('id', GUID()),
sa.Column('scope', sa.String(25)),
sa.Column('name', sa.String(255)),
sa.Column('did_type', DIDType.db_type(name='UPDATED_COL_REP_TYPE_CHK')),
sa.Column('rse_id', GUID()),
sa.Column('updated_at', sa.DateTime),
sa.Column('created_at', sa.DateTime))
if context.get_context().dialect.name != 'sqlite':
create_primary_key('UPDATED_COL_REP_PK', 'updated_col_rep', ['id'])
create_check_constraint('UPDATED_COL_REP_SCOPE_NN', 'updated_col_rep', 'scope IS NOT NULL')
create_check_constraint('UPDATED_COL_REP_NAME_NN', 'updated_col_rep', 'name IS NOT NULL')
create_index('UPDATED_COL_REP_SNR_IDX', 'updated_col_rep', ['scope', 'name', 'rse_id'])
def downgrade():
'''
downgrade method
'''
if context.get_context().dialect.name != 'sqlite':
drop_column('collection_replicas', 'available_replicas_cnt')
drop_column('collection_replicas', 'available_bytes')
if context.get_context().dialect.name == 'postgresql':
drop_constraint('UPDATED_COL_REP_PK', 'updated_col_rep', type_='primary')
drop_constraint('UPDATED_COL_REP_SCOPE_NN', 'updated_col_rep')
drop_constraint('UPDATED_COL_REP_NAME_NN', 'updated_col_rep')
drop_constraint('UPDATED_COL_REP_TYPE_CHK', 'updated_col_rep')
drop_index('UPDATED_COL_REP_SNR_IDX', 'updated_col_rep')
drop_table('updated_col_rep')
| # Copyright European Organization for Nuclear Research (CERN)
#
# 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
#
# Authors:
# - <NAME>, <<EMAIL>>, 2015
# - <NAME>, <<EMAIL>>, 2017
"""Create collection_replicas_updates table
Revision ID: 3ad36e2268b0
Revises: ae2a56fcc89
Create Date: 2015-03-16 15:32:59.620185
"""
from alembic import context
from alembic.op import (create_table, create_primary_key, add_column,
create_check_constraint, create_index,
drop_constraint, drop_column, drop_table, drop_index)
import sqlalchemy as sa
from rucio.db.sqla.constants import DIDType
from rucio.db.sqla.types import GUID
# revision identifiers, used by Alembic.
revision = '3ad36e2268b0'
down_revision = '42db2617c364'
def upgrade():
'''
upgrade method
'''
if context.get_context().dialect.name != 'sqlite':
add_column('collection_replicas', sa.Column('available_replicas_cnt', sa.BigInteger()))
add_column('collection_replicas', sa.Column('available_bytes', sa.BigInteger()))
create_table('updated_col_rep',
sa.Column('id', GUID()),
sa.Column('scope', sa.String(25)),
sa.Column('name', sa.String(255)),
sa.Column('did_type', DIDType.db_type(name='UPDATED_COL_REP_TYPE_CHK')),
sa.Column('rse_id', GUID()),
sa.Column('updated_at', sa.DateTime),
sa.Column('created_at', sa.DateTime))
if context.get_context().dialect.name != 'sqlite':
create_primary_key('UPDATED_COL_REP_PK', 'updated_col_rep', ['id'])
create_check_constraint('UPDATED_COL_REP_SCOPE_NN', 'updated_col_rep', 'scope IS NOT NULL')
create_check_constraint('UPDATED_COL_REP_NAME_NN', 'updated_col_rep', 'name IS NOT NULL')
create_index('UPDATED_COL_REP_SNR_IDX', 'updated_col_rep', ['scope', 'name', 'rse_id'])
def downgrade():
'''
downgrade method
'''
if context.get_context().dialect.name != 'sqlite':
drop_column('collection_replicas', 'available_replicas_cnt')
drop_column('collection_replicas', 'available_bytes')
if context.get_context().dialect.name == 'postgresql':
drop_constraint('UPDATED_COL_REP_PK', 'updated_col_rep', type_='primary')
drop_constraint('UPDATED_COL_REP_SCOPE_NN', 'updated_col_rep')
drop_constraint('UPDATED_COL_REP_NAME_NN', 'updated_col_rep')
drop_constraint('UPDATED_COL_REP_TYPE_CHK', 'updated_col_rep')
drop_index('UPDATED_COL_REP_SNR_IDX', 'updated_col_rep')
drop_table('updated_col_rep') | en | 0.653683 | # Copyright European Organization for Nuclear Research (CERN) # # 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 # # Authors: # - <NAME>, <<EMAIL>>, 2015 # - <NAME>, <<EMAIL>>, 2017 Create collection_replicas_updates table Revision ID: 3ad36e2268b0 Revises: ae2a56fcc89 Create Date: 2015-03-16 15:32:59.620185 # revision identifiers, used by Alembic. upgrade method downgrade method | 1.729181 | 2 |
slixmpp/plugins/xep_0333/stanza.py | marconfus/slixmpp | 0 | 6613348 | """
slixmpp: The Slick XMPP Library
Copyright (C) 2016 <NAME>
This file is part of slixmpp.
See the file LICENSE for copying permission.
"""
from slixmpp.xmlstream import ElementBase
class Markable(ElementBase):
name = 'markable'
plugin_attrib = 'markable'
namespace = 'urn:xmpp:chat-markers:0'
class Received(ElementBase):
name = 'received'
plugin_attrib = 'received'
namespace = 'urn:xmpp:chat-markers:0'
interfaces = {'id'}
class Displayed(ElementBase):
name = 'displayed'
plugin_attrib = 'displayed'
namespace = 'urn:xmpp:chat-markers:0'
interfaces = {'id'}
class Acknowledged(ElementBase):
name = 'acknowledged'
plugin_attrib = 'acknowledged'
namespace = 'urn:xmpp:chat-markers:0'
interfaces = {'id'}
| """
slixmpp: The Slick XMPP Library
Copyright (C) 2016 <NAME>
This file is part of slixmpp.
See the file LICENSE for copying permission.
"""
from slixmpp.xmlstream import ElementBase
class Markable(ElementBase):
name = 'markable'
plugin_attrib = 'markable'
namespace = 'urn:xmpp:chat-markers:0'
class Received(ElementBase):
name = 'received'
plugin_attrib = 'received'
namespace = 'urn:xmpp:chat-markers:0'
interfaces = {'id'}
class Displayed(ElementBase):
name = 'displayed'
plugin_attrib = 'displayed'
namespace = 'urn:xmpp:chat-markers:0'
interfaces = {'id'}
class Acknowledged(ElementBase):
name = 'acknowledged'
plugin_attrib = 'acknowledged'
namespace = 'urn:xmpp:chat-markers:0'
interfaces = {'id'}
| en | 0.726178 | slixmpp: The Slick XMPP Library Copyright (C) 2016 <NAME> This file is part of slixmpp. See the file LICENSE for copying permission. | 1.537792 | 2 |
ProjectFolder/Code/main.py | sebastianwindeck/DeepLearningProject | 1 | 6613349 | import matplotlib
import datetime
import inspect
import os
import numpy as np
from evaluate import final_score
from extractfeatures import prepareData
from model import Noiser, AMTNetwork
from visualize import visualize_input
matplotlib.use('Agg')
if __name__ == '__main__':
proj_root = os.path.join(os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))), '..')
# Define a parameter structure args
args = { # model parameters:
'model_name': 'baseline', 'init_lr': 1e-1,
'lr_decay': 'linear',
# parameters for audio
'bin_multiple': 3,
'residual': 'False',
'hop_length': 512,
'sr': 16000,
'spec_type': 'cqt',
'min_midi': 37, # 21 corresponds to A0 (lowest tone on a "normal" piano), 27.5Hz
'max_midi': 92, # 108 corresponds to C8 (highest tone on a "normal" piano), 4.2kHz
'window_size': 7, # choose higher value than 5
# training parameters:
'train_basemodel': True,
'epochs_on_clean': 1000,
'epochs_on_noisy': 50,
'noise_epochs': 10,
'min_difficulty_on_noisy': 0.19,
'max_difficulty_on_noisy': 0.60,
# noise parameters:
'noise_type': 'simplistic',
'noise_frames_per_epoch': 100,
'noise_initial_level': 0.03,
'noise_increase_factor': 2.5,
'noise_decrease_factor': 2,
'balance_classes': True,
# directories:
'proj_root': proj_root, # - root directory of maps (with substructure as given in maps):
'wav_dir': os.path.join(proj_root, 'Audiodaten'),
# - directory to store checkpoint files. All files are stored in this directory:
'checkpoint_root': os.path.join(proj_root, 'Checkpoints',
'train' + datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')),
'basemodel_root': os.path.join(proj_root, 'Basemodel'),
'maxFramesPerFile': -1, # set to -1 to ignore
'maxFrames': -1 # set to -1 to ignore
}
# derived parameters:
args['note_range'] = args['max_midi'] - args['min_midi'] + 1
args['feature_bins'] = args['note_range'] * args['bin_multiple']
args['input_shape'] = (args['window_size'], args['feature_bins'])
args['input_shape_channels'] = (args['window_size'], args['feature_bins'], 1)
args['n_bins'] = args['note_range'] * args['bin_multiple']
if os.path.exists(args['checkpoint_root']):
print("WARNING: Checkpoint root directory already exists!!")
else:
os.mkdir(args['checkpoint_root'])
print('Created checkpoint root.')
if os.path.exists(args['basemodel_root']):
print("WARNING: Basemodel root directory already exists!!")
else:
os.mkdir(args['basemodel_root'])
print('Created basemodel root.')
inputs, outputs, datapath = prepareData(args)
print("Inputs have shape: ", inputs.shape)
print("Outputs have shape: ", outputs.shape)
print("Total number of notes detected in input set ", np.sum(outputs))
print("Number of 1s in output: ", sum(sum(outputs == 1)))
print("Number of 0s in output: ", sum(sum(outputs == 0)))
print("Size of outputs: ", outputs.size)
print("=> 1s should be weighted ", sum(sum(outputs == 0)) / sum(sum(outputs == 1)))
input_level = np.mean(inputs)
print("Average sound level: ", input_level)
np.append(input_level, np.min(inputs))
np.append(input_level, np.max(inputs))
np.append(input_level, np.percentile(inputs, 25))
np.append(input_level, np.percentile(inputs, 50))
np.append(input_level, np.percentile(inputs, 75))
np.save(os.path.join(args['checkpoint_root'], "input_level"), input_level)
exit()
visualize_input(inputs, save_path=os.path.join(args['checkpoint_root'], 'input_heatmap.png'))
# initialize the amt model, and do an initial training
at = AMTNetwork(args)
baseModelPath = os.path.join(args['basemodel_root'], 'basemodel')
evaluatePath = os.path.join(args['checkpoint_root'], 'diagram')
if args['train_basemodel']:
# initial training, with clean data:
print("training initial basemodel")
at.compilation(outputs, save_path=os.path.join(args['checkpoint_root'], 'balance_weight.png'))
at.train(inputs, outputs, args=args, epochs=args['epochs_on_clean'], train_descr='initial')
at.save(model_path=baseModelPath)
else:
print("load existing basemodel")
# Load Basemodel:
bm = AMTNetwork(args)
bm.load(baseModelPath)
bm.compilation(outputs, save_path=os.path.join(args['checkpoint_root'], 'balance_weight.png'))
# Noise Model to Train:
at.load(baseModelPath)
at.compilation(outputs, save_path=os.path.join(args['checkpoint_root'], 'balance_weight.png'))
# initialize noiser:
noise_generator = Noiser(noise_type="gaussian", noise_size=args['input_shape'])
# Track f1 scores of the basemodel that is not further trained to noise
print("computing initial basemodel scores")
basemodel_score = bm.getscores(inputs, outputs)
print('scores of basemodel', basemodel_score)
# Save Noise levels
noise_levels = np.zeros(shape=1)
noise_level = args['noise_initial_level']
# Initialize base loss
idx = np.random.randint(0, inputs.shape[0], args['noise_frames_per_epoch'])
input_clean = inputs[idx]
y = outputs[idx]
res_old = at.evaluate_old(input_clean, y)
# loop over various noise epochs:
for noiseEpoch in range(args['noise_epochs']):
# a. Generate noise candidate (only) with current noise level [advTrain]
this_noise = noise_generator.generate(args['noise_frames_per_epoch'])
# indices of data samples to be noised.
idx = np.random.randint(0, inputs.shape[0], args['noise_frames_per_epoch'])
classi_change = 0.00
while True:
# Combine noise with clean data (noise and audio)
noisy_X = inputs[idx] + noise_level * this_noise
print("current noise level before test", noise_level)
noisy_Xold = inputs[idx] + noise_levels[noiseEpoch] * this_noise
print("current old noise level before test", noise_levels[noiseEpoch])
y = outputs[idx]
classi_change = at.evaluation(noisy_X, res_old, y)
print("classifier changed by", classi_change)
if noise_level > 10e8 or noise_level < 10e-8:
print("Noise Level is: ", noise_level, " in epoch ", noiseEpoch)
print("BREAK because of size threshold")
break
elif classi_change > args['max_difficulty_on_noisy']:
# “too hard for AMT” -> decrease noise level
print("too hard")
noise_level /= args['noise_decrease_factor']
print('Current noise level' + str(float(noise_level)) + ' in epoch ' + str(noiseEpoch))
continue # Jump to the next cycle
elif classi_change < args['min_difficulty_on_noisy']:
# “too easy for AMT” -> increase noise level
noise_level *= args['noise_increase_factor']
print("too easy")
print('Current noise level' + str(float(noise_level)) + ' in epoch ' + str(noiseEpoch))
continue # Jump to the next cycle
else:
print("Noise Level is: ", noise_level, " in epoch ", noiseEpoch)
print("Noise Level accepted for training")
# if we reach this point, the classi_perf is in the defined interval
# => Exit the while loop and train the amt with the new noisy data
break
# appending current noise level before training to numpy array "noise_levels"
noise_levels = np.append(noise_levels, noise_level)
# Train with noisy samples (for a given number of epochs, with intermed. Result saved)
this_noise = noise_generator.generate(inputs.shape[0])
noisy_inputs = inputs + np.random.uniform(0, noise_level, 1) * this_noise
print("start training")
at.train(noisy_inputs, outputs, args=args, epochs=args['epochs_on_noisy'],
train_descr='noisy_iter_' + str(noiseEpoch))
# compute loss for next loop evaluation
res_old = at.evaluate_old(noisy_X, y)
bm_pred = bm.getscores(noisy_inputs, outputs)
print("score of basemodel on noisy data", bm_pred)
basemodel_score = np.append(basemodel_score, bm_pred)
np.save(os.path.join(args['checkpoint_root'], "noise_levels"), noise_levels)
np.save(os.path.join(args['checkpoint_root'], "bm_score"), basemodel_score)
if noiseEpoch != 0 and ((noiseEpoch & (noiseEpoch - 1)) == 0):
y_pred = at.transcribe(noisy_inputs)
print(np.max(y_pred, axis=1))
y_pred = np.around(y_pred, decimals=0)
print(y_pred.shape)
y_true = outputs
print(y_true.shape)
final_score(y_pred=y_pred, y_true=y_true, description=str(noiseEpoch))
# Save np array of noise levels
np.save(os.path.join(args['checkpoint_root'], "noise_levels"), noise_levels)
print("all noise levels saved")
np.save(os.path.join(args['checkpoint_root'], "bm_score"), basemodel_score)
print("all basemodel scores on noise levels saved")
# Final evaluation:
final_score(y_pred=y_pred, y_true=y_true, description='final')
# pitch_confusion(y_pred=y_pred, y_true=y_true, save_path=evaluatePath, description='final')
print("DONE.")
| import matplotlib
import datetime
import inspect
import os
import numpy as np
from evaluate import final_score
from extractfeatures import prepareData
from model import Noiser, AMTNetwork
from visualize import visualize_input
matplotlib.use('Agg')
if __name__ == '__main__':
proj_root = os.path.join(os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))), '..')
# Define a parameter structure args
args = { # model parameters:
'model_name': 'baseline', 'init_lr': 1e-1,
'lr_decay': 'linear',
# parameters for audio
'bin_multiple': 3,
'residual': 'False',
'hop_length': 512,
'sr': 16000,
'spec_type': 'cqt',
'min_midi': 37, # 21 corresponds to A0 (lowest tone on a "normal" piano), 27.5Hz
'max_midi': 92, # 108 corresponds to C8 (highest tone on a "normal" piano), 4.2kHz
'window_size': 7, # choose higher value than 5
# training parameters:
'train_basemodel': True,
'epochs_on_clean': 1000,
'epochs_on_noisy': 50,
'noise_epochs': 10,
'min_difficulty_on_noisy': 0.19,
'max_difficulty_on_noisy': 0.60,
# noise parameters:
'noise_type': 'simplistic',
'noise_frames_per_epoch': 100,
'noise_initial_level': 0.03,
'noise_increase_factor': 2.5,
'noise_decrease_factor': 2,
'balance_classes': True,
# directories:
'proj_root': proj_root, # - root directory of maps (with substructure as given in maps):
'wav_dir': os.path.join(proj_root, 'Audiodaten'),
# - directory to store checkpoint files. All files are stored in this directory:
'checkpoint_root': os.path.join(proj_root, 'Checkpoints',
'train' + datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')),
'basemodel_root': os.path.join(proj_root, 'Basemodel'),
'maxFramesPerFile': -1, # set to -1 to ignore
'maxFrames': -1 # set to -1 to ignore
}
# derived parameters:
args['note_range'] = args['max_midi'] - args['min_midi'] + 1
args['feature_bins'] = args['note_range'] * args['bin_multiple']
args['input_shape'] = (args['window_size'], args['feature_bins'])
args['input_shape_channels'] = (args['window_size'], args['feature_bins'], 1)
args['n_bins'] = args['note_range'] * args['bin_multiple']
if os.path.exists(args['checkpoint_root']):
print("WARNING: Checkpoint root directory already exists!!")
else:
os.mkdir(args['checkpoint_root'])
print('Created checkpoint root.')
if os.path.exists(args['basemodel_root']):
print("WARNING: Basemodel root directory already exists!!")
else:
os.mkdir(args['basemodel_root'])
print('Created basemodel root.')
inputs, outputs, datapath = prepareData(args)
print("Inputs have shape: ", inputs.shape)
print("Outputs have shape: ", outputs.shape)
print("Total number of notes detected in input set ", np.sum(outputs))
print("Number of 1s in output: ", sum(sum(outputs == 1)))
print("Number of 0s in output: ", sum(sum(outputs == 0)))
print("Size of outputs: ", outputs.size)
print("=> 1s should be weighted ", sum(sum(outputs == 0)) / sum(sum(outputs == 1)))
input_level = np.mean(inputs)
print("Average sound level: ", input_level)
np.append(input_level, np.min(inputs))
np.append(input_level, np.max(inputs))
np.append(input_level, np.percentile(inputs, 25))
np.append(input_level, np.percentile(inputs, 50))
np.append(input_level, np.percentile(inputs, 75))
np.save(os.path.join(args['checkpoint_root'], "input_level"), input_level)
exit()
visualize_input(inputs, save_path=os.path.join(args['checkpoint_root'], 'input_heatmap.png'))
# initialize the amt model, and do an initial training
at = AMTNetwork(args)
baseModelPath = os.path.join(args['basemodel_root'], 'basemodel')
evaluatePath = os.path.join(args['checkpoint_root'], 'diagram')
if args['train_basemodel']:
# initial training, with clean data:
print("training initial basemodel")
at.compilation(outputs, save_path=os.path.join(args['checkpoint_root'], 'balance_weight.png'))
at.train(inputs, outputs, args=args, epochs=args['epochs_on_clean'], train_descr='initial')
at.save(model_path=baseModelPath)
else:
print("load existing basemodel")
# Load Basemodel:
bm = AMTNetwork(args)
bm.load(baseModelPath)
bm.compilation(outputs, save_path=os.path.join(args['checkpoint_root'], 'balance_weight.png'))
# Noise Model to Train:
at.load(baseModelPath)
at.compilation(outputs, save_path=os.path.join(args['checkpoint_root'], 'balance_weight.png'))
# initialize noiser:
noise_generator = Noiser(noise_type="gaussian", noise_size=args['input_shape'])
# Track f1 scores of the basemodel that is not further trained to noise
print("computing initial basemodel scores")
basemodel_score = bm.getscores(inputs, outputs)
print('scores of basemodel', basemodel_score)
# Save Noise levels
noise_levels = np.zeros(shape=1)
noise_level = args['noise_initial_level']
# Initialize base loss
idx = np.random.randint(0, inputs.shape[0], args['noise_frames_per_epoch'])
input_clean = inputs[idx]
y = outputs[idx]
res_old = at.evaluate_old(input_clean, y)
# loop over various noise epochs:
for noiseEpoch in range(args['noise_epochs']):
# a. Generate noise candidate (only) with current noise level [advTrain]
this_noise = noise_generator.generate(args['noise_frames_per_epoch'])
# indices of data samples to be noised.
idx = np.random.randint(0, inputs.shape[0], args['noise_frames_per_epoch'])
classi_change = 0.00
while True:
# Combine noise with clean data (noise and audio)
noisy_X = inputs[idx] + noise_level * this_noise
print("current noise level before test", noise_level)
noisy_Xold = inputs[idx] + noise_levels[noiseEpoch] * this_noise
print("current old noise level before test", noise_levels[noiseEpoch])
y = outputs[idx]
classi_change = at.evaluation(noisy_X, res_old, y)
print("classifier changed by", classi_change)
if noise_level > 10e8 or noise_level < 10e-8:
print("Noise Level is: ", noise_level, " in epoch ", noiseEpoch)
print("BREAK because of size threshold")
break
elif classi_change > args['max_difficulty_on_noisy']:
# “too hard for AMT” -> decrease noise level
print("too hard")
noise_level /= args['noise_decrease_factor']
print('Current noise level' + str(float(noise_level)) + ' in epoch ' + str(noiseEpoch))
continue # Jump to the next cycle
elif classi_change < args['min_difficulty_on_noisy']:
# “too easy for AMT” -> increase noise level
noise_level *= args['noise_increase_factor']
print("too easy")
print('Current noise level' + str(float(noise_level)) + ' in epoch ' + str(noiseEpoch))
continue # Jump to the next cycle
else:
print("Noise Level is: ", noise_level, " in epoch ", noiseEpoch)
print("Noise Level accepted for training")
# if we reach this point, the classi_perf is in the defined interval
# => Exit the while loop and train the amt with the new noisy data
break
# appending current noise level before training to numpy array "noise_levels"
noise_levels = np.append(noise_levels, noise_level)
# Train with noisy samples (for a given number of epochs, with intermed. Result saved)
this_noise = noise_generator.generate(inputs.shape[0])
noisy_inputs = inputs + np.random.uniform(0, noise_level, 1) * this_noise
print("start training")
at.train(noisy_inputs, outputs, args=args, epochs=args['epochs_on_noisy'],
train_descr='noisy_iter_' + str(noiseEpoch))
# compute loss for next loop evaluation
res_old = at.evaluate_old(noisy_X, y)
bm_pred = bm.getscores(noisy_inputs, outputs)
print("score of basemodel on noisy data", bm_pred)
basemodel_score = np.append(basemodel_score, bm_pred)
np.save(os.path.join(args['checkpoint_root'], "noise_levels"), noise_levels)
np.save(os.path.join(args['checkpoint_root'], "bm_score"), basemodel_score)
if noiseEpoch != 0 and ((noiseEpoch & (noiseEpoch - 1)) == 0):
y_pred = at.transcribe(noisy_inputs)
print(np.max(y_pred, axis=1))
y_pred = np.around(y_pred, decimals=0)
print(y_pred.shape)
y_true = outputs
print(y_true.shape)
final_score(y_pred=y_pred, y_true=y_true, description=str(noiseEpoch))
# Save np array of noise levels
np.save(os.path.join(args['checkpoint_root'], "noise_levels"), noise_levels)
print("all noise levels saved")
np.save(os.path.join(args['checkpoint_root'], "bm_score"), basemodel_score)
print("all basemodel scores on noise levels saved")
# Final evaluation:
final_score(y_pred=y_pred, y_true=y_true, description='final')
# pitch_confusion(y_pred=y_pred, y_true=y_true, save_path=evaluatePath, description='final')
print("DONE.")
| en | 0.774701 | # Define a parameter structure args # model parameters: # parameters for audio # 21 corresponds to A0 (lowest tone on a "normal" piano), 27.5Hz # 108 corresponds to C8 (highest tone on a "normal" piano), 4.2kHz # choose higher value than 5 # training parameters: # noise parameters: # directories: # - root directory of maps (with substructure as given in maps): # - directory to store checkpoint files. All files are stored in this directory: # set to -1 to ignore # set to -1 to ignore # derived parameters: # initialize the amt model, and do an initial training # initial training, with clean data: # Load Basemodel: # Noise Model to Train: # initialize noiser: # Track f1 scores of the basemodel that is not further trained to noise # Save Noise levels # Initialize base loss # loop over various noise epochs: # a. Generate noise candidate (only) with current noise level [advTrain] # indices of data samples to be noised. # Combine noise with clean data (noise and audio) # “too hard for AMT” -> decrease noise level # Jump to the next cycle # “too easy for AMT” -> increase noise level # Jump to the next cycle # if we reach this point, the classi_perf is in the defined interval # => Exit the while loop and train the amt with the new noisy data # appending current noise level before training to numpy array "noise_levels" # Train with noisy samples (for a given number of epochs, with intermed. Result saved) # compute loss for next loop evaluation # Save np array of noise levels # Final evaluation: # pitch_confusion(y_pred=y_pred, y_true=y_true, save_path=evaluatePath, description='final') | 2.102736 | 2 |
_autoclimate/occupancy.py | rr326/ad_app_autoclimate | 0 | 6613350 | <gh_stars>0
import datetime as dt
from typing import List
from _autoclimate.utils import climate_name
from adplus import Hass
from dateutil import tz
"""
Create new sensors
Reason: That way you do auto off if unoccupied since AND last_manual_change > X hours
* unoccupied_since:
* Last unoccupied
* None if no data
* datetime.max if currently occupied
* last_manual_change
* Timestamps as above
# TODO
* Offline - handle
"""
class Occupancy:
UNOCCUPIED_SINCE_OCCUPIED_VALUE = dt.datetime(dt.MAXYEAR, 12, 29, tzinfo=tz.tzutc())
def __init__(
self,
hass: Hass,
config: dict,
appname: str,
climates: list,
test_mode: bool,
):
self.hass = hass
self.aconfig = config
self.appname = appname
self.test_mode = test_mode
self.climates = climates
self.hass.run_in(self.create_occupancy_sensors, 0)
self.hass.run_in(self.init_occupancy_listeners, 0.1)
def unoccupied_sensor_name(self, climate):
return self.unoccupied_sensor_name_static(self.appname, climate)
@staticmethod
def unoccupied_sensor_name_static(appname, climate):
return f"sensor.{appname}_{climate_name(climate)}_unoccupied_since"
def create_occupancy_sensors(self, kwargs):
# Unoccupied Since Sensors
for climate in self.climates:
unoccupied_sensor_name = self.unoccupied_sensor_name(climate)
last_on_date = self.history_last_on_date(climate=climate)
self.hass.update_state(
unoccupied_sensor_name,
state=last_on_date,
attributes={
"freindly_name": f"{climate_name(climate)} - unoccupied since",
"device_class": "timestamp",
},
)
self.hass.log(
f"Created sensor: {unoccupied_sensor_name}. Initial state: {last_on_date}"
)
def init_occupancy_listeners(self, kwargs):
"""
This will create a different occupancy sensor for each climate,
so if multiple climates have the same oc_sensor, you'll get multiple
listeners.
"""
for climate in self.climates:
oc_sensor = self.get_sensor(climate=climate)
self.hass.log(f"listen_state: {oc_sensor}")
self.hass.listen_state(
self.update_occupancy_sensor,
entity=oc_sensor,
attribute="all",
climate=climate,
)
def update_occupancy_sensor(self, entity, attribute, old, new, kwargs):
climate = kwargs["climate"]
# self.hass.log(f'update_occupancy_sensor: {entity} -- {climate} -- {new} -- {attribute}')
last_on_date = self.oc_sensor_val_to_last_on_date(
new["state"], new["last_updated"]
)
unoccupied_sensor_name = self.unoccupied_sensor_name(climate)
self.hass.update_state(
unoccupied_sensor_name,
state=last_on_date,
)
self.hass.log(
f"update_occupancy_sensor - {unoccupied_sensor_name} - state: {last_on_date}"
)
def get_sensor(self, climate=None, sensor=None):
if climate and sensor:
raise RuntimeError(
f"Programming error - history_last_on_date: give climate OR sensor"
)
elif climate is None and sensor is None:
raise RuntimeError(
f"Programming error - need a climate or sensor. Got None."
)
elif sensor:
return sensor
else:
try:
oc_sensor = self.aconfig[climate]["occupancy_sensor"]
except KeyError:
raise RuntimeError(f"Unable to get occupancy_sensor for {climate}")
return oc_sensor
def oc_sensor_val_to_last_on_date(self, state, last_on_date):
if state == "on":
return self.UNOCCUPIED_SINCE_OCCUPIED_VALUE
elif state in ["off", "unavailable"]:
return last_on_date
else:
self.hass.log(f"Unexpected last_on_date state: {state}")
# Error or offline
return None
def history_last_on_date(self, climate=None, sensor=None):
state, duration_off, last_on_date = self.get_unoccupied_time_for(
climate, sensor
)
return self.oc_sensor_val_to_last_on_date(state, last_on_date)
def get_unoccupied_time_for(self, climate=None, sensor=None):
oc_sensor = self.get_sensor(climate=climate, sensor=sensor)
state, duration_off, last_on_date = self._history_occupancy_info(oc_sensor)
return state, duration_off, last_on_date
@staticmethod
def duration_off_static(hass, dateval):
"""
0 - currently on
> 0 - number hours off
< 0 / None - Error
"""
if isinstance(dateval, str):
dateval = dt.datetime.fromisoformat(dateval)
if dateval.tzinfo is None:
dateval.replace(tzinfo=tz.tzlocal())
now = hass.get_now()
if dateval > now:
return 0
duration_off_hours = round((now - dateval).total_seconds() / (60 * 60), 2)
return duration_off_hours
def _history_occupancy_info(self, sensor_id: str, days: int = 10):
"""
returns: state (on/off/unavailable), duration_off (hours float / None), last_on_date (datetime, None)
state = state of occupancy sensor
All based on an occupancy sensor's history data.
{
"entity_id": "binary_sensor.seattle_occupancy",
"state": "off", # on/off/unavailable
"attributes": {
"friendly_name": "<NAME>",
"device_class": "occupancy"
},
"last_changed": "2020-10-28T13:10:47.384057+00:00",
"last_updated": "2020-10-28T13:10:47.384057+00:00"
}
Note - it looks like the occupancy sensor properly handles offline by returning
an "unavailble" status. (Unlike temp sensors, which show the last value.)
"""
data: List = self.hass.get_history(entity_id=sensor_id, days=days) # type: ignore
if not data or len(data) == 0:
self.hass.warn(
f"get_history returned no data for entity: {sensor_id}. Exiting"
)
return "error", None, None
edata = data[0]
# the get_history() fn doesn't say it guarantees sort (though it appears to be)
edata = list(reversed(sorted(edata, key=lambda rec: rec["last_updated"])))
current_state = edata[0]["state"]
if current_state == "on":
return "on", None, None
last_on_date = None
now: dt.datetime = self.hass.get_now() # type: ignore
for rec in edata:
if rec.get("state") == "on":
last_on_date = dt.datetime.fromisoformat(rec["last_updated"])
duration_off_hours = round(
(now - last_on_date).total_seconds() / (60 * 60), 2
)
return current_state, duration_off_hours, last_on_date
# Can not find a last on time. Give the total time shown.
min_time_off = round(
(now - dt.datetime.fromisoformat(edata[-1]["last_updated"])).seconds
/ (60 * 60),
2,
)
return current_state, min_time_off, None
| import datetime as dt
from typing import List
from _autoclimate.utils import climate_name
from adplus import Hass
from dateutil import tz
"""
Create new sensors
Reason: That way you do auto off if unoccupied since AND last_manual_change > X hours
* unoccupied_since:
* Last unoccupied
* None if no data
* datetime.max if currently occupied
* last_manual_change
* Timestamps as above
# TODO
* Offline - handle
"""
class Occupancy:
UNOCCUPIED_SINCE_OCCUPIED_VALUE = dt.datetime(dt.MAXYEAR, 12, 29, tzinfo=tz.tzutc())
def __init__(
self,
hass: Hass,
config: dict,
appname: str,
climates: list,
test_mode: bool,
):
self.hass = hass
self.aconfig = config
self.appname = appname
self.test_mode = test_mode
self.climates = climates
self.hass.run_in(self.create_occupancy_sensors, 0)
self.hass.run_in(self.init_occupancy_listeners, 0.1)
def unoccupied_sensor_name(self, climate):
return self.unoccupied_sensor_name_static(self.appname, climate)
@staticmethod
def unoccupied_sensor_name_static(appname, climate):
return f"sensor.{appname}_{climate_name(climate)}_unoccupied_since"
def create_occupancy_sensors(self, kwargs):
# Unoccupied Since Sensors
for climate in self.climates:
unoccupied_sensor_name = self.unoccupied_sensor_name(climate)
last_on_date = self.history_last_on_date(climate=climate)
self.hass.update_state(
unoccupied_sensor_name,
state=last_on_date,
attributes={
"freindly_name": f"{climate_name(climate)} - unoccupied since",
"device_class": "timestamp",
},
)
self.hass.log(
f"Created sensor: {unoccupied_sensor_name}. Initial state: {last_on_date}"
)
def init_occupancy_listeners(self, kwargs):
"""
This will create a different occupancy sensor for each climate,
so if multiple climates have the same oc_sensor, you'll get multiple
listeners.
"""
for climate in self.climates:
oc_sensor = self.get_sensor(climate=climate)
self.hass.log(f"listen_state: {oc_sensor}")
self.hass.listen_state(
self.update_occupancy_sensor,
entity=oc_sensor,
attribute="all",
climate=climate,
)
def update_occupancy_sensor(self, entity, attribute, old, new, kwargs):
climate = kwargs["climate"]
# self.hass.log(f'update_occupancy_sensor: {entity} -- {climate} -- {new} -- {attribute}')
last_on_date = self.oc_sensor_val_to_last_on_date(
new["state"], new["last_updated"]
)
unoccupied_sensor_name = self.unoccupied_sensor_name(climate)
self.hass.update_state(
unoccupied_sensor_name,
state=last_on_date,
)
self.hass.log(
f"update_occupancy_sensor - {unoccupied_sensor_name} - state: {last_on_date}"
)
def get_sensor(self, climate=None, sensor=None):
if climate and sensor:
raise RuntimeError(
f"Programming error - history_last_on_date: give climate OR sensor"
)
elif climate is None and sensor is None:
raise RuntimeError(
f"Programming error - need a climate or sensor. Got None."
)
elif sensor:
return sensor
else:
try:
oc_sensor = self.aconfig[climate]["occupancy_sensor"]
except KeyError:
raise RuntimeError(f"Unable to get occupancy_sensor for {climate}")
return oc_sensor
def oc_sensor_val_to_last_on_date(self, state, last_on_date):
if state == "on":
return self.UNOCCUPIED_SINCE_OCCUPIED_VALUE
elif state in ["off", "unavailable"]:
return last_on_date
else:
self.hass.log(f"Unexpected last_on_date state: {state}")
# Error or offline
return None
def history_last_on_date(self, climate=None, sensor=None):
state, duration_off, last_on_date = self.get_unoccupied_time_for(
climate, sensor
)
return self.oc_sensor_val_to_last_on_date(state, last_on_date)
def get_unoccupied_time_for(self, climate=None, sensor=None):
oc_sensor = self.get_sensor(climate=climate, sensor=sensor)
state, duration_off, last_on_date = self._history_occupancy_info(oc_sensor)
return state, duration_off, last_on_date
@staticmethod
def duration_off_static(hass, dateval):
"""
0 - currently on
> 0 - number hours off
< 0 / None - Error
"""
if isinstance(dateval, str):
dateval = dt.datetime.fromisoformat(dateval)
if dateval.tzinfo is None:
dateval.replace(tzinfo=tz.tzlocal())
now = hass.get_now()
if dateval > now:
return 0
duration_off_hours = round((now - dateval).total_seconds() / (60 * 60), 2)
return duration_off_hours
def _history_occupancy_info(self, sensor_id: str, days: int = 10):
"""
returns: state (on/off/unavailable), duration_off (hours float / None), last_on_date (datetime, None)
state = state of occupancy sensor
All based on an occupancy sensor's history data.
{
"entity_id": "binary_sensor.seattle_occupancy",
"state": "off", # on/off/unavailable
"attributes": {
"friendly_name": "<NAME>",
"device_class": "occupancy"
},
"last_changed": "2020-10-28T13:10:47.384057+00:00",
"last_updated": "2020-10-28T13:10:47.384057+00:00"
}
Note - it looks like the occupancy sensor properly handles offline by returning
an "unavailble" status. (Unlike temp sensors, which show the last value.)
"""
data: List = self.hass.get_history(entity_id=sensor_id, days=days) # type: ignore
if not data or len(data) == 0:
self.hass.warn(
f"get_history returned no data for entity: {sensor_id}. Exiting"
)
return "error", None, None
edata = data[0]
# the get_history() fn doesn't say it guarantees sort (though it appears to be)
edata = list(reversed(sorted(edata, key=lambda rec: rec["last_updated"])))
current_state = edata[0]["state"]
if current_state == "on":
return "on", None, None
last_on_date = None
now: dt.datetime = self.hass.get_now() # type: ignore
for rec in edata:
if rec.get("state") == "on":
last_on_date = dt.datetime.fromisoformat(rec["last_updated"])
duration_off_hours = round(
(now - last_on_date).total_seconds() / (60 * 60), 2
)
return current_state, duration_off_hours, last_on_date
# Can not find a last on time. Give the total time shown.
min_time_off = round(
(now - dt.datetime.fromisoformat(edata[-1]["last_updated"])).seconds
/ (60 * 60),
2,
)
return current_state, min_time_off, None | en | 0.711951 | Create new sensors Reason: That way you do auto off if unoccupied since AND last_manual_change > X hours * unoccupied_since: * Last unoccupied * None if no data * datetime.max if currently occupied * last_manual_change * Timestamps as above # TODO * Offline - handle # Unoccupied Since Sensors This will create a different occupancy sensor for each climate, so if multiple climates have the same oc_sensor, you'll get multiple listeners. # self.hass.log(f'update_occupancy_sensor: {entity} -- {climate} -- {new} -- {attribute}') # Error or offline 0 - currently on > 0 - number hours off < 0 / None - Error returns: state (on/off/unavailable), duration_off (hours float / None), last_on_date (datetime, None) state = state of occupancy sensor All based on an occupancy sensor's history data. { "entity_id": "binary_sensor.seattle_occupancy", "state": "off", # on/off/unavailable "attributes": { "friendly_name": "<NAME>", "device_class": "occupancy" }, "last_changed": "2020-10-28T13:10:47.384057+00:00", "last_updated": "2020-10-28T13:10:47.384057+00:00" } Note - it looks like the occupancy sensor properly handles offline by returning an "unavailble" status. (Unlike temp sensors, which show the last value.) # type: ignore # the get_history() fn doesn't say it guarantees sort (though it appears to be) # type: ignore # Can not find a last on time. Give the total time shown. | 2.667934 | 3 |